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    "path": ".gitignore",
    "content": "*.log\n*.aux\nmain-blx.bib\nmain.bbl\nmain.blg\nmain.idx\nmain.ilg\nmain.ind\nmain.out\nmain.pdf\nmain.run.xml\nmain.synctex.gz\nmain.toc\nmain.upa\n*.DS_Store\n*gitignore~\n*.Rapp.history\n*~\nIcon[^/]\n\\#*\n*.dropbox\n_README\n*-deprecated*\n*.Rhistory\nOS4-201[89]-[01][0-9]-[0-3][0-9] [A-Z].pdf\nmain.synctex(busy)\n.Rproj.user\n"
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    "path": "LICENSE.md",
    "content": "\nOpenIntro Statistics is available at http://www.openintro.org under a Creative Commons Attribution-ShareAlike 3.0 Unported license (CC BY-SA):\n\nhttp://creativecommons.org/licenses/by-sa/3.0/\n\nThis `LICENSE` file describes guidelines when the textbook's source files are modified and / or shared. The CC BY-SA license guidelines supersede any guidelines put forth here; follow the CC BY-SA license if there is any discrepancy between that license and these guidelines.\n\nYou may contact us if you would like to request an alternative licensing option at\n\nhttps://www.openintro.org/contact\n\n1. Communication obligation. Any derivative work must communicate that it is licensed under a CC BY-SA license.\n\n2. Figure attribution. Some photographs may be owned by other creators who made the images available under a Creative Commons license and were used in this work. If you use a photograph, please check in the textbook whether the figure is a work of another party. If you use any such images, provide appropriate attribution to the original photographer (e.g. see OpenIntro Statistics for what we believe to be appropriate attribution in these instances).\n\n3. Derivative title. No derivative may include \"OpenIntro\" in the title, unless it is included in text of the form \"Derivative of OpenIntro\". Additionally, the title may not match any OpenIntro textbook (or be a translated equivalent) and also not imply a connection (e.g. \"[Introductory Statistics with Randomization and Simulation](https://openintro.org/book/isrs/) for Biology\" is not be permitted). A novel title is required to avoid product confusion or the appearance that your new resource is associated with OpenIntro.\n\nUse of the OpenIntro trademark and logo are strictly prohibited and are not licensed for use. The only appropriate use is when indicating the original resource that has been modified. Example: \"This book was built using 'OpenIntro Statistics', and that original book may be found at openintro.org/book/os.\"\n\n4. Below are other suggested guidelines for attribution.\n\n- The first two pages of any derivative work should be the title page and the copyright page. We encourage contributors to use the following two files provided in the textbook's source: file, extraTeX > preamble > title_derivative.tex, copyright_derivative.tex. We understand that it may be useful to modify them, so consider them an initial template.\n\n- We advise that contributing authors' names be listed in chronological order corresponding to their contribution. We also encourage contributing authors to provide a brief description of their contribution.\n"
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    "path": "README.md",
    "content": "Project Organization\n--------------------\n\n- Each chapter's content is in one of the eight chapter folders that start with \"ch_\". Within each folder, there is a \"figures\" folder and a \"TeX\" folder. The TeX folder contains the text files that are used to typeset the chapters in the textbook.\n- In many cases, R code is supplied with figures to regenerate the figure. It will often be necessary to install the \"openintro\" R package that is available from GitHub (https://github.com/OpenIntroOrg) if you would like to regenerate a figure. Other packages may also occasionally be required.\n- Exercise figures may be found in [chapter folder] > figures > eoce > [exercise figure folders]. \"EOCE\" means end-of-chapter exercises.\n- The extraTeX folder contains files for the front and back matter of the textbook and also the style files. Note that use of any style files, like all other files here, is under the Creative Commons license cited in the LICENSE file.\n\n- - -\n\nTypesetting the Textbook\n------------------------\n\nThe textbook may be typeset using the main.tex file. This file pulls in all of the necessary TeX files and figures. For a final typesetting event, typeset in the following order\n\n- LaTeX 3 times.\n- MakeIndex once.\n- BibTeX once.\n- LaTeX once.\n- MakeIndex once.\n- LaTeX once.\n\nThis isn't important for casual browsing, but it is important for a \"final\" version. The repetitive typesetting is to account for when typesetting changes references slightly, since typesetting the first few times can move content from one page to the next, e.g. as a \\ref{...} gets filled in.\n\n- - -\n\nLearning LaTeX\n--------------\n\nIf you are not familiar with LaTeX but would like to learn how to use it, check out the slides from two LaTeX mini-courses at\n\nhttps://github.com/OpenIntroOrg/mini-course-materials\n\nPDFs:\n\n[Basics of LaTeX](https://github.com/OpenIntroOrg/mini-course-materials/raw/master/LaTeX_Basics/basicsOfLatex.pdf)\n\n[Math and BibTeX](https://github.com/OpenIntroOrg/mini-course-materials/raw/master/LaTeX_Math_and_BibTeX/bibtexMathInLatex.pdf)\n\nFor a more authoritative review, the book \"Guide to LaTeX\" is an excellent resource.\n\nAlso, see the branches of [this repo](https://github.com/statkclee/mini-course-materials) by Kwangchun Lee for Korean translations of these mini-course materials.\n"
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    "path": "ch_distributions/TeX/binomial_distribution.tex",
    "content": "\\exercisesheader{}\n\n% 17\n\n\\eoce{\\qt{Underage drinking, Part I\\label{underage_drinking_intro}}\nData collected by the Substance Abuse and Mental Health\nServices Administration (SAMSHA) suggests that 69.7\\% of\n18-20 year olds consumed alcoholic beverages in any given\nyear.\\footfullcite{webpage:alcohol}\n\\begin{parts}\n\\item Suppose a random sample of ten 18-20 year olds is taken. Is the use \nof the binomial distribution appropriate for calculating the probability that \nexactly six consumed alcoholic beverages? Explain.\n\\item Calculate the probability that exactly 6 out of 10 randomly sampled 18-\n20 year olds consumed an alcoholic drink.\n\\item What is the probability that exactly four out of ten 18-20 year \nolds have \\textit{not} consumed an alcoholic beverage?\n\\item What is the probability that at most 2 out of 5 randomly sampled 18-20 \nyear olds have consumed alcoholic beverages?\n\\item What is the probability that at least 1 out of 5 randomly sampled 18-20 \nyear olds have consumed alcoholic beverages?\n\\end{parts}\n}{}\n\n% 18\n\n\\eoce{\\qt{Chickenpox, Part I\\label{chicken_pox_intro}} Boston Children's\nHospital estimates that 90\\% of Americans have had chickenpox by \nthe time they reach adulthood. \\footfullcite{bostonchildrenshospital:chickenpox}\n\\begin{parts}\n\\item Suppose we take a random sample of 100 American adults. Is the use of \nthe binomial distribution appropriate for calculating the probability that exactly 97 \nout of 100 randomly sampled American adults had chickenpox during childhood? Explain.\n\\item Calculate the probability that exactly 97 out of 100 randomly sampled \nAmerican adults had chickenpox during childhood.\n\\item What is the probability that exactly 3 out of a new sample of 100 \nAmerican adults have \\textit{not} had chickenpox in their childhood?\n\\item What is the probability that at least 1 out of 10 randomly sampled \nAmerican adults have had chickenpox?\n\\item What is the probability that at most 3 out of 10 randomly sampled \nAmerican adults have \\textit{not} had chickenpox?\n\\end{parts}\n}{}\n\n% 19\n\n\\eoce{\\qt{Underage drinking, Part II\\label{underage_drinking_normal_approx}}\nWe learned in Exercise~\\ref{underage_drinking_intro}\nthat about 70\\% of 18-20 year olds consumed alcoholic\nbeverages in any given year. We now consider a random \nsample of fifty 18-20 year olds.\n\\begin{parts}\n\\item How many people would you expect to have consumed alcoholic beverages? \nAnd with what standard deviation?\n\\item Would you be surprised if there were 45 or more people who have \nconsumed alcoholic beverages?\n\\item What is the probability that 45 or more people in this sample have \nconsumed alcoholic beverages? How does this probability relate to your answer \nto part (b)?\n\\end{parts}\n}{}\n\n% 20\n\n\\eoce{\\qt{Chickenpox, Part II\\label{chicken_pox_normal_approx}} We learned in \nExercise~\\ref{chicken_pox_intro} that about 90\\% of American adults had \nchickenpox before adulthood. We now consider a random sample of 120 American \nadults.\n\\begin{parts}\n\\item How many people in this sample would you expect to have had chickenpox \nin their childhood? And with what standard deviation?\n\\item Would you be surprised if there were 105 people who have had chickenpox \nin their childhood?\n\\item What is the probability that 105 or fewer people in this sample have \nhad chickenpox in their childhood? How does this probability relate to your \nanswer to part (b)?\n\\end{parts}\n}{}\n\n% 21\n\n\\eoce{\\qt{Game of dreidel\\label{dreidel}} A dreidel is a four-sided spinning top \nwith the Hebrew letters \\textit{nun}, \\textit{gimel}, \\textit{hei}, and \n\\textit{shin}, one on each side. Each side is equally likely to come up in a \nsingle spin of the dreidel. Suppose you spin a dreidel three times. Calculate \nthe probability of getting\n\n\\noindent\\begin{minipage}[c]{0.45\\textwidth}\n\\begin{parts}\n\\item at least one \\textit{nun}? \n\\item exactly 2 \\textit{nun}s? \n\\item exactly 1 \\textit{hei}? \n\\item at most 2 \\textit{gimel}s? \\vspace{3mm}\n\\end{parts}\n\\end{minipage}%\n\\begin{minipage}[c]{0.25\\textwidth}\n\\ \\vspace{2mm}\n\n\\Figures[An image of two wooden dreidels.]{0.95}{eoce/dreidel}{dreidel.jpg}\\vspace{2mm}\n\\end{minipage}%\n\\begin{minipage}[c]{0.28\\textwidth}%\n{\\footnotesize Photo by Staccabees, cropped \\\\\n  (\\oiRedirect{textbook-flickr_staccabees_dreidels}{http://flic.kr/p/7gLZTf}) \\\\\n  \\oiRedirect{textbook-CC_BY_2}{CC~BY~2.0~license}} \\\\\n\\end{minipage}\n}{}\n\n\\D{\\newpage}\n\n% 22\n\n\\eoce{\\qt{Arachnophobia\\label{arachnophobia}}\nA Gallup Poll found that 7\\% of teenagers (ages 13 to 17)\nsuffer from arachnophobia and are extremely afraid of spiders.\nAt a summer camp there are 10 teenagers sleeping in each tent.\nAssume that these 10 teenagers are independent of each other.%\n\\footfullcite{webpage:spiders}\n\\begin{parts}\n\\item Calculate the probability that at least one of them suffers from \narachnophobia.\n\\item Calculate the probability that exactly 2 of them suffer from \narachnophobia.\n\\item Calculate the probability that at most 1 of them suffers from \narachnophobia. \n\\item If the camp counselor wants to make sure no more than 1 teenager in \neach tent is afraid of spiders, does it seem reasonable for him to randomly \nassign teenagers to tents?\n\\end{parts}\n}{}\n\n% 23\n\n\\eoce{\\qt{Eye color, Part II\\label{eye_color_binomial}} \nExercise~\\ref{eye_color_geometric} introduces a husband and wife with brown \neyes who have 0.75 probability of having children with brown eyes, 0.125 \nprobability of having children with blue eyes, and 0.125 probability of \nhaving children with green eyes.\n\\begin{parts}\n\\item What is the probability that their first child will have green eyes and \nthe second will not?\n\\item What is the probability that exactly one of their two children will \nhave green eyes?\n\\item If they have six children, what is the probability that exactly two \nwill have green eyes?\n\\item If they have six children, what is the probability that at least one \nwill have green eyes?\n\\item What is the probability that the first green eyed child will be the \n$4^{th}$ child? \n\\item Would it be considered unusual if only 2 out of their 6 children had \nbrown eyes?\n\\end{parts}\n}{}\n\n% 24\n\n\\eoce{\\qt{Sickle cell anemia\\label{sickle_cell_anemia}} Sickle cell anemia is a \ngenetic blood disorder where red blood cells lose their flexibility and \nassume an abnormal, rigid, ``sickle\" shape, which results in a risk of \nvarious complications. If both parents are carriers of the disease, then a \nchild has a 25\\% chance of having the disease, 50\\% chance of being a \ncarrier, and 25\\% chance of neither having the disease nor being a carrier. \nIf two parents who are carriers of the disease have 3 children, what is the \nprobability that \n\\begin{parts}\n\\item two will have the disease?\n\\item none will have the disease?\n\\item at least one will neither have the disease nor be a carrier?\n\\item the first child with the disease will the be $3^{rd}$ child?\n\\end{parts}\n}{}\n\n% 25\n\n\\eoce{\\qt{Exploring permutations\\label{explore_combinations}} The formula for the \nnumber of ways to arrange $n$ objects is $n! = n\\times(n-1)\\times \\cdots \n\\times 2 \\times 1$. This exercise walks you through the derivation of this \nformula for a couple of special cases.\n\n\\indent A small company has five employees: Anna, Ben, Carl, Damian, and \nEddy. There are five parking spots in a row at the company, none of which are \nassigned, and each day the employees pull into a random parking spot. That \nis, all possible orderings of the cars in the row of spots are equally likely.\n\\begin{parts}\n\\item On a given day, what is the probability that the employees park in \nalphabetical order?\n\\item If the alphabetical order has an equal chance of occurring relative to \nall other possible orderings, how many ways must there be to arrange the five \ncars?\n\\item Now consider a sample of 8 employees instead. How many possible ways \nare there to order these 8 employees' cars?\n\\end{parts}\n}{}\n\n% 26\n\n\\eoce{\\qt{Male children\\label{male_children}} While it is often assumed that the \nprobabilities of having a boy or a girl are the same, the actual probability \nof having a boy is slightly higher at 0.51. Suppose a couple plans to have 3 \nkids. \n\\begin{parts}\n\\item Use the binomial model to calculate the probability that two of them \nwill be boys.\n\\item Write out all possible orderings of 3 children, 2 of whom are boys. Use \nthese scenarios to calculate the same probability from part (a) but using the \naddition rule for disjoint outcomes. Confirm that your answers from parts (a) \nand (b) match.\n\\item If we wanted to calculate the probability that a couple who plans to \nhave 8 kids will have 3 boys, briefly describe why the approach from part (b) \nwould be more tedious than the approach from part (a).\n\\end{parts}\n}{}\n"
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    "path": "ch_distributions/TeX/ch_distributions.tex",
    "content": "\\begin{chapterpage}{Distributions of random variables}\n  \\chaptertitle[30]{Distributions of random \\titlebreak{} variables}\n  \\label{ch_distributions}\n  \\chaptersection{normalDist}\n  %\\chaptersection{assessingNormal}\n  \\chaptersection{geomDist}\n  \\chaptersection{binomialModel}\n  \\chaptersection{negativeBinomial}\n  \\chaptersection{poisson}\n\\end{chapterpage}\n\\renewcommand{\\chapterfolder}{ch_distributions}\n\n\n\\chapterintro{In this chapter,\n  we discuss statistical distributions that frequently\n  arise in the context of data analysis or statistical\n  inference.\n  We start with the normal distribution in the first section,\n  which is used frequently in later chapters of this book.\n  The remaining sections will occasionally be referenced\n  but may be considered optional for the content in this\n  book.}\n\n%_________________\n\\section{Normal distribution}\n\\label{normalDist}\n\n\\index{distribution!normal|(}\n\\index{normal distribution|(}\n\nAmong all the distributions we see in practice,\none is overwhelmingly the most common.\nThe symmetric, unimodal, bell curve is ubiquitous\nthroughout statistics.\nIndeed it is so common, that people often know it as the\n\\termsub{normal curve}{normal distribution} or\n\\term{normal distribution}\\index{distribution!normal|textbf}%\n,\\footnote{It\n  is also introduced as the Gaussian distribution after Frederic\n  Gauss, the first person to formalize its mathematical\n  expression.}\nshown in Figure~\\ref{simpleNormal}.\nVariables such as SAT scores and heights of US adult males\nclosely follow the normal distribution.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A bell-shaped curve that is symmetric about its center is shown. This is the normal distribution. From the left, the curve starts low, grad lifting off the horizontal axis before more steeply rising, before it starts to rise more slowly and flattens at its peak. From the peak, it starts to decrease slowly and then more steeply, before gradually flattening out as it approaches the horizontal axis. This is the bell-shaped normal distribution, an it is the shape of many distributions we will encounter throughout this book. In general, going forward, this bell-shaped distribution shape should be remembered whenever the normal distribution is discussed.]{0.5}{simpleNormal}\n  \\caption{A normal curve.}\n  \\label{simpleNormal}\n\\end{figure}\n\n\\begin{onebox}{Normal distribution facts}\n  Many variables are nearly normal, but none are exactly normal.\n  Thus the normal distribution, while not perfect for any single\n  problem, is very useful for a variety of problems.\n  We will use it in data exploration and to solve important\n  problems in statistics.\n\\end{onebox}\n\n\n\\subsection{Normal distribution model}\n\nThe \\term{normal distribution} always describes a symmetric,\nunimodal, bell-shaped curve.\nHowever, these curves can look different depending on the\ndetails of the model.\nSpecifically, the normal distribution model can be adjusted\nusing two parameters: mean and standard deviation.\nAs you can probably guess, changing the mean shifts the bell\ncurve to the left or right, while changing the standard deviation\nstretches or constricts the curve.\nFigure~\\ref{twoSampleNormals} shows the normal distribution\nwith mean $0$ and standard deviation $1$ in the left panel\nand the normal distributions with mean $19$ and standard\ndeviation $4$ in the right panel.\nFigure~\\ref{twoSampleNormalsStacked} shows these distributions\non the same axis.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[Two normal distributions are shown. The first has a center of 0 and a standard deviation of 1, where the two tails of the normal distribution curve are essentially indistinguishable from a height of 0 for values less than -3 or larger than positive 3. The second normal distribution is centered at 19 and has a standard deviation of 4, where the height of the distribution is indistinguishable from 0 when it is more than 3 standard deviations from the mean.]{0.7}{twoSampleNormals}\n  \\caption{Both curves represent the normal distribution.\n      However, they differ in their center and spread.}\n  \\label{twoSampleNormals}\n\\end{figure}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[Two normal distributions are shown on the same plot. The first has a mean of 0 and a standard deviation of 1. The second has a mean of 19 and a standard deviation of 4. One important property visible in the plot is, because distributions are required to have an area of 1, the normal distribution with a standard deviation of 1 appears much narrower and but also much taller than the second distribution that has a standard deviation of 4.]{0.6}{twoSampleNormalsStacked}\n  \\caption{The normal distributions shown in\n      Figure~\\ref{twoSampleNormals} but plotted together\n      and on the same scale.}\n  \\label{twoSampleNormalsStacked}\n\\end{figure}\n\nIf a normal distribution has mean $\\mu$ and standard deviation\n$\\sigma$, we may write the distribution as $N(\\mu, \\sigma)$.\nThe two distributions in Figure~\\ref{twoSampleNormalsStacked}\nmay be written as\n\\begin{align*}\nN(\\mu=0,\\sigma=1)\n  \\quad \\text{and} \\quad\n  N(\\mu=19,\\sigma=4)\n\\end{align*}\nBecause the mean and standard deviation describe a normal\ndistribution exactly, they are called the distribution's\n\\termsub{parameters}{parameter}.\nThe normal distribution with mean $\\mu = 0$ and\nstandard deviation $\\sigma = 1$ is called the\n\\term{standard normal distribution}%\n\\index{normal distribution!standard|textbf}.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWrite down the short-hand for a normal distribution\nwith\\footnotemark{} \\\\\n%\\begin{enumerate}[(a)]\n%\\setlength{\\itemsep}{0mm}\n%\\item\n(a)\n    mean~5 and standard deviation~3, \\\\\n%\\item\n(b)\n    mean~-100 and standard deviation~10, and \\\\\n%\\item\n(c)\n    mean~2 and standard deviation~9.\n%\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~$N(\\mu=5,\\sigma=3)$.\n  (b)~$N(\\mu=-100, \\sigma=10)$.\n  (c)~$N(\\mu=2, \\sigma=9)$.}\n\n\n\\subsection{Standardizing with Z-scores}\n\n\\noindent%\nWe often want to put data onto a standardized scale,\nwhich can make comparisons more reasonable.\n\n\\newcommand{\\satmean}{1100}\n\\newcommand{\\satsd}{200}\n\\newcommand{\\actmean}{21}\n\\newcommand{\\actsd}{6}\n\\newcommand{\\annsatscore}{1300}\n\\newcommand{\\annsatzscore}{1}\n\\newcommand{\\tomsatscore}{24}\n\\newcommand{\\tomsatzscore}{0.5}\n\n\\begin{examplewrap}\n\\begin{nexample}{Table~\\vref{satACTstats} shows the mean\n    and standard deviation for total scores on the SAT and ACT.\n    The distribution of SAT and ACT scores are both nearly normal.\n    Suppose Ann scored \\annsatscore{} on her SAT and Tom scored\n    \\tomsatscore{} on his ACT.\n    Who performed better?}\n  \\label{actSAT}%\n  We use the standard deviation as a guide.\n  Ann is \\annsatzscore{} standard deviation above average\n  on the SAT: $\\satmean{} + \\satsd{} = \\annsatscore{}$.\n  Tom is \\tomsatzscore{} standard deviations above the mean\n  on the ACT:\n  $\\actmean{} + \\tomsatzscore{} \\times \\actsd{} = \\tomsatscore{}$.\n  In Figure~\\ref{satActNormals}, we can see that Ann tends\n  to do better with respect to everyone else than Tom did,\n  so her score was better.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l r r}\n  \\hline\n  & SAT & ACT \\\\\n  \\hline\n  Mean \\hspace{0.3cm} & \\satmean{} & \\actmean{} \\\\\n  SD & \\satsd{} & \\actsd{} \\\\\n  \\hline\n\\end{tabular}\n\\caption{Mean and standard deviation for the SAT and ACT.}\n\\label{satACTstats}\n\\end{figure}\n\n\\begin{figure}\n  \\centering\n  \\Figure[Ann's and Tom's scores shown against the SAT and ACT distributions, which are each shown as normal distributions. The SAT distribution has a mean of 1100 and a standard deviation of 200, while the ACT distribution has a mean of 21 and standard deviation of 6. Ann's score is 1300 for the SAT, and Tom's score is 24 for the ACT. Based on their positioning in their respective plots, it is evident that Ann has a higher relative value for her SAT distribution than Tom has for his ACT score.]{0.6}{satActNormals}\n  \\caption{Ann's and Tom's scores shown against\n      the SAT and ACT distributions.}\n  \\label{satActNormals}\n\\end{figure}\n\nExample~\\ref{actSAT} used a standardization technique called\na Z-score, a method most commonly employed for nearly normal\nobservations but that may be used with any distribution.\nThe \\term{Z-score}\\index{Z@$Z$} of an observation is defined\nas the number of standard deviations it falls above or below\nthe mean.\nIf the observation is one standard deviation above the mean,\nits Z-score is~1.\nIf it is 1.5 standard deviations \\emph{below} the mean,\nthen its Z-score is -1.5.\nIf $x$ is an observation from a distribution $N(\\mu, \\sigma)$,\nwe define the Z-score mathematically as\n\\begin{align*}\nZ = \\frac{x - \\mu}{\\sigma}\n\\end{align*}\nUsing $\\mu_{SAT} = \\satmean{}$, $\\sigma_{SAT} = \\satsd{}$,\nand $x_{_{\\text{Ann}}} = \\annsatscore{}$, we find Ann's Z-score:\n\\begin{align*}\nZ_{_{\\text{Ann}}}\n  = \\frac{x_{_{\\text{Ann}}} - \\mu_{_{\\text{SAT}}}}\n      {\\sigma_{_{\\text{SAT}}}}\n  = \\frac{\\annsatscore{} - \\satmean{}}{\\satsd{}}\n  = \\annsatzscore{}\n\\end{align*}\n\n\\begin{onebox}{The Z-score}\n  The Z-score of an observation is the number of standard\n  deviations it falls above or below the mean.\n  We compute the Z-score for an observation $x$ that follows\n  a distribution with mean $\\mu$ and standard deviation\n  $\\sigma$ using\n  \\begin{align*}\n  Z = \\frac{x - \\mu}{\\sigma}\n  \\end{align*}\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nUse Tom's ACT score, \\tomsatscore{}, along with the ACT mean and\nstandard deviation to find his Z-score.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$Z_{Tom}\n  = \\frac{x_{\\text{Tom}} - \\mu_{\\text{ACT}}}\n      {\\sigma_{\\text{ACT}}}\n  = \\frac{\\tomsatscore{} - \\actmean{}}{\\actsd{}}\n  = \\tomsatzscore{}$}\n\nObservations above the mean always have positive Z-scores,\nwhile those below the mean always have negative Z-scores.\nIf an observation is equal to the mean,\nsuch as an SAT score of \\satmean{}, then the Z-score is $0$.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nLet $X$ represent a random variable from $N(\\mu=3, \\sigma=2)$,\nand suppose we observe $x=5.19$. \\\\\n%\\begin{enumerate}[(a)]\n%\\setlength{\\itemsep}{0mm}\n%\\item\n(a)\n    Find the Z-score of $x$. \\\\\n%\\item\n(b)\n    Use the Z-score to determine how many standard deviations\n    above or below the mean $x$ falls.\\footnotemark{}\n%\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) Its Z-score is given by\n    $Z\n      = \\frac{x-\\mu}{\\sigma}\n      = \\frac{5.19 - 3}{2}\n      = 2.19/2\n      = 1.095$.\n    (b)~The observation $x$ is 1.095 standard deviations\n    \\emph{above} the mean.\n    We know it must be above the mean since $Z$ is positive.}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{headLZScore}\nHead lengths of brushtail possums follow a normal\ndistribution with mean 92.6 mm and standard deviation 3.6 mm.\nCompute the Z-scores for possums with head lengths of 95.4 mm\nand 85.8~mm.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{For $x_1=95.4$ mm:\n    $Z_1\n      = \\frac{x_1 - \\mu}{\\sigma}\n      = \\frac{95.4 - 92.6}{3.6}\n      = 0.78$.\n    For $x_2=85.8$ mm:\n    $Z_2 = \\frac{85.8 - 92.6}{3.6} = -1.89$.}\n\nWe can use Z-scores to roughly identify which observations\nare more unusual than others.\nAn observation $x_1$ is said to be more unusual than another\nobservation $x_2$ if the absolute value of its Z-score is larger\nthan the absolute value of the other observation's Z-score:\n$|Z_1| > |Z_2|$.\nThis technique is especially insightful when a distribution\nis symmetric.\n\n%\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhich of the observations in Guided Practice~\\ref{headLZScore}\nis more unusual?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Because the \\emph{absolute value} of Z-score\n  for the second observation is larger than that of the first,\n  the second observation has a more unusual head length.}\n\n\n\\subsection{Finding tail areas}\n\nIt's very useful in statistics to be able to identify tail areas\nof distributions.\nFor instance, what fraction of people have an SAT score below\nAnn's score of 1300?\nThis is the same as the \\term{percentile} Ann is at, which is\nthe percentage of cases that have lower scores than Ann.\nWe can visualize such a tail area like the curve and shading\nshown in Figure~\\ref{satBelow1300}.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A normal distribution is shown with a mean of 1100 and a standard deviation of 200. The distribution is shaded to the left of the value 1300, meaning the area bound by the horizontal axis, the bell-shaped curve (up to the horizontal value of 1300) and a vertical line at 1300 is shaded.]{0.45}{satBelow1300}\n  \\caption{The area to the left of $Z$ represents the\n      fraction of people who scored lower than Ann.}\n  \\label{satBelow1300}\n\\end{figure}\n\nThere are many techniques for doing this, and we'll discuss\nthree of the options.\n\\begin{enumerate}\n\\item\n    The most common approach in practice is to use\n    statistical software.\n    For example, in the program \\R{}, we could find the area\n    shown in Figure~\\ref{satBelow1300} using the\n    following command, which takes in the Z-score\n    and returns the lower tail area: \\\\\n    {\\color{white}.....}%\n        \\texttt{> pnorm(1)} \\\\\n    {\\color{white}.....}%\n        \\texttt{[1] 0.8413447} \\\\\n    According to this calculation,\n    the region shaded that is below 1300\n    represents the proportion 0.841 (84.1\\%) of SAT test\n    takers who had Z-scores below $Z = 1$.\n    More generally, we can also specify the cutoff explicitly\n    if we also note the mean and standard deviation: \\\\\n    {\\color{white}.....}%\n        \\texttt{> pnorm(1300, mean = 1100, sd = 200)} \\\\\n    {\\color{white}.....}%\n        \\texttt{[1] 0.8413447} %\\\\\n    %\\Add{More examples for using \\R{} are provided\n    %  at the end of the section.}\n\n\n    There are many other software options, such as Python or SAS;\n    even spreadsheet programs such as\n    Excel and Google Sheets support these calculations.\n\\item\n    A common strategy in classrooms is to use a graphing\n    calculator, such as a TI or Casio calculator.\n    These calculators require a series of button presses\n    that are less concisely described.\n    You can find instructions on using these calculators\n    for finding tail areas of a normal distribution in the\n    OpenIntro video library:\n    \\begin{center}\n    \\oiRedirect{textbook-openintro_videos}\n        {www.openintro.org/videos}\n    \\end{center}\n\\item\n    The last option for finding tail areas is to use\n    what's called a \\term{probability table};\n    these are occasionally used in classrooms\n    but rarely in practice.\n    Appendix~\\ref{normalProbabilityTable}\n    contains such a table and a guide for how to use it.\n\\end{enumerate}\nWe will solve normal distribution problems in this section\nby always first finding the Z-score.\nThe reason is that we will encounter close parallels\ncalled \\indexthis{test statistics}{test statistic}\nbeginning in Chapter~\\ref{ch_foundations_for_inf};\nthese are, in many instances, an equivalent of a Z-score.\n\n%No matter the approach you choose,\n%try the Guided Practice exercises in this section\n%using your preferred method.\n\n\n\\D{\\newpage}\n\n\\subsection{Normal probability examples}\n\\label{normal_probability_examples}\n\n\\noindent%\nCumulative SAT scores are approximated well by a normal model,\n$N(\\mu = \\satmean{}, \\sigma = \\satsd{})$.\n\n\\newcommand{\\shannonsat}{1190}\n\\newcommand{\\shannonsatz}{0.45}\n\\begin{examplewrap}\n\\begin{nexample}{Shannon is a randomly selected SAT taker,\n    and nothing is known about Shannon's SAT aptitude.\n    What is the probability Shannon scores at least\n    \\shannonsat{} on her SATs?}\n  \\label{satAbove1190Exam}%\n  First, always draw and label a picture of the normal\n  distribution.\n  (Drawings need not be exact to be useful.)\n  We are interested in the chance she scores above\n  \\shannonsat{}, so we shade this upper tail:\n  \\begin{center}\n  \\Figure[A normal distribution with a mean of 1100 and standard deviation of 200 has the area below the distribution shaded for horizontal values larger than 1300.]{0.4}{satAbove1190}\n  \\end{center}\n  The picture shows the mean and the values at\n  2~standard deviations above and below the mean.\n  The simplest way to find the shaded area under\n  the curve makes use of the Z-score of the cutoff value.\n  With $\\mu = \\satmean{}$, $\\sigma = \\satsd{}$,\n  and the cutoff value $x = \\shannonsat{}$,\n  the Z-score is computed as\n  \\begin{align*}\n  Z = \\frac{x - \\mu}{\\sigma}\n    = \\frac{\\shannonsat{} - \\satmean{}}{\\satsd{}}\n    = \\frac{90}{\\satsd{}}\n    = \\shannonsatz{}\n  \\end{align*}\n  Using statistical software (or another preferred method),\n  we can find the area left of $Z = \\shannonsatz{}$ as 0.6736.\n  %This is Shannon's \\term{percentile},\n  %which is the fraction of folks who scored below her score\n  %of \\shannonsat{}.\n  To find the area \\emph{above} $Z = \\shannonsatz{}$,\n  we compute one minus the area of the lower tail:\n  \\begin{center}\n  \\Figure[A full shaded normal distribution is shown, then a \"minus\" sign, then a normal distribution with most of its region shaded up to a little above the mean, then an equals sign, and then a normal distribution with an area in the upper tail shaded. Above those images is the text \"1.0000 minus 0.6736 equals 0.3264\". This visualization is intended to show how we can think of finding an upper tail of the normal distribution as taking the entire area below the distribution (which has a value of 1) and subtracting a portion of the area to the left to get an area to the right.]{0.4}{subtractingArea}\n  \\end{center}\n  The probability Shannon scores at least 1190 on the SAT\n  is 0.3264.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{Always draw a picture first,\n    and find the Z-score second}\n  For any normal probability situation,\n  \\emph{always always always} draw and label the\n  normal curve and shade the area of interest first.\n  The picture will provide an estimate of the probability.\n  After drawing a figure to represent the situation,\n  identify the Z-score for the value of interest.\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIf the probability of Shannon scoring at least \\shannonsat{}\nis 0.3264, then what is the probability she scores less than\n\\shannonsat{}?\nDraw the normal curve representing this exercise,\nshading the lower region instead of the upper one.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We found this probability in\n  Example~\\ref{satAbove1190Exam}: 0.6736. \\\\\n  \\Figures[A normal distribution with mean 1100 and standard deviation 200 is shaded from the left up to a vertical line a little above the distribution mean.]{0.35}{subtractingArea}{subtracted}}\n\n\\D{\\newpage}\n\n\\newcommand{\\edwardsat}{1030}\n\\newcommand{\\edwardsatz}{-0.35}\n\\newcommand{\\edwardsatlower}{0.3632}\n\\begin{examplewrap}\n\\begin{nexample}{Edward earned a \\edwardsat{} on his SAT.\n    What is his percentile?}\n  \\label{edwardSatBelow\\edwardsat{}}%\n  First, a picture is needed.\n  Edward's \\hiddenterm{percentile} is the proportion of people\n  who do not get as high as a \\edwardsat{}.\n  These are the scores to the left of \\edwardsat{}.\n\\begin{center}\n\\Figure[A normal distribution with mean 1100 and standard deviation 200 is shaded from the left up to a vertical line a little below the distribution mean. This area is labeled as \"40\\% (0.40)\".]{0.3}{satBelow1030}\n\\end{center}\nIdentifying the mean $\\mu=\\satmean{}$, the standard\ndeviation $\\sigma=\\satsd{}$, and the cutoff for the tail\narea $x=\\edwardsat{}$ makes it easy to compute the Z-score:\n\\begin{align*}\nZ\n  = \\frac{x - \\mu}{\\sigma}\n  = \\frac{\\edwardsat{} - \\satmean{}}{\\satsd{}}\n  = \\edwardsatz{}\n\\end{align*}\nUsing statistical software, we get a tail area of 0.3632.\nEdward is at the $36^{th}$ percentile.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nUse the results of Example~\\ref{edwardSatBelow\\edwardsat{}}\nto compute the proportion of SAT takers who did better than\nEdward.\nAlso draw a new picture.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{If Edward did better than 36\\% of SAT takers,\n  then about 64\\% must have done better than him. \\\\\n  \\Figures{0.25}{satBelow1030}{satAbove1030}}\n\n\\begin{onebox}{Finding areas to the right}\n  Many software programs return the area to the left\n  when given a Z-score.\n  If you would like the area to the right, first find the\n  area to the left and then subtract this amount from~one.\n\\end{onebox}\n\n\\newcommand{\\stuartsat}{1500}\n\\newcommand{\\stuarsatz}{2}\n\\begin{exercisewrap}\n\\begin{nexercise}\nStuart earned an SAT score of \\stuartsat{}.\nDraw a picture for each part. \\\\\n(a)~What is his percentile? \\\\\n(b)~What percent of SAT takers did better than\n  Stuart?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We leave the drawings to you.\n  (a) $Z = \\frac{\\stuartsat{} - \\satmean{}}{\\satsd{}}\n         = \\stuarsatz{}\n         \\to 0.9772$.\n  (b) $1 - 0.9772 = 0.0228$.}\n\nBased on a sample of 100 men, the heights of male adults\nin the US is nearly normal with mean 70.0''\nand standard deviation 3.3''.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nMike is 5'7'' and Jose is 6'4'', and they both live in the US. \\\\\n(a) What is Mike's height percentile? \\\\\n(b) What is Jose's height percentile? \\\\\nAlso draw one picture for each~part.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{First put the heights into inches:\n  67 and 76 inches.\n  Figures are shown below. \\\\\n  (a) $Z_{\\text{Mike}} = \\frac{67 - 70}{3.3} = -0.91\\ \\to\\ 0.1814$.\n  (b) $Z_{\\text{Jose}} = \\frac{76 - 70}{3.3} = 1.82\\ \\to\\ 0.9656$.\n  \\\\\n  \\Figure[Two plots are shown. The first plot is labeled \"Mike\" and shows a normal distribution with a mean of 70 and the left tail below 67 is shaded. The second plot is labeled \"Jose\" and shows a normal distribution with a mean of 70 and a large portion of the normal distribution up to the value 76 shaded.]{0.45}{mikeAndJosePercentiles}}\n\n\\D{\\newpage}\n\nThe last several problems have focused on finding the\npercentile (lower tail) or the upper tail for a particular observation.\nWhat if you would like to know the observation corresponding\nto a particular percentile?\n\n\\begin{examplewrap}\n\\begin{nexample}{Erik's height is at the $40^{th}$ percentile.\n    How tall is he?}\\label{normalExam40Perc}\n  As always, first draw the picture.\\vspace{-4mm}\n  \\begin{center}\n  \\Figure{0.3}{height40Perc}\\vspace{-1mm}\n  \\end{center}\n  In this case, the lower tail probability is known (0.40),\n  which can be shaded on the diagram.\n  We want to find the observation that corresponds to this value.\n  As a first step in this direction, we determine the Z-score\n  associated with the $40^{th}$ percentile.\n  Using software, we can obtain the corresponding Z-score\n  of about -0.25.\n\n  Knowing $Z_{Erik} = -0.25$ and the population parameters\n  $\\mu = 70$ and $\\sigma = 3.3$ inches, the Z-score formula can be\n  set up to determine Erik's unknown height, labeled\n  $x_{_{\\text{Erik}}}$:\n  \\begin{align*}\n  -0.25\n    = Z_{_{\\text{Erik}}}\n    = \\frac{x_{_{\\text{Erik}}} - \\mu}{\\sigma}\n    = \\frac{x_{_{\\text{Erik}}} - 70}{3.3}\n  \\end{align*}\n  Solving for $x_{_{\\text{Erik}}}$ yields a height of 69.18 inches.\n  That is, Erik is about 5'9''.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the adult male height at the\n    $82^{nd}$ percentile?}\n  Again, we draw the figure first.\\vspace{-3mm}\n  \\begin{center}\n  \\Figure[A normal distribution with mean 70 and standard deviation 3.3 is shaded from the left up to a vertical line a bit above the distribution mean. The shaded area to the left of the vertical line is labeled as \"82\\% (0.82)\" and the upper, unshaded tail is labeled \"18\\% (0.18)\".]{0.28}{height82Perc}\\vspace{-1mm}\n  \\end{center}\n  Next, we want to find the Z-score at the $82^{nd}$ percentile,\n  which will be a positive value and can be found using software\n  as $Z = 0.92$.\n  Finally, the height $x$ is found using the Z-score formula\n  with the known mean $\\mu$, standard deviation $\\sigma$,\n  and Z-score $Z = 0.92$:\n  \\begin{align*}\n  0.92 = Z = \\frac{x-\\mu}{\\sigma} = \\frac{x - 70}{3.3}\n  \\end{align*}\n  This yields 73.04 inches or about 6'1'' as the height\n  at the $82^{nd}$ percentile.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe SAT scores follow $N(\\satmean{}, \\satsd{})$.\\footnotemark{} \\\\\n(a) What is the $95^{th}$ percentile for SAT scores? \\\\\n(b) What is the $97.5^{th}$ percentile for SAT scores?\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Short answers:\n  (a) $Z_{95} = 1.6449 \\to 1429$ SAT score.\n  (b) $Z_{97.5} = 1.96 \\to 1492$ SAT score.}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{more74Less69}\nAdult male heights follow $N(70.0$''$, 3.3$''$)$.\\footnotemark{} \\\\\n(a)~What is the probability that a randomly selected male\n    adult is at least 6'2'' (74 inches)? \\\\\n(b)~What is the probability that a male adult is shorter\n    than 5'9'' (69 inches)?\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Short answers:\n  (a) $Z = 1.21 \\to 0.8869$, then subtract this value\n      from 1 to get 0.1131.\n  (b) $Z = -0.30 \\to 0.3821$.}\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the probability that a random adult\n    male is between 5'9'' and 6'2''?}\n  These heights correspond to 69 inches and 74 inches.\n  First, draw the figure.\n  The area of interest is no longer an upper or lower\n  tail.\\vspace{-2mm}\n  \\begin{center}\n  \\Figure[A normal distribution is shown with mean 70 and standard deviation 3.3. An area from just below the mean (69) up to a value further into the right tail (74) is shaded.]{0.35}{between59And62}\\vspace{-2mm}\n  \\end{center}\n  The total area under the curve is~1.\n  If we find the area of the two tails that are not shaded\n  (from Guided Practice~\\ref{more74Less69}, these areas are\n  $0.3821$ and $0.1131$), then we can find the middle\n  area:\\vspace{-2mm}\n  \\begin{center}\n  \\Figure[A plot is shown where we take the full distribution (1.0000), subtract off a lower tail (0.3821) and a small upper tail (0.1131), leaving a normal distribution with just a segment shaded, from just below the mean to a modest amount above the mean, and this last shaded area is labeled 0.5048.]{0.55}{subtracting2Areas}\\vspace{-2mm}\n  \\end{center}\n  That is, the probability of being between 5'9'' and 6'2''\n  is 0.5048.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nSAT scores follow $N(\\satmean{}, \\satsd{})$.\nWhat percent of SAT takers get between \\satmean{} and\n1400?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{This is an abbreviated solution.\n  (Be sure to draw a figure!)\n  First find the percent who get below \\satmean{}\n  and the percent that get above 1400:\n  $Z_{\\satmean{}} = 0.00 \\to 0.5000$ (area below),\n  $Z_{1400} = 1.5 \\to 0.0668$ (area above).\n  Final answer: $1.0000 - 0.5000 - 0.0668 = 0.4332$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nAdult male heights follow $N(70.0$''$, 3.3$''$)$.\nWhat percent of adult males are between 5'5''\nand 5'7''?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{5'5'' is 65 inches ($Z = -1.52$).\n  5'7'' is 67 inches ($Z = -0.91$).\n  Numerical solution: $1.000 - 0.0643 - 0.8186 = 0.1171$,\n  i.e. 11.71\\%.}\n\n\n\\D{\\newpage}\n\n\\subsection{68-95-99.7 rule}\n\nHere, we present a useful rule of thumb for the probability of falling within 1, 2, and 3 standard deviations of the mean in the normal distribution. This will be useful in a wide range of practical settings, especially when trying to make a quick estimate without a calculator or Z-table.\n\n\\begin{figure}[hht]\n\\centering\n\\Figure[A normal distribution is shown. The central region, from one standard deviation below the mean to one standard deviation above the mean, is shaded blue and is labeled with a value of 68\\%. The region further out to two standard deviations below the mean to two standard deviations above the mean is shaded green (besides the portion shaded blue) and is labeled with a value of 95\\%. The region further out to three standard deviations below the mean to three standard deviations above the mean is shaded yellow (besides the portions shaded green or blue) and is labeled with a value of 99.7\\%. Those percentages -- 68\\%, 95\\%, and 99.7\\% -- represent the portions of the area below a normal distribution within 1, 2, and 3 standard deviations of the mean.]{0.63}{6895997}\n\\caption{Probabilities for falling within 1, 2, and 3 standard deviations of the mean in a normal distribution.}\n\\label{6895997}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nUse software, a calculator, or a probability table\nto confirm that about 68\\%, 95\\%, and 99.7\\%\nof observations fall within 1, 2, and 3, standard deviations\nof the mean in the normal distribution, respectively.\nFor instance, first find the area that falls between $Z=-1$\nand $Z=1$, which should have an area of about 0.68.\nSimilarly there should be an area of about 0.95 between\n$Z=-2$ and $Z=2$.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{First draw the pictures.\n  Using software, we get 0.6827 within 1~standard deviation,\n  0.9545 within 2~standard deviations,\n  and 0.9973 within 3~standard deviations.}\n\nIt is possible for a normal random variable to fall 4,~5,\nor~even more standard deviations from the mean.\nHowever, these occurrences are very rare if the data are\nnearly normal.\nThe probability of being further than 4 standard deviations\nfrom the mean is about 1-in-15,000.\nFor 5 and 6 standard deviations, it is about 1-in-2 million\nand 1-in-500 million, respectively.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nSAT scores closely follow the normal model with mean\n$\\mu = \\satmean{}$ and standard deviation\n$\\sigma = \\satsd{}$.\\footnotemark{} \\\\\n(a) About what percent of test takers score 700 to 1500? \\\\\n(b) What percent score between \\satmean{} and 1500?\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) 700 and 1500 represent two standard deviations\n  below and above the mean, which means about 95\\% of test takers\n  will score between 700 and 1500.\n  (b)~We found that 700 to 1500 represents about 95\\% of test\n  takers.\n  These test takers would be evenly split by the center of\n  the distribution, \\satmean{},\n  so $\\frac{95\\%}{2} = 47.5\\%$ of all test takers\n  score between \\satmean{} and 1500.}\n\n\n{\\input{ch_distributions/TeX/normal_distribution.tex}}\n\n\n\n\n%%_________________\n%\\section{Evaluating the normal approximation}\n%\\label{assessingNormal}\n%\n%Many processes can be well approximated by the normal distribution.\n%We have already seen two good examples:\n%SAT scores and the heights of US adult males.\n%While using a normal model can be extremely convenient\n%and helpful, it is important to remember normality is\n%always an approximation.\n%Evaluating the appropriateness of the normal assumption\n%is a key step in many data analyses.\n%\n%\\index{normal probability plot|(}\n%\n%Example~\\ref{normalExam40Perc} in Section~\\ref{normalDist}\n%suggested the distribution of heights of US males is well\n%approximated by the normal model.\n%We are interested in proceeding under the assumption that\n%the data are normally distributed, but first we must check\n%to see if this is reasonable.\n%\n%There are two visual methods for checking the assumption of\n%normality, which can be implemented and interpreted quickly.\n%The first is a simple histogram with the best fitting normal\n%curve overlaid on the plot, as shown in the left panel of\n%Figure~\\ref{fcidMHeights}.\n%The sample mean $\\bar{x}$ and standard deviation $s$ are used\n%as the parameters of the best fitting normal curve.\n%The closer this curve fits the histogram, the more reasonable\n%the normal model assumption.\n%Another common method is examining a\n%\\term{normal probability plot},\\footnote{Also commonly\n%  called a \\term{quantile-quantile plot}.}\n%shown in the right panel of Figure~\\ref{fcidMHeights}.\n%The closer the points are to a perfect straight line,\n%the more confident we can be that the data follow the\n%normal model.\n%\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figure{0.7}{fcidMHeights}\n%  \\caption{A sample of 100 male heights.\n%      The observations are rounded to the nearest whole inch,\n%      explaining why the points appear to jump in increments\n%      in the normal probability plot.}\n%  \\label{fcidMHeights}\n%\\end{figure}\n%\n%\\begin{examplewrap}\n%\\begin{nexample}{Three data sets of 40, 100, and 400\n%    samples were simulated from a normal distribution,\n%    and the histograms and normal probability plots\n%    of the data sets are shown in Figure~\\ref{normalExamples}.\n%    These will provide a benchmark for what to look for\n%    in plots of real data.}\n%  \\label{normalExamplesExample}%\n%  The left panels show the histogram (top) and normal\n%  probability plot (bottom) for the simulated data set\n%  with 40 observations.\n%  The data set is too small to really see clear structure\n%  in the histogram.\n%  The normal probability plot also reflects this,\n%  where there are some deviations from the line.\n%  We should expect deviations of this amount for\n%  such a small data set.\n%\n%  The middle panels show diagnostic plots for the\n%  data set with 100 simulated observations.\n%  The histogram shows more normality and the normal\n%  probability plot shows a better fit.\n%  While there are a few observations that deviate\n%  noticeably from the line, they are not particularly\n%  extreme.\n%\n%  The data set with 400 observations has a histogram\n%  that greatly resembles the normal distribution,\n%  while the normal probability plot is nearly a perfect\n%  straight line.\n%  Again in the normal probability plot there is one\n%  observation (the largest) that deviates slightly from\n%  the line.\n%  If that observation had deviated 3 times further from\n%  the line, it would be of greater importance in a real\n%  data set.\n%  Apparent outliers can occur in normally distributed\n%  data but they are rare.\n%\n%  Notice the histograms look more normal as the sample\n%  size increases, and the normal probability plot becomes\n%  straighter and more stable.\n%\\end{nexample}\n%\\end{examplewrap}\n%\n%\\begin{figure}\n%  \\centering\n%  \\Figure{0.9}{normalExamples}\n%  \\caption{Histograms and normal probability plots for\n%      three simulated normal data sets; $n=40$ (left),\n%      $n=100$ (middle), $n=400$ (right).}\n%  \\label{normalExamples}\n%\\end{figure}\n%\n%\\begin{examplewrap}\n%\\begin{nexample}{Are NBA player heights normally distributed?\n%    Consider all 494 NBA players presented in\n%    Figure~\\ref{nbaNormal}.}\n%  We first create a histogram and normal probability plot\n%  of the NBA player heights.\n%  The histogram in the left panel appears to have too few\n%  observations at the upper end since the curve is notably\n%  above the histogram.\n%  The points in the normal probability plot\n%  follow a straight line for much of the center of the\n%  distribution, and then deviates more at the upper values.\n%  We can compare these characteristics to the sample of\n%  400 normally distributed observations in\n%  Example~\\ref{normalExamplesExample} and see that they\n%  represent much stronger deviations from the normal model.\n%  NBA player heights do not appear to come from a normal\n%  distribution.\n%\\end{nexample}\n%\\end{examplewrap}\n%\n%\\begin{examplewrap}\n%\\begin{nexample}{Can we approximate poker winnings by a normal distribution? We consider the poker winnings of an individual over 50 days. A histogram and normal probability plot of these data are shown in Figure~\\ref{pokerNormal}.}\n%The data are very strongly right skewed\\index{skew!example: very strong} in the histogram, which corresponds to the very strong deviations on the upper right component of the normal probability plot. If we compare these results to the sample of 40 normal observations in Example~\\ref{normalExamplesExample}, it is apparent that these data show very strong deviations from the normal model.\n%\\end{nexample}\n%\\end{examplewrap}\n%\n%\\begin{figure}\n%  \\centering\n%  \\Figure{0.8}{nbaNormal}\n%  \\caption{Histogram and normal probability plot\n%      for the NBA heights from the 2008-9 season.}\n%  \\label{nbaNormal}\n%\\end{figure}\n%\n%\\begin{figure}\n%  \\centering\n%  \\Figure{0.9}{pokerNormal}\n%  \\caption{A histogram of poker data with the best\n%      fitting normal plot and a normal probability plot.}\n%  \\label{pokerNormal}\n%\\end{figure}\n%\n%\\begin{exercisewrap}\n%\\begin{nexercise}\\label{normalQuantileExercise}%\n%Determine which data sets represented in\n%Figure~\\ref{normalQuantileExer} plausibly come from\n%a nearly normal distribution.\n%Are you confident in all of your conclusions?\n%There are 100 (top left), 50 (top right), 500 (bottom left),\n%and 15 points (bottom right) in the four plots.\\footnotemark{}\n%\\end{nexercise}\n%\\end{exercisewrap}\n%\\footnotetext{Answers may vary a little.\n%  The top-left plot shows some deviations in the smallest values\n%  in the data set;\n%  specifically, the left tail of the data set has some outliers\n%  we should be wary of.\n%  The top-right and bottom-left plots do not show any obvious\n%  or extreme deviations from the lines for their respective\n%  sample sizes, so a normal model would be reasonable for these\n%  data sets.\n%  The bottom-right plot has a consistent curvature that suggests\n%  it is not from the normal distribution.\n%  If we examine just the vertical coordinates of these\n%  observations, we see that there is a lot of data between\n%  -20 and 0, and then about five observations scattered\n%  between 0 and 70.\n%  This describes a distribution that has a strong right skew.}\n%\n%\\begin{figure}\n%  \\centering\n%  \\Figure{0.7}{normalQuantileExer}\n%  \\caption{Four normal probability plots for\n%      Guided Practice~\\ref{normalQuantileExercise}.}\n%  \\label{normalQuantileExer}\n%\\end{figure}\n%\n%\\begin{exercisewrap}\n%\\begin{nexercise}\n%\\label{normalQuantileExerciseAdditional}%\n%Figure~\\ref{normalQuantileExerAdditional} shows normal\n%probability plots for two distributions that are skewed.\n%One distribution is skewed to the low end (left skewed)\n%and the other to the high end (right skewed).\n%Which is which?\\footnotemark{}\n%\\end{nexercise}\n%\\end{exercisewrap}\n%\\footnotetext{Examine where the points fall along the\n%  vertical axis.\n%  In the first plot, most points are near the low end\n%  with fewer observations scattered along the high end;\n%  this describes a distribution that is skewed to the\n%  high end.\n%  The second plot shows the opposite features,\n%  and this distribution is skewed to the low end.}\n%\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figures{0.8}{normalQuantileExer}{normalQuantileExerAdditional}\n%  \\caption{Normal probability plots for\n%      Guided Practice~\\ref{normalQuantileExerciseAdditional}.}\n%  \\label{normalQuantileExerAdditional}\n%\\end{figure}\n%\n%\\index{normal probability plot|)}\n\\index{normal distribution|)}\n\\index{distribution!normal|)}\n\n\n\n\n%_________________\n\\section{Geometric distribution}\n\\label{geomDist}\n\nHow long should we expect to flip a coin until it turns up \\resp{heads}? Or how many times should we expect to roll a die until we get a \\resp{1}? These questions can be answered using the geometric distribution. We first formalize each trial -- such as a single coin flip or die toss -- using the Bernoulli distribution, and then we combine these with our tools from probability (Chapter~\\ref{probability}) to construct the geometric distribution.\n\n\\subsection{Bernoulli distribution}\n\\label{bernoulli}\n\n\\newcommand{\\insureSprob}{0.7}\n\\newcommand{\\insureSperc}{70\\%}\n\\newcommand{\\insureFprob}{0.3}\n\\newcommand{\\insureFperc}{30\\%}\n\\newcommand{\\insureDistA}{0.7}\n\\newcommand{\\insureDistB}{0.21}\n\\newcommand{\\insureDistC}{0.063}\n\\newcommand{\\insureDistD}{0.019}\n\\newcommand{\\insureDistE}{0.006}\n\\newcommand{\\insureCDistA}{0.7}\n\\newcommand{\\insureCDistB}{0.91}\n\\newcommand{\\insureCDistC}{0.973}\n\\newcommand{\\insureCDistCComplement}{0.027}\n\\newcommand{\\insureCDistD}{0.992}\n\\newcommand{\\insureCDistE}{0.998}\n\\newcommand{\\insureGeomMean}{1.43}\n\n\\index{distribution!Bernoulli|(}\n\nMany health insurance plans in the United States have\na deductible, where the insured individual is responsible\nfor costs up to the deductible, and then the costs above\nthe deductible are shared between the individual and\ninsurance company for the remainder of the year.\n\nSuppose a health insurance company found that \\insureSperc{} of the\npeople they insure stay below their deductible in any given year.\nEach of these people can be thought of as a \\term{trial}.\nWe label a person a \\term{success} if her healthcare costs\ndo not exceed the deductible.\nWe label a person a \\term{failure} if she does exceed her\ndeductible in the year.\nBecause 70\\% of the individuals will not hit their deductible,\nwe denote the \\term{probability of a success} as\n$p = \\insureSprob{}$.\nThe probability of a failure is sometimes denoted with\n$q = 1 - p$, which would be \\insureFprob{} for the insurance\nexample.\n\nWhen an individual trial only has two possible outcomes, often\nlabeled as \\resp{success} or \\resp{failure}, it is called a\n\\termsub{Bernoulli random variable}{distribution!Bernoulli}.\nWe chose to label a person who does not hit her deductible\nas a ``success'' and all others as ``failures''.\nHowever, we could just as easily have reversed these labels.\nThe mathematical framework we will build does not depend\non which outcome is labeled a success and which a failure,\nas long as we are consistent.\n\nBernoulli random variables are often denoted as \\resp{1}\nfor a success and \\resp{0} for a failure.\nIn addition to being convenient in entering data,\nit is also mathematically handy.\nSuppose we observe ten trials:\n\\begin{center}\n\\resp{1} \\resp{1} \\resp{1} \\resp{0} \\resp{1} \\resp{0} \\resp{0} \\resp{1} \\resp{1} \\resp{0}\n\\end{center}\nThen the \\term{sample proportion}, $\\hat{p}$, is the\nsample mean of these observations:\n\\begin{align*}\n\\hat{p} = \\frac{\\text{\\# of successes}}{\\text{\\# of trials}}\n    = \\frac{1+1+1+0+1+0+0+1+1+0}{10} = 0.6\n\\end{align*}%\nThis mathematical inquiry of Bernoulli random variables can\nbe extended even further.\n%\\Comment{Maybe the next footnote should instead be an EOCE?}\nBecause \\resp{0} and \\resp{1} are numerical outcomes,\nwe can define the {mean} and {standard deviation}\nof a Bernoulli random variable.\n(See Exercises~\\ref{bernoulli_mean_derivation}\nand~\\ref{bernoulli_sd_derivation}.)\n\n\\begin{onebox}{Bernoulli random variable}\n%  A Bernoulli random variable has exactly two possible\n%  outcomes, often labeled \\resp{1} for the ``success''\n%  outcome and \\resp{0} for the ``failure'' outcome.\\vspace{3mm}\n  If $X$ is a random variable that takes value 1 with\n  probability of success $p$ and 0 with probability $1-p$,\n  then $X$ is a Bernoulli random variable with mean\n  and standard deviation\n  \\begin{align*}\n  \\mu &= p\n      &\\sigma&= \\sqrt{p(1-p)}\n  \\end{align*}\n\\end{onebox}\n\nIn general, it is useful to think about a Bernoulli random variable as a random process with only two outcomes: a success or failure. Then we build our mathematical framework using the numerical labels \\resp{1} and \\resp{0} for successes and failures, respectively.\n\n\\index{distribution!Bernoulli|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Geometric distribution}\n\n\\index{distribution!geometric|(}\n\nThe \\termsub{geometric distribution}{distribution!geometric}\nis used to describe how\nmany trials it takes to observe a success.\nLet's first look at an example.\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose we are working at the insurance\n    company and need to find a case where the person did\n    not exceed her (or his) deductible as a case study.\n    If the probability a person will not exceed her\n    deductible is \\insureSprob{} and we are drawing people\n    at random, what are the chances that the first person\n    will not have exceeded her deductible, i.e. be a success?\n    The second person?\n    The third?\n    What about we pull $n - 1$ cases before we find\n    the first success, i.e. the first success is the\n    $n^{th}$ person?\n    (If the first success is the fifth person, then we say $n=5$.)}\n  \\label{waitForDeductible}%\n  The probability of stopping after the first person is just\n  the chance the first person will not hit her (or his)\n  deductible:~\\insureSprob{}.\n  The probability the second person is the first to hit\n  her deductible is\n  \\begin{align*}\n  &P(\\text{second person is the first to not hit deductible}) \\\\\n  &\\quad\n    = P(\\text{the first will, the second won't})\n    = (\\insureFprob{})(\\insureSprob{})\n    = \\insureDistB{}\n  \\end{align*}\n  Likewise, the probability it will be the third case is\n  $(\\insureFprob{})(\\insureFprob{})(\\insureSprob{})\n    = \\insureDistC$.\n\n  If the first success is on the $n^{th}$ person,\n  then there are $n-1$ failures and finally 1 success,\n  which corresponds to the probability\n  $(\\insureFprob{})^{n-1}(\\insureSprob{})$.\n  This is the same as\n  $(1-\\insureSprob{})^{n-1}(\\insureSprob{})$.\n\\end{nexample}\n\\end{examplewrap}\n\nExample~\\ref{waitForDeductible} illustrates what the\n\\termsub{geometric distribution}{distribution!geometric},\nwhich describes the waiting\ntime until a success for\n\\term{independent and identically distributed (iid)}\nBernoulli random variables.\nIn this case, the \\emph{independence} aspect just means\nthe individuals in the example don't affect each other,\nand \\emph{identical} means they each have the same probability\nof success.\n\nThe geometric distribution from Example~\\ref{waitForDeductible} is shown in Figure~\\ref{geometricDist70}. In general, the probabilities for a geometric distribution decrease \\term{exponentially} fast.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[The probability distribution of \"Number of Trials Until a Success for p = 0.7\" is shown, which appears as a bar plot. The possible values shown are 1, 2, 3, 4, 5, 6, 7, and 8. The probabilities for these are about 0.7, 0.21, 0.07, 0.02, 0.01, and then the values are nearly indistinguishable for the values of 6, 7, and 8.]{0.8}{geometricDist70}\n  \\caption{The geometric distribution when the probability\n      of success is $p = \\insureSprob{}$.}\n  \\label{geometricDist70}\n\\end{figure}\n\nWhile this text will not derive the formulas for the mean (expected) number of trials needed to find the first success or the standard deviation or variance of this distribution, we present general formulas for each.\n\n\\begin{onebox}{Geometric Distribution}\n  \\index{distribution!geometric|textbf}%\n  If the probability of a success in one trial is $p$\n  and the probability of a failure is $1-p$, then the\n  probability of finding the first success in the\n  $n^{th}$ trial is given by\\vspace{-1.5mm}\n  \\begin{align*}\n  (1-p)^{n-1}p\n  \\end{align*}\n  The mean (i.e. expected value), variance,\n  and standard deviation of this wait time are given by\n  \\begin{align*}\n  \\mu &= \\frac{1}{p}\n      &\\sigma^2 &=\\frac{1-p}{p^2}\n      &\\sigma &= \\sqrt{\\frac{1-p}{p^2}}\n  \\end{align*}\n\\end{onebox}\n\nIt is no accident that we use the symbol $\\mu$ for both the mean and expected value. The mean and the expected value are one and the same.\n\nIt takes, on average, $1/p$ trials to get a success under the geometric distribution. This mathematical result is consistent with what we would expect intuitively. If the probability of a success is high (e.g. 0.8), then we don't usually wait very long for a success: $1/0.8 = 1.25$ trials on average. If the probability of a success is low (e.g. 0.1), then we would expect to view many trials before we see a success: $1/0.1 = 10$ trials.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe probability that a particular case would not exceed their\ndeductible is said to be \\insureSprob{}.\nIf we were to examine cases until we found one that where\nthe person did not hit her deductible, how many cases should\nwe expect to check?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We would expect to see about\n    $1 / \\insureSprob{} \\approx \\insureGeomMean{}$\n    individuals to find the first success.}\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the chance that we would find\n    the first success within the first 3 cases?}\n  \\label{insureFirstSuccessInLT4}%\n  This is the chance it is the first ($n=1$), second ($n=2$),\n  or third ($n=3$) case is the first success, which are three\n  disjoint outcomes.\n  Because the individuals in the sample are randomly sampled\n  from a large population, they are independent.\n  We compute the probability of each case and add the separate\n  results:\n  \\begin{align*}\n  &P(n=1, 2, \\text{ or }3) \\\\\n    & \\quad = P(n=1)+P(n=2)+P(n=3) \\\\\n    & \\quad = (\\insureFprob{})^{1-1}(\\insureSprob{})\n        + (\\insureFprob{})^{2-1}(\\insureSprob{})\n        + (\\insureFprob{})^{3-1}(\\insureSprob{}) \\\\\n    & \\quad = \\insureCDistC{}\n  \\end{align*}\n  There is a probability of \\insureCDistC{} that we would\n  find a successful case within 3 cases.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nDetermine a more clever way to solve Example~\\ref{insureFirstSuccessInLT4}.\nShow that you get the same result.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{First find the probability of the complement:\n  $P($no success in first 3~trials$)\n      = \\insureFprob{}^3 = \\insureCDistCComplement{}$.\n  Next, compute one minus this probability:\n  $1 - P($no success in 3 trials$)\n      = 1 - \\insureCDistCComplement{}\n      = \\insureCDistC{}$.}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose a car insurer has determined\n    that 88\\% of its drivers will not exceed their deductible\n    in a given year.\n    If someone at the company were to randomly draw\n    driver files until they found one that had not exceeded\n    their deductible, what is the expected number of drivers\n    the insurance employee must check?\n    What is the standard deviation of the number of driver files\n    that must be drawn?}\n  \\label{carInsure08DrawOne}%\n  In this example, a success is again when someone will not\n  exceed the insurance deductible, which has probability\n  $p = 0.88$.\n  The expected number of people to be checked is\n  $1 / p = 1 / 0.88 = 1.14$ and the standard deviation is\n  $\\sqrt{(1-p)/p^2} = 0.39$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nUsing the results from Example~\\ref{carInsure08DrawOne},\n$\\mu = 1.14$ and $\\sigma = 0.39$, would it be appropriate\nto use the normal model to find what proportion\nof experiments would end in 3 or fewer trials?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{No. The geometric distribution is always\n  right skewed and can never be well-approximated by the\n  normal model.}\n\nThe independence assumption is crucial to the geometric\ndistribution's accurate description of a scenario.\nMathematically, we can see that to construct the probability\nof the success on the $n^{th}$ trial, we had to use the\nMultiplication Rule for Independent Processes.\nIt is no simple task to generalize the geometric model\nfor dependent trials.\n\n\\index{distribution!geometric|)}\n\n\n{\\input{ch_distributions/TeX/geometric_distribution.tex}}\n\n\n\n\n\n\\section{Binomial distribution}\n\\label{binomialModel}\n\n\\index{distribution!binomial|(}\n\nThe \\termsub{binomial distribution}{distribution!binomial}\nis used to describe\nthe number of successes in a fixed number of trials.\n%,\n%and this distribution is occasionally used in statistics,\n%especially when doing more careful analysis of samples\n%of data where simpler tools are not helpful.\nThis is different from the geometric distribution,\nwhich described the number of trials we must wait before\nwe observe a success.\n\n\n\\subsection{The binomial distribution}\n\n%\\newcommand{\\insureSprob}{0.7}\n%\\newcommand{\\insureSperc}{70\\%}\n%\\newcommand{\\insureFprob}{0.3}\n%\\newcommand{\\insureFperc}{30\\%}\n%\\newcommand{\\insureDistA}{0.7}\n%\\newcommand{\\insureDistB}{0.21}\n%\\newcommand{\\insureDistC}{0.063}\n%\\newcommand{\\insureDistD}{0.019}\n%\\newcommand{\\insureDistE}{0.006}\n%\\newcommand{\\insureCDistA}{0.7}\n%\\newcommand{\\insureCDistB}{0.91}\n%\\newcommand{\\insureCDistC}{0.973}\n%\\newcommand{\\insureCDistCComplement}{0.027}\n%\\newcommand{\\insureCDistD}{0.992}\n%\\newcommand{\\insureCDistE}{0.998}\n%\\newcommand{\\insureGeomMean}{1.43}\n\\newcommand{\\insureS}{\\resp{not}}\n\\newcommand{\\insureF}{\\resp{exceed}}\n% Doesn't consider binomial coefficient in next calculated value.\n\\newcommand{\\insureBinomCinDSingleScenario}{0.103}\n\\newcommand{\\insureBinomCinD}{0.412}\n\\newcommand{\\insureBinomEinHSingleScenario}{0.00454}\n\\newcommand{\\insureBinomEinH}{0.254}\n\\newcommand{\\insureBinomFourtyExpValue}{28}\n\\newcommand{\\insureBinomFourtySD}{2.9}\n\\newcommand{\\insureBinomFourtyLower}{22}\n\\newcommand{\\insureBinomFourtyUpper}{34}\n\n\\noindent%\nLet's again imagine ourselves back at the insurance agency\nwhere \\insureSperc{} of individuals do not exceed their\ndeductible.\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose the insurance agency is considering\n    a random sample of four individuals they insure.\n    What is the chance exactly one of them will exceed\n    the deductible and the other three will not?\n    Let's call the four people\n    Ariana ($A$),\n    Brittany ($B$),\n    Carlton ($C$),\n    and Damian ($D$)\n    for convenience.}\n  \\label{insureOneOfFourExceedsDeductible}%\n  Let's consider a scenario where one person exceeds\n  the deductible:\n  \\begin{align*}\n  &P(A=\\text{\\insureF{}},\n      \\text{ }B=\\text{\\insureS{}},\n      \\text{ }C=\\text{\\insureS{}},\n      \\text{ }D=\\text{\\insureS{}}) \\\\\n    &\\quad = P(A=\\text{\\insureF{}})\\ \n        P(B=\\text{\\insureS{}})\\ \n        P(C=\\text{\\insureS{}})\\ \n        P(D=\\text{\\insureS{}}) \\\\\n    &\\quad =  (\\insureFprob{})\n        (\\insureSprob{})\n        (\\insureSprob{})\n        (\\insureSprob{}) \\\\\n    &\\quad = (\\insureSprob{})^3 (\\insureFprob{})^1 \\\\\n    &\\quad = \\insureBinomCinDSingleScenario{}\n  \\end{align*}\n  But there are three other scenarios: Brittany, Carlton,\n  or Damian could have been the one to exceed the deductible.\n  In each of these cases, the probability is again\n  $(\\insureSprob{})^3 (\\insureFprob{})^1$.\n  These four scenarios exhaust all the possible ways that\n  exactly one of these four people could have exceeded\n  the deductible, so the total probability is\n  $4 \\times (\\insureSprob{})^3 (\\insureFprob{})^1\n      = \\insureBinomCinD{}$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nVerify that the scenario where Brittany is the only one\nto exceed the deductible has probability\n$(\\insureSprob{})^3 (\\insureFprob{})^1$.~\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{\n  $P(A=\\text{\\insureS{}},\n      \\text{ }B=\\text{\\insureF{}},\n      \\text{ }C=\\text{\\insureS{}},\n      \\text{ }D=\\text{\\insureS{}})\n    = (\\insureSprob{})(\\insureFprob{})\n        (\\insureSprob{})(\\insureSprob{})\n    = (\\insureSprob{})^3 (\\insureFprob{})^1$.}\n\n\nThe scenario outlined in Example~\\ref{insureOneOfFourExceedsDeductible} is an\nexample of a binomial distribution scenario.\nThe \\termsub{binomial distribution}{distribution!binomial}\ndescribes the probability of having exactly $k$ successes\nin $n$ independent Bernoulli trials with probability\nof a success $p$\n(in Example~\\ref{insureOneOfFourExceedsDeductible},\n$n=4$, $k=3$, $p=\\insureSprob{}$).\nWe would like to determine the probabilities associated\nwith the binomial distribution more generally,\ni.e. we want a formula where we can use $n$, $k$, and $p$\nto obtain the probability.\nTo do this, we reexamine each part of\nExample~\\ref{insureOneOfFourExceedsDeductible}.\n\nThere were four individuals who could have been the one\nto exceed the deductible, and each of these four scenarios\nhad the same probability.\nThus, we could identify the final probability as\n\\begin{align*}\n[\\text{\\# of scenarios}] \\times P(\\text{single scenario})\n\\end{align*}\nThe first component of this equation is the number of ways\nto arrange the $k=3$ successes among the $n=4$ trials.\nThe second component is the probability of any of the four\n(equally probable) scenarios.\n\n\\D{\\newpage}\n\nConsider $P($single scenario$)$ under the general case of\n$k$ successes and $n-k$ failures in the $n$ trials.\nIn any such scenario, we apply the Multiplication Rule\nfor independent events:\n\\begin{align*}\np^k (1 - p)^{n - k}\n\\end{align*}\nThis is our general formula for $P($single scenario$)$.\n\nSecondly, we introduce a general formula for the number\nof ways to choose $k$ successes in $n$ trials,\ni.e. arrange $k$ successes and $n - k$ failures:\n\\begin{align*}\n{n\\choose k} = \\frac{n!}{k! (n - k)!}\n\\end{align*}\nThe quantity ${n\\choose k}$ is read\n\\term{n choose k}.\\footnote{Other notation for\n  $n$ choose $k$ includes $_nC_k$, $C_n^k$, and $C(n,k)$.}\nThe exclamation point notation (e.g. $k!$) denotes\na \\term{factorial} expression.\\label{factorial_defined}\n\\begin{align*}\n& 0! = 1 \\\\\n& 1! = 1 \\\\\n& 2! = 2\\times1 = 2 \\\\\n& 3! = 3\\times2\\times1 = 6 \\\\\n& 4! = 4\\times3\\times2\\times1 = 24 \\\\\n& \\vdots \\\\\n& n! = n\\times(n-1)\\times...\\times3\\times2\\times1\n\\end{align*}\nUsing the formula, we can compute the number of ways\nto choose $k = 3$ successes in $n = 4$ trials:\n\\begin{align*}\n{4 \\choose 3} = \\frac{4!}{3!(4-3)!}\n  = \\frac{4!}{3!1!} \n  = \\frac{4\\times3\\times2\\times1}{(3\\times2\\times1) (1)}\n  = 4\n\\end{align*}\nThis result is exactly what we found by carefully thinking\nof each possible scenario in\nExample~\\ref{insureOneOfFourExceedsDeductible}.\n\nSubstituting $n$ choose $k$ for the number of scenarios\nand $p^k(1-p)^{n-k}$ for the single scenario probability\nyields the general binomial formula.\n\n\\begin{onebox}{Binomial distribution}\n  Suppose the probability of a single trial being\n  a success is $p$.\n  Then the probability of observing exactly $k$ successes\n  in $n$ independent trials is given by\\vspace{-1mm}\n  \\begin{align*}\n  {n\\choose k}p^k(1-p)^{n-k} = \\frac{n!}{k!(n-k)!}p^k(1-p)^{n-k}\n  \\end{align*}\n  The mean, variance, and standard deviation\n  of the number of observed successes are\\vspace{-2mm}\n  \\begin{align*}\n  \\mu &= np\n  &\\sigma^2 &= np(1-p)\n  &\\sigma&= \\sqrt{np(1-p)}\n  \\end{align*}\n\\end{onebox}\n\n\\begin{onebox}{Is it binomial? Four conditions to check.}\n  \\label{isItBinomialTipBox}%\n  (1) The trials are independent. \\\\\n  (2) The number of trials, $n$, is fixed. \\\\\n  (3) Each trial outcome can be classified as a \\emph{success}\n      or \\emph{failure}. \\\\\n  (4) The probability of a success, $p$, is the same for\n      each trial.\n\\end{onebox}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the probability that 3 of 8 randomly\n    selected individuals will have exceeded the insurance\n    deductible, i.e. that 5 of 8 will not exceed the deductible?\n    Recall that 70\\% of individuals will not exceed the\n    deductible.}\n  We would like to apply the binomial model,\n  so we check the conditions.\n  The number of trials is fixed ($n = 8$) (condition 2)\n  and each trial outcome can be classified as a success\n  or failure (condition 3).\n  Because the sample is random, the trials are independent\n  (condition~1) and the probability of a success is the same\n  for each trial (condition~4).\n\n  In the outcome of interest, there are $k = 5$ successes\n  in $n = 8$ trials (recall that a success is an individual\n  who does \\emph{not} exceed the deductible), and the\n  probability of a success is $p = \\insureSprob{}$.\n  So the probability that 5 of 8 will not exceed the\n  deductible and 3 will exceed the deductible is given by\n  \\begin{align*}\n  { 8 \\choose 5}(\\insureSprob{})^5\n  (1-\\insureSprob{})^{8-5}\n    &= \\frac{8!}{5!(8-5)!}\n        (\\insureSprob{})^5(1-\\insureSprob{})^{8-5} \\\\\n    &= \\frac{8!}{5!3!}\n        (\\insureSprob{})^5(\\insureFprob{})^3\n  \\end{align*}\n  Dealing with the factorial part:\n  \\begin{align*}\n  \\frac{8!}{5!3!}\n    = \\frac{8\\times7\\times6\\times5\\times4\\times3\\times2\\times1}\n        {(5\\times4\\times3\\times2\\times1)(3\\times2\\times1)}\n    = \\frac{8\\times7\\times6}{3\\times2\\times1}\n    = 56\n  \\end{align*}\n  Using $(\\insureSprob{})^5(\\insureFprob{})^3\n    \\approx \\insureBinomEinHSingleScenario{}$,\n  the final probability is about\n  $56 \\times \\insureBinomEinHSingleScenario{}\n    \\approx \\insureBinomEinH{}$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{Computing binomial probabilities}\n  The first step in using the binomial model is to check\n  that the model is appropriate.\n  The second step is to identify $n$, $p$, and $k$.\n  As the last stage use software or the formulas\n  to determine the probability, then interpret the results.%\n  \\vspace{3mm}\n\n  If you must do calculations by hand, it's often useful\n  to cancel out as many terms as possible in the top and\n  bottom of the binomial coefficient.\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIf we randomly sampled 40 case files from the insurance agency\ndiscussed earlier, how many of the cases would you expect to not\nhave exceeded the deductible in a given year?\nWhat is the standard deviation of the number that would not\nhave exceeded the deductible?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We are asked to determine the expected number\n  (the mean) and the standard deviation, both of which can\n  be directly computed from the formulas:\n  $\\mu = np = 40 \\times \\insureSprob{}\n    = \\insureBinomFourtyExpValue$\n  and $\\sigma = \\sqrt{np(1-p)}\n    = \\sqrt{40\\times \\insureSprob{}\\times \\insureFprob{}}\n    = \\insureBinomFourtySD{}$.\n  Because very roughly 95\\% of observations fall within\n  2~standard deviations of the mean\n  (see Section~\\ref{variability}), we would probably observe\n  at least \\insureBinomFourtyLower{}\n  but fewer than \\insureBinomFourtyUpper{} individuals\n  in our sample who would not exceed the deductible.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe probability that a random smoker will develop a severe\nlung condition in his or her lifetime is about $0.3$.\nIf you have 4 friends who smoke, are the conditions for the\nbinomial model satisfied?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{One possible answer:\n  if the friends know each other, then the independence\n  assumption is probably not satisfied.\n  For example, acquaintances may have similar smoking habits,\n  or those friends might make a pact to quit together.}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{noMoreThanOneFriendWSevereLungCondition}%\nSuppose these four friends do not know each other\nand we can treat them as if they were a random sample\nfrom the population.\nIs the binomial model appropriate?\nWhat is the probability that\\footnotemark{}\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}\n\\item\n    None of them will develop a severe lung condition?\n\\item\n    One will develop a severe lung condition?\n\\item\n    That no more than one will develop a severe lung condition?\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{To check if the binomial model is appropriate,\n  we must verify the conditions.\n  (i)~Since we are supposing we can treat the friends\n  as a random sample, they are independent.\n  (ii)~We have a fixed number of trials ($n=4$).\n  (iii)~Each outcome is a success or failure.\n  (iv)~The probability of a success is the same for each\n  trials since the individuals are like a random sample\n  ($p=0.3$ if we say a ``success'' is someone getting\n  a lung condition, a morbid choice).\n  Compute parts~(a) and~(b) using the binomial formula:\n  $P(0)\n    = {4 \\choose 0} (0.3)^0 (0.7)^4\n    = 1\\times1\\times0.7^4\n    = 0.2401$,\n  $P(1)\n    = {4 \\choose 1} (0.3)^1(0.7)^{3}\n    = 0.4116$.\n  Note: $0!=1$.\n  Part~(c) can be computed as the sum of parts~(a) and~(b):\n  $P(0) + P(1) = 0.2401 + 0.4116 = 0.6517$.\n  That is, there is about a 65\\% chance that no more than\n  one of your four smoking friends will develop a severe\n  lung condition.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat is the probability that at least 2 of your 4 smoking\nfriends will develop a severe lung condition in their\nlifetimes?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The complement (no more than one will develop\n  a severe lung condition) as computed in Guided\n  Practice~\\ref{noMoreThanOneFriendWSevereLungCondition}\n  as 0.6517, so we compute one minus this value:~0.3483.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nSuppose you have 7 friends who are smokers and they can\nbe treated as a random sample of smokers.\\footnotemark{}\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}\n\\item\n    How many would you expect to develop a severe lung\n    condition, i.e. what is the mean?\n\\item\n    What is the probability that at most 2 of your 7\n    friends will develop a severe lung condition.\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~$\\mu=0.3\\times7 = 2.1$.\n  (b)~$P($0, 1, or 2 develop severe lung condition$)\n      = P(k=0) + P(k=1)+P(k=2) = 0.6471$.}\n\nNext we consider the first term in the binomial probability,\n$n$ choose $k$ under some special scenarios.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhy is it true that ${n \\choose 0}=1$ and ${n \\choose n}=1$\nfor any number $n$?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Frame these expressions into words.\n  How many different ways are there to arrange 0 successes\n  and $n$ failures in $n$ trials?\n  (1 way.)\n  How many different ways are there to arrange $n$ successes\n  and 0 failures in $n$ trials?\n  (1 way.)}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nHow many ways can you arrange one success and $n-1$ failures\nin $n$ trials?\nHow many ways can you arrange $n-1$ successes and one failure\nin $n$ trials?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{One success and $n-1$ failures:\n  there are exactly $n$ unique places we can put\n  the success, so there are $n$ ways to arrange one\n  success and $n-1$ failures.\n  A~similar argument is used for the second question.\n  Mathematically, we show these results by verifying\n  the following two equations:\n  \\begin{align*}\n  {n \\choose 1} = n,\n    \\qquad {n \\choose n-1} = n\n  \\end{align*}}\n\n\n\\newpage\n\n\n\\subsection{Normal approximation to the binomial distribution}\n\\label{normalApproxBinomialDistSubsection}\n\n\\index{distribution!binomial!normal approximation|(}\n\nThe binomial formula is cumbersome when the sample size ($n$) is large, particularly when we consider a range of observations. In some cases we may use the normal distribution as an easier and faster way to estimate binomial probabilities.\n\n\\newcommand{\\smokeprop}{0.15}\n\\newcommand{\\smokeperc}{15\\%}\n\\newcommand{\\smokepropcomp}{0.85}\n\\newcommand{\\smokeperccomp}{85\\%}\n\\newcommand{\\smokex}{42}\n\\newcommand{\\smokexplusone}{43}\n\\newcommand{\\smoken}{400}\n\\newcommand{\\smokelowertailbinom}{0.0054}\n\\newcommand{\\smokemean}{60}\n\\newcommand{\\smokemeancomp}{340}\n\\newcommand{\\smokesd}{7.14}\n\\newcommand{\\smokez}{-2.52}\n\\newcommand{\\smokelowertailnormal}{0.0059}\n\n\\begin{examplewrap}\n\\begin{nexample}{Approximately \\smokeperc{} of the\n    US population smokes cigarettes.\n    A local government believed their community had\n    a lower smoker rate and commissioned a survey of\n    400 randomly selected individuals.\n    The survey found that only \\smokex{} of the\n    \\smoken{} participants smoke cigarettes.\n    If the true proportion of smokers in the community\n    was really \\smokeperc{}, what is the probability\n    of observing \\smokex{} or fewer smokers in a sample\n    of \\smoken{} people?}\n  \\label{exactBinomSmokerExSetup}%\n  We leave the usual verification that the four conditions\n  for the binomial model are valid as an exercise.\n\n  The question posed is equivalent to asking,\n  what is the probability of observing\n  $k=0$, 1, 2, ..., or \\smokex{} smokers in a sample of\n  $n = \\smoken{}$ when $p=\\smokeprop{}$?\n  We can compute these \\smokexplusone{} different\n  probabilities and add them together to find the answer:\n  \\begin{align*}\n  &P(k=0\\text{ or }k=1\\text{ or }\\cdots\\text{ or } k=\\smokex{}) \\\\\n\t&\\qquad = P(k=0) + P(k=1) + \\cdots + P(k=\\smokex{}) \\\\\n\t&\\qquad = \\smokelowertailbinom{}\n  \\end{align*}\n  If the true proportion of smokers in the community\n  is $p=\\smokeprop{}$, then the probability of observing\n  \\smokex{} or fewer smokers in a sample of $n=\\smoken{}$\n  is \\smokelowertailbinom{}.\n\\end{nexample}\n\\end{examplewrap}\n\nThe computations in Example~\\ref{exactBinomSmokerExSetup}\nare tedious and long.\nIn general, we should avoid such work if an alternative method\nexists that is faster, easier, and still accurate.\nRecall that calculating probabilities of a range of values\nis much easier in the normal model.\nWe might wonder, is it reasonable to use the normal model\nin place of the binomial distribution?\nSurprisingly, yes, if certain conditions are met.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nHere we consider the binomial model when the probability\nof a success is $p = 0.10$.\nFigure~\\ref{fourBinomialModelsShowingApproxToNormal}\nshows four hollow histograms for simulated samples from\nthe binomial distribution using four different sample sizes:\n$n = 10, 30, 100, 300$.\nWhat happens to the shape of the distributions as the sample\nsize increases?\nWhat distribution does the last hollow histogram\nresemble?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The distribution is transformed from a blocky\n  and skewed distribution into one that rather resembles the\n  normal distribution in last hollow histogram.}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[Four hollow histograms are shown, each in their own plot, based on a probability of p equals 0.10 and sample sizes of n equals 10, 30, 100, and 300. The first plot for n = 10 shows a distribution centered at 1 and is notably right skewed. The second plot for n = 30 shows a distribution centered at about 3, is just a bit right skewed, and is starting to look a little bit like a bell-shaped distribution. The third plot for n = 100 shows a distribution centered at about 10 and that is almost entirely symmetric with just the slightest indication it is right skewed. This third distribution also looks very bell-shaped. The fourth plot for n = 300 shows a distribution centered at about 30 and that is symmetric. This last plot looks very bell-shaped and resembles a normal distribution.]{0.92}{fourBinomialModelsShowingApproxToNormal}\n  \\caption{Hollow histograms of samples from the binomial\n      model when $p = 0.10$.\n      The sample sizes for the four plots are\n      $n = 10$, 30, 100, and 300, respectively.}\n  \\label{fourBinomialModelsShowingApproxToNormal}\n\\end{figure}\n\n\\begin{onebox}{Normal approximation of the binomial distribution}\n  The binomial distribution with probability of success\n  $p$ is nearly normal when the sample size $n$ is\n  sufficiently large that $np$ and $n(1-p)$ are both\n  at least 10.\n  The approximate normal distribution has parameters\n  corresponding to the mean and standard deviation of\n  the binomial distribution:\\vspace{-1.5mm}\n  \\begin{align*}\n  \\mu &= np\n      &\\sigma& = \\sqrt{np(1 - p)}\n  \\end{align*}\n\\end{onebox}\n\nThe normal approximation may be used when computing\nthe range of many possible successes.\nFor instance, we may apply the normal distribution to\nthe setting of Example~\\ref{exactBinomSmokerExSetup}.\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{How can we use the normal approximation\n    to estimate the probability of observing \\smokex{} or\n    fewer smokers in a sample of \\smoken{}, if the true\n    proportion of smokers is $p = \\smokeprop{}$?}\n  \\label{approxNormalForSmokerBinomEx}\n  Showing that the binomial model is reasonable was a\n  suggested exercise in Example~\\ref{exactBinomSmokerExSetup}.\n  We also verify that both $np$ and $n(1-p)$ are at least 10:\n  \\begin{align*}\n  np &= \\smoken{} \\times \\smokeprop{} = \\smokemean{}\n  &n (1 - p) &= \\smoken{} \\times \\smokepropcomp{}\n      = \\smokemeancomp{}\n  \\end{align*}\n  With these conditions checked, we may use the normal\n  approximation in place of the binomial distribution\n  using the mean and standard deviation from the binomial\n  model:\n  \\begin{align*}\n  \\mu &= np = \\smokemean{}\n  &\\sigma &= \\sqrt{np(1 - p)} = \\smokesd{}\n  \\end{align*}\n  We want to find the probability of observing\n  \\smokex{} or fewer smokers using this model.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nUse the normal model $N(\\mu = \\smokemean{}, \\sigma = \\smokesd{})$\nto estimate the probability of observing \\smokex{} or fewer\nsmokers.\nYour answer should be approximately equal to the solution\nof Example~\\ref{exactBinomSmokerExSetup}:%\n~\\smokelowertailbinom{}.~\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Compute the Z-score first:\n  $Z = \\frac{\\smokex{} - \\smokemean{}}{\\smokesd{}} = \\smokez{}$.\n  The corresponding left tail area is \\smokelowertailnormal{}.}\n\n\n\n\\newpage\n\n\n\\subsection{The normal approximation breaks down on small intervals}\n\nThe normal approximation to the binomial distribution tends to perform poorly when estimating the probability of a small range of counts, even when the conditions are met.\n\n\\newcommand{\\smokeA}{49}\n\\newcommand{\\smokeB}{50}\n\\newcommand{\\smokeC}{51}\n\\newcommand{\\smokeABCBinom}{0.0649}\n\\newcommand{\\smokeABCNormal}{0.0421}\n\\newcommand{\\smokeABCNormalFixed}{0.0633}\n\nSuppose we wanted to compute the probability of observing\n\\smokeA{}, \\smokeB{}, or \\smokeC{} smokers in \\smoken{}\nwhen $p = \\smokeprop{}$.\nWith such a large sample, we might be tempted to apply\nthe normal approximation and use the range \\smokeA{} to \\smokeC{}.\nHowever, we would find that the binomial solution and the normal\napproximation notably differ:\n\\begin{align*}\n\\text{Binomial:}&\\ \\smokeABCBinom{}\n&\\text{Normal:}&\\ \\smokeABCNormal{}\n\\end{align*}\nWe can identify the cause of this discrepancy using\nFigure~\\ref{normApproxToBinomFail}, which shows the areas\nrepresenting the binomial probability (outlined) and normal\napproximation (shaded).\nNotice that the width of the area under the normal\ndistribution is 0.5 units too slim on both sides of\nthe interval.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A normal distribution centered at 60 with a standard deviation of about 7 is shown. (The determination that the standard deviation is about 7 was based on the normal distribution being very close to 0 a distance of about 20 from the mean, and this happens about 3 standard deviations from the mean.) A region of this distribution is shaded from 49 to 51. Additionally, a red outlined area is boxed out between 48.5 and 51.5 that represents the exact binomial distribution.]{1.0}{normApproxToBinomFail}\n  \\caption{A normal curve with the area between\n      \\smokeA{} and \\smokeC{} shaded.\n      The outlined area represents the exact binomial\n      probability.}\n  \\label{normApproxToBinomFail}\n\\end{figure}\n\n\\begin{onebox}{Improving the normal approximation\n    for the binomial distribution}\n  The normal approximation to the binomial distribution\n  for intervals of values is usually improved if cutoff\n  values are modified slightly.\n  The cutoff values for the lower end of a shaded region\n  should be reduced by 0.5, and the cutoff value for the\n  upper end should be increased by 0.5.\n\\end{onebox}\n\nThe tip to add extra area when applying the normal\napproximation is most often useful when examining\na range of observations.\nIn the example above, the revised normal distribution\nestimate is \\smokeABCNormalFixed{}, much closer to the\nexact value of \\smokeABCBinom{}.\nWhile it is possible to also apply this correction when\ncomputing a tail area, the benefit of the modification\nusually disappears since the total interval is typically\nquite wide.\n\n\\index{distribution!binomial!normal approximation|)}\n\\index{distribution!binomial|)}\n\n\n{\\input{ch_distributions/TeX/binomial_distribution.tex}}\n\n\n\n\n%_________________\n\\section{Negative binomial distribution}\n\\label{negativeBinomial}\n\n\\index{distribution!negative binomial|(}\n\nThe geometric distribution describes the probability of observing the first success on the $n^{th}$ trial. The \\termsub{negative binomial distribution}{distribution!negative binomial} is more general: it describes the probability of observing the $k^{th}$ success on the $n^{th}$ trial.\n\n\\begin{examplewrap}\n\\begin{nexample}{Each day a high school football coach tells his star kicker, Brian, that he can go home after he successfully kicks four 35 yard field goals. Suppose we say each kick has a probability $p$ of being successful. If $p$ is small -- e.g. close to 0.1 -- would we expect Brian to need many attempts before he successfully kicks his fourth field goal?}\nWe are waiting for the fourth success ($k=4$). If the probability of a success ($p$) is small, then the number of attempts ($n$) will probably be large. This means that Brian is more likely to need many attempts before he gets $k=4$ successes. To put this another way, the probability of $n$ being small is low.\n\\end{nexample}\n\\end{examplewrap}\n\nTo identify a negative binomial case, we check 4 conditions. The first three are common to the binomial distribution.\n\n\\begin{onebox}{Is it negative binomial? Four conditions to check}\n(1) The trials are independent. \\\\\n(2) Each trial outcome can be classified as a success or failure. \\\\\n(3) The probability of a success ($p$) is the same for each trial. \\\\\n(4) The last trial must be a success.\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nSuppose Brian is very diligent in his attempts and he makes each 35 yard field goal with probability $p=0.8$. Take a guess at how many attempts he would need before making his fourth kick.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{One possible answer: since he is likely to make each field goal attempt, it will take him at least 4 attempts but probably not more than 6 or 7.}\n\n\\begin{examplewrap}\n\\begin{nexample}{In yesterday's practice, it took Brian only 6 tries to get his fourth field goal. Write out each of the possible sequence of kicks.} \\label{eachSeqOfSixTriesToGetFourSuccesses}\nBecause it took Brian six tries to get the fourth success, we know the last kick must have been a success. That leaves three successful kicks and two unsuccessful kicks (we label these as failures) that make up the first five attempts. There are ten possible sequences of these first five kicks, which are shown in Figure~\\ref{successFailureOrdersForBriansFieldGoals}. If Brian achieved his fourth success ($k=4$) on his sixth attempt ($n=6$), then his order of successes and failures must be one of these ten possible sequences.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[ht]\n\\newcommand{\\succObs}[1]{{\\color{oiB}$\\stackrel{#1}{S}$}}\n\\centering\n\\begin{tabular}{c|c ccc cl | r}\n\\multicolumn{8}{c}{\\hspace{10mm}Kick Attempt} \\\\\n& & 1 & 2 & 3 & 4 & \\multicolumn{2}{l}{5\\hfill6} \\\\\n\\hline\n1&& $F$ & $F$ & \\succObs{1} & \\succObs{2} & \\succObs{3} & \\succObs{4} \\\\\n2&& $F$ & \\succObs{1} & $F$ & \\succObs{2} & \\succObs{3} & \\succObs{4} \\\\\n3&& $F$ & \\succObs{1} & \\succObs{2} & $F$ & \\succObs{3} & \\succObs{4} \\\\\n4&& $F$ & \\succObs{1} & \\succObs{2} & \\succObs{3} & $F$ & \\succObs{4} \\\\\n5&& \\succObs{1} & $F$ & $F$ & \\succObs{2} & \\succObs{3} & \\succObs{4} \\\\\n6&& \\succObs{1} & $F$ & \\succObs{2} & $F$ & \\succObs{3} & \\succObs{4} \\\\\n7&& \\succObs{1} & $F$ & \\succObs{2} & \\succObs{3} & $F$ & \\succObs{4} \\\\\n8&& \\succObs{1} & \\succObs{2} & $F$ & $F$ & \\succObs{3} & \\succObs{4} \\\\\n9&& \\succObs{1} & \\succObs{2} & $F$ & \\succObs{3} & $F$ & \\succObs{4} \\\\\n10&& \\succObs{1} & \\succObs{2} & \\succObs{3} & $F$ & $F$ & \\succObs{4} \\\\\n\\end{tabular}\n\\caption{The ten possible sequences when the fourth successful kick is on the sixth attempt.}\n\\label{successFailureOrdersForBriansFieldGoals}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{probOfEachSeqOfSixTriesToGetFourSuccesses}\nEach sequence in Figure~\\ref{successFailureOrdersForBriansFieldGoals} has exactly two failures and four successes with the last attempt always being a success. If the probability of a success is $p=0.8$, find the probability of the first sequence.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The first sequence:\n  $0.2 \\times 0.2 \\times 0.8 \\times\n      0.8 \\times 0.8 \\times 0.8\n    = 0.0164$.}\n\n\\D{\\newpage}\n\nIf the probability Brian kicks a 35 yard field goal is $p=0.8$, what is the probability it takes Brian exactly six tries to get his fourth successful kick? We can write this as\n{\\small\\begin{align*}\n&P(\\text{it takes Brian six tries to make four field goals}) \\\\\n& \\quad = P(\\text{Brian makes three of his first five field goals, and he makes the sixth one}) \\\\\n& \\quad = P(\\text{$1^{st}$ sequence OR $2^{nd}$ sequence OR ... OR $10^{th}$ sequence})\n\\end{align*}\n}where the sequences are from Figure~\\ref{successFailureOrdersForBriansFieldGoals}. We can break down this last probability into the sum of ten disjoint possibilities:\n{\\small\\begin{align*}\n&P(\\text{$1^{st}$ sequence OR $2^{nd}$ sequence OR ... OR $10^{th}$ sequence}) \\\\\n&\\quad = P(\\text{$1^{st}$ sequence}) + P(\\text{$2^{nd}$ sequence}) + \\cdots + P(\\text{$10^{th}$ sequence})\n\\end{align*}\n}The probability of the first sequence was identified in Guided Practice~\\ref{probOfEachSeqOfSixTriesToGetFourSuccesses} as 0.0164, and each of the other sequences have the same probability. Since each of the ten sequence has the same probability, the total probability is ten times that of any individual sequence.\n\nThe way to compute this negative binomial probability is similar to how the binomial problems were solved in Section~\\ref{binomialModel}. The probability is broken into two pieces:\n\\begin{align*}\n&P(\\text{it takes Brian six tries to make four field goals}) \\\\\n&= [\\text{Number of possible sequences}] \\times P(\\text{Single sequence})\n\\end{align*}\nEach part is examined separately, then we multiply to get the final result.\n\nWe first identify the probability of a single sequence. One particular case is to first observe all the failures ($n-k$ of them) followed by the $k$ successes:\n\\begin{align*}\n&P(\\text{Single sequence}) \\\\\n&= P(\\text{$n-k$ failures and then $k$ successes}) \\\\\n&= (1-p)^{n-k} p^{k}\n\\end{align*}\n\n\\D{\\newpage}\n\nWe must also identify the number of sequences for the general case. Above, ten sequences were identified where the fourth success came on the sixth attempt. These sequences were identified by fixing the last observation as a success and looking for all the ways to arrange the other observations. In other words, how many ways could we arrange $k-1$ successes in $n-1$ trials? This can be found using the $n$ choose $k$ coefficient but for $n-1$ and $k-1$ instead:\n\\begin{align*}\n{n-1 \\choose k-1} = \\frac{(n-1)!}{(k-1)! \\left((n-1) - (k-1)\\right)!} = \\frac{(n-1)!}{(k-1)! \\left(n - k\\right)!}\n\\end{align*}\nThis is the number of different ways we can order $k-1$ successes and $n-k$ failures in $n-1$ trials. If the factorial notation (the exclamation point) is unfamiliar, see page~\\pageref{factorial_defined}.\n\n\\begin{onebox}{Negative binomial distribution}\n  The negative binomial distribution describes the\n  probability of observing the $k^{th}$ success on\n  the $n^{th}$ trial, where all trials are independent:\n  \\begin{align*}\n  P(\\text{the $k^{th}$ success on the $n^{th}$ trial})\n      = {n-1 \\choose k-1} p^{k}(1-p)^{n-k}\n  \\end{align*}\n  The value $p$ represents the probability that\n  an individual trial is a success.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{Show using the formula for the negative binomial distribution that the probability Brian kicks his fourth successful field goal on the sixth attempt is 0.164.}\nThe probability of a single success is $p=0.8$, the number of successes is $k=4$, and the number of necessary attempts under this scenario is $n=6$.\n\\begin{align*}\n{n-1 \\choose k-1}p^k(1-p)^{n-k}\\ \n\t=\\ \\frac{5!}{3!2!} (0.8)^4 (0.2)^2\\ \n\t=\\ 10\\times 0.0164\\ \n\t=\\ 0.164\n\\end{align*}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe negative binomial distribution requires that each kick attempt by Brian is independent. Do you think it is reasonable to suggest that each of Brian's kick attempts are independent?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Answers may vary. We cannot conclusively say they are or are not independent. However, many statistical reviews of athletic performance suggests such attempts are very nearly independent.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nAssume Brian's kick attempts are independent. What is the probability that Brian will kick his fourth field goal within 5 attempts?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{If his fourth field goal ($k=4$) is within five attempts, it either took him four or five tries ($n=4$ or $n=5$). We have $p=0.8$ from earlier. Use the negative binomial distribution to compute the probability of $n = 4$ tries and $n=5$ tries, then add those probabilities together:\n\\begin{align*}\n& P(n=4\\text{ OR }n=5) = P(n=4) + P(n=5) \\\\\n&\\quad = {4-1 \\choose 4-1} 0.8^4 + {5-1 \\choose 4-1} (0.8)^4(1-0.8) = 1\\times 0.41 + 4\\times 0.082 = 0.41 + 0.33 = 0.74\n\\end{align*}}\n\n\\D{\\newpage}\n\n\\begin{onebox}{Binomial versus negative binomial}\n  In the binomial case, we typically have a fixed number\n  of trials and instead consider the number of successes.\n  In the negative binomial case, we examine how many trials\n  it takes to observe a fixed number of successes and\n  require that the last observation be a success.\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nOn 70\\% of days, a hospital admits at least one heart attack patient. On 30\\% of the days, no heart attack patients are admitted. Identify each case below as a binomial or negative binomial case, and compute the probability.\\footnotemark\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}\n\\item What is the probability the hospital will admit\n    a heart attack patient on exactly three days this week?\n\n\\item What is the probability the second day with a heart\n    attack patient will be the fourth day of the week?\n\n\\item What is the probability the fifth day of next month\n    will be the first day with a heart attack patient?\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{In each part, $p=0.7$. (a) The number of days is fixed, so this is binomial. The parameters are $k=3$ and $n=7$: 0.097. (b) The last ``success'' (admitting a heart attack patient) is fixed to the last day, so we should apply the negative binomial distribution. The parameters are $k=2$, $n=4$: 0.132. (c) This problem is negative binomial with $k=1$ and $n=5$: 0.006. Note that the negative binomial case when $k=1$ is the same as using the geometric distribution.}\n\n\\index{distribution!negative binomial|)}\n\n\n{\\input{ch_distributions/TeX/negative_binomial_distribution.tex}}\n\n\n\n\n\n%_________________\n\\section{Poisson distribution}\n\\label{poisson}\n\n\\index{distribution!Poisson|(}\n\n\\begin{examplewrap}\n\\begin{nexample}{There are about 8 million individuals\n    in New York City.\n    How many individuals might we expect to be hospitalized\n    for acute myocardial infarction (AMI), i.e. a heart attack,\n    each day?\n    According to historical records, the average number is\n    about 4.4 individuals.\n    However, we would also like to know the approximate\n    distribution of counts.\n    What would a histogram of the number of AMI occurrences\n    each day look like if we recorded the daily counts over\n    an entire year?}\n  \\label{amiIncidencesEachDayOver1YearInNYCExample}%\n  A histogram of the number of occurrences of AMI on 365 days\n  for NYC is shown in\n  Figure~\\ref{amiIncidencesOver100Days}.\\footnotemark{}\n  The sample mean (4.38) is similar to the historical average\n  of~4.4.\n  The sample standard deviation is about 2, and the histogram\n  indicates that about 70\\% of the data fall between 2.4 and~6.4.\n  The distribution's shape is unimodal and skewed to the right.\n\\end{nexample}\n\\end{examplewrap}\n\\footnotetext{These data are simulated. In practice, we should check for an association between successive days.}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A histogram is shown for \"AMI Events (by Day)\". There are 11 non-zero values shown: a frequency of about 15 at a value of 1, a frequency of 50 at 2, 70 at 3, 85 at 4, 55 at 5, 45 at 6, 25 at 7, 20 at 8, 5 at 9, 5 at 10, and a frequency of about 2 at 11.]{0.6}{amiIncidencesOver100Days}\n  \\caption{A histogram of the number of occurrences\n      of AMI on 365 separate days in NYC.}\n  \\label{amiIncidencesOver100Days}\n\\end{figure}\n\nThe \\termsub{Poisson distribution}{distribution!Poisson} is often useful for estimating the number of events in a large population over a unit of time. For instance, consider each of the following events:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item having a heart attack,\n\\item getting married, and\n\\item getting struck by lightning.\n\\end{itemize}\nThe Poisson distribution helps us describe the number of such events that will occur in a day for a fixed population if the individuals within the population are independent. The Poisson distribution could also be used over another unit of time, such as an hour or a~week.\n\nThe histogram in Figure~\\ref{amiIncidencesOver100Days} approximates a Poisson distribution with rate equal to 4.4. The \\term{rate} for a Poisson distribution is the average number of occurrences in a mostly-fixed population per unit of time. In Example~\\ref{amiIncidencesEachDayOver1YearInNYCExample}, the time unit is a day, the population is all New York City residents, and the historical rate is 4.4. The parameter in the Poisson distribution is the rate -- or how many events we expect to observe -- and it is typically denoted by $\\lambda$\\index{Greek!lambda@lambda ($\\lambda$)}\n(the Greek letter \\emph{lambda})  or $\\mu$. Using the rate, we can describe the probability of observing exactly $k$ events in a single unit of time.\n\n\\D{\\newpage}\n\n\\begin{onebox}{Poisson distribution}\n  Suppose we are watching for events and the number\n  of observed events follows a Poisson distribution\n  with rate $\\lambda$.\n  Then\n  \\begin{align*}\n  P(\\text{observe $k$ events})\n      = \\frac{\\lambda^{k} e^{-\\lambda}}{k!}\n  \\end{align*}\n  where $k$ may take a value 0, 1, 2, and so on,\n  and $k!$ represents $k$-factorial, as described on\n  page~\\pageref{factorial_defined}.\n  The letter $e\\approx2.718$ is the base of the natural\n  logarithm.\n  The mean and standard deviation of this distribution\n  are $\\lambda$ and $\\sqrt{\\lambda}$, respectively.\n\\end{onebox}\n\nWe will leave a rigorous set of conditions for the Poisson distribution to a later course. However, we offer a few simple guidelines that can be used for an initial evaluation of whether the Poisson model would be appropriate.\n\nA random variable may follow a Poisson distribution if we are looking for the number of events, the population that generates such events is large, and the events occur independently of each other.\n\nEven when events are not really independent --\nfor instance, Saturdays and Sundays are especially\npopular for weddings --\na Poisson model may sometimes still be reasonable\nif we allow it to have a different rate for different\ntimes.\nIn the wedding example, the rate would be modeled as\nhigher on weekends than on weekdays.\nThe idea of modeling rates for a Poisson distribution\nagainst a second variable such as the day of week forms\nthe foundation of some more advanced methods that fall\nin the realm of \\termsub{generalized linear models}\n    {generalized linear model}.\nIn Chapters~\\ref{linRegrForTwoVar}\nand~\\ref{multipleAndLogisticRegression},\nwe will discuss a foundation of linear models.\n\n\\index{distribution!Poisson|)}\n\n\n{\\input{ch_distributions/TeX/poisson_distribution.tex}}\n"
  },
  {
    "path": "ch_distributions/TeX/geometric_distribution.tex",
    "content": "\\exercisesheader{}\n\n% 11\n\n\\eoce{\\qtq{Is it Bernoulli\\label{is_it_bernouilli}} Determine if each trial can be \nconsidered an independent Bernoulli trial for the following situations.\n\\begin{parts}\n\\item Cards dealt in a hand of poker.\n\\item Outcome of each roll of a die.\n\\end{parts}\n}{}\n\n% 12\n\n\\eoce{\\qt{With and without replacement\\label{with_without_replacement}} In the \nfollowing situations assume that half of the specified population is male and \nthe other half is female.\n\\begin{parts}\n\\item Suppose you're sampling from a room with 10 people. What is the \nprobability of sampling two females in a row when sampling with replacement? \nWhat is the probability when sampling without replacement?\n\\item Now suppose you're sampling from a stadium with 10,000 people. What is \nthe probability of sampling two females in a row when sampling with \nreplacement? What is the probability when sampling without replacement?\n\\item We often treat individuals who are sampled from a large population as \nindependent. Using your findings from parts~(a) and~(b), explain whether or \nnot this assumption is reasonable.\n\\end{parts}\n}{}\n\n% 13\n\n\\eoce{\\qt{Eye color, Part I\\label{eye_color_geometric}} A husband and wife both \nhave brown eyes but carry genes that make it possible for their children to \nhave brown eyes (probability 0.75), blue eyes (0.125), or green eyes (0.125).\n\\begin{parts}\n\\item What is the probability the first blue-eyed child they have is their \nthird child? Assume that the eye colors of the children are independent of \neach other.\n\\item On average, how many children would such a pair of parents have before \nhaving a blue-eyed child? What is the standard deviation of the number of \nchildren they would expect to have until the first blue-eyed child?\n\\end{parts}\n}{}\n\n% 14\n\n\\eoce{\\qt{Defective rate\\label{defective_rate}} A machine that produces a special \ntype of transistor (a component of computers) has a 2\\% defective rate. The \nproduction is considered a random process where each transistor is \nindependent of the others.\n\\begin{parts}\n\\item What is the probability that the $10^{th}$ transistor produced is the \nfirst with a defect?\n\\item What is the probability that the machine produces no defective \ntransistors in a batch of 100?\n\\item On average, how many transistors would you expect to be produced before \nthe first with a defect? What is the standard deviation?\n\\item Another machine that also produces transistors has a 5\\% defective rate \nwhere each transistor is produced independent of the others. On average how \nmany transistors would you expect to be produced with this machine before the \nfirst with a defect? What is the standard deviation?\n\\item Based on your answers to parts (c) and (d), how does increasing the \nprobability of an event affect the mean and standard deviation of the wait \ntime until success?\n\\end{parts}\n}{}\n\n% 15\n\n\\eoce{\\qt{Bernoulli, the mean\\label{bernoulli_mean_derivation}}\nUse the probability rules from\nSection~\\ref{randomVariablesSection}\nto derive the mean of a Bernoulli random variable,\ni.e. a random variable $X$ that takes value 1\nwith probability $p$ and value 0 with probability $1 - p$.\nThat is, compute the expected value of a generic\nBernoulli random variable.\n}{}\n\n% 16\n\n\\eoce{\\qt{Bernoulli, the standard deviation\\label{bernoulli_sd_derivation}}\nUse the probability rules from\nSection~\\ref{randomVariablesSection}\nto derive the standard deviation of a Bernoulli random variable,\ni.e. a random variable $X$ that takes value 1\nwith probability $p$ and value 0 with probability $1 - p$.\nThat is, compute the square root of the variance of a generic\nBernoulli random variable.\n}{}\n"
  },
  {
    "path": "ch_distributions/TeX/negative_binomial_distribution.tex",
    "content": "\\exercisesheader{}\n\n% 27\n\n\\eoce{\\qt{Rolling a die\\label{roll_die}} Calculate the \nfollowing probabilities and indicate which probability distribution model is \nappropriate in each case. You roll a fair die 5 times. What is the \nprobability of rolling\n\\begin{parts}\n\\item the first 6 on the fifth roll?\n\\item exactly three 6s?\n\\item the third 6 on the fifth roll?\n\\end{parts}\n}{}\n\n% 28\n\n\\eoce{\\qt{Playing darts\\label{play_darts}} Calculate the following probabilities \nand indicate which probability distribution model is appropriate in each \ncase. A very good darts player can hit the bull's eye (red circle in the \ncenter of the dart board) 65\\% of the time. What is the probability that he\n\\begin{parts}\n\\item hits the bullseye for the $10^{th}$ time on the $15^{th}$ try?\n\\item hits the bullseye 10 times in 15 tries?\n\\item hits the first bullseye on the third try?\n\\end{parts}\n}{}\n\n% 29\n\n\\eoce{\\qt{Sampling at school\\label{sampling_at_school}} For a sociology class \nproject you are asked to conduct a survey on 20 students at your school. You \ndecide to stand outside of your dorm's cafeteria and conduct the survey on a \nrandom sample of 20 students leaving the cafeteria after dinner one evening. \nYour dorm is comprised of 45\\% males and 55\\% females.\n\\begin{parts}\n\\item Which probability model is most appropriate for calculating the \nprobability that the $4^{th}$ person you survey is the $2^{nd}$ female? \nExplain.\n\\item Compute the probability from part (a).\n\\item The three possible scenarios that lead to $4^{th}$ person you survey \nbeing the $2^{nd}$ female are\n\\[ \\{M, M, F, F\\}, \\{M, F, M, F\\}, \\{F, M, M, F\\} \\]\nOne common feature among these scenarios is that the last trial is always \nfemale. In the first three trials there are 2 males and 1 female. Use the \nbinomial coefficient to confirm that there are 3 ways of ordering 2 males and \n1 female. \n\\item Use the findings presented in part (c) to explain why the formula for \nthe coefficient for the negative binomial is ${n-1 \\choose k-1}$ while the \nformula for the binomial coefficient is ${n \\choose k}$.\n\\end{parts}\n}{}\n\n% 30\n\n\\eoce{\\qt{Serving in volleyball\\label{serving_volleyball}} A not-so-skilled \nvolleyball player has a 15\\% chance of making the serve, which involves \nhitting the ball so it passes over the net on a trajectory such that it will \nland in the opposing team's court. Suppose that her serves are independent of \neach other.\n\\begin{parts}\n\\item What is the probability that on the $10^{th}$ try she will make her \n$3^{rd}$ successful serve?\n\\item Suppose she has made two successful serves in nine attempts. What is \nthe probability that her $10^{th}$ serve will be successful?\n\\item Even though parts (a) and (b) discuss the same scenario, the \nprobabilities you calculated should be different. Can you explain the reason \nfor this discrepancy?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_distributions/TeX/normal_distribution.tex",
    "content": "\\exercisesheader{}\n\n% 1\n\n\\eoce{\\qt{Area under the curve, Part I\\label{area_under_curve_1}} What percent of a \nstandard normal distribution $N(\\mu=0, \\sigma=1)$ is found in each region? \nBe sure to draw a graph. \\vspace{-3mm}\n\\begin{multicols}{4}\n\\begin{parts}\n\\item $Z < -1.35$\n\\item $Z > 1.48$\n\\item $-0.4 < Z < 1.5$\n\\item $|Z| > 2$\n\\end{parts}\n\\end{multicols}\n}{}\n\n% 2\n\n\\eoce{\\qt{Area under the curve, Part II\\label{area_under_curve_2}} What percent of \na standard normal distribution $N(\\mu=0, \\sigma=1)$ is found in each region? \nBe sure to draw a graph. \\vspace{-3mm}\n\\begin{multicols}{4}\n\\begin{parts}\n\\item $Z > -1.13$\n\\item $Z < 0.18$\n\\item $Z > 8$\n\\item $|Z| < 0.5$\n\\end{parts}\n\\end{multicols}\n}{}\n\n% 3\n\n\\eoce{\\qt{GRE scores, Part I\\label{GRE_intro}} Sophia who took the Graduate Record \nExamination (GRE) scored 160 on the Verbal Reasoning section and 157 on the \nQuantitative Reasoning section. The mean score for Verbal Reasoning section \nfor all test takers was 151 with a standard deviation of 7, and the mean \nscore for the Quantitative Reasoning was 153 with a standard deviation of \n7.67. Suppose that both distributions are nearly normal. \n\\begin{parts}\n\\item Write down the short-hand for these two normal distributions.\n\\item What is  Sophia's Z-score on the Verbal Reasoning section? On the \nQuantitative Reasoning section? Draw a standard normal distribution curve and \nmark these two Z-scores.\n\\item What do these Z-scores tell you?\n\\item Relative to others, which section did she do better on?\n\\item Find her percentile scores for the two exams.\n\\item What percent of the test takers did better than her on the Verbal \nReasoning section? On the Quantitative Reasoning section?\n\\item Explain why simply comparing raw scores from the two sections could lead \nto an incorrect conclusion as to which section a student did better on.\n\\item If the distributions of the scores on these exams are not nearly \nnormal, would your answers to parts (b) - (f) change? Explain your reasoning.\n\\end{parts}\n}{}\n\n% 4\n\n\\eoce{\\qt{Triathlon times, Part I\\label{triathlon_times_intro}} In triathlons, it \nis common for racers to be placed into age and gender groups. Friends Leo and \nMary both completed the Hermosa Beach Triathlon, where Leo competed in the \n\\textit{Men, Ages 30 - 34} group while Mary competed in the \\textit{Women, \nAges 25 - 29} group. Leo completed the race in 1:22:28 (4948 seconds), while \nMary completed the race in 1:31:53 (5513 seconds). Obviously Leo finished \nfaster, but they are curious about how they did within their respective \ngroups. Can you help them? Here is some information on the performance of \ntheir groups:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item The finishing times of the \\textit{Men, Ages 30 - 34} group has a mean \nof 4313 seconds with a standard deviation of 583 seconds.\n\\item The finishing times of the \\textit{Women, Ages 25 - 29} group has a \nmean of 5261 seconds with a standard deviation of 807 seconds.\n\\item The distributions of finishing times for both groups are approximately \nNormal.\n\\end{itemize}\nRemember: a better performance corresponds to a faster finish.\n\\begin{parts}\n\\item Write down the short-hand for these two normal distributions.\n\\item What are the Z-scores for Leo's and Mary's finishing times? What do \nthese Z-scores tell you?\n\\item Did Leo or Mary rank better in their respective groups? Explain your \nreasoning.\n\\item What percent of the triathletes did Leo finish faster than in his group?\n\\item What percent of the triathletes did Mary finish faster than in her \ngroup?\n\\item If the distributions of finishing times are not nearly normal, would \nyour answers to parts (b)~-~(e) change? Explain your reasoning.\n\\end{parts}\n}{}\n\n% 5\n\n\\eoce{\\qt{GRE scores, Part II\\label{GRE_cutoffs}} In Exercise~\\ref{GRE_intro} we \nsaw two distributions for GRE scores: $N(\\mu=151, \\sigma=7)$ for the verbal \npart of the exam and $N(\\mu=153, \\sigma=7.67)$ for the quantitative part. Use \nthis information to compute each of the following:\n\\begin{parts}\n\\item The score of a student who scored in the $80^{th}$ percentile on the \nQuantitative Reasoning section.\n\\item The score of a student who scored worse than 70\\% of the test takers in \nthe Verbal Reasoning section.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 6\n\n\\eoce{\\qt{Triathlon times, Part II\\label{triathlon_times_cutoffs}} In \nExercise~\\ref{triathlon_times_intro} we saw two distributions for triathlon \ntimes: $N(\\mu=4313, \\sigma=583)$ for \\emph{Men, Ages 30 - 34} and \n$N(\\mu=5261, \\sigma=807)$ for the \\emph{Women, Ages 25 - 29} group. Times are \nlisted in seconds. Use this information to compute each of the following:\n\\begin{parts}\n\\item The cutoff time for the fastest 5\\% of athletes in the men's group, i.e. those \nwho took the shortest 5\\% of time to finish. \n\\item The cutoff time for the slowest 10\\% of athletes in the women's group. \n\\end{parts}\n}{}\n\n% 7\n\n\\eoce{\\qt{LA weather, Part I\\label{la_weather_intro}} The average daily high \ntemperature in June in LA is 77\\degree F with a standard deviation of \n5\\degree F. Suppose that the temperatures in June closely follow a normal \ndistribution. \n\\begin{parts}\n\\item What is the probability of observing an 83\\degree F temperature or \nhigher in LA during a randomly chosen day in June?\n\\item How cool are the coldest 10\\% of the days (days with lowest \nhigh temperature) during June in LA?\n\\end{parts}\n}{}\n\n% 8\n\n\\eoce{\\qt{CAPM\\label{CAPM}} The Capital Asset Pricing Model (CAPM) is a financial \nmodel that assumes returns on a portfolio are normally distributed. Suppose a \nportfolio has an average annual return of 14.7\\% (i.e. an average gain of \n14.7\\%) with a standard deviation of 33\\%. A return of 0\\% means the value of \nthe portfolio doesn't change, a negative return means that the portfolio \nloses money, and a positive return means that the portfolio gains money.\n\\begin{parts}\n\\item What percent of years does this portfolio lose money, i.e. have a \nreturn less than 0\\%?\n\\item What is the cutoff for the highest 15\\% of annual returns with this \nportfolio?\n\\end{parts}\n}{}\n\n% 9\n\n\\eoce{\\qt{LA weather, Part II\\label{la_weather_unit_change}} \nExercise~\\ref{la_weather_intro} states that average daily high temperature in \nJune in LA is 77\\degree F with a standard deviation of 5\\degree F, and it can \nbe assumed that they to follow a normal distribution. We use the following \nequation to convert \\degree F (Fahrenheit) to \\degree C (Celsius):\n\\[ C = (F - 32) \\times \\frac{5}{9}. \\]\n\\begin{parts}\n\\item Write the probability model for the distribution of temperature in \n\\degree C in June in LA.\n\\item What is the probability of observing a 28\\degree C (which roughly \ncorresponds to 83\\degree F) temperature or higher in June in LA? Calculate \nusing the \\degree C model from part (a).\n\\item Did you get the same answer or different answers in part (b) of this \nquestion and part (a) of Exercise~\\ref{la_weather_intro}? Are you surprised? Explain.\n\\item Estimate the IQR of the temperatures (in \\degree C) in June in LA.\n\\end{parts}\n}{}\n\n% 10\n\n\\eoce{\\qt{Find the SD\\label{find_sd_cholesterol}}\nCholesterol levels for women aged 20 to 34 follow an\napproximately normal distribution with mean 185 milligrams\nper deciliter (mg/dl).\nWomen with cholesterol levels above 220 mg/dl are considered\nto have high cholesterol and about 18.5\\% of women fall into\nthis category.\nWhat is the standard deviation of the \ndistribution of cholesterol levels for women aged 20 to~34?\n}{}\n"
  },
  {
    "path": "ch_distributions/TeX/poisson_distribution.tex",
    "content": "\\exercisesheader{}\n\n% 31\n\n\\eoce{\\qt{Customers at a coffee shop\\label{coffee_shop_customers}} A coffee shop \nserves an average of 75 customers per hour during the morning rush.\n\\begin{parts}\n\\item\n  Which distribution have we studied that is most appropriate\n  for calculating the probability of a given number of customers\n  arriving within one hour \n  during this time of day?\n\\item What are the mean and the standard deviation of the number of customers \nthis coffee shop serves in one hour during this time of day?\n\\item Would it be considered unusually low if only 60 customers showed up to \nthis coffee shop in one hour during this time of day?\n\\item Calculate the probability that this coffee shop serves 70 customers in \none hour during this time of day.\n\\end{parts}\n}{}\n\n% 32\n\n\\eoce{\\qt{Stenographer's typos\\label{stenographer_typos}} A very skilled \ncourt stenographer makes one typographical error (typo) per hour on average.\n\\begin{parts}\n\\item What probability distribution is most appropriate for calculating the \nprobability of a given number of typos this stenographer makes in an hour?\n\\item What are the mean and the standard deviation of the number of typos \nthis stenographer makes?\n\\item Would it be considered unusual if this stenographer made 4 typos in a \ngiven hour? \n\\item Calculate the probability that this stenographer makes at most 2 typos \nin a given hour.\n\\end{parts}\n}{}\n\n% 33\n\n\\eoce{\\qtq{How many cars show up\\label{cars_in_parking_lot}}\nFor Monday through Thursday when there isn't a holiday,\nthe average number of vehicles that visit a particular\nretailer between 2pm and 3pm each afternoon is 6.5,\nand the number of cars that show up on any given day\nfollows a Poisson distribution.\n\\begin{parts}\n\\item\n    What is the probability that exactly\n    5 cars will show up next Monday?\n\\item\n    What is the probability that\n    0, 1, or 2 cars will show up next Monday\n    between 2pm and 3pm?\n\\item\n    There is an average of 11.7 people who visit during\n    those same hours from vehicles.\n    Is it likely that the number of people visiting\n    by car during this hour is also Poisson?\n    Explain.\n\\end{parts}\n}{}\n\n% 34\n\n\\eoce{\\qt{Lost baggage\\label{lost_baggage}}\nOccasionally an airline will lose a bag.\nSuppose a small airline has found it can reasonably\nmodel the number of bags lost each weekday using a\nPoisson model with a mean of 2.2 bags.\n\\begin{parts}\n\\item\n    What is the probability that the airline\n    will lose no bags next Monday?\n\\item\n    What is the probability that the airline\n    will lose 0, 1, or 2 bags on next Monday?\n\\item\n    Suppose the airline expands over the course\n    of the next 3 years, doubling the number of\n    flights it makes, and the CEO asks you if\n    it's reasonable for them to continue\n    using the Poisson model with a mean of~2.2.\n    What is an appropriate recommendation?\n    Explain.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_distributions/TeX/review_exercises.tex",
    "content": "\\reviewexercisesheader{}\n\n% 35\n\n\\eoce{\\qt{Roulette winnings\\label{roulette_winnings}} In the game of roulette, a \nwheel is spun and you place bets on where it will stop. One popular bet is \nthat it will stop on a red slot; such a bet has an 18/38 chance of winning. \nIf it stops on red, you double the money you bet. If not, you lose the money \nyou bet. Suppose you play 3 times, each time with a \\$1 bet. Let Y represent \nthe total amount won or lost. Write a probability model for Y.\n}{}\n\n% 36\n\n\\eoce{\\qt{Speeding on the I-5, Part I\\label{speeding_i5_intro}} The distribution of \npassenger vehicle speeds traveling on the Interstate 5 Freeway (I-5) in \nCalifornia is nearly normal with a mean of 72.6 miles/hour and a standard \ndeviation of 4.78 miles/hour.\\footfullcite{Johnson+Murray:2010}\n\\begin{parts}\n\\item What percent of passenger vehicles travel slower than 80 miles/hour?\n\\item What percent of passenger vehicles travel between 60 and 80 miles/hour?\n\\item How fast do the fastest 5\\% of passenger vehicles travel?\n\\item The speed limit on this stretch of the I-5 is 70 miles/hour. \nApproximate what percentage of the passenger vehicles travel above the speed \nlimit on this stretch of the I-5.\n\\end{parts}\n}{}\n\n% 37\n\n\\eoce{\\qt{University admissions\\label{university_admissions}} Suppose a university \nannounced that it admitted 2,500 students for the following year's freshman \nclass. However, the university has dorm room spots for only 1,786 freshman \nstudents. If there is a 70\\% chance that an admitted student will decide to \naccept the offer and attend this university, what is the approximate \nprobability that the university will not have enough dormitory room spots for \nthe freshman class?\n}{}\n\n% 38\n\n\\eoce{\\qt{Speeding on the I-5, Part II\\label{speeding_i5_geometric}} \nExercise~\\ref{speeding_i5_intro} states that the distribution of speeds of \ncars traveling on the Interstate 5 Freeway (I-5) in California is nearly \nnormal with a mean of 72.6 miles/hour and a standard deviation of 4.78 \nmiles/hour. The speed limit on this stretch of the I-5 is 70 miles/hour.\n\\begin{parts}\n\\item A highway patrol officer is hidden on the side of the freeway. What is \nthe probability that 5~cars pass and none are speeding? Assume that the \nspeeds of the cars are independent of each other.\n\\item On average, how many cars would the highway patrol officer expect to \nwatch until the first car that is speeding? What is the standard deviation of \nthe number of cars he would expect to watch?\n\\end{parts}\n}{}\n\n% 39\n\n\\eoce{\\qt{Auto insurance premiums\\label{auto_insurance_premiums}} Suppose a \nnewspaper article states that the distribution of auto insurance premiums for \nresidents of California is approximately normal with a mean of \\$1,650. The \narticle also states that 25\\% of California residents pay more than \\$1,800. \n\\begin{parts}\n\\item What is the Z-score that corresponds to the top 25\\% (or the $75^{th}$ \npercentile) of the standard normal distribution?\n\\item What is the mean insurance cost? What is the cutoff for the 75th \npercentile?\n\\item Identify the standard deviation of insurance premiums in California.\n\\end{parts}\n}{}\n\n% 40\n\n\\eoce{\\qt{SAT scores\\label{sat_scores}}\nSAT scores (out of 1600) are distributed \nnormally with a mean of 1100 and a standard deviation of 200.\nSuppose a school council awards a certificate of excellence\nto all students who score at least 1350 on the SAT,\nand suppose we pick one of the recognized students at random.\nWhat is the probability this student's score will be\nat least 1500?\n(The material covered in\nSection~\\ref{conditionalProbabilitySection}\non conditional probability\nwould be useful for this question.)\n}{}\n\n% 41\n\n\\eoce{\\qt{Married women} \\label{married_women} The American Community Survey \nestimates that 47.1\\% of women ages 15 years and over are married.\n\\footfullcite{marWomenACS}\n\\begin{parts}\n\\item We randomly select three women between these ages. What is the \nprobability that the third woman selected is the only one who is married?\n\\item What is the probability that all three randomly selected women are \nmarried?\n\\item On average, how many women would you expect to sample before selecting \na married woman? What is the standard deviation?\n\\item If the proportion of married women was actually 30\\%, how many women \nwould you expect to sample before selecting a married woman? What is the \nstandard deviation?\n\\item Based on your answers to parts (c) and (d), how does decreasing the \nprobability of an event affect the mean and standard deviation of the wait \ntime until success?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 42\n\n\\eoce{\\qt{Survey response rate\\label{survey_response_rate}} Pew Research reported \nthat the typical response rate to their surveys is only 9\\%. If for a \nparticular survey 15,000 households are contacted, what is the probability \nthat at least 1,500 will agree to respond? \\footfullcite{surveysPew}\n}{}\n\n% 43\n\n\\eoce{\\qt{Overweight baggage\\label{overweight_baggage}} Suppose weights of the \nchecked baggage of airline passengers follow a nearly normal distribution \nwith mean 45 pounds and standard deviation 3.2 pounds. Most airlines charge a \nfee for baggage that weigh in excess of 50 pounds. Determine what percent of \nairline passengers incur this fee.\n}{}\n\n% 44\n\n\\eoce{\\qt{Heights of 10 year olds, Part I\\label{heights_10_yrs}}\nHeights of 10 year olds, regardless of gender, closely follow\na normal distribution with mean 55 inches and standard deviation\n6~inches.\n\\begin{parts}\n\\item\n    What is the probability that a randomly chosen 10 year old\n    is shorter than 48 inches?\n\\item\n    What is the probability that a randomly chosen 10 year old\n    is between 60 and 65 inches?\n\\item\n    If the tallest 10\\% of the class is considered\n    ``very tall'',\n    what is the height cutoff for ``very tall\"?\n\\end{parts}\n}{}\n\n% 45\n\n\\eoce{\\qt{Buying books on Ebay\\label{buy_boooks_ebay}}\nSuppose you're considering buying your expensive chemistry\ntextbook on Ebay.\nLooking at past auctions suggests that the \nprices of this textbook follow an approximately normal\ndistribution with mean \\$89 and standard deviation \\$15.\n\\begin{parts}\n\n\\item What is the probability that a randomly selected auction for this book \ncloses at more than \\$100?\n\n\\item Ebay allows you to set your maximum bid price so that if someone \noutbids you on an auction you can automatically outbid them, up to the \nmaximum bid price you set. If you are only bidding on one auction, what are \nthe advantages and disadvantages of setting a bid price too high or too low? \nWhat if you are bidding on multiple auctions?\n\n\\item If you watched 10 auctions, roughly what percentile might you use for a \nmaximum bid cutoff to be somewhat sure that you will win one of these ten \nauctions? Is it possible to find a cutoff point that will ensure that you win \nan auction?\n\n\\item If you are willing to track up to ten auctions closely, about what \nprice might you use as your maximum bid price if you want to be somewhat sure \nthat you will buy one of these ten books?\n\n\\end{parts}\n}{}\n\n% 46\n\n\\eoce{\\qt{Heights of 10 year olds, Part II\\label{heights_10_yrs_prob}}\nHeights of 10 year olds, regardless of gender, closely follow\na normal distribution with mean 55 inches and standard deviation\n6~inches.\n\\begin{parts}\n\\item\n    The height requirement for \\textit{Batman the Ride} at\n    Six Flags Magic Mountain is 54 inches.\n    What percent of 10 year olds cannot go on this ride?\n\\item\n    Suppose there are four 10 year olds.\n    What is the chance that at least two of them\n    will be able to ride \\emph{Batman the Ride}?\n\\item\n    Suppose you work at the park to help them better\n    understand their customers' demographics, and\n    you are counting people as they enter\n    the park.\n    What is the chance that the first 10 year old\n    you see who can ride \\emph{Batman the Ride} is\n    the 3rd 10 year old who enters the park?\n\\item\n    What is the chance that the fifth 10 year old\n    you see who can ride \\emph{Batman the Ride} is\n    the 12th 10 year old who enters the park?\n\\end{parts}\n}{}\n\n% 47\n\n\\eoce{\\qt{Heights of 10 year olds, Part III\\label{heights_10_yrs_dist}}\nHeights of 10 year olds, regardless of gender, closely follow\na normal distribution with mean 55 inches and standard deviation\n6~inches.\n\\begin{parts}\n\\item\n    What fraction of 10 year olds are taller than\n    76 inches?\n\\item\\label{heights_10_yrs_dist_76_inches}\n    If there are 2,000 10 year olds entering\n    Six Flags Magic Mountain in a single day,\n    then compute the expected number of\n    10 year olds who are at least 76 inches tall.\n    (You may assume the heights of the 10-year olds\n    are independent.)\n\\item\n    Using the binomial distribution,\n    compute the probability that 0 of the 2,000\n    10 year olds will be at least 76 inches tall.\n\\item\n    The number of 10 year olds who enter\n    Six Flags Magic Mountain and are\n    at least 76 inches tall in a given day\n    follows a Poisson distribution with\n    mean equal to the value found in\n    part~(\\ref{heights_10_yrs_dist_76_inches}).\n    Use the Poisson distribution to identify\n    the probability no 10 year old will enter\n    the park who is 76 inches or taller.\n\\end{parts}\n}{}\n\n% 48\n\n\\eoce{\\qt{Multiple choice quiz\\label{mc_quiz}} In a multiple choice quiz there are \n5 questions and 4 choices for each question (a, b, c, d). Robin has not \nstudied for the quiz at all, and decides to randomly guess the answers. What \nis the probability that\n\\begin{parts}\n\\item the first question she gets right is the $3^{rd}$ question?\n\\item she gets exactly 3 or exactly 4 questions right?\n\\item she gets the majority of the questions right?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_distributions/figures/6895997/6895997.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"6895997.pdf\", 5, 2.5,\n      mar = c(2, 0, 0, 0))\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\nplot(X, Y,\n     type = 'n',\n     axes = FALSE,\n     xlim = c(-3.2, 3.2),\n     ylim = c(0, 0.4))\nabline(h = 0, col = COL[6])\nat <- -3:3\nlabels <- expression(mu - 3 * sigma,\n                     mu - 2 * sigma,\n                     mu - sigma,\n                     mu,\n                     mu + sigma,\n                     mu + 2 * sigma,\n                     mu + 3 * sigma)\naxis(1, at, labels)\nfor (i in 3:1) {\n  these <- (i - 1 <= X & X <= i)\n  polygon(c(i - 1, X[these], i),\n          c(0, Y[these], 0),\n          col = COL[i],\n          border = COL[i])\n  these <- (-i <= X & X <= -i + 1)\n  polygon(c(-i, X[these], -i + 1),\n          c(0, Y[these], 0),\n          col = COL[i],\n          border = COL[i])\n}\n\n# _____ Label 99.7 _____ #\narrows(-3, 0.03,\n       3, 0.03,\n       code = 3,\n       col = '#444444',\n       length = 0.15)\ntext(0, 0.02, '99.7%', pos = 3)\n\n# _____ Label 95 _____ #\narrows(-2, 0.13,\n       2, 0.13,\n       code = 3,\n       col = '#444444',\n       length = 0.15)\ntext(0, 0.12, '95%', pos = 3)\n\n# _____ Label 68 _____ #\narrows(-1, 0.23,\n       1, 0.23,\n       code = 3,\n       col = '#444444',\n       length = 0.15)\ntext(0, 0.22, '68%', pos = 3)\n\nlines(X, Y, col = '#888888')\nabline(h = 0, col = '#AAAAAA')\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/amiIncidencesOver100Days/amiIncidencesOver100Days.R",
    "content": "library(openintro)\n\nx <- ami.occurrences$ami\n\nmyPDF(\"amiIncidencesOver100Days.pdf\", 5, 2.4,\n       mar = c(3, 3.5, 0.5, 1))\nhistPlot(x,\n         breaks = (0:max(2 * x + 1)) / 2 - 0.25,\n         axes = FALSE,\n         col = COL[1],\n         xlab = \"\",\n         ylab = \"\")\nat     <- 0:1000\nlabels <- rep(\"\", length(at))\naxis(1, at = at, labels = labels, tcl = -0.18)\naxis(1, at = seq(0, 1000, 5), tcl = -0.35)\naxis(2, at = seq(0, 1000, 20))\npar(las = 0)\nmtext(\"AMI Events (by Day)\", 1, 1.8)\nmtext(\"Frequency\", 2, 2.4)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/between59And62/between59And62.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('between59And62.pdf', 2.5, 0.9,\n      mar = c(1.4, 0, 0, 0),\n      mgp = c(3, 0.45, 0))\nnormTail(70, 3.3,\n         M = c(69, 74),\n         col = COL[1],\n         axes = FALSE)\nlabels <- round(70 + 3.3 * c(-2, 0, 2), 2)\naxis(1, labels, cex.axis = 0.8)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/eoce/GRE_intro/gre_intro.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# set input data ----------------------------------------------------\n\nmean_v = 151\nsd_v = 7\nsophia_v = 160\nsophia_v_Z = (sophia_v - mean_v) / sd_v\n\nmean_q = 153\nsd_q = 7.67\nsophia_q = 157  \nsophia_q_Z = (sophia_q - mean_q) / sd_q\n\n# gre_intro ---------------------------------------------------------\n\npdf(\"gre_intro.pdf\", height = 3, width = 5)\n\npar(mar = c(0,0,0,0), las = 1, mgp = c(3,1,0))\n\nm = 0\ns = 1\n\nX <- m + s*seq(-3.2,3.2,0.01)\nY <- dnorm(X, m, s)\n\nplot(X, Y, type='l', axes = F, \n     xlim = c(-3.4,3.4), ylim = c(-0.11, 0.4), \n     ylab = \"\")\nlines(X, rep(0,length(X)))\n\nlines(c(0,0), dnorm(0)*c(0.01,0.99), col = COL[6], lty=3)\n\nz = sophia_v_Z\ntext(x = z+0.1, dnorm(z)*1.05, \"VR\", pos=3, col= COL[1], cex = 1.5)\ntext(x = z + 0.5, y = -0.03, paste(\"Z =\", round(sophia_v_Z, 2)), \n     col = COL[1], cex = 1.5)\nlines(c(z,z), dnorm(z, m, s)*c(0.01,0.99), lty=2, col= COL[1])\n\nz = sophia_q_Z\ntext(x = z+0.1, dnorm(z)*1.05, \"QR\", pos=3, col= COL[4], cex = 1.5)\ntext(x = z - 0.5, y = -0.03, paste(\"Z =\", round(sophia_q_Z, 2)), \n     col = COL[4], cex = 1.5)\nlines(c(z,z), dnorm(z, m, s)*c(0.01,0.99), lty=2, col= COL[4])\n\ndev.off()\n\n# gre_intro_VR ---------------------------------------------------------\n\npdf(\"gre_intro_VR.pdf\", height = 2, width = 4)\n\npar(mar = c(2,0,0,0), las = 1, mgp = c(3,1,0), \n    cex.lab = 1.25, cex.axis = 0.9)\n\nnormTail(m = mean_v, s = sd_v, L = sophia_v, col = COL[1])\n\ndev.off()\n\n# gre_intro_QR ---------------------------------------------------------\n\npdf(\"gre_intro_QR.pdf\", height = 2, width = 4)\n\npar(mar = c(2,0,0,0), las = 1, mgp = c(3,1,0), \n    cex.lab = 1.25, cex.axis = 0.9)\n\nnormTail(m = mean_q, s = sd_q, L = sophia_q, col = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_distributions/figures/eoce/area_under_curve_1/area_under_curve_1.R",
    "content": "# load packages -----------------------------------------------------\n\nlibrary(openintro)\n\n# Z < -1.35 ---------------------------------------------------------\n\npdf(\"zltNeg.pdf\", height = 3, width = 5)\n\npar(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))\n\nm = 0\ns = 1\nl = -1.35\nu = NA\n\nnormTail(m = m, s = s, L = l, U = u, \n         axes = FALSE, col = COL[1], \n         xlab = \"(a)\", cex.lab = 2)\naxis(1, at = c(m - 3*s, l, m, u, m + 3*s), \n     label = c(NA,l,m,u,NA), cex.axis = 2)\n\ndev.off()\n\n# Z > 1.48 ----------------------------------------------------------\n\npdf(\"zgtPos.pdf\", height = 3, width = 5)\n\npar(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))\n\nm = 0\ns = 1\nl = NA\nu = 1.48\n\nnormTail(m = m, s = s, L = l, U = u, \n         axes = FALSE, col = COL[1], \n         xlab = \"(b)\", cex.lab = 2)\naxis(1, at = c(m - 3*s, l, m, u, m + 3*s), \n     label = c(NA,l,m,u,NA), cex.axis = 2)\n\ndev.off()\n\n# -0.4 < Z < 1.5-----------------------------------------------------\n\npdf(\"zBet.pdf\", height = 3, width = 5)\n\npar(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))\n\nm = 0\ns = 1\nl = NA\nu = NA\nM = c(-0.4,1.5)\n\nnormTail(m = m, s = s, L = l, U = u, M = M,\n         axes = FALSE, col = COL[1], \n         xlab = \"(c)\", cex.lab = 2)\naxis(1, at = c(m - 3*s, l, m, u, m + 3*s), \n     label = c(NA,l,m,u,NA), cex.axis = 2)\n\ndev.off()\n\n# -2 < Z < 2---------------------------------------------------------\n\npdf(\"zgtAbs.pdf\", height = 3, width = 5)\n\npar(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))\n\nm = 0\ns = 1\nl = -2\nu = 2\nM = NA\n\nnormTail(m = m, s = s, L = l, U = u, M = M,\n         axes = FALSE, col = COL[1], \n         xlab = \"(d)\", cex.lab = 2)\naxis(1, at = c(m - 3*s, l, m, u, m + 3*s), \n     label = c(NA,l,m,u,NA), cex.axis = 2)\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/eoce/area_under_curve_2/area_under_curve_2.R",
    "content": "# load packages -----------------------------------------------------\n\nlibrary(openintro)\n\n# Z > -1.13 ---------------------------------------------------------\n\npdf(\"zgtNeg.pdf\", height = 3, width = 5)\n\npar(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))\n\nm = 0\ns = 1\nl = NA\nu = -1.13\nM = NA\n\nnormTail(m = m, s = s, L = l, U = u, M = M,\n         axes = FALSE, col = COL[1], \n         xlab = \"(a)\", cex.lab = 2)\naxis(1, at = c(m - 3*s, l, m, u, m + 3*s), \n     label = c(NA,l,m,u,NA), cex.axis = 2)\n\ndev.off()\n\n# Z < 0.18 ----------------------------------------------------------\n\npdf(\"zltPos.pdf\", height = 3, width = 5)\n\npar(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))\n\nm = 0\ns = 1\nl = 0.18\nu = NA\nM = NA\n\nnormTail(m = m, s = s, L = l, U = u, \n         axes = FALSE, col = COL[1], \n         xlab = \"(b)\", cex.lab = 2)\naxis(1, at = c(m - 3*s, l, m, u, m + 3*s), \n     label = c(NA,l,m,u,NA), cex.axis = 2)\n\ndev.off()\n\n# Z > 8 -------------------------------------------------------------\n\npdf(\"zgt8.pdf\", height = 3, width = 5)\n\npar(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))\n\nm = 0\ns = 1\nl = NA\nu = 8\nM = NA\n\nnormTail(m = m, s = s, L = l, U = u, M = M,\n         axes = FALSE, col = COL[1], \n         xlab = \"(c)\", cex.lab = 2)\naxis(1, at = c(m - 3*s, l, m, u, m + 3*s), \n     label = c(NA,l,m,u,NA), cex.axis = 2)\n\ndev.off()\n\n# -0.5 < Z < 0.5 ----------------------------------------------------\n\npdf(\"zgtAbs.pdf\", height = 3, width = 5)\n\npar(mar = c(5,0,0,0), las = 1, mgp = c(3,1,0), mfrow = c(1,1))\n\nm = 0\ns = 1\nl = NA\nu = NA\nM = c(-0.5,0.5)\n\nnormTail(m = m, s = s, L = l, U = u, M = M,\n         axes = FALSE, col = COL[1], \n         xlab = \"(d)\", cex.lab = 2)\naxis(1, at = c(m - 3*s, l, m, u, m + 3*s), \n     label = c(NA,l,m,u,NA), cex.axis = 2)\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/eoce/college_fem_heights/college_fem_heights.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\n\nheights = c(54, 55, 56, 56, 57, 58, 58, 59, 60, 60, 60, 61, \n            61, 62, 62, 63, 63, 63, 64, 65, 65, 67, 67, 69, 73)\n\n# format data for including in text ---------------------------------\n\ncat(paste(\"\\\\stackrel{\", 1:25, \"}{\", sort(heights), \"}\", sep=\"\"), sep=\", \")\n\n# plot histogram of heights -----------------------------------------\n\npdf(\"heightsFcoll_hist.pdf\", height = 4, width = 6)\n\npar(mar=c(3.7,2.2,1,1), las=1, mgp=c(2.5,0.7,0), mfrow = c(1,1), cex.lab = 1.5, cex.axis = 1.5)\n\nhistPlot(heights, col = COL[1], xlab = \"Heights\", ylab = \"\", probability = TRUE, axes = FALSE, ylim = c(0,0.085))\naxis(1)\n#axis(2, labels = NA)\n\nx = heights\nxfit = seq(min(x)-5, max(x)+5, length = 400)\nyfit = dnorm(xfit, mean = mean(x), sd = sd(x))\nlines(xfit, yfit, col = COL[4], lwd = 2)\n\ndev.off()\n\n# normal probability plot of heights --------------------------------\n\npdf(\"heightsFcoll_qq.pdf\", height = 4, width = 6)\n\npar(mar=c(3.7,3.7,1,1), las=1, mgp=c(2.5,0.7,0), mfrow = c(1,1), cex.lab = 1.5, cex.axis = 1.5)\n\nqqnorm(heights, col = COL[1], pch = 19, main = \"\", axes = FALSE)\naxis(1)\naxis(2)\nqqline(heights, col = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_distributions/figures/eoce/stats_scores/stats_scores.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\n\nscores = c(79, 83, 57, 82, 94, 83, 72, 74, 73, 71, \n           66, 89, 78, 81, 78, 81, 88, 69, 77, 79)\n\n# format data for including in text ---------------------------------\n\ncat(paste(\"\\\\stackrel{\", 1:20, \"}{\", sort(scores), \"}\", sep=\"\"), sep=\", \")\n\n# plot histogram of scores  -----------------------------------------\n\npdf(\"scores_hist.pdf\", height = 4, width = 6)\n\npar(mar = c(3.7, 2.2, 1, 1), las = 1, \n    mgp = c(2.5,0.7,0), mfrow = c(1,1), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nhistPlot(scores, col = COL[1], \n         xlab = \"Scores\", ylab = \"\", \n         probability = TRUE, \n         axes = FALSE)\naxis(1)\n#axis(2, labels = NA)\n\nx = scores\nxfit = seq(min(x)-5, max(x)+5, length = 400)\nyfit = dnorm(xfit, mean = mean(x), sd = sd(x))\nlines(xfit, yfit, col = COL[4], lwd = 2)\n\ndev.off()\n\n# normal probability plot of scores  --------------------------------\n\npdf(\"scores_qq.pdf\", height = 4, width = 6)\n\npar(mar=c(3.7,3.7,1,1), las=1, \n    mgp=c(2.5,0.7,0), mfrow = c(1,1), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nqqnorm(scores, col = COL[1], \n       pch = 19, main = \"\", \n       axes = FALSE)\naxis(1)\naxis(2)\nqqline(scores, col = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_distributions/figures/fcidMHeights/fcidMHeights-helpers.R",
    "content": "\nQQNorm <- function(x, M, SD, col) {\n  qqnorm(x,\n         cex = 0.7,\n         main = '',\n         axes = FALSE,\n         ylab = 'male heights (in.)',\n         col = col)\n  axis(1)\n  axis(2)\n  abline(M, SD)\n}\n\nNormalHist <- function(obs, hold, M, SD, col) {\n  plot(0, 0,\n       type = 'n',\n       xlab = 'Male heights (inches)',\n       ylab = '',\n       axes = FALSE,\n       main = '',\n       xlim = M + c(-3, 3) * SD,\n       ylim = c(0, max(hold$density)))\n  for (i in 1:length(hold$counts)) {\n    rect(hold$breaks[i], 0,\n         hold$breaks[i + 1], hold$density[i],\n         col = col)\n  }\n  axis(1)\n  x <- seq(min(obs) - 2, max(obs) + 2, 0.01)\n  y <- dnorm(x, M, SD)\n  lines(x, y, lwd = 1.5)\n}"
  },
  {
    "path": "ch_distributions/figures/fcidMHeights/fcidMHeights.R",
    "content": "library(openintro)\n\nobs <- male_heights_fcid$height_inch\nsource(\"fcidMHeights-helpers.R\")\n\nhold <- hist(obs, plot = FALSE)\n\nmyPDF(\"fcidMHeights.pdf\", 6, 2.7,\n      mfrow = c(1, 2),\n      mgp = c(2, 0.7, 0),\n      mar = c(3, 0.2, 1, 0.8))\nNormalHist(obs, hold, mean(obs), sd(obs), COL[1])\n\npar(mar = c(3,4,1,0))\nqqnorm(obs,\n       cex = 0.7,\n       main = '',\n       axes = FALSE,\n       ylab = 'Male Heights (inches)',\n       col = COL[1])\naxis(1)\naxis(2)\nqqline(obs)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/fourBinomialModelsShowingApproxToNormal/fourBinomialModelsShowingApproxToNormal.R",
    "content": "library(openintro)\ndata(COL)\n\nk  <- -50:500\np  <- 0.1\nn  <- c(10, 30, 100, 300)\nxl <- c(0, 0, 0, 10) - 1\nxu <- c(7, 11, 24, 50) - 1\naxis1 <- list()\naxis1[[1]] <- seq(0, 6, 2)\naxis1[[2]] <- seq(0, 10, 2)\naxis1[[3]] <- seq(0, 20, 5)\naxis1[[4]] <- seq(10, 50, 10)\n\nmyPDF('fourBinomialModelsShowingApproxToNormal.pdf', 5.5, 4.1,\n      mfrow = c(2, 2),\n      mar = c(3.9, 1, 0.5, 1),\n      mgp = c(2.2, 0.6, 0))\n\nfor (i in 1:4) {\n  plot(k - 0.05, dbinom(k, n[i], p),\n       type = 's',\n       xlim = c(xl[i], xu[i]),\n       axes = FALSE,\n       xlab = paste(\"n  = \", n[i]),\n       ylab = \"\",\n       col = COL[1],\n       lwd = 2)\n  axis(1, axis1[[i]])\n  abline(h = 0)\n  if (i == 2) {\n  \tpar(mar = c(3.25, 1, 0.9, 1))\n  }\n}\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/geometricDist35/geometricDist35.R",
    "content": "library(openintro)\ndata(COL)\n\np <- 0.35\nx <- 1:100\ny <- (1 - p)^(x - 1) * p\nmyPDF('geometricDist35.pdf', 6, 3.1,\n      mar = c(2.6, 3.6, 0.5, 0.5),\n      mgp = c(2.5, 0.34, 0))\nplot(x, y,\n     xlim = c(0.5, 14.5),\n     type = 'n',\n     axes = FALSE,\n     xlab = '',\n     ylab = 'Probability')\nmtext('Number of Trials', line = 1.5, side = 1)\naxis(1, at = seq(2, 14, 2))\npar(mgp = c(2.25, 0.5, 0))\naxis(2, seq(0, 0.3, 0.1))\nfor (i in 1:14) {\n  rect(x[i] - 0.4, 0,\n       x[i] + 0.4, y[i],\n       col = COL[1])\n}\nabline(h = 0)\ntext(14.7, 0.003, '...', col = '#444444')\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/geometricDist70/geometricDist70.R",
    "content": "library(openintro)\ndata(COL)\n\np <- 0.7\nx <- 1:100\ny <- (1 - p)^(x - 1) * p\nmyPDF('geometricDist70.pdf', 6, 3.1,\n      mar = c(2.6, 3.6, 0.5, 0.5),\n      mgp = c(2.5, 0.34, 0))\nplot(x, y,\n     xlim = c(0.5, 8.5),\n     type = 'n',\n     axes = FALSE,\n     xlab = '',\n     ylab = 'Probability')\nmtext(paste('Number of Trials Until a Success for p =', p),\n    line = 1.5, side = 1)\naxis(1, at = seq(1, 20, 1))\npar(mgp = c(2.25, 0.5, 0))\naxis(2, seq(0, 0.6, 0.2))\naxis(2, seq(0, 0.7, 0.1), rep(\"\", 8), tcl = -0.15)\nfor (i in 1:14) {\n  rect(x[i] - 0.4, 0,\n       x[i] + 0.4, y[i],\n       col = COL[1])\n}\nabline(h = 0)\ntext(14.7, 0.003, '...', col = '#444444')\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/height40Perc/height40Perc.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('height40Perc.pdf', 2.15, 0.95,\n      mar = c(1.31, 0, 0.01, 0),\n      mgp = c(3, 0.45, 0))\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-3.1, 3.1))\naxis(1,\n     at = c(-2, 0, 2),\n     labels = round(70 + 3.3 * c(-2, 0, 2), 2),\n     cex.axis = 0.8)\nthese <- which(X <= -0.25)\npolygon(c(X[these[1]], X[these], X[rev(these)[1]]),\n        c(0, Y[these], 0),\n        col = COL[1])\n\ntext(-2, 0.24, '  40%\\n(0.40)', cex = 0.8, col = COL[1])\n\nlines(X, Y)\nabline(h = 0)\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/height82Perc/height82Perc.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('height82Perc.pdf', 2.15, 1,\n      mar = c(1.31, 0, 0.01, 0),\n      mgp = c(3, 0.45, 0))\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\n\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-3.4, 3.4))\naxis(1,\n     at = c(-2, 0, 2),\n     labels = round(70 + 3.3 * c(-2, 0, 2), 2),\n     cex.axis = 0.8)\nthese <- which(X <= 0.92)\npolygon(c(X[these[1]], X[these], X[rev(these)[1]]),\n        c(0, Y[these], 0), col = COL[1])\n\ntext(-2, 0.23, '  82%\\n(0.82)', cex = 0.8, col = COL[1])\n\narrows(2, 0.2, 1.45, 0.07, length = 0.07)\ntext(2.1, 0.18, '  18%\\n(0.18)', cex = 0.8, pos = 3)\n\nlines(X, Y)\nabline(h = 0)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/mikeAndJosePercentiles/mikeAndJosePercentiles.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"mikeAndJosePercentiles.pdf\", 7, 1.3,\n      mar = c(2, 0.2, 0.2, 0.2),\n      mgp = c(3, 0.8, 0),\n      tcl = -0.4)\nlayout(matrix(0:2, 1), c(0.5, 2, 2), 1)\n\nnormTail(70, 3.3,\n         L = 67,\n         axes = FALSE,\n         col = COL[1])\naxis(1,\n     at = c(-100, 67, 70, 1000),\n     cex.axis = 1.7)\ntext(62, 0.07, \"Mike\", cex = 2)\n\nnormTail(70, 3.3,\n         L = 76,\n         axes = FALSE,\n         col = COL[1])\naxis(1,\n     at = c(-100, 70, 76, 1000),\n     cex.axis = 1.7)\ntext(62, 0.07, \"Jose\", cex = 2)\n\ndev.off()"
  },
  {
    "path": "ch_distributions/figures/nbaNormal/nbaNormal-helpers.R",
    "content": "\nQQNorm <- function(x, M, SD, col) {\n  qqnorm(x,\n         cex = 0.7,\n         main = '',\n         axes = FALSE,\n         ylab = 'Observed',\n         col = col)\n  axis(1)\n  axis(2)\n  qqline(x)\n}\n\nNormalHist <- function(obs, hold, M, SD, col) {\n  x <- seq(min(obs) - 2, max(obs) + 2, 0.01)\n  y <- dnorm(x, M, SD)\n  plot(0, 0,\n       type = 'n',\n       xlab = 'Height (inches)',\n       ylab = '',\n       axes = FALSE,\n       main = '',\n       xlim = M + c(-3, 3) * SD,\n       ylim = c(0, max(hold$density, y)))\n  for (i in 1:length(hold$counts)) {\n    rect(hold$breaks[i], 0,\n         hold$breaks[i + 1], hold$density[i],\n         col = col)\n  }\n  axis(1)\n  lines(x, y, lwd = 1.5)\n}"
  },
  {
    "path": "ch_distributions/figures/nbaNormal/nbaNormal.R",
    "content": "library(openintro)\ndim(nba_players_19)\nhead(nba_players_19)\n\nsource(\"nbaNormal-helpers.R\")\n\nobs <- nba_players_19$height\nM  <- mean(obs)\nSD <- sd(obs)\nhold <- hist(obs, plot = FALSE)\n\nmyPDF(\"nbaNormal.pdf\", 6, 2.5,\n      mfrow = c(1, 2),\n      mgp = c(2, 0.5, 0),\n      mar = c(3, 0.5, 0.5, 2),\n      cex.axis = 0.8)\nNormalHist(obs, hold, M, SD, COL[1])\npar(mar = c(3, 4, 0.5, 0.5))\nQQNorm(obs, M, SD, COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/normApproxToBinomFail/normApproxToBinomFail.R",
    "content": "library(openintro)\ndata(COL)\n\nk <- 0:400\np <- 0.15\nn <- 400\nx1 <- 49\nx2 <- 51\nm <- n * p\ns <- sqrt(n * p * (1 - p))\n\nmyPDF('normApproxToBinomFail.pdf', 7.5, 2.6,\n      mar = c(1.9, 1, 0.3, 1),\n      mgp = c(2.2, 0.6, 0),\n      tcl = -0.35)\n\nX <- seq(0, 100, 0.01)\nY <- dnorm(X, m, s)\nplot(X, Y,\n     type = \"l\",\n     xlim = c(37, 83),\n     axes = FALSE,\n     xlab = \"\",\n     ylab = \"\")\npolygon(c(x1, x1, x2, x2),\n        dnorm(c(-1000, x1, x2, -1000), m, s),\n        col = COL[1])\npolygon(rep(c(x1 - 1.1, x1, x1 + 1, x2 + 0.1), rep(2, 4)) + 0.5,\n        dbinom(c(-1000, x1, x1, x1 + 1, x1 + 1, x2, x2, -1000),\n            n, p),\n        border = COL[4],\n        lwd = 2)\naxis(1)\naxis(1,\n     1:200,\n     rep(\"\", 200),\n     tcl = -0.12)\nabline(h = 0)\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/normalExamples/normalExamples-helpers.R",
    "content": "\nQQNorm <- function(x, M, SD, col) {\n  qqnorm(x,\n         cex = 0.7,\n         main = '',\n         axes = FALSE,\n         ylab = 'observed',\n         col = col)\n  axis(1, cex.axis = 1.2)\n  axis(2, cex.axis = 1.2)\n  qqline(x)\n}\n\nNormalHist <- function(obs, hold, M, SD, col) {\n  plot(0, 0,\n       type = 'n',\n       xlab = '',\n       ylab = '',\n       axes = FALSE,\n       main = '',\n       xlim = c(-3, 3),\n       ylim = c(0, max(hold$density)))\n  for (i in 1:length(hold$counts)) {\n    rect(hold$breaks[i], 0,\n         hold$breaks[i + 1], hold$density[i],\n         col = col)\n  }\n  axis(1, cex.axis = 1.2)\n  x <- seq(min(obs) - 2, max(obs) + 2, 0.01)\n  y <- dnorm(x, M, SD)\n  lines(x, y, lwd = 1.5)\n}"
  },
  {
    "path": "ch_distributions/figures/normalExamples/normalExamples.R",
    "content": "library(openintro)\ndata(COL)\n\nobs1 <- simulated_normal$n40\nobs2 <- simulated_normal$n100\nobs3 <- simulated_normal$n400\n\nhold1 <- hist(obs1, plot=FALSE)\nM1    <- mean(obs1)\nSD1   <- sd(obs1)\n\nhold2 <- hist(obs2, breaks=10, plot=FALSE)\nM2    <- mean(obs2)\nSD2   <- sd(obs2)\n\nhold3 <- hist(obs3, breaks=12, plot=FALSE)\nM3    <- mean(obs3)\nSD3   <- sd(obs3)\n\nsource(\"normalExamples-helpers.R\")\n\nmyPDF(\"normalExamples.pdf\", 7.3, 4.4,\n      mfrow = c(2, 3),\n      mgp = c(2, 0.7, 0),\n      mar = c(3, 0, 1, 1))\nNormalHist(obs1, hold1, M1, SD1, COL[1])\nNormalHist(obs2, hold2, M2, SD2, COL[2])\nNormalHist(obs3, hold3, M3, SD3, COL[3])\n\npar(mar = c(3,2.85,1,1.8))\nQQNorm(obs1, M1, SD1, COL[1])\nQQNorm(obs2, M2, SD2, COL[2])\nQQNorm(obs3, M3, SD3, \"#B09A00\")\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/normalQuantileExer/QQNorm.R",
    "content": "\nQQNorm <- function(obs, at  =  pretty(obs), lwd = 2) {\n  qqnorm(obs,\n         cex = 0.9,\n         main = '',\n         axes = FALSE,\n         ylab = 'Observed',\n         xlab = \"\",\n         col = COL[1],\n         lwd = lwd)\n  mtext(\"Theoretical quantiles\",\n        1,\n        1.8,\n        cex = 0.8)\n  axis(1, cex.axis = 1.1)\n  axis(2, at = at, cex.axis = 1.1)\n}\n"
  },
  {
    "path": "ch_distributions/figures/normalQuantileExer/normalQuantileExer-data.R",
    "content": ""
  },
  {
    "path": "ch_distributions/figures/normalQuantileExer/normalQuantileExer.R",
    "content": "library(openintro)\ndata(COL)\n\n\nobs1 <- simulated_dist$d1\nobs2 <- simulated_dist$d2\nobs3 <- simulated_dist$d3\nobs4 <- simulated_dist$d4\n\nsource(\"QQNorm.R\")\n\nmyPDF(\"normalQuantileExer.pdf\", 6, 5.3,\n      mfrow = c(2,2),\n      mgp = c(2.4,.55,0),\n      mar = c(3.5,3.45,1,1),\n      cex.lab = 1.1)\nQQNorm(obs1, seq(0, 120, 40), lwd = 1.5)\nQQNorm(obs2, lwd = 1.5)\nQQNorm(obs3, seq(-3, -1, 1), lwd = 1.5)\nQQNorm(obs4, lwd = 1.5)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/normalQuantileExer/normalQuantileExerAdditional.R",
    "content": "library(openintro)\ndata(COL)\n\nsource(\"QQNorm.R\")\n\nobs1 <- simulated_dist$d5\nobs2 <- simulated_dist$d6\n\n\nmyPDF(\"normalQuantileExerAdditional.pdf\", 7.2, 3.18,\n      mfrow = c(1, 2),\n      mgp = c(2.4, 0.55, 0),\n      mar = c(3.5, 3.45, 1, 1),\n      cex.lab = 1.1)\n\nQQNorm(obs1, 0:2, lwd = 2)\nQQNorm(obs2, seq(5, 15, 5), lwd = 2)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/normalTails/normalTails.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"normalTails.pdf\", 4.3, 1,\n      mar = c(0.81, 1, 0.3, 1),\n      mgp = c(3, -0.2, 0),\n      mfrow = c(1,2))\nnormTail(0, 1,\n         -0.8,\n         col = COL[1],\n         axes = FALSE)\nat <- c(-5, 0, 5)\nlabels <- c(-5, 'Negative Z', 5)\ncex.axis <- 0.7\ntick <- FALSE\naxis(1, at, labels, cex.axis = cex.axis, tick = tick)\nlines(c(0, 0),\n      dnorm(0) * c(0.01, 0.99),\n      col = COL[6],\n      lty = 3,\n      lwd = 1.5)\n\nnormTail(0, 1,\n         0.8,\n         col = COL[1],\n         axes = FALSE)\nlabels <- c(-5, 'Positive Z', 5)\naxis(1, at, labels, cex.axis = cex.axis, tick = tick)\nlines(c(0, 0),\n      dnorm(0) * c(0.01, 0.99),\n      col = COL[6],\n      lty = 3,\n      lwd = 1.5)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/pokerNormal/pokerNormal.R",
    "content": "library(openintro)\ndata(COL)\n\nobs <- c(-110, -9, -60, 316, -200, -196,\n         320, -160, 31, 331, 1731, 21,\n         -926, -475, 914, -300, -15, 1,\n         -29, 829, 761, 227, -141, -672,\n         352, 385, 24, 103, -826, 95,\n         115, 39, -9, -1000, -35, -200,\n         -200, 235, 70, 307, 135, 60,\n         -100, -295, -1000, 361, -95,\n         337, 3712, -255)\n\nM  <- mean(obs)\nSD <- sd(obs)\nx <- seq(min(obs) - 3000,\n         max(obs) + 3000,\n         1)\ny <- dnorm(x, M, SD)\nmyPDF(\"pokerNormal.pdf\", 6.5, 2.7,\n      mfrow = 1:2,\n      mgp = c(2, 0.5, 0),\n      mar = c(3, 0.5, 0.5, 2))\nhistPlot(obs,\n         xlab = 'Poker earnings (US$)',\n         ylab = '',\n         axes = FALSE,\n         main = '',\n         xlim = c(-2000, 4000),\n         probability = TRUE,\n         col = COL[1])\naxis(1,\n     cex.axis = 0.7,\n     mgp = c(2, 0.35, 0))\nlines(x, y,\n      lwd = 1.5)\n\npar(mar = c(3, 4, 0.5, 0.5),\n    mgp = c(2.8, 0.5, 0),\n    cex.axis = 0.8)\nqqnorm(obs,\n       cex = 0.8, col = COL[1], lwd = 2,\n       main = '',\n       axes = FALSE,\n       xlab = '',\n       ylab = 'Observed')\nmtext('Theoretical Quantiles',\n      line = 2,\n      side = 1)\naxis(1)\naxis(2)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/satAbove1190/satAbove1190.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"satAbove1190.pdf\", 3, 1.4,\n      mar = c(1.2, 0, 0, 0),\n      mgp = c(3, 0.17, 0))\nnormTail(1100, 200,\n         U = 1190,\n         axes = FALSE,\n         col = COL[1])\naxis(1, at = c(700, 1100, 1500),\n     cex.axis = 0.8)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/satActNormals/satActNormals.R",
    "content": "library(openintro)\ndata(COL)\n\nset.seed(1)\n\npdf('satActNormals.pdf', 6, 3.5)\npar(mfrow = c(2, 1),\n    las = 1,\n    mar = c(2.5, 0, 0.5, 0))\n\n# _____ Curve 1 _____ #\nm <- 1100\ns <- 200\nX <- m + s * seq(-6, 6, 0.01)\nY <- dnorm(X, m, s)\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = m + s * 2.7 * c(-1, 1))\naxis(1, at = m + s * (-3:3))\nabline(h = 0)\nlines(c(m, m),\n      dnorm(m, m, s) * c(0.01, 0.99),\n      lty = 2,\n      col = '#EEEEEE')\nlines(c(m, m) + s,\n      dnorm(m + s, m, s) * c(0.01, 1.25),\n      lty = 2, col = COL[1])\ntext(m + s,\n     dnorm(m + s, m, s) * 1.25,\n     'Ann',\n     pos = 3,\n     col = COL[1])\n\n\n# _____ Curve 2 _____ #\npar(mar = c(2, 0, 1, 0))\nm <- 21\ns <- 6\nX <- m + s * seq(-6, 6, 0.01)\nY <- dnorm(X, m, s)\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = m + s * 2.7 * c(-1, 1))\naxis(1, at = m + s * (-3:3))\nabline(h = 0)\nlines(c(m, m),\n      dnorm(m, m, s) * c(0.01, 0.99),\n      lty = 2,\n      col = '#EEEEEE')\nlines(c(m, m) + 3,\n      dnorm(m + 3, m, s) * c(0.01, 1.2),\n      lty = 2,\n      col = COL[1])\ntext(m + 3,\n     dnorm(m + 3, m, s) * 1.05,\n     'Tom',\n     pos = 4,\n     col = COL[1])\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/satBelow1030/satBelow1030.R",
    "content": "library(openintro)\ndata(COL)\n\n\nmyPDF('satBelow1030.pdf', 2.875, 1,\n      mar = c(1.5, 0, 0, 0),\n      mgp = c(3, 0.45, 0))\nnormTail(1100, 200, 1030,\n         axes = FALSE,\n         col = COL[1])\naxis(1, at = c(700, 1100, 1500))\ndev.off()\n\n\nmyPDF('satAbove1030.pdf', 3, 1,\n      mar = c(1.5, 4, 0, 0),\n      mgp = c(3, 0.45, 0))\nnormTail(1100, 200,\n         U = 1030,\n         axes = FALSE,\n         col = COL[1])\naxis(1, at = c(700, 1100, 1500))\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/satBelow1300/satBelow1300.R",
    "content": "library(openintro)\ndata(COL)\n\n#===> plot <===#\nmyPDF(\"satBelow1300.pdf\", 2.25, 1,\n      mar = c(1.2, 0, 0, 0),\n      mgp = c(3, 0.17, 0))\nnormTail(1100, 200,\n         L = 1300,\n         col = COL[1],\n         cex.axis = 0.6)\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/simpleNormal/simpleNormal.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"simpleNormal.pdf\", 4.3, 1.5,\n      mar = 0.1 * rep(1, 4))\n\nX <- seq(-5,5,0.01)\nY <- dnorm(X)\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-4, 4),\n     lwd = 2,\n     col = COL[5])\n#axis(1, at = -3:3)\nabline(h = -0.002, col = COL[5])\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/smallNormalTails/smallNormalTails.R",
    "content": "library(openintro)\n\nmyPDF(\"smallNormalTails.pdf\", 4.56, 1.2,\n      mar = c(1.3, 1, 0.5, 1),\n      mgp = c(3, 0.27, 0),\n      mfrow = c(1, 2))\n\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\n\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-3.4, 3.4))\nat = c(-5, -0.8, 0, 5)\nlabels = c(-5, '-Z', 0, 5)\naxis(1, at, labels, cex.axis = 0.7)\nthese <- which(X < -0.799)\npolygon(c(X[these[1]], X[these], X[rev(these)[1]]),\n        c(0, Y[these], 0),\n        col = '#CCCCCC')\nlines(X, Y)\nabline(h = 0)\nlines(c(0, 0), c(0, dnorm(0)),\n      col = '#CCCCCC',\n      lty = 3)\n\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-3.4, 3.4))\naxis(1,\n     at = c(-5, 0.8, 0, 5),\n     labels = c(-5, 'Z', 0,5),\n     cex.axis = 0.7)\nthese <- which(X > 0.801)\npolygon(c(X[these[1]], X[these],X[rev(these)[1]]),\n        c(0, Y[these], 0),\n        col = '#CCCCCC')\nlines(X, Y)\nabline(h = 0)\nlines(c(0, 0),\n      c(0, dnorm(0)),\n      col = '#CCCCCC',\n      lty = 3)\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/standardNormal/standardNormal.R",
    "content": "library(openintro)\n\nset.seed(1)\nx <- rnorm(1e5)\nhold <- hist(x, breaks = 50, plot = FALSE)\n\nmyPDF(\"standardNormal.pdf\", 1250 / 255, 650 / 255,\n      mar = c(2, 0, 0.5, 0))\n\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\n\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-3.4, 3.4))\naxis(1, at = -3:3)\nfor(i in 1:length(hold$counts)){\n  rect(hold$breaks[i], 0,\n       hold$breaks[i+1], hold$density[i],\n       border = '#DDDDDD',\n       col = '#F4F4F4')\n}\nlines(X, Y)\nabline(h = 0)\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/subtracting2Areas/subtracting2Areas.R",
    "content": "library(openintro)\ndata(COL)\n\nAddShadedPlot <- function(x, y, offset,\n                          shade.start = -8,\n                          shade.until = 8) {\n  lines(x + offset, y)\n  lines(x + offset, rep(0, length(x)))\n  these <- which(shade.start <= x & x <= shade.until)\n  polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,\n          c(0, y[these], 0),\n          col = COL[1])\n  lines(x + offset, y)\n}\nAddText <- function(x, text) {\n  text(x, 0.549283, text)\n}\n\npdf('subtracting2Areas.pdf', 4, 0.7)\npar(las = 1,\n    mar = rep(0, 4),\n    mgp = c(3, 0, 0))\nX <- seq(-3.2, 3.2, 0.01)\nY <- dnorm(X)\n\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-3.4, 24 + 3.4),\n     ylim = c(0, 0.622))\n\nAddShadedPlot(X, Y, 0)\nAddText(0, format(c(1, 0.0001), scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 8, -8, -0.3)\nAddText(8, format(0.3821, scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 16, 1.21, 8)\nAddText(16, format(0.1131, scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 24, -0.3, 1.21)\nAddText(24, format(0.5048, scientific = FALSE)[1])\n\nlines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)\nlines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)\nlines(c(8 + 3.72, 8 + 4.28), rep(0.549283, 2), lwd = 2)\nlines(c(8 + 3, 2 * 8 - 3), c(0.2, 0.2), lwd = 3)\n\ntext(20, 0.549283,\n     ' = ')\nsegments(rep(19, 2), c(0.17, 0.23), rep(21, 2), lwd = 3)\ndev.off()\n\n"
  },
  {
    "path": "ch_distributions/figures/subtractingArea/subtractingArea.R",
    "content": "library(openintro)\n\nAddShadedPlot <- function(x, y, offset,\n                          shade.start = -8,\n                          shade.until = 8) {\n  lines(x + offset, y)\n  lines(x + offset, rep(0, length(x)))\n  these <- which(shade.start <= x & x <= shade.until)\n  polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,\n          c(0, y[these], 0),\n          col = COL[1])\n  lines(x + offset, y)\n}\nAddText <- function(x, text) {\n  text(x, 0.549283, text, cex = 2)\n}\n\npdf('subtractingArea.pdf', 6, 1.4)\npar(las = 1,\n    mar = rep(0, 4),\n    mgp = c(3, 0, 0))\nX <- seq(-3.2, 3.2, 0.01)\nY <- dnorm(X)\n\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-3.4, 16 + 3.4),\n     ylim = c(0, 0.622))\n\nAddShadedPlot(X, Y, 0)\nAddText(0, format(c(1, 0.0001), scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 8, -8, 0.45)\nAddText(8, format(0.6736, scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 16, 0.45, 8)\nAddText(16, format(0.3264, scientific = FALSE)[1])\n\nlines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)\nlines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)\n\ntext(12, 0.549283,\n     ' = ',\n     cex = 2)\nsegments(c(11, 11), c(0.17, 0.23), c(13, 13), lwd = 3)\ndev.off()\n\n\npdf('subtracted.pdf', 3, 0.95)\npar(las = 1,\n    mar = c(1.5, 3, 0, 0),\n    mgp = c(3, 0.55, 0))\nnormTail(1100, 200, L = 1190, col = COL[1], axes = FALSE)\naxis(1, at = c(700, 1100, 1500))\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/twoSampleNormals/twoSampleNormals.R",
    "content": "library(openintro)\ndata(COL)\n\nset.seed(1)\nx <- rnorm(100000)\nhold <- hist(x,\n             breaks = 50,\n             plot = FALSE)\n\nmyPDF(\"twoSampleNormals.pdf\", 6, 2,\n      mfrow = c(1,2), las = 1, mar = c(2.5,1,0.5,1))\n\n# curve 1\nX <- seq(-4,4,0.01)\nY <- dnorm(X)\nplot(X, Y,\n     type = 'l',\n     col = COL[1],\n     axes = FALSE,\n     xlim = c(-3.4, 3.4))\naxis(1, at = -3:3)\nfor (i in 1:length(hold$counts)) {\n  rect(hold$breaks[i], 0,\n       hold$breaks[i+1], hold$density[i],\n       border = COL[5,4], col = COL[7,3])\n}\nlines(X, Y, col = COL[1], lwd = 2)\nabline(h = 0)\n\n# curve 2\nX <- seq(3,35,0.01)\nY <- dnorm(X, 19, 4)\nplot(X, Y, type = 'l', col = COL[2], axes = FALSE, xlim = c(5.4,32.6))\naxis(1, at = 19+4*(-3:3))\n\nfor (i in 1:length(hold$counts)) {\n  rect(19 + 4 * hold$breaks[i], 0,\n       19 + 4 * hold$breaks[i + 1], hold$density[i] / 4,\n       border = COL[5, 4], col = COL[7, 3])\n}\nlines(X, Y, col = COL[2], lwd = 2)\nabline(h = 0)\n\ndev.off()\n"
  },
  {
    "path": "ch_distributions/figures/twoSampleNormalsStacked/twoSampleNormalsStacked.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"twoSampleNormalsStacked.pdf\", 4.65, 2,\n      mar = c(1.7,1,0.1,1))\n\n# curve 1\nX <- seq(-4,4,0.01)\nY <- dnorm(X)\nplot(X, Y,\n     type = 'l',\n     col = COL[1],\n     axes = FALSE,\n     xlim = c(-5, 35))\naxis(1, at = seq(-10, 40, 10))\nlines(X, Y, col = COL[1], lwd = 3)\nabline(h = 0)\n\n# curve 2\nX <- seq(4, 35, 0.01)\nY <- dnorm(X, 19, 4)\nlines(X, Y, col = COL[2], lwd = 3)\n\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/TeX/ch_foundations_for_inf.tex",
    "content": "\\begin{chapterpage}{Foundations for inference}\n  \\chaptertitle{Foundations for inference}\n  \\label{foundationsForInference}\n  \\label{ch_foundations_for_inf}\n  \\chaptersection{pointEstimates}\n  \\chaptersection{confidenceIntervals}\n  \\chaptersection{hypothesisTesting}\n\\end{chapterpage}\n\\renewcommand{\\chapterfolder}{ch_foundations_for_inf}\n\n\\chapterintro{Statistical inference is primarily\n  concerned with understanding and quantifying the\n  uncertainty of parameter estimates.\n  While the equations and details change\n  depending on the setting, the foundations for inference\n  are the same throughout all of statistics. \\\\\n\n  \\noindent%\n  We start with a familiar topic:\n  the idea of using a sample proportion to estimate\n  a population proportion.\n  Next, we create what's called a\n  \\emph{\\hiddenterm{confidence interval}}, which is a range\n  of plausible values where we may find the true population\n  value.\n  Finally, we introduce the\n  \\emph{hypothesis testing framework},\n  which allows us to formally evaluate claims about the\n  population, such as whether a survey provides strong\n  evidence that a candidate has the support of a majority\n  of the voting population.}\n\n\n\n%__________________\n\\section{Point estimates and sampling variability}\n\\label{pointEstimates}\n\n\\index{data!solar survey|(}\n\nCompanies such as Pew Research frequently conduct\npolls as a way to understand the state of public opinion\nor knowledge on many topics, including politics,\nscientific understanding, brand recognition, and more.\n%These polls typically reach a sample of 300 to\n%10,000 people.\nThe ultimate goal in taking a poll is generally to use\nthe responses to estimate the opinion or knowledge of the\nbroader population.\n\n%These polls are often based on 500 to 5000 people,\n%and a polling company such as Pew would use this sample\n%to estimate the opinions of the broader population.\n%For example, Pew frequently conducts a poll on about\n%1000 adults about their feelings about the direction\n%of their country.\n%In early 2019, they found that \n%Through this and future sections,\n%we'll use some new notation and terminology:\n%\\begin{itemize}\n%\\item\n%    For all inference problems concerning proportions,\n%    the population proportion will be written as $p$.\n%    When discussing a population summary such as $p$,\n%    it is common to refer to the value as a population\n%    \\term{parameter}.\n%    In the solar survey,\n%    $p$ represents the proportion of \\emph{all}\n%    American adults who support solar energy.\n%\\item Using Pew Research sample, we can estimate that the proportion\n%    of American adults who support expanding solar energy is\n%    somewhere near \\pewsolarpollpercent{}.\n%    This is called the \\term{sample proportion},\n%    and it gets a special label of $\\hat{p}$\n%    (spoken as \\emph{p-hat}).\n%\\item The size of a sample will generally\n%    be denoted by $n$. In the case of this Pew Research poll,\n%    the \\term{sample size} is $n = \\pewsolarpollsize{}$.\n%\\end{itemize}\n\n\n\n%In the United States, those 1000 adults would be used\n%to generalize out to a population of about \\emph{250 million}\n%American adults.\n%A~natural question arises:\n%\\begin{quote}\n%\\em\n%If the poll was based on only a thousand people,\n%how reliable is it?\n%\\end{quote}\n%For instance, if we took another poll,\n%we wouldn't get the exact same answer,\n%so how trustworthy is the result?\n%This is the topic of this first inference section,\n%where we hope to understand how variable estimates\n%are from one sample to the next,\n%which will give us an idea of how much trust we should\n%(or shouldn't) put into such polls.\n\n\n\\subsection{Point estimates and error}\n\n\\index{point estimate|(}\n\nSuppose a poll suggested the US President's approval\nrating is 45\\%.\nWe would consider 45\\% to be a\n\\term{point estimate}\\index{estimate} of the approval\nrating we might see if we collected responses from the\nentire population.\n%\\footnote{When we collect responses from the\n%  entire population, it is called a \\term{census}.\n%  It is often expensive to conduct a census,\n%  which is why we often instead take a sample.}\nThis entire-population response proportion is\ngenerally referred to as the \\term{parameter}\nof interest.\nWhen the parameter is a proportion,\nit is often denoted by $p$,\n%We typically estimate the parameter by collecting\n%information from a sample of the population;\n%we compute the observed proportion in the sample;\n%also called a \\term{point estimate},\nand we often refer to the sample proportion as $\\hat{p}$\n(pronounced \\emph{p-hat}\\footnote{Not to be confused with\n  \\emph{phat}, the slang term used for something cool,\n  like this book.}).\nUnless we collect responses from every individual in the population,\n$p$ remains unknown, and we use $\\hat{p}$ as our estimate of~$p$.\nThe difference we observe from the poll versus\nthe parameter is called the \\term{error} in the estimate.\n%There are other considerations that can influence\n%the error in a sample's estimate can be influenced\n%by other factors, too.\n%it is not the complete story.\n%For this reason, we will also find it convenient to track\n%the \\term{sample size}, which is generally referred to using\n%the letter $n$.\nGenerally, the error consists of two aspects:\nsampling error and bias.\n%Throughout the rest of this section,\n%we discuss what a point estimate like\n%\\pewsolarpollpercent{} represents\n%and the sampling uncertainty associated with such an estimate.\n%If we take a simple random sample of 1000 American adults\n%and ask them for their opinion about solar energy,\n%will we tend to get a result close to the\n%\\pewsolarpollpercent{} value,\n%or might we see observations far from the truth?\n\n\n%\n%Suppose that we know that \\pewsolarpollpercent{}\n%of American adults \n%\n%American adults' attitudes towards different forms of energy.\n%They found that \\pewsolarpollpercent{} of respondents\n%favored expanding\n%solar energy.\n%In this case, Pew Research worked to ensure\n%that the sample was representative.\n%However, a~natural question remains:\n%\\begin{quote}\n%\\em\n%If the poll was based on only a thousand people,\n%how reliable is it?\n%\\end{quote}\n%If we took another poll, we wouldn't get the exact same answer.\n%Maybe we'd get 90\\%, or perhaps even 80\\%.\n%Ultimately, it's unlikely that the actual proportion of\n%Americans who support expanding solar energy is\n%\\emph{exactly}~\\pewsolarpollpercent{}, but the data suggest\n%the actual support is close to \\pewsolarpollpercent{}.\n%This type of uncertainty --\n%the variability in the estimate from one sample to the next --\n%is called the \\term{sampling error},\n%and it is a major focus throughout the rest of this book.\n\n%\\footnote{Another major form\n%  of error is \\term{bias}, which basically is a systematic\n%  tendency to over or under-estimate the true population value.\n%  For instance, if we took a political poll and undersampled\n%  one of the political parties, the sample would not be\n%  representative and would skew in a particular direction.}\n%Ultimately, it's unlikely that the actual proportion of Americans\n%who support expanding solar energy is \\emph{exactly}\n%\\pewsolarpollpercent{}, but the data suggest the actual\n%support is close to \\pewsolarpollpercent{}.\n\n\n%The Pew Research poll is a point estimate\n%of the actual proportion\n%of American adults who support expanding solar energy.\n%This estimate of \\pewsolarpollpercent{} is unlikely\n%to be perfect,\n%and it's quite possible for the population proportion\n%to be a little lower or a little higher than the\n%sample proportion.\n%The difference between a point estimate and\n%the parameter is called the estimate's \\term{error}.\n\n\\termsub{Sampling error}{sampling error},\nsometimes called \\emph{\\hiddenterm{sampling uncertainty}},\ndescribes how much an estimate will tend to vary from\none sample to the next.\nFor instance, the estimate from one sample might be 1\\% too low\nwhile in another it may be 3\\% too high.\nMuch of statistics, including much of this book,\nis focused on understanding and quantifying sampling error,\nand we will find it useful to consider a sample's size\nto help us quantify this error;\nthe \\term{sample size} is often represented by the letter $n$.\n%Intuitively, a larger sample would tend to produce a more\n%accurate estimate than what we would\n%obtain from a smaller sample.\n%This is exactly the ref\n%estimate from a smaller sample,\n%and this is generally true.\n\n\\termsub{Bias}{bias} describes a systematic tendency\nto over- or under-estimate the true population value.\nFor~example, if we were taking a student poll asking\nabout support for a new college stadium, we'd probably\nget a biased estimate of the stadium's level of student\nsupport by wording the question as,\n\\emph{Do you support your school by supporting funding\n  for the new stadium?}\nWe try to minimize bias through thoughtful data\ncollection procedures, which were discussed in\nChapter~\\ref{ch_intro_to_data}\nand are the topic of many other books.\n\n%While bias is an incredibly important topic,\n%it's forms are so varied that \n%so vast and context-specific that we \n\n%\\begin{onebox}{Sampling error vs bias}\n%  \\termsub{Sampling error}{sampling error} is uncertainty\n%  in a point estimate that happens naturally from one sample\n%  to the next.\n%  The methods we discuss are useful for understanding,\n%  quantifying, and working with sampling errors.\n%  \\stdvspace{}\n%\n%  In contrast, another common form of error is \\term{bias},\n%  which is a systematic tendency to over or under-estimate\n%  the true population value.\n%  For instance, if we took a political poll but our sample\n%  didn't include a roughly representative distribution of\n%  the political parties, the sample would likely skew\n%  in a particular direction and be biased.\n%\\end{onebox}\n\n\n\n\n\\subsection{Understanding the variability of a point estimate}\n\\label{simulationForUnderstandingVariabilitySection}\n\n\\newcommand{\\pewsolarpollsize}{1000}\n\\newcommand{\\pewsolarparprop}{0.88}\n\\newcommand{\\pewsolarparpropcomplement}{0.12}\n\\newcommand{\\pewsolarparpercent}{88\\%}\n\\newcommand{\\pewsolarparpercentcomplement}{12\\%}\n\\newcommand{\\pewsolarpollprop}{0.887}\n\\newcommand{\\pewsolarpollpropcomplement}{0.113}\n\\newcommand{\\pewsolarpollpercent}{88.7\\%}\n\\newcommand{\\pewsolarpollpercentcomplement}{11.3\\%}\n\\newcommand{\\pewsolarpollcount}{887}\n\\newcommand{\\pewsolarpollexpcount}{880}\n\\newcommand{\\pewsolarpollcountcomplement}{113}\n\\newcommand{\\pewsolarpollexpcountcomplement}{120}\n\\newcommand{\\pewsolarpollse}{0.010}\n\nSuppose the proportion of American adults who support\nthe expansion of solar energy is $p = \\pewsolarparprop{}$,\nwhich is our parameter of interest.\\footnote{We haven't\n  actually conducted a census to measure this value perfectly.\n  However, a very large sample has suggested the actual\n  level of support is about \\pewsolarparpercent{}.}\nIf we were to take a poll of \\pewsolarpollsize{} American adults\non this topic, the estimate would not be perfect,\nbut how close might we expect the sample proportion\nin the poll would be to \\pewsolarparpercent{}?\nWe want to understand, \\emph{how does the\nsample proportion $\\hat{p}$ behave when the true population\nproportion is\n\\pewsolarparprop{}}.\\footnote{\\pewsolarparpercent{}\n  written as a proportion would be\n  \\pewsolarparprop{}.\n  It is common to switch between proportion and percent.\n  However, formulas presented in this book always refer\n  to the proportion, not the percent.}\nLet's find out!\nWe can simulate responses we would get from a simple\nrandom sample of 1000 American adults,\nwhich is only possible because we know the actual\nsupport for expanding solar energy is \\pewsolarparprop{}.\n%\n%\n%We could\n%run the survey again to see how consistent the results\n%are, but who has the time and money for that? Instead,\n%we can investigate the properties of $\\hat{p}$ using simulations.\n%\n%To simulate the sample, we'll suppose that the population\n%proportion is exactly \\pewsolarpollpercent{}.\n%Now, we know\n%the population proportion isn't exactly \\pewsolarpollpercent\\%,\n%but we do expect it to be close, so this simulation will offer\n%us some insights about the property of $\\hat{p}$.\n%If we took a random sample\n%from this population, how accurate would the point estimate be?\nHere's how we might go about constructing such a simulation:\n%simulate it:\n\\begin{enumerate}\n\\item There were about 250 million American adults in 2018.\n    On 250 million pieces of paper, write ``support''\n    on \\pewsolarparpercent{} of them and ``not'' on\n    the other \\pewsolarparpercentcomplement{}.\n\\item Mix up the pieces of paper and pull out \\pewsolarpollsize{}\n    pieces to represent our sample of \\pewsolarpollsize{}\n    American adults.\n\\item Compute the fraction of the sample that say ``support''.\n\\end{enumerate}\nAny volunteers to conduct this simulation? Probably not. Running\nthis simulation with 250 million pieces of paper would be\ntime-consuming and very costly, but we can simulate it\nusing computer code; we've written a short program in\nFigure~\\ref{solarPollSimulationCodeR}\nin case you are curious what the computer code looks like.\nIn this simulation, the sample gave a point estimate of\n$\\hat{p}_1 = 0.894$. We~know the population proportion\nfor the simulation was $p = \\pewsolarparprop{}$, so we know\nthe estimate had an error of\n$0.894 - \\pewsolarparprop{} = \\text{+0.014}$.\n\n%\\setlength\\textwidth{\\officialtextwidth-10mm}\n\\begin{figure}[h]\n\\texttt{\\# 1.\\ Create a set of 250 million entries,\nwhere \\pewsolarparpercent{} of them are \"support\" \\\\\n\\#\\ \\ \\ \\ and \\pewsolarparpercentcomplement{} are \"not\". \\\\\npop\\us{}size <- 250000000 \\\\\npossible\\_entries <- c(rep(\"support\", \\pewsolarparprop{} * pop\\us{}size), rep(\"not\", \\pewsolarparpropcomplement{} * pop\\us{}size))\n\\\\[3mm]\n\\# 2.\\ Sample \\pewsolarpollsize{} entries without replacement. \\\\\nsampled\\_entries <- sample(possible\\_entries, size = \\pewsolarpollsize{}) \\\\[3mm]\n\\# 3.\\ Compute p-hat:~count the number that are \"support\",\nthen divide by \\\\\n\\#\\ \\ \\ \\ the sample size. \\\\\nsum(sampled\\_entries == \"support\") / \\pewsolarpollsize{}}\n\\caption{For those curious, this is code for\n    a single $\\hat{p}$ simulation using the\n    statistical software called \\R{}\\index{R}.\n    Each line that starts with \\texttt{\\#} is a\n    \\term{code comment},\n    which is used to describe in regular language what the\n    code is doing.\n    We've provided software labs in \\R{} at\n    \\oiRedirect{os}{openintro.org/book/os}\n    for anyone interested in learning more.}\n\\label{solarPollSimulationCodeR}\n\\end{figure}\n% \\setlength\\textwidth{\\officialtextwidth}\n\nOne simulation isn't enough to get a great sense of the\ndistribution of estimates we might expect in the simulation,\nso we should run more simulations.\nIn a second simulation,\nwe get $\\hat{p}_2 = 0.885$, which has an error of~+0.005.\nIn another, $\\hat{p}_3 = 0.878$ for an error of -0.002.\nAnd in another,\nan estimate of $\\hat{p}_4 = 0.859$ with an error of -0.021.\nWith the help of a computer, we've run the simulation 10,000 times\nand created a histogram of the results from all 10,000 simulations\nin Figure~\\ref{sampling_10k_prop_88p}. This\ndistribution of sample proportions is called a\n\\term{sampling distribution}.\nWe can characterize this sampling distribution as follows:\n\\begin{description}\n\\setlength{\\itemsep}{0mm}\n\\item[Center.]\n    The center of the distribution is\n    $\\bar{x}_{\\hat{p}} = \\pewsolarparprop{}0$,\n    which is the same as the parameter.\n    Notice that the simulation mimicked a simple random sample\n    of the population, which is a straightforward sampling\n    strategy that helps avoid sampling bias.\n%    That~is, we see that the sample proportion is an\n%    \\termsub{unbiased estimate}{unbiased}\n%    of the population proportion.\n\\item[Spread.]\n    The standard deviation of the distribution\n    is $s_{\\hat{p}} = \\pewsolarpollse{}$.\n    When we're talking about\n    a sampling distribution or the variability of\n    a point estimate, we typically use the term\n    \\termsub{standard error}{standard error (SE)}\n    rather than \\emph{standard deviation},\n    and the notation $SE_{\\hat{p}}$ is used for the standard\n    error associated with the sample proportion.\n\\item[Shape.]\n    The distribution is symmetric and bell-shaped,\n    and it \\emph{resembles a normal distribution}.\n\\end{description}\nThese findings are encouraging!\nWhen the population\nproportion is $p = \\pewsolarparprop{}$ and the sample size is\n$n = \\pewsolarpollsize{}$,\nthe sample proportion $\\hat{p}$ tends to give\na pretty good estimate\nof the population proportion.\nWe also have the interesting observation\nthat the histogram resembles a normal distribution.\n\n\\begin{figure}[h]\n   \\centering\n   \\Figure[A histogram is shown for 10,000 sample proportions where each sample is taken from a population where the population proportion is \\pewsolarparprop{} and the sample size is $n = \\pewsolarpollsize{}$. The distribution is bell-shaped (appears nearly normal), is centered at 0.88 and has a standard deviation of about 0.01.]{0.8}{sampling_10k_prop_88p}\n   %\\Figure{0.8}{sampling_10k_prop_887p}\n   \\caption{A histogram of 10,000 sample proportions,\n       where each sample is taken from a population\n       where the population proportion is\n       \\pewsolarparprop{} and the sample size\n       is $n = \\pewsolarpollsize{}$.}\n   \\label{sampling_10k_prop_88p}\n   %\\label{sampling_10k_prop_887p}\n\\end{figure}\n\n\\begin{onebox}{Sampling distributions are\n    never observed, but we keep them in mind}\n  In real-world applications, we never actually observe the\n  sampling distribution, yet it is useful to always think of\n  a point estimate as coming from such a hypothetical\n  distribution.\n  \\mbox{Understanding} the sampling distribution will help us\n  characterize and make sense of the point estimates that we\n  do observe.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{If we used a much smaller sample size of $n = 50$,\nwould you guess that the standard error for $\\hat{p}$ would be larger\nor smaller than when we used $n = \\pewsolarpollsize{}$?}\n\\label{smallerSampleWhatHappensToPropErrorExercise}\nIntuitively, it seems like more data is better\nthan less data, and generally that is correct! The typical error\nwhen $p = \\pewsolarparprop{}$ and $n = 50$ would be larger\nthan the error we would expect when $n = \\pewsolarpollsize{}$.\n\\end{nexample}\n\\end{examplewrap}\n\n%\\noindent\nExample~\\ref{smallerSampleWhatHappensToPropErrorExercise}\nhighlights an important property we will see again and again:\na bigger sample tends to provide a more precise point estimate\nthan a smaller sample.\n\n\\index{point estimate|)}\n\n\n\\subsection{Central Limit Theorem}\n\nThe distribution in\nFigure~\\ref{sampling_10k_prop_88p} looks an awful lot like\na normal distribution. That is no anomaly; it~is the result\nof a general principle called the\n\\index{Central Limit Theorem!proportion|textbf}\n\\term{Central Limit Theorem}.\n\n\\begin{onebox}{Central Limit Theorem and the success-failure condition}\n  When observations are independent and the sample size is\n  sufficiently large, the sample proportion $\\hat{p}$ will tend\n  to follow a normal distribution with the following mean and\n  standard error:%\\footnotemark{}\n  \\begin{align*}\n    \\mu_{\\hat{p}} &= p\n    &SE_{\\hat{p}} &= \\sqrt{\\frac{p (1 - p)}{n}}\n  \\end{align*}\n  In order for the Central Limit Theorem to hold,\n  the sample size is typically considered sufficiently large\n  when $np \\geq 10$ and $n(1-p) \\geq 10$, which is called the\n  \\term{success-failure condition}.\n\\end{onebox}\n%\\footnotetext{Some statisticians will say what we\n%  have written for $SE_{\\hat{p}}$ should be called\n%  the \\emph{standard deviation of $\\hat{p}$}\n%  and the standard error is a term for\n%  an estimated version (that we'll first encounter\n%  in Section~\\ref{apply_clt_real_world_setting}).\n%  We adhere to simpler terminology in this book\n%  that is also accepted,\n%  where the listed formula also can be called the\n%  \\emph{standard error}.}\n\nThe Central Limit Theorem is incredibly important, and it provides\na foundation for much of statistics.\nAs we begin applying\nthe Central Limit Theorem, be mindful of the two\ntechnical conditions:\nthe observations must be independent, and the sample size must\nbe sufficiently large such that $np \\geq 10$ and $n(1-p) \\geq 10$.\n\n\\begin{examplewrap}\n\\begin{nexample}{Earlier we estimated the mean and standard\nerror of $\\hat{p}$ using simulated data when\n$p = \\pewsolarparprop{}$ and $n = \\pewsolarpollsize{}$.\nConfirm that the Central Limit Theorem applies\nand the sampling  distribution is approximately\nnormal.}\\label{sample_p88_n1000_confirm_normal}\n\\begin{description}\n\\item[Independence.] There are $n = \\pewsolarpollsize{}$\n    observations for each\n    sample proportion $\\hat{p}$, and each of those observations\n    are independent draws. \\emph{The most common way for\n    observations to be considered independent is if they are from\n    a simple random sample.}\n    \\index{independent}\n    \\index{independence}\n    \\index{Central Limit Theorem!independence}\n\\item[Success-failure condition.] We can confirm the sample size\n    is sufficiently large by checking the success-failure condition\n    and confirming the two calculated values are greater than~10:\n    \\begin{align*}\n    np &= \\pewsolarpollsize{} \\times \\pewsolarparprop{}\n        = \\pewsolarpollexpcount{}\n        \\geq 10\n    &n(1-p) &= \\pewsolarpollsize{} \\times (1 - \\pewsolarparprop{})\n        = \\pewsolarpollexpcountcomplement{}\n        \\geq 10\n    \\end{align*}\n\\end{description}\nThe independence and success-failure conditions are both\nsatisfied, so the Central Limit Theorem applies, and it's\nreasonable to model $\\hat{p}$ using a normal distribution.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{How to verify sample observations are independent}\n  Subjects in an experiment are considered independent\n  if they undergo random assignment to the treatment\n  groups.\\stdvspace{}\n\n  If the observations are from a simple random sample,\n  then they are independent.\\stdvspace{}\n\n  If a sample is from a seemingly random process,\n  e.g. an occasional error on an assembly line,\n  checking independence is more difficult. In~this case,\n  use your best judgement.\n\\end{onebox}\n\nAn additional condition that is sometimes added for samples\nfrom a population is that they are no larger than 10\\% of\nthe population.\nWhen the sample exceeds 10\\% of the population size,\nthe methods we discuss tend to overestimate the sampling error\nslightly versus what we would get using more advanced\nmethods.\\footnote{For example, we could use what's called the\n  \\term{finite population correction factor}:\n  if the sample is of size $n$ and the population size is $N$,\n  then we can multiply the typical standard error formula by\n  $\\sqrt{\\frac{N-n}{N-1}}$\n  to obtain a smaller, more precise estimate of the\n  actual standard error.\n  When $n < 0.1 \\times N$, this correction factor is\n  relatively small.}\nThis is very rarely an issue, and when it is an issue,\nour methods tend to be conservative, so we consider this\nadditional check as optional.\n\n\\begin{examplewrap}\n\\begin{nexample}{Compute the theoretical mean and standard error\nof $\\hat{p}$ when\n$p = \\pewsolarparprop{}$ and $n = \\pewsolarpollsize{}$,\naccording to the\nCentral Limit Theorem.}\\label{sample_p88_n1000_mean_se}\nThe mean of the $\\hat{p}$'s is simply the population proportion:\n$\\mu_{\\hat{p}} = \\pewsolarparprop{}$.\n\nThe calculation of the standard error of $\\hat{p}$ uses\nthe following formula:\n\\begin{align*}\nSE_{\\hat{p}}\n    = \\sqrt{\\frac{p (1 - p)}{n}}\n    = \\sqrt{\\frac{\\pewsolarparprop{} (1 - \\pewsolarparprop{})}\n        {\\pewsolarpollsize{}}}\n    = \\pewsolarpollse{}\n\\end{align*}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Estimate how frequently the sample proportion\n$\\hat{p}$ should be within 0.02 (2\\%) of the population value,\n$p = \\pewsolarparprop{}$. Based on\nExamples~\\ref{sample_p88_n1000_confirm_normal}\nand~\\ref{sample_p88_n1000_mean_se},\nwe know that the distribution is approximately\n$N(\\mu_{\\hat{p}} = \\pewsolarparprop{}, SE_{\\hat{p}} = \\pewsolarpollse{})$.}\n\\label{sampling_10k_prop_887p-prop_from_867_to_907}\nAfter so much practice in Section~\\ref{normalDist},\nthis normal distribution example will hopefully feel familiar!\nWe would like to understand the fraction of $\\hat{p}$'s\nbetween 0.86 and 0.90:\n\\begin{center}\n\\Figure[A normal distribution centered at 0.88 with a standard deviation of 0.01 is shown, where the region between 0.86 and 0.90 has been shaded.]{0.35}{p-hat_from_86_and_90}\n\\end{center}\nWith $\\mu_{\\hat{p}} = \\pewsolarparprop{}$ and\n$SE_{\\hat{p}} = \\pewsolarpollse{}$,\nwe can compute the Z-score for both the left and right cutoffs:\n\\begin{align*}\nZ_{0.86}\n  &= \\frac{0.86 - \\pewsolarparprop{}}{\\pewsolarpollse{}}\n  = -2\n&Z_{0.90}\n  &= \\frac{0.90 - \\pewsolarparprop{}}{\\pewsolarpollse{}}\n  = 2\n\\end{align*}\nWe can use either statistical software, a graphing calculator,\nor a table to find the areas to the tails, and in any case we\nwill find that they are each 0.0228. The total tail areas are\n$2 \\times 0.0228 = 0.0456$, which leaves the shaded area of\n0.9544. That is, about 95.44\\% of the sampling distribution\nin Figure~\\ref{sampling_10k_prop_88p} is within $\\pm0.02$\nof the population proportion, $p = \\pewsolarparprop{}$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIn Example~\\ref{smallerSampleWhatHappensToPropErrorExercise}\nwe discussed how a smaller sample would tend\nto produce a less reliable estimate. Explain how this intuition\nis reflected in the formula for\n$SE_{\\hat{p}} = \\sqrt{\\frac{p (1 - p)}{n}}$.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Since the\n  sample size $n$ is in the denominator\n  (on the bottom) of the fraction,\n  a bigger sample size means the entire\n  expression when calculated will tend to be smaller.\n  That is, a larger sample size would correspond to\n  a smaller standard error.}\n\n\n\\subsection{Applying the Central Limit Theorem to\n    a real-world setting}\n\\label{apply_clt_real_world_setting}\n\nWe do not actually know the population proportion\nunless we conduct an expensive poll of all individuals\nin the population.\nOur earlier value of $p = 0.88$ was based on poll\nconducted by Pew Research of \\pewsolarpollsize{}\nAmerican adults that found\n$\\hat{p} = \\pewsolarpollprop{}$ of them favored\nexpanding solar energy.\nThe researchers might have wondered:\ndoes the sample proportion from the poll approximately\nfollow a normal distribution?\nWe can check the conditions from the Central Limit Theorem:\n\\begin{description}\n\\item[Independence.] The poll is a simple random sample of\n    American adults, which means that the observations are\n    independent.\n\\item[Success-failure condition.] To check this condition,\n    we need the population proportion, $p$, to check if both\n    $np$ and $n(1-p)$ are greater than 10.\n    However, we do not actually know $p$, which\n    is exactly why the pollsters would take a sample!\n    In cases like these, we often use $\\hat{p}$\n    as our next best way to check the success-failure condition:\n    \\begin{align*}\n    n\\hat{p}\n        &= \\pewsolarpollsize{} \\times \\pewsolarpollprop{}\n        = \\pewsolarpollcount{}\n    &n (1 - \\hat{p})\n        &= \\pewsolarpollsize{} \\times (1 - \\pewsolarpollprop{})\n        = \\pewsolarpollcountcomplement{}\n    \\end{align*}\n    The sample proportion $\\hat{p}$ acts as\n    a reasonable substitute for $p$ during this check,\n    and each value in this case is well above the minimum of 10.\n\\end{description}\n\nThis \\term{substitution approximation} of using $\\hat{p}$ in\nplace of $p$ is also useful when computing the standard error\nof the sample proportion:\n\\begin{align*}\nSE_{\\hat{p}}\n    = \\sqrt{\\frac{p (1 - p)}{n}}\n    \\approx \\sqrt{\\frac{\\hat{p} (1 - \\hat{p})}{n}}\n    = \\sqrt{\\frac{\\pewsolarpollprop{}\n        (1 - \\pewsolarpollprop{})}{\\pewsolarpollsize{}}}\n    = \\pewsolarpollse{}\n\\end{align*}\nThis substitution technique is sometimes\nreferred to as the ``\\hiddenterm{plug-in principle}''.\nIn this case, $SE_{\\hat{p}}$ didn't change enough to\nbe detected using only 3 decimal places\nversus when we completed the calculation with\n\\pewsolarparprop{} earlier.\nThe computed standard error tends to be reasonably stable\neven when observing slightly different proportions in one\nsample or another.\n\n\n\\D{\\newpage}\n\n\\subsection{More details regarding the Central Limit Theorem}\n\n\\noindent%\nWe've applied the Central Limit Theorem in numerous examples\nso far this chapter:\n\\begin{quote}{\\em\nWhen observations are independent and the sample size is\nsufficiently large, the distribution of $\\hat{p}$ resembles\na normal distribution with\n\\begin{align*}\n  \\mu_{\\hat{p}} &= p\n  &SE_{\\hat{p}} &= \\sqrt{\\frac{p (1 - p)}{n}}\n\\end{align*}\nThe sample size is considered sufficiently large\nwhen $n p \\geq 10$ and $n (1 - p) \\geq 10$.\n}\\end{quote}\nIn this section, we'll explore the success-failure\ncondition and seek to better understand the\nCentral Limit Theorem.\n\nAn interesting question to answer is, \\emph{what happens when\n$np < 10$ or $n(1-p) < 10$?} As we did in\nSection~\\ref{simulationForUnderstandingVariabilitySection},\nwe can simulate drawing samples of different sizes where,\nsay, the true proportion is $p = 0.25$.\nHere's a sample of size~10:\n\\begin{center}\n% paste(sample(c(\"yes\", \"no\"), 10, TRUE, c(.25, .75)), collapse = \", \")\nno, no, yes, yes, no, no, no, no, no, no\n\\end{center}\nIn this sample, we observe a sample proportion of yeses\nof $\\hat{p} = \\frac{2}{10} = 0.2$. We can simulate many such\nproportions to understand the sampling distribution of\n$\\hat{p}$ when $n = 10$ and $p = 0.25$, which we've plotted\nin Figure~\\ref{sampling_10_prop_25p}\nalongside a normal distribution with the\nsame mean and variability.\nThese distributions have a number of important differences.\n\n\\begin{figure}[h]\n   \\centering\n   \\Figure[There are two plots. The first plot is a histogram of 10,000 simulations of p-hat when the sample size is n equals 10 and the population proportion is p equals 0.25. The possible values are 0.0, 0.1, 0.2, and so on up to 1.0, though the graph only shows values up to 0.8. The distribution is centered at about 0.25, and is slightly right-skewed. The frequencies are about 500 for 0.0, 1900 for 0.1, 2800 for 0.2, 2400 for 0.3, 1500 for 0.4, 500 for 0.5, 100 for 0.6, and the bin heights for the remaining values have bin heights that are not visually distinguishable from zero. The second plot shows a normal distribution centered at 0.25 with a standard deviation of 0.137. The plot has a vertical line located at 0.0, which makes it more visually evident that a portion of the area under the normal distribution -- about 5\\% of this area -- represents values below 0.0.]{0.97}{sampling_10_prop_25p}\n   \\caption{Left: simulations of $\\hat{p}$ when the sample size\n       is $n = 10$ and the population proportion is $p = 0.25$.\n       Right: a normal distribution with the same mean (0.25)\n       and standard deviation (0.137).}\n   \\label{sampling_10_prop_25p}\n\\end{figure}\n\n\\begin{figure}\n  \\centering\n  \\Figures[Sampling distributions are shown for several scenarios for parameters p and n. The graphs are arranged in a grid of 5 rows representing proportions 0.1, 0.2, 0.5, 0.8, and 0.9 and 2 columns of sample sizes n equals 10 and 25. In each graph, the distribution is centered at the proportion. Given that these are proportions based on relatively small sample sizes, the bins do look relatively discrete (jumpy from one to the next), though less so for the distributions based on n equals 25. In cases where the true underlying proportion is near the lower bound of 0 or the upper bound of 1, the distribution tends to skew away from that boundary. This is most noticeable for both the distributions representing proportions closer to either boundary and for the smaller sample size. One distribution stands out among the 10 shown: the sample with p equals 0.5 and n equals 25, which shows a bell-shaped distribution resembling the normal distribution, though the data are still somewhat discrete.]{}{clt_prop_grid}{clt_prop_grid_1}\n  \\caption{Sampling distributions for several scenarios\n      of $p$ and $n$. \\\\\n      Rows: $p = 0.10$, $p = 0.20$, $p = 0.50$,\n      $p = 0.80$, and $p = 0.90$. \\\\\n      Columns: $n = 10$ and $n = 25$.}\n  \\label{clt_prop_grid_1}\n\\end{figure}\n\n\\begin{figure}\n  \\centering\n  \\Figures[Sampling distributions are shown for several scenarios for parameters p and n. The graphs are arranged in a grid of 5 rows representing proportions 0.1, 0.2, 0.5, 0.8, and 0.9 and 3 columns of sample sizes n equals 50, 100, and 250. Relative to the previous figure, which considered similar proportion scenarios but with n equals 10 and 25, the data in these graphs looks less discrete -- that is, they appear to almost be continuous. This is most evident for the largest sample sizes. Nearly all of the graphs shown also closely resemble the normal distribution, in some cases with the larger sample sizes that it resembles it so closely that there are not substantial visual differences. One aspect less evident -- but still present -- in the last figure but that continues into and becomes much more obvious in this figure, is that the distributions of the sample proportions tend to have a much smaller standard deviation with the larger sample sizes. That is, the sample proportion distributions for larger sample sizes tend to be smaller than they were for smaller sample sizes. Also, the variability within a graph also appears to be largest for the proportion p equals 0.5 than it is for the other proportions when considering a single proportion -- and this property is apparent upon inspection of a distribution based on any of the considered sample sizes.]{}{clt_prop_grid}{clt_prop_grid_2}\n  \\caption{Sampling distributions for several scenarios\n      of $p$ and $n$. \\\\\n      Rows: $p = 0.10$, $p = 0.20$, $p = 0.50$,\n      $p = 0.80$, and $p = 0.90$. \\\\\n      Columns: $n = 50$, $n = 100$, and $n = 250$.}\n  \\label{clt_prop_grid_2}\n\\end{figure}\n\n\\begin{center}\n\\begin{tabular}{lccc}\n\\hline\n    &  Unimodal?  &  Smooth?  &  Symmetric? \\\\\n\\hline\nNormal: $N(0.25, 0.14)$  &\n    \\highlightO{Yes}  &\n    \\highlightO{Yes}  &\n    \\highlightO{Yes} \\\\\n$n = 10$, $p = 0.25$  &\n    \\highlightO{Yes}  &\n    \\highlightT{No}  &\n    \\highlightT{No} \\\\\n\\hline\n\\end{tabular}\n\\end{center}\nNotice that the success-failure condition\nwas not satisfied when $n = 10$ and $p = 0.25$:\n\\begin{align*}\nn p = 10 \\times 0.25 = 2.5 &&\n    n (1 - p) = 10 \\times 0.75 = 7.5\n\\end{align*}\nThis single sampling distribution does not show that\nthe success-failure condition is the perfect guideline,\nbut we have found that the guideline did correctly\nidentify that a normal distribution might not be appropriate.\n\nWe can complete several additional simulations,\nshown in\nFigures~\\ref{clt_prop_grid_1}\nand~\\ref{clt_prop_grid_2},\nand we can see some trends:\n\\begin{enumerate}\n\\item When either $np$ or $n(1 - p)$ is small, the\n    distribution is more \\term{discrete},\n    i.e. \\emph{not continuous}.\n\\item When $np$ or $n(1-p)$ is smaller than~10,\n    the skew in the distribution is more noteworthy.\n\\item The larger both $np$ \\emph{and} $n(1 - p)$,\n    the more normal the distribution.\n    This may be a little harder to see for the larger\n    sample size in these plots as the variability\n    also becomes much smaller.\n\\item When $np$ and $n(1 - p)$ are both very large,\n    the distribution's discreteness is hardly evident,\n    and the distribution looks much more\n    like a normal distribution.\n\\end{enumerate}\n\n\\D{\\newpage}\n\nSo far we've only focused on the skew and discreteness\nof the distributions.\nWe haven't considered how the mean and standard error\nof the distributions change.\nTake a moment to look back at the graphs,\nand pay attention to three things:\n\\begin{enumerate}\n\\item The centers of the distribution are always at\n    the population proportion, $p$, that was used to\n    generate the simulation. Because the sampling\n    distribution of $\\hat{p}$ is always centered at\n    the population parameter $p$, it means the sample\n    proportion $\\hat{p}$ is \\term{unbiased} when\n    the data are independent and drawn from such\n    a population.\n\\item For a particular population proportion $p$,\n    the variability in the sampling distribution\n    decreases as the sample size~$n$ becomes larger.\n    This will likely align with your intuition:\n    an estimate based on a larger sample size will\n    tend to be more accurate.\n\\item For a particular sample size, the variability\n    will be largest when $p = 0.5$. The differences\n    may be a little subtle, so take a close look.\n    This reflects the role of the proportion\n    $p$ in the standard error formula:\n    $SE = \\sqrt{\\frac{p (1 - p)}{n}}$.\n    The standard error is largest when $p = 0.5$.\n\\end{enumerate}\n\nAt no point will the distribution of $\\hat{p}$ look\n\\emph{perfectly} normal, since $\\hat{p}$ will always\ntake discrete values ($x / n$).\nIt is always a matter of degree, and we will use\nthe standard success-failure condition with minimums\nof 10 for $np$ and $n (1 - p)$ as our guideline\nwithin this~book.\n\n\n\\subsection{Extending the framework for other statistics}\n\nThe strategy of using a sample statistic to estimate\na parameter is quite common, and it's a strategy that\nwe can apply to other statistics besides a proportion.\nFor instance, if we want to estimate the average salary\nfor graduates from a particular college, we could\nsurvey a random sample of recent graduates;\nin that example, we'd be using a sample mean $\\bar{x}$\nto estimate the population mean~$\\mu$ for all graduates.\nAs another example, if we want to estimate the\ndifference in product prices for two websites,\nwe might take a random sample of products available\non both sites, check the prices on each,\nand then compute the average difference;\nthis strategy certainly would give us some idea\nof the actual difference through a point estimate.\n\nWhile this chapter emphasizes a single proportion\ncontext, we'll encounter many different contexts\nthroughout this book where these methods will be\napplied.\nThe principles and general ideas are the same,\neven if the details change a little.\nWe've also sprinkled some other contexts into\nthe exercises to help you start thinking about\nhow the ideas generalize.\n\n\n{\\input{ch_foundations_for_inf/TeX/variability_in_estimates.tex}}\n\n\n\n\n\n%__________________\n\\section{Confidence intervals for a proportion}\n\\label{confidenceIntervals}\n\n\\index{confidence interval|(}\n\nThe sample proportion $\\hat{p}$ provides a single plausible value\nfor the population proportion $p$. However, the sample proportion\nisn't perfect and will have some \\emph{standard error}\nassociated with it.\nWhen stating an estimate for the population  proportion,\nit is better practice to provide a plausible\n\\emph{range of values} instead of supplying just the point\nestimate.\n\n\n\\subsection{Capturing the population parameter}\n\nUsing only a point estimate is like fishing in a murky\nlake with a spear. We can throw a spear where we\nsaw a fish, but we will probably miss. On the other hand,\nif we toss a net in that area, we have a good chance of\ncatching the fish.\nA \\term{confidence interval} is like fishing with a net,\nand it represents a range of plausible values where we\nare likely to find the population parameter.\n\nIf we report a point estimate $\\hat{p}$, we probably\nwill not hit the exact population proportion. On the\nother hand, if we report a range of plausible values,\nrepresenting a confidence interval,\nwe have a good shot at capturing the parameter.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIf we want to be very certain we capture the population\nproportion in an interval, should we use a wider interval\nor a smaller interval?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{If we want to be more\n    certain we will capture the fish, we might use a\n    wider net. Likewise, we use a wider confidence interval\n    if we want to be more certain that we capture the\n    parameter.}\n\n\\subsection{Constructing a 95\\% confidence interval}\n\nOur sample proportion $\\hat{p}$ is the most plausible\nvalue of the population proportion, so it makes sense\nto build a confidence interval around this point estimate.\nThe standard error\\index{standard error (SE)|textbf}\nprovides a guide for how\nlarge we should make the confidence interval.\n\nThe standard error represents the standard deviation\nof the point estimate, and when the Central\nLimit Theorem conditions are satisfied,\nthe point estimate closely follows a normal distribution.\nIn a normal distribution, 95\\% of\nthe data is within 1.96~standard deviations of the mean.\nUsing this principle, we can construct a confidence\ninterval that extends 1.96~standard errors from the sample\nproportion to be \\termsub{95\\% confident}\n    {confident!95\\% confident}\\index{confident|textbf}\nthat the interval captures the population proportion:\n\\begin{align*}\n\\text{point estimate}\\ &\\pm\\ 1.96 \\times SE \\\\\n\\hat{p}\\ &\\pm\\ 1.96 \\times \\sqrt{\\frac{p (1 - p)}{n}}\n%\\label{95PercentConfidenceIntervalFormula}\n\\end{align*}\nBut what does ``95\\% confident'' mean? Suppose we took\nmany samples and built a 95\\% confidence interval from\neach. Then about 95\\% of those intervals would\ncontain the parameter,~$p$.\nFigure~\\ref{95PercentConfidenceInterval} shows the\nprocess of creating 25 intervals from 25 samples\nfrom the simulation in\nSection~\\ref{simulationForUnderstandingVariabilitySection},\nwhere 24 of the resulting confidence intervals contain\nthe simulation's population proportion of\n$p = \\pewsolarparprop{}$, and one interval does not.\n\n\\D{\\newpage}\n\n\\begin{figure}\n  \\centering\n  \\Figure[Twenty-five point estimates and confidence intervals from the simulations in Section~\\ref{simulationForUnderstandingVariabilitySection} are shown. These intervals are shown relative to the population proportion p equals \\pewsolarparprop{}. The point estimates vary around the true population proportion of 0.88, but most of their confidence intervals overlap the value p equals 0.88. One of the 25 intervals does not have a confidence interval that overlaps the population proportion, and this interval has been bolded. We might say that this confidence interval did not \"capture\" the parameter p equals 0.88.]{0.75}{95PercentConfidenceInterval}\n  \\caption{Twenty-five point estimates and confidence\n      intervals from the simulations in\n      Section~\\ref{simulationForUnderstandingVariabilitySection}.\n      These intervals are shown relative to the population\n      proportion $p = \\pewsolarparprop{}$.\n      Only~1 of these~25\n      intervals did not capture the population\n      proportion, and this interval has been bolded.}\n  \\label{95PercentConfidenceInterval}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{In Figure~\\ref{95PercentConfidenceInterval},\none interval does not contain $p = \\pewsolarparprop{}$.\nDoes this imply that the population proportion used\nin the simulation could not have been\n$p = \\pewsolarparprop{}$?}\nJust as some observations naturally\noccur more than 1.96~standard deviations\nfrom the mean, some point estimates will be more than\n1.96~standard errors from the parameter of interest.\nA confidence interval only provides a plausible range\nof values.\nWhile we might say other values are implausible\nbased on the data, this does not mean they are impossible.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{95\\% confidence interval for a parameter}\n  \\index{confidence interval!95\\%}\n  When the distribution of a point estimate qualifies for\n  the Central Limit Theorem and\n  therefore closely follows a normal distribution,\n  we can construct a 95\\% confidence interval as\n  \\begin{align*}\n  \\text{point estimate} &\\pm 1.96 \\times SE\n  \\end{align*}\n  % This confidence interval only accounts for sampling error,\n  % not bias.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{In Section~\\ref{pointEstimates} we learned about\n    a Pew Research poll where\n    \\pewsolarpollpercent{} of a random sample of\n    \\pewsolarpollsize{} American adults\n    supported expanding the role of solar power.\n    Compute and\n    interpret a 95\\% confidence interval for the population\n    proportion.} \\label{95p_ci_for_pew_solar_support}\n  We earlier confirmed that $\\hat{p}$ follows a normal\n  distribution and has a standard error of\n  $SE_{\\hat{p}} = \\pewsolarpollse{}$.\n  To compute the 95\\% confidence interval, plug the\n  point estimate $\\hat{p} = \\pewsolarpollprop{}$ and\n  standard error into the 95\\% confidence interval formula:\n  \\begin{align*}\n  \\hat{p} \\pm 1.96 \\times SE_{\\hat{p}}\n  \\quad\\to\\quad\n  \\pewsolarpollprop{} \\pm 1.96 \\times \\pewsolarpollse{}\n  \\quad\\to\\quad\n  (0.8674, 0.9066)\n  \\end{align*}\n  We are 95\\% confident that the actual proportion of\n  American adults who support expanding solar power is\n  between 86.7\\% and 90.7\\%.\n  (It's common to round to the nearest percentage point\n  or nearest tenth of a percentage point when reporting\n  a confidence interval.)\n\\end{nexample}\n\\end{examplewrap}\n\n\n\\D{\\newpage}\n\n\\subsection{Changing the confidence level}\n\\label{changingTheConfidenceLevelSection}\n\n\\index{confidence interval!confidence level|(}\n\nSuppose we want to consider confidence intervals where the confidence\nlevel is higher than 95\\%, such as a confidence\nlevel of~99\\%. Think back to the analogy about trying to catch a fish:\nif~we want to be more sure that we will catch the fish, we should use\na wider net. To create a 99\\% confidence level, we must also widen our\n95\\% interval. On the other hand, if we want an interval with lower\nconfidence, such as 90\\%, we could use a slightly narrower\ninterval than our original 95\\% interval.\n\nThe 95\\% confidence interval structure provides guidance in\nhow to make intervals with different confidence levels.\nThe general 95\\% confidence interval for a point estimate\nthat follows a normal distribution is\n\\begin{eqnarray*}\n\\text{point estimate}\\ \\pm\\ 1.96 \\times SE\n\\end{eqnarray*}\nThere are three components to this interval: the point estimate,\n``1.96'', and the standard error. The choice of $1.96\\times SE$ was\nbased on capturing 95\\% of the data since the estimate is within\n1.96 standard errors of the parameter about 95\\% of the time.\nThe choice of 1.96 corresponds to a 95\\% confidence level. \n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{leadInForMakingA99PercentCIExercise}\nIf $X$ is a normally distributed random variable, what is the\nprobability of the value $X$ being\nwithin 2.58~standard deviations of the mean?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{This is equivalent to asking how often the\n    Z-score will be larger than -2.58 but less than 2.58.\n    For a picture, see Figure~\\ref{choosingZForCI}.\n    To determine this probability, we can use statistical software,\n    a calculator, or a table to look up -2.58 and 2.58 for\n    a normal distribution: 0.0049 and 0.9951.\n    Thus, there is a $0.9951-0.0049 \\approx 0.99$ probability\n    that an unobserved normal random variable\n    $X$ will be within 2.58~standard deviations of $\\mu$.}\n\nGuided Practice~\\ref{leadInForMakingA99PercentCIExercise} highlights\nthat 99\\% of the time a normal random variable will be within\n2.58~standard deviations of the mean.\nTo create a 99\\% confidence interval, change 1.96 in the 95\\%\nconfidence interval formula to be $2.58$.\nThat is, the formula\nfor a 99\\% confidence interval is\n\\begin{align*}\n\\text{point estimate}\\ \\pm\\ 2.58 \\times SE\n%\\label{99PercCIForProp}\n\\end{align*}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A standard normal distribution is shown, where \"standard\" is the term used to indicate that the normal distribution is centered at 0 and has a standard deviation of 1. Portions of the normal distribution have been shaded. First, the central 95\\% portion of the distribution has been shaded in a dark blue, and this region has an annotation stating \"95\\%, extends from -1.96 to 1.96\". Recall that the value of 1.96 closely matches our 68-95-99.7 rule for the normal distribution, which had stated that about 95\\% of the area under the normal distribution lied within 2 standard deviations of the mean. Second, a slightly broader region of the normal distribution is shaded, in this case from about -2.5 to positive 2.5, and this has an annotation stating, \"99\\%, extends -2.58 to 2.58\". The values described here -- 1.96 and 2.58 -- are the z-star values that we would use for 95\\% and 99\\% confidence intervals, respectively.]{}{choosingZForCI}\n  \\caption{The area between -$z^{\\star}$ and $z^{\\star}$ increases as\n      $z^{\\star}$ becomes larger. If the confidence level is 99\\%,\n      we choose $z^{\\star}$ such that 99\\% of a normal\n      normal distribution is between -$z^{\\star}$ and $z^{\\star}$,\n      which corresponds to 0.5\\%\n      in the lower tail and 0.5\\% in the upper tail:\n      $z^{\\star}=2.58$.}\n\\label{choosingZForCI}\n\\index{confidence interval!confidence level|)}\n\\end{figure}\n\n\\D{\\newpage}\n\nThis approach -- using the Z-scores in the\nnormal model to compute confidence levels --\nis appropriate when a point estimate such as $\\hat{p}$\nis associated with a normal distribution.\n%For the context of sample proportions, the\n%normal distribution is reasonable when the sample\n%observations are independent and the success-failure condition\n%holds ($np$ and $n(1-p)$ are both at least 10).\nFor some other point estimates, a normal model is not a good fit;\nin these cases, we'll use alternative distributions that better\nrepresent the sampling distribution.\n\n\\begin{onebox}{Confidence interval using any confidence level}\n  If a point estimate closely follows a normal model\n  with standard error $SE$, then a confidence interval\n  for the population parameter is\n  \\begin{align*}\n  \\text{point estimate}\\ \\pm\\ z^{\\star} \\times SE\n  \\end{align*}\n  where $z^{\\star}$ corresponds to the confidence\n  level selected.\n\\end{onebox}\n\nFigure~\\ref{choosingZForCI} provides a picture of how to identify\n$z^{\\star}$ based on a confidence level. We~select $z^{\\star}$\nso that the area between -$z^{\\star}$ and $z^{\\star}$ in the\nstandard normal distribution\\index{standard normal distribution}\\index{normal distribution!standard}\\index{distribution!normal!standard},\n$N(0, 1)$, corresponds to the confidence level.\n\n\\begin{onebox}{Margin of error}\n  \\label{marginOfErrorTermBox}%\n  In a confidence interval, $z^{\\star}\\times SE$ is called the\n  \\term{margin of error}.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{Use the data in\n    Example~\\ref{95p_ci_for_pew_solar_support} to\n    create a 90\\% confidence interval for the proportion of American\n    adults that support expanding the use of solar power.\n    We have already verified conditions for normality.}\n  We first find $z^{\\star}$ such that 90\\% of the distribution falls\n  between -$z^{\\star}$ and $z^{\\star}$ in the\n  \\index{standard normal distribution}%\n  \\index{normal distribution!standard}%\n  \\index{distribution!normal!standard}%\n  standard normal distribution, $N(\\mu = 0, \\sigma = 1)$.\n  We can do this using a graphing calculator,\n  statistical software, or a probability table by looking for an\n  upper tail of 5\\% (the other 5\\% is in the lower tail):\n  $z^{\\star}=1.65$.\n  The 90\\% confidence interval can then be computed as\n  \\begin{align*}\n  \\hat{p}\\ \\pm\\ 1.6449 \\times SE_{\\hat{p}}\n      \\quad\\to\\quad 0.887\\ \\pm\\ 1.65 \\times 0.0100\n      \\quad\\to\\quad (0.8705, 0.9034)\n  \\end{align*}\n  That is, we are 90\\% confident that 87.1\\% to 90.3\\% of American\n  adults supported the expansion of solar power in 2018.\n\\end{nexample}\n\\end{examplewrap}\n\n\\newcommand{\\onepropconfintsummary}[0]{\n\\begin{onebox}{Confidence interval for a single proportion}\n  Once you've determined a one-proportion confidence interval\n  would be helpful for an application,\n  there are four steps to constructing the interval:\n  \\begin{description}\n  \\item[Prepare.]\n      Identify $\\hat{p}$ and $n$, and determine what\n      confidence level you wish to use.\n  \\item[Check.]\n      Verify the conditions to ensure $\\hat{p}$\n      is nearly normal.\n      For one-proportion confidence intervals,\n      use $\\hat{p}$ in place of $p$ to check\n      the success-failure condition.\n  \\item[Calculate.]\n      If the conditions hold, compute $SE$ using $\\hat{p}$,\n      find $z^{\\star}$, and construct the interval.\n  \\item[Conclude.]\n      Interpret the confidence interval in the context\n      of the problem.\n  \\end{description}\n\\end{onebox}\n}\n\\onepropconfintsummary{}\n\n\n\\D{\\newpage}\n\n\\subsection{More case studies}\n\n\\index{data!Ebola poll|(}\n\n\\newcommand{\\wsjebolapollsize}{1042}\n\\newcommand{\\wsjebolapollsizecomma}{1,042}\n\\newcommand{\\wsjebolapollprop}{0.82}\n\\newcommand{\\wsjebolapollpropcomplement}{0.18}\n\\newcommand{\\wsjebolapollpercent}{82}\n\\newcommand{\\wsjebolapollpercentcomplement}{18}\n\\newcommand{\\wsjebolapollcount}{854}\n\\newcommand{\\wsjebolapollcountcomplement}{188}\n\\newcommand{\\wsjebolapollse}{0.012}\n\n\nIn New York City on October 23rd, 2014, a doctor who had recently been\ntreating Ebola patients in Guinea went to the hospital with a slight fever\nand was subsequently diagnosed with Ebola. Soon thereafter,\nan NBC~4 New York/The Wall Street Journal/Marist Poll found that\n\\wsjebolapollpercent{}\\% of New Yorkers favored a ``mandatory 21-day\nquarantine for anyone who has come in contact with an Ebola\npatient''. This poll included responses\nof \\wsjebolapollsizecomma{} New York adults between\nOct 26th and~28th, 2014.\n%\\footnote{This survey, like the others\n%  you'll see in this book, ...}\n%We may want a confidence interval for the proportion of New York\n%adults who favored a mandatory quarantine of anyone who had been in\n%contact with an Ebola patient.\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the point estimate in this case,\n    and is it reasonable to\n    use a normal distribution to model that point estimate?}\n  The point estimate, based on a sample of size $n = \\wsjebolapollsize{}$,\n  is $\\hat{p} = \\wsjebolapollprop{}$.\n  To check whether $\\hat{p}$ can be reasonably\n  modeled using a normal distribution, we check independence\n  (the poll is based on a simple random sample) and the\n  success-failure condition\n  ($\\wsjebolapollsize{} \\times \\hat{p} \\approx \\wsjebolapollcount{}$\n  and $\\wsjebolapollsize{} \\times (1 - \\hat{p})\n      \\approx \\wsjebolapollcountcomplement{}$,\n  both easily greater than~10).\n  With the conditions met, we are assured\n  that the sampling distribution of $\\hat{p}$ can be\n  reasonably modeled using a normal distribution.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Estimate the standard error of\n    $\\hat{p} = \\wsjebolapollprop{}$ from the Ebola survey.}\n  \\label{seOfPropOfNYEbolaSurvey}%\n  We'll use the substitution approximation of\n  $p \\approx \\hat{p} = \\wsjebolapollprop{}$ to compute\n  the standard error:\n  \\begin{align*}\n  SE_{\\hat{p}}\n    = \\sqrt{\\frac{p(1-p)}{n}}\n    \\approx \\sqrt{\\frac{\\wsjebolapollprop{}\n        (1 - \\wsjebolapollprop{})}{\\wsjebolapollsize{}}}\n    = \\wsjebolapollse{}\n  \\end{align*}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Construct a 95\\% confidence interval for $p$,\n    the proportion of New York adults who supported a quarantine\n    for anyone who has come into contact with an Ebola patient.}\n  \\label{ex_ci_ny_ebola_quarantine}%\n  Using the standard error $SE = 0.012$ from\n  Example~\\ref{seOfPropOfNYEbolaSurvey},\n  the point estimate \\wsjebolapollprop{}, and $z^{\\star} = 1.96$\n  for a 95\\% confidence level, the confidence interval is\n  \\begin{eqnarray*}\n  \\text{point estimate} \\ \\pm\\ z^{\\star} \\times SE\n    \\quad\\to\\quad \\wsjebolapollprop{} \\ \\pm\\ 1.96\\times \\wsjebolapollse{}\n    \\quad\\to\\quad (0.796, 0.844)\n  \\end{eqnarray*}\n  We are 95\\% confident that the proportion of New York adults\n  in October 2014 who supported a quarantine for anyone who had come\n  into contact with an Ebola patient was between 0.796 and 0.844.\n\\index{data!Ebola poll|)}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nAnswer the following two questions about the confidence interval\nfrom Example~\\ref{ex_ci_ny_ebola_quarantine}:\\footnotemark{}\n\\begin{enumerate}[(a)]\n\\item\n    What does 95\\% confident mean in this context?\n\\item\n    Do you think the confidence interval is still valid\n    for the opinions of New Yorkers today?\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~If we took many such samples and computed\n  a 95\\% confidence interval for each, then about 95\\% of those\n  intervals would contain the actual proportion of New York\n  adults who supported a quarantine for anyone who has come into\n  contact with an Ebola patient. \\\\\n  (b)~Not necessarily. The poll was taken at a\n  time where there was a huge public safety concern.\n  Now that people have had some time to step back,\n  they may have changed their opinions.\n  We would need to run a new poll if we wanted to get an\n  estimate of the current proportion of New York adults who\n  would support such a quarantine period.}\n\n\\D{\\newpage}\n\n\\index{data!wind turbine survey|(}\n\n\\newcommand{\\pewwindpollsize}{\\pewsolarpollsize}\n\\newcommand{\\pewwindpollprop}{0.848}\n\\newcommand{\\pewwindpollpropcomplement}{0.152}\n\\newcommand{\\pewwindpollpercent}{84.8}\n\\newcommand{\\pewwindpollpercentcomplement}{15.2}\n\\newcommand{\\pewwindpollcount}{848}\n\\newcommand{\\pewwindpollcountcomplement}{152}\n\\newcommand{\\pewwindpollse}{0.0114}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{pew_wind_turbine_support_normal_dist_gp}%\nIn the Pew Research poll about solar energy, they\nalso inquired about other forms of energy,\nand \\pewwindpollpercent{}\\% of the \\pewwindpollsize{}\nrespondents supported expanding the use of wind\nturbines.\\footnotemark{}\n\\begin{enumerate}[(a)]\n\\item\n    Is it reasonable to model the proportion\n    of US adults who support expanding wind turbines\n    using a normal distribution?\n\\item\n    Create a 99\\% confidence interval for the level of American\n    support for expanding the use of wind turbines for power\n    generation.\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~The survey was a random sample\n  and counts are both $\\geq 10$\n  ($\\pewwindpollsize{} \\times \\pewwindpollprop{}\n    = \\pewwindpollcount{}$\n  and $\\pewwindpollsize{} \\times \\pewwindpollpropcomplement{}\n    = \\pewwindpollcountcomplement$),\n  so independence and the success-failure condition\n  are satisfied, and\n  $\\hat{p} = \\pewwindpollprop{}$ can be\n  modeled using a normal distribution. \\\\\n  (b)~Guided\n  Practice~\\ref{pew_wind_turbine_support_normal_dist_gp}\n  confirmed that $\\hat{p}$ closely follows\n  a normal distribution, so we can use the C.I.~formula:\n  \\begin{align*}\n  \\text{point estimate} \\pm z^{\\star} \\times SE\n  \\end{align*}\n  In this case, the point estimate is\n  $\\hat{p} = \\pewwindpollprop{}$.\n  For a 99\\% confidence interval, $z^{\\star} = 2.58$.\n  Computing the standard error:\n  $SE_{\\hat{p}}\n    = \\sqrt{\\frac{\\pewwindpollprop{}(1 - \\pewwindpollprop{})}\n        {\\pewwindpollsize{}}}\n    = \\pewwindpollse{}$.\n  Finally, we compute the interval as\n  $\\pewwindpollprop{} \\pm 2.58 \\times \\pewwindpollse{}\n    \\to (0.8186,   0.8774)$.\n  It is also important to \\emph{always} provide an interpretation\n  for the interval: we are 99\\% confident the proportion of\n  American adults that support expanding the use of wind\n  turbines in 2018 is between 81.9\\% and 87.7\\%.}\n\nWe can also construct confidence intervals for other\nparameters, such as a population mean.\nIn these cases, a confidence interval would be computed\nin a similar way to that of a single proportion:\na point estimate plus/minus some margin of error.\nWe'll dive into these details in later chapters.\n\n\n\\subsection{Interpreting confidence intervals}\n\\label{interpretingCIs}\n\n\\index{confidence interval!interpretation|(}\n\nIn each of the examples, we described the confidence\nintervals by putting them into the context of the data and also\nusing somewhat formal language:\n\\begin{description}\n  \\item[Solar.] We are 90\\% confident that 87.1\\% to 90.4\\% of\n      American adults support the expansion of solar power in 2018.\n  \\item[Ebola.] We are 95\\% confident that the proportion\n      of New York adults in October 2014 who supported a quarantine\n      for anyone who had come into contact with an Ebola patient was\n      between 0.796 and 0.844.\n  \\item[Wind Turbine.] We are 99\\% confident the proportion of\n      Americans adults that support expanding the use of wind\n      turbines is between 81.9\\% and 87.7\\% in 2018.\n\\end{description}\nFirst, notice that the statements are always about the population\nparameter, which considers \\emph{all} American adults for the\nenergy polls or \\emph{all} New York adults for the quarantine poll.\n\nWe also avoided another common mistake:\n\\emph{incorrect} language might try to describe the confidence interval\nas capturing the population parameter with a certain probability.\nMaking a probability interpretation is a common error:\nwhile it might be useful to think of it as a probability,\nthe confidence level only quantifies how plausible\nit is that the parameter is in the given interval.\n\nAnother important consideration of confidence intervals is that they\nare \\emph{only about the population parameter}.\nA confidence interval says nothing about individual\nobservations or point estimates.\nConfidence intervals only provide a plausible range for\npopulation parameters.\n\n\\index{bias|(}\nLastly, keep in mind the methods we discussed only apply\nto sampling error, not to bias.\nIf a data set is collected in a way that will tend to\nsystematically under-estimate\n(or over-estimate) the population parameter, the techniques\nwe have discussed will not address that problem.\nInstead, we rely on careful data collection procedures to\nhelp protect against bias in the examples we have considered,\nwhich is a common practice employed by data scientists\nto combat bias.\n\\index{bias|)}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nConsider the 90\\% confidence interval for the solar\nenergy survey: 87.1\\% to 90.4\\%.\nIf~we ran the survey again, can we say that we're\n90\\% confident that the new survey's proportion\nwill be between 87.1\\% and 90.4\\%?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{\n  No, a confidence interval only provides a range of plausible\n  values for a parameter,\n  not future point estimates.}\n\n\n\n\n\\index{data!wind turbine survey|)}\n\\index{data!solar survey|)}\n\\index{confidence interval!interpretation|)}\n\n\\CalculatorVideos{confidence intervals for a single proportion}\n\n\\index{confidence interval|)}\n\n\n{\\input{ch_foundations_for_inf/TeX/confidence_intervals.tex}}\n\n\n\n\n\n\n%__________________\n\\section{Hypothesis testing for a proportion}\n\\label{hypothesisTesting}\n\n\\index{hypothesis testing|(}\n\nThe following question comes from a book written by\nHans Rosling, Anna Rosling R{\\\"o}nnlund, and Ola Rosling\ncalled \\emph{\\oiRedirect{amazon_factfulness}{Factfulness}}:\n\\begin{quote}\n  {\\em How many of the world's 1~year old children today\n  have been vaccinated against some disease:\n  \\begin{enumerate}[a.]\n  \\setlength{\\itemsep}{0mm}\n  \\item 20\\%\n  \\item 50\\%\n  \\item 80\\%\n  \\end{enumerate}}\n\\end{quote}\nWrite down what your answer (or guess),\nand when you're ready, find the answer in the\nfootnote.\\footnote{The correct answer is (c):\n  80\\% of the world's 1~year olds have been vaccinated\n  against some disease.}\n\nIn this section,\nwe'll be exploring how people with a 4-year college\ndegree perform on this and other world health questions\nas we learn about hypothesis tests, which are\na framework used to rigorously evaluate competing\nideas and claims.\n\n\\newcommand{\\roslingAsize}{50}\n\\newcommand{\\roslingAprop}{0.24}\n\\newcommand{\\roslingApropcomplement}{0.76}\n\\newcommand{\\roslingApercent}{24}\n\\newcommand{\\roslingApercentcomplement}{76}\n\\newcommand{\\roslingAcount}{12}\n\\newcommand{\\roslingAcountcomplement}{38}\n\\newcommand{\\roslingAse}{0.060}\n% n <- 50; x <- 12; (p <- x/n); (se <- sqrt(p * (1 - p) / n)); p + c(-1, 1) * 1.96 * se\n\n%There's an adage in United States financial markets that\n%it is better to get out of investments during the six ``summer''\n%months: \\emph{sell in May and go away!}\\footnote{Summer in the\n%northern hemisphere, anyways. \\rotatebox[origin=c]{180}{(Hello\n%Australia!)}} While this clever saying does rhyme, that doesn't\n%mean it is sound financial advice. Let's investigate.\n\n%so is this is a pretty strong statement, since the stock\n%market has a very strong historical trend of moving upwards.\n%\n%To test this theory, we've retrieved the \n%\n%If this adage holds meaning, we would expect that about half of the time the market would be in decline each year. Of course, we also would care to learn if it happens to be up more often than not, so we will also check that!\n\n%Finance is a field where a lot of money can be made or lost. We're going to explore a few topics in relation to the US stock market and \n\n%The United States stock market moves down and up in unpredictable ways, and it can be useful to look for small inconsistencies in the market behavior that can be leveraged for minor gains. We will test three theories about the stock market in this section:\n\n%\\item We might wonder whether the stock market is more likely to go up or down in any given day. Of course, the average return each day has been historically positive, and so this exploration will allow us to better understand if that is also reflected in the fraction of days that are up.\n%\\item Each week there is a 65.5 hours window from the time the market closes on Friday to when it opens on the weekdays. That's a lot of time for good news and bad news that can affect the returns on Mondays. We'll see whether we \n\n%The market has the same chance of going up or down on any given day of the week. For example, we would be interested to learn if the stock market goes up a little more often on, say, Fridays, that could be useful for \n\n\n\\subsection{Hypothesis testing framework}\n\nWe’re interested in understanding how much people know\nabout world health and development.\nIf we take a multiple choice\nworld health question, then we might like to understand~if\n\\begin{description}\n\\item[$\\mathbf{H_0}$:]\n    People never learn these particular topics and their\n    responses are simply equivalent to random guesses.\n\\item[$\\mathbf{H_A}$:]\n    People have knowledge that helps them do better\n    than random guessing, or perhaps, they have false knowledge\n    that leads them to actually do worse than random guessing.\n\\end{description}\nThese competing ideas are called \\term{hypotheses}.\nWe call $H_0$ the null hypothesis and $H_A$ the alternative\nhypothesis.\nWhen there is a subscript 0 like in $H_0$,\ndata scientists pronounce it as ``nought''\n(e.g.~$H_0$ is pronounced ``H-nought'').\n\n\\begin{onebox}{Null and alternative hypotheses}\n  The \\term{null hypothesis ($H_0$)} often represents\n  a skeptical perspective or a claim to be tested.\n  The \\term{alternative hypothesis ($H_A$)} represents an\n  alternative claim under consideration and is often\n  represented by a range of possible parameter values.\n  \\stdvspace{}\n  \n  Our job as data scientists is to play the role of a skeptic:\n  before we buy into the alternative hypothesis, we need to\n  see strong supporting evidence.\n\\end{onebox}\n\nThe null hypothesis often represents a skeptical position\nor a perspective of ``no difference''.\nIn our first example, we'll consider whether\nthe typical person does any different than random guessing\non Roslings' question about infant vaccinations.\n\nThe alternative hypothesis generally represents a new\nor stronger perspective. In the case of the question\nabout infant vaccinations,\nit would certainly be interesting to learn whether\npeople do better than random guessing, since that would\nmean that the typical person knows something about\nworld health statistics.\nIt would also be very interesting if we learned\nthat people do \\emph{worse} than random guessing,\nwhich would suggest people believe\nincorrect information about world health.\n\nThe hypothesis testing framework is a very general tool, and we often use it without a second thought. If a person makes a somewhat unbelievable claim, we are initially skeptical. However, if~there is sufficient evidence that supports the claim, we set aside our skepticism and reject the null hypothesis in favor of the alternative. The hallmarks of hypothesis testing are also found in the US~court system. \n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{hypTestCourtExample}\nA US court considers two possible claims about a defendant: she is either innocent or guilty. If we set these claims up in a hypothesis framework, which would be the null hypothesis and which the alternative?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The jury considers whether the evidence is so\n    convincing (strong) that there is no reasonable doubt\n    regarding the person's guilt;\n    in such a case, the jury rejects innocence\n    (the null hypothesis) and concludes the defendant\n    is guilty (alternative hypothesis).}\n\nJurors examine the evidence to see whether it convincingly\nshows a defendant is guilty.\nEven if the jurors leave unconvinced of guilt beyond\na reasonable doubt, this does not mean they believe the\ndefendant is innocent.\nThis is also the case with hypothesis testing:\n\\emph{even if we fail to reject the null hypothesis,\nwe typically do not accept the null hypothesis as true}.\nFailing to find strong evidence for the alternative\nhypothesis is not equivalent to accepting\nthe null hypothesis.\n\nWhen considering Roslings' question about infant vaccination,\nthe null hypothesis represents the notion that the people\nwe will be considering -- college-educated adults --\nare as accurate as random guessing.\nThat is, the proportion\n$p$ of respondents who pick the correct\nanswer, that 80\\% of 1~year olds have been vaccinated\nagainst some disease, is about 33.3\\%\n(or 1-in-3 if wanting to be perfectly precise).\nThe alternative hypothesis is that this proportion is something\nother than 33.3\\%. While it's helpful to write these hypotheses\nin words, it can be useful to write them using mathematical\nnotation:\n\\begin{description}\n\\item[$H_0$:] $p = 0.333$\n\\item[$H_A$:] $p \\neq 0.333$\n\\end{description}\nIn this hypothesis setup, we want to make a conclusion about\nthe population parameter $p$. The value we are comparing the\nparameter to is called the \\term{null value}, which in this\ncase is 0.333. It's common to label the null value with the\nsame symbol as the parameter but with a subscript~`0'.\nThat is, in this case, the null value is $p_0 = 0.333$\n(pronounced ``p-nought equals 0.333'').\n\n\\begin{examplewrap}\n\\begin{nexample}{It may seem impossible that the\n    proportion of people who get the correct answer\n    is \\emph{exactly} 33.3\\%. If we don't believe the\n    null hypothesis, should we simply reject it?}\n  No. While we may not buy into the notion that\n  the proportion is exactly 33.3\\%, the hypothesis testing\n  framework requires that there be strong evidence before\n  we reject the null hypothesis and conclude something\n  more interesting.\n\n  After all, even if we don't believe the proportion is\n  \\emph{exactly} 33.3\\%, that doesn't really tell us anything\n  useful! We would still be stuck with the original question:\n  do people do better or worse than random guessing on\n  Roslings' question?\n  Without data that strongly\n  points in one direction or the other, it is both\n  uninteresting and pointless to reject $H_0$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n  Another example of a real-world hypothesis testing situation\n  is evaluating whether a new drug is better or worse\n  than an existing drug at treating a particular disease.\n  What should we use for the null and alternative hypotheses in\n  this case?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The null hypothesis ($H_0$) in this case is\n    the declaration of \\emph{no difference}: the drugs are equally\n    effective. The alternative hypothesis ($H_A$) is that the\n    new drug performs differently than the original,\n    i.e. it could perform better or worse.}\n\n\n\\D{\\newpage}\n\n\\subsection{Testing hypotheses using confidence intervals}\n\\label{utilizingOurCI}\n\nWe will use the \\data{rosling\\us{}responses}\ndata set to evaluate\nthe hypothesis test evaluating whether college-educated adults\nwho get the question about infant vaccination correct is different\nfrom 33.3\\%.\nThis data set summarizes the answers of \\roslingAsize{}\ncollege-educated adults.\nOf these \\roslingAsize{} adults, \\roslingApercent{}\\%~of\nrespondents got the question correct that 80\\% of 1~year olds\nhave been vaccinated against some disease.\n\nUp until now, our discussion has been philosophical.\nHowever, now that we have data, we might ask ourselves:\ndoes the data provide strong evidence that the proportion\nof all college-educated adults who would answer this\nquestion correctly is different than 33.3\\%?\n\nWe learned in Section~\\ref{pointEstimates} that there is\nfluctuation from one sample to another, and it is unlikely\nthat our sample proportion, $\\hat{p}$,\nwill exactly equal $p$, but we want to make\na conclusion about~$p$.\nWe~have a nagging concern:\nis this deviation of \\roslingApercent{}\\%\nfrom 33.3\\% simply due to chance,\nor~does the data provide strong evidence that the\npopulation proportion is different from 33.3\\%?\n\nIn Section~\\ref{confidenceIntervals}, we learned how to\nquantify the uncertainty in our estimate using confidence\nintervals. \nThe same method for measuring variability can be useful\nfor the hypothesis test.\n\n\\begin{examplewrap}\n\\begin{nexample}{Check whether it is reasonable to construct\n    a confidence interval for $p$ using the sample data, and\n    if so, construct a 95\\% confidence interval.}\n  The conditions are met for $\\hat{p}$ to be approximately\n  normal: the data come from a simple random sample (satisfies\n  independence), and $n\\hat{p} = \\roslingAcount$ and\n  $n(1 - \\hat{p}) = \\roslingAcountcomplement$ are both\n  at least 10 (success-failure condition).\n\n  To construct the confidence interval, we will need to identify\n  the point estimate ($\\hat{p} = \\roslingAprop$),\n  the critical value for\n  the 95\\% confidence level ($z^{\\star} = 1.96$), and the standard\n  error of $\\hat{p}$\n  ($SE_{\\hat{p}} = \\sqrt{\\hat{p}(1 - \\hat{p}) / n} = \\roslingAse$).\n  With those pieces, the confidence interval for $p$ can be\n  constructed:\n  \\begin{align*}\n    &\\hat{p} \\pm z^{\\star} \\times SE_{\\hat{p}} \\\\\n    &\\roslingAprop \\pm 1.96 \\times \\roslingAse \\\\\n    &(0.122, 0.358)\n  \\end{align*}\n  We are 95\\% confident that the proportion of all\n  college-educated adults to correctly answer this\n  particular question about infant vaccination is between\n  12.2\\% and 35.8\\%.\n\\end{nexample}\n\\end{examplewrap}\n%At a first glance, it looks like it might be. After all,\n%36\\% isn't that close to 50\\%, so maybe this data constitutes\n%\\emph{strong evidence}. We need to \n\nBecause the null value in the hypothesis test is $p_0 = 0.333$,\nwhich falls within the range of plausible values from the\nconfidence interval, we cannot say the null value is\nimplausible.\\footnote{Arguably this method is slightly imprecise.\n  As we'll see in a few pages, the standard error is often\n  computed slightly differently in the context of a hypothesis\n  test for a proportion.}\nThat is, the data do not provide sufficient evidence to reject\nthe notion that the performance of college-educated\nadults was different than random guessing,\nand we do not reject the null hypothesis,~$H_0$.\n\n\\begin{examplewrap}\n\\begin{nexample}{Explain why we cannot conclude that\n    college-educated adults simply guessed on the\n    infant vaccination question.}\n  While we failed to reject $H_0$, that does not\n  necessarily mean the null hypothesis is true.\n  Perhaps there was an actual difference,\n  but we were not able to detect it with the\n  relatively small sample of~\\roslingAsize{}.\n\n%  Second, we are only evaluating the proportion,\n%  and if the population proportion is 0.333,\n%  there are still multiple ways to arrive at that proportion.\n%  For example,\n%  perhaps some adults guessed but others did not.\n%  And of those who didn't guess,\n%  their past knowledge simply wasn't very useful on this\n%  question and so most of them still got it wrong.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{Double negatives can sometimes be used in statistics}\n  In many statistical explanations, we use double negatives.\n  For instance, we might say that the null hypothesis is\n  \\emph{not implausible} or we \\emph{failed to reject}\n  the null hypothesis.\n  Double negatives are used to communicate that while we\n  are not rejecting a position, we are also not saying it\n  is correct.\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{roslingB_hypothesis_setup}%\nLet's move onto a second question posed by the Roslings:\n\\begin{quote}{\\em\n  There are 2 billion children in the world today\n  aged 0-15 years old, how many children will there\n  be in year 2100 according to the United Nations?\n  \\begin{enumerate}[a.]\n  \\setlength{\\itemsep}{0mm}\n  \\item 4 billion.\n  \\item 3 billion.\n  \\item 2 billion.\n  \\end{enumerate}\n}\\end{quote}\nSet up appropriate hypotheses to evaluate whether\ncollege-educated adults are better than random guessing\non this question.\nAlso, see if you can guess the correct answer before checking\nthe answer in the footnote!\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{%\nThe appropriate hypotheses are:\n\n$H_0$: the proportion who get the answer correct is the same\nas random guessing: 1-in-3, or $p = 0.333$.\n\n$H_A$: the proportion who get the answer correct is different\nthan random guessing, $p \\neq 0.333$.\n\nThe correct answer to the question is 2~billion.\nWhile the world population is projected to increase,\nthe average age is also expected to rise.\nThat is, the majority of the population growth will\nhappen in older age groups, meaning people are projected\nto live longer in the future across much of the world.}\n\n% n <- 228; x <- 39; p <- x / n; n; p; 1 - p; x; n - x; sqrt(p*(1-p)/n)\n\\newcommand{\\roslingBsize}{228}\n\\newcommand{\\roslingBprop}{0.149}\n\\newcommand{\\roslingBpropcomplement}{0.851}\n\\newcommand{\\roslingBpercent}{14.9\\%}\n\\newcommand{\\roslingBpercentcomplement}{85.1\\%}\n\\newcommand{\\roslingBcount}{34}\n\\newcommand{\\roslingBcountcomplement}{194}\n\\newcommand{\\roslingBse}{0.024}\n% n <- 228; x <- 34; (p <- x/n); (se <- sqrt(p * (1 - p) / n)); p + c(-1, 1) * 1.96 * se\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{roslingB_normality}%\nThis time we took a larger sample of\n\\roslingBsize{} college-educated adults,\n\\roslingBcount{} (\\roslingBpercent{}) selected the correct\nanswer to the question in Guided\nPractice~\\ref{roslingB_hypothesis_setup}: 2~billion.\nCan we model the sample proportion using a normal distribution\nand construct a confidence interval?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We check both conditions, which are satisfied,\nso it is reasonable to use\na normal distribution for $\\hat{p}$: \\\\\n\\textbf{Independence.} Since the data are from a simple\n    random sample, the observations are independent. \\\\\n\\textbf{Success-failure.} We'll use $\\hat{p}$ in place of $p$\n    to check: $n\\hat{p} = \\roslingBcount$\n    and $n(1 - \\hat{p}) = \\roslingBcountcomplement$.\n    Both are greater than 10, so the success-failure condition\n    is satisfied.}\n\n\\begin{examplewrap}\n\\begin{nexample}{Compute a 95\\% confidence interval for the\n    fraction of college-educated adults who answered the\n    children-in-2100 question correctly, and evaluate the\n    hypotheses in Guided\n    Practice~\\ref{roslingB_hypothesis_setup}.}\n  To compute the standard error, we'll again use\n  $\\hat{p}$\n  in place of $p$ for the calculation:\n  \\begin{align*}\n  SE_{\\hat{p}}\n      = \\sqrt{\\frac{\\hat{p}(1 - \\hat{p})}{n}}\n      = \\sqrt{\\frac{\\roslingBprop{}(1 - \\roslingBprop{})}\n          {\\roslingBsize{}}}\n      = \\roslingBse{}\n  \\end{align*}\n  In Guided Practice~\\ref{roslingB_normality},\n  we found that $\\hat{p}$ can be modeled using\n  a normal distribution,\n  which ensures a 95\\% confidence interval may be accurately\n  constructed as\n  \\begin{align*}\n  \\hat{p}~\\pm~z^{\\star} \\times SE\n  \\quad\\to\\quad\n  \\roslingBprop{}~\\pm~1.96 \\times \\roslingBse{}\n  \\quad\\to\\quad\n  (0.103, 0.195)\n  \\end{align*}\n  Because the null value, $p_0 = 0.333$, is not in the\n  confidence interval, a population proportion of 0.333\n  is implausible and we reject the null hypothesis.\n  That is, the data provide statistically significant\n  evidence that the actual proportion of college adults\n  who get the children-in-2100 question correct is\n  different from random guessing. Because the entire\n  95\\% confidence interval\n  is below 0.333, we can conclude college-educated adults\n  do \\emph{worse} than random guessing on this question.\n\n  One subtle consideration is that we used a\n  95\\% confidence interval.\n  What if we had used a 99\\% confidence level?\n  Or even a 99.9\\% confidence level?\n  It's possible to come to a different conclusion\n  if using a different confidence level.\n  Therefore, when we make a conclusion based\n  on confidence interval, we should also be sure\n  it is clear what confidence level we used.\n\\end{nexample}\n\\end{examplewrap}\n\nThe worse-than-random performance on this\nlast question is not a fluke:\nthere are many such world health questions where people\ndo worse than random guessing.\nIn general, the answers suggest that people tend to be\nmore pessimistic about progress than reality suggests.\nThis topic is discussed in much greater detail in\nthe Roslings' book,\n\\emph{\\oiRedirect{amazon_factfulness}{Factfulness}}.\n\n\n\\D{\\newpage}\n\n\\subsection{Decision errors}\n\n\\index{hypothesis testing!decision errors|(}\n\nHypothesis tests are not flawless: we can make an incorrect\ndecision in a statistical hypothesis test based on the data.\nFor example, in the court system innocent people are\nsometimes wrongly convicted and the guilty sometimes walk free.\n%Unfortunately, we never truly know if $H_0$ or $H_A$ holds true.\nOne key distinction with statistical hypothesis tests is that\nwe have the tools necessary to probabilistically quantify how\noften we make errors in our conclusions.\n\nRecall that there are two competing hypotheses:\nthe null and the alternative.\nIn a hypothesis test, we make a statement about which one might\nbe true, but we might choose incorrectly. There are four possible\nscenarios, which are summarized in Figure~\\ref{fourHTScenarios}.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l l c c}\n& & \\multicolumn{2}{c}{\\textbf{Test conclusion}} \\\\\n\\cline{3-4}\n\\vspace{-3.7mm} \\\\\n& & do not reject $H_0$ &  reject $H_0$ in favor of $H_A$ \\\\\n\\cline{2-4}\n\\vspace{-3.7mm} \\\\\n& $H_0$ true &\n    okay &  \\highlight{Type~1 Error} \\\\\n\\raisebox{1.5ex}{\\textbf{Truth}} & $H_A$ true &\n    \\highlight{Type~2 Error} & okay \\\\\n\\cline{2-4}\n\\end{tabular}\n\\caption{Four different scenarios for hypothesis tests.}\n\\label{fourHTScenarios}\n\\end{figure}\n\nA \\term{Type~1 Error} is rejecting the null hypothesis when\n$H_0$ is actually true.\nA \\term{Type~2 Error} is failing to\nreject the null hypothesis when the alternative is actually\ntrue.\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{whatAreTheErrorTypesInUSCourts}\nIn a US court, the defendant is either innocent ($H_0$) or\nguilty ($H_A$).\nWhat does a Type~1 Error represent in this context?\nWhat does a Type~2 Error represent?\nFigure~\\ref{fourHTScenarios} may be useful.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{If\n  the court makes a Type~1 Error, this means the defendant\n  is innocent ($H_0$ true) but wrongly convicted.\n  Note that a Type~1 Error is only possible if we've rejected\n  the null hypothesis.\n\n  A Type~2 Error means the court failed to reject $H_0$\n  (i.e. failed to convict the person) when she was\n  in fact guilty ($H_A$ true).\n  Note that a Type~2 Error is only possible if we have\n  failed to reject the null hypothesis.}\n\n\\begin{examplewrap}\n\\begin{nexample}{How could we reduce the Type~1 Error rate\n    in US courts?\n    What influence would this have on the Type~2 Error rate?}\n    \\label{howToReduceType1ErrorsInUSCourts}%\n  To lower the Type~1 Error rate, we might\n  raise our standard for conviction from\n  ``beyond a reasonable doubt'' to\n  ``beyond a conceivable doubt'' so fewer people would\n  be wrongly convicted. However, this would also make\n  it more difficult to convict the people who are\n  actually guilty, so we would make more Type~2 Errors.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{howToReduceType2ErrorsInUSCourts}\nHow could we reduce the Type~2 Error rate in US courts?\nWhat influence would this have on the Type~1 Error\nrate?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{To lower the Type~2 Error rate, we want\n  to convict more guilty people. We could lower the\n  standards for conviction from ``beyond a reasonable\n  doubt'' to ``beyond a little doubt''. Lowering the bar\n  for guilt will also result in more wrongful convictions,\n  raising the Type~1 Error rate.}\n\n\\index{hypothesis testing!decision errors|)}\n\nExercises~\\ref{whatAreTheErrorTypesInUSCourts}-\\ref{howToReduceType2ErrorsInUSCourts} provide\nan important lesson: if we reduce how often we make\n one type of error, we generally make more of the\n other type.\n\nHypothesis testing is built around rejecting or failing\nto reject the null hypothesis.\nThat is, we do not reject $H_0$ unless we have strong evidence.\nBut what precisely does \\emph{strong evidence} mean?\nAs a general rule of thumb, for those cases where the null\nhypothesis is actually true, we do not want to incorrectly\nreject $H_0$ more than 5\\% of the time.\nThis corresponds to a \\term{significance level}%\n\\index{hypothesis testing!significance level} of 0.05.\nThat is, if the null hypothesis is true,\nthe significance level indicates how often\nthe data lead us to incorrectly reject $H_0$.\nWe often write the significance level using $\\alpha$\n(the Greek letter \\emph{alpha}\\index{Greek!alpha@alpha ($\\alpha$)}):\n$\\alpha = 0.05$.\nWe discuss the appropriateness of different significance\nlevels in Section~\\ref{significanceLevel}.\n\n\\D{\\newpage}\n\nIf we use a 95\\% confidence interval to evaluate a\nhypothesis test and the null hypothesis happens to be true,\nwe will make an error whenever the point estimate is\nat least 1.96 standard errors away from the population\nparameter.\nThis happens about 5\\% of the time (2.5\\% in each tail).\nSimilarly, using a 99\\% confidence interval to evaluate\na hypothesis is equivalent to a significance level of\n$\\alpha = 0.01$.\n\nA confidence interval is very helpful in determining\nwhether or not to reject the null hypothesis.\nHowever, the confidence interval approach isn't always\nsustainable.\nIn several sections, we will encounter situations where\na confidence interval cannot be constructed.\nFor example, if we wanted to evaluate the hypothesis\nthat several proportions are equal, it isn't clear how\nto construct and compare many confidence intervals\naltogether.\n\nNext we will introduce a statistic called the \\emph{p-value}\nto help us expand our statistical toolkit, which will\nenable us to both better understand the strength of\nevidence and work in more complex data scenarios in\nlater sections.\n\n\n\n\\subsection{Formal testing using p-values}\n\n\\label{pValue}\n\n\\index{hypothesis testing!p-value|(}\n\nThe p-value is a way of quantifying the strength of the\nevidence against the null hypothesis and in favor of the\nalternative hypothesis.\nStatistical hypothesis testing typically uses the\np-value method rather than making a decision based\non  confidence intervals.\n\n\\begin{onebox}{p-value}\n  The \\term{p-value}\\index{hypothesis testing!p-value|textbf}\n  is the probability of observing data at least as favorable\n  to the alternative hypothesis as our current data set,\n  if the null hypothesis were true. We typically use a summary\n  statistic of the data, in this section the sample proportion,\n  to help compute the p-value and evaluate the hypotheses.\n\\end{onebox}\n\n%To apply the normal distribution framework in the context of a hypothesis test for a proportion, the independence and success-failure conditions must be satisfied. In a hypothesis test, the success-failure condition is checked using the null proportion: we verify $np_0$ and $n(1-p_0)$ are at least 10, where $p_0$ is the null value.\n\n\\index{data!coal power support|(}\n\n\\newcommand{\\pewcoalpollsize}{1000}\n\\newcommand{\\pewcoalpollprop}{0.37}\n\\newcommand{\\pewcoalpollpropcomplement}{0.63}\n\\newcommand{\\pewcoalpollpercent}{37\\%}\n\\newcommand{\\pewcoalpollpercentcomplement}{63\\%}\n\\newcommand{\\pewcoalpollcount}{370}\n\\newcommand{\\pewcoalpollcountcomplement}{630}\n\\newcommand{\\pewcoalpollse}{0.0153}\n\\newcommand{\\pewcoalpollnullvalue}{0.5}\n\\newcommand{\\pewcoalpollnullse}{0.016}\n\n\\begin{examplewrap}\n\\begin{nexample}{Pew Research asked a random sample of\n    \\pewcoalpollsize{} American\n    adults whether they supported the increased usage of coal to\n    produce energy.\n    Set up hypotheses to evaluate whether\n    a majority of American adults support or oppose\n    the increased usage of coal.}\n  The uninteresting result is that there is no majority either way:\n  half of Americans support and the other half oppose expanding the\n  use of coal to produce energy. The alternative hypothesis would\n  be that there is a majority support or oppose\n  (though we do not known which one!) expanding the\n  use of coal. If $p$ represents the proportion supporting, then\n  we can write the hypotheses as\n  \\begin{description}\n    \\item[$H_0$:] $p = 0.5$\n    \\item[$H_A$:] $p \\neq 0.5$\n  \\end{description}\n  In this case, the null value is $p_0 = 0.5$.\n\\end{nexample}\n\\end{examplewrap}\n\n%\\begin{examplewrap}\n%\\begin{nexample}{Suppose the null value, $p_0 = 0.5$,\n%    was the actual level of support for coal usage.\n%    Describe how we could simulate a survey of\n%    \\pewcoalpollsize{} responses when $p_0 = 0.5$.}\n%  \\label{simOnePropExample}%\n%  If we pick a random person to participate in the survey,\n%  then \\emph{under the null hypothesis},\n%  the chances they would support coal usage is $p_0 = 0.5$.\n%  If this were true, then it's the same as flipping a fair coin.\n%  That is, we can simulate an individual person's response by\n%  flipping a coin;\n%  if it's heads, we say \\resp{support},\n%  and if it's tails, \\resp{oppose}.\n%  To simulate \\pewcoalpollsize{} independent responses,\n%  we can flip the coin a total of 1000 times and compute the\n%  fraction of instances that were heads as the observed\n%  proportion.\n%  We did this and observed 487 heads for a proportion\n%  of $\\hat{p}_{\\text{sim, 1}} = 0.487$.\n%\\end{nexample}\n%\\end{examplewrap}\n%\n%Example~\\ref{simOnePropExample} described how we could\n%simulate a survey result under the null hypothesis that\n%the population proportion is equal to $p_0$.\n%In this way, we check what kind of sample observations\n%we might expect to see \\emph{if the null hypothesis were true}.\n%Of course, a single simulation is interesting, but not that\n%informative.\n%If we run the simulation again, we get a value of\n%$\\hat{p}_{\\text{sim, 2}} = 0.502$.\n%And again: $\\hat{p}_{\\text{sim, 3}} = 0.523$.\n%We can do this many times on a computer,\n%just like we did for a population proportion of 0.88\n%in Section~\\ref{pointEstimates}.\n%The results of 5,000 simulated surveys are summarized\n%in a histogram in Figure~\\ref{sampling_5k_prop_50p}.\n%\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figure{0.8}{sampling_5k_prop_50p}\n%  \\caption{\n%      Simulated surveys proportion\n%      \\emph{if} the population proportion\n%      were equal to the null value, $p_0 = 0.5$.\n%      All 5,000 simulated sample proportions\n%      lie between 0.44 and 0.56.}\n%  \\label{sampling_5k_prop_50p}\n%\\end{figure}\n%\n%\\begin{examplewrap}\n%\\begin{nexample}{The actual Pew Research survey found that\n%    \\pewcoalpollpercent{} of the \\pewcoalpollsize{}\n%    respondents supported increasing the use of coal.\n%    Use Figure~\\ref{sampling_5k_prop_50p}\n%    to estimate how frequently we might observe a proportion\n%    of \\pewcoalpollprop{} if the null hypothesis that\n%    the population proportion is 0.5 were actually true.\n%    What might you conclude from this finding?}\n%  Not one of the 5,000 simulations yielded a sample proportion\n%  of \\pewcoalpollpercent{} or further from 0.5.\n%  That is, \\emph{if} the actually population proportion is\n%  actually 0.5, then we observed something so rare that we\n%  wouldn't necessarily see it if we repeated the process\n%  5,000 times.\n%  Ultimately, the observed sample result is nearly\n%  impossible (extremely improbable!) if we believe that\n%  the population proportion is 0.5.\n%  This evidence casts significant doubt on the notion that\n%  $p = 0.5$, and we should reject the null hypothesis,~$H_0$.\n%\\end{nexample}\n%\\end{examplewrap}\n\nWhen evaluating hypotheses for proportions using the\np-value method,\nwe will slightly modify how we check the success-failure\ncondition and compute the standard error for the\nsingle proportion case.\nThese changes aren't dramatic, but pay close attention\nto how we use the null value, $p_0$.\n\n\\begin{examplewrap}\n\\begin{nexample}{Pew Research's sample show that\n    \\pewcoalpollpercent{}\n    of American adults support increased usage of coal.\n    We now wonder, does \\pewcoalpollpercent{} represent\n    a real difference from the null hypothesis of 50\\%?\n    What would the sampling distribution of $\\hat{p}$\n    look like if the null hypothesis were true?}\n  If the null hypothesis were true, the population proportion\n  would be the null value, 0.5.\n  We~previously learned that\n  the sampling distribution of $\\hat{p}$ will be normal when\n  two conditions are~met:\n  \\begin{description}\n    \\item[Independence.]\n        The poll was based on a simple random sample,\n        so independence is satisfied.\n    \\item[Success-failure.]\n        Based on the poll's sample size of\n        $n = \\pewcoalpollsize{}$,\n        the success-failure condition is met, since\n        \\begin{align*}\n        np ~ \\stackrel{H_0}{=}\n            ~ \\pewcoalpollsize{} \\times \\pewcoalpollnullvalue{}\n            = 500\n        \\qquad\\qquad\n        n (1 - p) ~ \\stackrel{H_0}{=}\n            ~ \\pewcoalpollsize{} \\times\n                (1 - \\pewcoalpollnullvalue{})\n            = 500\n        \\end{align*}\n        are both at least 10.\n        Note that the success-failure condition was checked\n        using the null value, $p_0 = 0.5$;\n        this is the first procedural difference from\n        confidence intervals.\n  \\end{description}\n  If the null hypothesis were true, the sampling distribution\n  indicates that a sample proportion based on\n  $n = \\pewcoalpollsize{}$ observations\n  would be normally distributed. Next, we can compute the standard\n  error, where we will again use the null value $p_0 = 0.5$ in the\n  calculation:\n  \\begin{align*}\n  SE_{\\hat{p}}\n    = \\sqrt{\\frac{p (1 - p)}{n}}\n    \\quad \\stackrel{H_0}{=} \\quad\n        \\sqrt{\\frac{\\pewcoalpollnullvalue{} \\times\n            (1 - \\pewcoalpollnullvalue{})}{\\pewcoalpollsize{}}}\n    = \\pewcoalpollnullse{}\n  \\end{align*}\n  This marks the other procedural difference from confidence\n  intervals: since the sampling distribution is determined\n  under the null proportion, the null value $p_0$ was used for\n  the proportion in the calculation rather than $\\hat{p}$.\n\n  Ultimately, if the null hypothesis were true, then the sample\n  proportion should follow a normal distribution with mean\n  \\pewcoalpollnullvalue{}\n  and a standard error of \\pewcoalpollnullse{}.\n  This distribution is shown in\n  Figure~\\ref{normal_dist_mean_500_se_016}.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A normal distribution centered at 0.5 with a standard deviation of 0.016 is shown. Additionally, an annotation is located at 0.37 that states, \"Observed p-hat equals 0.37\".]{0.7}{normal_dist_mean_500_se_016}\n  \\caption{\n      If the null hypothesis were true,\n      this normal distribution describes the\n      distribution of $\\hat{p}$.}\n  \\label{normal_dist_mean_500_se_016}\n\\end{figure}\n\n\\begin{onebox}{Checking success-failure and computing\n      $\\mathbf{SE_{\\hat{p}}}$\n      for a hypothesis test}\n  When using the p-value method to evaluate a hypothesis test,\n  we check the conditions for $\\hat{p}$ and construct the\n  standard error using the null value, $p_0$, instead of using\n  the sample proportion. \\stdvspace{}\n\n  In a hypothesis test with a p-value, we are supposing the\n  null hypothesis is true,\n  which is a different mindset than when we compute\n  a confidence interval.\n  This is why we use $p_0$ instead of $\\hat{p}$\n  when we check conditions and compute the standard error\n  in this context.\n\\end{onebox}\n\nWhen we identify the sampling distribution under the null hypothesis,\nit has a special name: the \\term{null distribution}.\nThe p-value represents the probability of the observed $\\hat{p}$,\nor a $\\hat{p}$ that is more extreme,\nif the null hypothesis were true.\nTo find the p-value, we generally find the null distribution,\nand then we find a tail area in that distribution corresponding\nto our point estimate.\n%In some cases, as in this particular instance,\n%the null distribution is a normal distribution.\n\n\\begin{examplewrap}\n\\begin{nexample}{If the null hypothesis were true,\n    determine the chance of finding $\\hat{p}$ at least\n    as far into the tails as \\pewcoalpollprop{}\n    under the null distribution,\n    which is a normal distribution with mean\n    $\\mu = \\pewcoalpollnullvalue{}$\n    and $SE = \\pewcoalpollnullse{}$.}\n%  When we compute the p-value, we think about the chance\n%  of our observation, if the null hypothesis were true.\n%\n  This is a normal probability problem where\n  $x = \\pewcoalpollprop{}$.\n  First, we draw a simple graph to represent the situation,\n  similar to what is shown in\n  Figure~\\ref{normal_dist_mean_500_se_016}.\n  Since $\\hat{p}$ is so far out in the tail, we know the\n  tail area is going to be very small. To find it, we start\n  by computing the Z-score using the mean of 0.5 and the\n  standard error of \\pewcoalpollnullse{}:\n  \\begin{align*}\n  Z = \\frac{\\pewcoalpollprop{} - 0.5}{\\pewcoalpollnullse{}} = -8.125 \n  \\end{align*}\n  We can use software to find the tail area:\n  $2.2 \\times 10^{-16}$\n  (0.00000000000000022).\n  If using the normal probability table in\n  Appendix~\\ref{normalProbabilityTable},\n  we'd find that $Z = -8.125$ is off the table,\n  so we would use the smallest area listed: 0.0002.\n\n  The potential $\\hat{p}$'s in the upper tail beyond\n  \\pewcoalpollpropcomplement{}, which are shown\n  in Figure~\\ref{normal_dist_mean_500_se_016_with_upper},\n  also represent observations at least as extreme as\n  the observed value of \\pewcoalpollprop{}.\n  To account for these values that are also more\n  extreme under the hypothesis setup,\n  we double the lower tail to get an estimate\n  of the p-value: $4.4 \\times 10^{-16}$\n  (or if using the table method, 0.0004).\n\n  The p-value represents the probability of observing\n  such an extreme sample proportion by chance, if the null\n  hypothesis were true.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[A normal distribution centered at 0.5 with a standard deviation of 0.016 is shown. Additionally, the tail areas below 0.37 and above 0.63 are emphasized -- the regions under the normal distribution are nearly zero. Two annotations also appear. First, an annotation located at 0.37 states, \"Tail area for p-hat\". Second, an annotation located at 0.68 states, \"Equally unlikely if H-sub-zero (the null hypothesis) is true\".]{0.7}{normal_dist_mean_500_se_016}\n      {normal_dist_mean_500_se_016_with_upper}\n  \\caption{\n      If $H_0$ were true, then the values above\n      \\pewcoalpollpropcomplement{} are just\n      as unlikely as values below \\pewcoalpollprop{}.}\n  \\label{normal_dist_mean_500_se_016_with_upper}\n\\end{figure}\n\n\n\n\n\\begin{examplewrap}\n\\begin{nexample}{How should we evaluate the hypotheses using the\n    p-value of $4.4 \\times 10^{-16}$?\n    Use the standard significance level of $\\alpha = 0.05$.}\n  If the null hypothesis were true, there's only an incredibly\n  small chance of observing such an extreme deviation of\n  $\\hat{p}$ from 0.5.\n  This means one of the following must be true:\n  \\begin{enumerate}\n    \\item The null hypothesis is true, and we just happened\n        to observe something so extreme that it only happens\n        about once in every 23 quadrillion times\n        (1~quadrillion = 1~million $\\times$ 1~billion).\n    \\item The alternative hypothesis is true,\n        which would be consistent\n        with observing a sample proportion far from 0.5.\n  \\end{enumerate}\n  The first scenario is laughably improbable,\n  while the second scenario seems much more plausible.\n\n  Formally, when we evaluate a hypothesis test,\n  we compare the p-value to the significance level,\n  which in this case is $\\alpha = 0.05$.\n  Since the p-value is less than $\\alpha$,\n  we reject the null hypothesis.\n  That is, the data provide strong evidence against $H_0$.\n  The data indicate the direction of the difference:\n  a majority of Americans do not support\n  expanding the use of coal-powered energy.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!coal power support|)}\n\n\\begin{onebox}{Compare the p-value to $\\pmb{\\alpha}$ to\n      evaluate $\\pmb{H_0}$}\n  When the p-value is less than the significance level, $\\alpha$,\n  reject $H_0$. We would report a conclusion that the data provide\n  strong evidence supporting the alternative hypothesis. \\\\[2mm]\n  When the p-value is greater than $\\alpha$, do not reject $H_0$,\n  and report that we do not have sufficient evidence to reject the\n  null hypothesis. \\\\[2mm]\n  In either case, it is important to describe the conclusion\n  in the context of the data.\n\\end{onebox}\n\n\n\n\n\n\n\n\\index{data!nuclear arms reduction|(}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nDo a majority of Americans support or oppose nuclear arms\nreduction? Set up hypotheses to evaluate this\nquestion.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We would like to understand if a majority\n  supports or opposes, or ultimately, if there is no difference.\n  If $p$ is the proportion of Americans who support nuclear\n  arms reduction, then\n  $H_0$: $p = 0.50$ and $H_A$: $p \\neq 0.50$.}\n\n\\newcommand{\\gallupnucleararmspollsize}{1028}\n\\newcommand{\\gallupnucleararmspollprop}{0.56}\n\\newcommand{\\gallupnucleararmspollpropcomplement}{0.44}\n\\newcommand{\\gallupnucleararmspollpercent}{56}\n\\newcommand{\\gallupnucleararmspollpercentcomplement}{44}\n\\newcommand{\\gallupnucleararmspollnullcount}{514}\n\\newcommand{\\gallupnucleararmspollse}{0.0155}\n\\newcommand{\\gallupnucleararmspollnullvalue}{0.5}\n\\newcommand{\\gallupnucleararmspollnullse}{0.0156}\n\n\\begin{examplewrap}\n\\begin{nexample}{A simple random sample of\n    \\gallupnucleararmspollsize{} US adults\n    in March 2013 show that\n    \\gallupnucleararmspollpercent{}\\% support nuclear arms\n    reduction.\n    Does this provide convincing evidence that a majority\n    of Americans supported nuclear arms reduction at the\n    5\\% significance level?} \\label{NuclearArmsInferenceExample}\n  First, check conditions:\n  \\begin{description}\n  \\item[Independence.] The poll was of a simple random sample\n      of US adults, meaning the observations are independent.\n  \\item[Success-failure.] In a one-proportion hypothesis test,\n      this condition is checked using the null proportion,\n      which is $p_0 = \\gallupnucleararmspollnullvalue{}$\n      in this context:\n      $n p_0 = n (1 - p_0)\n          = \\gallupnucleararmspollsize{} \\times\n              \\gallupnucleararmspollnullvalue{}\n          = \\gallupnucleararmspollnullcount{} \\geq 10$.\n  \\end{description}\n  With these conditions verified,\n  we can model $\\hat{p}$ using a normal model.\n\n  Next the standard error can be computed.\n  The null value $p_0$ is used again here,\n  because this is a hypothesis test for a single proportion.\n  \\begin{align*}\n  SE_{\\hat{p}}\n      = \\sqrt{\\frac{p_0 (1 - p_0)}{n}}\n      = \\sqrt{\\frac{\\gallupnucleararmspollnullvalue{}\n          (1 - \\gallupnucleararmspollnullvalue{})}\n          {\\gallupnucleararmspollsize{}}}\n      = \\gallupnucleararmspollnullse{}\n  \\end{align*}\n  Based on the normal model, the test statistic can be\n  computed as the Z-score of the point estimate:\n  \\begin{align*}\n  Z = \\frac{\\text{point estimate} - \\text{null value}}{SE}\n      = \\frac{\\gallupnucleararmspollprop{} - 0.50}\n          {\\gallupnucleararmspollnullse{}}\n      = 3.85\n  \\end{align*}\n  It's generally helpful to draw null distribution and\n  the tail areas of interest for computing the p-value:\n  \\begin{center}\n  \\Figures[A normal distribution centered at 0.5 is shown, which has a standard deviation of about 0.0156. Two tails several standard deviations away from the center are emphasized. The first, at and above 0.56, is annotated with the text \"upper tail\". The second, which appears to be at and below 0.44, is annotated with the text \"lower tail\".]{0.48}{nuclearArmsReduction}{nuclearArmsReductionPValue}\n  \\end{center}\n  The upper tail area is about 0.0001,\n  and we double this tail area to get the p-value: 0.0002.\n  Because the p-value is smaller than 0.05, we reject $H_0$.\n  The poll provides convincing evidence that a majority\n  of Americans supported nuclear arms reduction efforts\n  in March 2013.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!nuclear arms reduction|)}\n\n\\D{\\newpage}\n\n\\newcommand{\\oneprophtsummary}{\n\\begin{onebox}{Hypothesis testing for a single proportion}\n  Once you've determined a one-proportion hypothesis test is the\n  correct procedure, there are four steps to completing the\n  test:\n  \\begin{description}\n  \\item[Prepare.]\n      Identify the parameter of interest,\n      list hypotheses,\n      identify the significance level,\n      and identify $\\hat{p}$ and $n$.\n  \\item[Check.]\n      Verify conditions\n      to ensure $\\hat{p}$ is nearly normal under $H_0$.\n      For one-proportion hypothesis tests, use the null\n      value to check the success-failure condition.\n  \\item[Calculate.]\n      If the conditions hold, compute the standard\n      error, again using $p_0$, compute the Z-score,\n      and identify the p-value.\n  \\item[Conclude.]\n      Evaluate the hypothesis test by comparing the p-value\n      to $\\alpha$, and provide a conclusion in the context\n      of the problem.\n  \\end{description}\n\\end{onebox}\n}\n\\oneprophtsummary{}\n\n\\CalculatorVideos{hypothesis tests for a single proportion}\n\n\n\\subsection{Choosing a significance level}\n\\label{significanceLevel}\n\n\\index{hypothesis testing!significance level|(}\n\\index{significance level|(}\n\nChoosing a significance level for a test is important in\nmany contexts, and the traditional level is $\\alpha = 0.05$.\nHowever, it can be helpful to adjust the significance level\nbased on the application. We may select a level that is\nsmaller or larger than 0.05 depending on the consequences\nof any conclusions reached from the test.\n\nIf making a Type~1 Error is dangerous or especially costly,\nwe should choose a small significance level (e.g. 0.01).\nUnder this scenario we want to be very cautious about\nrejecting the null hypothesis, so we demand very strong\nevidence favoring $H_A$ before we would reject $H_0$.\n\nIf a Type~2 Error is relatively more dangerous or much more\ncostly than a Type~1 Error, then we might choose a higher\nsignificance level (e.g. 0.10). Here we want to be cautious\nabout failing to reject $H_0$ when the alternative hypothesis\nis actually true.\n\nAdditionally, if the cost of collecting data is small relative\nto the cost of a Type~2 Error, then it may also be a good\nstrategy to collect more data.\nUnder this strategy, the Type~2 Error can be reduced\nwhile not affecting the Type~1 Error rate.\nOf course, collecting extra data is often costly,\nso~there is typically a cost-benefit analysis to be considered.\n%We'll discuss this topic a bit more in\n%Sections~\\ref{} and~\\ref{}.\n%\\Comment{Fix this reference.}\n\n\\newcommand{\\doorhingeflawrate}{0.2}\n\n\\begin{examplewrap}\n\\begin{nexample}{A car manufacturer is considering switching\n    to a new, higher quality piece of equipment that constructs\n    vehicle door hinges.\n    They figure that they will save money in the long run\n    if this new machine produces hinges\n    that have flaws less than\n    \\doorhingeflawrate{}\\% of the time.\n    However, if the hinges are flawed more than\n    \\doorhingeflawrate{}\\% of\n    the time, they wouldn't get a good enough\n    return-on-investment from the new piece of equipment,\n    and they would lose money.\n    Is there good reason to modify the significance level\n    in such a hypothesis test?}\n  The null hypothesis would be that the rate of flawed\n  hinges is \\doorhingeflawrate{}\\%,\n  while the alternative is that it the rate\n  is different than \\doorhingeflawrate{}\\%.\n  This decision is just one of many that have a marginal\n  impact on the car and company.\n  A significance level of 0.05 seems reasonable since\n  neither a Type~1 or Type~2 Error should be dangerous\n  or (relatively) much more expensive.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{The same car manufacturer is considering\n    a slightly more expensive supplier for parts related\n    to safety, not door hinges.\n    If the durability of these\n    safety components is shown to be better than the\n    current supplier, they will switch manufacturers.\n    Is there good reason to modify the significance level\n    in such an evaluation?}\n  The null hypothesis would be that the suppliers' parts\n  are equally reliable. Because safety is involved,\n  the car company should be eager to switch to the slightly\n  more expensive manufacturer (reject $H_0$), even if the\n  evidence of increased safety is only moderately strong.\n  A slightly larger significance level,\n  such as $\\alpha=0.10$, might be appropriate.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nA part inside of a machine is very expensive to replace.\nHowever, the machine usually functions properly even if\nthis part is broken, so the part is replaced only if we\nare extremely certain it is broken based on a series of\nmeasurements.\nIdentify appropriate hypotheses for this test\n(in plain language) and suggest an appropriate significance\nlevel.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Here\n  the null hypothesis is that the part is not broken,\n  and the alternative is that it is broken.\n  If we don't have sufficient evidence to reject $H_0$,\n  we would not replace the part.\n  It sounds like failing to fix the part if it is broken\n  ($H_0$ false, $H_A$ true) is not very problematic,\n  and replacing the part is expensive.\n  Thus, we should require very strong evidence against\n  $H_0$ before we replace the part.\n  Choose a small significance level, such as $\\alpha=0.01$.}\n\n\\begin{onebox}{Why is 0.05 the default?}\n  The $\\alpha = 0.05$ threshold is most common. But why?\n  Maybe the standard level should be smaller, or perhaps larger.\n  If you're a little puzzled, you're reading with an\n  extra critical eye -- good job!\n  We've made a 5-minute task to help clarify \\emph{why 0.05}:\n  \\begin{center}\n  \\oiRedirect{textbook-why05}{www.openintro.org/why05}\n  \\end{center}\n\\end{onebox}\n\n\n\\index{significance level|)}\n\\index{hypothesis testing!significance level|)}\n\\index{hypothesis testing|)}\n\n\n\\subsection{Statistical significance versus practical significance}\n\nWhen the sample size becomes larger,\npoint estimates become more precise and any real differences\nin the mean and null value become easier to detect and recognize.\nEven a very small difference would likely be detected if we took\na large enough sample.\nSometimes researchers will take such large samples that even\nthe slightest difference is detected, even differences where\nthere is no practical value.\nIn such cases, we still say the difference is\n\\term{statistically significant},\nbut it is not \\term{practically significant}.\nFor example, an online experiment might identify that placing\nadditional ads on a movie review website statistically\nsignificantly increases viewership of a TV show by 0.001\\%,\nbut this increase might not have any practical value.\n\n%Statistically significant differences are sometimes\n%so minor that they are not practically relevant.\n%This is especially important to research:\n%if we conduct a study, we want to focus on finding\n%a meaningful result.\n%We don't want to spend lots of money finding results\n%that hold no practical value.\n\nOne role of a data scientist in conducting a study often\nincludes planning the size of the study.\nThe data scientist might first consult experts or scientific\nliterature to learn what would be the smallest meaningful\ndifference from the null value.\nShe also would obtain other information,\nsuch as a very rough estimate of the true proportion $p$,\nso that she could roughly estimate the standard error.\nFrom here, she can suggest a sample size that is sufficiently\nlarge that, if there is a real difference that is meaningful,\nwe could detect it.\nWhile larger sample sizes may still be used,\nthese calculations are especially helpful when considering\ncosts or potential risks, such as possible health impacts\nto volunteers in a medical study.\n\n\n\\D{\\newpage}\n\n\\subsection{One-sided hypothesis tests (special topic)}\n\nSo far we've only considered what are called \\term{two-sided\nhypothesis tests}, where we care about detecting whether $p$\nis either above or below some null value $p_0$.\nThere is a second type of hypothesis test called a\n\\term{one-sided hypothesis test}.\nFor a one-sided hypothesis test,\nthe hypotheses take one of the following forms:\n\\begin{enumerate}\n\\item There's only value in detecting if the population\n    parameter is \\emph{less than} some value~$p_0$.\n    In~this case, the alternative hypothesis is written\n    as $p < p_0$ for some null value $p_0$.\n\\item There's only value in detecting if the population\n    parameter is \\emph{more than} some value~$p_0$:\n    In~this case, the alternative hypothesis is written\n    as $p > p_0$.\n\\end{enumerate}\nWhile we adjust the form of the alternative hypothesis,\nwe continue to write the null hypothesis using an equals-sign\nin the one-sided hypothesis test case.\n\nIn the entire hypothesis testing procedure,\nthere is only one difference in evaluating a one-sided\nhypothesis test vs a two-sided hypothesis test:\nhow to compute the p-value.\nIn a one-sided hypothesis test, we compute the p-value as\nthe tail area in the \\emph{direction of the alternative\nhypothesis only}, meaning it is represented by a single\ntail area. Herein lies the reason why one-sided tests\nare sometimes interesting: if we don't have to double\nthe tail area to get the p-value, then the p-value is\nsmaller and the level of evidence required to identify\nan interesting finding in the direction of the\nalternative hypothesis goes down.\nHowever, one-sided tests aren't all sunshine and rainbows:\nthe heavy price paid is that any interesting findings\nin the opposite direction must be disregarded.\n\n\\begin{examplewrap}\n\\begin{nexample}{\n    In Section~\\ref{basicExampleOfStentsAndStrokes},\n    we encountered an example where doctors were interested\n    in determining whether stents would help people who had\n    a high risk of stroke.\n    The researchers believed the stents would help.\n    Unfortunately, the data showed the opposite:\n    patients who received stents actually did worse.\n    Why was using a two-sided test so important in\n    this context?}\n    \\label{basicExampleOfStentsAndStrokesOneSided}\n  Before the study, researchers had reason to believe\n  that stents would help patients since existing research\n  suggested stents helped in patients with heart attacks.\n  It would surely have been tempting to use a one-sided\n  test in this situation, and had they done this,\n  they would have limited their ability to identify\n  potential harm to patients.\n\\end{nexample}\n\\end{examplewrap}\n\nExample~\\ref{basicExampleOfStentsAndStrokesOneSided}\nhighlights that using a one-sided hypothesis creates\na risk of overlooking data supporting the opposite\nconclusion.\nWe could have made a similar error when reviewing\nthe Roslings' question data this section;\nif we had a pre-conceived notion that\ncollege-educated people wouldn't do worse than random\nguessing and so used a one-sided test,\nwe would have missed the really interesting finding\nthat many people have incorrect knowledge about\nglobal public health.\n%Here are a few other situations where it has been,\n%or would have been, very useful to have an open mind\n%and consider the contrarian view:\n%\\begin{itemize}\n%\\item The 2008 financial crisis. There were warning signs,\n%    but few people recognized them.\n%    In fact, some financial firms essentially bought into\n%    the notion that housing prices could only rise, not fall.\n%\\item \n%    \n%\\end{itemize}\n\nWhen might a one-sided test be appropriate to use?\n\\emph{Very rarely.}\nShould you ever find yourself considering using a\none-sided test, carefully answer the following question:\n\\begin{quote}{\\em\n  What would I, or others, conclude if the data happens\n  to go clearly in the opposite direction than my\n  alternative hypothesis?\n}\\end{quote}\nIf you or others would find any value in making\na conclusion about the data that goes in the opposite\ndirection of a one-sided test, then a two-sided hypothesis\ntest should actually be used.\nThese considerations can be subtle, so exercise caution.\nWe will only apply two-sided tests in the rest of\nthis book.\n\n\\begin{examplewrap}\n\\begin{nexample}{\n    Why can't we simply run a one-sided\n    test that goes in the direction of the data?}\n  We've been building a careful framework that\n  controls for the Type~1 Error, which is the\n  significance level $\\alpha$ in a hypothesis test.\n  We'll use the $\\alpha = 0.05$ below to keep\n  things simple.\n\n  Imagine we could pick the one-sided test after\n  we saw the data. What will go wrong?\n  \\begin{itemize}\n  \\item If $\\hat{p}$ is \\emph{smaller} than\n      the null value,\n      then a one-sided test where $p < p_0$ would\n      mean that any observation in the\n      \\emph{lower} 5\\% tail of the null distribution\n      would lead to us rejecting $H_0$.\n  \\item If $\\hat{p}$ is \\emph{larger} than\n      the null value,\n      then a one-sided test where $p > p_0$ would\n      mean that any observation in the\n      \\emph{upper} 5\\% tail of the null distribution\n      would lead to us rejecting $H_0$.\n  \\end{itemize}\n  Then if $H_0$ were true, there's a 10\\% chance of\n  being in one of the two tails, so our testing error\n  is actually $\\alpha = 0.10$, not 0.05.\n  That is,\n  not being careful about when to use one-sided tests\n  effectively undermines the methods we're working\n  so hard to develop and utilize.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{hypothesis testing|)}\n\n\n{\\input{ch_foundations_for_inf/TeX/hypothesis_testing.tex}}\n"
  },
  {
    "path": "ch_foundations_for_inf/TeX/confidence_intervals.tex",
    "content": "\\exercisesheader{}\n\n% 7\n\n\\eoce{\\qt{Chronic illness, Part I\\label{chronic_illness_intro}} \nIn 2013, the Pew Research Foundation reported that ``45\\% of U.S. adults report \nthat they live with one or more chronic conditions''.\n\\footfullcite{data:pewdiagnosis:2013} However, this value was based on a sample, \nso it may not be a perfect estimate for the population parameter of interest on \nits own. The study reported a standard error of about 1.2\\%, and a normal model \nmay reasonably be used in this setting. Create a 95\\% confidence interval for \nthe proportion of U.S. adults who live with one or more chronic conditions. Also \ninterpret the confidence interval in the context of the study.\n}{}\n\n% 8\n\n\\eoce{\\qt{Twitter users and news, Part I\\label{twitter_users_intro}} \nA poll conducted in 2013 found that 52\\% of U.S. adult Twitter users \nget at least some news on Twitter.\\footfullcite{data:pewtwitternews:2013}. \nThe standard error for this estimate was 2.4\\%, and a normal distribution \nmay be used to model the sample proportion. Construct a 99\\% confidence \ninterval for the fraction of U.S. adult Twitter users who get some \nnews on Twitter, and interpret the confidence interval in context.\n}{}\n\n% 9\n\n\\eoce{\\qt{Chronic illness, Part II\\label{chronic_illness_tf}} In 2013, the Pew Research Foundation reported that \n``45\\% of U.S. adults report that they live with one or more chronic \nconditions'', and the standard error for this estimate is 1.2\\%. Identify each \nof the following statements as true or false. Provide an explanation to justify \neach of your answers.\n\\begin{parts}\n\\item We can say with certainty that the confidence interval from \nExercise~\\ref{chronic_illness_intro} contains the true percentage of U.S. adults who \nsuffer from a chronic illness.\n\\item If we repeated this study 1,000 times and constructed a 95\\% confidence \ninterval for each study, then approximately 950 of those confidence intervals \nwould contain the true fraction of U.S. adults who suffer from chronic illnesses.\n\\item The poll provides statistically significant evidence (at the \n$\\alpha = 0.05$ level) that the percentage of U.S. adults who suffer from \nchronic illnesses is below 50\\%.\n\\item Since the standard error is 1.2\\%, only 1.2\\% of people in the study \ncommunicated uncertainty about their answer.\n\\end{parts}\n}{}\n\n% 10\n\n\\eoce{\\qt{Twitter users and news, Part II\\label{twitter_users_tf}} A poll conducted in 2013 found that 52\\% of \nU.S. adult Twitter users get at least some news on Twitter, and the standard \nerror for this estimate was 2.4\\%. Identify each of the following statements as \ntrue or false. Provide an explanation to justify each of your answers.\n\\begin{parts}\n\\item The data provide statistically significant evidence that more than half of \nU.S. adult Twitter users get some news through Twitter. Use a significance level \nof $\\alpha = 0.01$.\n(This part uses concepts from Section~\\ref{hypothesisTesting} and will be\ncorrected in a future edition.)\n\\item Since the standard error is 2.4\\%, we can conclude that 97.6\\% of all U.S. \nadult Twitter users were included in the study.\n\\item If we want to reduce the standard error of the estimate, we should collect \nless data.\n\\item If we construct a 90\\% confidence interval for the percentage of U.S. \nadults Twitter users who get some news through Twitter, this confidence interval \nwill be wider than a corresponding 99\\% confidence interval.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 11\n\n\\eoce{\\qt{Waiting at an ER, Part I\\label{er_wait_intro_prop_ok}} A hospital administrator \nhoping to improve wait times decides to estimate the average emergency \nroom waiting time at her hospital. She collects a simple random sample \nof 64 patients and determines the time (in minutes) between when they \nchecked in to the ER until they were first seen by a doctor. A 95\\% \nconfidence interval based on this sample is (128 minutes, 147 minutes), \nwhich is based on the normal model for the mean. Determine whether the \nfollowing statements are true or false, and explain your reasoning.\n\\begin{parts}\n\\item We are 95\\% confident that the average waiting time of these 64 emergency \nroom patients is between 128 and 147 minutes.\n\\item We are 95\\% confident that the average waiting time of all patients at \nthis hospital's emergency room is between 128 and 147 minutes.\n\\item 95\\% of random samples have a sample mean between 128 and 147 minutes.\n\\item A 99\\% confidence interval would be narrower than the 95\\% confidence \ninterval since we need to be more sure of our estimate.\n\\item The margin of error is 9.5 and the sample mean is 137.5.\n\\item In order to decrease the margin of error of a 95\\% confidence interval to \nhalf of what it is now, we would need to double the sample size.\n(Hint: the margin of error for a mean scales in the same way with sample size\nas the margin of error for a proportion.)\n\\end{parts}\n}{}\n\n% 12\n\n\\eoce{\\qt{Mental health\\label{mental_health}}\nThe General Social Survey asked the question:\n``For how many days during the past 30 days was your \nmental health, which includes stress, depression,\nand problems with emotions, not good?\"\nBased on responses from 1,151 US residents,\nthe survey reported a 95\\% confidence interval of\n3.40 to 4.24 days in 2010.\n\\begin{parts}\n\\item\n    Interpret this interval in context of the data.\n\\item\n    What does ``95\\% confident\" mean? Explain in the\n    context of the application.\n\\item\n    Suppose the researchers think a 99\\% confidence level\n    would be more appropriate for this interval.\n    Will this new interval be smaller or wider than the\n    95\\% confidence interval?\n\\item\n    If a new survey were to be done with 500 Americans,\n    do you think the standard error of the estimate be\n    larger, smaller, or about the same.\n\\end{parts}\n}{}\n\n% 13\n\n\\eoce{\\qt{Website registration\\label{website_registration_design_prop}}\nA website is trying to increase registration for first-time visitors,\nexposing 1\\% of these visitors to a new site design.\nOf 752 randomly sampled visitors over a month who saw the\nnew design, 64 registered.\n\\begin{parts}\n\\item\n    Check any conditions required for constructing a confidence\n    interval.\n\\item\n    Compute the standard error.\n\\item\n    Construct and interpret a 90\\% confidence interval for the\n    fraction of first-time visitors of the site who would register\n    under the new design\n    (assuming stable behaviors by new visitors over time).\n\\end{parts}\n}{}\n\n% 14\n\n\\eoce{\\qt{Coupons driving visits\\label{store_coupon_prop}}\nA store randomly samples 603 shoppers over the course of a year\nand finds that 142 of them made their visit because of a coupon\nthey'd received in the mail.\nConstruct a 95\\% confidence interval for the fraction of all shoppers\nduring the year whose visit was because of a coupon they'd received\nin the mail.\n}{}\n"
  },
  {
    "path": "ch_foundations_for_inf/TeX/hypothesis_testing.tex",
    "content": "\\exercisesheader{}\n\n% 15\n\n\\eoce{\\qt{Identify hypotheses, Part I\\label{\n}}\nWrite the null and alternative hypotheses in words and then symbols\nfor each of the following  situations.\n\\begin{parts}\n\\item\n    A tutoring company would like to understand if most\n    students tend to improve their grades (or not) after\n    they use their services.\n    They sample 200 of the students who used their service\n    in the past year and ask them if their grades have\n    improved or declined from the previous year.\n\\item\n    Employers at a firm are worried about the effect of March Madness,\n    a basketball championship held each spring in the US, on employee\n    productivity.\n    They estimate that on a regular business day employees spend on\n    average 15 minutes of company time checking personal email,\n    making personal phone calls, etc.\n    They also collect data on how much company time employees spend\n    on such non-business activities during March Madness.\n    They want to determine if these data provide convincing evidence\n    that employee productivity changed during March Madness.\n\\end{parts}\n}{}\n\n% 16\n\n\\eoce{\\qt{Identify hypotheses, Part II\\label{identify_hypotheses_prop_and_mean_2}} \nWrite the null and alternative hypotheses in words and using symbols \nfor each of the following situations.\n\\begin{parts}\n\\item\n    Since 2008, chain restaurants in California have been required\n    to display calorie counts of each menu item. Prior to menus\n    displaying calorie counts, the average calorie intake of diners\n    at a restaurant was 1100 calories.\n    After calorie counts started to be displayed on menus,\n    a nutritionist collected data on the number of calories consumed\n    at this restaurant from a random sample of diners.\n    Do these data provide convincing evidence of a difference in the\n    average calorie intake of a diners at this restaurant?\n\\item\n    The state of Wisconsin would like to understand\n    the fraction of its adult residents that consumed alcohol\n    in the last year,\n    specifically if the rate is different from the\n    national rate of 70\\%.\n    To help them answer this question, they conduct\n    a random sample of 852 residents and ask them\n    about their alcohol consumption.\n\\end{parts}\n}{}\n\n% 17\n\n\\eoce{\\qt{Online communication\\label{online_communication_prop_ht_errors}}\nA study suggests that 60\\% of college student spend\n10~or more hours per week communicating with others online.\nYou believe that this is incorrect and decide to collect your \nown sample for a hypothesis test.\nYou randomly sample 160 students from your dorm\nand find that 70\\% spent 10~or more hours a week\ncommunicating with others online.\nA~friend of yours, who offers to help you with\nthe hypothesis test, comes up with the following\nset of hypotheses.\nIndicate any errors you see.\n\\begin{align*}\nH_0&: \\hat{p} < 0.6 \\\\\nH_A&: \\hat{p} > 0.7\n\\end{align*}\n}{}\n\n% 18\n\n\\eoce{\\qt{Married at 25\\label{married_at_25_prop_ht_errors}}\nA study suggests that the 25\\% of 25 year olds have\ngotten married.\nYou believe that this is incorrect and decide to collect\nyour own sample for a hypothesis test.\nFrom a random sample of 25 year olds in census data\nwith size 776,\nyou find that 24\\% of them are married.\nA friend of yours offers to help you with setting\nup the hypothesis test and comes up with the following\nhypotheses.\nIndicate any errors you see.\n\\begin{align*}\nH_0&: \\hat{p} = 0.24 \\\\\nH_A&: \\hat{p} \\neq 0.24\n\\end{align*}\n}{}\n\n% 19\n\n\\eoce{\\qt{Cyberbullying rates\\label{cyberbullying_prop_ci_ht}}\nTeens were surveyed about cyberbullying, and\n54\\% to 64\\% reported experiencing cyberbullying\n(95\\% confidence interval).\\footfullcite{pew_cyber_bully_2018}\nAnswer the following questions based on this interval.\n\\begin{parts}\n\\item \n    A newspaper claims that a majority of teens\n    have experienced cyberbullying.\n    Is this claim supported by the confidence interval?\n    Explain your reasoning.\n\\item\\label{cyberbullying_prop_ci_ht_researcher}\n    A researcher conjectured that 70\\% of teens have\n    experienced cyberbullying.\n    Is this claim supported by the confidence interval?\n    Explain your reasoning.\n\\item\n    Without actually calculating the interval, determine\n    if the claim of the researcher from\n    part~(\\ref{cyberbullying_prop_ci_ht_researcher})\n    would be supported based on a 90\\% confidence interval?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 20\n\n\\eoce{\\qt{Waiting at an ER, Part II\\label{er_wait_ci_ht_prop_ok}}\nExercise~\\ref{er_wait_intro_prop_ok} \nprovides a 95\\% confidence interval for the mean waiting\ntime at an emergency room (ER) of (128 minutes, 147 minutes).\nAnswer the following questions based on this interval.\n\\begin{parts}\n\\item\n    A local newspaper claims that the average waiting time\n    at this ER exceeds 3 hours.\n    Is this claim supported by the confidence interval?\n    Explain your reasoning.\n\\item\\label{er_wait_ci_ht_prop_ok_dean}\n    The Dean of Medicine at this hospital claims the\n    average wait time is 2.2 hours.\n    Is this claim supported by the confidence interval?\n    Explain your reasoning.\n\\item\n    Without actually calculating the interval,\n    determine if the claim of the Dean from\n    part~(\\ref{er_wait_ci_ht_prop_ok_dean})\n    would be supported based on a 99\\% confidence interval?\n\\end{parts}\n}{}\n\n% 21\n\n\\eoce{\\qt{Minimum wage, Part I\\label{minimum_wage_prop_1}}\nDo a majority of US adults believe raising\nthe minimum wage will help the economy,\nor is there a majority who do not believe this?\nA~Rasmussen Reports survey of a random sample of 1,000 US adults found\nthat 42\\% believe it will help the\neconomy.\\footfullcite{webpage:rasmussen-2019-raise-minimum-wage}\nConduct an appropriate hypothesis test to help\nanswer the research question.\n}{}\n\n% 22\n\n\\eoce{\\qt{Getting enough sleep\\label{univ_students_enough_sleep}}\n400 students were randomly sampled from a large university,\nand 289 said they did not get enough sleep.\nConduct a hypothesis test to check whether this\nrepresents a statistically significant difference\nfrom 50\\%, and use a significance level of 0.01.\n}{}\n\n% 23\n\n\\eoce{\\qt{Working backwards, Part I\\label{backwards_prop_1}}\nYou are given the following hypotheses:\n\\begin{align*}\nH_0&: p = 0.3 \\\\\nH_A&: p \\ne 0.3\n\\end{align*}\nWe know the sample size is 90.\nFor what sample proportion would the p-value be equal to 0.05?\nAssume that all conditions  necessary for inference are satisfied.\n}{}\n\n% 24\n\n\\eoce{\\qt{Working backwards, Part II\\label{backwards_prop_2}}\nYou are given the following hypotheses:\n\\begin{align*}\nH_0&: p = 0.9 \\\\\nH_A&: p \\ne 0.9\n\\end{align*}\nWe know that the sample size is 1,429.\nFor what sample proportion would the p-value be equal to 0.01?\nAssume that all conditions necessary for inference are satisfied.\n}{}\n\n% 25\n\n\\eoce{\\qt{Testing for Fibromyalgia\\label{errors_fibromyalgia}} A patient named Diana \nwas diagnosed with Fibromyalgia, a long-term syndrome of body pain, and was \nprescribed anti-depressants. Being the skeptic that she is, Diana didn't \ninitially believe that anti-depressants would help her symptoms. However after \na couple months of being on the medication she decides that the \nanti-depressants are working, because she feels like her symptoms are in fact \ngetting better.\n\\begin{parts}\n\\item Write the hypotheses in words for Diana's skeptical position when she \nstarted taking the anti-depressants.\n\\item What is a Type~1 Error in this context?\n\\item What is a Type~2 Error in this context?\n\\end{parts}\n}{}\n\n% 26\n\n\\eoce{\\qtq{Which is higher\\label{prop_which_higher_found_inf}}\nIn each part below, there is a value of interest and two\nscenarios (I and II).\nFor each part, report if the value of interest is larger\nunder scenario I, scenario II, or whether the value is\nequal under the scenarios.\n\\begin{parts}\n\\item\n     The standard error of $\\hat{p}$ when\n     (I)~$n = 125$ or (II)~$n = 500$.\n\\item\n    The margin of error of a confidence interval\n    when the confidence level is\n    (I)~90\\% or (II)~80\\%.\n\\item\n    The p-value for a Z-statistic of 2.5 calculated\n    based on a (I)~sample with $n = 500$ or based on\n    a (II)~sample with $n = 1000$.\n\\item\n    The probability of making a Type~2 Error when the\n    alternative hypothesis is true and the significance\n    level is (I)~0.05 or (II)~0.10.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_foundations_for_inf/TeX/one_sided_tests.tex",
    "content": "\\subsection{One-sided hypothesis tests (special topic)}\n\n\\Comment{This section needs a lot of work. Maybe it shouldn't\n    even be mentioned? It absolutely should not be so aggressive\n    and also much shorter.}\n\n\\emph{One-sided hypothesis testing is an advanced\n    topic due to the nuances around using this method.\n    You need only read this section if you are ever asked\n    to complete a \\term{one-sided hypothesis test}.} \\\\\n\nSo far we've only considered what are called \\term{two-sided\nhypothesis tests}, where we care about detecting whether $p$\nis either above or below some null value $p_0$.\nThere is a second type of hypothesis test called a\n\\term{one-sided hypothesis test}.\nFor a one-sided hypothesis test,\nthe hypotheses take the form of one of the following:\n\\begin{enumerate}\n\\item If we truly only care about detecting if the population\n    parameter were \\emph{less than} some value~$p_0$:\n  \\begin{description}\n  \\item[$\\mathbf{H_0}$:] $p = p_0$.\n  \\item[$\\mathbf{H_A}$:] $p < p_0$. The parameter $p$ is less\n      than the null value $p_0$.\n  \\end{description}\n\\item If we truly only care about detecting if the population\n    parameter were \\emph{more than} some value~$p_0$:\n  \\begin{description}\n  \\item[$\\mathbf{H_0}$:] $p = p_0$.\n  \\item[$\\mathbf{H_A}$:] $p > p_0$. The parameter $p$ is more\n      than the null value $p_0$.\n  \\end{description}\n\\end{enumerate}\nNotice that we still write the null hypothesis using an\nequality in the one-sided hypothesis test case.\n\nWhile this one-sided test approach is common in many\nintroductory statistics textbooks, these tests create\nsome philosophical problems that we lightly touch on\nhere. In some instances, such as in clinical trials\nwhere we might test out whether a new drug is effective,\none-sided tests are banned.\n\n\\begin{example}{Suppose we're on a business team that is\n    considering whether to go into a new market. If they\n    more than 20\\% of the buyers would be interested in\n    their product, they will move into that market. If not,\n    they will not enter the market. Set up an appropriate\n    one-sided hypothesis test for this situation.}\n  We care about determining whether there is convincing\n  evidence that the population proportion $p$ is greater\n  than 20\\%, so we make this our alternative hypothesis\n  and use equality for the null:\n  \\begin{description}\n  \\item[$\\mathbf{H_0}$:] $p = 0.20$\n  \\item[$\\mathbf{H_A}$:] $p > 0.20$\n  \\end{description}\n\\end{example}\n\n\\begin{example}{The business runs a survey of a simple\n    random sample of 400 people in the market of interest,\n    and 21\\% of the people express interest in the\n    business' product. Will (should) the business decide\n    to enter the market?}\n    \\label{business_one_sided_20_21}\n  There is only one difference in evaluating a one-sided\n  hypothesis test vs a two-sided hypothesis test: how to\n  compute the p-value.\n  In a one-sided hypothesis test, we compute the p-value as\n  the tail area in the \\emph{direction of the alternative\n  hypothesis}. In this example, here we only care about\n  detecting whether $p$ is greater than 20\\%, so we compute\n  the upper tail area and use this as the p-value.\n  \\begin{description}\n  \\item[Conditions.] The data come from a simple random sample\n      and the success failure condition is satisfied\n      ($n \\times p_0 = 80$ and $n \\times (1 - p_0) = 320$).\n  \\item[Compute.] Compute the standard error using the null\n      value: $SE_{\\hat{p}} = \\sqrt{0.2 (1 - 0.8) / 400} = 0.01$.\n      Next compute\n      $Z = \\frac{\\hat{p} - p_0}{SE_{\\hat{p}}}\n         = \\frac{0.21 - 0.20}{0.01}\n         = 1.00$.\n      Finally, compute the tail area where $p > 0.20$,\n      we consider the upper tail:\n      \\begin{center}\n      \\includegraphics[width=0.3\\textwidth]{ch_inference_for_props/figures/business_one_sided_20_21-p_value/business_one_sided_20_21-p_value}\n      \\end{center}\n      We can find the p-value from software or using the\n      normal probability table: 0.1587.\n  \\item[Conclude.] Since the p-value is greater than 0.05,\n      we do not find convincing evidence that the fraction\n      of the market that's interested in the company's\n      product is greater than 20\\%. In this case, the\n      company would not enter the market.\n  \\end{description}\n\\end{example}\n\nThere's a piece of human behavior that we left off in\nExample~\\ref{business_one_sided_20_21}: the company\nwould not have entered the market \\emph{yet}. One-sided\nhypothesis tests work well in isolation. However,\nthey also define not just a decision but what we can\n\\emph{learn} from the data, if we are to be thoughtful\nabout our analysis. %controlling Type~1 Errors.\nIn the next example, we consider the hypothetical\nsituation where the survey data came back with\na much smaller percent.\n\n%The one-sided hypothesis test presented in\n%Example~\\ref{business_one_sided_20_21}\n%didn't throw up any surprises when it comes to\n%questioning whether a one-sided test was reasonable\n%\n%In Example~\\ref{business_one_sided_20_21},\n%we considered some data and evaluated the\n%one-sided test. However, the examples that follow will dive\n%into the logic and philosophy behind one-sided tests and\n%whether we can be robotic enough to apply them properly.\n\n\\begin{example}{Suppose the survey had actually come back\n    with a result that only 7\\% of the 400 people were\n    interested in their product. In this case, the Z-score\n    would have been $Z = -13$. This corresponds to a lower\n    tail area of very nearly~0 and an upper tail are of\n    very nearly~1.\n    How would we correctly interpret this finding when using\n    the one-sided alternative hypothesis that $p > 0.20$?}\n    \\label{business_one_sided_20_7}\n  In this one-sided analysis, the p-value would be larger\n  than 0.05, and we would simply conclude that we do not\n  have strong evidence that the true proportion is greater\n  than 20\\%.\n  \n  This is the only conclusion we can make. Our p-value\n  doesn't say \\emph{anything} about that the result went\n  in completely the opposite direction.\n\\end{example}\n\n\\begin{example}{Suppose the company board saw the\n    hypothetical survey results from\n    Example~\\ref{business_one_sided_20_7}\n    where the survey findings were that only 7\\% of\n    the 400 surveyed people were interested in the product.\n    How do you think they would interpret those results?}\n    \\label{business_one_sided_20_7-exec_interpretation}\n  The board is probably going to feel comfortable with\n  their decision to not enter the market, as they should\n  since the p-value is large. However, they may now believe\n  the actual proportion to be notably \\emph{less than}\n  20\\%. Unfortunately this is not a valid statistical\n  conclusion if we are using a one-sided test: we should\n  not attempt to describe or infer the magnitude of the\n  difference in the opposite direction of a one-sided\n  $H_A$ since this means we are actually running a\n  two-sided test.\n\\end{example}\n\n\\emph{You can't have your cake and eat it, too.}\nUsing a one-sided test to get a slightly smaller p-value\n\\emph{if} the data goes in the direction of interest means\nwe cannot later change our minds and make an assertive\nconclusion in the opposite direction.\nOur natural human tendencies to learn from data and use that\nknowledge in the future will generally undermine the validity\nof a one-sided hypothesis test. That is, unless there is\nan astoundingly good reason and special situation,\nonly use two-sided tests.\nWe will not present any additional one-sided scenarios\nin this textbook due to the problems we've outlined here,\nand because we haven't been able to outline a situation\nwhere this arose.\n%even in our\n%contrived example where we attempted to set up a situation\n%where a one-sided test would be appropriate, we've\n%stumbled into a reason why it would actually \\emph{not}\n%be appropriate.\n\n\\begin{termBox}{\\tBoxTitle{The risk of flipping\n    a one-sided test to a two-sided test inflates\n    the Type~1 Error}\n  We've been working very hard to build a rigorous\n  system for analyzing data. If we introduce the risk\n  of flip-flopping into that system, we undermine the\n  the principles we're using in statistics.}\n\\end{termBox}\n\n\\begin{example}{\n    In Section~\\ref{basicExampleOfStentsAndStrokes},\n    we encountered an example where doctors were interested\n    in determining whether stents would help people were at\n    a high risk of stroke.\n    The researchers believed the stents would help.\n    Unfortunately, they did not, and the study found strong\n    evidence that patients who received stents actually did\n    worse.\n    Why was using a two-sided test so important in\n    this context?}\n  Before the study, researchers strongly believed that stents\n  would, at worst, help patients. Had they used a one-sided\n  test, they couldn't have legitimately identified the strong\n  evidence that the stents were in fact \\emph{harming} the\n  types of patients they considered. Without being able to\n  recognize and acknowledge that there was likely harm to\n  the patients, these doctors (or other doctors) might have\n  instead tried to complete a larger study to try to find\n  evidence that stents help -- and in the process, they would\n  put patients in harm's way.\n\\end{example}\n\n\n"
  },
  {
    "path": "ch_foundations_for_inf/TeX/review_exercises.tex",
    "content": "\\reviewexercisesheader{}\n\n% 27\n\n\\eoce{\\qt{Relaxing after work\\label{relax_after_work}} The General Social Survey asked the question:\n``After an average work day, about how many hours do you have to relax or pursue \nactivities that you enjoy?\" to a random sample of 1,155 Americans.\\footfullcite{data:gss} A 95\\% confidence interval for the mean number of hours spent \nrelaxing or pursuing activities they enjoy was (1.38, 1.92).\n\\begin{parts}\n\\item Interpret this interval in context of the data.\n\\item Suppose another set of researchers reported a confidence interval with a \nlarger margin of error based on the same sample of 1,155 Americans. How does \ntheir confidence level compare to the confidence level of the interval stated \nabove?\n\\item Suppose next year a new survey asking the same question is conducted, and \nthis time the sample size is 2,500. Assuming that the population \ncharacteristics, with respect to how much time people spend relaxing after work, \nhave not changed much within a year. How will the margin of error of the 95\\% \nconfidence interval constructed based on data from the new survey compare to the \nmargin of error of the interval stated above?\n\\end{parts}\n}{}\n\n% 28\n\n\\eoce{\\qt{Minimum wage, Part II\\label{minimum_wage_prop_2}}\nIn Exercise~\\ref{minimum_wage_prop_1},\nwe learned that a Rasmussen Reports survey\nof 1,000 US adults found that 42\\% believe\nraising the minimum wage will help the economy.\nConstruct a 99\\% confidence interval for the\ntrue proportion of US adults who believe this.\n}{}\n\n% 29\n\n\\eoce{\\qt{Testing for food safety\\label{errors_food_safety}} A food safety inspector \nis called upon to investigate a restaurant with a few customer reports of poor \nsanitation practices. The food safety inspector uses a hypothesis testing \nframework to evaluate whether regulations are not being met. If he decides \nthe restaurant is in gross violation, its license to serve food will be revoked.\n\\begin{parts}\n\\item Write the hypotheses in words.\n\\item What is a Type~1 Error in this context?\n\\item What is a Type~2 Error in this context?\n\\item Which error is more problematic for the restaurant owner? Why?\n\\item Which error is more problematic for the diners? Why?\n\\item As a diner, would you prefer that the food safety inspector requires \nstrong evidence or very strong evidence of health concerns before revoking a \nrestaurant's license? Explain your reasoning.\n\\end{parts}\n}{}\n\n% 30\n\n\\eoce{\\qt{True or false\\label{tf_found_inf_prop_friendly}}\nDetermine if the following statements are true or false, and \nexplain your reasoning. If false, state how it could be corrected.\n\\begin{parts}\n\\item If a given value (for example, the null hypothesized value of a parameter) \nis within a 95\\% confidence interval, it will also be within a 99\\% confidence \ninterval.\n\\item Decreasing the significance level ($\\alpha$) will increase the probability \nof making a Type~1 Error.\n\\item Suppose the null hypothesis is $p = 0.5$ and we fail to reject $H_0$. \nUnder this scenario, the true population proportion is 0.5.\n\\item With large sample sizes, even small differences between the null value and \nthe observed point estimate, a difference often called the\neffect size\\index{effect size}, will be identified as statistically significant.\n\\end{parts}\n}{}\n\n% 31\n\n\\eoce{\\qt{Unemployment and relationship problems\\label{unemployment_relationship}} \nA USA Today/Gallup poll asked a group of\nunemployed and underemployed Americans if they have\nhad major problems in their  relationships with their\nspouse or another close family member as a result of\nnot having a job (if unemployed) or not having\na full-time job (if underemployed).\n27\\%~of the 1,145 unemployed respondents and\n25\\%~of the 675 underemployed respondents said they had\nmajor problems in relationships as a  result of their\nemployment status.\n\\begin{parts}\n\\item\n    What are the hypotheses for evaluating if the proportions\n    of unemployed and underemployed people who had relationship\n    problems were different?\n\\item\n    The p-value for this hypothesis test is approximately 0.35.\n    Explain what this means in context of the hypothesis test\n    and the data.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 32\n\n\\eoce{\\qt{Nearsighted\\label{nearsighted_updated}}\nIt is believed that nearsightedness affects about 8\\% of \nall children.\nIn a random sample of 194 children, 21 are nearsighted.\nConduct a hypothesis test for the following question:\ndo these data provide evidence that the 8\\% value is inaccurate?\n}{}\n\n% 33\n\n\\eoce{\\qt{Nutrition labels\\label{nutrition_labels}}\nThe nutrition label on a bag of potato chips says\nthat a one ounce (28~gram) serving of potato chips\nhas 130 calories and contains ten grams of fat,\nwith three grams of saturated fat.\nA~random sample of 35 bags yielded\na confidence interval for the number of calories\nper bag of 128.2 to 139.8 calories.\nIs there evidence that the nutrition label does not \nprovide an accurate measure of calories in the bags\nof potato chips?\n}{}\n\n% 34\n\n\\eoce{\\qt{CLT for proportions\\label{CLT_prop}}\nDefine the term ``sampling distribution\" of the sample proportion,\nand describe how the shape, center, and spread of the sampling\ndistribution change as the sample size increases when $p = 0.1$.\n}{}\n\n% 35\n\n\\eoce{\\qt{Practical vs. statistical significance\\label{prac_stat_sig}}\nDetermine whether the following statement is true\nor false, and explain your reasoning:\n``With large sample sizes, even small differences\nbetween the null value and the observed point\nestimate can be statistically significant.''\n}{}\n\n% 36\n\n\\eoce{\\qt{Same observation, different sample size\\label{same_obs_diff_n}} Suppose you \nconduct a hypothesis test based on a sample where the sample size is $n = 50$, \nand arrive at a p-value of 0.08. You then refer back to your notes and discover \nthat you made a careless mistake, the sample size should have been $n = 500$. \nWill your p-value increase, decrease, or stay the same? Explain.\n}{}\n\n% 37\n\n\\eoce{\\qt{Gender pay gap in medicine\\label{gender_pay_gap_medicine}}\nA study examined the average pay for men and women\nentering the workforce as doctors for 21 different\npositions.\\footfullcite{LoSassoMedicineGenderPayGap}\n\\begin{parts}\n\\item\\label{gender_pay_gap_medicine_hypotheses}\n    If each gender was equally paid, then we would expect\n    about half of those positions to have men paid more\n    than women and women would be paid more than men in\n    the other half of positions.\n    Write appropriate hypotheses to test this scenario.\n\\item\n    Men were, on average, paid more in 19 of those\n    21 positions.\n    Supposing these 21 positions represent a simple random sample,\n    complete a hypothesis test using your hypotheses\n    from part~(\\ref{gender_pay_gap_medicine_hypotheses}).\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_foundations_for_inf/TeX/variability_in_estimates.tex",
    "content": "\\exercisesheader{}\n\n% 1\n\n\\eoce{\\qt{Identify the parameter, Part I\\label{identify_parameter_1}} For each of the following situations, state \nwhether the parameter of interest is a mean or a proportion. It may be helpful \nto examine whether individual responses are numerical or categorical.\n\\begin{parts}\n\\item In a survey, one hundred college students are asked how many hours per \nweek they spend on the Internet.\n\\item In a survey, one hundred college students are asked: ``What percentage of \nthe time you spend on the Internet is part of your course work?\"\n\\item In a survey, one hundred college students are asked whether or not they \ncited information from Wikipedia in their papers.\n\\item In a survey, one hundred college students are asked what percentage of \ntheir total weekly spending is on alcoholic beverages.\n\\item In a sample of one hundred recent college graduates, it is found that 85 \npercent expect to get a job within one year of their graduation date.\n\\end{parts}\n}{}\n\n% 2\n\n\\eoce{\\qt{Identify the parameter, Part II\\label{identify_parameter_2}} For each of the \nfollowing situations, state whether the parameter of interest is a mean or a \nproportion. \n\\begin{parts}\n\\item A poll shows that 64\\% of Americans personally worry a great deal about \nfederal spending and the budget deficit.\n\\item A survey reports that local TV news has shown a 17\\% increase in revenue \nwithin a two year period while newspaper revenues decreased by 6.4\\% during this \ntime period.\n\\item In a survey, high school and college students are asked whether or not \nthey use geolocation services on their smart phones.\n\\item In a survey, smart phone users are asked whether or not they use a web-based taxi service.\n\\item In a survey, smart phone users are asked how many times they used a web-based taxi service over the last year.\n\\end{parts}\n}{}\n\n% 3\n\n\\eoce{\\qt{Quality control\\label{comp_chips_quality_ctrl_prop}}\nAs part of a quality control process for computer chips,\nan engineer at a factory randomly samples 212 chips\nduring a week of production to test the current rate of\nchips with severe defects.\nShe finds that 27 of the chips are defective.\n\\begin{parts}\n\\item\n    What population is under consideration in the data set?\n\\item\n    What parameter is being estimated?\n\\item\\label{comp_chips_quality_ctrl_prop_pt_est}%\n    What is the point estimate for the parameter?\n\\item\\label{comp_chips_quality_ctrl_prop_se_name}%\n    What is the name of the statistic we use to measure\n    the uncertainty of the point estimate?\n\\item\\label{comp_chips_quality_ctrl_prop_se_calc_w_pt_est}%\n    Compute the value from\n    part~(\\ref{comp_chips_quality_ctrl_prop_se_name})\n    for this context.\n\\item\n    The historical rate of defects is 10\\%.\n    Should the engineer be surprised by the observed\n    rate of defects during the current week?\n\\item\n    Suppose the true population value was found to be 10\\%.\n    If we use this proportion to recompute the value in\n    part~(\\ref{comp_chips_quality_ctrl_prop_se_calc_w_pt_est})\n    using $p = 0.1$ instead of $\\hat{p}$,\n    does the resulting value change much?\n\\end{parts}\n}{}\n\n% 4\n\n\\eoce{\\qt{Unexpected expense\\label{us_emergency_expense_prop}}\nIn a random sample 765 adults in the United States, 322 say\nthey could not cover a \\$400 unexpected expense without borrowing\nmoney or going into debt.\n% Ref: https://www.federalreserve.gov/publications/files/2017-report-economic-well-being-us-households-201805.pdf\n\\begin{parts}\n\\item\n    What population is under consideration in the data set?\n\\item\n    What parameter is being estimated?\n\\item\\label{us_emergency_expense_prop_pt_est}%\n    What is the point estimate for the parameter?\n\\item\\label{us_emergency_expense_prop_se_name}%\n    What is the name of the statistic we use to measure\n    the uncertainty of the point estimate?\n\\item\\label{us_emergency_expense_prop_se_calc_w_pt_est}%\n    Compute the value from\n    part~(\\ref{us_emergency_expense_prop_se_name})\n    for this context.\n\\item\n    A cable news pundit thinks the value is actually 50\\%.\n    Should she be surprised by the data?\n\\item\n    Suppose the true population value was found to be 40\\%.\n    If we use this proportion to recompute the value in\n    part~(\\ref{us_emergency_expense_prop_se_calc_w_pt_est})\n    using $p = 0.4$ instead of $\\hat{p}$,\n    does the resulting value change much?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 5\n\n\\eoce{\\qt{Repeated water samples\\label{repeated_water_samples_prop}}\nA nonprofit wants to understand the fraction of households that\nhave elevated levels of lead in their drinking water.\nThey expect at least 5\\% of homes will have elevated levels of\nlead, but not more than about 30\\%.\nThey randomly sample 800 homes and work with the owners to retrieve\nwater samples, and they compute the fraction of these homes\nwith elevated lead levels.\nThey repeat this 1,000 times and build a distribution\nof sample proportions.\n\\begin{parts}\n\\item\n    What is this distribution called?\n\\item\n    Would you expect the shape of this distribution to be\n    symmetric, right skewed, or left skewed?\n    Explain your reasoning.\n\\item\n    If the proportions are distributed around 8\\%,\n    what is the variability of the distribution?\n\\item\n    What is the formal name of the value you computed in~(c)?\n\\item\n    Suppose the researchers' budget is reduced, and they are only\n    able to collect 250 observations per sample, but they can still\n    collect 1,000 samples.\n    They build a new distribution of sample proportions.\n    How will the variability of this new distribution compare\n    to the variability of the distribution when each sample\n    contained 800 observations?\n\\end{parts}\n}{}\n\n% 6\n\n\\eoce{\\qt{Repeated student samples\\label{repeated_student_samples_prop}}\nOf all freshman at a large college, 16\\% made the dean's list\nin the current year.\nAs part of a class project, students randomly sample 40 students\nand check if those students made the list.\nThey repeat this 1,000 times and build a distribution\nof sample proportions.\n\\begin{parts}\n\\item\n    What is this distribution called?\n\\item\n    Would you expect the shape of this distribution to be\n    symmetric, right skewed, or left skewed?\n    Explain your reasoning.\n\\item\n    Calculate the variability of this distribution.\n\\item\n    What is the formal name of the value you computed in~(c)?\n\\item\n    Suppose the students decide to sample again,\n    this time collecting 90 students per sample,\n    and they again collect 1,000 samples.\n    They build a new distribution of sample proportions.\n    How will the variability of this new distribution compare\n    to the variability of the distribution when each sample\n    contained 40 observations?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/95PercentConfidenceInterval/95PercentConfidenceInterval.R",
    "content": "library(openintro)\ndata(COL)\ndata(run10)\nset.seed(52)\n\n# This still references run10, but the actual range of values\n# isn't shown, so just tweaking the printed value.\n\nmyPDF('95PercentConfidenceInterval.pdf', 6, 3.4,\n      mar = c(1.7, 1, 0, 1),\n      mgp = c(2.7, 0.7, 0))\nm <- 94.52\ns <- 16.0\nn <- 100\nk <- 25\nSE <- s/sqrt(n)\n\nset.seed(3)\nmeans <- c()\nSE    <- c()\nfor(i in 1:k){\n  temp <- sample(nrow(run10), n)\n  d    <- run10$time[temp]\n  means[i] <- mean(d, na.rm = TRUE)\n  SE[i]    <- sd(d)/sqrt(n)\n}\nxR <- m + 4 * c(-1, 1) * s / sqrt(n)\nyR <- c(0.7, 25.3)\nplot(xR, yR,\n     type = 'n',\n     xlab = 'run time (minutes)',\n     ylab = '',\n     axes = FALSE)\nabline(v = m, lty = 2, col = COL[5,2])\naxis(1, at = m, \"p = 0.88\")\nfor(i in 1:k){\n  ci <- means[i] + 2 * c(-1, 1) * SE[i]\n  if(abs(means[i] - m) > 1.96 * SE[i]){\n    col <- COL[4]\n    points(means[i], i, cex = 1.4, col = col)\n    lines(ci, rep(i, 2), col = col, lwd = 4)\n  } else {\n    col <- COL[1]\n  }\n  points(means[i], i, pch = 20, cex = 1.2, col = col)\n  lines(ci, rep(i, 2), col = col)\n}\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/ARCHIVE/sampling_10k_prop_56p/sampling_10k_prop_56p.R",
    "content": "set.seed(1)\nlibrary(openintro)\ndata(COL)\n\nn.sim <- 10000\nsamp.size <- 1000\n\nsamples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(0.44, 0.56)), n.sim)\nresults <- apply(samples, 1, mean)\nmean(results)\nsd(results)\n\nmyPDF('sampling_10k_prop_56p.pdf', 6.5, 3.2,\n    mar = c(3.5, 3.5, 0.7, 0.7),\n    mgp = c(2.3, 0.6, 0),\n    yaxs = \"i\")\nhistPlot(results,\n    col = COL[1], breaks = 25,\n    xlab = \"Sample Proportions\",\n    ylab = \"\",\n    axes = FALSE)\nat <- seq(0, 1, 0.05)\naxis(1, at = seq(0, 1, 0.01), labels = rep(\"\", 101))\naxis(1, at = at)\n# axis(2, at = seq(0, 1200, 100), label = rep(\"\", 13))\naxis(2, at = seq(0, 1200, 200))\n# abline(v = 0.56, col = COL[4])\n\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove10WithDF4/chiSquareAreaAbove10WithDF4.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove10WithDF4.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(10,\n              4,\n              c(0, 18),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove11Point7WithDF7/chiSquareAreaAbove11Point7WithDF7.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove11Point7WithDF7.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(11.7,\n              7,\n              c(0, 25),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove4Point3WithDF2/chiSquareAreaAbove4WithDF2.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove4Point3WithDF2.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(4.3,\n              2,\n              c(0, 15),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove5Point1WithDF5/chiSquareAreaAbove5Point1WithDF5.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove5Point1WithDF5.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(5.1,\n              5,\n              c(0, 25),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove6Point25WithDF3/chiSquareAreaAbove6Point25WithDF3.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove6Point25WithDF3.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(6.25,\n              3,\n              c(0, 15),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove9Point21WithDF3/chiSquareAreaAbove9Point21WithDF3.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove9Point21WithDF3.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(9.21,\n              3,\n              c(0, 15),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/bladesTwoSampleHTPValueQC/bladesTwoSampleHTPValueQC.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('bladesTwoSampleHTPValueQC.pdf', 3.04, 1.56,\n      mar = c(2.4, 0, 0.5, 0),\n      mgp = c(3, 0.45, 0))\nnormTail(U = 2.3, col = COL[1], axes = FALSE)\nat <- c(-5, 0, 2.3, 5)\nlabels <- c(0, 0.03, 0.059, 0)\naxis(1, at, labels, cex.axis = 0.9)\npar(mgp = c(5, 1.3, 0))\naxis(1, at = 0, '(null value)', cex.axis = 0.7)\narrows(2.5, 0.19,\n       2.5, 0.05,\n       length = 0.1,\n       col = COL[1])\ntext(2.5, 0.18, \"0.006\",\n     pos = 3,\n     cex = 0.8,\n     col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/business_one_sided_20_21-p_value/business_one_sided_20_21-p_value.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('business_one_sided_20_21-p_value.pdf', 2.15, 0.95,\n      mar = c(1.31, 0, 0.01, 0),\n      mgp = c(3, 0.45, 0))\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\nnormTail(0.20, 0.01, U = 0.21, cex.axis = 0.8, axes = FALSE, col = COL[1])\nat <- c(0.18, 0.20, 0.22)\naxis(1, at, cex.axis = 0.8)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/chiSquareDistributionWithInceasingDF/chiSquareDistributionWithInceasingDF.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareDistributionWithInceasingDF.pdf', 6.5, 3,\n      mar = c(2, 0.5, 0.25, 0.5),\n      mgp = c(2.1, 0.7, 0))\nx <- c(0, seq(0.0000001, 40, 0.05))\nDF <- c(2.0000001, 4, 9)\ny <- list()\nfor (i in 1:length(DF)) {\n  y[[i]] <- dchisq(x, DF[i])\n}\nplot(0, 0,\n     type = 'n',\n     xlim = c(0, 25),\n     ylim = range(c(y, recursive = TRUE)),\n     axes = FALSE)\nfor (i in 1:length(DF)) {\n  lines(x, y[[i]],\n        lty = i,\n        col = COL[ifelse(i == 3, 4, i)],\n        lwd = 1.5 + i / 2)\n}\nabline(h = 0)\naxis(1)\nlegend('topright',\n       lwd = 0.3 + 1:4 / 1.25,\n       col = COL[c(1, 2, 4)],\n       lty = 1:4,\n       legend = paste(round(DF)),\n       title = 'Degrees of Freedom',\n       cex = 1)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/choosingZForCI/choosingZForCI.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('choosingZForCI.pdf', 7.56, 3.84,\n      mar=c(3.3, 1, 0.5, 1),\n      mgp=c(2.1, 0.6, 0))\nnormTail(M = c(-1.96, 1.96),\n         df = 10,\n         col = COL[1],\n         xlim = 3.3 * c(-1, 1),\n         ylim = c(0, 0.59),\n         xlab='Standard Deviations from the Mean')\nX <- rev(seq(-4, 4, 0.025))\nY <- dt(X, 10) # makes better visual\n\nyMax <- 0.41\n\nthese <- (-2.58 < X & X < 2.58)\nx <- c(2.58, X[these], -2.58)\ny <- c(0, dt(X[these], 10), 0)\npolygon(x, y, col=COL[1,3], border='#00000000')\n\nlines(1.96*c(-1,1), rep(yMax,2), lwd=2)\nlines(rep(-1.96,2), c(0,yMax), lty=2, col=COL[6])\nlines(rep( 1.96,2), c(0,yMax), lty=2, col=COL[6])\ntext(0, yMax, '95%, extends -1.96 to 1.96', pos=3)\n\nyMax <- 0.53\nlines(2.58*c(-1,1), rep(yMax,2), lwd=2)\nlines(rep(-2.58,2), c(0,yMax), lty=2, col='#00000055')\nlines(rep( 2.58,2), c(0,yMax), lty=2, col='#00000055')\ntext(0, yMax, '99%, extends -2.58 to 2.58', pos=3)\n\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/clt_prop_grid/clt_prop_grid.R",
    "content": "library(openintro)\ndata(COL)\n\nprops <- c(0, 0.1, 0.2, 0.50, 0.8, 0.9)\nsamp.size.1 <- c(0, 10, 25)\nsamp.size.2 <- c(50, 100, 250)\n\nplot.width <- 7\nplot.height <- 10\n\n\nSetupLayout <- function(show) {\n  myMat <- rbind(matrix(1:18, nrow = 6, ncol = 3, byrow = TRUE))\n  if (show) {\n    myW <- c(0.5, rep(1, 2))\n  } else {\n    myW <- rep(1, 3)\n  }\n  myH <- c(0.5, rep(1, 5))\n  layout(myMat, myW, myH)\n}\n\nPlotSampDist <- function(n, p, main) {\n  par(mar = mar)\n  x <- seq(0, n)\n  y <- dbinom(x, n, p)\n  p.hat <- x / n\n  width <- 0.2 / n\n  plot(p.hat, y, type = \"n\", axes = FALSE,\n      xlab = \"\", ylab = \"\")\n  axis(1)\n  rect(p.hat - width, 0, p.hat + width, y, border = COL[1], col = COL[1])\n  abline(h = 0)\n}\n\nTextPlot <- function(text, cex = 2.5, vertical = FALSE) {\n  plot(0:1, 0:1, axes = FALSE, type = \"n\", xlab = \"\", ylab = \"\")\n  text(0.5, 0.5, text, cex = cex)\n}\n\nBuildGrid <- function(props, samp.size) {\n  for (p in props) {\n    for (n in samp.size) {\n      par(mar = rep(0, 4))\n      if (p == 0 && n == 0) {\n        TextPlot(\"\")\n      } else if (p > 0 && n == 0) {\n        TextPlot(paste(\"p =\", p))\n      } else if (p == 0 && n > 0) {\n        TextPlot(paste(\"n =\", n))\n      } else {\n        PlotSampDist(n, p)\n      }\n    }\n  }\n}\n\n\nmar <- c(3.5, 1.5, 0.7, 1.5)\n\nmyPDF('clt_prop_grid_1.pdf', plot.width, plot.height,\n    mgp = c(2.3, 0.6, 0),\n    yaxs = \"i\",\n    mfrow = c(5, 2))\nSetupLayout(TRUE)\nBuildGrid(props, samp.size.1)\ndev.off()\n\n\nmyPDF('clt_prop_grid_2.pdf', plot.width, plot.height,\n    mar = c(3.5, 3, 0.7, 0.2),\n    mgp = c(2.3, 0.6, 0),\n    yaxs = \"i\",\n    mfrow = c(5, 2))\nSetupLayout(FALSE)\nBuildGrid(props, samp.size.2)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/communityCollegeClaimedHousingExpenseDistribution/communityCollegeClaimedHousingExpenseDistribution.R",
    "content": "library(openintro)\ndata(COL)\n\nx <- student.housing$price\nt.test(x, mu = 650)\nmean(x)\nsd(x)\nlength(x)\n\nmyPDF('communityCollegeClaimedHousingExpenseDistribution.pdf',\n      6.5, 3.4,\n      mar = c(3.2, 3.5, 1, 1),\n      mgp = c(1.9, 0.7, 0))\nhistPlot(x,\n         breaks = 20,\n         xlab = 'Housing Expense (dollars)',\n         ylab = '',\n         col = COL[1],\n         axes = FALSE)\naxis(1, at = seq(400, 1200, 200))\naxis(2, at = seq(0, 30, 5))\nmtext('Freqency', side = 2, line = 2.3, las = 0)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/adult_heights/adult_heights.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(bdims)\n\n# histogram of heights ----------------------------------------------\n\npdf(\"adult_heights_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.5,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(bdims$hgt, col = COL[1], xlab = \"Height\", ylab = \"\")\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/age_at_first_marriage_intro/age_at_first_marriage_intro.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(ageAtMar)\n\n# histogram of age at first marriage --------------------------------\npdf(\"age_at_first_marriage_intro_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.7,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(ageAtMar$age, col = COL[1], xlab = \"Age at first marriage\", ylab = \"\")\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/assisted_reproduction_one_sample_randomization/assisted_reproduction_one_sample_randomization.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# set sample size and number of simulations -------------------------\nn = 25\nN = 10^4\n\n# randomize ---------------------------------------------------------\n\nset.seed(15)\n\np <- 0.31\n\npHat <- rbinom(N, n, p)/n\nM    <- max(pHat)*n\n\npHatObs <- 0.4\n\nsum(pHat >= pHatObs)/N\n\n# plot randomization dist for question ------------------------------\n\npdf(\"assisted_reproduction_one_sample_randomization.pdf\", height = 3, width = 6)\n\npar(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))\n\nhistPlot(pHat, breaks = (-1:(2*M)+0.75)/2/n, \n         xlab = expression(hat(p)[sim]*\"    \"), \n         col = COL[7,3], ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at = (0:3)*N/20, labels=c(\"0\",\"0.05\",\"0.10\",\"0.15\"))\nabline(h = 0)\n\nabline(h = seq(250, 1500, 250), lty = 3, lwd = 2, col = COL[7])\n\ndev.off()\n\n# plot randomization dist for solution ------------------------------\n\npdf(\"assisted_reproduction_one_sample_randomization_soln.pdf\", height = 3, width = 6)\n\npar(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))\n\nhistPlot(pHat, breaks = (-1:(2*M)+0.75)/2/n, \n         xlab = expression(hat(p)[sim]*\"    \"), \n         col = COL[7,3], ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at = (0:3)*N/20, labels=c(\"0\",\"0.05\",\"0.10\",\"0.15\"))\nabline(h = 0)\n\nhistPlot(pHat[pHat >= pHatObs], breaks = (-1:(2*M)+0.75)/2/n, \n         col = COL[1], add = TRUE)\n\nlines(rep(pHatObs, 2), c(0, 3)*N/22, lty=3, lwd=1.7)\ntext(x = pHatObs, y = 3*N/22, as.character(pHatObs), pos=3, cex=1.25)\n\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/cflbs/cflbs.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# inputs ------------------------------------------------------------\n\nm = 9000\ns = 1000\nn = 15\nse = s / sqrt(n)\n\n# plot sketch -------------------------------------------------------\n\npdf(\"cflbs_sketch.pdf\", height = 3, width = 6)\n\npar(mar = c(2,1,1,0), las = 1, mgp = c(3,1,0))\n\n# population\n\nX <- seq((m - 3 * s),(m + 3 * s), 1)\nY <- dnorm(X, m, s)\n\nplot(X, Y, type = 'l', axes = FALSE, \n     xlim = c(min(X), max(X)), ylim = c(0, 0.0015))\n     ylab = \"\", lwd = 2.5)\nlines(X, rep(0, length(X)), lwd = 1.5)\naxis(1, at = seq((m - 3 * s), (m + 3 * s),s), cex.axis = 1.25)\n\n# sampling\n\nX <- seq((m - 5 * se),(m + 5 * se), 1)\nY <- dnorm(X, m, se)\n\nlines(X, Y, type = 'l', lty = 2, lwd = 2.5, col = COL[1])\n\nlegend(\"topright\", c(\"Population\",\"Sampling (n = 15)\"), \n       lty = c(1,2), col = c(\"black\", COL[1]), inset = 0.03, \n       cex = 1.25, lwd = c(2.5,2.5))\n\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/college_credits/college_credits.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(credits)\n\n# histogram of college credits --------------------------------------\n\npdf(\"college_credits_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(credits[,1], col = COL[1], xlab = \"Number of credits\", ylab = \"\")\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/egypt_revolution_one_sample_randomization/egypt_revolution_one_sample_randomization.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# set sample size and number of simulations -------------------------\nn = 20\nN = 10^4\n\n# randomize ---------------------------------------------------------\n\nset.seed(5)\n\npHat <- rbinom(N, n, 0.69)/n\nM    <- max(pHat)*n\n\npHatObs <- 0.57\n\nsum(pHat <= pHatObs)/N\n\n# plot randomization dist for question ------------------------------\n\npdf(\"egypt_revolution_one_sample_randomization.pdf\", height = 3, width = 6)\n\npar(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))\n\nhistPlot(pHat, breaks = (11:(2*M)+0.75)/2/n, \n         xlab = expression(hat(p)[sim]*\"    \"), \n         col = COL[7,3], ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at=(0:3)*N/20, labels=c(\"0\",\"0.05\",\"0.10\",\"0.15\"))\nabline(h = 0)\n\nabline(h = seq(250,1500,250), lty = 3, lwd = 2, col = COL[7])\n\ndev.off()\n\n# plot randomization dist for solution ------------------------------\n\npdf(\"egypt_revolution_one_sample_randomization_soln.pdf\", height = 3, width = 6)\n\npar(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))\n\nhistPlot(pHat, breaks = (11:(2*M)+0.75)/2/n, \n         xlab = expression(hat(p)[sim]*\"    \"), \n         col = COL[7,3], ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at=(0:3)*N/20, labels=c(\"0\",\"0.05\",\"0.10\",\"0.15\"))\nabline(h = 0)\n\nhistPlot(pHat[pHat <= pHatObs], breaks = (-1:(2*M)+0.75)/2/n, \n         col = COL[1], add = TRUE)\n\nlines(rep(pHatObs, 2), c(0, 3)*N/22, lty=3, lwd=1.7)\ntext(x = pHatObs, y = 3*N/22, as.character(pHatObs), pos=3, cex=1.25)\n\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/exclusive_relationships/exclusive_relationships.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(dplyr)\n\n# load data ---------------------------------------------------------\nsurvey <- read.csv(\"survey.csv\")\n\n# sample size -------------------------------------------------------\nn <- survey %>%\n  filter(!is.na(excl_relation)) %>%\n  nrow() # 203\n\n# histogram ---------------------------------------------------------\n\npdf(\"exclusive_relationships_rel_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(survey$excl_relation, col = COL[1], xlab = \"Number of exclusive relationships\", ylab = \"\")\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/exclusive_relationships/survey.csv",
    "content": "\"excl_relation\"\n2\n4\n1\n4\nNA\n2\n2\n2\n1\n4\n2\n4\n2\n7\nNA\n1\nNA\n1\n9\nNA\n4\n1\n2\n4\n2\n1\n5\n1\n9\n1\n2\n1\n4\n4\n1\n8\nNA\n1\n6\n4\n1\n1\n2\n2\n4\n2\n5\n4\n1\n1\n5\n5\n4\n4\n1\n5\n4\n4\n5\n2\n6\n1\n1\n4\n1\n7\n5\n5\n5\n1\n1\n7\n6\n2\nNA\n1\n2\n6\n1\nNA\nNA\n4\n1\n2\n4\n1\n4\nNA\n5\n2\n5\n4\n4\n4\n1\n1\n6\n6\nNA\n2\n2\n2\n5\n4\n2\n7\n1\n2\n5\n4\n1\n4\n6\n1\n4\n4\n1\n7\n5\n5\n7\n2\n5\n4\n1\n8\n5\n6\n1\n2\n2\n1\n1\n4\n2\n4\n1\n1\nNA\n2\n10\n4\n2\n4\n1\n2\n5\n2\n2\n2\n4\n2\n5\n1\n2\n4\n4\n2\n1\n1\n2\n4\nNA\n5\n2\n1\n2\nNA\n6\n4\n2\n2\n4\n4\n4\n4\n4\n4\n5\n4\n1\n5\n4\n4\n5\n4\n4\n3\n4\n4\n2\nNA\n2\n1\n2\n4\n2\n2\n1\n1\n1\nNA\n1\n3\n5\n4\n6\n1\n2\n5\n1\n8\n4\n2\n1\n2\n2\n5\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/gifted_children_ht/gifted_children_ht.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(gifted)\n\n# plot mom's IQ -----------------------------------------------------\npdf(\"gifted_children_ht_momIQ_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(gifted$motheriq, col = COL[1], \n         xlab = \"Mother's IQ\", ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at = c(0,4,8,12))\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/gifted_children_intro/gifted_children_intro.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(gifted)\n\n# plot counts -------------------------------------------------------\npdf(\"gifted_children_ht_count_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(gifted$count, col = COL[1], \n         xlab = \"Age child first counted to 10 (in months)\", ylab = \"\", \n         axes = FALSE)\naxis(1)\naxis(2, at = c(0,3,6))\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/identify_dist_ls_pop/identify_dist_ls_pop.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\nset.seed(85479)\na  = rbeta(1e6, 3.5, 2)\nb = a * 94\n\n# plot population ---------------------------------------------------\nmyPDF(\"identify_dist_ls_pop.pdf\", 4.25, 1.95, mar=c(2.3,0,0,0), \n      mgp=c(2.7,0.5,0), las = 1)\ndensityPlot(b, bw = 1, from = 0, to = 101, col = COL[5], \n            fadingBorder = \"66\", histo = \"faded\", xlab = \"\", axes = FALSE, ylab = \"\")\naxis(1)\ntext(x = 10, y = 0.015, \"Population\")\ntext(x = 10, y = 0.0125, expression(paste(mu, \" = 60\")))\ntext(x = 10, y = 0.01, expression(paste(sigma, \" = 18\")))\ndev.off()\n\n# plot sample -------------------------------------------------------\nset.seed(2452)\nsamp = sample(b, size = 500)\n\nmyPDF(\"identify_dist_ls_samp.pdf\", 3.2, 2, mar=c(3.3,2,0.5,0.5), mgp=c(2.1,0.5,0))\nhist(samp, col = COL[1], xlab = \"Plot B\", ylab = \"\", main = \"\", axes=FALSE)\naxis(1)\naxis(2, at=c(0, 50, 100))\ndev.off()\n\n# plot sampling, n = 5 ----------------------------------------------\n\nset.seed(24524)\n\nsampling_18 = rep(0, 500)\nn = 18\n\nfor(i in 1:500){\n  temp <- sample(b, n)\n  sampling_18[i] <- mean(temp)\n}\n\nmyPDF(\"identify_dist_ls_sampling_n18.pdf\", 3.2, 2, mar=c(3.3,2,0.5,0.5), mgp=c(2.1,0.5,0))\nhist(sampling_18, col = COL[1], xlab = \"Plot C\", ylab = \"\", main = \"\", axes=FALSE)\naxis(1)\naxis(2, at=c(0, 50, 100))\ndev.off()\n\n# plot sampling, n = 81 ---------------------------------------------\n\nset.seed(3563)\nsampling_81 = rep(0, 500)\nn = 81\n\nfor(i in 1:500){\n  temp <- sample(b, n)\n  sampling_81[i] <- mean(temp)\n}\n\nmyPDF(\"identify_dist_ls_sampling_n81.pdf\", 3.2, 2, mar=c(3.3,2,0.5,0.5), mgp=c(2.1,0.5,0))\nhist(sampling_81, col = COL[1], xlab = \"Plot A\", ylab = \"\", main = \"\", axes=FALSE)\naxis(1)\naxis(2, at=c(0, 50, 100))\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/identify_dist_symm_pop/identify_dist_symm_pop.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\nset.seed(85479)\na  = rnorm(1e6, 10, 3)\n\n# plot population ---------------------------------------------------\nmyPDF(\"identify_dist_symm_pop.pdf\", 4.25, 1.95, mar=c(2.3,0,0,0), \n      mgp=c(2.7,0.5,0), las = 1)\ndensityPlot(a, bw = 1/4, from = -2, to = 22, col = COL[5], \n            fadingBorder = \"66\", histo = \"faded\", xlab = \"\", \n            axes = FALSE, ylab = \"\", breaks = 60, xlim=c(0, 20))\naxis(1, at = seq(0,20,5), labels = seq(0,20,5))\ntext(x = 17, y = 0.103, \"Population\")\ntext(x = 17, y = 0.085, expression(paste(mu, \" = 10\")))\ntext(x = 17, y = 0.07, expression(paste(sigma, \" = 3\")))\ndev.off()\n\n# plot sample -------------------------------------------------------\nset.seed(9582)\nsamp = sample(a, size = 100)\n\nmyPDF(\"identify_dist_symm_samp.pdf\", 3.2, 2, mar=c(3.3,2,0.5,0.5), mgp=c(2.1,0.5,0))\nhist(samp, col = COL[1], xlab = \"Plot B\", ylab = \"\", main = \"\", axes=FALSE)\naxis(1)\naxis(2, at=c(0, 10, 20))\ndev.off()\n\n# plot sampling, n = 5 ----------------------------------------------\n\nset.seed(7793)\nsampling_5 = rep(0, 100)\nn = 5\n\nfor(i in 1:100){\n  \ttemp <- sample(a, n)\n   \tsampling_5[i] <- mean(temp)\n   \t}\n\nmyPDF(\"identify_dist_symm_sampling_n5.pdf\", 3.2, 2, mar=c(3.3,2,0.5,0.5), \n      mgp=c(2.1,0.5,0))\nhist(sampling_5, col = COL[1], xlab = \"Plot A\", ylab = \"\", main = \"\", axes=FALSE)\naxis(1)\naxis(2, at=c(0, 10, 20))\ndev.off()\n\n# plot sampling, n = 25 ---------------------------------------------\n\nset.seed(3563)\nsampling_25 = rep(0, 100)\nn = 25\n\nfor(i in 1:100){\n  \ttemp <- sample(a, n)\n   \tsampling_25[i] <- mean(temp)\n   \t}\n\nmyPDF(\"identify_dist_symm_sampling_n25.pdf\", 3.2, 2, mar=c(3.3,2,0.5,0.5), \n      mgp=c(2.1,0.5,0))\nhist(sampling_25, col = COL[1], xlab = \"Plot C\", ylab = \"\", main = \"\", axes = FALSE)\naxis(2, at=seq(0, 20, 10))\naxis(1, at = 9:11, labels = 9:11)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/pennies_ages/pennies_ages.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\nload(\"penniesAges.Rda\")\n\n# plot population ---------------------------------------------------\npdf(\"pennies_ages_pop.pdf\", height = 3, width = 5.8)\npar(mar=c(2,2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(penniesAges$age, col = COL[1], xlab = \"Penny ages\", \n         ylab = \"\", axes = FALSE)\naxis(1)\ndev.off()\n\n# plot sampling, n = 5 ----------------------------------------------\n\nset.seed(123)\nxbar = c()\nfor(i in 1:5000){\n\tsub = sample(c(1:nrow(penniesAges)), size = 5, replace = TRUE)\n\txbar = c(xbar, mean(penniesAges$age[sub]))\n}\nxbar5 = xbar\n\nmyPDF(\"pennies_ages_sampling_n5.pdf\", 3, 2.4, \n      mar=c(3.5,0.5,0.5,0.5), las=1, mgp=c(2.1,0.4,0))\nhistPlot(xbar5, col = COL[1], \n         xlab = expression(bar(x)[\" n = 5\"]), ylab = \"\", \n         axes = FALSE)\naxis(1)\ndev.off()\n\n# plot sampling, n = 30 ----------------------------------------------\n\nset.seed(234)\nxbar = c()\nfor(i in 1:5000){\n  sub = sample(c(1:nrow(penniesAges)), size = 30, replace = TRUE)\n  xbar = c(xbar, mean(penniesAges$age[sub]))\n}\nxbar30 = xbar\n\nmyPDF(\"pennies_ages_sampling_n30.pdf\", 3, 2.4, \n      mar=c(3.5,0.5,0.5,0.5), las=1, mgp=c(2.1,0.4,0))\nhistPlot(xbar30, col = COL[1], \n         xlab = expression(bar(x)[\" n = 30\"]), ylab = \"\", \n         axes = FALSE)\naxis(1)\ndev.off()\n\n# plot sampling, n = 100 --------------------------------------------\n\nset.seed(345)\nxbar = c()\nfor(i in 1:5000){\n  sub = sample(c(1:nrow(penniesAges)), size = 100, replace = TRUE)\n  xbar = c(xbar, mean(penniesAges$age[sub]))\n}\nxbar100 = xbar\n\nmyPDF(\"pennies_ages_sampling_n100.pdf\", 3, 2.4, \n      mar=c(3.5,0.5,0.5,0.5), las=1, mgp=c(2.1,0.4,0))\nhistPlot(xbar100, col = COL[1], \n         xlab = expression(bar(x)[\" n = 100\"]), ylab = \"\", \n         axes = FALSE)\naxis(1)\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/penny_weights/penny_weights.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# input -------------------------------------------------------------\n\nm = 2.5\ns = 0.03\nn = 10\nse = s / sqrt(n)\n\n# plot sketch -------------------------------------------------------\n\npdf(\"penny_weights_sketch.pdf\", height = 3, width = 6)\n\npar(mar=c(2,0,0,0), las=1, mgp=c(3,1,0), mfrow = c(1,1))\n\n# population\n\nX <- seq((m - 3 * s), (m + 3 * s), 0.001)\nY <- dnorm(X, m, s)\n\nplot(X, Y, type = 'l', axes = FALSE, \n     xlim = c(min(X), max(X)), ylim = c(0, 42), \n     ylab = \"\", lwd = 2.5)\nlines(X, rep(0, length(X)), lwd = 1.5)\naxis(1, at = seq((m - 3 * s), (m + 3 * s),s), cex.axis = 1.25)\n\n# sampling\n\nX <- seq((m - 5 * se), (m + 5 * se), 0.001)\nY <- dnorm(X, m, se)\n\nlines(X, Y, type = 'l', lty = 2, lwd = 2.5, col = COL[1])\n\nlegend(\"topright\", c(\"Population\",\"Sampling (n = 10)\"), \n       lty = c(1,2), col = c(\"black\",COL[1]), \n       inset = 0.03, cex = 1.25, lwd = c(2.5,2.5))\n\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/social_experiment_two_sample_randomization/social_experiment_two_sample_randomization.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# set number of simulations -----------------------------------------\nN = 10^4\n\n# randomize ---------------------------------------------------------\n\npHatObs = -0.35\n\nset.seed(3)\n\nsc <- c(rep(\"p\", 20), rep(\"c\",25))\nint <- c(rep(c(\"y\", \"n\"), c(5, 15)), rep(c(\"y\", \"n\"), c(15, 10)))\n\nd <- rep(NA, N)\nfor(i in 1:N){\n\tscf  <- sample(sc)\n\tp1   <- sum(int[scf == \"p\"] == \"y\") / 20\n\tp2   <- sum(int[scf == \"c\"] == \"y\") / 25\n\td[i] <- p1 - p2\n}\nsum((d) <= pHatObs) / N\n\n# plot randomization dist for question ------------------------------\n\npdf(\"social_experiment_two_sample_randomization.pdf\", height = 3, width = 6)\n\npar(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))\n\ntemp1 <- sort(unique(d))\ntemp2 <- diff(temp1[1:2])/2\nbr    <- seq(temp1[1]-temp2/2, tail(temp1,1)+temp2/2, temp2)\n\nhistPlot(d, breaks = br, col=COL[7,4], \n         main=\"\", xlab=expression(hat(p)[pr_sim] - hat(p)[con_sim]*\"    \"), \n         ylab=\"\", axes=FALSE)\naxis(1, seq(-0.4, 0.4, 0.2))\naxis(2, at=(0:4)*N/20, labels=c(0, NA, 2, NA, 4)/20)\nabline(h = 0)\n\nabline(h = c((1:4)*N/20), lty = 3, lwd = 2, col = COL[7])\n\ndev.off()\n\n# plot randomization dist for solution ------------------------------\n\npdf(\"social_experiment_two_sample_randomization_soln.pdf\", height = 3, width = 6)\n\npar(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))\n\ntemp1 <- sort(unique(d))\ntemp2 <- diff(temp1[1:2])/2\nbr    <- seq(temp1[1]-temp2/2, tail(temp1,1)+temp2/2, temp2)\n\nhistPlot(d, breaks = br, col=COL[7,4], \n         main=\"\", xlab=expression(hat(p)[pr_sim] - hat(p)[con_sim]*\"    \"), \n         ylab=\"\", axes=FALSE)\naxis(1, seq(-0.4, 0.4, 0.2))\naxis(2, at=(0:4)*N/20, labels=c(0, NA, 2, NA, 4)/20)\nabline(h = 0)\n\nhistPlot(d[d <= pHatObs], breaks=br, col=COL[1], add=TRUE)\nabline(h=0)\nlines(rep(pHatObs, 2), c(0, 3)*N/25, lty=3, lwd=1.7)\ntext(pHatObs, 3*N/25, as.character(pHatObs), pos=3, cex=1.25)\n\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/songs_on_ipod/songs_on_ipod.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(ipod)\n\n# population histogram ----------------------------------------------\npdf(\"songs_on_ipod_pop_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(ipod$songLength, col = COL[1], \n         xlab = \"Length of song\", ylab = \"\")\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/thanksgiving_spending_intro/thanksgiving_spending_intro.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(tgSpending)\n\n# population histogram ----------------------------------------------\npdf(\"thanksgiving_spending_intro_pop_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(tgSpending$spending, col = COL[1], \n         xlab = \"Spending\", ylab = \"\")\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/eoce/yawning_two_sample_randomization/yawning_two_sample_randomization.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# set number of simulations -----------------------------------------\nN = 10^4\n\n# randomize ---------------------------------------------------------\n\npHatObs = 0.04\n\nset.seed(29)\n\ngr <- c(rep(\"trtmt\", 34), rep(\"ctrl\",16))\nyawn <- c(rep(c(\"y\", \"n\"), c(10, 24)), rep(c(\"y\", \"n\"), c(4, 12)))\n\nd <- rep(NA, N)\nfor(i in 1:N){\n  grf  <- sample(gr)\n  p1   <- sum(yawn[grf == \"trtmt\"] == \"y\") / 34\n  p2   <- sum(yawn[grf == \"ctrl\"] == \"y\") / 16\n  d[i] <- p2 - p1\n}\nsum((d) >= pHatObs) / N\n\n# plot randomization dist for question ------------------------------\n\npdf(\"yawning_two_sample_randomization.pdf\", height = 3.5, width = 6.7)\n\npar(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))\n\nhistPlot(d, breaks=seq(-0.6, 0.7, 0.02), col=COL[7,4], \n         main=\"\", xlab=expression(hat(p)[trtmt] - hat(p)[ctrl]*\"    \"), \n         ylab=\"\", axes=FALSE)\naxis(1)\naxis(2, at=(0:5)*N/20, labels=c(0, NA, 2, NA, 4, NA)/20)\nabline(h = 0)\n\nabline(h = c((1:5)*N/20), lty = 3, lwd = 2, col = COL[7])\n\ndev.off()\n\n# plot randomization dist for solution ------------------------------\n\npdf(\"yawning_two_sample_randomization_soln.pdf\", height = 3.5, width = 6.7)\n\npar(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))\n\nhistPlot(d, breaks=seq(-0.6, 0.7, 0.02), col=COL[7,4], \n         main=\"\", xlab=expression(hat(p)[trtmt] - hat(p)[ctrl]*\"    \"), \n         ylab=\"\", axes=FALSE)\naxis(1)\naxis(2, at=(0:5)*N/20, labels=c(0, NA, 2, NA, 4, NA)/20)\nabline(h = 0)\n\nhistPlot(d[d >= pHatObs], breaks=seq(-0.6, 0.7, 0.02), col=COL[1], add=TRUE)\nabline(h=0)\nlines(rep(pHatObs, 2), c(0, 6.1)*N/25, lty=3, lwd=1.7)\ntext(pHatObs, 6*N/25, as.character(pHatObs), pos=3, cex=1.25)\n\ndev.off()"
  },
  {
    "path": "ch_foundations_for_inf/figures/geomFitEvaluationForSP500For1990To2011/geomFitEvaluationForSP500For1990To2011.R",
    "content": "library(openintro)\ndata(COL)\nlibrary(stockPortfolio)\ngr <- getReturns(\"^GSPC\",\n                 freq = \"d\",\n                 start = \"1990-01-01\",\n                 end = \"2011-12-31\")\nR  <- ifelse(gr$R[gr$R != 0] > 0, 1, 0)\nCC <- table(diff(which(R == 1)))\nCC[names(CC) == 7] <- sum(CC[names(CC) %in% 7:9])\nCC <- CC[- which(names(CC) %in% 8:9)]\np  <- mean(R)\npr <- p * (1 - p)^(0:5)\npr <- append(pr, 1 - sum(pr))\n\nCC <- c(CC)\nC  <- rep(1:7, CC)\nEE <- round(pr * sum(CC))\nE  <- rep(1:7, EE)\n\nmyPDF('geomFitEvaluationForSP500For1990To2011.pdf', 7, 3.5,\n      mar = c(3.2, 4.2, 0.2, 1),\n      mgp = c(2.1, 0.7, 0))\nhistPlot(C - 0.13,\n         breaks = seq(0, 8, 0.25),\n         xlim = c(0.5, 7.5),\n         ylim = c(0, 1600),\n         xlab = 'Wait until positive day',\n         ylab = '',\n         axes = FALSE,\n         col = COL[1])\nhistPlot(E + 0.13,\n         breaks = seq(0, 8, 0.25),\n         add = TRUE,\n         col = COL[3])\naxis(1, 1:7, c(1:6, \"7+\"))\naxis(2, at = seq(0, 1200, 400))\npar(las = 0)\nmtext('Frequency', 2, line = 3)\nlegend('topright',\n       fill = COL[c(1, 3)],\n       legend = c('Observed', 'Expected'))\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/geomFitPValueForSP500For1990To2011/geomFitPValueForSP500For1990To2011.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('geomFitPValueForSP500For1990To2011.pdf', 6.6, 2.387,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.5, 0))\nChiSquareTail(15.08,\n              6,\n              c(0, 30),\n              col = COL[1])\narrows(15.1, max(y) / 3,\n       15.5, max(y) / 10,\n       length = 0.1,\n       col = COL[1])\ntext(15.1, max(y)/3, 'Area representing\\nthe p-value',\n     pos = 3,\n     col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/googleHTForDiffAlgPerformancePValue/googleHTForDiffAlgPerformancePValue.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('googleHTForDiffAlgPerformancePValue.pdf', 5, 2.25,\n    mar = c(2, 1, 1, 1), mgp = c(2.1, 0.7, 0))\nChiSquareTail(6.12,\n              2,\n              c(0, 16),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/helpers.R",
    "content": "\nRunSimulation <- function(p, n.sim, samp.size, xlim, xlab, show = \"n\") {\n\n  samples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - p, p)), n.sim)\n  results <- apply(samples, 1, mean)\n  breaks <- seq(-0.0025, 1.0025, 0.005)\n  if (samp.size < 100) {\n    breaks <- seq(-0.01, 1.01, 0.02)\n  }\n  if (missing(xlim)) {\n    xlim <- range(results)\n  }\n  if (missing(xlab)) {\n    xlab <- \"Sample Proportions\"\n  }\n  histPlot(results,\n      col = COL[1], breaks = breaks,\n      xlim = xlim,\n      xlab = xlab,\n      ylab = \"\",\n      axes = FALSE)\n  spread <- format(c(0.001, round(sqrt(p * (1 - p) / samp.size), 3)))[2]\n  main <- bquote(\n      \"n = \"*.(samp.size)~~~~~\n      mu[hat(p)]*\" = \"*.(p)~~~~~\n      sigma[hat(p)]*\" = \"*.(spread))\n  if (show == \"p\") {\n    main <- bquote(\n        \"p = \"*.(p)~~~~~\n        sigma[hat(p)]*\" = \"*.(spread))\n  }\n  mtext(main, line = 0.4, cex = 0.9)\n  if (all(xlim == c(0, 1))) {\n    at1 <- seq(0, 1, 0.1)\n    at2 <- seq(0, 1, 0.2)\n  } else {\n    at1 <- seq(0, 1, 0.025)\n    at2 <- seq(0, 1, 0.05)\n  }\n  axis(1, at = at1, labels = rep(\"\", length(at1)))\n  axis(1, at = at2)\n  # axis(2, at = seq(0, 1200, 100), label = rep(\"\", 13))\n  # axis(2, at = seq(0, 1200, 200))\n  results\n}\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/jurorHTPValueShown/jurorHTPValueShown.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('jurorHTPValueShown.pdf', 4.4, 1.87,\n      mar = c(1.5, 1, 0.2, 1),\n      mgp = c(2.1, 0.45, 0))\nChiSquareTail(5.89,\n              3,\n              c(0, 16),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/mammograms/mammograms.R",
    "content": "require(openintro)\ndata(COL)\n\nfn <- 'mammogramPValue.pdf'\nmyPDF(fn, 4, 1.2,\n      mar = c(1.5, 0, 0.1, 0),\n      mgp = c(3, 0.3, 0))\nnormTail(L = -0.17, U = 0.17,\n        col = COL[1],\n        axes = FALSE,\n        xlim = c(-3.2, 3.2))\nat <- c(-10, -2, 0, 2, 10)\nlabels <- c(0, -0.0014, 0, 0.0014, 0)\naxis(1, at, labels, cex.axis = 0.9)\n# lines(rep(0, 2), c(0, dnorm(0)), col = COL[4])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/normal_dist_mean_500_se_016/normal_dist_mean_500_se_016.R",
    "content": "require(openintro)\ndata(COL)\n\nfn1 <- 'normal_dist_mean_500_se_016.pdf'\nfn2 <- 'normal_dist_mean_500_se_016_with_upper.pdf'\n\nGenerateGraph <- function(show.tails = FALSE) {\n  normTail(0.5, 0.016, L = 0.37, U = 0.63, col = COL[1],\n      xlim = c(0.32, 0.68), axes = FALSE)\n  at <- c(-1, 0.37, 0.5, 0.63, 2)\n  font.36 <- 1\n  if (!show.tails) {\n    at <- c(-1, 0.37, 0.5, 2)\n    font.36 <- 2\n  }\n  axis(1, at, cex.axis = 0.9)\n  if (show.tails) {\n    lines(c(-1, 0.37), rep(0, 2), lwd = 5, col = COL[1])\n    arrows(0.37, 7, 0.35, 1,\n           length = 0.1,\n           lwd = 2,\n           col = COL[1])\n    expr <- expression(\"Tail Area for \"*hat(p))\n    text(0.39, 7, expr, pos = 3, col = COL[1],\n        font = font.36)\n    lines(c(1, 0.63), rep(0, 2), lwd = 5, col = COL[1])\n    arrows(0.63, 7, 0.65, 1,\n           length = 0.1,\n           lwd = 2,\n           col = COL[1])\n    expr <- expression(\"Equally unlikely if \"*H[0]*\" is true\")\n    text(0.61, 7, expr, pos = 3, col = COL[1], cex = 0.8)\n  } else {\n    arrows(0.38, 7, 0.371, 1,\n           length = 0.1,\n           lwd = 2,\n           col = COL[1])\n    expr <- expression(\"Observed \"*hat(p)*\" = 0.37\")\n    text(0.39, 7, expr, pos = 3, col = COL[1],\n        font = font.36)\n  }\n}\n\n\nmyPDF(fn1, 5, 1.5,\n      mar = c(1.55, 0, 0.1, 0),\n      mgp = c(3, 0.5, 0))\nGenerateGraph()\ndev.off()\n\nmyPDF(fn2, 5, 1.5,\n      mar = c(1.55, 0, 0.1, 0),\n      mgp = c(3, 0.5, 0))\nGenerateGraph(TRUE)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/nuclearArmsReduction/nuclearArmsReduction.R",
    "content": "require(openintro)\ndata(COL)\n\nfn <- 'nuclearArmsReductionPValue.pdf'\nmyPDF(fn, 3.5, 1,\n      mar = c(1.55, 0, 0.1, 0),\n      mgp = c(3, 0.5, 0))\nnormTail(U = 3.75, col = COL[1], axes = FALSE,\n         xlim = c(-6, 6))\nat <- c(-10, 0, 3.75, 10)\nlabels <- expression(0, 0.50, 0.56, 0)\naxis(1, at, labels, cex.axis = 0.9)\nlines(c(3.75, 10), rep(0, 2), lwd = 5, col = COL[1])\nlines(c(-3.75, -10), rep(0, 2), lwd = 5, col = COL[1])\narrows(4.3, 0.1, 4.5, 0.03,\n       length = 0.1,\n       lwd = 2,\n       col = COL[1])\ntext(4.3, 0.1, \"upper tail\", pos = 3, col = COL[1], font = 2)\narrows(-4.3, 0.1, -4.5, 0.03,\n       length = 0.1,\n       lwd = 2,\n       col = COL[1])\ntext(-4.3, 0.1, \"lower tail\", pos = 3, col = COL[1], font = 2)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/p-hat_from_53_and_59-not-used/p-hat_from_53_and_59.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('p-hat_from_53_and_59.pdf', 2.15, 0.95,\n      mar = c(1.31, 0, 0.01, 0),\n      mgp = c(3, 0.45, 0))\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\nnormTail(0.56, 0.0156, M = c(0.53, 0.59), cex.axis = 0.8, axes = FALSE, col = COL[1])\nat <- c(0.53, 0.56, 0.59)\naxis(1, at, cex.axis = 0.8)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/p-hat_from_53_and_59_computation/NormTailsCalc.R",
    "content": "NormTailsCalc <- function(z1, z2, file.name) {\n\n  if (!missing(file.name)) {\n    pdf(paste0(file.name, '.pdf'), 4, 0.7)\n  }\n  par(las = 1,\n      mar = rep(0, 4),\n      mgp = c(3, 0, 0))\n\n\n  AddShadedPlot <- function(\n      x, y, offset,\n      shade.start = -8,\n      shade.until = 8) {\n\n    lines(x + offset, y)\n    lines(x + offset, rep(0, length(x)))\n    these <- which(shade.start <= x & x <= shade.until)\n    polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,\n            c(0, y[these], 0),\n            col = COL[1])\n    lines(x + offset, y)\n  }\n\n  AddText <- function(x, text) {\n    text(x, 0.549283, text)\n  }\n\n  X <- seq(-3.2, 3.2, 0.01)\n  Y <- dnorm(X)\n\n  plot(X, Y,\n      type = 'l',\n      axes = FALSE,\n      xlim = c(-3.4, 24 + 3.4),\n      ylim = c(0, 0.622))\n\n  AddShadedPlot(X, Y, 0)\n  AddText(0, format(c(1, 0.0001), scientific = FALSE)[1])\n\n  AddShadedPlot(X, Y, 8, -8, -0.3)\n  AddText(8, format(0.3821, scientific = FALSE)[1])\n\n  AddShadedPlot(X, Y, 16, 1.21, 8)\n  AddText(16, format(0.1131, scientific = FALSE)[1])\n\n  AddShadedPlot(X, Y, 24, -0.3, 1.21)\n  AddText(24, format(0.5048, scientific = FALSE)[1])\n\n  lines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)\n  lines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)\n  lines(c(8 + 3.72, 8 + 4.28), rep(0.549283, 2), lwd = 2)\n  lines(c(8 + 3, 2 * 8 - 3), c(0.2, 0.2), lwd = 3)\n\n  text(20, 0.549283, ' = ')\n  segments(rep(19, 2), c(0.17, 0.23), rep(21, 2), lwd = 3)\n  if (!missing(file.name)) {\n    dev.off()\n  }\n}\n\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/p-hat_from_53_and_59_computation/p-hat_from_53_and_59_computation.R",
    "content": "library(openintro)\ndata(COL)\n\nAddShadedPlot <- function(x, y, offset,\n                          shade.start = -8,\n                          shade.until = 8) {\n  lines(x + offset, y)\n  lines(x + offset, rep(0, length(x)))\n  these <- which(shade.start <= x & x <= shade.until)\n  polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,\n          c(0, y[these], 0),\n          col = COL[1])\n  lines(x + offset, y)\n}\nAddText <- function(x, text) {\n  text(x, 0.549283, text)\n}\n\npdf('p-hat_from_53_and_59_computation.pdf', 4, 0.7)\npar(las = 1,\n    mar = rep(0, 4),\n    mgp = c(3, 0, 0))\nX <- seq(-3.2, 3.2, 0.01)\nY <- dnorm(X)\n\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-3.4, 24 + 3.4),\n     ylim = c(0, 0.622))\n\nAddShadedPlot(X, Y, 0)\nAddText(0, format(c(1, 0.0001), scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 8, -8, -0.3)\nAddText(8, format(0.3821, scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 16, 1.21, 8)\nAddText(16, format(0.1131, scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 24, -0.3, 1.21)\nAddText(24, format(0.5048, scientific = FALSE)[1])\n\nlines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)\nlines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)\nlines(c(8 + 3.72, 8 + 4.28), rep(0.549283, 2), lwd = 2)\nlines(c(8 + 3, 2 * 8 - 3), c(0.2, 0.2), lwd = 3)\n\ntext(20, 0.549283,\n     ' = ')\nsegments(rep(19, 2), c(0.17, 0.23), rep(21, 2), lwd = 3)\ndev.off()\n\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/p-hat_from_867_and_907-not-used/p-hat_from_867_and_907.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('p-hat_from_867_and_907.pdf', 2.15, 0.95,\n      mar = c(1.31, 0, 0.01, 0),\n      mgp = c(3, 0.45, 0))\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\nnormTail(0.887, 0.0100, M = c(0.867, 0.907), cex.axis = 0.8, axes = FALSE, col = COL[1])\nat <- c(0.867, 0.887, 0.907)\naxis(1, at, cex.axis = 0.8)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/p-hat_from_86_and_90/p-hat_from_86_and_90.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('p-hat_from_86_and_90.pdf', 2.15, 0.95,\n      mar = c(1.31, 0, 0.01, 0),\n      mgp = c(3, 0.45, 0))\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\nnormTail(0.88, 0.0100, M = c(0.86, 0.90), cex.axis = 0.8, axes = FALSE, col = COL[1])\nat <- c(0.86, 0.88, 0.90)\naxis(1, at, cex.axis = 0.8)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/quadcopter/quadcopter_attribution.txt",
    "content": "https://secure.flickr.com/photos/sebilden/14642916088\n\nPhotographer: David J\nLicense: CC BY 2.0\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/sampling_100_prop_X/sampling_100_prop_X.R",
    "content": "set.seed(4)\nlibrary(openintro)\ndata(COL)\nsource(\"../helpers.R\")\n\np <- c(0.03, 0.20, 0.50, 0.80, 0.97)\n# Must sub p's actual value into expression() below.\nn.sim <- 50000\nsamp.size <- 100\nmar <- c(3.5, 1.5, 2.3, 1.5)\n\n\nmyPDF('sampling_100_prop_X_12.pdf', 8, 2.8,\n    mfrow = c(1, 2),\n    yaxs = \"i\",\n    mar = mar,\n    mgp = c(2.3, 0.6, 0))\nfor (p. in p[1:2]) {\n  if (p. == 0.05) {\n    par(mar = c(3.5, 0.2, 2.3, 2))\n  } else if (p. == 0.2) {\n    par(mar = c(3.5, 2, 2.3, 0.2))\n  }\n  xlab <- \"\"\n  RunSimulation(p., n.sim, samp.size, xlab = xlab, show = \"p\")\n}\ndev.off()\n\nmyPDF('sampling_100_prop_X_3.pdf', 4.5, 2.8,\n    yaxs = \"i\",\n    mar = mar,\n    mgp = c(2.3, 0.6, 0))\nfor (p. in p[3]) {\n  par(mar = c(3.5, 0.2, 2.3, 0.2))\n  xlab <- \"\"\n  RunSimulation(p., n.sim, samp.size, xlab = xlab, show = \"p\")\n}\ndev.off()\n\nmyPDF('sampling_100_prop_X_45.pdf', 8, 2.8,\n    mfrow = c(1, 2),\n    yaxs = \"i\",\n    mar = mar,\n    mgp = c(2.3, 0.6, 0))\nfor (p. in p[4:5]) {\n  if (p. %in% c(0.80)) {\n    par(mar = c(3.5, 0.2, 2.3, 2))\n  } else {\n    par(mar = c(3.5, 2, 2.3, 0.2))\n  }\n  xlab <- \"Sample Proportion\"\n  RunSimulation(p., n.sim, samp.size, xlab = xlab, show = \"p\")\n}\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/sampling_10_prop_25p/sampling_10_prop_25p - one figure.R",
    "content": "set.seed(3)\nlibrary(openintro)\n\nn.sim <- 10000\nsamp.size <- 10  # 2541\nprop <- 0.25\nwidth <- 0.025\n\nsamples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)\nresults <- apply(samples, 1, mean)\nmean(results)\nsd(results)\n\nmyPDF('sampling_10_prop_25p.pdf', 4.5, 2.4,\n    mar = c(3.5, 3, 0.7, 0.2),\n    mgp = c(2.3, 0.6, 0),\n    xaxs = \"i\")\nhistPlot(results,\n    col = COL[1],\n    breaks = seq(-2 * width,\n        max(results) + 2 * width, width) - width / 2,\n    xlab = \"Sample Proportions\",\n    ylab = \"\",\n    xlim = c(-0.2, 1.05),\n    axes = FALSE)\nat <- seq(-0.2, 1, 0.1)\naxis(1, at = seq(0, 1, 0.1), labels = rep(\"\", 11))\naxis(1, at = at)\naxis(2, at = seq(0, 2000, 1000))\nabline(h = 0, lwd = 2)\nx <- seq(-1, 2, 0.001)\ny <- dnorm(x, prop, sd(results))\nbin.max <- 0.98 * max(table(results))\ny <- y * bin.max / max(y)\nlines(x, y)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/sampling_10_prop_25p/sampling_10_prop_25p.R",
    "content": "set.seed(3)\nlibrary(openintro)\ndata(COL)\n\nn.sim <- 10000\nsamp.size <- 10  # 2541\nprop <- 0.25\nwidth <- 0.025\n\nsamples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)\nresults <- apply(samples, 1, mean)\nmean(results)\nsd(results)\n\nmyPDF('sampling_10_prop_25p.pdf', 9, 2.4,\n    mar = c(3.5, 4, 0.7, 0.2),\n    mgp = c(2.3, 0.6, 0),\n    yaxs = \"i\",\n    mfrow = c(1, 2))\nhistPlot(results,\n    col = COL[1], breaks = seq(0, max(results) + 2 * width, width) - width / 2,\n    xlab = \"Sample Proportions\",\n    ylab = \"\",\n    axes = FALSE)\nat <- seq(0, 1, 0.1)\naxis(1, at = seq(0, 1, 0.1), labels = rep(\"\", 11))\naxis(1, at = at)\naxis(2, at = seq(0, 2000, 1000))\nabline(h = 0, lwd = 2)\npar(las = 0)\nmtext(\"Frequency\", 2, 2.9)\npar(las = 1)\n# x <- seq(-1, 2, 0.001)\n# y <- dnorm(x, prop, sd(results))\n# bin.max <- max(table(results))\n# y <- y * bin.max / max(y)\n# lines(x, y)\n\npar(yaxs = \"r\", mar = c(3.5, 2.5, 0.4, 0.2))\nnormTail(prop, sd(results), L = -1000, lwd = 2, axes = FALSE)\naxis(1, seq(-1, 2, 0.2))\nabline(v = 0, lty = 2)\n\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/sampling_10k_prop_887p/sampling_10k_prop_887p.R",
    "content": "set.seed(4)\nlibrary(openintro)\ndata(COL)\n\nn.sim <- 10000\nsamp.size <- 1000  # 2541\nprop <- 0.887\n\nsamples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)\nresults <- apply(samples, 1, mean)\nmean(results)\nsd(results)\n\nmyPDF('sampling_10k_prop_887p.pdf', 6.5, 3.2,\n    mar = c(3.5, 3.8, 1.8, 0.7),\n    mgp = c(2.3, 0.6, 0),\n    yaxs = \"i\")\nhistPlot(results,\n    col = COL[1], breaks = 50,\n    xlab = \"Sample Proportions\",\n    ylab = \"\",\n    axes = FALSE)\nat <- seq(0, 1, 0.02)\naxis(1, at = seq(0, 1, 0.01), labels = rep(\"\", 101))\naxis(1, at = at)\n# axis(2, at = seq(0, 1200, 100), label = rep(\"\", 13))\naxis(2, at = seq(0, 750, 250))\n# abline(v = 0.89, col = COL[4])\npar(las = 0)\nmtext(\"Frequency\", 2, 2.7)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/sampling_10k_prop_88p/sampling_10k_prop_88p.R",
    "content": "set.seed(4)\nlibrary(openintro)\ndata(COL)\n\nn.sim <- 10000\nsamp.size <- 1000  # 2541\nprop <- 0.88\n\nsamples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)\nresults <- apply(samples, 1, mean)\nmean(results)\nsd(results)\n\nmyPDF('sampling_10k_prop_88p.pdf', 6.5, 3.2,\n    mar = c(3.5, 3.8, 1.8, 0.7),\n    mgp = c(2.3, 0.6, 0),\n    yaxs = \"i\")\nhistPlot(results,\n    col = COL[1], breaks = 50,\n    xlab = \"Sample Proportions\",\n    ylab = \"\",\n    axes = FALSE)\nat <- seq(0, 1, 0.02)\naxis(1, at = seq(0, 1, 0.01), labels = rep(\"\", 101))\naxis(1, at = at)\n# axis(2, at = seq(0, 1200, 100), label = rep(\"\", 13))\naxis(2, at = seq(0, 750, 250))\n# abline(v = 0.89, col = COL[4])\npar(las = 0)\nmtext(\"Frequency\", 2, 2.7)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/sampling_5k_prop_50p/sampling_5k_prop_50p.R",
    "content": "set.seed(3)\nlibrary(openintro)\ndata(COL)\n\nn.sim <- 5000\nsamp.size <- 1000\nprop <- 0.5\n\nsamples <- matrix(sample(0:1, n.sim * samp.size, TRUE, c(1 - prop, prop)), n.sim)\nresults <- apply(samples, 1, mean)\nmean(results)\nsd(results)\n\nmyPDF('sampling_5k_prop_50p.pdf', 6.5, 3.2,\n    mar = c(3.5, 3.8, 1.8, 0.7),\n    mgp = c(2.3, 0.6, 0),\n    yaxs = \"i\")\nhistPlot(results,\n    col = COL[1], breaks = 50,\n    xlab = \"Sample Proportions\",\n    ylab = \"\",\n    axes = FALSE,\n    xlim = c(0.35, 0.65))\nat <- seq(0, 1, 0.02)\naxis(1, at = seq(0, 1, 0.01), labels = rep(\"\", 101))\naxis(1, at = seq(0, 1, 0.05))\n# axis(1, at = at)\n# axis(2, at = seq(0, 1200, 100), label = rep(\"\", 13))\naxis(2, at = seq(0, 200, 100))\npar(las = 0)\nmtext(\"Frequency\", 2, 2.7)\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/sampling_X_prop_56p/sampling_X_prop_56p.R",
    "content": "set.seed(4)\nlibrary(openintro)\ndata(COL)\nsource(\"../helpers.R\")\n\np <- 0.56\n# Must sub p's actual value into expression() below.\nn.sim <- 50000\nsamp.size <- c(5, 25, 100) # , 1000)\nmar <- c(3.5, 1.5, 2.3, 1.5)\n\n\nmyPDF('sampling_X_prop_56p.pdf', 4, 5,\n    mfrow = c(3, 1),\n    yaxs = \"i\",\n    mar = mar,\n    mgp = c(2.3, 0.6, 0))\nfor (ss in samp.size) {\n  par(mar = c(3.5, 0.2, 2.3, 0.2))\n  xlab <- ifelse(ss < 100, \"\", \"Sample Proportion\")\n  RunSimulation(p, n.sim, ss, xlim = c(0, 1), xlab = xlab)\n}\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/sulphStudyFindPValueUsingNormalApprox/sulphStudyFindPValueUsingNormalApprox.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('sulphStudyFindPValueUsingNormalApprox.pdf', 6.7, 2.4,\n      mar = c(2, 0, 0.5, 0),\n      mgp = c(3, 0.65, 0))\nnormTail(U = 1.9,\n         df = 20,\n         col = COL[1],\n         axes = FALSE,\n         xlim = c(-3.5, 3.5))\nat <- c(-5, 0, 1.9, 5)\nlabels <- expression(0, 'null diff. = 0   ',\n                     '   obs. diff. = 0.025', 0)\naxis(1, at, labels)\nyMax <- 0.4\n\ntext(0, yMax * 0.4, '0.973')\n\narrows(2.3, yMax / 2,\n       2.3, yMax / 9,\n       length = 0.1,\n       col = COL[1],\n       lwd = 1.5)\ntext(2.3, yMax / 2, 'p-value\\n 0.027',\n     pos = 3,\n     col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_foundations_for_inf/figures/whyWeWantPValue/whyWeWantPValue.R",
    "content": "library(openintro)\ndata(COL)\n\nBuildWhyWeWantPValuePlot <- function(\n    file.name = 'whyWeWantPValue.pdf',\n    expression1 = expression('Distribution of '*bar(x)),\n    expression2 = expression('observed '*bar(x))) {\n\n  myPDF(file.name, 6.3, 2.5,\n        mar = c(2, 1, 0.5, 1),\n        mgp = c(2.1, 0.6, 0))\n  normTail(L = -5, df = 20,\n           axes = FALSE, xlim = c(-6, 3),\n           lwd = 2.5, curveColor = COL[5])\n  at <- seq(-10, 5, 5)\n  labels <- expression('', 'null value '*-5*'×SE   ',\n                       'null value', '')\n  axis(1, at, labels)\n  yMax <- 0.4\n  text(0, yMax / 2 - 0.02,\n      expression1,\n      cex = 1.1, col = COL[5])\n  text(0, yMax / 3 - 0.01,\n      expression('if '*H[0]*' was true'),\n      cex = 1.1, col = COL[5])\n  arrows(-5, yMax / 3, -5, yMax / 20,\n      length = 0.1, lwd = 2, col = COL[1])\n  text(-5, yMax / 3, expression2,\n      cex = 1.1, pos = 3, col = COL[1])\n  dev.off()\n}\n\nBuildWhyWeWantPValuePlot()\nBuildWhyWeWantPValuePlot(\n    \"whyWeWantPValueProp.pdf\",\n    expression(\"Distribution of \"*hat(p)*\",\"),\n    expression(\"Observed \" *hat(p)))  # \"Observed proportion\")\n"
  },
  {
    "path": "ch_inference_for_means/TeX/ch_inference_for_means.tex",
    "content": "\\begin{chapterpage}{Inference for numerical data}\n  \\chaptertitle{Inference for numerical data}\n  \\label{inferenceForNumericalData}\n  \\label{ch_inference_for_means}\n  \\chaptersection{oneSampleMeansWithTDistribution}\n  \\chaptersection{pairedData}\n  \\chaptersection{differenceOfTwoMeans}\n  \\chaptersection{PowerForDifferenceOfTwoMeans}\n  \\chaptersection{anovaAndRegrWithCategoricalVariables}\n\\end{chapterpage}\n\\renewcommand{\\chapterfolder}{ch_inference_for_means}\n\n\n\\chapterintro{Chapter~\\ref{ch_foundations_for_inf}\n  introduced a framework for statistical inference based\n  on confidence intervals and hypotheses using the\n  normal distribution for sample proportions.\n  In this chapter, we encounter several new point estimates\n  and a couple new distributions.\n  In each case, the inference ideas remain the same:\n  determine which point estimate or test statistic is useful,\n  identify an appropriate distribution for the point estimate\n  or test statistic, and\n  apply the ideas of inference.}\n\n\n\n%__________________\n\\section[One-sample means with the $t$-distribution]\n    {One-sample means with the\n        $\\pmb{\\MakeLowercase{t}}$-distribution}\n\\label{oneSampleMeansWithTDistribution}\n\n\\noindent%\nSimilar to how we can model the behavior of the\nsample proportion $\\hat{p}$ using a normal distribution,\nthe sample mean $\\bar{x}$ can also be modeled using\na normal distribution when certain conditions are met.\n\\index{point estimate!single mean}\nHowever, we'll soon learn that a new distribution,\ncalled the $t$-distribution,\ntends to be more useful when working with the sample mean.\nWe'll first learn about this new distribution,\nthen we'll use it to construct confidence intervals\nand conduct hypothesis tests for the mean.\n\n\n\\subsection[The distribution of $\\bar{x}$]\n    {The sampling distribution of $\\pmb{\\bar{x}}$}\n\nThe sample mean tends to follow\na normal distribution centered at the population mean,~$\\mu$,\nwhen certain conditions are met.\nAdditionally, we can compute a standard error for the sample\nmean using the population standard deviation $\\sigma$\nand the sample size $n$.\n\n\\begin{onebox}{Central Limit Theorem for the sample mean}\n  When we collect a sufficiently large sample of\n  $n$~independent observations from a population with\n  mean $\\mu$ and standard deviation $\\sigma$,\n  the sampling distribution of $\\bar{x}$ will be nearly\n  normal with\n  \\begin{align*}\n  &\\text{Mean}=\\mu\n  &&\\text{Standard Error }(SE) = \\frac{\\sigma}{\\sqrt{n}}\n  \\end{align*}\n\\end{onebox}\n\n\\noindent%\nBefore diving into confidence intervals and hypothesis\ntests using $\\bar{x}$, we first need to cover two topics:\n\\begin{itemize}\n\\item\n    When we modeled $\\hat{p}$ using the normal distribution,\n    certain conditions had to be satisfied.\n    The conditions for working with $\\bar{x}$\n    are a little more complex, and we'll spend\n    Section~\\ref{x_bar_conditions} discussing\n    how to check conditions for inference.\n\\item\n    The standard error is dependent on the population\n    standard deviation, $\\sigma$.\n    However, we rarely know $\\sigma$, and instead\n    we must estimate it.\n    Because this estimation is itself imperfect,\n    we use a new distribution called the\n    $t$-distribution\\index{t-distribution@$t$-distribution}\n    to fix this problem, which we discuss in\n%    While we can use the plug-in principle,\n%    using the sample standard deviation $s$ in place of $\\sigma$,\n%    this is not quite enough to resolve the issue entirely.\n%    and .\n%    We'll cover this topic in\n    Section~\\ref{introducingTheTDistribution}.\n\\end{itemize}\n\n\n\\subsection[Evaluating the two conditions required for\n    modeling $\\bar{x}$]\n  {Evaluating the two conditions required for\n      modeling $\\pmb{\\bar{x}}$}\n\\label{x_bar_conditions}\n\n\\noindent%\nTwo conditions are required to apply the\nCentral Limit Theorem\\index{Central Limit Theorem}\nfor a sample mean~$\\bar{x}$:\n\\begin{description}\n\\item[Independence.]\n    The sample observations must be independent,\n    The most common way to satisfy this condition is\n    when the sample is a simple random sample from the\n    population.\n    If the data come from a random process,\n    analogous to rolling a die,\n    this would also satisfy the independence condition.\n\\item[Normality.]\n    When a sample is small,\n    we also require that the sample observations\n    come from a normally distributed population.\n    We can relax this condition more and more\n    for larger and larger sample sizes.\n    This condition is obviously vague,\n    making it difficult to evaluate,\n    so next we introduce a couple rules of thumb\n    to make checking this condition easier.\n\\end{description}\n%%Before we get to the sample size consideration, let's\n%%consider a special case of the normal distribution\n%%where any sample size is sufficient.\n%\n%%There is also a special case of the Central Limit Theorem\n%%for when the data come from a nearly normal distribution.\n%%In this case the sample mean will be nearly normal\n%%regardless of sample size.\n%\n%\\begin{onebox}{Special case of the Central Limit Theorem\n%    for normally distributed data}\n%  The sampling distribution of $\\bar{x}$ is nearly normal when\n%  the sample observations are independent and come from a nearly\n%  normal distribution.\n%  This is true for any sample size.\n%\\end{onebox}\n%\n%%For population distributions that are not normal,\n%%the sample mean $\\bar{x}$ will still look normal if the sample\n%%size is large enough.\n%%To check what is \\emph{large enough}, we ask two questions:\n%%\\begin{itemize}\n%%\\item\n%%    Is the sample show evident skew or outliers?\n%%    If so, then if t\n%%\\end{itemize}\n%\n%In practice, the population never exactly follows\n%a normal distribution,\n%and the more ``non-normal'' a population\n%distribution, the larger the required sample size required for\n%$\\bar{x}$ to be reasonably modeled using a normal distribution.\n%The rough rule of thumb is, if you don't see any clear outliers\n%and we don't have reason to believe particularly extreme outliers\n%are present in population, then this condition is satisfied.\n\n\\begin{onebox}{Rules of thumb:\n    how to perform the normality check}\n  There is no perfect way to check the normality condition,\n  so instead we use two rules of thumb: %,\n%  one for small samples ($n < 30$)\n%  and another for large samples ($n \\geq 30$):\n  \\begin{description}\n  \\setlength{\\itemsep}{0mm}\n  \\item[$\\mathbf{n < 30}$:]\n      If the sample size $n$ is less than 30\n      and there are no clear outliers in the data,\n      then we typically assume the data come from\n      a nearly normal distribution to satisfy the\n      condition.\n  \\item[$\\mathbf{n \\geq 30}$:]\n      If the sample size $n$ is at least 30\n      and there are no \\emph{particularly extreme} outliers,\n      then we typically assume the sampling distribution\n      of $\\bar{x}$ is nearly normal, even if the underlying\n      distribution of individual observations is not.\n  \\end{description}\n\\end{onebox}\n\nIn this first course in statistics, you aren't expected\nto develop perfect judgement on the normality condition.\nHowever, you are expected to be able to handle\nclear cut cases based on the rules of thumb.\\footnote{More\n  nuanced guidelines would consider further relaxing\n  the \\emph{particularly extreme outlier} check when the\n  sample size is very large.\n  However, we'll leave further discussion here to a future course.}\n\n\\begin{examplewrap}\n\\begin{nexample}{Consider the following two plots\n    that come from simple random samples from\n    different populations.\n    Their sample sizes are $n_1 = 15$ and $n_2 = 50$.\n    \\begin{center}\n    \\Figure[Two histograms are shown, one for \"Sample 1 Observations\" and one for \"Sample 2 Observations\". The histogram for Sample 1 Observations has values ranging from 0 to 7 with a bin width of 1 for a total of 7 bins with frequencies of 2, 1, 4, 3, 2, 0, and 3. The histogram for Sample 2 Observations has values ranging from 0 to 22, with a bin width of 1. Most of the data is located near zero, with half of the observations located in the bin from 0 to 1. There is only non-zero bin beyond 5, which appears to have a height of 1 and is the bin from 21 to 22.]{0.85}{outliers_and_ss_condition}\n    \\end{center}\n    Are the independence and normality conditions met\n    in each case?}\n  \\label{outliers_and_ss_condition_ex}%\n  Each samples is from a simple random sample of its\n  respective population, so the independence condition\n  is satisfied.\n  Let's next check the normality condition for\n  each using the rule of thumb.\n  \n  The first sample has fewer than 30 observations,\n  so we are watching for any clear outliers.\n  None are present; while there is a small gap in the\n  histogram between 5 and~6, this gap is small and\n  20\\% of the observations in this small sample\n  are represented in that far right bar of the histogram,\n  so we can hardly call these clear outliers.\n  With no clear outliers, the normality condition\n  is reasonably~met.\n\n  The second sample has a sample size greater than 30 and\n  includes an outlier that appears to be roughly 5 times\n  further from the center of the distribution than the\n  next furthest observation.\n  This is an example of a particularly extreme outlier,\n  so the normality condition would not be satisfied.\n\\end{nexample}\n\\end{examplewrap}\n\nIn practice, it's typical to also do a mental check to evaluate\nwhether we have reason to believe the underlying population\nwould have moderate skew (if $n < 30$)\nor have particularly extreme outliers ($n \\geq 30$)\nbeyond what we observe in the data.\nFor example, consider the number of followers\nfor each individual account on Twitter,\nand then imagine this distribution.\nThe large majority of accounts have built up\na couple thousand followers or fewer,\nwhile a relatively tiny fraction have amassed\ntens of millions of followers,\nmeaning the distribution is extremely skewed.\nWhen we know the data come from such an extremely\nskewed distribution,\nit takes some effort to understand what sample\nsize is large enough for the normality condition\nto be satisfied.\n\n%if we were sampling accounts from Twitter\n%and examining the distribution of followers on the sampled\n%accounts, we can expect that the vast majority of accounts\n%will have fewer than 1,000 followers and that there\n%will be some very extreme outliers who have tens of millions\n%of followers.\n\n%Distribution of the number of subscribers for\n%      anyone who has uploaded a video to YouTube.\n%      Most such individuals will have built little to\n%      no following, while others will have amassed tens\n%      of millions of subscribers.\n%Generally, we do not presume you to always know when the\n%underlying population has particularly extreme outliers.\n%That~is, besides looking at the data itself,\n%considering the mental check for whether particularly extreme\n%outliers are likely to be a sanity check, not a formal check.\n\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figure{0.8}{outliers_and_ss_condition}\n%  \\caption{Sample observations for\n%      Example~\\ref{outliers_and_ss_condition_ex}.}\n%  \\label{outliers_and_ss_condition}\n%\\end{figure}\n\n%A more thorough sample size condition assessment would\n%also consider two additional aspects beyond the core\n%guidance above.\n\n%The most nuanced checks are then when the sample size\n%is very small -- and we have almost no observational data\n%to allow us to check the condition.\n%\\begin{description}\n%\\item[Population knowledge.]\n%    If we have information about the population beyond\n%    what we've observed in the sample, we would consider\n%    this information as well.\n%    For example, if the sample size is under 30\n%    and the population is known to be moderately skewed\n%    (something difficult to detect with a small sample\n%    in the observed data),\n%    we might still not consider the \n%    For example, if the sample size is under 30 but the\n%    population is known to be moderately skewed,\n%    then the sample size condition is not reasonable.\n%    Likewise, if the population is known to have particularly\n%    extreme observations (examples below), then we may\n%    require a particularly large sample size if we want\n%    to use the normal model for $\\bar{x}$.\n%\\item[Relaxing the extreme outlier condition.]\n%    When the sample size gets very large,\n%    we may even be able to overcome issues with\n%    particularly extreme outliers.\n%    However, there isn't clear guidance, and instead,\n%    custom simulations can be helpful but are beyond\n%    the scope of this book.\n%\\end{description}\n%In this first course in statistics,\n%you won't (and aren't expected to) have perfect judgement\n%on when the sample size condition is or is not met.\n%However, you are expected to be able to handle the\n%clear cut cases based on the core guidelines.\n\n%For those wanting to do more rigorous checks\n%or for the situation that the ,\n%then we add a \n%below are slightly more careful checks, it's convenient\n%to break down \n%\\begin{description}\n%\\item[Sample size under 30.]\n%    If the sample size is less than 30, then we simply follow\n%    the rule of thumb and there isn't\n%    extreme skew in the data (usually punctuated by\n%    extreme outliers), then we can proceed.\n%\\item[Sample size at least 30.]\n%    If the sample size is at least 30 and there isn't\n%    extreme skew in the data (usually punctuated by\n%    extreme outliers), then we can proceed.\n%\\end{description}\n%then it's generally reasonable to consider $\\bar{x}$\n%as following a nearly normal distribution.\n\n%\\Comment{Check the ``99\\%'' and ``hundreds'' claim in the\n%  income example below.}\n%\n%\\begin{examplewrap}\n%\\begin{nexample}{Describe a couple populations that you know\n%    would have particularly extreme outliers.}\n%  Wealth distributions in many countries have\n%      particularly extreme outliers.\n%      For example, over 99\\% of the population\n%      has fewer than \\$10 million saved,\n%      while there are hundreds individuals in the\n%      United States with over \\$1~billion and who\n%      are unlikely to be captured in even a moderate-sized\n%      sample.\n%\n%  Distribution of the number of subscribers for\n%      anyone who has uploaded a video to YouTube.\n%      Most such individuals will have built little to\n%      no following, while others will have amassed tens\n%      of millions of subscribers.\n%\n%%  So while we won't be quizzing you on a variety of applications\n%%  in this book, when you apply these skills elsewhere it is\n%%  important to keep this consideration in mind and do some\n%%  research if you aren't sure about outliers.\n%\\end{nexample}\n%\\end{examplewrap}\n%\n%Generally, we do not presume you to always know when the\n%underlying population has particularly extreme outliers.\n%That~is, besides looking at the data itself,\n%considering the mental check for whether particularly extreme\n%outliers are likely to be a sanity check, not a formal check.\n\n%\\begin{examplewrap}\n%\\begin{nexample}{Suppose we randomly sampled 20 individuals\n%    from the United States and considered their incomes. %,\n%    % which are shown in the following distribution:\n%    However, the population is known to have particularly\n%    extreme outliers, e.g. some individuals with incomes\n%    above \\$10 million.\n%    No matter what we observe in the original 20 observations,\n%    can you say whether we should proceed with modeling\n%    $\\bar{x}$ using a normal distribution?}\n%  When we know the population distribution to have particularly\n%  extreme outliers, then even if we observe no outliers in our\n%  sample, we should not proceed to model $\\bar{x}$ using\n%  a normal distribution.\n%\n%  Generally, we do not presume you to always know when the\n%  underlying population has particularly extreme outliers.\n%  So while we won't be quizzing you on a variety of applications\n%  in this book, when you apply these skills elsewhere it is\n%  important to keep this consideration in mind and do some\n%  research if you aren't sure about outliers.\n%\\end{nexample}\n%\\end{examplewrap}\n\n%However, if one or more of clear outliers are present are evidently present,\n%the guidelines around a reasonable minimum sample become murky.\n%If \n%We'll see some other examples throughout the rest of this book,\n%which will help in developing some intuition around this topic,\n%but in many cases, data with .\n\n\\index{Central Limit Theorem!normal data|)}\n\n\n\n\\subsection[Introducing the $t$-distribution]\n    {Introducing the $\\pmb{t}$-distribution}\n\\label{introducingTheTDistribution}\n\n\\index{t-distribution@$t$-distribution|(}\n\\index{distribution!t@$t$|(}\n\nIn practice, we cannot directly calculate the standard error\nfor $\\bar{x}$ since we do not know the population standard\ndeviation,~$\\sigma$.\nWe encountered a similar issue when computing the standard\nerror for a sample proportion, which relied on the population\nproportion,~$p$.\nOur solution in the proportion context was to use sample\nvalue in place\nof the population value when computing the standard error.\nWe'll employ a similar strategy for computing the standard\nerror of $\\bar{x}$, using the sample\nstandard deviation $s$ in place of $\\sigma$:\n\\begin{align*}\nSE = \\frac{\\sigma}{\\sqrt{n}} \\approx \\frac{s}{\\sqrt{n}}\n\\end{align*}\nThis strategy tends to work well when we have\na lot of data and can estimate $\\sigma$ using $s$ accurately.\nHowever, the estimate is less precise with smaller samples,\nand this leads to problems when using the normal\ndistribution to model $\\bar{x}$.\n% --\n%when the sample size is large --\n%but it is less reliable when the sample size is smaller\n%than about 30. % independent observations.\n\nWe'll find it useful to use a new distribution for\ninference calculations called the\n\\termsub{$\\pmb{t}$-distribution}{t-distribution@$t$-distribution}.\nA~$t$-distribution, shown as a solid line in\nFigure~\\ref{tDistCompareToNormalDist}, has a bell shape.\nHowever, its tails are thicker than the normal distribution's,\nmeaning observations are more likely to fall beyond two\nstandard deviations from the mean than under the normal\ndistribution. %\\footnote{The standard deviation of the\n  %$t$-distribution is actually a little more than 1.\n  %However, it is useful to always think of the $t$-distribution\n  %as having a standard deviation of 1 in all of our applications.}\n%This distribution is important since it accounts for\n%a key challenge with modeling the sample mean:\n%the standard error of the sample mean isn't as\n%precise when the sample size is small.\nThe extra thick tails of the $t$-distribution are exactly\nthe correction needed to resolve the problem of using~$s$\nin place of $\\sigma$ in the $SE$ calculation.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A standard normal distribution and a t-distribution are shown. The t-distribution also has a bell-shape, but it is more sharply peaked than the normal distribution and also has thicker tails than the normal distribution. For example, the is a sizable fraction of the distribution -- perhaps 5\\% for this particular t-distribution -- that extends below -3 and above positive 3, while the normal distribution is very close to zero when looking below -3 or above positive 3.]{0.7}{tDistCompareToNormalDist}\n  \\caption{Comparison of a $t$-distribution\n      and a normal distribution.}\n  \\label{tDistCompareToNormalDist}\n\\end{figure}\n\nThe $t$-distribution is always centered at zero and\nhas a single parameter: degrees of freedom.\nThe \\termsub{degrees of freedom ($\\pmb{df}$)}\n    {degrees of freedom ($df$)!$t$-distribution}\ndescribes the precise form of the bell-shaped $t$-distribution.\nSeveral $t$-distributions are shown in\nFigure~\\ref{tDistConvergeToNormalDist}\nin comparison to the normal distribution.\n\nIn general, we'll use a $t$-distribution\nwith $df = n - 1$ to model the sample mean\nwhen the sample size is $n$.\nThat is, when we have more observations,\nthe degrees of freedom will be larger and\nthe $t$-distribution will look more like the\nstandard normal distribution;\nwhen the degrees of freedom is about 30 or more,\nthe $t$-distribution is nearly indistinguishable\nfrom the normal distribution.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[Four t-distributions with degrees of freedom of 1, 2, 4, and 8 are shown along with a normal distribution on the same plot. The larger the degrees of freedom, the more closely the t-distribution aligns with the normal distribution, meaning that the shape of the peak becomes less sharp and the less \"thick\" the distributions tails appear.]{0.75}{tDistConvergeToNormalDist}\n  \\caption{The larger the degrees of freedom, the more\n      closely the $t$-distribution resembles the standard\n      normal distribution.}\n  \\label{tDistConvergeToNormalDist}\n\\end{figure}\n\n\\begin{onebox}{Degrees of freedom\n    ($\\pmb{\\MakeLowercase{df}}$)}\n  The degrees of freedom describes the shape of the\n  $t$-distribution.\n  The larger the degrees of freedom, the more closely\n  the distribution approximates the normal model. \\stdvspace{}\n\n  When modeling $\\bar{x}$ using the $t$-distribution,\n  use $df = n - 1$.\n\\end{onebox}\n\n%\\Comment{Cut this next sentence?}\n%In Section~\\ref{tDistSolutionToSEProblem},\n%we relate degrees of freedom to sample size.\n\nThe $t$-distribution allows us greater flexibility than\nthe normal distribution when analyzing numerical data.\nIn~practice, it's common to use statistical software,\nsuch as R, Python, or SAS for these analyses.\nAlternatively, a graphing calculator or a\n\\termsub{$\\pmb{t}$-table}{t-table@$t$-table} may be used;\nthe $t$-table is similar to the normal distribution table,\nand it may be found in Appendix~\\ref{tDistributionTable},\nwhich includes usage instructions and examples\nfor those who wish to use this option.\nNo matter the approach you choose, apply your method\nusing the examples below to confirm your working\nunderstanding of the $t$-distribution.\n\n\\begin{examplewrap}\n\\begin{nexample}{What proportion of the $t$-distribution\n    with 18 degrees of freedom falls below -2.10?}\n  Just like a normal probability problem, we first draw\n  the picture in Figure~\\ref{tDistDF18LeftTail2Point10}\n  and shade the area below -2.10.\n%  If this were a normal distribution, the area would be\n%  a little less than 0.025, since about 5\\% of the area\n%  under a normal curve goes out beyond $\\pm 1.96$ standard\n%  deviations.\n  Using statistical software, we can obtain a precise\n  value: 0.0250.\n%  The tail area below -2.10 in the $t$-distribution with\n%  $df = 18$ is the same as the tail area below -1.96 in\n%  the normal distribution.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}\n  \\centering\n  \\Figure[A t-distribution with 18 degrees of freedom is shown, where the region below -2.10 is shaded and appears to represent very roughly 2\\% to 5\\% of the distribution. For the most part, when the degrees of freedom are larger than about 10, like in this case, the differences between the t-distribution and the normal distribution are visually subtle, even if the distinction remains important for our calculations.]{0.42}{tDistDF18LeftTail2Point10}\n  \\caption{The $t$-distribution with 18 degrees of freedom.\n      The area below -2.10 has been shaded.}\n  \\label{tDistDF18LeftTail2Point10}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{A $t$-distribution with 20 degrees of freedom\n    is shown in the left panel of\n    Figure~\\ref{tDistDF20RightTail1Point65}.\n    Estimate the proportion of the distribution falling\n    above 1.65.}\n  With a normal distribution, this would correspond to\n  about~0.05, so we should expect the $t$-distribution\n  to give us a value in this neighborhood.\n  Using statistical software: 0.0573.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}\n  \\centering\n  \\Figure[Two t-distributions are shown on two separate plots. The first plot shows a t-distribution with 20 degrees of freedom with the region above positive 1.65 is shaded, which appears to be very roughly 5\\% of the total distribution area. The second plot shows a t-distribution with 2 degrees of freedom with the region below -3 and above positive 3 shaded. Because the degrees of freedom are so small, the tails are much thicker in this distribution, and its center is also more sharply peaked. Each of these tails appears to represent very roughly 2\\% to 5\\% of the area under this distribution.]{0.72}{tDistDF20RightTail1Point65}\n  \\caption{Left: The $t$-distribution with 20 degrees\n      of freedom, with the area above 1.65 shaded.\n      Right:~The $t$-distribution with 2 degrees of freedom,\n      with the area further than 3 units from 0 shaded.}\n  \\label{tDistDF20RightTail1Point65}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{A $t$-distribution with 2 degrees of freedom\n    is shown in the right panel of\n    Figure~\\ref{tDistDF20RightTail1Point65}.\n    Estimate the proportion of the distribution falling more\n    than 3~units from the mean (above or below).}\n  With so few degrees of freedom, the $t$-distribution will\n  give a more notably different value than the normal\n  distribution.\n  Under a normal distribution, the area would be about\n  0.003 using the 68-95-99.7 rule.\n  For a $t$-distribution with $df = 2$, the area in both\n  tails beyond 3~units totals 0.0955.\n  This area is dramatically different than what\n  we obtain from the normal distribution.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat proportion of the $t$-distribution with 19 degrees\nof freedom falls above -1.79 units?\nUse your preferred method for finding tail areas.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We want to find the shaded area \\emph{above}\n  -1.79 (we leave the picture to you).\n  The lower tail area has an area of 0.0447,\n  so the upper area would have an area of $1 - 0.0447 = 0.9553$.}\n\n\\index{distribution!t@$t$|)}\n\\index{t-distribution@$t$-distribution|)}\n\n\n%\\subsection{Conditions for using the $\\mathbf{t}$-distribution\n%    for inference on a sample mean}\n%\\label{tDistSolutionToSEProblem}\n%\n%\\noindent%\n%To proceed with the $t$-distribution for inference about a single mean, we first check two conditions.\n%\\begin{description}\n%\\item[Independence.]\n%    We verify this condition just as we did before.\n%    We collect a simple random sample, or if the data are from\n%    an experiment or random process, we check to the best of our\n%    abilities that the observations were independent.\n%\\item[Sample size.]\n%    We use the earlier rule of thumb to evaluate this condition:\n%\n%    If the sample size $n$ is less than 30\n%    and there are no clear outliers in the data,\n%    then the sample size condition is satisfied.\n%\n%    If the sample size $n$ is at least 30\n%    and there are no \\emph{particularly extreme} outliers,\n%    then the sample size condition is satisfied.\n%\\end{description}\n%When examining a sample mean and estimated standard error\n%from a sample of $n$ independent and nearly normal observations,\n%we use a $t$-distribution with $n - 1$ degrees of freedom~($df$).\n%For example, if the sample size was 19, then we would use the\n%$t$-distribution with $df = 19 - 1 = 18$ degrees of freedom\n%and proceed in a way similar to how we worked with proportions.\n\n\n\\D{\\newpage}\n\n\\subsection[One sample $t$-confidence intervals]\n    {One sample $\\pmb{t}$-confidence intervals}\n\\label{oneSampleTConfidenceIntervals}\n\n\\index{data!dolphins and mercury|(}\n\nLet's get our first taste of applying the $t$-distribution\nin the context of an example about the mercury content\nof dolphin muscle.\n%Dolphins are at the top of the oceanic food chain, which causes dangerous substances such as mercury to concentrate in their organs and muscles.\nElevated mercury concentrations are an important problem\nfor both dolphins\nand other animals, like humans, who occasionally eat them.\n\\captionsetup{width=86mm}\n\n\\begin{figure}[h]\n\\centering\n\\Figures[A Risso's dolphin is shown surfacing in water. The area forward of its face is mostly white, and then its body is gray and white streaked together.]{0.8}{rissosDolphin}{rissosDolphin.jpg}  \\\\\n\\addvspace{2mm}\n\\begin{minipage}{\\textwidth}\n   \\caption[rissosDolphinPic]{A Risso's dolphin.\\vspace{-1mm} \\\\\n   -----------------------------\\vspace{-2mm}\\\\\n   {\\footnotesize Photo by Mike Baird (\\oiRedirect{textbook-bairdphotos_com}{www.bairdphotos.com}). \\oiRedirect{textbook-CC_BY_2}{CC~BY~2.0~license}.}\\vspace{-8mm}}\n   \\label{rissosDolphin}\n\\end{minipage}\n\\stdvspace{}\n\\end{figure}\n\\captionsetup{width=\\mycaptionwidth}\n\nWe will identify a confidence interval for the average mercury content in dolphin muscle using a sample of 19 Risso's dolphins from the Taiji area in Japan. The data are summarized in Figure~\\ref{summaryStatsOfHgInMuscleOfRissosDolphins}. The minimum and maximum observed values can be used to evaluate whether or not there are clear outliers.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{ccc cc}\n\\hline\n$n$ & $\\bar{x}$ & $s$ & minimum & maximum \\\\\n19   & 4.4\t  & 2.3  & 1.7\t       & 9.2 \\\\\n\\hline\n\\end{tabular}\n\\caption{Summary of mercury content in the muscle of\n    19 Risso's dolphins from the Taiji area.\n    Measurements are in micrograms of mercury per wet gram\n    of muscle ($\\mu$g/wet g).}\n\\label{summaryStatsOfHgInMuscleOfRissosDolphins}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Are the independence and\n    normality conditions satisfied for this data~set?}\n  The observations are a simple random sample,\n  therefore independence is reasonable.\n  The summary statistics in\n  Figure~\\ref{summaryStatsOfHgInMuscleOfRissosDolphins}\n  do not suggest any clear outliers, since\n  all observations are within 2.5 standard deviations\n  of the mean.\n  Based on this evidence, the normality condition\n  seems reasonable.\n\\end{nexample}\n\\end{examplewrap}\n\nIn the normal model, we used $z^{\\star}$ and the standard error to determine the width of a confidence interval. We revise the confidence interval formula slightly when using the $t$-distribution:\n\\begin{align*}\n&\\text{point estimate} \\ \\pm\\  t^{\\star}_{df} \\times SE\n&&\\to\n&&\\bar{x} \\ \\pm\\  t^{\\star}_{df} \\times \\frac{s}{\\sqrt{n}}\n\\end{align*}\n%The sample mean is the point estimate of interest.\n%The standard error is computed using $SE = s/\\sqrt{n}$.\n\n\\begin{examplewrap}\n\\begin{nexample}{Using the summary statistics in\n    Figure~\\ref{summaryStatsOfHgInMuscleOfRissosDolphins},\n    compute the standard error for the average\n    mercury content in the $n = 19$ dolphins.}\n  We plug in $s$ and $n$ into the formula:\n  $\n  %\\begin{align*}\n  SE\n    = s / \\sqrt{n}\n    = 2.3 / \\sqrt{19}\n    = 0.528\n  %\\end{align*}\n  $.\n\\end{nexample}\n\\end{examplewrap}\n\nThe value $t^{\\star}_{df}$ is a cutoff we obtain based on the\nconfidence level and the $t$-distribution with $df$ degrees\nof freedom.\nThat cutoff is found in the same way as with a normal\ndistribution: we find $t^{\\star}_{df}$ such that\nthe fraction of the $t$-distribution with $df$ degrees\nof freedom within a distance $t^{\\star}_{df}$\nof 0 matches the confidence level of interest.\n\n\\begin{examplewrap}\n\\begin{nexample}{When $n = 19$, what is the appropriate\n    degrees of freedom?\n    Find $t^{\\star}_{df}$ for this degrees of freedom\n    and the confidence level of 95\\%}\n  The degrees of freedom is easy to calculate:\n  $df = n - 1 = 18$.\n  \n  Using statistical software, we find the cutoff where\n  the upper tail is equal to 2.5\\%:\n  $t^{\\star}_{18} = 2.10$.\n  The area below -2.10 will also be equal to 2.5\\%.\n  That is, 95\\% of the $t$-distribution with $df = 18$\n  lies within 2.10 units of~0.\n\\end{nexample}\n\\end{examplewrap}\n\n%\\begin{onebox}{Degrees of freedom for a single sample}\n%If the sample has $n$ observations and we are examining a single mean, then we use the $t$-distribution with $df=n-1$ degrees of freedom.\n%\\end{onebox}\n\n%In our current example, we should use the $t$-distribution\n%with $df=19-1=18$ degrees of freedom.\n%We can generally identify $t_{18}^{\\star}$\n%using statistical software.\n%Alternatively, we could use the $t$-table in\n%Appendix~\\ref{tDistributionTable}.\n%Generally the value of $t^{\\star}_{df}$ is slightly larger\n%than what we would get under the normal model with~$z^{\\star}$.\n\n\\begin{examplewrap}\n\\begin{nexample}{Compute and interpret the 95\\% confidence interval\n    for the average mercury content in Risso's dolphins.}\n  We can construct the confidence interval as\n  \\begin{align*}\n  \\bar{x} \\ \\pm\\  t^{\\star}_{18} \\times SE\n    \\quad \\to \\quad 4.4 \\ \\pm\\  2.10 \\times 0.528\n    \\quad \\to \\quad (3.29, 5.51)\n  \\end{align*}\n  We are 95\\% confident the average mercury content of muscles\n  in Risso's dolphins is between 3.29 and 5.51 $\\mu$g/wet gram,\n  which is considered extremely high.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!dolphins and mercury|)}\n\n\\begin{onebox}{Finding a\n    $\\pmb{\\MakeLowercase{t}}$-confidence interval\n    for the mean}\n  Based on a sample of $n$ independent and nearly normal\n  observations, a confidence interval for the population\n  mean is\n  \\begin{align*}\n  &\\text{point estimate} \\ \\pm\\  t^{\\star}_{df} \\times SE\n  &&\\to\n  &&\\bar{x} \\ \\pm\\  t^{\\star}_{df} \\times \\frac{s}{\\sqrt{n}}\n  \\end{align*}\n  where $\\bar{x}$ is the sample mean, $t^{\\star}_{df}$\n  corresponds to the confidence level and degrees of freedom\n  $df$, and $SE$ is the standard error as estimated by\n  the sample.\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{croakerWhiteFishPacificExerConditions}\n\\index{data!white fish and mercury|(}\nThe FDA's webpage provides some data on mercury content of fish.\nBased on a sample of 15 croaker white fish (Pacific),\na sample mean and standard deviation were computed as 0.287\nand 0.069 ppm (parts per million), respectively.\nThe 15 observations ranged from 0.18 to 0.41 ppm.\nWe will assume these observations are independent.\nBased on the summary statistics of the data,\ndo you have any objections to the normality condition\nof the individual observations?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The sample size is under 30,\n  so we check for obvious outliers:\n  since all observations are within 2 standard deviations\n  of the mean, there are no such clear outliers.}\n\n\\begin{examplewrap}\n\\begin{nexample}{Estimate the standard error of\n    $\\bar{x} = 0.287$ ppm using the data summaries in\n    Guided Practice~\\ref{croakerWhiteFishPacificExerConditions}.\n    If we are to use the $t$-distribution to create a\n    90\\% confidence interval for the actual mean of the\n    mercury content, identify the degrees of freedom\n    and $t^{\\star}_{df}$.}\n  \\label{croakerWhiteFishPacificExerSEDFTStar}%\n  The standard error: $SE = \\frac{0.069}{\\sqrt{15}} = 0.0178$.\n\n  Degrees of freedom: $df = n - 1 = 14$.\n\n  Since the goal is a 90\\% confidence interval,\n  we choose $t_{14}^{\\star}$ so that the two-tail area\n  is 0.1:\n  $t^{\\star}_{14} = 1.76$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{Confidence interval for a single mean}\n  Once you've determined a one-mean confidence interval\n  would be helpful for an application,\n  there are four steps to constructing the interval:\n  \\begin{description}\n  \\item[Prepare.]\n      Identify $\\bar{x}$, $s$, $n$, and determine what\n      confidence level you wish to use.\n  \\item[Check.]\n      Verify the conditions to ensure $\\bar{x}$\n      is nearly normal.\n  \\item[Calculate.]\n      If the conditions hold, compute $SE$,\n      find $t_{df}^{\\star}$, and construct the interval.\n  \\item[Conclude.]\n      Interpret the confidence interval in the context\n      of the problem.\n  \\end{description}\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{croakerWhiteFish90ci}\nUsing the information and results of Guided Practice~\\ref{croakerWhiteFishPacificExerConditions} and Example~\\ref{croakerWhiteFishPacificExerSEDFTStar}, compute a 90\\% confidence interval for the average mercury content of croaker white fish (Pacific).\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{\n  $\\bar{x} \\ \\pm\\ t^{\\star}_{14} \\times SE\n      \\ \\to\\  0.287 \\ \\pm\\  1.76 \\times 0.0178\n      \\ \\to\\ (0.256, 0.318)$.\n  We are 90\\% confident that the average mercury content\n  of croaker white fish (Pacific) is between 0.256 and 0.318 ppm.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe 90\\% confidence interval from\nGuided Practice~\\ref{croakerWhiteFish90ci}\nis 0.256 ppm to 0.318 ppm.\nCan we say that 90\\% of croaker white fish (Pacific)\nhave mercury levels between 0.256 and 0.318 ppm?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{\n  No, a confidence interval only provides a range\n  of plausible values for a population parameter,\n  in this case the population mean.\n  It does not describe what we might observe\n  for individual observations.}\n\n\\index{data!white fish and mercury|)}\n\n%Now that we've whet \\Comment{spelling?} your palette with confidence\n%intervals for a mean, let's speed on through to\n%hypothesis tests for the mean.\n\n\n\\subsection[One sample $t$-tests]\n    {One sample $\\pmb{t}$-tests}\n\\label{oneSampleTTests}\n\n\\newcommand{\\cherryblossomn}{100}\n\\newcommand{\\cherryblossommean}{97.32}\n\\newcommand{\\cherryblossomnull}{93.29}\n\\newcommand{\\cherryblossomsd}{16.98}\n\\newcommand{\\cherryblossomse}{1.70}\n\\newcommand{\\cherryblossomz}{2.37}\n\n\\noindent%\nIs the typical US runner getting faster or slower over time? We consider this question in the context of the Cherry Blossom Race, which is a 10-mile race in Washington, DC each~spring.\n\nThe average time for all runners who finished the Cherry Blossom Race in 2006 was \\cherryblossomnull{} minutes (93 minutes and about 17 seconds). We want to determine using data from \\cherryblossomn{} participants in the 2017 Cherry Blossom Race whether runners in this race are getting faster or slower, versus the other possibility that there has been no change.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat are appropriate hypotheses for this context?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$H_0$: The average 10-mile run time was the same for 2006 and 2017. $\\mu = \\cherryblossomnull{}$ minutes. $H_A$: The average 10-mile run time for 2017 was \\emph{different} than that of 2006. $\\mu \\neq \\cherryblossomnull{}$ minutes.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe data come from a simple random sample of all participants,\nso the observations are independent.\nHowever, should we be worried about the normality condition?\nSee Figure~\\ref{run10SampTimeHistogram} for a histogram\nof the differences and evaluate if we can move\nforward.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{With a sample of \\cherryblossomn{},\n  we should only be concerned if there is are particularly\n  extreme outliers.\n  The histogram of the data doesn't show any outliers of concern\n  (and arguably, no outliers at all).}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[A histogram of \"time\" for the sample Cherry Blossom Race data is shown. The data are nearly symmetric with a center at about 100 minutes and a standard deviation of roughly 15 to 20 minutes. All times lie between 50 and 140 minutes.]{0.65}{run10SampTimeHistogram}{run17SampTimeHistogram} \n  \\caption{A histogram of \\var{time} for the sample\n      Cherry Blossom Race data.}\n  \\label{run10SampTimeHistogram}\n\\end{figure}\n\nWhen completing a hypothesis test for the one-sample mean,\nthe process is nearly identical to completing a hypothesis\ntest for a single proportion.\nFirst, we find the Z-score using the observed value,\nnull value, and standard error;\nhowever, we call it a \\term{T-score} since we use\na $t$-distribution for calculating the tail area.\nThen we find the p-value using the same ideas we used\npreviously: find the one-tail area under the sampling\ndistribution, and double it.\n\n\\D{\\newpage}\n\n%\\begin{exampleewrap}\n%\\begin{nexample}{With independence satisfied and normality\n%    not a concern, we can proceed with performing a hypothesis\n%    test using the $t$-distribution.\n%    The sample mean and sample standard deviation of the\n%    sample of \\cherryblossomn{} runners from the 2017 Cherry\n%    Blossom Race are \\cherryblossommean{} and\n%    \\cherryblossomsd{} minutes, respectively.\n%    Recall that the sample size is 100.\n%    What is the p-value for the test, and what is your\n%    conclusion?}\n%\\end{nexercise}\n%\\end{exercisewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{With both the independence\n    and normality conditions satisfied,\n    we can proceed with a hypothesis test using\n    the $t$-distribution.\n    The sample mean and sample standard deviation\n    of the sample\n    of \\cherryblossomn{} runners from the\n    2017 Cherry Blossom Race\n    are \\cherryblossommean{} and \\cherryblossomsd{} minutes,\n    respectively.\n    Recall that the sample size is 100\n    and the average run time in 2006 was\n    \\cherryblossomnull{} minutes.\n    Find the test statistic and p-value.\n    What is your conclusion?}\n\n  To find the test statistic (T-score),\n  we first must determine the standard error:\n  \\begin{align*}\n  SE\n    = \\cherryblossomsd{} / \\sqrt{\\cherryblossomn{}}\n    = \\cherryblossomse{}\n  \\end{align*}\n  Now we can compute the \\emph{T-score}\n  using the sample mean (\\cherryblossommean{}),\n  null value (\\cherryblossomnull{}), and $SE$:\n  \\begin{align*}\n  T\n    = \\frac{\\cherryblossommean{} - \\cherryblossomnull{}}\n        {\\cherryblossomse{}}\n    = \\cherryblossomz{}\n  \\end{align*}\n  For $df = \\cherryblossomn{} - 1 = 99$,\n  we can determine using statistical software\n  (or a $t$-table) that the one-tail area is 0.01,\n  which we double to get the p-value:~0.02.\n\n  Because the p-value is smaller than 0.05,\n  we reject the null hypothesis.\n  That is, the data provide strong evidence that the average\n  run time for the Cherry Blossom Run in 2017 is different\n  than the 2006 average.\n  Since the observed value is above the null value\n  and we have rejected the null hypothesis, we would conclude\n  that runners in the race were slower on average in 2017\n  than in 2006.\n\\end{nexample}\n\\end{examplewrap}\n\n%%\\begin{onebox}{When using a $t$-distribution, we use a T-score (same as Z-score)}\n%To help us remember to use the $t$-distribution,\n%we use a $T$ to represent the test statistic,\n%and we often call this a \\term{T-score}.\n%The Z-score and T-score are computed in the exact same way\n%and are conceptually identical:\n%each represents how many standard errors the observed value\n%is from the null value.\n%%\\end{onebox}\n\n\\begin{onebox}{Hypothesis testing for a single mean}\n  Once you've determined a one-mean hypothesis test is the\n  correct procedure, there are four steps to completing the\n  test:\n  \\begin{description}\n  \\item[Prepare.]\n      Identify the parameter of interest,\n      list out hypotheses,\n      identify the significance level,\n      and identify $\\bar{x}$, $s$, and $n$.\n  \\item[Check.]\n      Verify conditions to ensure $\\bar{x}$ is nearly normal.\n  \\item[Calculate.]\n      If the conditions hold, compute $SE$,\n      compute the T-score, and identify the p-value.\n  \\item[Conclude.]\n      Evaluate the hypothesis test by comparing the p-value\n      to $\\alpha$, and provide a conclusion in the context\n      of the problem.\n  \\end{description}\n\\end{onebox}\n\n\\CalculatorVideos{confidence intervals and hypothesis tests for a single mean}\n\n\n{\\input{ch_inference_for_means/TeX/one-sample_means_with_the_t-distribution.tex}}\n\n\n\n\n\n%__________________\n\\section{Paired data}\n\\label{pairedData}\n\n\\newcommand{\\uclabookN}{68}\n\\newcommand{\\uclabookDF}{67}\n\\newcommand{\\uclabookM}{3.58}\n\\newcommand{\\uclabookSD}{13.42}\n\\newcommand{\\uclabookSE}{1.63}\n\n\\index{paired|(}\n\\index{data!textbooks|(}\n\n\\noindent%\nIn an earlier edition of this textbook,\nwe found that Amazon prices were, on average,\nlower than those of the UCLA Bookstore for UCLA courses\nin 2010.\nIt's been several years, and many stores have adapted\nto the online market, so we wondered,\nhow is the UCLA Bookstore doing today?\n\nWe sampled 201 UCLA courses.\nOf those, \\uclabookN{}\nrequired books could be found on Amazon.\nA~portion of the data set from these courses\nis shown in Figure~\\ref{textbooksDF},\nwhere prices are in US dollars.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{r ll ccc}\n  \\hline\n & subject &\n     course\\us{}number &\n     bookstore &\n     amazon &\n     price\\us{}difference \\\\ \n  \\hline\n  1 & American Indian Studies & M10 & 47.97 & 47.45 & 0.52 \\\\ \n  2 & Anthropology & 2 & 14.26 & 13.55 & 0.71 \\\\ \n  3 & Arts and Architecture & 10 & 13.50 & 12.53 & 0.97 \\\\\n  $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ \\\\\n  %\\uclabookDF{} & Korean & 1 & 24.96 & 23.79 & 1.17 \\\\ \n  \\uclabookN{} & Jewish Studies & M10 & 35.96 & 32.40 & 3.56 \\\\\n  \\hline\n\\end{tabular}\n\\caption{Four cases of the \\data{textbooks} data set.%\n    \\vspace{-3mm}}\n\\label{textbooksDF}\n\\end{figure}\n% library(openintro); library(xtable); library(dplyr); d <- select(ucla_textbooks_f18, subject, course_num, bookstore_new, amazon_new); d$price_diff <- d$bookstore_new - d$amazon_new; d <- subset(d, !is.na(bookstore_new) & !is.na(amazon_new)); rownames(d) <- NULL; xtable(d[c(1:3, nrow(d) - 1:0),])\n\n\\subsection{Paired observations}\n\nEach textbook has two corresponding prices in the data set:\none for the UCLA Bookstore and one for Amazon.\nWhen two sets of observations have this special\ncorrespondence, they are said to be \\term{paired}.\n\n\\begin{onebox}{Paired data}\n  Two sets of observations are \\emph{paired} if each\n  observation in one set has a special correspondence\n  or connection with exactly one observation in the other\n  data set.\n\\end{onebox}\n\nTo analyze paired data, it is often useful to look\nat the difference in outcomes of each pair of observations.\nIn the textbook data, we look at the differences\nin prices, which is represented as the\n\\var{price\\us{}difference} variable\nin the data set.\nHere the differences are taken as\n\\begin{align*}\n\\text{UCLA Bookstore price} - \\text{Amazon price}\n\\end{align*}\n%for each book.\nIt is important that we always subtract using\na consistent order;\nhere Amazon prices are always subtracted from UCLA prices.\nThe first difference shown in Figure~\\ref{textbooksDF}\nis computed as $47.97 - 47.45 = 0.52$.\nSimilarly, the second difference is computed as\n$14.26 - 13.55 = 0.71$,\n and the third is $13.50 - 12.53 = 0.97$.\nA histogram of the differences is shown in\nFigure~\\ref{diffInTextbookPricesF18}.\nUsing differences between paired observations\nis a common and useful way to analyze paired data.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[A histogram is shown for \"UCLA bookstore Price minus Amazon Price, in US dollars\", where values range from -\\$20 to positive \\$80. The distribution has a prominent peak at or slightly above \\$0, with the wide majority of data lying between \\$20 and positive \\$20. There are also 4 bins above \\$20 that have non-zero heights: bin \\$20 to \\$30 has a height of 2, bin \\$30 to \\$40 has a height of 2, bin \\$50 to \\$60 has a height of 1, and bin \\$70 to \\$80 has a height of 1.]{0.63}{textbooksF18}{diffInTextbookPricesF18}\n  \\caption{Histogram of the difference in price for\n      each book sampled.}\n  \\label{diffInTextbookPricesF18}\n\\end{figure}\n\n\n\\subsection{Inference for paired data}\n\nTo analyze a paired data set,\nwe simply analyze the differences.\nWe can use the same $t$-distribution techniques\nwe applied in\nSection~\\ref{oneSampleMeansWithTDistribution}.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{ccccc}\n\\hline\n$n_{_{\\text{\\emph{diff}}}}$\t&\\hspace{3mm}& $\\bar{x}_{_{\\text{\\emph{diff}}}}$\t&\\hspace{3mm}& $s_{_{\\text{\\emph{diff}}}}$ \\vspace{1mm}\\\\\n\\uclabookN{}  && \\uclabookM{}  && \\uclabookSD{} \\\\\n\\hline\n\\end{tabular}\n\\caption{Summary statistics for the \\uclabookN{} price differences.}\n\\label{textbooksSummaryStats}\n\\end{figure}\n\n%\\Comment{Consider breaking the next example into two pieces.}\n\n\\begin{examplewrap}\n\\begin{nexample}{Set up a hypothesis test\n    to determine whether, on average, there is a difference\n    between Amazon's price for a book and the UCLA\n    bookstore's price.\n    Also, check the conditions for whether we can move\n    forward with the test using the $t$-distribution.}\n  \\label{htSetupTextbookPriceDiff}%\n  We are considering two scenarios: there is no difference\n  or there is some difference in average prices.\n  \\begin{itemize}\n  \\setlength{\\itemsep}{0mm}\n  \\item[$H_0$:]\n      $\\mu_{\\text{\\emph{diff}}} = 0$.\n      There is no difference in the average textbook price.\n  \\item[$H_A$:]\n      $\\mu_{\\text{\\emph{diff}}} \\neq 0$.\n      There is a difference in average prices.\n  \\end{itemize}\n\n  Next, we check the independence and normality conditions.\n  The observations are based on a simple random sample,\n  so independence is reasonable.\n  While there are some outliers,\n  $n = \\uclabookN{}$ and none of the outliers\n  are particularly extreme, so the normality\n  of $\\bar{x}$ is satisfied.\n  With these conditions satisfied,\n  we can move forward with the $t$-distribution.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Complete the hypothesis test started\n    in Example~\\ref{htSetupTextbookPriceDiff}.}\n  \\label{SEAndTScoreTextbookPriceDiff}\n  To compute the test  compute the standard error associated with\n  $\\bar{x}_{\\text{\\emph{diff}}}$ using the standard\n  deviation of the differences\n  ($s_{_{\\text{\\emph{diff}}}} = \\uclabookSD{}$)\n  and the number of differences\n  ($n_{_{\\text{\\emph{diff}}}} = \\uclabookN{}$):\n  \\begin{align*}\n  SE_{\\bar{x}_{\\text{\\emph{diff}}}}\n    = \\frac{s_{\\text{\\emph{diff}}}}{\\sqrt{n_{\\text{\\emph{diff}}}}}\n    = \\frac{\\uclabookSD{}}{\\sqrt{\\uclabookN{}}} = \\uclabookSE{}\n  \\end{align*}\n  The test statistic is the T-score of\n  $\\bar{x}_{\\text{\\emph{diff}}}$\n  under the null condition that the actual mean\n  difference is~0:\n  \\begin{align*}\n  T\n    = \\frac{\\bar{x}_{\\text{\\emph{diff}}} - 0}\n        {SE_{\\bar{x}_{\\text{\\emph{diff}}}}}\n    = \\frac{\\uclabookM{} - 0}{\\uclabookSE{}} = 2.20\n  \\end{align*}\n  To visualize the p-value, the sampling distribution\n  of $\\bar{x}_{\\text{\\emph{diff}}}$ is drawn as though\n  $H_0$ is true,\n  and the p-value is represented by the two shaded tails:\n  \\begin{center}\n  \\Figures[A bell-shaped distribution is shown, with a center of mu-sub-0, which has a value of 0. The area under the distribution above x-bar-sub-diff equals 3.58 is shaded, as is the corresponding tail below -3.58.]{0.53}{textbooksF18}{textbooksF18HTTails}\n  \\end{center}\n  The degrees of freedom is\n  $df = \\uclabookN{} - 1 = \\uclabookDF{}$.\n  Using statistical software, we find the\n  one-tail area of 0.0156.\n  Doubling this area gives the p-value: 0.0312.\n\n  Because the p-value is less than 0.05,\n  we reject the null hypothesis.\n  Amazon prices are, on average, lower than the\n  UCLA Bookstore prices for UCLA courses.\n\\end{nexample}\n\\end{examplewrap}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nCreate a 95\\% confidence interval for the average\nprice difference between books at the UCLA bookstore\nand books on Amazon.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Conditions\n  have already verified and the standard error\n  computed in\n  Example~\\ref{htSetupTextbookPriceDiff}.\n  To find the interval,\n  identify $t^{\\star}_{\\uclabookDF{}}$ using statistical software\n  or the $t$-table ($t^{\\star}_{\\uclabookDF{}} = 2.00$),\n  and plug it, the point estimate,\n  and the standard error into the confidence\n  interval formula:\n  \\begin{align*}\n  \\text{point estimate} \\ \\pm\\ z^{\\star} \\times SE\n      \\quad\\to\\quad\n          \\uclabookM{} \\ \\pm\\ 2.00 \\times \\uclabookSE{}\n      \\quad\\to\\quad (0.32, 6.84)\n  \\end{align*}\n  We are 95\\% confident that Amazon is, on average,\n  between \\$0.32 and \\$6.84 less expensive\n  than the UCLA Bookstore for UCLA course books.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWe have strong evidence that Amazon is,\non average, less expensive.\nHow should this conclusion affect UCLA student\nbuying habits?\nShould UCLA students always buy their books\non Amazon?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The average price difference\n  is only mildly useful for this question.\n  Examine the distribution shown in\n  Figure~\\ref{diffInTextbookPricesF18}.\n  There are certainly a handful of cases where\n  Amazon prices are far below the UCLA Bookstore's,\n  which suggests it is worth checking Amazon\n  (and probably other online sites) before purchasing.\n  However, in many cases the Amazon price is\n  above what the UCLA Bookstore charges,\n  and most of the time the price isn't that different.\n  Ultimately, if getting a book immediately from\n  the bookstore is notably more convenient,\n  e.g. to get started on reading or homework,\n  it's likely a good idea to go with the UCLA\n  Bookstore unless the price difference on a\n  specific book happens to be quite large.\n\n  For reference, this is a very different result\n  from what we (the authors) had seen in a similar\n  data set from 2010.\n  At that time, Amazon prices were almost uniformly\n  lower than those of the UCLA Bookstore's and by\n  a large margin, making the case to use Amazon over\n  the UCLA Bookstore quite compelling at that time.\n  Now we frequently check multiple websites\n  to find the best price.}\n\n\\index{data!textbooks|)}\n\\index{paired|)}\n\n{\\input{ch_inference_for_means/TeX/paired_data.tex}}\n\n\n\n\n\n\n%__________________\n\\section{Difference of two means}\n\\label{differenceOfTwoMeans}\n\n\\noindent%\nIn this section we consider a difference in\ntwo population means, $\\mu_1 - \\mu_2$, under the condition\nthat the data are not paired.\nJust as with a single sample, we identify conditions to ensure\nwe can use the $t$-distribution with a point estimate\nof the difference, $\\bar{x}_1 - \\bar{x}_2$,\nand a new standard error formula.\nOther than these two differences, the details are almost\nidentical to the one-mean procedures.\n\nWe apply these methods in three contexts: determining whether\nstem cells can improve heart function,\nexploring the relationship between pregnant womens' smoking\nhabits and birth weights of newborns,\nand exploring whether there is statistically significant\nevidence that one variation of an exam is harder than\nanother variation.\nThis section is motivated by questions like\n``Is there convincing evidence that newborns from mothers\nwho smoke have a different average birth weight than newborns\nfrom mothers who don't smoke?''\n\n\n\\subsection{Confidence interval for a difference of means}\n\n\\index{data!stem cells, heart function|(}\n\\index{point estimate!difference of means|(}\n\nDoes treatment using embryonic stem cells (ESCs)\nhelp improve heart function following a heart attack?\nFigure~\\ref{statsSheepEscStudy} contains summary statistics\nfor an experiment to test ESCs in sheep that had a heart attack.\nEach of these sheep was randomly assigned to the ESC\nor control group, and the change in their hearts' pumping\ncapacity was measured in the study.\nFigure~\\ref{stemCellTherapyForHearts} provides\nhistograms of the two data sets.\nA~positive value corresponds to increased pumping capacity,\nwhich generally suggests a stronger recovery.\nOur goal will be to identify a 95\\% confidence interval\nfor the effect of ESCs on the change in heart pumping\ncapacity relative to the control group.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l rrrrr}\n\\hline\n\\hspace{10mm}\t& $n$\t& $\\bar{x}$\t& $s$  \\\\\n\\hline\nESCs     & 9  & 3.50   & 5.17  \\\\\ncontrol  & 9  & -4.33  & 2.76  \\\\\n\\hline\n\\end{tabular}\n\\caption{Summary statistics of the embryonic stem cell study.}\n\\label{statsSheepEscStudy}\n\\end{figure}\n\nThe point estimate of the difference in the heart pumping variable\nis straightforward to find: it is the difference in the sample means.\n\\begin{align*}\n\\bar{x}_{esc} - \\bar{x}_{control}\\ \n  =\\ 3.50 - (-4.33)\\ \n  =\\ 7.83\n\\end{align*}\nFor the question of whether we can model this difference\nusing a $t$-distribution, we'll need to check new conditions.\nLike the 2-proportion cases, we will require a more\nrobust version of independence so we are confident\nthe two groups are also independent.\nSecondly, we also check for normality in each\ngroup separately, which in practice is a check\nfor outliers.\n\n\\index{point estimate!difference of means|)}\n\n%\\begin{examplewrap}\n%\\begin{nexample}{Set up hypotheses that will be used to test whether there is convincing evidence that ESCs actually increase the amount of blood the heart pumps. Also, check conditions for using the $t$-distribution for inference with the point estimate $\\bar{x}_1 - \\bar{x}_2$. To assist in this assessment, the data are presented in Figure~\\ref{stemCellTherapyForHearts}.}\\label{exampleToEvaluteWhetherESCsAreHelpfulInImprovingHeartFunctionInSheep}\n%We first setup the hypotheses:\n%\\begin{itemize}\n%\\setlength{\\itemsep}{0mm}\n%\\item[$H_0$:] The stem cells do not improve heart pumping function. $\\mu_{esc} - \\mu_{control} = 0$.\n%\\item[$H_A$:] The stem cells do improve heart pumping function. $\\mu_{esc} - \\mu_{control} > 0$.\n%\\end{itemize}\n%\\end{nexample}\n%\\end{examplewrap}\n\n\\begin{onebox}{Using the\n    $\\pmb{\\MakeLowercase{t}}$-distribution\n    for a difference in means}\n  \\label{ConditionsForTwoSampleTDist}%\n  The $t$-distribution can be used for inference when working\n  with the standardized difference of two means if\n  \\begin{itemize}\n  \\setlength{\\itemsep}{0mm}\n  \\item \\emph{Independence, extended.}\n    The data are independent within and between\n    the two groups, e.g. the data come from\n    independent random samples or from a\n    randomized experiment.\n  \\item \\emph{Normality.}\n    We check the outliers rules of thumb for\n    each group separately.\n  \\end{itemize}\n  The standard error may be computed as\n  \\begin{align*}\n  SE%_{\\bar{x}_{1} - \\bar{x}_{2}}\n    = \\sqrt{\\frac{\\sigma_1^2}{n_1} + \\frac{\\sigma_2^2}{n_2}}\n    %\\approx \\sqrt{\\frac{s_1^2}{n_1} + \\frac{s_2^2}{n_2}}\n  \\index{standard error (SE)!difference in means}\n  \\end{align*}\n  The official formula for the degrees of freedom is quite\n  complex %\\footnotemark{}\n  and is generally computed using software,\n  so instead you may use the smaller of\n  $n_1 - 1$ and $n_2 - 1$ for the degrees of freedom\n  if software isn't readily available.\n\\end{onebox}\n%\\footnotetext{$df = \n%    \\left.\n%    \\left[\\frac{s_1^2}{n_1} + \\frac{s_2^2}{n_2}\\right]^2\n%    \\middle/\n%    \\left[\\frac{(s_1^2 / n_1)^2}{n_1 - 1} +\n%        \\frac{(s_2^2 / n_2)^2}{n_2 - 1}\\right]\n%    \\right.$}\n\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{Can the $t$-distribution be used to make\n    inference using the point estimate,\n    $\\bar{x}_{esc} - \\bar{x}_{control} = 7.83$?}\n  First, we check for independence.\n  Because the sheep were randomized into\n  the groups, independence within\n  and between groups is satisfied.\n\n  Figure~\\ref{stemCellTherapyForHearts}\n  does not reveal any clear outliers\n  in either group.\n  (The ESC group does look a bit more variability,\n  but this is not the same as having clear outliers.)\n\n  With both conditions met, we can use the\n  $t$-distribution to model the difference of sample means.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[Two histograms are shown, one for \"Embryonic stem cell transplant\" and one for \"Control (no treatment)\". The data for the first histogram for the treatment group are roughly centered at about 3\\%, with values ranging from about -5\\% to positive 15\\%. The data for the second histogram, which represents the control group, is approximately centered at -3\\%, with values ranging from -10\\% to about positive 2\\%.]{0.63}{stemCellTherapyForHearts}\n  \\caption{Histograms for both the embryonic stem cell\n      and control group.}\n  \\label{stemCellTherapyForHearts}\n\\end{figure}\n\n%\\begin{onebox}{Conditions for applying the $t$-distribution to $\\bar{x}_1 - \\bar{x}_2$}\n%If the sample means, $\\bar{x}_1$ and $\\bar{x}_2$, each meet the criteria for using the $t$-distribution and the observations in the two samples are independent, then we can analyze the difference in sample means using the $t$-distribution.\n%\\end{onebox}\n\n%In addition to new conditions, we also will need an updated\n%formula for the standard error for the difference of two means.\n%\n%\\begin{onebox}{Distribution of a difference of sample means}\n%  The sample difference of two means, $\\bar{x}_1 - \\bar{x}_2$,\n%  can be modeled using the $t$-distribution and the standard error\n%  \\begin{align*}\n%  SE%_{\\bar{x}_{1} - \\bar{x}_{2}}\n%    = \\sqrt{\\frac{\\sigma_1^2}{n_1} + \\frac{\\sigma_2^2}{n_2}}\n%    %\\approx \\sqrt{\\frac{s_1^2}{n_1} + \\frac{s_2^2}{n_2}}\n%  \\end{align*}\n%  when each sample mean can itself be modeled using\n%  a $t$-distribution and the samples are independent.\n%  The official formula for the degrees of freedom is quite\n%  complex %\\footnotemark{}\n%  and is generally computed using software,\n%  so instead you may use the smaller of\n%  $n_1 - 1$ and $n_2 - 1$ for the degrees of freedom\n%  if software isn't readily available.\n%\\end{onebox}\n%%\\footnotetext{$df = \n%%    \\left.\n%%    \\left[\\frac{s_1^2}{n_1} + \\frac{s_2^2}{n_2}\\right]^2\n%%    \\middle/\n%%    \\left[\\frac{(s_1^2 / n_1)^2}{n_1 - 1} +\n%%        \\frac{(s_2^2 / n_2)^2}{n_2 - 1}\\right]\n%%    \\right.$}\n\n%We can quantify the variability in the point estimate,\n%$\\bar{x}_{esc} - \\bar{x}_{\\text{control}}$,\n%using the following formula for its standard error:\n%\\index{standard error (SE)!difference in means}\n%\\begin{align*}\n%SE%_{\\bar{x}_{esc} - \\bar{x}_{control}}\n%  = \\sqrt{\\frac{\\sigma_{esc}^2}{n_{esc}}\n%      + \\frac{\\sigma_{control}^2}{n_{control}}}\n%\\end{align*}\nAs with the one-sample case, we always compute the\nstandard error using sample standard deviations rather\nthan population standard deviations:\n\\begin{align*}\nSE%_{\\bar{x}_{esc} - \\bar{x}_{control}}\n\t%= \\sqrt{\\frac{\\sigma_{esc}^2}{n_{esc}} + \\frac{\\sigma_{control}^2}{n_{control}}} %\\\\\n\t= \\sqrt{\\frac{s_{esc}^2}{n_{esc}} + \\frac{s_{control}^2}{n_{control}}}\n\t= \\sqrt{\\frac{5.17^2}{9} + \\frac{2.76^2}{9}} = 1.95\n\\end{align*}\nGenerally, we use statistical software to find the appropriate\ndegrees of freedom, or if software isn't available,\nwe can use the smaller\nof $n_1 - 1$ and $n_2 - 1$ for the degrees of freedom,\ne.g. if using a $t$-table to find tail areas.\nFor transparency in the Examples and Guided Practice,\nwe'll use the latter approach for finding $df$;\nin the case of the ESC example, this means we'll use $df = 8$.\n\n\\begin{examplewrap}\n\\begin{nexample}{Calculate a 95\\% confidence interval for the\n    effect of ESCs on the change in heart pumping capacity of\n    sheep after they've suffered a heart attack.}\n  We will use the sample difference and the standard error that \n  we computed earlier calculations:\n  \\begin{align*}\n  \\bar{x}_{esc} - \\bar{x}_{control} = 7.83\n  && SE = \\sqrt{\\frac{5.17^2}{9} + \\frac{2.76^2}{9}} = 1.95\n  \\end{align*}\n  Using $df = 8$, we can identify the\n  critical value of $t^{\\star}_{8} = 2.31$\n  for a 95\\% confidence interval.\n  Finally, we can enter the values into the confidence\n  interval formula:\n  \\begin{align*}\n  \\text{point estimate} \\ \\pm\\ t^{\\star} \\times SE\n    \\quad\\rightarrow\\quad 7.83 \\ \\pm\\ 2.31\\times 1.95\n    \\quad\\rightarrow\\quad (3.32, 12.34)\n  \\end{align*}\n  We are 95\\% confident that embryonic stem cells improve\n  the heart's pumping function in sheep that have suffered\n  a heart attack by 3.32\\% to 12.34\\%.\n  \n%  Had we used software to get a more precise degrees\n%  of freedom ($df = 12.225$), the confidence interval\n%  would have been slightly slimmer.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!stem cells, heart function|)}\n\n\\noindent%\nAs with past statistical inference applications,\nthere is a well-trodden procedure.\n\\begin{description}\n\\setlength{\\itemsep}{0mm}\n\\item[Prepare.]\n    Retrieve critical contextual information,\n    and if appropriate, set up hypotheses.\n\\item[Check.]\n    Ensure the required conditions are reasonably\n    satisfied.\n\\item[Calculate.]\n    Find the standard error, and then construct\n    a confidence interval, or if conducting\n    a hypothesis test, find a test statistic\n    and p-value.\n\\item[Conclude.]\n    Interpret the results in the context of the\n    application.\n\\end{description}\nThe details change a little from one setting to the next,\nbut this general approach remain the same.\n\n\n%\\D{\\newpage}\n\n\\subsection{Hypothesis tests for the difference of two means}\n\n\\index{data!baby\\_smoke|(}\n\nA data set called \\data{ncbirths} represents a random sample of 150 cases of mothers and their newborns in North Carolina over a year. Four cases from this data set are represented in Figure~\\ref{babySmokeDF}. We are particularly interested in two variables: \\var{weight} and \\var{smoke}. The \\var{weight} variable represents the weights of the newborns and the \\var{smoke} variable describes which mothers smoked during pregnancy. We would like to know, is there convincing evidence that newborns from mothers who smoke have a different average birth weight than newborns from mothers who don't smoke? We will use the North Carolina sample to try to answer this question. The smoking group includes 50 cases and the nonsmoking group contains 100 cases.\n%Figure~\\ref{babySmokePlotOfTwoGroupsToExamineSkew}.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{rrrrrll}\n  \\hline\n & fage & mage & weeks & weight & sex & smoke \\\\ \n  \\hline\n1 & NA & 13 &  37 & 5.00 & female & nonsmoker \\\\ \n  2 & NA & 14 &  36 & 5.88 & female & nonsmoker \\\\ \n  3 & 19 & 15 &  41 & 8.13 & male & smoker \\\\ \n  $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ \\\\\n  150 & 45 & 50 &  36 & 9.25 & female & nonsmoker \\\\ \n   \\hline\n\\end{tabular}\n\\caption{Four cases from the \\data{ncbirths} data set. The value ``NA'', shown for the first two entries of the first variable, indicates that piece of data is missing.}\n\\label{babySmokeDF}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Set up appropriate hypotheses to evaluate\n    whether there is a relationship between a mother smoking\n    and average birth weight.}\n  \\label{babySmokeHTForWeight}%\n  The null hypothesis represents the case of no difference\n  between the groups.\n  \\begin{itemize}\n  \\setlength{\\itemsep}{0mm}\n  \\item[$H_0$:]\n      There is no difference in average birth weight for\n      newborns from mothers who did and did not smoke.\n      In statistical notation: $\\mu_{n} - \\mu_{s} = 0$,\n      where $\\mu_{n}$ represents non-smoking mothers and\n      $\\mu_s$ represents mothers who smoked.\n  \\item[$H_A$:]\n      There is some difference in average newborn weights\n      from mothers who did and did not smoke\n      ($\\mu_{n} - \\mu_{s} \\neq 0$).\n  \\end{itemize}\n\\end{nexample}\n\\end{examplewrap}\n\nWe check the two conditions necessary to model the difference\nin sample means using the $t$-distribution.\n\\begin{itemize}\n\\item\n    Because the data come from a simple random sample,\n    the observations are independent,\n    both within and between samples.\n\\item\n    With both data sets over 30 observations,\n    we inspect the data in\n    Figure~\\ref{babySmokePlotOfTwoGroupsToExamineSkew}\n    for any particularly extreme outliers\n    and find none.\n\\end{itemize}\nSince both conditions are satisfied, the difference\nin sample means may be modeled using a $t$-distribution.\n\n\\begin{figure}[hhh]\n  \\centering\n  \\Figure[Two histograms are shown for \"Newborn Weights, in pounds\", one for \"Mothers Who Smoked\" and one for \"Mothers Who Did Not Smoke\". The histogram for \"Mothers Who Smoked\" is centered at about 7 and is left-skewed, with values ranging from about 1 pound to 10 pounds. The histogram for \"Mothers Who Did Not Smoke\" is centered at about 7.5 and is left-skewed, with values ranging from about 1 pound to 11 pounds.]{}{babySmokePlotOfTwoGroupsToExamineSkew}\n  \\caption{The left panel represents birth weights for infants\n      whose mothers smoked.\n      The right panel represents the birth weights for\n      infants whose mothers who did not smoke.}\n  \\label{babySmokePlotOfTwoGroupsToExamineSkew}\n\\end{figure}\n\n%Summary statistics are shown for each sample in Figure~\\ref{SumStatsBirthWeightNewbornsSmoke}.\n\n%\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{babySmokeCalcForWeight}\nThe summary statistics in\nFigure~\\ref{SumStatsBirthWeightNewbornsSmoke} may be useful\nfor this Guided Practice.\\footnotemark{}\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}\n\\item\n    What is the point estimate of the population difference,\n    $\\mu_{n} - \\mu_{s}$?\n\\item\n    Compute the standard error of the point estimate from\n    part~(a).\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~The difference in sample means is an\n  appropriate point estimate: $\\bar{x}_{n} - \\bar{x}_{s} = 0.40$.\n  (b)~The standard error of the estimate can be\n  calculated using the standard error formula:\n  \\begin{align*}\n  SE\n    = \\sqrt{\\frac{\\sigma_n^2}{n_n} + \\frac{\\sigma_s^2}{n_s}}\n      \\approx \\sqrt{\\frac{s_n^2}{n_n} + \\frac{s_s^2}{n_s}}\n    = \\sqrt{\\frac{1.60^2}{100} + \\frac{1.43^2}{50}}\n    = 0.26\n  \\end{align*}}\n\n\\begin{figure}[hhh]\n\\centering\n\\begin{tabular}{lrr}\n\\hline\n& \\resp{smoker} & \\resp{nonsmoker} \\\\\n\\hline\nmean & 6.78 & 7.18 \\\\\nst. dev. & 1.43 & 1.60 \\\\\nsamp. size & 50 & 100 \\\\\n\\hline\n\\end{tabular}\n\\caption{Summary statistics for the \\data{ncbirths} data set.}\n\\label{SumStatsBirthWeightNewbornsSmoke}\n\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{Complete the\n    hypothesis test started in\n    Example~\\ref{babySmokeHTForWeight}\n    and Guided Practice~\\ref{babySmokeCalcForWeight}.\n    Use a significance level of $\\alpha=0.05$.\n    For reference, $\\bar{x}_{n} - \\bar{x}_{s} = 0.40$,\n    $SE = 0.26$, and the sample sizes were $n_n = 100$\n    and $n_s = 50$.}\n  \\label{babySmokeHTForWeightComputePValueAndEvalHT}%\n  We can find the test statistic for this test\n  using the values from\n  Guided Practice~\\ref{babySmokeCalcForWeight}:\n  \\begin{align*}\n  T = \\frac{\\ 0.40 - 0\\ }{0.26} = 1.54\n  \\end{align*}\n  The p-value is represented by the two shaded tails\n  in the following plot:\n  \\begin{center}\n    \\Figure[A bell-shaped curve that resembles a normal distribution is shown centered at \"mu-sub-n minus mu-sub-s equals 0\". The upper tail is shaded above a value marked as \"observed difference\", and the corresponding lower tail is also shaded. These tails together appear to represent about 10\\% to 15\\% of the area under the distribution.]{0.5}{distOfDiffOfSampleMeansForBWOfBabySmokeData}\n  \\end{center}\n  We find the single tail area using software\n  (or the $t$-table in Appendix~\\ref{tDistributionTable}).\n  We'll use the\n  smaller of $n_n - 1 = 99$ and $n_s - 1 = 49$ as the\n  degrees of freedom: $df = 49$.\n  The one tail area is 0.065;\n  doubling this value gives the two-tail area and p-value,\n  0.135.\n\n  The p-value is larger than the significance value, 0.05,\n  so we do not reject the null hypothesis.\n  There is insufficient evidence to say there is a difference\n  in average birth weight of newborns from North Carolina mothers\n  who did smoke during pregnancy and newborns from North Carolina\n  mothers who did not smoke during pregnancy.\n\\end{nexample}\n\\end{examplewrap}\n\n%\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWe've seen much research suggesting smoking is harmful\nduring pregnancy, so how could we fail to reject the null\nhypothesis in\nExample~\\ref{babySmokeHTForWeightComputePValueAndEvalHT}?\n\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{It is possible that there is a difference\n  but we did not detect it.\n  If there is a difference, we made a Type~2 Error.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{babySmokeHTIDingHowToDetectDifferences}%\nIf we made a Type~2 Error and there is a difference,\nwhat could we have done differently in data collection\nto be more likely to detect the difference?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We could have collected more data.\n  If the sample sizes are larger, we tend to have\n  a better shot at finding a difference if one exists.\n  In fact, this is exactly what we would find if we\n  examined a larger data set!}\n\nPublic service announcement: while we have used this relatively\nsmall data set as an example, larger data sets show that women\nwho smoke tend to have smaller newborns.\nIn~fact, some in the tobacco industry actually had the audacity\nto tout that as a \\emph{benefit} of~smoking:\n\\begin{quotation}\n  \\noindent%\n  \\emph{It's true.\n  The babies born from women who smoke are smaller,\n  but they're just as healthy as the babies born from\n  women who do not smoke.\n  And some women would prefer having smaller babies.} \\\\[2mm]\n  \\indent\\indent\\indent\\indent\\indent\\indent%\n    - Joseph Cullman, Philip Morris' Chairman of the Board \\\\\n  \\indent\\indent\\indent\\indent\\indent\\indent%\n  {\\color{white}...}on CBS' \\emph{Face the Nation}, Jan 3,~1971\n\\end{quotation}\nFact check: the babies from women who smoke are not actually\nas healthy as the babies from women who do not\nsmoke.\\footnote{You can watch an episode of John Oliver\n  on \\emph{Last Week Tonight} to explore the present day\n  offenses of the tobacco industry.\n  Please be aware that there is some adult language:\n  \\oiRedirect{textbook-johnoliver_tobacco}{youtu.be/6UsHHOCH4q8}.}\n% Resource on this topic:\n% http://archive.tobacco.org/Documents/documentquotes.html\n\n\\index{data!baby\\_smoke|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Case study: two versions of a course exam}\n\n\\index{data!two exam comparison|(}\n\nAn instructor decided to run two slight variations of the same exam. Prior to passing out the exams, she shuffled the exams together to ensure each student received a random version. Summary statistics for how students performed on these two exams are shown in Figure~\\ref{summaryStatsForTwoVersionsOfExams}. Anticipating complaints from students who took Version~B, she would like to evaluate whether the difference observed in the groups is so large that it provides convincing evidence that Version~B was more difficult (on average) than Version~A.\n\n\\begin{figure}[hht]\n\\centering\n\\begin{tabular}{l rrrrr}\n\\hline\nVersion\\hspace{2mm}\t& $n$\t& $\\bar{x}$\t& $s$\t& min\t& max  \\\\\n\\hline\nA\t\t& 30\t\t& 79.4\t\t& 14 \t& 45\t\t& 100 \\\\\nB\t\t& 27\t\t& 74.1\t\t& 20\t\t& 32\t\t& 100 \\\\\n\\hline\n\\end{tabular}\n\\caption{Summary statistics of scores for each exam version.}\n\\label{summaryStatsForTwoVersionsOfExams}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{htSetupForEvaluatingTwoExamVersions}%\nConstruct hypotheses to evaluate whether the observed\ndifference in sample means, $\\bar{x}_A - \\bar{x}_B=5.3$,\nis due to chance. We will later evaluate these hypotheses\nusing $\\alpha = 0.01$.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$H_0$: the exams are equally difficult, on average. $\\mu_A - \\mu_B = 0$. $H_A$: one exam was more difficult than the other, on average. $\\mu_A - \\mu_B \\neq 0$.}\n\n%\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{conditionsForTDistForEvaluatingTwoExamVersions}%\nTo evaluate the hypotheses in Guided Practice~\\ref{htSetupForEvaluatingTwoExamVersions} using the $t$-distribution, we must first verify conditions.\\footnotemark{}\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}\n\\item\n    Does it seem reasonable that the scores are independent?\n\\item\n    Any concerns about outliers?\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~Since the exams were shuffled,\n  the ``treatment'' in this case was randomly assigned,\n  so independence within and between groups is satisfied.\n  (b)~The summary statistics suggest the data are roughly\n  symmetric about the mean, and the min/max values don't\n  suggest any outliers of concern.}\n\nAfter verifying the conditions for each sample and confirming the samples are independent of each other, we are ready to conduct the test using the $t$-distribution. In this case, we are estimating the true difference in average test scores using the sample data, so the point estimate is $\\bar{x}_A - \\bar{x}_B = 5.3$. The standard error of the estimate can be calculated~as\n\\begin{align*}\nSE\n  = \\sqrt{\\frac{s_A^2}{n_A} + \\frac{s_B^2}{n_B}}\n  = \\sqrt{\\frac{14^2}{30} + \\frac{20^2}{27}}\n  = 4.62\n\\end{align*}\nFinally, we construct the test statistic:\n\\begin{align*}\nT\n  = \\frac{\\text{point estimate} - \\text{null value}}{SE}\n  = \\frac{(79.4-74.1) - 0}{4.62}\n  = 1.15\n\\end{align*}\nIf we have a computer handy, we can identify the degrees\nof freedom as 45.97.\nOtherwise we use the smaller of $n_1-1$ and $n_2-1$: $df=26$.\n\n\\D{\\newpage}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A t-distribution with 26 degrees of freedom is shown along with the p-value from the exam example represented as shaded area. The t-distribution shown is centered at zero, and the upper tail area above T equals 1.15 is shaded along with the area below about -1.15. These shaded tail areas appear to represent roughly 25\\% of the distribution.]{0.63}{pValueOfTwoTailAreaOfExamVersionsWhereDFIs26}\n  \\caption{The $t$-distribution with 26 degrees of freedom\n      and the p-value from exam example represented\n      as the shaded areas.}\n  \\label{pValueOfTwoTailAreaOfExamVersionsWhereDFIs26}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Identify the p-value depicted in\n    Figure~\\ref{pValueOfTwoTailAreaOfExamVersionsWhereDFIs26}\n    using $df = 26$, and provide a conclusion in the\n    context of the case study.}\n  Using software, we can find the one-tail area (0.13)\n  and then double this value to get the two-tail area,\n  which is the p-value: 0.26.\n  (Alternatively, we could use the $t$-table in\n  Appendix~\\ref{tDistributionTable}.)\n\n  In Guided\n  Practice~\\ref{htSetupForEvaluatingTwoExamVersions},\n  we specified that we would use $\\alpha = 0.01$.\n  Since the p-value is larger than $\\alpha$,\n  we do not reject the null hypothesis.\n  That is, the data do not convincingly show that one exam\n  version is more difficult than the other, and the teacher\n  should not be convinced that she should add points to the\n  Version~B exam scores.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!two exam comparison|)}\n\n%\\subsection{Summary for inference using the $t$-distribution}\n%\n%\\Comment{This subsection should be heavily updated.}\n%\n%%When considering the difference of two means, there are two common cases: the two samples are paired or they are independent. (There are instances where the data are neither paired nor independent, e.g. see blocking in Section~\\ref{experimentalDesignPrinciples}.) The paired case was treated in Section~\\ref{pairedData}, where the one-sample methods were applied to the differences from the paired observations. We examined the second and more complex scenario in this section.\n%\n%\\textbf{Hypothesis tests.} When applying the $t$-distribution for a hypothesis test, we proceed as follows:\n%\\begin{itemize}\n%\\setlength{\\itemsep}{0mm}\n%\\item Write appropriate hypotheses.\n%\\item Verify conditions for using the $t$-distribution.\n%\\begin{itemize}\n%\\item One-sample or differences from paired data: the observations (or differences) must be independent and nearly normal. For larger sample sizes, we can relax the nearly normal requirement, e.g. slight skew is okay for sample sizes of 15, moderate skew for sample sizes of 30, and strong skew for sample sizes of 60.\n%\\item For a difference of means when the data are not paired: each sample mean must separately satisfy the one-sample conditions for the $t$-distribution, and the data in the groups must also be independent.\n%\\end{itemize}\n%\\item Compute the point estimate of interest, the standard error, and the degrees of freedom. For $df$, use $n-1$ for one sample, and for two samples use either statistical software or the smaller of $n_1 - 1$ and $n_2 - 1$.\n%\\item Compute the T-score and p-value.\n%\\item Make a conclusion based on the p-value, and write a conclusion in context and in plain language so anyone can understand the result.\n%\\end{itemize}\n%\\noindent\\textbf{Confidence intervals.} Similarly, the following is how we generally computed a confidence interval using a $t$-distribution:\n%\\begin{itemize}\n%\\item Verify conditions for using the $t$-distribution. (See above.)\n%\\item Compute the point estimate of interest, the standard error, the degrees of freedom, and $t^{\\star}_{df}$.\n%\\item Calculate the confidence interval using the general formula, point estimate $\\pm\\ t_{df}^{\\star} SE$.\n%\\item Put the conclusions in context and in plain language so even non-data scientists can understand the results.\n%\\end{itemize}\n%\n%\\CalculatorVideos{confidence intervals and hypothesis tests for a difference of means}\n\n\n%\\subsection{Examining the standard error formula (special topic)}\n%\n%The formula for the standard error of the difference in two means is similar to the formula for other standard errors. Recall that the standard error of a single mean, $\\bar{x}_1$, can be approximated by\n%\\begin{align*}\n%SE_{\\bar{x}_1} = \\frac{s_1}{\\ \\sqrt{n_1}\\ }\n%\\end{align*}\n%where $s_1$ and $n_1$ represent the sample standard deviation and sample size.\n%\n%The standard error of the difference of two sample means can be constructed from the standard errors of the separate sample means:\n%\\begin{align*}\n%SE_{\\bar{x}_{1} - \\bar{x}_{2}}\n%\t= \\sqrt{SE_{\\bar{x}_1}^2 + SE_{\\bar{x}_2}^2}\n%\t= \\sqrt{\\frac{s_1^2}{{n_1}} + \\frac{s_2^2}{{n_2}}}\n%\\end{align*}\n%This special relationship follows from probability theory.\n%\n%\\begin{exercisewrap}\n%\\begin{nexercise}\n%\\label{derivingSEForDiffOfTwoMeansExercise}%\n%Prerequisite: Section~\\ref{randomVariablesSection}.\n%We can rewrite the equation above in a different way:\n%\\begin{align*}\n%SE_{\\bar{x}_{1} - \\bar{x}_{2}}^2\n%  = SE_{\\bar{x}_1}^2 + SE_{\\bar{x}_2}^2\n%\\end{align*}\n%Explain where this formula comes from using the ideas of probability theory.\\footnotemark{}\n%\\end{nexercise}\n%\\end{exercisewrap}\n%\\footnotetext{The standard error squared represents the variance of the estimate. If $X$ and $Y$ are two random variables with variances $\\sigma_x^2$ and $\\sigma_y^2$, then the variance of $X-Y$ is $\\sigma_x^2 + \\sigma_y^2$. Likewise, the variance corresponding to $\\bar{x}_1 - \\bar{x}_2$ is $\\sigma_{\\bar{x}_1}^2 + \\sigma_{\\bar{x}_2}^2$. Because $\\sigma_{\\bar{x}_1}^2$ and $\\sigma_{\\bar{x}_2}^2$ are just another way of writing $SE_{\\bar{x}_1}^2$ and  $SE_{\\bar{x}_2}^2$, the variance associated with $\\bar{x}_1 - \\bar{x}_2$ may be written as $SE_{\\bar{x}_1}^2 + SE_{\\bar{x}_2}^2$.}\n\n\n%\\D{\\newpage}\n\n\\subsection{Pooled standard deviation estimate (special topic)}\n\\label{pooledStandardDeviations}\n\nOccasionally, two populations will have standard deviations\nthat are so similar that they can be treated as identical.\nFor example, historical data or a well-understood biological\nmechanism may justify this strong assumption.\nIn such cases, we can make the $t$-distribution approach\nslightly more precise by using a pooled standard deviation.\n\nThe \\term{pooled standard deviation} of two groups is a way\nto use data from both samples to better estimate the standard\ndeviation and standard error.\nIf $s_1^{}$ and $s_2^{}$ are the standard deviations\nof groups~1 and~2 and there are very good reasons to believe\nthat the population standard deviations are equal,\nthen we can obtain an improved estimate of the group variances\nby pooling their data:\n\\begin{align*}\ns_{pooled}^2 = \\frac{s_1^2\\times (n_1-1) + s_2^2\\times (n_2-1)}{n_1 + n_2 - 2}\n\\end{align*}\nwhere $n_1$ and $n_2$ are the sample sizes, as before.\nTo use this new statistic, we substitute $s_{pooled}^2$\nin place of $s_1^2$ and $s_2^2$ in the standard error formula,\nand we use an updated formula for the degrees of freedom:\n\\begin{align*}\ndf = n_1 + n_2 - 2\n\\end{align*}\n\nThe benefits of pooling the standard deviation are realized\nthrough obtaining a better estimate of the standard deviation\nfor each group and using a larger degrees of freedom parameter\nfor the $t$-distribution.\nBoth of these changes may permit a more accurate model of the\nsampling distribution of $\\bar{x}_1 - \\bar{x}_2$,\nif the standard deviations of the two groups are indeed equal.\n\n\\begin{onebox}\n    {Pool standard deviations only after careful consideration}\n  A pooled standard deviation is only appropriate when\n  background research indicates the population standard\n  deviations are nearly equal.\n  When the sample size is large and the condition\n  may be adequately checked with data, the benefits\n  of pooling the standard deviations greatly diminishes.\n\\end{onebox}\n\n\n{\\input{ch_inference_for_means/TeX/difference_of_two_means.tex}}\n\n\n\n\n\n\n\n%__________________\n\\section{Power calculations for a difference of means}\n\\label{PowerForDifferenceOfTwoMeans}\n\n\\noindent%\nOften times in experiment planning,\nthere are two competing considerations:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item\n    We want to collect enough data that we can detect\n    important effects.\n\\item\n    Collecting data can be expensive, and in experiments\n    involving people, there may be some risk to patients.\n\\end{itemize}\nIn this section, we focus on the context of a clinical trial,\nwhich is a health-related experiment where the subject\nare people, and we will determine an appropriate sample size\nwhere we can be 80\\% sure that we would detect any practically\nimportant effects.\\footnote{Even though we don't cover it\n  explicitly, similar sample size planning is also helpful\n  for observational studies.}\n\n\n\\subsection{Going through the motions of a test}\n\nWe're going to go through the motions of a hypothesis test.\nThis will help us frame our calculations for determining\nan appropriate sample size for the study.\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose a pharmaceutical company has developed\n    a new drug for lowering blood pressure, and they are\n    preparing a clinical trial (experiment) to test the\n    drug's effectiveness.\n    They recruit people who are taking a particular standard\n    blood pressure medication.\n    People in the control group will continue to take their\n    current medication through generic-looking pills to ensure\n    blinding.\n    Write down the hypotheses for a two-sided hypothesis test\n    in this context.}\n  Generally, clinical trials use a two-sided alternative\n  hypothesis, so below are suitable hypotheses for this context:\n  \\begin{description}\n  \\setlength{\\itemsep}{0mm}\n  \\item[$H_0$:]\n      The new drug performs exactly as well as the\n      standard medication. \\\\\n      $\\mu_{trmt} - \\mu_{ctrl} = 0$.\n  \\item[$H_A$:]\n      The new drug's performance differs from the\n      standard medication. \\\\\n      $\\mu_{trmt} - \\mu_{ctrl} \\neq 0$.\n  \\end{description}\n%  This two-sided test ensures we'll be alerted if either\n%  the new drug works better or worse than the standard\n%  medication.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{The researchers would like to run the clinical\n    trial on patients with systolic blood pressures between 140\n    and 180~mmHg.\n    Suppose previously published studies suggest that the\n    standard deviation of the patients' blood pressures will\n    be about 12~mmHg and the distribution of patient blood\n    pressures will be approximately symmetric.\\footnotemark{}\n    If~we had 100 patients per group, what would be the\n    approximate standard error for\n    $\\bar{x}_{trmt} - \\bar{x}_{ctrl}$?}\n  The standard error is calculated as follows:\n  \\begin{align*}\n  SE_{\\bar{x}_{trmt} - \\bar{x}_{ctrl}}\n    = \\sqrt{\\frac{s_{trmt}^2}{n_{trmt}} +\n        \\frac{s_{ctrl}^2}{n_{ctrl}}}\n    = \\sqrt{\\frac{12^2}{100} + \\frac{12^2}{100}}\n    = 1.70\n  \\end{align*}\n  This may be an imperfect estimate of\n  $SE_{\\bar{x}_{trmt} - \\bar{x}_{ctrl}}$,\n  since the standard deviation estimate we used may not\n  be perfectly correct for this group of patients.\n  However, it is sufficient for our purposes.\n\\end{nexample}\n\\end{examplewrap}\n\\footnotetext{In this particular study, we'd generally measure\n  each patient's blood pressure at the beginning and end\n  of the study, and then the outcome measurement for\n  the study would be the average change in blood pressure.\n  That is, both $\\mu_{trmt}$ and $\\mu_{ctrl}$ would\n  represent average differences.\n  This is what you might think of as a 2-sample paired\n  testing structure, and we'd analyze it exactly just like\n  a hypothesis test for a difference in the average change\n  for patients.\n  In the calculations we perform here, we'll suppose\n  that 12~mmHg is the predicted standard deviation of\n  a patient's blood pressure difference over the course\n  of the study.}\n\n\\begin{examplewrap}\n\\begin{nexample}{What does the null distribution of\n    $\\bar{x}_{trmt} - \\bar{x}_{ctrl}$ look like?}\n  The degrees of freedom are greater than 30, so the\n  distribution of $\\bar{x}_{trmt} - \\bar{x}_{ctrl}$\n  will be approximately normal.\n  The standard deviation of this distribution\n  (the standard error) would be about 1.70, and under\n  the null hypothesis, its mean would be 0.\n  \\begin{center}\n  \\Figures[A normal distribution is shown for \"x-bar-sub-treatment minus x-bar-sub-control\", where the distribution is centered at zero and has a standard deviation of about 1.6. The distribution is labeled as \"Null distribution\".]{0.93}{power_null_0_1-7}{power_null_A_0_1-7}\n  \\end{center}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{For what values of\n    $\\bar{x}_{trmt} - \\bar{x}_{ctrl}$ would we reject\n    the null hypothesis?}\n  For $\\alpha = 0.05$, we would reject $H_0$ if the difference\n  is in the lower 2.5\\% or upper 2.5\\% tail:\n  \\begin{description}\n  \\setlength{\\itemsep}{0mm}\n  \\item[Lower 2.5\\%:]\n      For the normal model, this is 1.96 standard errors\n      below~0, so any difference smaller than\n      $-1.96 \\times 1.70 = -3.332$~mmHg.\n  \\item[Upper 2.5\\%:]\n      For the normal model, this is 1.96 standard errors\n      above~0, so any difference larger than\n      $1.96 \\times 1.70 = 3.332$~mmHg.\n  \\end{description}\n  The boundaries of these \\term{rejection regions} are shown below:\n  \\begin{center}\n  \\Figures[A normal distribution is shown for \"x-bar-sub-treatment minus x-bar-sub-control\", where the distribution is centered at zero and has a standard deviation of about 1.6. The distribution is labeled as \"Null distribution\". Three regions are labeled: the region between about -3.3 and positive 3.3 is labeled as \"Do not reject H-sub-0\", while the two regions on either side of this central region are labeled with \"Reject H-sub-zero\".]{0.93}{power_null_0_1-7}{power_null_B_0_1-7_with_rejection_region}\n  \\end{center}\n\\end{nexample}\n\\end{examplewrap}\n\nNext, we'll perform some hypothetical calculations to determine\nthe probability we reject the null hypothesis, if the alternative\nhypothesis were actually true.\n\n\n\\subsection%[Computing the power for a 2-sample test]\n    {Computing the power for a 2-sample test}\n\nWhen planning a study, we want to know how likely we are\nto detect an effect we care about.\nIn~other words, if there is a real effect, and that effect\nis large enough that it has practical value, then what's\nthe probability that we detect that effect?\nThis probability is called the \\term{power}, and we can\ncompute it for different sample sizes or for different\n\\emph{effect sizes}.\n\nWe first determine what is a practically significant result.\nSuppose that the company researchers care about finding any\neffect on blood pressure that is 3~mmHg or larger vs the\nstandard medication.\nHere, 3~mmHg is the minimum \\term{effect size} of interest,\nand we want to know how likely we are to detect this size\nof an effect in the study.\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose we decided to move forward with\n    100 patients per treatment group and the new drug reduces\n    blood pressure by an additional 3~mmHg relative to the\n    standard medication.\n    What is the probability that we detect a drop?}\n  \\label{PowerFor100AtNeg3}%\n  Before we even do any calculations, notice that if\n  $\\bar{x}_{trmt} - \\bar{x}_{ctrl} = -3$~mmHg, there\n  wouldn't even be sufficient evidence to reject $H_0$.\n  That's not a good sign.\n\n  To calculate the probability that we will reject $H_0$,\n  we need to determine a few things:\n  \\begin{itemize}\n  \\setlength{\\itemsep}{0mm}\n  \\item\n      The sampling distribution for\n      $\\bar{x}_{trmt} - \\bar{x}_{ctrl}$ when the true difference\n      is -3~mmHg.\n      This is the same as the null distribution,\n      except it is shifted to the left by~3:\n      \\begin{center}\n      \\Figures[A normal distribution is shown for \"x-bar-sub-treatment minus x-bar-sub-control\", where the distribution is centered at zero and has a standard deviation of about 1.6. The distribution is labeled as \"Null distribution\". A second normal distribution is also shown centered at -3 with a standard deviation of about 1.6, and this distribution is labeled \"Distribution with mu-sub-treatment minus mu-sub-control equals -3\". The lines demarking the \"reject\" regions and the \"do-not-reject\" regions from an earlier plot are also shown.]{0.87}{power_null_0_1-7}\n          {power_null_C_0_1-7_with_alt_at_3}\n      \\end{center}\n  \\item\n      The rejection regions, which are outside of the\n      dotted lines above.\n  \\item\n      The fraction of the distribution that falls in the\n      rejection region.\n  \\end{itemize}\n  In short, we need to calculate the probability that\n  $x < -3.332$ for a normal distribution with mean -3\n  and standard deviation~1.7.\n  To do so, we first shade the area we want to calculate:\n  \\begin{center}\n    \\Figures[A normal distribution is shown for \"x-bar-sub-treatment minus x-bar-sub-control\", where the distribution is centered at zero and has a standard deviation of about 1.6. The distribution is labeled as \"Null distribution\". A second normal distribution is also shown centered at -3 with a standard deviation of about 1.6, and this distribution is labeled \"Distribution with mu-sub-treatment minus mu-sub-control equals -3\". The lines demarking the \"reject\" regions and the \"do-not-reject\" regions from an earlier plot are also shown, and the region of the second distribution centered at -3 that is below the lower demarkation line at about -3.2 is shaded, representing just under half of that distribution.]{0.93}{power_null_0_1-7}\n        {power_null_D_0_1-7_with_alt_at_3_and_shaded}\n  \\end{center}\n  We'll use a normal approximation, which is good approximation\n  when the degrees of freedom is about 30 or more.\n  We'll start by calculating the Z-score and find the tail area\n  using either statistical software or the probability table:\n  \\begin{align*}\n  Z = \\frac{-3.332 - (-3)}{1.7} = -0.20 \\qquad \\to \\qquad 0.42\n  \\end{align*}\n  The power for the test is about 42\\% when\n  $\\mu_{trmt} - \\mu_{ctrl} = -3$ and each group has\n  a sample size of~100.\n\\end{nexample}\n\\end{examplewrap}\n\nIn Example~\\ref{PowerFor100AtNeg3}, we ignored the upper\nrejection region in the calculation, which was in the\nopposite direction of the hypothetical truth, i.e. -3.\nThe reasoning?\nThere wouldn't be any value in rejecting the null hypothesis\nand concluding there was an increase when in fact there was\na decrease.\n\nWe've also used a normal distribution instead\nof the $t$-distribution.\nThis is a convenience, and if the sample size is too small,\nwe'd need to revert back to using the $t$-distribution.\nWe'll discuss this a bit further at the end of this section.\n\n\n\\D{\\newpage}\n\n\\subsection{Determining a proper sample size}\n\nIn the last example, we found that if we have a sample size\nof 100 in each group, we can only detect an effect size of\n3~mmHg with a probability of about 0.42.\nSuppose the researchers moved forward and only used\n100 patients per group, and the data did not support\nthe alternative hypothesis,\ni.e. the researchers did not reject $H_0$.\nThis is a very bad situation to be in for a few reasons:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item\n    In the back of the researchers' minds, they'd all be\n    wondering, \\emph{maybe there is a real and meaningful\n    difference, but we weren't able to detect it with such\n    a small sample}. \n\\item\n    The company probably invested hundreds of millions\n    of dollars in developing the new drug, so now they\n    are left with great uncertainty about its potential\n    since the experiment didn't have a great shot at\n    detecting effects that could still be important.\n\\item\n    Patients were subjected to the drug, and we can't even\n    say with much certainty that the drug doesn't help\n    (or harm) patients.\n\\item\n    Another clinical trial may need to be run to get a more\n    conclusive answer as to whether the drug does hold any\n    practical value, and conducting a second clinical trial\n    may take years and many millions of dollars.\n\\end{itemize}\nWe want to avoid this situation, so we need to determine\nan appropriate sample size to ensure we can be pretty\nconfident that we'll detect any effects that are practically\nimportant.\nAs mentioned earlier, a change of 3~mmHg was deemed to be the\nminimum difference that was practically important.\nAs~a first step, we could calculate power for several\ndifferent sample sizes.\nFor instance, let's try 500 patients per group.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nCalculate the power to detect a change of -3~mmHg when using\na sample size of 500 per group.\\footnotemark{}\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}\n\\item\n    Determine the standard error (recall that the standard\n    deviation for patients was expected to be about 12~mmHg).\n\\item\n    Identify the null distribution and rejection regions.\n\\item\n    Identify the alternative distribution when\n    $\\mu_{trmt} - \\mu_{ctrl} = -3$.\n\\item\n    Compute the probability we reject the null hypothesis.\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) The standard error is given as\n  $SE = \\sqrt{\\frac{12^2}{500} + \\frac{12^2}{500}} = 0.76$.\\\\\n  (b)~\\&~(c)~The null distribution, rejection boundaries,\n  and alternative distribution are shown below: \\\\\n  \\indent%\n  \\Figures[A normal distribution is shown for \"x-bar-sub-treatment minus x-bar-sub-control\", where the distribution is centered at zero and has a standard deviation of about 0.76 (note that this is a much smaller than in earlier plots). The distribution is labeled as \"Null distribution\". A second normal distribution is also shown centered at -3 with a standard deviation of about 0.76, and this distribution is labeled \"Distribution with mu-sub-treatment minus mu-sub-control equals -3\". The overlap of these two normal distributions is much smaller than in the last plot. Lines are shown demarking \"reject\" regions for the null distribution are shown at about -1.5 and positive 1.5, and the region of the second distribution centered at -3 that is below the lower demarkation line at about -1.5 is shaded, representing a bit over 95\\% of the distribution.]{0.7}{power_null_0_0-76}\n      {power_null_0_0-76_with_alt_at_3_and_shaded} \\\\\n  The rejection regions are the areas on the outside of the\n  two dotted lines and are at $\\pm 0.76 \\times 1.96 = \\pm 1.49$. \\\\\n  (d)~The area of the alternative distribution where\n  $\\mu_{trmt} - \\mu_{ctrl} = -3$ has been shaded.\n  We compute the Z-score and find the tail area:\n  $Z = \\frac{-1.49 - (-3)}{0.76} = 1.99 \\to 0.977$.\n%  (can use $df = 500$ from the minimum of the two sample\n%  sizes minus 1),\n%  which is the power of the test for a difference of 3~mmHg.\n  With 500 patients per group, we would be about 97.7\\% sure\n  (or~more) that we'd detect any effects that are at least\n  3~mmHg in size.}\n\nThe researchers decided 3~mmHg was the minimum difference\nthat was practically important, and with a sample size of~500,\nwe can be very certain (97.7\\% or better) that we will detect\nany such difference.\nWe now have moved to another extreme where we are exposing\nan unnecessary number of patients to the new drug in the\nclinical trial.\nNot only is this ethically questionable, but it would also\ncost a lot more money than is necessary to be quite sure\nwe'd detect any important effects.\n\nThe most common practice is to identify the sample size where\nthe power is around 80\\%, and sometimes 90\\%.\nOther values may be reasonable for a specific context,\nbut 80\\% and 90\\% are most commonly targeted as a good\nbalance between high power and not exposing too many\npatients to a new treatment (or wasting too much money).\n\nWe could compute the power of the test at several other\npossible sample sizes until we find one that's close to~80\\%,\nbut there's a better way.\nWe should solve the problem backwards.\n\n\\begin{examplewrap}\n\\begin{nexample}{What sample size will lead to a power of 80\\%? Use $\\alpha = 0.05$.}\n  \\label{sample_size_for_80_percent_power}%  This is referenced in EOCE.\n  We'll assume we have a large enough sample that the normal\n  distribution is a good approximation for the test statistic,\n  since the normal distribution and the $t$-distribution\n  look almost identical when the degrees of freedom are\n  moderately large (e.g. $df \\geq 30$).\n  If that doesn't turn out to be true, then we'd need to make\n  a correction.\n\n  We start by identifying the Z-score that would give us a lower\n  tail of 80\\%.\n  For a moderately large sample size per group,\n  the Z-score for a lower tail of 80\\% would be about $Z = 0.84$.\n%  (If our calculations suggest a very sample size,\n%  we should recalculate this part and basically do the\n%  problem one more time.)\n  \\begin{center}\n    \\Figure[A normal distribution is shown for \"x-bar-sub-treatment minus x-bar-sub-control\", where the distribution is centered at zero and has a standard deviation of about 1.1 (note that this is different than earlier plots). The distribution is labeled as \"Null distribution\". A second normal distribution is also shown centered at -3 with a standard deviation of about 1.1, and this distribution is labeled \"Distribution with mu-sub-treatment minus mu-sub-control equals -3\". Lines are shown demarking \"reject\" regions for the null distribution are shown at about -2.2 and positive 2.2, and the region of the second distribution centered at -3 that is below the lower demarkation line at about -1.5 is shaded, representing a bit over 80\\% of the distribution. The distance from 0 to the rejection region line at 2.2 is labeled \"1.96 times SE\", and the distance between the rejection region line and -3 is labeled \"0.84 times SE\".]{0.93}{power_best_sample_size}\n  \\end{center}\n  Additionally, the rejection region extends\n  $1.96\\times SE$ from the center of the null distribution\n  for $\\alpha = 0.05$.\n  This allows us to calculate the target distance between\n  the center of the null and alternative distributions in\n  terms of the standard error:\n  \\begin{align*}\n  0.84 \\times SE + 1.96 \\times SE = 2.8 \\times SE\n  \\end{align*}\n  In our example, we want the distance between the null\n  and alternative distributions' centers to equal the minimum\n  effect size of interest, 3~mmHg, which allows us to set up\n  an equation between this difference and the standard error:\n  \\begin{align*}\n  3 &= 2.8 \\times SE \\\\\n  3 &= 2.8 \\times \\sqrt{\\frac{12^2}{n} + \\frac{12^2}{n}} \\\\\n  n &= \\frac{2.8^2}{3^2} \\times \\left( 12^2 + 12^2 \\right)\n    = 250.88 \\\\\n  \\end{align*}\n  We should target 251 patients per group in order to achieve\n  80\\% power at the 0.05 significance level for this context.\n\\end{nexample}\n\\end{examplewrap}\n\nThe standard error difference of $2.8 \\times SE$ is specific\nto a context where the targeted power is 80\\% and the\nsignificance level is $\\alpha = 0.05$.\nIf the targeted power is 90\\% or if we use a different\nsignificance level, then we'll use something a little\ndifferent than $2.8 \\times SE$.\n\nHad the suggested sample size been relatively small\n-- roughly 30 or smaller -- it would have been a good idea\nto rework the calculations using the degrees of fredom\nfor the smaller sample size under that initial sample size.\nThat is, we would have revised the 0.84 and 1.96\nvalues based on degrees of freedom implied by the initial\nsample size.\nThe revised sample size target would generally have then\nbeen a little larger.\n\n%\\begin{examplewrap}\n%\\begin{nexample}{Suppose the suggested sample size from\n%    the power calculation was 15 per group.\n%    This is a relatively small sample size,\n%    and the conditions about the sample size being\n%    large in Example~\\ref{}\n%    wouldn't be valid.\n%    What should we do?}\n%  First, recognizing that there is \\emph{something}\n%  to do is already great here:\n%  it's easy to forget the earlier assumption about\n%  a moderately large sample size.\n%  So if you catch yourself here, that is something\n%  to be commended!\n%\n%  Next, we basically update the values of 0.84 and 1.96\n%  in the calculations.\n%  First, we identify the degrees of freedom\n%  ($df = 14$ as a rough guide, though \n%  We'd find the values\n%  corresponding to this more precise $t$-distribution.\n%  For example, had the sample-size per group been suggested\n%  as~15, we would have used $df = 14$;\n%  this would have led to a T-score of 0.87 (in place of 0.84)\n%  and a rejection region cutoff of 2.14.\n%  The reworked sample size would then have been suggested\n%  as about 16\\% larger.\n%  If we did not do this extra step, our estimated power would\n%  drop from 80\\% to 74\\%.\n%  While that would not be the end of the world, being precise\n%  is part of the job of being a data scientist!\n%\\end{nexample}\n%\\end{examplewrap}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nSuppose the targeted power was 90\\% and we were using\n$\\alpha = 0.01$.\nHow many standard errors should separate the centers\nof the null and alternative distribution, where the\nalternative distribution is centered at the minimum\neffect size of interest?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{First, find the Z-score such that 90\\% of the\n  distribution is below it: $Z = 1.28$.\n  Next, find the cutoffs for the rejection regions: $\\pm 2.58$.\n  Then the difference in centers should be about\n  $1.28 \\times SE + 2.58 \\times SE = 3.86 \\times SE$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat are some considerations that are important in determining\nwhat the power should be for an experiment?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Answers will vary, but here are a few\n  important considerations:\n  \\begin{itemize}\n  \\setlength{\\itemsep}{0mm}\n  \\item Whether there is any risk to patients in the study.\n  \\item The cost of enrolling more patients.\n  \\item The potential downside of not detecting an effect\n      of interest.\n  \\end{itemize}}\n\nFigure~\\ref{power_curve_neg-3} shows the power for sample\nsizes from 20~patients to 5,000~patients when $\\alpha = 0.05$\nand the true difference is -3.\nThis curve was constructed by writing a program to compute\nthe power for many different sample sizes.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[A line plot is shown with \"Sample Size Per Group\" on the horizontal axis and \"Power\" on the vertical axis. The horizontal axis values grow exponentially and has values going from 20 to 50 to 100 to 200 to 500 to 1,000 to 2,000 and finally to 5,000. The line starts t about (20, 0.1) and slowly climbs up to about (50, 0.25), then climbs more quickly up to (100, 0.4), then (200, 0.7), where its growth starts tapering off as nearly flattens at about (500, 0.98). The height of the line is indistinguishable from 1 for sample sizes per group of 1,000 and higher.]{0.9}{power_curve}{power_curve_neg-3}\n  \\caption{The curve shows the power for different sample\n      sizes in the context of the blood pressure example when\n      the true difference is~-3.\n      Having more than about 250 to 350 observations doesn't\n      provide much additional value in detecting an effect when\n      $\\alpha = 0.05$.}\n  \\label{power_curve_neg-3}\n\\end{figure}\n\n%\\begin{exercisewrap}\n%\\begin{nexercise}\n%\n%\\end{nexercise}\n%\\end{exercisewrap}\n\nPower calculations for expensive or risky experiments are\ncritical.\nHowever, what about experiments that are inexpensive and\nwhere the ethical considerations are minimal?\nFor example, if we are doing final testing on a new feature\non a popular website, how would our sample size considerations\nchange?\nAs before, we'd want to make sure the sample is big enough.\nHowever, suppose the feature has undergone some testing and\nis known to perform well\n(e.g.~the website's users seem to enjoy the feature).\nThen it may be reasonable to run a larger experiment\nif there's value from having a more precise estimate\nof the feature's effect, such as helping guide the\ndevelopment of the next useful feature.\n\n\n{\\input{ch_inference_for_means/TeX/power_calculations_for_a_difference_of_means.tex}}\n\n\n\n\n\n%__________________\n\\section{Comparing many means with ANOVA}\n\\label{anovaAndRegrWithCategoricalVariables}\n\n\\index{analysis of variance (ANOVA)|(}\n\n\\noindent%\nSometimes we want to compare means across many groups.\nWe might initially think to do pairwise comparisons.\nFor example, if there were three groups, we might be tempted\nto compare the first mean with the second,\nthen with the third,\nand then finally compare the second and third means for\na total of three comparisons.\nHowever, this strategy can be treacherous.\nIf we have many groups and do many comparisons,\nit is likely that we will eventually find a difference\njust by chance, even if there is no difference in the\npopulations.\nInstead, we should apply a holistic test to check whether\nthere is evidence that at least one pair groups are\nin fact different, and this is where \\emph{ANOVA} saves\nthe~day.\n\n\n\\subsection{Core ideas of ANOVA}\n\nIn this section, we will learn a new method called\n\\term{analysis of variance (ANOVA)} and a new test\nstatistic called $F$.\nANOVA uses a single hypothesis test to check whether\nthe means across many groups are equal:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[$H_0$:] The mean outcome is the same across all groups. In statistical notation, $\\mu_1 = \\mu_2 = \\cdots = \\mu_k$ where $\\mu_i$ represents the mean of the outcome for observations in category $i$.\n\\item[$H_A$:] At least one mean is different.\n\\end{itemize}\nGenerally we must check three conditions on the data before performing ANOVA:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item the observations are independent within and across groups,\n\\item the data within each group are nearly normal, and\n\\item the variability across the groups is about equal.\n\\end{itemize}\nWhen these three conditions are met, we may perform an ANOVA to determine whether the data provide strong evidence against the null hypothesis that all the $\\mu_i$ are equal.\n\n\\begin{examplewrap}\n\\begin{nexample}{College departments commonly run multiple\n    lectures of the same introductory course each semester\n    because of high demand.\n    Consider a statistics department that runs three lectures\n    of an introductory statistics course.\n    We might like to determine whether there are statistically\n    significant differences in first exam scores in these three\n    classes ($A$,~$B$, and~$C$).\n    Describe appropriate hypotheses to determine whether\n    there are any differences between the three classes.}\n  \\label{firstExampleForThreeStatisticsClassesAndANOVA}%\n  The hypotheses may be written in the following form:\n  \\begin{itemize}\n  \\setlength{\\itemsep}{0mm}\n  \\item[$H_0$:]\n      The average score is identical in all lectures.\n      Any observed difference is due to chance.\n      Notationally, we write $\\mu_A=\\mu_B=\\mu_C$.\n  \\item[$H_A$:]\n      The average score varies by class.\n      We would reject the null hypothesis in favor of the\n      alternative hypothesis if there were larger differences\n      among the class averages than what we might expect\n      from chance alone.\n  \\end{itemize}\n\\end{nexample}\n\\end{examplewrap}\n\nStrong evidence favoring the alternative hypothesis in ANOVA\nis described by unusually large differences among the group means.\nWe will soon learn that assessing the variability of the group\nmeans relative to the variability among individual observations\nwithin each group is key to ANOVA's success.\n\n\\begin{examplewrap}\n\\begin{nexample}{Examine Figure~\\ref{toyANOVA}.\n    Compare groups I, II, and III.\n    Can you visually determine if the differences in the group\n    centers is due to chance or not? Now compare\n    groups IV, V, and~VI.\n    Do these differences appear to be due to chance?}\n  Any real difference in the means of groups I, II, and~III\n  is difficult to discern, because the data within each group\n  are very volatile relative to any differences in the\n  average outcome.\n  On the other hand, it appears there are differences\n  in the centers of groups IV, V, and~VI.\n  For instance, group~V appears to have a higher mean than\n  that of the other two groups.\n  Investigating groups IV, V, and~VI, we see the differences\n  in the groups' centers are noticeable because those\n  differences are large \\emph{relative to the variability\n  in the individual observations within each group}.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[Side-by-side dot plots are shown for groups I, II, III, IV, V, and VI. The means for I and IV are the same, the means of II and V and are the same, and the means of III and VI are also the same. However, the variability of the data shown in groups I, II, and III are larger than the variability of the groups IV, V, and VI.]{0.68}{toyANOVA}\n  \\caption{Side-by-side dot plot for the outcomes for six groups.}\n  \\label{toyANOVA}\n\\end{figure}\n\n\n\\subsection{Is batting performance related to player position in MLB?}\n\n\\index{data!MLB batting|(}\n\n\\newcommand{\\mlbdata}{\\data{bat18}}\n\\newcommand{\\mlbN}{429}\n\\newcommand{\\mlbK}{3}\n\\newcommand{\\mlbMinAB}{100}\n\\newcommand{\\mlbDFA}{2}\n\\newcommand{\\mlbDFB}{426}\n\\newcommand{\\mlbF}{5.077}\n\\newcommand{\\mlbPvalue}{0.0066}\n\nWe would like to discern whether there are real differences\nbetween the batting performance of baseball players according\nto their position:\noutfielder (\\resp{OF}), infielder (\\resp{IF}),\n%designated hitter (\\resp{DH}),\nand catcher (\\resp{C}).\nWe will use a data set called \\mlbdata{},\nwhich includes batting records of \\mlbN{} Major League\nBaseball (MLB) players from the 2018 season who had\nat least \\mlbMinAB{} at bats.\nSix of the \\mlbN{} cases represented in \\mlbdata{}\nare shown in Figure~\\ref{mlbBat18DataMatrix},\nand descriptions for each variable are provided\nin Figure~\\ref{mlbBat18Variables}.\nThe measure we will use for the player batting\nperformance (the outcome variable) is on-base\npercentage (\\var{OBP}).\nThe on-base percentage roughly represents the fraction\nof the time a player successfully gets on base or hits\na home run.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{r lll ccc ccc}\n  \\hline\n  & name & team & position & AB & H & HR &RBI & AVG & OBP \\\\ \n  \\hline\n  1 &  Abreu, J & CWS & IF &  499 &  132 &   22 &\n      78 & 0.265 & 0.325 \\\\\n  2 &  Acuna Jr., R & ATL & OF &  433 &  127 &   26 &\n      64 & 0.293 & 0.366 \\\\\n  3 &  Adames, W & TB & IF &  288 &   80 &   10 &\n      34 & 0.278 & 0.348 \\\\\n  $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ &\n      $\\vdots$ & $\\vdots$ & $\\vdots$ \\\\\n  427 &  Zimmerman, R & WSH & IF & 288 &   76 &\n      13 &   51 & 0.264 & 0.337 \\\\\n  428 & Zobrist, B & CHC & IF & 455 & 139 & 9 &\n      58 & 0.305 & 0.378 \\\\\n  \\mlbN{} &  Zunino, M & SEA & C &  373 &   75 &   20 &\n      44 & 0.201 & 0.259 \\\\\n   \\hline\n\\end{tabular}\n\\caption{Six cases from the \\mlbdata{} data matrix.}\n\\label{mlbBat18DataMatrix}\n\\end{figure}\n\n\\begin{figure}[h]\n\\centering\\small\n\\begin{tabular}{lp{8.5cm}}\n\\hline\n{\\bf variable} & {\\bf description} \\\\\n\\hline\n\\var{name} & Player name \\\\\n\\var{team} & The abbreviated name of the player's team \\\\\n\\var{position} &\n    The player's primary field position\n    (\\resp{OF}, \\resp{IF}, \\resp{C}) \\\\\n\\var{AB} & Number of opportunities at bat \\\\\n\\var{H} & Number of hits \\\\\n\\var{HR} & Number of home runs \\\\\n\\var{RBI} & Number of runs batted in \\\\\n\\var{AVG} &\n    Batting average, which is equal to $\\resp{H}/\\resp{AB}$ \\\\\n\\var{OBP} &\n    On-base percentage, which is roughly equal to the fraction\n    of times a player gets on base or hits a home run \\\\\n\\hline\n\\end{tabular}\n\\caption{Variables and their descriptions for the\n    \\mlbdata{} data set.}\n\\label{mlbBat18Variables}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{nullHypForOBPAgainstPosition}%\nThe null hypothesis under consideration is the following:\n$\\mu_{\\resp{OF}} = \\mu_{\\resp{IF}} = %\\mu_{\\resp{DH}} = \n    \\mu_{\\resp{C}}$.\nWrite the null and corresponding alternative hypotheses\nin plain language.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$H_0$: The average on-base percentage is equal\n  across the three positions.\n  $H_A$: The average on-base percentage varies across some\n  (or all) groups.}\n\n\\begin{examplewrap}\n\\begin{nexample}{The player positions have been divided\n    into three groups: outfield (\\resp{OF}), infield (\\resp{IF}),\n    %designated hitter (\\resp{DH}),\n    and catcher~(\\resp{C}).\n    What would be an appropriate point estimate of the on-base\n    percentage by outfielders, $\\mu_{\\resp{OF}}$?}\n  A good estimate of the on-base percentage by outfielders would\n  be the sample average of \\var{OBP} for just those players\n  whose position is outfield: $\\bar{x}_{OF} = 0.320$.\n\\end{nexample}\n\\end{examplewrap}\n\nFigure~\\ref{mlbHRPerABSummaryTable} provides summary statistics for each group. A side-by-side box plot for the on-base percentage is shown in Figure~\\ref{mlbANOVABoxPlot}. Notice that the variability appears to be approximately constant across groups; nearly constant variance across groups is an important assumption that must be satisfied before we consider the ANOVA approach.\n\n\\begin{figure}[h]\n\\centering\\small\n\\begin{tabular}{l rrr}\n\\hline\n\t& \\resp{OF} & \\resp{IF} & \\resp{C} \\\\\n\\hline\nSample size ($n_i$) & 160 & 205 & 64 \\\\\nSample mean ($\\bar{x}_i$) & 0.320 & 0.318 & 0.302 \\\\\nSample SD ($s_i$) & 0.043 & 0.038 & 0.038 \\\\\n\\hline\n\\end{tabular}\n\\caption{Summary statistics of on-base percentage, split by player position.}\n\\label{mlbHRPerABSummaryTable}\n\\end{figure}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[Side-by-side box plot of the on-base percentage for \\mlbN{} players across three groups. The boxes for outfield (OF) and infield (IF) groups are about 0.30 to 0.34 with a median of about 0.32, while the catcher (C) box is 0.28 to 0.33 with a median of 0.30. The whiskers for outfield and infield extend down to about 0.25 and up to 0.42, while the catcher box plot whiskers extend down to 0.23 and up to 0.38. With over a hundred players in both the infield and outfield groups, a few individual points are shown but are not concerning.]{0.6}{mlbANOVA}{mlbANOVABoxPlot}\n  \\caption{Side-by-side box plot of the on-base percentage\n      for \\mlbN{} players across three groups.\n      With over a hundred players in both the infield and\n      outfield groups, the apparent outliers are not a concern.}\n  \\label{mlbANOVABoxPlot}\n\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{The largest difference between the sample means\n    is between the catcher and the outfielder positions.\n    Consider again the original hypotheses:\n    \\begin{itemize}\n    \\setlength{\\itemsep}{0mm}\n    \\item[$H_0$:]\n        $\\mu_{\\resp{OF}} = \\mu_{\\resp{IF}} = \\mu_{\\resp{C}}$\n    \\item[$H_A$:]\n        The average on-base percentage ($\\mu_i$) varies\n        across some (or all) groups.\n    \\end{itemize}\n    Why might it be inappropriate to run the test by simply\n    estimating whether the difference of $\\mu_{\\var{C}}$ and\n    $\\mu_{\\resp{OF}}$ is statistically significant at a 0.05\n    significance level?}\n  \\label{multCompExIncDiscOfClassrooms}%\n  The primary issue here is that we are inspecting the data\n  before picking the groups that will be compared.\n  It is inappropriate to examine all data by eye\n  (informal testing) and only afterwards decide which parts\n  to formally test.\n  This is called \\term{data snooping} or \\term{data fishing}.\n  Naturally, we would pick the groups with the large\n  differences for the formal test, and this would leading\n  to an inflation in the Type~1 Error rate.\n  To understand this better, let's consider a slightly\n  different problem.\n\n  Suppose we are to measure the aptitude for students in\n  20~classes in a large elementary school at the beginning\n  of the year.\n  In this school, all students are randomly assigned to\n  classrooms, so any differences we observe between the\n  classes at the start of the year are completely due\n  to chance.\n  However, with so many groups, we will probably observe\n  a few groups that look rather different from each other.\n  If we select only these classes that look so different\n  and then perform a formal test,\n  we will probably make the wrong conclusion that the\n  assignment wasn't random.\n  While we might only formally test differences\n  for a few pairs of classes, we informally evaluated\n  the other classes by eye before choosing the most extreme\n  cases for a comparison.\n\\end{nexample}\n\\end{examplewrap}\n\nFor additional information on the ideas expressed in\nExample~\\ref{multCompExIncDiscOfClassrooms}, we recommend\nreading about the\n\\term{prosecutor's fallacy}.\\footnote{See, for example,\n  \\oiRedirect{textbook-prosecutors_fallacy}\n      {statmodeling.stat.columbia.edu/2007/05/18/the\\_prosecutors}.}\n\nIn the next section we will learn how to use the $F$~statistic\nand ANOVA to test whether observed differences in sample means\ncould have happened just by chance even if there was no\ndifference in the respective population means.\n\n\n\\D{\\newpage}\n\n\\subsection{Analysis of variance (ANOVA)\n    and the $\\pmb{F}$-test}\n\nThe method of analysis of variance in this context focuses\non answering one question:\nis the variability in the sample means so large that it seems\nunlikely to be from chance alone?\nThis question is different from earlier testing procedures\nsince we will \\emph{simultaneously} consider many groups,\nand evaluate whether their sample means differ more than\nwe would expect from natural variation.\nWe~call this variability the\n\\term{mean square between groups ($MSG$)},\nand it has an associated degrees of freedom,\n$df_{G} = k - 1$ when there are\n$k$~groups.\\index{degrees of freedom (df)!ANOVA}\nThe $MSG$ can be thought of as a scaled variance formula\nfor means.\nIf the null hypothesis is true, any variation in the sample\nmeans is due to chance and shouldn't be too large.\nDetails of $MSG$ calculations are provided in the\nfootnote.\\footnote{Let $\\bar{x}$ represent the mean of\n  outcomes across all groups.\n  Then the mean square between groups is computed as\n  \\begin{align*}\n  MSG\n    = \\frac{1}{df_{G}}SSG\n    = \\frac{1}{k-1}\\sum_{i=1}^{k} n_{i}\n        \\left(\\bar{x}_{i} - \\bar{x}\\right)^2\n  \\end{align*}\n  where $SSG$ is called the \\term{sum of squares between groups}\n  and $n_{i}$ is the sample size of group $i$.}\nHowever, we typically use software for these computations.\n\nThe mean square between the groups is, on its own, quite useless\nin a hypothesis test.\nWe~need a benchmark value for how much variability should\nbe expected among the sample means if the null hypothesis is true.\nTo this end, we compute a pooled variance estimate,\noften abbreviated as the \\term{mean square error ($MSE$)},\nwhich has an associated degrees of freedom value $df_E = n - k$.\nIt is helpful to think of $MSE$ as a measure of the variability\nwithin the groups.\nDetails of the computations of the $MSE$ and a link to an\nextra online section for ANOVA calculations are provided\nin the footnote\\footnote{Let $\\bar{x}$ represent the mean\n  of outcomes across all groups.\n  Then the \\term{sum of squares total ($SST$)} is computed as\n  \\begin{align*}\n  SST = \\sum_{i=1}^{n} \\left(x_{i} - \\bar{x}\\right)^2\n  \\end{align*}\n  where the sum is over all observations in the data set.\n  Then we compute the \\term{sum of squared errors ($SSE$)}\n  in one of two equivalent ways:\n  \\begin{align*}\n  SSE &= SST - SSG \\\\\n  \t&= (n_1-1)s_1^2 + (n_2-1)s_2^2 + \\cdots + (n_k-1)s_k^2\n  \\end{align*}\n  where $s_i^2$ is the sample variance (square of the standard\n  deviation) of the residuals in group $i$.\n  Then the $MSE$ is the standardized form of $SSE$:\n  $MSE = \\frac{1}{df_{E}}SSE$.\n  \n  \\noindent%\n  For additional details on ANOVA calculations, see\n  \\oiRedirect{stat_extra_anova_calculations}\n      {www.openintro.org/d?file=stat\\_extra\\_anova\\_calculations}}\nfor interested readers.\n\nWhen the null hypothesis is true, any differences among the\nsample means are only due to chance, and the $MSG$ and $MSE$\nshould be about equal.\nAs~a test statistic for ANOVA, we examine the fraction of $MSG$\nand~$MSE$:\n\\begin{align*}\nF = \\frac{MSG}{MSE}\n\\end{align*}\nThe $MSG$ represents a measure of the between-group variability,\nand $MSE$ measures the variability within each of the groups.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nFor the baseball data, $MSG = 0.00803$ and $MSE=0.00158$.\nIdentify the degrees of freedom associated with MSG and\nMSE and verify the $F$ statistic is approximately\n\\mlbF{}.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{There are $k = \\mlbK{}$ groups,\n  so $df_{G} = k - 1 = \\mlbDFA{}$.\n  There are $n = n_1 + n_2 + n_3 = \\mlbN{}$ total observations,\n  so $df_{E} = n - k = \\mlbDFB{}$.\n  Then the $F$ statistic is computed as the ratio of $MSG$\n  and $MSE$:\n  $F\n    = \\frac{MSG}{MSE}\n    = \\frac{0.00803}{0.00158}\n    = 5.082\n    \\approx \\mlbF{}$.\n  ($F = \\mlbF{}$ was computed by using values for $MSG$\n  and $MSE$ that were not rounded.)}\n\nWe can use the $F$ statistic to evaluate the hypotheses in\nwhat is called an \\termsub{$\\pmb{F}$-test}{F-test@$F$-test}.\nA p-value can be computed from the $F$ statistic using\nan $F$~distribution, which has two associated parameters:\n$df_{1}$ and~$df_{2}$.\nFor the $F$ statistic in ANOVA,\n$df_{1} = df_{G}$ and $df_{2} = df_{E}$.\nAn $F$ distribution with \\mlbDFA{} and \\mlbDFB{} degrees\nof freedom, corresponding to the $F$ statistic for the\nbaseball hypothesis test, is shown in\nFigure~\\ref{fDist2And423Shaded}.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[An F distribution with df-sub-1 equals 2 and df-sub-2 equals 426 is shown. This distribution starts at zero and runs up (and past) a value of 8. The distribution is strongly right skewed. The distribution peaks right at 0 and tapers off quickly, with about 5\\% to 10\\% of the distribution lying above a value of 2. The distribution is indistinguishable from the horizontal axis by about 5. The figure also annotates a small tail area at and above values of 5.]{0.6}{fDist2And423}{fDist2And423Shaded}\n  \\caption{An $F$ distribution with $df_1=2$ and $df_2=426$.}\n  \\label{fDist2And423Shaded}\n\\end{figure}\n\n\\D{\\newpage}\n\nThe larger the observed variability in the sample\nmeans ($MSG$) relative to the within-group observations ($MSE$),\nthe larger $F$ will be and the stronger the evidence against\nthe null hypothesis.\nBecause larger values of $F$ represent stronger evidence against\nthe null hypothesis, we use the upper tail of the distribution\nto compute a p-value.\n\n\\begin{onebox}{The $\\pmb{F}$ statistic and the\n    $\\pmb{F}$-test}\n  Analysis of variance (ANOVA) is used to test whether\n  the mean outcome differs across 2~or more groups.\n  ANOVA uses a test statistic $F$, which represents\n  a standardized ratio of variability in the sample means\n  relative to the variability within the groups.\n  If~$H_0$ is true and the model conditions are satisfied,\n  the statistic $F$ follows an $F$ distribution with\n  parameters $df_{1} = k - 1$ and $df_{2} = n - k$.\n  The upper tail of the $F$ distribution is used to\n  represent the p-value.\n\\end{onebox}\n\n%\\begin{exercisewrap}\n%\\begin{nexercise}\n%\\label{describePValueAreaForFDistributionInMLBOBPExample}%\n%The test statistic for the baseball example is $F = \\mlbF{}$.\n%Shade the area corresponding to the p-value in\n%Figure~\\ref{fDist2And423}. \\footnotemark{}\n%\\end{nexercise}\n%\\end{exercisewrap}\n%\\footnotetext{\\ \\vspace{-4mm}\\\\%\n%  \\Figures{0.5}{fDist2And423}{fDist2And423Shaded}}\n\n\\begin{examplewrap}\n\\begin{nexample}{The p-value corresponding to\n    the shaded area in\n    Figure~\\ref{fDist2And423Shaded}\n    is equal to about \\mlbPvalue{}.\n    Does this provide strong evidence against the\n    null hypothesis?}\n  The p-value is smaller than 0.05, indicating the evidence\n  is strong enough to reject the null hypothesis\n  at a significance level of 0.05.\n  That is, the data provide strong evidence that the average\n  on-base percentage varies by player's primary field position.\n\\end{nexample}\n\\end{examplewrap}\n\n\n\\subsection{Reading an ANOVA table from software}\n\nThe calculations required to perform an ANOVA by hand are\ntedious and prone to human error.\nFor these reasons, it is common to use statistical software\nto calculate the $F$ statistic and p-value.\n\nAn ANOVA can be summarized in a table very similar to that\nof a regression summary, which we will see in\nChapters~\\ref{linRegrForTwoVar}\nand~\\ref{multipleAndLogisticRegression}.\nFigure~\\ref{anovaSummaryTableForOBPAgainstPosition}\nshows an ANOVA summary to test whether the mean of on-base\npercentage varies by player positions in the MLB.\nMany of these values should look familiar;\nin particular, the $F$-test statistic and p-value\ncan be retrieved from the last two columns.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{lrrrrr}\n  \\hline\n  & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\\\ \n  \\hline\n  position & \\mlbDFA{} & 0.0161 & 0.0080 & 5.0766 & 0.0066 \\\\ \n  Residuals & \\mlbDFB{} & 0.6740 & 0.0016 &  &  \\\\ \n\n  \\hline\n\\multicolumn{6}{r}{$s_{pooled} = 0.040$ on $df = 423$}\n\\end{tabular}\n\\caption{ANOVA summary for testing whether the average\n    on-base percentage differs across player positions.}\n\\label{anovaSummaryTableForOBPAgainstPosition}\n\\end{figure}\n\n\n\\D{\\newpage}\n\n\\subsection{Graphical diagnostics for an ANOVA analysis}\n\nThere are three conditions we must check for an ANOVA analysis:\nall observations must be independent,\nthe data in each group must be nearly normal,\nand the variance within each group must be approximately equal.\n\\begin{description}\n\\item[Independence.]\n    If the data are a simple random sample,\n    this condition is satisfied.\n    For processes and experiments, carefully consider whether\n    the data may be independent (e.g. no pairing).\n    For example, in the MLB data, the data were not sampled.\n    However, there are not obvious reasons why independence\n    would not hold for most or all observations.\n\\item[Approximately normal.]\n    As with one- and two-sample testing for means,\n    the normality assumption is especially important\n    when the sample size is quite small when it is\n    ironically difficult to check for non-normality.\n    A histogram of the observations from each group\n    is shown in Figure~\\ref{mlbANOVADiagNormalityGroups}.\n    Since each of the groups we're considering have\n    relatively large sample sizes,\n    what we're looking for are major outliers.\n    None are apparent, so this conditions is reasonably met.\n    \\begin{figure}[h]\n      \\centering\n      \\Figures[Three histograms are shown, one for Outfielders, one for Infielders, and one for Catchers. The Outfielders and Infielders are centered slightly above 0.3, while the Catchers distribution is centered at about 0.3. The variability in each group is about 0.03. Each of the distributions somewhat resemble normal distributions and do not have any major outliers.]{}{mlbANOVA}{mlbANOVADiagNormalityGroups}\n      \\caption{Histograms of OBP for each field position.}\n      \\label{mlbANOVADiagNormalityGroups}\n    \\end{figure}\n\\item[Constant variance.]\n    The last assumption is that the variance in the\n    groups is about equal from one group to the next.\n    This assumption can be checked by examining a\n    side-by-side box plot of the outcomes across the\n    groups, as in Figure~\\vref{mlbANOVABoxPlot}.\n    In this case, the variability is similar in the\n    three groups but not identical.\n    We see in Table~\\vref{mlbHRPerABSummaryTable}\n    that the standard deviation doesn't vary much\n    from one group to the next.\n\\end{description}\n\n\\index{data!MLB batting|)}\n\n\\begin{onebox}{Diagnostics for an ANOVA analysis}\n  Independence is always important to an ANOVA analysis.\n  The normality condition is very important when the sample\n  sizes for each group are relatively small.\n  The constant variance condition is especially important\n  when the sample sizes differ between groups.\n\\end{onebox}\n\n\n\\D{\\newpage}\n\n\\subsection{Multiple comparisons and controlling Type~1 Error rate}\n\\label{multipleComparisonsAndControllingTheType1ErrorRate}\n\n\\index{significance level!multiple comparisons|(}\n\nWhen we reject the null hypothesis in an ANOVA analysis,\nwe might wonder, which of these groups have different means?\nTo answer this question, we compare the means of each possible\npair of groups.\nFor instance, if there are three groups and there is strong\nevidence that there are some differences in the group means,\nthere are three comparisons to make:\ngroup~1 to group~2, group~1 to group~3, and group~2 to group~3.\nThese comparisons can be accomplished using\na two-sample $t$-test, but we use a modified significance level\nand a pooled estimate of the standard deviation across groups.\nUsually this pooled standard deviation can be found in the\nANOVA table, e.g. along the bottom of\nFigure~\\ref{anovaSummaryTableForOBPAgainstPosition}.\n\n\\begin{examplewrap}\n\\begin{nexample}{\n    Example~\\vref{firstExampleForThreeStatisticsClassesAndANOVA}\n    discussed three statistics lectures, all taught during the\n    same semester.\n    Figure~\\ref{summaryStatisticsForClassTestData}\n    shows summary statistics for these three courses,\n    and a side-by-side box plot of the data is shown\n    in Figure~\\ref{classDataSBSBoxPlot}.\n    We would like to conduct an ANOVA for these data.\n    Do you see any deviations from the three conditions\n    for ANOVA?}\n  In this case (like many others) it is difficult to check\n  independence in a rigorous way.\n  Instead, the best we can do is use common sense to consider\n  reasons the assumption of independence may not hold.\n  For instance, the independence assumption may not be\n  reasonable if there is a star teaching assistant that only\n  half of the students may access;\n  such a scenario would divide a class into two subgroups.\n  No such situations were evident for these particular data,\n  and we believe that independence is acceptable.\n\n  The distributions in the side-by-side box plot appear\n  to be roughly symmetric and show no noticeable outliers.\n\n  The box plots show approximately equal variability,\n  which can be verified in\n  Figure~\\ref{summaryStatisticsForClassTestData},\n  supporting the constant variance assumption.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{lrrr}\n  \\hline\nClass $i$\t& A\t& B\t& C \\\\ \n  \\hline\n$n_i$\t\t& 58\t& 55\t& 51 \\\\ \n$\\bar{x}_i$\t& 75.1\t& 72.0\t& 78.9 \\\\ \n$s_i$\t\t& 13.9\t& 13.8\t& 13.1 \\\\ \n\\hline\n\\end{tabular}\n\\caption{Summary statistics for the first midterm scores\n    in three different lectures of the same course.}\n\\label{summaryStatisticsForClassTestData}\n\\end{figure}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[Side-by-side box plot for the first midterm scores in three different lectures of the same course. Lecture A has a box from about 65 to 85, a median of 73, and whiskers that extend down to 45 and up to 100. Lecture B has a box from about 62 to 82, a median of 72, and whiskers that extend down to 40 and up to 100. Lecture A has a box from about 73 to 88, a median of 82, and whiskers that extend down to 45 and up to 100.]{0.72}{classData}{classDataSBSBoxPlot}\n  \\caption{Side-by-side box plot for the first midterm\n      scores in three different lectures of the same course.}\n  \\label{classDataSBSBoxPlot}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{exerExaminingAnovaSummaryTableForMidtermData}%\nANOVA was conducted for the midterm data,\nand summary results are shown in\nFigure~\\ref{anovaSummaryTableForMidtermData}.\nWhat should we conclude?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The p-value of the test is 0.0330,\n  less than the default significance level of 0.05.\n  Therefore, we reject the null hypothesis and conclude\n  that the difference in the average midterm scores are\n  not due to chance.}\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{lrrrrr}\n  \\hline\n & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\\\ \n  \\hline\nlecture & 2 & 1290.11 & 645.06 & 3.48 & 0.0330 \\\\ \n  Residuals & 161 & 29810.13 & 185.16 &  &  \\\\ \n   \\hline\n\\multicolumn{6}{r}{$s_{pooled}=13.61$ on $df=161$}\n\\end{tabular}\n\\caption{ANOVA summary table for the midterm data.}\n\\label{anovaSummaryTableForMidtermData}\n\\end{figure}\n\nThere is strong evidence that the different means in each\nof the three classes is not simply due to chance.\nWe might wonder, which of the classes are actually different?\nAs discussed in earlier chapters, a two-sample $t$-test\ncould be used to test for differences in each possible pair\nof groups.\nHowever, one pitfall was discussed in\nExample~\\vref{multCompExIncDiscOfClassrooms}:\nwhen we run so many tests, the Type~1 Error rate increases.\nThis issue is resolved by using a modified significance level.\n\n\\begin{onebox}{Multiple comparisons and the Bonferroni\n    correction for $\\pmb{\\alpha}$}\n  The scenario of testing many pairs of groups is called\n  \\term{multiple comparisons}.\n  The \\term{Bonferroni correction} suggests that a more\n  stringent significance level is more appropriate for\n  these tests:\n  \\begin{align*}\n  \\alpha^{\\star} = \\alpha / K\n  \\end{align*}\n  where $K$ is the number of comparisons being considered\n  (formally or informally).\n  If there are $k$ groups, then usually all possible pairs\n  are compared and $K=\\frac{k(k-1)}{2}$.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{In Guided\n    Practice~\\ref{exerExaminingAnovaSummaryTableForMidtermData},\n    you found strong evidence of differences in the average\n    midterm grades between the three lectures.\n    Complete the three possible pairwise comparisons using\n    the Bonferroni correction and report any differences.}\n  \\label{multipleComparisonsOfThreeStatClasses}%\n  We use a modified significance level of\n  $\\alpha^{\\star} = 0.05 / 3 = 0.0167$.\n  Additionally, we use the pooled estimate of the standard\n  deviation:\n  $s_{pooled}=13.61$ on $df=161$,\n  which is provided in the ANOVA summary table.\n\n  Lecture A versus Lecture B:\n  The estimated difference and standard error are,\n  respectively,\n  \\begin{align*}\n  \\bar{x}_A - \\bar{x}_{B} &= 75.1 - 72 = 3.1\n  &&SE = \\sqrt{\\frac{13.61^2}{58} + \\frac{13.61^2}{55}} = 2.56\n  \\end{align*}\n  (See Section~\\vref{pooledStandardDeviations}\n  for additional details.)\n  This results in a T-score of 1.21 on $df = 161$\n  (we use the $df$ associated with $s_{pooled}$).\n  Statistical software was used to precisely identify the two-sided\n  p-value since the modified significance level of 0.0167 is not\n  found in the $t$-table.\n  The p-value (0.228) is larger than $\\alpha^*=0.0167$,\n  so there is not strong evidence of a difference in the means\n  of lectures A and~B.\n\n  Lecture A versus Lecture C: The estimated difference and\n  standard error are 3.8 and 2.61, respectively.\n  This results in a $T$ score of 1.46 on $df = 161$\n  and a two-sided p-value of 0.1462.\n  This p-value is larger than $\\alpha^*$, so there is not\n  strong evidence of a difference in the means of lectures\n  A and~C.\n\n  Lecture B versus Lecture C: The estimated difference\n  and standard error are 6.9 and 2.65, respectively.\n  This results in a $T$ score of 2.60 on $df = 161$\n  and a two-sided p-value of 0.0102.\n  This p-value is smaller than $\\alpha^*$.\n  Here we find strong evidence of a difference in the\n  means of lectures B and~C.\n\\end{nexample}\n\\end{examplewrap}\n\n\\D{\\newpage}\n\n\\noindent%\nWe might summarize the findings of the analysis from\nExample~\\ref{multipleComparisonsOfThreeStatClasses}\nusing the following notation:\n\\begin{align*}\n\\mu_A &\\stackrel{?}{=} \\mu_B\n\t&\\mu_A &\\stackrel{?}{=} \\mu_C\n\t&\\mu_B &\\neq \\mu_C\n\\end{align*}\nThe midterm mean in lecture A is not statistically\ndistinguishable from those of lectures B or C.\nHowever, there is strong evidence that lectures B and~C\nare different.\nIn~the first two pairwise comparisons, we did not have\nsufficient evidence to reject the null hypothesis.\nRecall that failing to reject $H_0$ does not imply $H_0$ is true.\n\n\\begin{onebox}{Reject $\\pmb{H_0}$ with ANOVA\n    but find no differences in group means}\n  It is possible to reject the null hypothesis using ANOVA\n  and then to not subsequently identify differences in the\n  pairwise comparisons.\n  However, \\emph{this does not invalidate the ANOVA conclusion}.\n  It only means we have not been able to successfully identify\n  which specific groups differ in their means.\n\\end{onebox}\n\nThe ANOVA procedure examines the big picture:\nit considers all groups simultaneously to decipher whether\nthere is evidence that some difference exists.\nEven if the test indicates that there is strong evidence\nof differences in group means, identifying with\nhigh confidence a specific difference as statistically\nsignificant is more difficult.\n\nConsider the following analogy:\nwe observe a Wall Street firm that makes large quantities\nof money based on predicting mergers.\nMergers are generally difficult to predict,\nand if the prediction success rate is extremely high,\nthat may be considered sufficiently strong evidence\nto warrant investigation by the Securities and Exchange\nCommission~(SEC).\nWhile the SEC may be quite certain that there is insider\ntrading taking place at the firm, the evidence against\nany single trader may not be very strong.\nIt is only when the SEC considers all the data that they\nidentify the pattern.\nThis is effectively the strategy of ANOVA:\nstand back and consider all the groups simultaneously.\n\n\\index{significance level!multiple comparisons|)}\n\\index{analysis of variance (ANOVA)|)}\n\n\n{\\input{ch_inference_for_means/TeX/comparing_many_means_with_anova.tex}}\n"
  },
  {
    "path": "ch_inference_for_means/TeX/comparing_many_means_with_anova.tex",
    "content": "\\exercisesheader{}\n\n% 35\n\n\\eoce{\\qt{Fill in the blank\\label{fitb_anova}} When doing an ANOVA, you observe \nlarge differences in means between groups. Within the ANOVA framework, this \nwould most likely be interpreted as evidence strongly favoring the \\underline{\\hspace{20mm}} hypothesis.\n}{}\n\n% 36\n\n\\eoce{\\qtq{Which test\\label{which_test_anova}} We would like to test if \nstudents who are in the social sciences, natural sciences, arts and \nhumanities, and other fields spend the same amount of time studying for \nthis course. What type of test should we use? Explain your reasoning.\n}{}\n\n% 37\n\n\\eoce{\\qt{Chicken diet and weight, Part III\\label{chick_wts_anova}} In Exercises~\\ref{chick_wts_linseed_horsebean} and \\ref{chick_wts_casein_soybean} we compared the effects of two types of feed at a time. A better analysis would first consider all feed types at once: casein, horsebean, linseed, meat meal, soybean, and sunflower. The ANOVA output below can be used to test for differences between the average weights of chicks on different diets.\n\\begin{center}\n\\begin{tabular}{lrrrrr}\n\\hline\n        & Df    & Sum Sq        & Mean Sq   & F value   & Pr($>$F) \\\\ \n\\hline\nfeed        & 5     & 231,129.16    & 46,225.83     & 15.36     & 0.0000 \\\\ \nResiduals   & 65 & 195,556.02   & 3,008.55  &       &  \\\\ \n\\hline\n%\\multicolumn{6}{r}{$s_{pooled} = 55.85$ on $df=65$}\n\\end{tabular}\n\\end{center}\nConduct a hypothesis test to determine if these data provide convincing evidence that the average weight of chicks varies across some (or all) groups. Make sure to check relevant conditions. Figures and summary statistics are shown below.\n\n\\begin{minipage}[c]{0.65\\textwidth}\n\\begin{center}\n\\FigureFullPath[A side-by-side box plot is shown for \"Weight, in grams\" for several feed types. The width of the data range for each feed type spans about 150 grams. However, they are centered at different locations: about 325 for \"casein\", about 150 for \"horsebean\", about 225 for \"linseed\", about 275 for \"meatmeal\", about 250 for \"soybean\", and about 325 for \"sunflower\".]{}{ch_inference_for_means/figures/eoce/chick_wts_anova/chick_wts_box.pdf}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.35\\textwidth}\n{\\footnotesize\\begin{tabular}{l c c c}\n\\hline\n            & Mean      & SD        & n \\\\\n\\hline\ncasein          & 323.58        & 64.43 & 12 \\\\\nhorsebean   & 160.20        & 38.63 & 10 \\\\\nlinseed         & 218.75        & 52.24 & 12 \\\\\nmeatmeal    & 276.91        & 64.90 & 11 \\\\\nsoybean         & 246.43        & 54.13 & 14 \\\\\nsunflower       & 328.92        & 48.84 & 12 \\\\\n\\hline\n\\end{tabular}}\n\\end{minipage}\n}{}\n\n% 38\n\n\\eoce{\\qt{Teaching descriptive statistics\\label{teach_descriptive_stats}} A study \ncompared five different methods for teaching descriptive statistics. The five \nmethods were traditional lecture and discussion, programmed textbook \ninstruction, programmed text with lectures, computer instruction, and computer \ninstruction with lectures. 45 students were randomly assigned, 9 to each \nmethod. After completing the course, students took a 1-hour exam. \n\\begin{parts}\n\\item What are the hypotheses for evaluating if the average test scores are \ndifferent for the different teaching methods?\n\\item What are the degrees of freedom associated with the $F$-test for \nevaluating these hypotheses?\n\\item Suppose the p-value for this test is 0.0168. What is the conclusion?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 39\n\n\\eoce{\\qt{Coffee, depression, and physical activity\\label{coffee_depression_phys_act}} \nCaffeine is the world's most widely used stimulant, with approximately 80\\% consumed \nin the form of coffee. Participants in a study investigating the relationship between \ncoffee consumption and exercise were asked to report the number of hours they spent per \nweek on moderate (e.g., brisk walking) and vigorous (e.g., strenuous sports and jogging) \nexercise. Based on these data the researchers estimated the total hours of metabolic \nequivalent tasks (MET) per week, a value always greater than 0. The table below gives \nsummary statistics of MET for women in this study based on the amount of coffee consumed.\n\\footfullcite{Lucas:2011}\n \n\\begin{adjustwidth}{-4em}{-4em}\n\\begin{center}\n\\begin{tabular}{l  r  r  r  r  r  r}\n                & \\multicolumn{5}{c}{\\textit{Caffeinated coffee consumption}} \\\\\n\\cline{2-6}\n                & $\\le$ 1 cup/week  & 2-6 cups/week & 1 cup/day \n                                            & 2-3 cups/day  & $\\ge$ 4 cups/day  & Total \\\\\n\\hline\nMean            & 18.7              & 19.6          & 19.3  \n                                            & 18.9          & 17.5 \\\\\nSD              & 21.1              & 25.5          & 22.5  \n                                            & 22.0          & 22.0 \\\\\nn               & 12,215            & 6,617         & 17,234    \n                                            & 12,290        & 2,383             & 50,739 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\end{adjustwidth}\n\n\\begin{parts}\n\n\\item Write the hypotheses for evaluating if the average physical activity level \nvaries among the different levels of coffee consumption.\n\n\\item Check conditions and describe any assumptions you must make to proceed with \nthe test.\n\n\\item Below is part of the output associated with this test. Fill in the empty cells.\n\n\\begin{center}\n\\renewcommand{\\arraystretch}{1.25}\n\\begin{tabular}{lrrrrr}\n  \\hline\n            & Df\n                        & Sum Sq\n                                    & Mean Sq\n                                                & F value\n                                                            & Pr($>$F) \\\\ \n  \\hline\ncoffee      & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}}    \n                        & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}}      \n                                    & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}}           \n                                                & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}}\n                                                            & 0.0003 \\\\ \nResiduals   & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}} \n                        & 25,564,819\n                                    & \\fbox{\\textcolor{white}{{\\footnotesize  XXXXX}}}\n                                                &\n                                                            &  \\\\ \n   \\hline\nTotal       & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}} \n                        & 25,575,327\n\\end{tabular}\n\\end{center}\n\n\\item What is the conclusion of the test?\n\n\\end{parts}\n}{}\n\n% 40\n\n\\eoce{\\qt{Student performance across discussion sections\\label{student_performance_sections}} A professor who teaches a large introductory statistics class (197 students) with eight discussion sections would like to test if student performance differs by discussion section, where each discussion section has a different teaching assistant. The summary table below shows the average final exam score for each discussion section as well as the standard deviation of scores and the number of students in each section.\n\\begin{center}\n\\begin{tabular}{rrrrrrrrr}\n  \\hline\n            & Sec 1 & Sec 2 & Sec 3 & Sec 4 & Sec 5 & Sec 6 & Sec 7 & Sec 8 \\\\ \n  \\hline\n$n_i$       & 33 & 19 & 10 & 29 & 33 & 10 & 32 & 31 \\\\ \n$\\bar{x}_i$ & 92.94 & 91.11 & 91.80 & 92.45 & 89.30 & 88.30 & 90.12 & 93.35 \\\\ \n$s_i$       & 4.21 & 5.58 & 3.43 & 5.92 & 9.32 & 7.27 & 6.93 & 4.57 \\\\ \n   \\hline\n\\end{tabular}\n\\end{center}\nThe ANOVA output below can be used to test for differences between the average scores from the different discussion sections.\n\\begin{center}\n\\begin{tabular}{lrrrrr}\n\\hline\n            & Df        & Sum Sq & Mean Sq  & F value & Pr($>$F) \\\\ \n\\hline\nsection         & 7         & 525.01    & 75.00         & 1.87  & 0.0767 \\\\ \nResiduals   & 189   & 7584.11   & 40.13         &       &  \\\\ \n\\hline\n\\end{tabular}\n\\end{center}\nConduct a hypothesis test to determine if these data provide convincing evidence that the average score varies across some (or all) groups. Check conditions and describe any assumptions you must make to proceed with the test.\n}{}\n\n\\D{\\newpage}\n\n% 41\n\n\\eoce{\\qt{GPA and major\\label{gpa_major}} Undergraduate students taking an introductory statistics course at Duke University conducted a survey about GPA and major. The side-by-side box plots show the distribution of GPA among three groups of majors. Also provided is the ANOVA output.\n\\begin{center}\n\\FigureFullPath[Side-by-side box plot for GPA in three different groups of majors. \"Arts and Humanities\" has a box from about 3.3 to 3.8, a median of 3.6, and whiskers that extend down to 3.1 to 4.0. \"Natural Sciences\" has a box from about 3.4 to 3.8, a median of 3.7, and whiskers that extend down to 2.9 to 4.0. \"Social Sciences\" has a box from about 3.3 to 3.8, a median of 3.6, whiskers that extend down to 2.8 to 4.0, and a single point beyond the lower whisker at about 2.6.]{0.55}{ch_inference_for_means/figures/eoce/gpa_major/gpa_major.pdf}\n\\end{center}\n\\begin{center}\n\\begin{tabular}{lrrrrr}\n  \\hline\n            & Df    & Sum Sq    & Mean Sq   & F value   & Pr($>$F) \\\\ \n  \\hline\nmajor       & 2     & 0.03      & 0.015      & 0.185     & 0.8313 \\\\ \nResiduals   & 195   & 15.77     & 0.081      &           &  \\\\ \n   \\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Write the hypotheses for testing for a difference between average GPA across majors.\n\\item What is the conclusion of the hypothesis test?\n\\item How many students answered these questions on the survey, i.e. what is the sample size?\n\\end{parts}\n}{}\n\n% 42\n\n\\eoce{\\qt{Work hours and education\\label{work_hours_education}} The General Social Survey \ncollects data on demographics, education, and work, among many other characteristics \nof US residents. \\footfullcite{data:gss} Using ANOVA, we can consider \neducational attainment levels for all 1,172 respondents at once. Below are the \ndistributions of hours worked by educational attainment and relevant summary \nstatistics that will be helpful in carrying out this analysis.\n\\begin{center}\n\n\\begin{tabular}{l  r  r  r  r  r  r}\n                & \\multicolumn{5}{c}{\\textit{Educational attainment}} \\\\\n\\cline{2-6}\n                & Less than HS  & HS    & Jr Coll   & Bachelor's & Graduate & Total \\\\\n\\hline\nMean            & 38.67         & 39.6  & 41.39     & 42.55     & 40.85     & 40.45 \\\\\nSD              & 15.81         & 14.97 & 18.1      & 13.62     & 15.51     & 15.17 \\\\\nn               & 121           & 546   & 97        & 253       & 155       & 1,172 \\\\\n\\hline\n\\end{tabular}\n\n\\FigureFullPath[Side-by-side box plot for \"Hours worked per week\" for five different levels of education. \"Less than High School\" has a box from about 31 to 46, a median of 40, and whiskers that extend down to 9 and up to 69. \"High School\" has a box from about 32 to 48, a median of 41, and whiskers that extend down to 33 and up to 49. \"Junior College\" has a box from about 31 to 50, a median of 42, and whiskers that extend down to 0 and up to 49. \"Bachelor's\" has a box from about 42 to 50, a median of 42, and whiskers that extend down to 31 and up to 62. \"Graduate\" has a box from about 38 to 48, a median of 42, and whiskers that extend down to 20 and up to 72. All boxes have a few points extending beyond the whiskers, with the exception of Bachelor's, which has a large number of points below the lower whisker extending close to 0.]{0.78}{ch_inference_for_means/figures/eoce/work_hours_education/work_hours_education.pdf}\n\\end{center}\n\\begin{parts}\n\\item Write hypotheses for evaluating whether the average number of hours \nworked varies across the five groups.\n\\item Check conditions and describe any assumptions you must make to proceed \nwith the test.\n\\item Below is part of the output associated with this test. Fill in the \nempty cells.\n\n\\begin{center}\n\\renewcommand{\\arraystretch}{1.25}\n\\begin{tabular}{lrrrrr}\n  \\hline\n            & Df    \n                    & Sum Sq        \n                            & Mean Sq       \n                                    & F-value      \n                                            & Pr($>$F) \\\\ \n  \\hline\ndegree      & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}}       \n                    & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}}       \n                            & 501.54    \n                                    & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}}   \n                                            & 0.0682 \\\\ \nResiduals   & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}} \n                    & 267,382     \n                            & \\fbox{\\textcolor{white}{{\\footnotesize  XXXXX}}}          \n                                    &       \n                                            &  \\\\ \n   \\hline\nTotal       & \\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}} \n                    &\\fbox{\\textcolor{white}{{\\footnotesize XXXXX}}}\n\\end{tabular}\n\\end{center}\n\n\\item What is the conclusion of the test?\n\n\\end{parts}\n}{}\n\n% 43\n\n\\eoce{\\qt{True / False: ANOVA, Part I\\label{tf_anova_1}} Determine if the following statements are true or false in ANOVA, and explain your reasoning for statements you identify as false.\n\\begin{parts}\n\\item As the number of groups increases, the modified significance level for pairwise tests increases as well.\n\\item As the total sample size increases, the degrees of freedom for the residuals increases as well.\n\\item The constant variance condition can be somewhat relaxed when the sample sizes are relatively consistent across groups.\n\\item The independence assumption can be relaxed when the total sample size is large.\n\\end{parts}\n}{}\n\n% 44\n\n\\eoce{\\qt{Child care hours\\label{child_care_hours}} The China Health and Nutrition \nSurvey aims to examine the effects of the health, nutrition, and family planning \npolicies and programs implemented by national and local governments.\\footfullcite{data:china} It, for example, collects information on number of hours Chinese parents spend \ntaking care of their children under age 6. The side-by-side box plots below \nshow the distribution of this variable by educational attainment of the parent. \nAlso provided below is the ANOVA output for comparing average hours across \neducational attainment categories.\n\\begin{center}\n\\FigureFullPath[Side-by-side box plot for \"Child care hours\" for five different levels of education. The \"Primary school\", \"Lower middle school\", \"Upper middle school\", and \"College\" have very similar box plots: a box from about 5 to 30, a median of 15, whiskers that extend down to 0 and up to about 60, and several points above the upper whisker. \"Technical or vocational\" has a box from about 5 to 50, a median of 20, whiskers that extend down to 0 and up to 90, with a handful of points above the upper whisker.]{}{ch_inference_for_means/figures/eoce/child_care_hours/child_care_hours}\n\\end{center}\n\\begin{center}\n\\begin{tabular}{lrrrrr}\n\\hline\n            & Df    & Sum Sq    & Mean Sq   & F value   & Pr($>$F) \\\\ \n\\hline\neducation   & 4     & 4142.09   & 1035.52   & 1.26      & 0.2846 \\\\ \nResiduals   & 794   & 653047.83 & 822.48    &           &  \\\\ \n\\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Write the hypotheses for testing for a difference between the average \nnumber of hours spent on child care across educational attainment levels.\n\\item What is the conclusion of the hypothesis test?\n\\end{parts}\n}{}\n\n% 45\n\n\\eoce{\\qt{Prison isolation experiment, Part II\\label{prison_isolation_anova}} Exercise~\\ref{prison_isolation_T} introduced an experiment that was conducted with the goal of identifying a treatment that reduces subjects' psychopathic deviant T scores, where this score measures a person's need for control or his rebellion against control. In Exercise~\\ref{prison_isolation_T} you evaluated the success of each treatment individually. An alternative analysis involves comparing the success of treatments.\nThe relevant ANOVA output is given below,\nand we have checked for you that there are no meaningful\ndifferences in variability across the groups.\n\\begin{center}\n\\begin{tabular}{lrrrrr}\n  \\hline\n & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\\\ \n  \\hline\ntreatment & 2 & 639.48 & 319.74 & 3.33 & 0.0461 \\\\ \n  Residuals & 39 & 3740.43 & 95.91 &  &  \\\\ \n   \\hline\n\\multicolumn{6}{r}{$s_{pooled} = 9.793$ on $df=39$}\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item What are the hypotheses?\n\\item\\label{prison_isolation_anova_test_conclusion}\n    What is the conclusion of the test?\n    Use a 5\\% significance level.\n\\item\n    If in\n    part~(\\ref{prison_isolation_anova_test_conclusion})\n    you determined that the test is significant,\n    conduct pairwise tests to determine which groups\n    are different from each other.\n    If you did not reject the null hypothesis in\n    part~(\\ref{prison_isolation_anova_test_conclusion}),\n    recheck your answer.\n    Summary statistics for each group are provided below.\n\\begin{center}\n\\begin{tabular}{l  r  r  r  r  }\n\\hline\n                & Tr 1  & Tr 2  & Tr 3      \\\\\n\\hline\nMean            & 6.21  & 2.86  & -3.21           \\\\\nSD              & 12.3  & 7.94  & 8.57       \\\\\nn               & 14        & 14        & 14     \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\end{parts}\n}{}\n\n% 46\n\n\\eoce{\\qt{True / False: ANOVA, Part II\\label{tf_anova_2}} Determine if the following statements are true or false, and explain your reasoning for statements you identify as false.\n\nIf the null hypothesis that the means of four groups are all the same is rejected using ANOVA at a 5\\% significance level, then ...\n\\begin{parts}\n\\item we can then conclude that all the means are different from one another.\n\\item the standardized variability between groups is higher than the standardized variability within groups.\n\\item the pairwise analysis will identify at least one pair of means that are significantly different.\n\\item the appropriate $\\alpha$ to be used in pairwise comparisons is 0.05 / 4 = 0.0125 since there are four groups.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_inference_for_means/TeX/difference_of_two_means.tex",
    "content": "\\exercisesheader{}\n\n% 23\n\n\\eoce{\\qt{Friday the 13$^{\\text{th}}$, Part I\\label{friday_13th_traffic}} In the \nearly 1990's, researchers in the UK collected data on traffic flow, number of \nshoppers, and traffic accident related emergency room admissions on Friday the \n13$^{\\text{th}}$ and the previous Friday, Friday the 6$^{\\text{th}}$. The \nhistograms below show the distribution of number of cars passing by a specific \nintersection on Friday the 6$^{\\text{th}}$ and Friday the 13$^{\\text{th}}$ for \nmany such date pairs. Also given are some sample statistics, where the \ndifference is the number of cars on the 6th minus the number of cars on the 13th.\\footfullcite{Scanlon:1993}\n\\begin{center}\n\\FigureFullPath[Three histograms are shown. The first histogram is for \"Friday the 6th\", which has values ranging from 110,000 to 140,000. The second histogram is for \"Friday the 13th\", which also has values ranging from 110,000 to 140,000. The third histogram is for \"Difference\", with values ranging from 0 to 5,000. While the first two distributions are relatively uniform across the range, the last distribution has most of its distribution ranging between 0 and 3,000, with one observation in the 4,000 to 5,000 bin, which represents one value.]{}{ch_inference_for_means/figures/eoce/friday_13th_traffic/friday_13th_traffic_hist} \\\\\n$\\:$ \\\\\n{\\small\n\\begin{tabular}{l c c c}\n\\hline\n        & 6$^{\\text{th}}$   & 13$^{\\text{th}}$  & Diff.\\\\\n\\hline  \n$\\bar{x}$   &128,385            & 126,550       & 1,835 \\\\\n$s$     &7,259          & 7,664         & 1,176 \\\\\n$n$     &10             & 10                & 10 \\\\\n\\hline\n\\end{tabular}\n}\n\\end{center}\n\\begin{parts}\n\\item Are there any underlying structures in these data that should be \nconsidered in an analysis? Explain.\n\\item What are the hypotheses for evaluating whether the number of people out \non Friday the 6$^{\\text{th}}$ is different than the number out on Friday the \n13$^{\\text{th}}$?\n\\item Check conditions to carry out the hypothesis test from part~(b).\n\\item Calculate the test statistic and the p-value.\n\\item What is the conclusion of the hypothesis test?\n\\item Interpret the p-value in this context.\n\\item What type of error might have been made in the conclusion of your test? \nExplain.\n\\end{parts}\n}{}\n\n% 24\n\n\\eoce{\\qt{Diamonds, Part I\\label{diamonds_1}} Prices of diamonds are determined by \nwhat is known as the 4 Cs: cut, clarity, color, and carat weight. The prices of \ndiamonds go up as the carat weight increases, but the increase is not smooth. \nFor example, the difference between the size of a 0.99 carat diamond and a 1 \ncarat diamond is undetectable to the naked human eye, but the price of a 1 \ncarat diamond tends to be much higher than the price of a 0.99 diamond. In this \nquestion we use two random samples of diamonds, 0.99 carats and 1 carat, each \nsample of size 23, and compare the average prices of the diamonds. In order to \nbe able to compare equivalent units, we first divide the price for each diamond \nby 100 times its weight in carats. That is, for a 0.99 carat diamond, we divide \nthe price by 99. For a 1 carat diamond, we divide the price by 100. The \ndistributions and some sample statistics are shown below.\\footfullcite{ggplot2} \\\\[1mm]\n\\begin{minipage}[c]{0.57\\textwidth}\nConduct a hypothesis test to evaluate if there is a difference between the \naverage standardized prices of 0.99 and 1 carat diamonds. Make sure to state \nyour hypotheses clearly, check relevant conditions, and interpret your results \nin context of the data. \\\\[2mm]\n\\begin{tabular}{l c c }\n\\hline\n        & 0.99 carats       & 1 carat\\\\\n\\hline  \nMean    & \\$44.51          & \\$56.81           \\\\\nSD      & \\$13.32          &\\$16.13            \\\\\nn       &23             & 23 \\\\\n\\hline\n\\end{tabular}\n\\end{minipage}%\n\\begin{minipage}[c]{0.43\\textwidth}\n\\begin{center}\n\\FigureFullPath[Side-by-side box plot for \"Point price, in dollars\". The two categories shown are for \"0.99 carats\" and \"1 carat\" diamonds. The 0.99 carat diamonds have their box running from about \\$36 to \\$57, a median of about \\$49, and the whiskers spanning about \\$19 to \\$62. The 1 carat diamonds have their box running from about \\$48 to \\$72, a median of about \\$55, and the whiskers spanning about \\$34 to \\$72.]{0.875}{ch_inference_for_means/figures/eoce/diamonds_1/diamonds_box.pdf}\n\\end{center}\n\\end{minipage}\n}{}\n\n\\D{\\newpage}\n\n% 25\n\n\\eoce{\\qt{Friday the 13$^{\\text{th}}$, Part II\\label{friday_13th_accident}}\nThe Friday the $13^{th}$ study reported in\nExercise~\\ref{friday_13th_traffic} also provides data on traffic\naccident related emergency room admissions.\nThe distributions of these counts from Friday the 6$^{\\text{th}}$ and\nFriday the 13$^{\\text{th}}$ are shown below for six such paired dates\nalong with summary statistics.\nYou may assume that conditions for inference are met.\n\\begin{center}\n\\FigureFullPath[Three histograms are shown. The first histogram is for \"Friday the 6th\", which has values ranging across 3 to 12. The second histogram is for \"Friday the 13th\", which has values ranging from 4 to 14. The third histogram is for \"Difference\", with values ranging from -8 to positive 2.]{0.9}{ch_inference_for_means/figures/eoce/friday_13th_accident/friday_13th_accident_hist} \\\\\n$\\:$ \\\\\n\\begin{minipage}[c]{0.32\\textwidth}\n\\begin{tabular}{l c c c}\n\\hline\n        & 6$^{\\text{th}}$   & 13$^{\\text{th}}$  & diff\\\\\n\\hline  \nMean    &7.5                & 10.83             & -3.33 \\\\\nSD      &3.33           & 3.6               & 3.01 \\\\\nn       &6              & 6             & 6 \\\\\n\\hline\n\\end{tabular}\n\\end{minipage}\n\\end{center}\n\n\\begin{parts}\n\\item Conduct a hypothesis test to evaluate if there is a difference between \nthe average numbers of traffic accident related emergency room admissions \nbetween Friday the 6$^{\\text{th}}$ and Friday the~13$^{\\text{th}}$.\n\\item Calculate a 95\\% confidence interval for the difference between the \naverage numbers of traffic accident related emergency room admissions between \nFriday the 6$^{\\text{th}}$ and Friday the 13$^{\\text{th}}$.\n\\item The conclusion of the original study states, ``Friday 13th is unlucky for \nsome. The risk of hospital admission as a result of a transport accident may be \nincreased by as much as 52\\%. Staying at home is recommended.'' Do you agree \nwith this statement? Explain your reasoning.\n\\end{parts}\n}{}\n\n% 26\n\n\\eoce{\\qt{Diamonds, Part II\\label{diamonds_2}} In Exercise~\\ref{diamonds_1}, we \ndiscussed diamond prices (standardized by weight) for diamonds with weights 0.\n99 carats and 1 carat. See the table for summary statistics, and then construct \na 95\\% confidence interval for the average difference between the standardized \nprices of 0.99 and 1 carat diamonds. You may assume the conditions for \ninference are met.\n\\begin{center}\n\\begin{tabular}{l c c }\n\\hline\n        & 0.99 carats       & 1 carat\\\\\n\\hline  \nMean    & \\$44.51          & \\$56.81           \\\\\nSD      & \\$13.32          &\\$16.13            \\\\\nn       &23             & 23 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n}{}\n\n% 27\n\n\\eoce{\\qt{Chicken diet and weight,\n    Part I\\label{chick_wts_linseed_horsebean}}\nChicken farming is a multi-billion dollar industry,\nand any methods that increase the growth rate of young\nchicks can reduce consumer costs while increasing\ncompany profits, possibly by millions of dollars.\nAn experiment was conducted to measure and compare\nthe effectiveness of various feed supplements on the\ngrowth rate of chickens.\nNewly hatched chicks were randomly allocated into six groups, \nand each group was given a different feed supplement.\nBelow are some summary statistics from this data set along\nwith box plots showing the distribution of weights by\nfeed type.\\footfullcite{data:chickwts}\n\n\\noindent\\begin{minipage}[c]{0.65\\textwidth}\n\\begin{center}\n\\FigureFullPath[A side-by-side box plot is shown for \"Weight, in grams\" for several feed types. The width of the data range for each feed type spans about 150 grams. However, they are centered at different locations: about 325 for \"casein\", about 150 for \"horsebean\", about 225 for \"linseed\", about 275 for \"meatmeal\", about 250 for \"soybean\", and about 325 for \"sunflower\".]{}{ch_inference_for_means/figures/eoce/chick_wts_linseed_horsebean/chick_wts_box.pdf}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.35\\textwidth}\n{\\footnotesize\\begin{tabular}{l c c c}\n\\hline\n            & Mean      & SD        & n \\\\\n\\hline\ncasein          & 323.58        & 64.43 & 12 \\\\\nhorsebean   & 160.20        & 38.63 & 10 \\\\\nlinseed         & 218.75        & 52.24 & 12 \\\\\nmeatmeal    & 276.91        & 64.90 & 11 \\\\\nsoybean         & 246.43        & 54.13 & 14 \\\\\nsunflower       & 328.92        & 48.84 & 12 \\\\\n\\hline\n\\end{tabular}}\n\\end{minipage} \n\n\\begin{parts}\n\\item Describe the distributions of weights of chickens that were fed linseed \nand horsebean.\n\\item Do these data provide strong evidence that the average weights of \nchickens that were fed linseed and horsebean are different? Use a 5\\% \nsignificance level.\n\\item What type of error might we have committed? Explain.\n\\item Would your conclusion change if we used $\\alpha = 0.01$?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 28\n\n\\eoce{\\qt{Fuel efficiency of manual and automatic cars, Part I\\label{fuel_eff_city}} \nEach year the US Environmental Protection Agency (EPA)\nreleases fuel economy data on cars manufactured in that year.\nBelow are summary statistics on fuel efficiency (in miles/gallon)\nfrom random samples of cars with manual and automatic transmissions.\nDo these data provide strong evidence of a difference between the\naverage fuel efficiency of cars with manual and automatic\ntransmissions in terms of their average city mileage?\nAssume that conditions for inference are\nsatisfied. \\footfullcite{data:epaMPG}\n\n\\noindent\\begin{minipage}[c]{0.38\\textwidth}\n\\begin{center}\n\\begin{tabular}{l c c }\n\\hline\n        & \\multicolumn{2}{c}{City MPG} \\\\\n\\hline\n        & Automatic     & Manual         \\\\\nMean    & 16.12         & 19.85      \\\\\nSD      & 3.58          & 4.51       \\\\\nn       & 26            & 26 \\\\\n\\hline\n& \\\\\n& \\\\\n\\end{tabular}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.6\\textwidth}\n\\begin{center}\n\\FigureFullPath[A side-by-side box plot is shown for \"City MPG\" for \"automatic\" and \"manual\" cars. The \"automatic\" box plot has its box spanning approximately 14 to 19, has a median of about 16, and its whiskers extending down to about 7 and up to about 24. The \"manual\" box plot has its box spanning approximately 18 to 24, has a median of about 21, and its whiskers extending down to about 8 and up to about 31.]{0.7}{ch_inference_for_means/figures/eoce/fuel_eff_city/fuel_eff_city_box.pdf}\n\\end{center}\n\\end{minipage}\n}{}\n\n% 29\n\n\\eoce{\\qt{Chicken diet and weight, Part II\\label{chick_wts_casein_soybean}} Casein is \na common weight gain supplement for humans. Does it have an effect on chickens? \nUsing data provided in Exercise~\\ref{chick_wts_linseed_horsebean}, test the \nhypothesis that the average weight of chickens that were fed casein is \ndifferent than the average weight of chickens that were fed soybean. If your \nhypothesis test yields a statistically significant result, discuss whether or \nnot the higher average weight of chickens can be attributed to the casein diet. \nAssume that conditions for inference are satisfied.\n}{}\n\n% 30\n\n\\eoce{\\qt{Fuel efficiency of manual and automatic cars, Part II\\label{fuel_eff_hway}} \nThe table provides summary statistics on highway fuel economy\nof the same 52 cars from Exercise~\\ref{fuel_eff_city}.\nUse these statistics to calculate a 98\\% confidence interval\nfor the difference between average highway mileage of manual\nand automatic cars, and interpret this interval in the context\nof the data.\\footfullcite{data:epaMPG}\n\n\\noindent\\begin{minipage}[c]{0.38\\textwidth}\n\\begin{center}\n\\begin{tabular}{l c c }\n\\hline\n        & \\multicolumn{2}{c}{Hwy MPG} \\\\\n\\hline\n            & Automatic     & Manual         \\\\\nMean    & 22.92         & 27.88          \\\\\nSD      & 5.29          & 5.01           \\\\\nn       & 26            & 26 \\\\\n\\hline\n& \\\\\n& \\\\\n\\end{tabular}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.6\\textwidth}\n\\begin{center}\n\\FigureFullPath[A side-by-side box plot is shown for \"Highway MPG\" for \"automatic\" and \"manual\" cars. The \"automatic\" box plot has its box spanning approximately 20 to 26, has a median of about 23, and its whiskers extending down to about 14 and up to about 34. The \"manual\" box plot has its box spanning approximately 26 to 32, has a median of about 29, and its whiskers extending down to about 17 and up to about 38.]{0.7}{ch_inference_for_means/figures/eoce/fuel_eff_hway/fuel_eff_hway_box.pdf}\n\\end{center}\n\\end{minipage}\n}{}\n\n\\D{\\newpage}\n\n% 31\n\n\\eoce{\\qt{Prison isolation experiment, Part I\\label{prison_isolation_T}}\nSubjects from Central Prison in Raleigh, NC, volunteered\nfor an experiment involving an ``isolation'' experience.\nThe goal of the experiment was to find a treatment \nthat reduces subjects' psychopathic deviant T scores.\nThis score measures a person's need for control or their rebellion against \ncontrol, and it is part of a commonly used mental health test called the \nMinnesota Multiphasic Personality Inventory (MMPI) test. The experiment had \nthree treatment groups: \n\\begin{enumerate}[(1)]\n\\setlength{\\itemsep}{0mm}\n\\item\n    Four hours of sensory restriction plus a 15 minute\n    ``therapeutic\" tape advising that professional help\n    is available.\n\\item\n    Four hours of sensory restriction plus a 15 minute\n    ``emotionally neutral'' tape on training hunting dogs.\n\\item\n    Four hours of  sensory restriction but no taped message.\n\\end{enumerate}\nForty-two subjects were randomly assigned to these treatment groups, and an \nMMPI test was administered before and after the treatment. Distributions of the \ndifferences between pre and post treatment scores (pre - post) are shown below, \nalong with some sample statistics. Use this information to independently test \nthe effectiveness of each treatment. Make sure to clearly state your \nhypotheses, check conditions, and interpret results in the context of the data.\\footfullcite{data:prison}\n\n\\begin{center}\n\\FigureFullPath[Three box plots are shown for Treatments 1, 2, and 3. The box plot for \"Treatment 1\" is slightly right skewed with values ranging from about -10 to about positive 40, and this distribution has one borderline outlier between 30 and 40. The box plot for \"Treatment 2\" is about symmetric with values ranging from about -20 to about positive 20. The box plot for \"Treatment 3\" is left skewed with values ranging from about -30 to about positive 10.]{}{ch_inference_for_means/figures/eoce/prison_isolation_T/prison_isolation_hist} \\\\\n$\\:$ \\\\\n\\begin{tabular}{l  r  r  r  r  }\n\\hline\n                & Tr 1  & Tr 2  & Tr 3      \\\\\n\\hline\nMean            & 6.21  & 2.86  & -3.21           \\\\\nSD              & 12.3  & 7.94  & 8.57       \\\\\nn               & 14        & 14        & 14     \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n}{}\n\n% 32\n\n\\eoce{\\qt{True / False: comparing means\\label{tf_compare_means}} Determine if the \nfollowing statements are true or false, and explain your reasoning for \nstatements you identify as false.\n\\begin{parts}\n\\item When comparing means of two samples where $n_1 = 20$ and $n_2 = 40$, we \ncan use the normal model for the difference in means since $n_2 \\ge 30$.\n\\item As the degrees of freedom increases, the $t$-distribution approaches \nnormality.\n\\item We use a pooled standard error for calculating the standard error of the \ndifference between means when sample sizes of groups are equal to each other.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_inference_for_means/TeX/one-sample_means_with_the_t-distribution.tex",
    "content": "\\exercisesheader{}\n\n% 1\n\n\\eoce{\\qt{Identify the critical $t$\\label{identify_critical_t}} An independent random \nsample is selected from an approximately normal population with unknown \nstandard deviation. Find the degrees of freedom and the critical $t$-value \n(t$^\\star$) for the given sample size and confidence level.\n%\\begin{multicols}{4}\n\\begin{parts}\n\\item $n = 6$, CL = 90\\%\n\\item $n = 21$, CL = 98\\%\n\\item $n = 29$, CL = 95\\%\n\\item $n = 12$, CL = 99\\%\n\\end{parts}\n%\\end{multicols}\n}{}\n\n% 2\n\n\\eoce{\\qt{$t$-distribution\\label{t_distribution}}\nThe figure on the right shows three \nunimodal and symmetric curves:\nthe standard normal (z) distribution,\nthe $t$-distribution with 5 degrees of freedom,\nand the $t$-distribution with 1 degree of freedom.\nDetermine which is which, and explain your reasoning.\n\\begin{center}\n\n\\FigureFullPath[Three distributions are shown, all symmetric, bell-shaped, and centered at zero. The first is shown as a solid line and has the broadest peak of the three distributions, and the tails of this distribution also visually approach zero at about -3 and positive 3. The second curve that is shown as a dashed line has a less broad, slightly sharper peak than the distribution based on solid line. The tails of the distribution with the dashed line has tails that visually approach zero at values of about -4 and positive 4. The third curve is shown as a dotted line and has the sharpest peak of the three distributions. The tails of the dotted line distribution has tails that visually approach zero further out, beyond the limits shown in this plot of -4 and positive 4.]{0.4}{ch_inference_for_means/figures/eoce/t_distribution/t_distribution}\n\\end{center}\n}{}\n\n% 3\n\n\\eoce{\\qt{Find the p-value, Part I\\label{find_T_pval_1_2_sided}}\nAn independent random sample \nis selected from an approximately normal population\nwith an unknown standard \ndeviation.\nFind the p-value for the given sample size and test statistic.\nAlso determine if the null hypothesis would be rejected at \n$\\alpha = 0.05$.\n\\begin{parts}\n\\item $n = 11$, $T = 1.91$\n\\item $n = 17$, $T = -3.45$\n\\item $n = 7$, $T = 0.83$\n\\item $n = 28$, $T = 2.13$\n\\end{parts}\n}{}\n\n% 4\n\n\\eoce{\\qt{Find the p-value, Part II\\label{find_T_pval_2_2_sided}}\nAn independent random sample \nis selected from an approximately normal population\nwith an unknown standard \ndeviation.\nFind the p-value for the given sample size and test statistic.\nAlso determine if the null hypothesis would be rejected at \n$\\alpha = 0.01$.\n\\begin{parts}\n\\item $n = 26$, $T = 2.485$\n\\item $n = 18$, $T = 0.5$\n\\end{parts}\n}{}\n\n% 5\n\n\\eoce{\\qt{Working backwards, Part I\\label{work_backwards_1}} A 95\\% confidence \ninterval for a population mean, $\\mu$, is given as (18.985, 21.015). This \nconfidence interval is based on a simple random sample of 36 observations. \nCalculate the sample mean and standard deviation. Assume that all conditions \nnecessary for inference are satisfied. Use the $t$-distribution in any \ncalculations.\n}{}\n\n% 6\n\n\\eoce{\\qt{Working backwards, Part II\\label{work_backwards_2}} A 90\\% confidence \ninterval for a population mean is (65, 77). The population distribution is \napproximately normal and the population standard deviation is unknown. This \nconfidence interval is based on a simple random sample of 25 observations. \nCalculate the sample mean, the margin of error, and the sample standard \ndeviation.\n}{}\n\n\\D{\\newpage}\n\n% 7\n\n\\eoce{\\qt{Sleep habits of New Yorkers\\label{ny_sleep_habits_2_sided}}\nNew York is known as \n``the city that never sleeps\".\nA random sample of 25 New Yorkers were asked how \nmuch sleep they get per night.\nStatistical summaries of these data are shown \nbelow.\nThe point estimate suggests New Yorkers sleep less than\n8~hours a night on average.\nIs the result statistically significant?\n\\begin{center}\n\\begin{tabular}{rrrrrr}\n \\hline\nn   & $\\bar{x}$ & s     & min   & max \\\\ \n \\hline\n25  & 7.73      & 0.77  & 6.17  & 9.78 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\n\\begin{parts}\n\\item Write the hypotheses in symbols and in words.\n\\item Check conditions, then calculate the test statistic, $T$, and the \nassociated degrees of freedom.\n\\item Find and interpret the p-value in this context. Drawing a picture may be \nhelpful.\n\\item What is the conclusion of the hypothesis test?\n\\item If you were to construct a 90\\% confidence interval that corresponded to \nthis hypothesis test, would you expect 8 hours to be in the interval?\n\\end{parts}\n}{}\n\n% 8\n\n\\eoce{\\qt{Heights of adults\\label{adult_heights}}\nResearchers studying anthropometry \ncollected body girth measurements and skeletal diameter measurements, as well as \nage, weight, height and gender, for 507 physically active individuals. The \nhistogram below shows the sample distribution of heights in centimeters. \n\\footfullcite{Heinz:2003} \\\\\n\\begin{minipage}[c]{0.75\\textwidth}\n\\begin{center}\n\\FigureFullPath[A histogram is shown for \"Height\" with values ranging from  140 to 200, with a bin width of 5. The distribution is roughly symmetric with a center at about 170. The bin heights, starting with the bin from 145 to 150, are about 3, 17, 55, 70, 100, 85, 95, 50, 30, 15, and 3.]{}{ch_inference_for_means/figures/eoce/adult_heights/adult_heights_hist}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.23\\textwidth}\n\\begin{center}\n\\begin{tabular}{l|r l}\nMin     & 147.2 \\\\\nQ1      & 163.8 \\\\\nMedian  & 170.3 \\\\\nMean    & 171.1 \\\\\nSD      &  9.4 \\\\\nQ3      & 177.8 \\\\\nMax     & 198.1 \\\\\n\\end{tabular}\n\\end{center}\n\\end{minipage}\n\\begin{parts}\n\\item What is the point estimate for the average height of active individuals? \nWhat about the median?\n\\item What is the point estimate for the standard deviation of the heights of \nactive individuals? What about the IQR?\n\\item Is a person who is 1m 80cm (180 cm) tall considered unusually tall? And is \na person who is 1m 55cm (155cm) considered unusually short? Explain your \nreasoning.\n\\item The researchers take another random sample of physically active \nindividuals. Would you expect the mean and the standard deviation of this new \nsample to be the ones given above? Explain your reasoning.\n\\item The sample means obtained are point estimates for the mean height of all \nactive individuals, if the sample of individuals is equivalent to a simple \nrandom sample.\nWhat measure do we use to quantify the variability of such an estimate?\nCompute \nthis quantity using the data from the original sample under the condition that \nthe data are a simple random sample. \n\\end{parts}\n}{}\n\n% 9\n\n\\eoce{\\qt{Find the mean\\label{find_mean_2_sided}}\nYou are given the following hypotheses:\n\\begin{align*}\nH_0&: \\mu = 60 \\\\\nH_A&: \\mu \\neq 60\n\\end{align*}\nWe know that the sample standard deviation is 8\nand the sample size is 20.\nFor what sample mean would the p-value be equal to 0.05?\nAssume that all conditions necessary for inference are satisfied.\n}{}\n\n\\D{\\newpage}\n\n% 10\n\n\\eoce{\\qt{$t^\\star$ vs. $z^\\star$\\label{critical_t_vs_z}} For a given confidence \nlevel, $t^{\\star}_{df}$ is larger than $z^{\\star}$. Explain how $t^{*}_{df}$ \nbeing slightly larger than $z^{*}$ affects the width of the confidence interval.\n}{}\n\n% 11\n\n\\eoce{\\qt{Play the piano\\label{play_piano_2_sided}}\nGeorgianna claims that in a small city \nrenowned for its music school, the average child takes less than 5 years of \npiano lessons. We have a random sample of 20 children from the city, with a \nmean of 4.6 years of piano lessons and a standard deviation of 2.2 years.\n\\begin{parts}\n\\item\n    Evaluate Georgianna's claim (or that the opposite might be true)\n    using a hypothesis test.\n\\item\n    Construct a 95\\% confidence interval for the number of years\n    students in this city take piano lessons, and interpret it\n    in context of the data.\n\\item\n    Do your results from the hypothesis test and the confidence\n    interval agree?\n    Explain your reasoning.\n\\end{parts}\n}{}\n\n% 12\n\n\\eoce{\\qt{Auto exhaust and\n    lead exposure\\label{auto_exhaust_lead_exposure_2_sided}} \nResearchers interested in lead exposure due to car exhaust\nsampled the blood of 52 police officers subjected to constant\ninhalation of automobile exhaust fumes while working traffic\nenforcement in a primarily urban environment.\nThe blood samples of these officers had an average lead\nconcentration of 124.32 $\\mu$g/l and a SD of 37.74 $\\mu$g/l;\na previous study of individuals from a nearby suburb,\nwith no history of exposure, found an average blood level\nconcentration \nof 35 $\\mu$g/l.\\footfullcite{Mortada:2000}\n\\begin{parts}\n\\item\n    Write down the hypotheses that would be appropriate for\n    testing if the police officers appear to have been exposed\n    to a different concentration of lead.\n\\item\\label{auto_exhaust_lead_exposure_2_sided_cond}\n    Explicitly state and check all conditions necessary for\n    inference on these data.\n\\item\n    Regardless of your answers in\n    part~(\\ref{auto_exhaust_lead_exposure_2_sided_cond}),\n    test the hypothesis that the downtown police officers have\n    a higher lead exposure than the group in the previous study.\n    Interpret your results in context.\n\\end{parts}\n}{}\n\n% 13\n\n\\eoce{\\qt{Car insurance savings\\label{car_insurance_savings}}\nA market researcher wants to evaluate car insurance savings\nat a competing company.\nBased on past studies he is assuming that the standard\ndeviation of savings is \\$100.\nHe wants to collect data such that he can get a margin of\nerror of no more than \\$10 at a 95\\% confidence level.\nHow large of a sample should he collect?\n}{}\n\n% 14\n\n\\eoce{\\qt{SAT scores\\label{sat_scores_CI}}\nThe standard deviation of SAT scores for students at\na particular Ivy League college is 250 points.\nTwo statistics students, Raina and Luke, want to estimate\nthe average SAT score of students at this college as part\nof a class project.\nThey want their margin of error to be no more than 25 points.\n\\begin{parts}\n\\item\n    Raina wants to use a 90\\% confidence interval.\n    How large a sample should she collect?\n\\item\n    Luke wants to use a 99\\% confidence interval.\n    Without calculating the actual sample size, determine\n    whether his sample should be larger or smaller\n    than Raina's, and explain your reasoning.\n\\item\n    Calculate the minimum required sample size for Luke.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_inference_for_means/TeX/paired_data.tex",
    "content": "\\exercisesheader{}\n\n% 15\n\n\\eoce{\\qt{Air quality\\label{air_quality_shortened}}\nAir quality measurements were collected in \na random sample of 25 country capitals in 2013, and then again in the same \ncities in 2014. We would like to use these data to compare\naverage air quality between the two years.\nShould we use a paired or non-paired test? Explain your reasoning.\n}{}\n\n% 16\n\n\\eoce{\\qt{True / False: paired\\label{tf_paired}} Determine if the following \nstatements are true or false. If false, explain.\n\\begin{parts}\n\\item In a paired analysis we first take the difference of each pair of observations, \nand then we do inference on these differences.\n\\item Two data sets of different sizes cannot be analyzed as paired data.\n\\item Consider two sets of data that are paired with each other.\nEach observation in one data set has a natural correspondence with \nexactly one observation from the other data set.\n\\item Consider two sets of data that are paired with each other.\nEach observation in one data set is subtracted from the average of the \nother data set's observations.\n\\end{parts}\n}{}\n\n% 17\n\n\\eoce{\\qt{Paired or not? Part I\\label{paired_or_not_1}} In each of the following \nscenarios, determine if the data are paired.\n\\begin{parts}\n\\item Compare pre- (beginning of semester) and post-test (end of semester) \nscores of students.\n\\item Assess gender-related salary gap by comparing salaries of randomly \nsampled men and women.\n\\item Compare artery thicknesses at the beginning of a study and after 2 years \nof taking Vitamin E for the same group of patients.\n\\item Assess effectiveness of a diet regimen by comparing the before and after \nweights of subjects.\n\\end{parts}\n}{}\n\n% 18\n\n\\eoce{\\qt{Paired or not? Part II\\label{paired_or_not_2}} In each of the following \nscenarios, determine if the data are paired.\n\\begin{parts}\n\\item We would like to know if Intel's stock and Southwest Airlines' stock have \nsimilar rates of return. To find out, we take a random sample of 50 days, and \nrecord Intel's and Southwest's stock on those same days.\n\\item We randomly sample 50 items from Target stores and note the price for \neach. Then we visit Walmart and collect the price for each of those same 50 \nitems.\n\\item A school board would like to determine whether there is a difference in \naverage SAT scores for students at one high school versus another high school \nin the district. To check, they take a simple random sample of 100 students \nfrom each high school.\n\\end{parts}\n}{}\n\n% 19\n\n\\eoce{\\qt{Global warming, Part I\\label{global_warming_v2_1}}\nLet's consider a limited set of climate data,\nexamining temperature differences in 1948 vs~2018.\nWe sampled 197 locations from the\nNational Oceanic and Atmospheric Administration's\n(NOAA) historical data,\nwhere the data was available for both years of interest.\nWe want to know: were there more days with temperatures\nexceeding 90\\textdegree{}F in 2018 or\nin~1948?\\footfullcite{webpage:noaa_1948_2018}\nThe difference in number of days exceeding 90\\textdegree{}F\n(number of days in 2018 - number of days in 1948) was calculated\nfor each of the 197 locations.\nThe average of these differences was 2.9 days with \na standard deviation of 17.2 days.\nWe are interested in determining whether these data provide\nstrong evidence that there were more days in 2018 that\nexceeded 90\\textdegree{}F from NOAA's weather\nstations.\\vspace{3mm}\n\n\\noindent%\n\\begin{minipage}[c]{0.65\\textwidth}\n\\begin{parts}\n\\item\n    Is there a relationship between the observations collected\n    in 1948 and 2018?\n    Or are the observations in the two groups independent?\n    Explain.\n\\item\n    Write hypotheses for this research in symbols and in words.\n\\item\n    Check the conditions required to complete this test.\n    A histogram of the differences is given to the right.\n\\item\n    Calculate the test statistic and find the p-value.\n\\item\n    Use $\\alpha = 0.05$ to evaluate the test,\n    and interpret your conclusion in context.\n\\item\n    What type of error might we have made?\n    Explain in context what the error means.\n\\item\n    Based on the results of this hypothesis test,\n    would you expect a confidence interval for the\n    average difference between the number of days\n    exceeding 90\\textdegree{}F from 1948 and 2018\n    to include 0?\n    Explain your reasoning.\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.02\\textwidth}\n\\ \n\\end{minipage}\n\\begin{minipage}[c]{0.32\\textwidth}\n\\FigureFullPath[A histogram is shown for \"Differences in Number of Days\", which has bins between -70 and 60, where the bin width is 10. There is a prominent peak around zero, where much of the data lies between -40 and positive 40. The non-zero bins beyond this range are -70 to -60 has a bin height of 1, the 40 to 50 bin has a bin height of 2, and the 50 to 60 bin has a bin height of 1.]{}{ch_inference_for_means/figures/eoce/global_warming_v2_1/global_warming_v2_1_diffs}\n\\end{minipage}\n% library(openintro); d <- climate70$dx90_2018 - climate70$dx90_1948; mean(d); sd(d); length(d); t.test(d)\n}{}\n\n\\D{\\newpage}\n\n% 20\n\n\\eoce{\\qt{High School and Beyond, Part I\\label{hs_beyond_1}} The National Center of \nEducation Statistics conducted a survey of high school seniors, collecting test \ndata on reading, writing, and several other subjects. Here we examine a simple \nrandom sample of 200 students from this survey. Side-by-side box plots of \nreading and writing scores as well as a histogram of the differences in scores \nare shown below.\n\\begin{center}\n\\FigureFullPath[A side-by-side box plot with dot plots also overlaid for each box plot. There are two categories shown, \"read\" and \"write\", for values ranging from about 27 to 77. The box portion of each distribution is nearly identical, ranging from about 45 to 60. The median of \"read\" is about 49 while the median of \"write\" is about 53. The whiskers for \"read\" extend down to about 27 and up to 77, while the whiskers for \"write\" extend down to about 32 and up to about 67. No points are shown beyond the whiskers for either box plot.]{0.44}{ch_inference_for_means/figures/eoce/hs_beyond_1/hs_beyond_read_write_box.pdf}\n\\FigureFullPath[A histogram is shown for \"Difference in scores (read minus write)\", which is centered at approximately zero and is roughly bell-shaped with values ranging from -25 to positive 25.]{0.54}{ch_inference_for_means/figures/eoce/hs_beyond_1/hs_beyond_diff_hist.pdf}\n\\end{center}\n\\begin{parts}\n\\item Is there a clear difference in the average reading and writing scores?\n\\item Are the reading and writing scores of each student independent of each \nother?\n\\item Create hypotheses appropriate for the following research question: is \nthere an evident difference in the average scores of students in the reading \nand writing exam?\n% is there evidence that students on average perform differently on the reading and writing exam?\n\\item Check the conditions required to complete this test.\n\\item The average observed difference in scores is \n$\\bar{x}_{read-write} = -0.545$, and the standard deviation of the differences \nis 8.887 points. Do these data provide convincing evidence of a difference \nbetween the average scores on the two exams?\n\\item What type of error might we have made? Explain what the error means in \nthe context of the application.\n\\item Based on the results of this hypothesis test, would you expect a \nconfidence interval for the average difference between the reading and writing \nscores to include 0? Explain your reasoning.\n\\end{parts}\n}{}\n\n% 21\n\n\\eoce{\\qt{Global warming, Part II\\label{global_warming_v2_2}}\nWe considered the change in the number of days exceeding\n90\\textdegree{}F from 1948 and 2018 at 197 randomly sampled\nlocations from the NOAA database in\nExercise~\\ref{global_warming_v2_1}.\nThe mean and standard deviation of the reported differences\nare 2.9 days and 17.2 days.\n\\begin{parts}\n\\item\n    Calculate a 90\\% confidence interval for the average\n    difference between number of days exceeding 90\\textdegree{}F\n    between 1948 and 2018.\n    We've already checked the conditions for you.\n\\item\n    Interpret the interval in context.\n\\item\n    Does the confidence interval provide convincing evidence\n    that there were more days exceeding 90\\textdegree{}F\n    in 2018 than in 1948 at NOAA stations?\n    Explain.\n\\end{parts}\n}{}\n\n% 22\n\n\\eoce{\\qt{High school and beyond, Part II\\label{hs_beyond_2}} We considered the \ndifferences between the reading and writing scores of a random sample of 200 \nstudents who took the High School and Beyond Survey in Exercise~\\ref{hs_beyond_1}. The \nmean and standard deviation of the differences are \n$\\bar{x}_{read-write} = -0.545$ and 8.887 points.\n\\begin{parts}\n\\item Calculate a 95\\% confidence interval for the average difference between \nthe reading and writing scores of all students.\n\\item Interpret this interval in context.\n\\item Does the confidence interval provide convincing evidence that there is a \nreal difference in the average scores? Explain.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_inference_for_means/TeX/power_calculations_for_a_difference_of_means.tex",
    "content": "\\exercisesheader{}\n\n% 33\n\n\\eoce{\\qt{Increasing corn yield\\label{increase_corn_yield}} A large farm wants to \ntry out a new type of fertilizer to evaluate whether it will improve the \nfarm's corn production. The land is broken into plots that produce an \naverage of 1,215 pounds of corn with a standard deviation of 94 pounds per \nplot. The owner is interested in detecting any average difference of at \nleast 40 pounds per plot. How many plots of land would be needed for the \nexperiment if the desired power level is 90\\%?\nUse $\\alpha = 0.05$.\nAssume each plot of land gets \ntreated with either the current fertilizer or the new fertilizer.\n}{}\n\n% 34\n\n\\eoce{\\qt{Email outreach efforts\\label{email_outreach_efforts}} A medical research \ngroup is recruiting people to complete short surveys about their medical \nhistory. For example, one survey asks for information on a person's family \nhistory in regards to cancer. Another survey asks about what topics were \ndiscussed during the person's last visit to a hospital. So far, as people \nsign up, they complete an average of just 4~surveys, and the standard \ndeviation of the number of surveys is about~2.2. The research group wants to \ntry a new interface that they think will encourage new enrollees to complete \nmore surveys, where they will randomize each enrollee to either get the new \ninterface or the current interface. How many new enrollees do they need for \neach interface to detect an effect size of 0.5 surveys per enrollee, if the \ndesired power level is 80\\%?\nUse $\\alpha = 0.05$.\n}{}\n"
  },
  {
    "path": "ch_inference_for_means/TeX/review_exercises.tex",
    "content": "\\reviewexercisesheader{}\n\n% 47\n\n\\eoce{\\qt{Gaming and distracted eating, Part I\\label{gaming_distracted_eating_intake}}\nA group of researchers are interested in the possible effects of distracting \nstimuli during eating, such as an increase or decrease in the amount of food \nconsumption. To test this hypothesis, they monitored food intake for a group of \n44 patients who were randomized into two equal groups. The treatment group ate \nlunch while playing solitaire, and the control group ate lunch without any \nadded distractions. Patients in the treatment group ate 52.1 grams of biscuits, \nwith a standard deviation of 45.1 grams, and patients in the control group ate \n27.1 grams of biscuits, with a standard deviation of 26.4 grams. Do these data \nprovide convincing evidence that the average food intake (measured in amount of \nbiscuits consumed) is different for the patients in the treatment group? Assume \nthat conditions for inference are satisfied. \\footfullcite{Oldham:2011}\n}{}\n\n% 48\n\n\\eoce{\\qt{Gaming and distracted eating, Part II\\label{gaming_distracted_eating_recall}} \nThe researchers from Exercise~\\ref{gaming_distracted_eating_intake} also \ninvestigated the effects of being distracted by a game on how much people eat. \nThe 22 patients in the treatment group who ate their lunch while playing \nsolitaire were asked to do a serial-order recall of the food lunch items they \nate. The average number of items recalled by the patients in this group was 4.\n9, with a standard deviation of 1.8. The average number of items recalled by \nthe patients in the control group (no distraction) was 6.1, with a standard \ndeviation of 1.8. Do these data provide strong evidence that the average number \nof food items recalled by the patients in the treatment and control groups are \ndifferent?\n}{}\n\n% 49\n\n\\eoce{\\qt{Sample size and pairing\\label{sample_size_pairing}} Determine if the \nfollowing statement is true or false, and if false, explain your reasoning: If \ncomparing means of two groups with equal sample sizes, always use a paired test.\n}{}\n\n% 50\n\n\\eoce{\\qt{College credits\\label{college_credits}}\nA college counselor is interested in \nestimating how many credits a student typically enrolls\nin each semester.\nThe counselor decides to randomly sample 100 students\nby using the registrar's \ndatabase of students.\nThe histogram below shows the distribution of the number \nof credits taken by these students.\nSample statistics for this distribution are \nalso provided.\\\\\n\\begin{minipage}[c]{0.1\\textwidth}\n\\ \n\\end{minipage}\n\\begin{minipage}[c]{0.5\\textwidth}\n\\begin{center}\n\\FigureFullPath[A histogram is shown for \"Number of credits\". The distribution is centered at about 13 and is very roughly bell-shaped with data ranging from 8 to 18 with no apparent outliers.]{}{ch_inference_for_means/figures/eoce/college_credits/college_credits_hist}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.32\\textwidth}\n\\begin{center}\n\\begin{tabular}{l|r l}\nMin     & 8 \\\\\nQ1      & 13 \\\\\nMedian  & 14 \\\\\nMean    & 13.65 \\\\\nSD      & 1.91 \\\\\nQ3      & 15 \\\\\nMax     & 18 \\\\\n\\end{tabular}\n\\end{center}\n\\end{minipage}\n\\begin{parts}\n\\item What is the point estimate for the average\nnumber of credits taken per semester by students at this college?\nWhat about the median?\n\\item What is the point estimate for the standard deviation\nof the number of credits taken per semester by students at\nthis college?\nWhat about the IQR?\n\\item Is a load of 16 credits unusually high for this college?\nWhat about 18 credits?\nExplain your reasoning.\n\\item The college counselor takes another\nrandom sample of 100 students and this \ntime finds a sample mean of 14.02 units.\nShould she be surprised that this sample\nstatistic is slightly different than the\none from the original sample? \nExplain your reasoning.\n\\item\nThe sample means given above are point estimates\nfor the mean number of \ncredits taken by all students at that college.\nWhat measures do we use to \nquantify the variability of this estimate?\nCompute this quantity using the data \nfrom the original sample.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 51\n\n\\eoce{\\qt{Hen eggs\\label{hen_eggs}} The distribution of the number of eggs laid \nby a certain species of hen during their breeding period has a mean of 35 eggs \nwith a standard deviation of 18.2. Suppose a group of researchers \nrandomly samples 45 hens of this species, counts the number of eggs laid \nduring their breeding period, and records the sample mean. They repeat \nthis 1,000 times, and build a distribution of sample \nmeans. \n\\begin{parts}\n\\item What is this distribution called? \n\\item Would you expect the shape of this distribution to be symmetric, right \nskewed, or left skewed? Explain your reasoning.\n\\item Calculate the variability of this distribution and state the appropriate \nterm used to refer to this value.\n\\item Suppose the researchers' budget is reduced and they are only able to \ncollect random samples of 10 hens. The sample mean of the number of eggs is \nrecorded, and we repeat this 1,000 times, and build a new distribution of sample \nmeans. How will the variability of this new distribution compare to the \nvariability of the original distribution?\n\\end{parts}\n}{}\n\n% 52\n\n\\eoce{\\qt{Forest management\\label{forest_mgmt_tree_growth}}\nForest rangers wanted to better understand the rate\nof growth for younger trees in the park.\nThey took measurements of a random sample of 50 young trees\nin 2009 and again measured those same trees in 2019.\nThe data below summarize their measurements,\nwhere the heights are in feet:\n\\begin{center}\n\\begin{tabular}{l c c c}\n\\hline\n          & 2009   & 2019  & Differences\\\\\n\\hline  \n$\\bar{x}$ & 12.0  & 24.5  & 12.5 \\\\\n$s$       & 3.5   & 9.5   & 7.2 \\\\\n$n$       & 50    & 50    & 50 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\nConstruct a 99\\% confidence interval for the\naverage growth of (what had been) younger trees\nin the park over 2009-2019.\n}{}\n\n% 53\n\n\\eoce{\\qt{Experiment resizing\\label{tech_exp_resizing}}\nAt a startup company running a new weather app, an engineering\nteam generally runs experiments where a random sample of 1\\%\nof the app's visitors in the control group and another\n1\\% were in the treatment group to test each new feature.\nThe team's core goal is to increase a metric\ncalled \\emph{daily visitors},\nwhich is essentially the number of visitors to the app\neach day.\nThey track this metric in each experiment arm and\nas their core experiment metric.\nIn their most recent experiment, the team tested\nincluding a new animation when the app started,\nand the number of daily visitors in this experiment\nstabilized at +1.2\\% with a 95\\% confidence interval\nof (-0.2\\%, +2.6\\%).\nThis means if this new app start animation was launched,\nthe team thinks they might lose as many as 0.2\\% of daily\nvisitors or gain as many as 2.6\\% more daily visitors.\nSuppose you are consulting as the team's data scientist,\nand after discussing with the team,\nyou and they agree that they should run\nanother experiment that is bigger.\nYou also agree that this new experiment\nshould be able to detect a gain in the daily visitors\nmetric of 1.0\\% or more with 80\\% power.\nNow they turn to you and ask,\n``How big of an experiment do we need to run\nto ensure we can detect this effect?''\n\\begin{parts}\n\\item\\label{tech_exp_resizing_target_se}\n    How small must the standard error be if\n    the team is to be able to detect an effect\n    of 1.0\\% with 80\\% power and a significance\n    level of $\\alpha = 0.05$?\n    You may safely assume the percent change in\n    daily visitors metric follows a normal distribution.\n\\item\\label{tech_exp_resizing_original_se}\n    Consider the first experiment, where\n    the point estimate was +1.2\\% and the\n    95\\% confidence interval was (-0.2\\%, +2.6\\%).\n    If that point estimate followed a normal\n    distribution, what was the standard error\n    of the estimate?\n\\item\\label{tech_exp_resizing_ratio}\n    The ratio of the standard error from\n    part~(\\ref{tech_exp_resizing_target_se})\n    vs the standard error from\n    part~(\\ref{tech_exp_resizing_original_se})\n    should be~1.97.\n    How much bigger of an experiment is needed\n    to shrink a standard error by a factor of~1.97?\n\\item\n    Using your answer from\n    part~(\\ref{tech_exp_resizing_ratio})\n    and that the original experiment was\n    a 1\\% vs 1\\% experiment to recommend\n    an experiment size to the team.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 54\n\n\\eoce{\\qt{Torque on a rusty bolt\\label{torque_on_rusty_bolt}}\nProject Farm is a YouTube channel that routinely\ncompares different products.\nIn one episode, the channel evaluated different\noptions for loosening rusty\nbolts.\\footfullcite{youtube:torque_on_rusty_bolt}\nEight options were evaluated,\nincluding a control group where no treatment was given\n(``none'' in the graph),\nto determine which was most effective.\nFor all treatments, there were four bolts tested,\nexcept for a treatment of heat with a blow torch,\nwhere only two data points were collected.\nThe results are shown in the figure below:\n\\begin{center}\n\\FigureFullPath[A side-by-side dot plot is shown for \"Torque required to loosen a rusty bolt, in foot-pounds\". There are only 2 to 4 observations per option, which are roughly as follows: Heat (82, 98), WD-40 (106, 118, 129, 131), Royal Purple (108, 114, 122, 132), PB Blaster (110, 124, 127, 128), Liquid Wrench (85, 88, 98, 114), AeroKroil (107, 125, 132, 134), Acetone/ATF (105, 107, 114, 129), and \"none\" (110, 123, 129, 142).)]{0.8}{ch_inference_for_means/figures/eoce/torque_on_rusty_bolt/torque_on_rusty_bolt_dot_plot}\n\\end{center}\n\\begin{parts}\n\\item\\label{torque_on_rusty_bolt_appropriate}\n    Do you think it is reasonable to apply ANOVA in this case?\n\\item\n    Regardless of your answer in\n    part~(\\ref{torque_on_rusty_bolt_appropriate}),\n    describe hypotheses for ANOVA in this context,\n    and use the table below to carry out the test.\n    Give your conclusion in the context of the data.\n    \\begin{center}\n    \\begin{tabular}{lrrrrr}\n    \\hline\n    & Df & Sum Sq & Mean Sq & F value & Pr($>$F) \\\\ \n    \\hline\n    treatment & 7 & 3603.43 & 514.78 & 4.03 & 0.0056 \\\\ \n    Residuals & 22 & 2812.80 & 127.85 &  &  \\\\ \n    \\hline\n    \\end{tabular}\n    \\end{center}\n\\item\\label{torque_on_rusty_bolt_pvalues}\n    The table below are p-values for pairwise $t$-tests\n    comparing each of the different groups.\n    These p-values have not been corrected for multiple\n    comparisons.\n    Which pair of groups appears most likely to represent\n    a difference?\n    \\begin{center}\\footnotesize\n    \\begin{tabular}{l ccc ccc c}\n    \\hline\n    & AeroKroil & Heat & Liquid Wrench & none &\n        PB Blaster & Royal Purple & WD-40 \\\\ \n    \\hline\n    Acetone/ATF & 0.2026 & 0.0308 & 0.0476 & 0.1542 &\n        0.3294 & 0.5222 & 0.3744 \\\\ \n    AeroKroil &  & 0.0027 & 0.0025 & 0.8723 & 0.7551 &\n        0.5143 & 0.6883 \\\\ \n    Heat &  &  & 0.5580 & 0.0020 & 0.0050 & 0.0096 &\n        0.0059 \\\\ \n    Liquid Wrench &  &  &  & 0.0017 & 0.0053 &\n        0.0117 & 0.0065 \\\\ \n    none &  &  &  &  & 0.6371 & 0.4180 & 0.5751 \\\\ \n    PB Blaster &  &  &  &  &  & 0.7318 & 0.9286 \\\\ \n    Royal Purple &  &  &  &  &  &  & 0.8000 \\\\\n    \\hline\n    \\end{tabular}\n    \\end{center}\n\\item\n    There are 28 p-values shown in the table in\n    part~(\\ref{torque_on_rusty_bolt_pvalues}).\n    Determine if any of them are statistically\n    significant after correcting for multiple\n    comparisons.\n    If so, which one(s)?\n    Explain your answer.\n\\end{parts}\n}{}\n\n% 55\n\n\\eoce{\\qt{Exclusive relationships\\label{exclusive_relationships}} A survey conducted \non a reasonably random sample of 203 undergraduates asked, among many other \nquestions, about the number of exclusive relationships these students have been \nin. The histogram below shows the distribution of the data from this sample. \nThe sample average is 3.2 with a standard deviation of 1.97.\n\\begin{center}\n\\FigureFullPath[A histogram is shown for \"Number of exclusive relationships\". The distribution has a peak between 1 and 2 of about 101, a substantial dip for the 2 to 3 bin at a value of about 2, and the 3 to 4 bin is about 50, 4 to 5 bin a value of about 25, and the data continues to taper off with a maximum value of \"10\" shown.]{0.6}{ch_inference_for_means/figures/eoce/exclusive_relationships/exclusive_relationships_rel_hist}\n\\end{center}\nEstimate the average number of exclusive relationships Duke students have been \nin using a 90\\% confidence interval and interpret this interval in context. \nCheck any conditions required for inference, and note any assumptions you must \nmake as you proceed with your calculations and conclusions.\n}{}\n\n% 56\n\n\\eoce{\\qt{Age at first marriage, Part I\\label{age_at_first_marriage_intro}} \nThe National Survey of Family Growth conducted by the Centers for Disease \nControl gathers information on family life, marriage and divorce, pregnancy, \ninfertility, use of contraception, and men's and women's health. One of the \nvariables collected on this survey is the age at first marriage. The histogram \nbelow shows the distribution of ages at first marriage of 5,534 randomly sampled \nwomen between 2006 and 2010. The average age at first marriage among these women \nis 23.44 with a standard deviation of 4.72.\\footfullcite{data:nsfg:2010}\n\\begin{center}\n\\FigureFullPath[A histogram is shown for \"Age at first marriage\". The distribution is right-skewed, centered at about 23, has a standard deviation of about 5. The data smoothly tapers off in each direction but do not extend below about 12 or above 45.]{0.6}{ch_inference_for_means/figures/eoce/age_at_first_marriage_intro/age_at_first_marriage_intro_hist}\n\\end{center}\nEstimate the average age at first marriage of women using a 95\\% confidence \ninterval, and interpret this interval in context. Discuss any relevant \nassumptions.\n}{}\n\n% 57\n\n\\eoce{\\qt{Online communication\\label{online_communication}} A study suggests that the \naverage college student spends 10 hours per week communicating with others \nonline. You believe that this is an underestimate and decide to collect your \nown sample for a hypothesis test. You randomly sample 60 students from your \ndorm and find that on average they spent 13.5 hours a week communicating with \nothers online. A friend of yours, who offers to help you with the hypothesis \ntest, comes up with the following set of hypotheses. Indicate any errors you see.\n\\begin{align*}\nH_0&: \\bar{x} < 10~hours \\\\\nH_A&: \\bar{x} > 13.5~hours\n\\end{align*}\n}{}\n\n% 58\n\n\\eoce{\\qt{Age at first marriage, Part II\\label{age_at_first_marriage_hyp_errors}} Exercise~\\ref{age_at_first_marriage_intro} presents the results \nof a 2006 - 2010 survey showing that the average age of women at first marriage \nis 23.44.\nSuppose a social scientist thinks this value has changed \nsince the survey was taken.\nBelow is how she set up her hypotheses.\nIndicate any errors you see.\n\\begin{align*}\nH_0&: \\bar{x} \\neq 23.44~years~old \\\\\nH_A&: \\bar{x} = 23.44~years~old\n\\end{align*}\n}{}\n"
  },
  {
    "path": "ch_inference_for_means/figures/babySmokePlotOfTwoGroupsToExamineSkew/babySmokePlotOfTwoGroupsToExamineSkew.R",
    "content": "library(openintro)\ndata(COL)\ndata(births)\nd <- births\n\n\nmyPDF('babySmokePlotOfTwoGroupsToExamineSkew.pdf', 2 * 4.5, 2.3,\n      mfrow = 1:2, #2:1,\n      mar = c(3, 1, 2.5, 1),\n      mgp = c(1.7, 0.55, 0))\nxlab.start <- 'Newborn Weights (lbs)'\nhistPlot(d$weight[d$smoke == 'smoker'],\n         xlim = c(0, 11),\n         axes = FALSE,\n         xlab = xlab.start,\n         main = 'Mothers Who Smoked',\n         col = COL[1])\naxis(1)\n\n# par(mar = c(2.8, 1, 0.5, 1))\nhistPlot(d$weight[d$smoke == 'nonsmoker'],\n         xlim = c(0, 11),\n         axes = FALSE,\n         xlab = xlab.start,\n         main = 'Mothers Who Did Not Smoke',\n         col = COL[1])\naxis(1)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/cbrRunTimesMenWomen/cbrRunTimesMenWomen.R",
    "content": "library(openintro)\ndata(COL)\ndata(run10Samp)\n\nset.seed(1)\nm <- run10Samp$time[run10Samp$gender=='M']\nmean(m); sd(m)\nf <- run10Samp$time[run10Samp$gender=='F']\nmean(f); sd(f)\n\nmyPDF('cbrRunTimesMenWomen.pdf', 3.8, 3,\n      mgp = c(2.5, 0.7, 0),\n      mar = c(2, 4, 0.5, 1))\nboxPlot(m,\n        at = 1,\n        xlim = c(0.5, 2.5),\n        ylim = c(45, 150),\n        axes = FALSE,\n        ylab = 'run time (minutes)',\n        lcol = COL[1],\n        col = COL[1,3],\n        lwd = 1)\nboxPlot(f,\n        add = 2,\n        axes = FALSE,\n        lcol = COL[1],\n        col = COL[1, 3],\n        lwd = 1)\naxis(1, at = 1:2, labels = c('men', 'women'))\naxis(2, at = c(50, 100, 150))\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/classData/classData.R",
    "content": "library(openintro)\ndata(COL)\nlibrary(xtable)\ndata(classData)\n\nmyPDF(\"classDataSBSBoxPlot.pdf\", 5.5, 2.7,\n      mgp = c(2.3, 0.5, 0),\n      mar = c(3.4, 3.2, 0.5, 0.5))\nboxPlot(classData$m1, classData$lecture,\n        axes = FALSE,\n        xlab = \"Lecture\",\n        ylab = \"Midterm Scores\",\n        lcol = COL[1],\n        lwd = 1.3,\n        medianLwd = 2.5)\naxis(1, c(-50, 1:3, 50), c(\"\", \"A\", \"B\", \"C\", \"\"))\naxis(2, seq(0, 100, 20))\ndev.off()\n\nby(classData$m1, classData$lecture, length)\nby(classData$m1, classData$lecture, mean)\nby(classData$m1, classData$lecture, sd)\n\nanova(lm(m1 ~ lecture, classData))\nsummary(lm(m1 ~ lecture, classData))\nxtable(anova(lm(m1 ~ lecture, classData)))\n"
  },
  {
    "path": "ch_inference_for_means/figures/distOfDiffOfSampleMeansForBWOfBabySmokeData/distOfDiffOfSampleMeansForBWOfBabySmokeData.R",
    "content": "library(openintro)\ndata(COL)\ndata(births)\nd <- births\n\nmyPDF('distOfDiffOfSampleMeansForBWOfBabySmokeData.pdf', 3.5, 1.2,\n      mar=c(1.6, 0, 0, 0),\n      mgp=c(3, 0.5, 0))\nnormTail(0, 1,\n         L = -1.54,\n         U = 1.54,\n         df = 20, # Aesthetics\n         col = COL[1],\n         axes = FALSE)\nat <- c(-5, 0, 1.54, 5)\nlabels <- expression(0, mu[n]-mu[s]*' = 0', 'obs. diff', 0)\naxis(1, at, labels, cex.axis=0.9)\n# abline(h=0)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/adult_heights/adult_heights.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(bdims)\n\n# histogram of heights ----------------------------------------------\n\npdf(\"adult_heights_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.5,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(bdims$hgt, col = COL[1], xlab = \"Height\", ylab = \"\")\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/age_at_first_marriage_intro/age_at_first_marriage_intro.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(ageAtMar)\n\n# histogram of age at first marriage --------------------------------\npdf(\"age_at_first_marriage_intro_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.7,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(ageAtMar$age, col = COL[1], xlab = \"Age at first marriage\", ylab = \"\")\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/anova_exercise_1/anova_exercise_1.R",
    "content": "library(openintro)\n\nd <- penetrating_oil\n\nmyPDF(\"torque_on_rusty_bolt_dot_plot.pdf\", 7, 3.2,\n    mar = c(3.5, 6.5, 0.1, 0.3),\n    mgp = c(2.3, 0.55, 0))\ndotPlot(d$torque, d$treatment,\n    pch = 19, col = COL[1, 2], cex = 2,\n    vertical = FALSE,\n    xlab = paste(\n        \"Torque Required to Loosen Rusty Bolt,\",\n        \"in Foot-Pounds\"),\n    ylab = \"\")\nabline(h = 1:8, col = COL[5, 7])\ndev.off()\n\nanova(lm(d$torque ~ d$treatment))\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/chick_wts_anova/chick_wts.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(dplyr)\n\n# load data ---------------------------------------------------------\ndata(chickwts)\n\n# summary stats ----------------------------------------------------\nchickwts %>%\n  group_by(feed) %>%\n  summarise(mean = round(mean(weight), 2),\n            sd = round(sd(weight), 2),\n            length = n())\n\n# side-by-side box plots of weight by feed -------------------------\npdf(\"chick_wts_box.pdf\", height = 4, width = 8)\npar(mar=c(2, 4, 0.5, 0.5), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\nboxPlot(chickwts$weight, fact = chickwts$feed, \n        h = T, col = COL[1], horiz = FALSE, \n        ylab = \"Weight (in grams)\",\n        lwd = 1.5, medianLwd = 2.5, lcol = COL[1])\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/chick_wts_linseed_horsebean/chick_wts.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(dplyr)\n\n# load data ---------------------------------------------------------\ndata(chickwts)\n\n# summary stats ----------------------------------------------------\nchickwts %>%\n  group_by(feed) %>%\n  summarise(mean = round(mean(weight), 2),\n            sd = round(sd(weight), 2),\n            length = n())\n\n# side-by-side box plots of weight by feed -------------------------\npdf(\"chick_wts_box.pdf\", height = 4, width = 8)\npar(mar=c(2, 4, 0.5, 0.5), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\nboxPlot(chickwts$weight, fact = chickwts$feed, \n\t\th = T, col = COL[1], lwd = 1.5, medianLwd = 2.5, lcol = COL[1],\n\t\thoriz = FALSE, ylab = \"Weight (in grams)\")\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/child_care_hours/child_care_hours.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\nchina <- read.csv(\"china.csv\")\n\n# subset and clean data ---------------------------------------------\nchina <- china[!is.na(china$gender) & !is.na(china$child_care) \n               & !is.na(china$edu) & china$child_care != -99 & china$edu != 9,]\n\nchina$edu[china$edu == 1] <- \"Primary school\"\nchina$edu[china$edu == 2] <- \"Lower middle school\"\nchina$edu[china$edu == 3] <- \"Upper middle school\"\nchina$edu[china$edu == 4] <- \"Technical or vocational\"\nchina$edu[china$edu == 5] <- \"College\"\nchina$edu <- factor(china$edu, \n                    levels = c(\"Primary school\", \"Lower middle school\", \n                               \"Upper middle school\", \"Technical or vocational\",\n                               \"College\"))\n\n# summary stats -----------------------------------------------------\n\nby(china$child_care, china$edu, mean)\nby(china$child_care, china$edu, sd)\nby(china$child_care, china$edu, length)\n\n# plot --------------------------------------------------------------\n\npdf(\"child_care_hours.pdf\", height = 4, width = 15)\n\npar(mar = c(2,4,1,5), las = 1, mgp = c(2.7,0.7,0), \n    cex.lab = 1.45, cex.axis = 1.45)\nboxPlot(china$child_care, fact = china$edu, ylab = \"Child care hours\", \n        col = COL[1,2], xlim = c(0.6, 5.4),\n        lcol = COL[1], lwd = 1.5, medianLwd = 2.5)\ndev.off()\n\n# anova -------------------------------------------------------------\nxtable(anova(lm(china$child_care ~ china$edu)), digits = 2)"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/child_care_hours/china.csv",
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r2,NA,NA\r1,2,NA\r2,2,NA\r1,NA,NA\r1,NA,NA\r1,NA,NA\r2,NA,NA\r1,1,NA\r1,2,NA\r1,1,NA\r2,NA,NA\r1,2,NA\r1,2,NA\r1,1,NA\r2,2,NA\r2,NA,NA\r1,NA,NA\r2,NA,NA\r1,NA,NA\r1,4,NA\r2,2,NA\r1,NA,NA\r2,NA,NA\r2,NA,NA\r1,2,NA\r2,NA,NA\r1,1,NA\r1,2,NA\r2,NA,NA\r1,NA,NA\r1,NA,NA\r1,NA,NA\r2,NA,NA\r1,1,NA\r1,NA,NA\r1,NA,NA\r2,NA,NA\r2,1,NA\r1,2,NA\r1,1,NA\r2,NA,NA\r1,1,NA\r2,NA,NA\r1,NA,NA\r2,NA,NA\r1,NA,NA\r2,NA,NA\r1,NA,NA\r2,NA,NA\r1,1,NA\r2,NA,NA\r2,2,NA\r2,NA,NA\r1,NA,NA\r1,NA,NA\r2,NA,NA\r1,NA,NA\r2,NA,NA\r1,NA,NA\r2,NA,NA\r1,2,NA\r2,NA,NA\r1,2,NA\r2,3,NA\r1,NA,NA\r2,NA,NA\r1,2,NA\r2,1,NA\r1,1,NA\r2,NA,NA\r1,2,NA\r1,1,NA\r1,2,NA\r1,NA,NA\r2,NA,NA\r2,1,NA"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/cleveland_sacramento/cleveland_sacramento.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# take a sample -----------------------------------------------------\ncle_sac = cle_sac[!is.na(cle_sac$personal_income),]\n\nset.seed(8957)\nsac = sample(cle_sac$personal_income[cle_sac$city == \"Sacramento\"], 17)\ncle = sample(cle_sac$personal_income[cle_sac$city == \"Cleveland\"], 21)\n\n# plot of total personal income in Cle and Sac ----------------------\npdf(\"cleveland_sacramento_hist.pdf\", height = 5, width = 7)\n\npar(mar = c(3.7, 2, 1,1), las = 1, mgp = c(2.5, 0.7, 0), \n    mfrow = c(2,1), cex.lab = 1.25)\n\nhistPlot(cle, xlim = c(0, 180000), ylim = c(0,10),\n         ylab = \"\", xlab = \"\", col = COL[1], breaks = 8, axes = FALSE)\naxis(1, at = seq(0,180000,45000))\naxis(2, at = seq(0,10,5))\ntext(x = 120000, y = 8, labels = \"Cleveland, OH\", pos = 4, cex = 1.25)\n\nhistPlot(sac, xlim = c(0,180000), ylim = c(0,10), \n         ylab = \"\", xlab = \"Total personal income\", col = COL[1], breaks = 8,\n         axes = FALSE)\naxis(1, at = seq(0,180000,45000))\naxis(2, at = seq(0,10,5))\ntext(x = 120000, y = 8, labels = \"Sacramento, CA\", pos = 4, cex = 1.25)\n\ndev.off()\n\n# summary stats -----------------------------------------------------\nmean(cle, na.rm = TRUE)\nsd(cle, na.rm = TRUE)\nlength(cle)\n\nmean(sac, na.rm = TRUE)\nsd(sac, na.rm = TRUE)\nlength(sac)\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/college_credits/college_credits.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(credits)\n\n# histogram of college credits --------------------------------------\n\npdf(\"college_credits_hist.pdf\", height = 2, width = 4)\npar(mar=c(3.4,3.4,0.5,0.5), las=1, mgp=c(2.2,0.7,0), cex.lab = 1)\nhistPlot(credits$credits, col = COL[1],\n    xlab = \"Number of credits\",\n    ylab = \"Frequency\",\n    axes = FALSE)\naxis(1)\naxis(2, seq(0, 30, 10))\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/diamonds_1/diamonds.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(ggplot2)\n\n# load data ---------------------------------------------------------\ndata(diamonds)\n\n# calculate ppc: price per carat ------------------------------------\ndiamonds$ppc <- diamonds$price / (diamonds$carat * 100)\n\n# subset for cara = 1 or carat = 0.99 -------------------------------\ndiamonds_100_99 <- diamonds[diamonds$carat == 1 | diamonds$carat == 0.99,]\n\n# take samples ------------------------------------------------------\nnn <- diamonds_100_99$ppc[diamonds_100_99$carat == 0.99]\n\nset.seed(123)\none <- sample(diamonds_100_99$ppc[diamonds_100_99$carat == 1], size = 23, replace = FALSE)\n\n# create variables --------------------------------------------------\nppc <- c(nn, one)\ncarat <- c(rep(\"0.99 carats\",23), rep(\"1 carat\",23))\n\n# box plots ---------------------------------------------------------\npdf(\"diamonds_box.pdf\", height = 3, width = 4)\npar(mar = c(2, 4, 1, 1), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\nboxPlot(ppc, fact = carat, ylab = \"Point price (in dollars)\", axes = FALSE,\n\t\tlcol = COL[1], lwd = 1.5, medianLwd = 2.5)\naxis(1, at = c(1,2), labels = c(\"0.99 carats\", \"1 carat\"))\naxis(2, at = seq(20, 80, 20))\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/exclusive_relationships/exclusive_relationships.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(dplyr)\n\n# load data ---------------------------------------------------------\nsurvey <- exclusive_relationship\n\n# sample size -------------------------------------------------------\nn <- survey %>%\n  na.omit(excl_relation) %>%\n  nrow() # 203\n\n# histogram ---------------------------------------------------------\n\npdf(\"exclusive_relationships_rel_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(survey$excl_relation, col = COL[1], xlab = \"Number of exclusive relationships\", ylab = \"\", xlim = c(0, 10))\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/exclusive_relationships/survey.csv",
    "content": "\"excl_relation\"\n2\n4\n1\n4\nNA\n2\n2\n2\n1\n4\n2\n4\n2\n7\nNA\n1\nNA\n1\n9\nNA\n4\n1\n2\n4\n2\n1\n5\n1\n9\n1\n2\n1\n4\n4\n1\n8\nNA\n1\n6\n4\n1\n1\n2\n2\n4\n2\n5\n4\n1\n1\n5\n5\n4\n4\n1\n5\n4\n4\n5\n2\n6\n1\n1\n4\n1\n7\n5\n5\n5\n1\n1\n7\n6\n2\nNA\n1\n2\n6\n1\nNA\nNA\n4\n1\n2\n4\n1\n4\nNA\n5\n2\n5\n4\n4\n4\n1\n1\n6\n6\nNA\n2\n2\n2\n5\n4\n2\n7\n1\n2\n5\n4\n1\n4\n6\n1\n4\n4\n1\n7\n5\n5\n7\n2\n5\n4\n1\n8\n5\n6\n1\n2\n2\n1\n1\n4\n2\n4\n1\n1\nNA\n2\n10\n4\n2\n4\n1\n2\n5\n2\n2\n2\n4\n2\n5\n1\n2\n4\n4\n2\n1\n1\n2\n4\nNA\n5\n2\n1\n2\nNA\n6\n4\n2\n2\n4\n4\n4\n4\n4\n4\n5\n4\n1\n5\n4\n4\n5\n4\n4\n3\n4\n4\n2\nNA\n2\n1\n2\n4\n2\n2\n1\n1\n1\nNA\n1\n3\n5\n4\n6\n1\n2\n5\n1\n8\n4\n2\n1\n2\n2\n5\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/friday_13th_accident/friday_13th_accident.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# subset for accidents ----------------------------------------------\nfriday_acc <- friday[friday$type == \"accident\",]\n\n# Hist of 6th vs. 13th accidents ------------------\nH <- function(x, xlab) {\n  tmp <- hist(x,\n      col = COL[1],\n      xlab = xlab,\n      ylab = \"\",\n      main = \"\",\n      axes = FALSE)\n  axis(1, at = pretty(tmp$breaks, n = 3))\n  axis(2, at = seq(0, max(tmp$counts)))\n  # rug(x)\n  return(tmp)\n}\n\nmyPDF(\"friday_13th_accident_hist.pdf\", 7, 1.9 * 7.5 / 9,\n    mar = c(3.2, 2.5, 0.5, 2.5),\n    mgp = c(2, 0.7, 0),\n    mfrow = c(1,3),\n    cex.lab = 1.25)\nH(friday_acc$sixth, \"Friday the 6th\")\nH(friday_acc$thirteenth, \"Friday the 13th\")\nH(friday_acc$sixth - friday_acc$thirteenth, \"Difference\")\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/friday_13th_traffic/friday_13th_traffic.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(friday)\n\n# subset for accidents ----------------------------------------------\nfriday_tr <- friday[friday$type == \"traffic\",]\n\n# Hist of 6th vs. 13th vs. diff traffic -------------------------\nH <- function(x, xlab) {\n  tmp <- hist(x,\n      col = COL[1],\n      xlab = xlab,\n      ylab = \"\",\n      main = \"\",\n      axes = FALSE)\n  axis(1, at = pretty(tmp$breaks, n = 3))\n  axis(2, at = seq(0, max(tmp$counts)))\n  # rug(x)\n  return(tmp)\n}\n\nmyPDF(\"friday_13th_traffic_hist.pdf\", 9, 2,\n    mar = c(4, 2.5, 0.5, 2.5),\n    mgp = c(2.9, 0.7, 0),\n    mfrow = c(1,3),\n    cex.lab = 1.25)\nH(friday_tr$sixth, \"Friday the 6th\")\nH(friday_tr$thirteenth, \"Friday the 13th\")\nH(friday_tr$sixth - friday_tr$thirteenth, \"Difference\")\ndev.off()\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/fuel_eff_city/fuel_eff.csv",
    "content": "model_yr,mfr_name,division,carline,mfr_code,model_type_index,engine_displacement,no_cylinders,transmission_speed,city_mpg,hwy_mpg,comb_mpg,guzzler,air_aspir_method,air_aspir_method_desc,transmission,transmission_desc,no_gears,trans_lockup,trans_creeper_gear,drive_sys,drive_desc,fuel_usage,fuel_usage_desc,class,car_truck,release_date,fuel_cell\r2012,aston martin,Aston Martin Lagonda Ltd,V12 Vantage,ASX,8,5.9,12,Manual(M6),11,17,13,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,V8 Vantage,ASX,2,4.7,8,Auto(AM6),14,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/11/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,V8 Vantage,ASX,11,4.7,8,Auto(AM7),14,21,16,Y,NA,Naturally Aspirated,AM,Automated Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/9/12,N\r2012,aston martin,Aston Martin Lagonda Ltd,V8 Vantage,ASX,1,4.7,8,Manual(M6),13,19,15,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/11/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,V8 Vantage S,ASX,3,4.7,8,Auto(AM7),14,21,16,Y,NA,Naturally Aspirated,AM,Automated Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/11/11,N\r2012,Audi,Audi,R8,ADX,73,4.2,8,Auto(AM6),13,21,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/26/11,\r2012,Audi,Audi,R8,ADX,75,4.2,8,Manual(M6),11,20,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,6/7/11,\r2012,Audi,Audi,R8,ADX,41,5.2,10,Auto(AM6),13,19,15,Y,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/6/11,\r2012,Audi,Audi,R8,ADX,43,5.2,10,Manual(M6),12,19,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/9/11,\r2012,Audi,Audi,R8 Spyder,ADX,66,4.2,8,Auto(AM6),13,21,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/26/11,\r2012,Audi,Audi,R8 Spyder,ADX,74,4.2,8,Manual(M6),11,20,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,6/7/11,\r2012,Audi,Audi,R8 Spyder,ADX,40,5.2,10,Auto(AM6),13,19,15,Y,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/6/11,\r2012,Audi,Audi,R8 Spyder,ADX,42,5.2,10,Manual(M6),12,19,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/9/11,\r2012,Audi,Audi,TT ROADSTER QUATTRO,ADX,71,2,4,Auto(S6),23,31,26,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,6/7/11,N\r2012,Bentley,Bentley Motors Ltd.,Continental Supersports,BEX,15,6,12,Auto(S6),12,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,2/15/11,N\r2012,BMW,BMW,Z4 sDrive28i,BMX,428,2,4,Auto(A8),24,33,27,N,TC,Turbocharged,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,9/28/11,\r2012,BMW,BMW,Z4 sDrive28i,BMX,429,2,4,Manual(M6),23,34,27,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,12/9/11,\r2012,BMW,BMW,Z4 sDrive35i,BMX,436,3,6,Auto(S7),17,24,19,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,9/24/11,\r2012,BMW,BMW,Z4 sDrive35i,BMX,435,3,6,Manual(M6),19,26,21,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,9/24/11,\r2012,BMW,BMW,Z4 sDrive35is,BMX,438,3,6,Auto(S7),17,24,19,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,9/24/11,\r2012,Bugatti,Bugatti,Veyron,BGT,85,8,16,Auto(S7),8,15,10,Y,TC,Turbocharged,SA,Semi-Automatic,7,N,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,7/8/11,\r2012,General Motors,Chevrolet,CORVETTE,GMX,42,6.2,8,Auto(S6),15,25,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Two Seaters,car,6/2/11,\r2012,General Motors,Chevrolet,CORVETTE,GMX,43,6.2,8,Manual(M6),16,26,19,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Two Seaters,car,6/2/11,\r2012,General Motors,Chevrolet,CORVETTE,GMX,44,6.2,8,Manual(M6),14,21,17,Y,SC,Supercharged,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,6/23/11,\r2012,General Motors,Chevrolet,CORVETTE,GMX,45,7,8,Manual(M6),15,24,18,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,6/23/11,\r2012,Honda,Honda,CR-Z,HNX,9,1.5,4,Auto(AV-S7),35,39,37,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),7,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Two Seaters,car,9/30/11,N\r2012,Honda,Honda,CR-Z,HNX,8,1.5,4,Manual(M6),31,37,34,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Two Seaters,car,9/30/11,N\r2012,Lamborghini,Lamborghini,Aventador Coupe,NLX,7,6.5,12,Auto(S7),11,17,13,Y,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,2/28/11,\r2012,Audi,Lamborghini,Gallardo Coupe,ADX,62,5.2,10,Auto(AM6),13,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/25/11,\r2012,Audi,Lamborghini,Gallardo Coupe,ADX,64,5.2,10,Manual(M6),12,20,15,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/25/11,\r2012,Audi,Lamborghini,Gallardo Spyder,ADX,63,5.2,10,Auto(AM6),13,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/25/11,\r2012,Lamborghini,Lamborghini,Gallardo Spyder,NLX,65,5.2,10,Manual(M6),12,20,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/25/11,\r2012,Toyota,LEXUS,LFA,TYX,3,4.8,10,Auto(S6),11,16,12,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,1/3/11,\r2012,MAZDA,MAZDA,MX-5,TKX,8,2,4,Auto(S6),21,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/12/11,\r2012,MAZDA,MAZDA,MX-5,TKX,6,2,4,Manual(M5),22,28,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/12/11,\r2012,MAZDA,MAZDA,MX-5,TKX,7,2,4,Manual(M6),21,28,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,SL 550,MBX,222,5.5,8,Auto(A7),14,22,17,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,6/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,SL 63 AMG,MBX,226,6.2,8,Auto(A7),12,19,14,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,7/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,SLK 250,MBX,232,1.8,4,Auto(A7),23,33,26,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,2/23/12,\r2012,Mercedes-Benz,Mercedes-Benz,SLK 250,MBX,233,1.8,4,Manual(M6),22,32,26,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,2/23/12,\r2012,Mercedes-Benz,Mercedes-Benz,SLK 350,MBX,236,3.5,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,5/30/11,\r2012,Mercedes-Benz,Mercedes-Benz,SLK 55 AMG,MBX,238,5.5,8,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,2/1/12,\r2012,Mercedes-Benz,Mercedes-Benz,SLS AMG,MBX,270,6.2,8,Auto(AM7),14,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,7/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,SLS AMG Roadster,MBX,271,6.2,8,Auto(AM7),14,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,8/31/11,\r2012,Mercedes-Benz,Mercedes-Benz,Smart fortwo (CABRIOLET),MBX,703,1,3,Auto(AM5),34,38,36,N,NA,Naturally Aspirated,AM,Automated Manual,5,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,10/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,Smart fortwo (COUPE),MBX,702,1,3,Auto(AM5),34,38,36,N,NA,Naturally Aspirated,AM,Automated Manual,5,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,10/10/11,\r2012,BMW,Mini,Mini Cooper Coupe,BMX,40,1.6,4,Auto(S6),28,36,31,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Coupe,BMX,41,1.6,4,Manual(M6),29,37,32,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Roadster,BMX,42,1.6,4,Auto(S6),27,35,30,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Roadster,BMX,43,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Coupe,BMX,44,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Coupe,BMX,45,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Roadster,BMX,46,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Roadster,BMX,47,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works Coupe,BMX,48,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works Roadster,BMX,49,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,Nissan,NISSAN,370Z,NSX,56,3.7,6,Auto(S7),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,9/24/11,\r2012,Nissan,NISSAN,370Z,NSX,57,3.7,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,9/24/11,\r2012,Nissan,NISSAN,370Z ROADSTER,NSX,58,3.7,6,Auto(S7),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,10/4/11,\r2012,Nissan,NISSAN,370Z ROADSTER,NSX,59,3.7,6,Manual(M6),18,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,10/4/11,\r2012,Porsche,Porsche,911 Speedster,PRX,65,3.8,6,Auto(A7),19,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,2/11/11,\r2012,Porsche,Porsche,Boxster,PRX,31,2.9,6,Auto(A7),20,29,24,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,4/1/11,\r2012,Porsche,Porsche,Boxster,PRX,30,2.9,6,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,4/1/11,\r2012,Porsche,Porsche,Boxster S,PRX,36,3.4,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Boxster S,PRX,35,3.4,6,Manual(M6),19,26,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Boxster Spyder,PRX,40,3.4,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Boxster Spyder,PRX,39,3.4,6,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Cayman,PRX,33,2.9,6,Auto(A7),20,29,24,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,4/1/11,\r2012,Porsche,Porsche,Cayman,PRX,32,2.9,6,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,4/1/11,\r2012,Porsche,Porsche,Cayman R,PRX,42,3.4,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Cayman R,PRX,41,3.4,6,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Cayman S,PRX,38,3.4,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Cayman S,PRX,37,3.4,6,Manual(M6),19,26,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,aston martin,Aston Martin Lagonda Ltd,DB9,ASX,6,5.9,12,Auto(S6),13,20,15,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,DB9,ASX,10,5.9,12,Manual(M6),11,17,13,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,DBS,ASX,5,5.9,12,Auto(S6),12,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,DBS,ASX,4,5.9,12,Manual(M6),11,17,13,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,Virage,ASX,9,5.9,12,Auto(S6),13,18,15,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,Chrysler Group LLC,FIAT,500,CRX,601,1.4,4,Auto(A6),27,34,30,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/21/11,\r2012,Chrysler Group LLC,FIAT,500,CRX,600,1.4,4,Manual(M5),30,38,33,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/3/11,\r2012,Chrysler Group LLC,FIAT,500 Abarth,CRX,603,1.4,4,Manual(M5),28,34,31,N,TC,Turbocharged,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/10/12,\r2012,Chrysler Group LLC,FIAT,500 Cabrio,CRX,602,1.4,4,Auto(A6),27,32,29,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XK,JCX,4,5,8,Auto(S6),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XK,JCX,10,5,8,Auto(S6),15,22,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XK Convertible,JCX,2,5,8,Auto(S6),15,22,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XK Convertible,JCX,3,5,8,Auto(S6),16,22,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,7/14/11,N\r2012,Lotus,Lotus Cars Ltd,Evora,LTX,5,3.5,6,Auto(S6),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/2/12,\r2012,Lotus,Lotus Cars Ltd,Evora,LTX,6,3.5,6,Auto(S6),19,28,22,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,11/18/11,\r2012,Lotus,Lotus Cars Ltd,Evora,LTX,3,3.5,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/2/12,\r2012,Lotus,Lotus Cars Ltd,Evora,LTX,4,3.5,6,Manual(M6),17,26,20,N,SC,Supercharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/2/12,\r2012,BMW,Mini,Mini Cooper,BMX,10,1.6,4,Auto(S6),28,36,31,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper,BMX,11,1.6,4,Manual(M6),29,37,32,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Convertible,BMX,14,1.6,4,Auto(S6),27,35,30,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Convertible,BMX,15,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S,BMX,16,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S,BMX,17,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Convertible,BMX,20,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Convertible,BMX,21,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works,BMX,23,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works Conv,BMX,24,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE SPYDER,DSX,322,2.4,4,Auto(S4),20,27,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Minicompact Cars,car,1/24/11,N\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE SPYDER,DSX,324,3.8,6,Auto(S5),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/24/11,N\r2012,Porsche,Porsche,911 C4 GTS,PRX,67,3.8,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,6/10/11,\r2012,Porsche,Porsche,911 C4 GTS,PRX,66,3.8,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,6/10/11,\r2012,Porsche,Porsche,911 C4 GTS Cabriolet,PRX,69,3.8,6,Auto(A7),18,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,6/10/11,\r2012,Porsche,Porsche,911 C4 GTS Cabriolet,PRX,68,3.8,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,6/10/11,\r2012,Porsche,Porsche,911 Carrera,PRX,11,3.6,6,Auto(A7),19,27,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera,PRX,10,3.6,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4,PRX,19,3.6,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4,PRX,18,3.6,6,Manual(M6),18,24,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4 Cabriolet,PRX,21,3.6,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4 Cabriolet,PRX,20,3.6,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4 Targa,PRX,27,3.6,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4 Targa,PRX,26,3.6,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S,PRX,23,3.8,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S,PRX,22,3.8,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S Cabriolet,PRX,25,3.8,6,Auto(A7),18,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S Cabriolet,PRX,24,3.8,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S Targa,PRX,29,3.8,6,Auto(A7),18,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S Targa,PRX,28,3.8,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera Cabriolet,PRX,13,3.6,6,Auto(A7),19,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera Cabriolet,PRX,12,3.6,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera S,PRX,15,3.8,6,Auto(A7),19,26,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera S,PRX,14,3.8,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera S Cabriolet,PRX,17,3.8,6,Auto(A7),19,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera S Cabriolet,PRX,16,3.8,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 GTS,PRX,62,3.8,6,Auto(A7),19,26,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 GTS,PRX,61,3.8,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 GTS Cabriolet,PRX,64,3.8,6,Auto(A7),19,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 GTS Cabriolet,PRX,63,3.8,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo Cabriolet,PRX,51,3.8,6,Auto(A7),16,24,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo Cabriolet,PRX,55,3.8,6,Manual(M6),16,24,19,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo Coupe,PRX,50,3.8,6,Auto(A7),17,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo Coupe,PRX,54,3.8,6,Manual(M6),16,24,19,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo S Cabriolet,PRX,53,3.8,6,Auto(A7),16,24,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo S Coupe,PRX,52,3.8,6,Auto(A7),17,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,New 911 Carrera,PRX,102,3.4,6,Auto(S7),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera,PRX,101,3.4,6,Manual(M7),19,27,22,N,NA,Naturally Aspirated,M,Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera Cabriolet,PRX,104,3.4,6,Auto(S7),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera Cabriolet,PRX,103,3.4,6,Manual(M7),19,27,22,N,NA,Naturally Aspirated,M,Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera S,PRX,106,3.8,6,Auto(S7),20,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera S,PRX,105,3.8,6,Manual(M7),19,27,22,N,NA,Naturally Aspirated,M,Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera S Cabriolet,PRX,108,3.8,6,Auto(S7),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera S Cabriolet,PRX,107,3.8,6,Manual(M7),19,27,22,N,NA,Naturally Aspirated,M,Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Toyota,SCION,iQ,TYX,11,1.3,4,Auto(AV),36,37,37,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Minicompact Cars,car,8/20/11,\r2012,aston martin,Aston Martin Lagonda Ltd,Rapide,ASX,7,5.9,12,Auto(S6),13,19,15,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/6/11,N\r2012,Audi,Audi,A5 Cabriolet,ADX,21,2,4,Auto(AV),22,30,25,N,TC,Turbocharged,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,4/18/11,\r2012,Audi,Audi,A5 Cabriolet quattro,ADX,32,2,4,Auto(S8),21,29,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/4/11,\r2012,Audi,Audi,A5 QUATTRO,ADX,30,2,4,Auto(S8),21,29,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/4/11,\r2012,Audi,Audi,A5 QUATTRO,ADX,34,2,4,Manual(M6),21,31,25,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/4/11,\r2012,Audi,Audi,S5,ADX,57,4.2,8,Auto(S6),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/13/11,N\r2012,Audi,Audi,S5,ADX,56,4.2,8,Manual(M6),14,22,17,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/13/11,N\r2012,Audi,Audi,S5 Cabriolet,ADX,38,3,6,Auto(S7),17,26,20,N,SC,Supercharged,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/5/11,\r2012,Audi,Audi,TT COUPE QUATTRO,ADX,70,2,4,Auto(S6),23,31,26,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,6/7/11,N\r2012,Audi,Audi,TTRS COUPE,ADX,80,2.5,5,Manual(M6),18,25,20,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,6/13/11,N\r2012,Bentley,Bentley Motors Ltd.,Continental GTC,BEX,88,6,12,Auto(S6),11,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,10/10/11,N\r2012,Bentley,Bentley Motors Ltd.,Continental Supersports Convt,BEX,13,6,12,Auto(S6),12,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,2/15/11,N\r2012,BMW,BMW,128Ci Convertible,BMX,130,3,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,128Ci Convertible,BMX,131,3,6,Manual(M6),18,28,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,N\r2012,BMW,BMW,128i,BMX,128,3,6,Auto(S6),18,28,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,N\r2012,BMW,BMW,128i,BMX,129,3,6,Manual(M6),18,28,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,N\r2012,BMW,BMW,135i,BMX,135,3,6,Auto(S7),18,25,21,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,135i,BMX,136,3,6,Manual(M6),20,28,23,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,135i Convertible,BMX,137,3,6,Auto(S7),18,25,20,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,135i Convertible,BMX,138,3,6,Manual(M6),19,28,22,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,328Ci Convertible,BMX,312,3,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328Ci Convertible,BMX,313,3,6,Manual(M6),17,26,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328i Coupe,BMX,302,3,6,Auto(S6),18,28,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328i Coupe,BMX,303,3,6,Manual(M6),18,28,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328i Coupe xDrive,BMX,306,3,6,Auto(S6),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328i Coupe xDrive,BMX,307,3,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,335Ci Convertible,BMX,347,3,6,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/23/11,\r2012,BMW,BMW,335Ci Convertible,BMX,348,3,6,Manual(M6),19,28,22,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/23/11,\r2012,BMW,BMW,335i Coupe,BMX,337,3,6,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/23/11,\r2012,BMW,BMW,335i Coupe,BMX,338,3,6,Manual(M6),19,28,22,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/23/11,\r2012,BMW,BMW,335i Coupe xDrive,BMX,341,3,6,Auto(S6),18,27,21,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335i Coupe xDrive,BMX,342,3,6,Manual(M6),19,27,22,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335is Convertible,BMX,345,3,6,Auto(S7),17,24,19,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335is Convertible,BMX,346,3,6,Manual(M6),18,26,21,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335is Coupe,BMX,343,3,6,Auto(S7),17,24,19,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335is Coupe,BMX,344,3,6,Manual(M6),18,26,21,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,M3 Convertible,BMX,365,4,8,Auto(S7),14,20,16,Y,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,M3 Convertible,BMX,364,4,8,Manual(M6),13,20,16,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,M3 Coupe,BMX,363,4,8,Auto(S7),14,20,16,Y,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,M3 Coupe,BMX,362,4,8,Manual(M6),14,20,16,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,General Motors,Chevrolet,SONIC 5,GMX,101,1.4,4,Manual(M6),29,40,33,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,9/12/11,\r2012,General Motors,Chevrolet,SONIC 5,GMX,35,1.8,4,Auto(S6),25,35,28,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,7/11/11,\r2012,General Motors,Chevrolet,SONIC 5,GMX,36,1.8,4,Manual(M5),26,35,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,8/11/11,\r2012,Coda,CODA Automotive Inc,CODA,CDA,1,0,,Auto(A1),77,68,73,N,,,A,Automatic,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Subcompact Cars,car,2/27/12,N\r2012,Ford Motor Company,Ford Division,Fiesta FWD,FMX,1,1.6,4,Auto(AM6),29,39,33,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,Fiesta FWD,FMX,2,1.6,4,Manual(M5),29,38,33,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,Fiesta SFE FWD,FMX,189,1.6,4,Auto(AM6),29,40,33,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,27,3.7,6,Auto(A6),19,31,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,28,3.7,6,Manual(M6),19,29,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,25,5,8,Auto(A6),18,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,10/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,26,5,8,Manual(M6),17,26,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,24,5.4,8,Manual(M6),15,23,17,N,SC,Supercharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG CONVERTIBLE,FMX,29,3.7,6,Auto(A6),19,30,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS COUPE,HYX,18,2,4,Auto(A5),20,30,23,N,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS COUPE,HYX,19,2,4,Manual(M6),21,30,24,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS COUPE,HYX,20,3.8,6,Auto(A6),17,27,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS COUPE,HYX,21,3.8,6,Manual(M6),17,26,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,6/24/11,\r2012,Nissan,INFINITI,G37 CONVERTIBLE,NSX,54,3.7,6,Auto(S7),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37 CONVERTIBLE,NSX,55,3.7,6,Manual(M6),16,24,19,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37 COUPE,NSX,73,3.7,6,Auto(S7),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37 COUPE,NSX,72,3.7,6,Manual(M6),17,25,19,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37x COUPE,NSX,74,3.7,6,Auto(S7),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Toyota,LEXUS,IS 250 AWD,TYX,25,2.5,6,Auto(S6),20,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS 250/IS 250C,TYX,27,2.5,6,Auto(S6),21,30,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS 250/IS 250C,TYX,26,2.5,6,Manual(M6),19,28,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS 350 AWD,TYX,23,3.5,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS 350/IS 350C,TYX,24,3.5,6,Auto(S6),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS F,TYX,32,5,8,Auto(S8),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,10/1/11,\r2012,Maserati,MASERATI,GRANTURISMO,MAX,21,4.7,8,Auto(A6),13,21,15,Y,NA,Naturally Aspirated,A,Automatic,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,7/22/11,N\r2012,Maserati,MASERATI,Granturismo Convertible,MAX,25,4.7,8,Auto(A6),13,20,15,Y,NA,Naturally Aspirated,A,Automatic,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,7/22/11,N\r2012,Mercedes-Benz,Mercedes-Benz,C 250 (Coupe),MBX,102,1.8,4,Auto(A7),21,31,25,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,10/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 350 (Coupe),MBX,112,3.5,6,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,8/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 63 AMG Coupe,MBX,69,6.2,8,Auto(A7),13,19,15,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,8/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 63 Black Series AMG Coupe,MBX,110,6.2,8,Auto(A7),13,19,15,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/25/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 (CONVERTIBLE),MBX,141,3.5,6,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,7/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 (CONVERTIBLE),MBX,818,3.5,6,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/1/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 (coupe),MBX,131,3.5,6,Auto(A7),19,29,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,1/31/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 (coupe),MBX,819,3.5,6,Auto(A7),20,28,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/1/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 4MATIC (coupe),MBX,133,3.5,6,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,1/31/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 4MATIC (coupe),MBX,820,3.5,6,Auto(A7),19,27,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/1/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 550 (CONVERTIBLE),MBX,142,4.7,8,Auto(A7),16,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,10/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 550 (COUPE),MBX,132,4.7,8,Auto(A7),17,27,21,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,10/5/11,\r2012,BMW,Mini,Mini Cooper Clubman,BMX,12,1.6,4,Auto(S6),27,35,30,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Clubman,BMX,13,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Clubman,BMX,18,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Clubman,BMX,19,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works Clubman,BMX,22,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,i-MiEV,MTX,141,0,,Auto(A1),126,99,112,N,,,A,Automatic,1,Y,N,R,\"2-Wheel Drive, Rear\",EL,Electricity,Subcompact Cars,car,10/17/11,N\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE,DSX,312,2.4,4,Auto(S4),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/24/11,N\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE,DSX,311,2.4,4,Manual(M5),20,28,23,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/24/11,N\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE,DSX,314,3.8,6,Auto(S5),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,1/24/11,N\r2012,Nissan,NISSAN,ALTIMA COUPE,NSX,25,2.5,4,Auto(AV-S6),23,32,26,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/27/11,N\r2012,Nissan,NISSAN,ALTIMA COUPE,NSX,26,2.5,4,Manual(M6),23,31,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/27/11,N\r2012,Nissan,NISSAN,ALTIMA COUPE,NSX,43,3.5,6,Auto(AV-S6),20,27,23,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/24/11,N\r2012,Nissan,NISSAN,ALTIMA COUPE,NSX,44,3.5,6,Manual(M6),18,27,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/24/11,N\r2012,Nissan,NISSAN,GT-R,NSX,71,3.8,6,Auto(AM6),16,23,19,N,TC,Turbocharged,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/3/11,\r2012,Roush,\"Roush Industries, Inc.\",Roush Stage 3 Mustang,RII,2,5,8,Auto(A6),15,22,18,N,SC,Supercharged,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,11/30/11,\r2012,Roush,\"Roush Industries, Inc.\",Roush Stage 3 Mustang,RII,1,5,8,Manual(M6),14,21,16,Y,SC,Supercharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,5/2/11,N\r2012,Toyota,SCION,xD,TYX,13,1.8,4,Auto(A4),27,33,29,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,9/17/11,\r2012,Toyota,SCION,xD,TYX,14,1.8,4,Manual(M5),27,33,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,9/17/11,\r2012,Volkswagen,Volkswagen,BEETLE,VWX,45,2,4,Auto(S6),22,30,25,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/9/11,\r2012,Volkswagen,Volkswagen,BEETLE,VWX,86,2,4,Manual(M6),21,30,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,10/17/11,N\r2012,Volkswagen,Volkswagen,BEETLE,VWX,25,2.5,5,Auto(S6),22,29,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/9/11,\r2012,Volkswagen,Volkswagen,BEETLE,VWX,87,2.5,5,Manual(M5),22,31,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,10/17/11,\r2012,Volkswagen,Volkswagen,EOS,VWX,5,2,4,Auto(S6),22,30,25,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,1/21/11,\r2012,Volvo,\"Volvo Cars of North America, LLC\",C70 FWD,VVX,69,2.5,5,Auto(S5),18,28,21,N,TC,Turbocharged,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,6/20/11,N\r2012,Honda,Acura,TSX,HNX,20,2.4,4,Auto(S5),22,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/21/11,N\r2012,Honda,Acura,TSX,HNX,19,2.4,4,Manual(M6),21,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/21/11,N\r2012,Honda,Acura,TSX,HNX,24,3.5,6,Auto(S5),19,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/21/11,N\r2012,Audi,Audi,A4,ADX,20,2,4,Auto(AV),22,30,25,N,TC,Turbocharged,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,4/18/11,\r2012,Audi,Audi,A4 QUATTRO,ADX,29,2,4,Auto(S8),21,29,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/4/11,\r2012,Audi,Audi,A4 QUATTRO,ADX,33,2,4,Manual(M6),21,31,25,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/4/11,\r2012,Audi,Audi,S4,ADX,37,3,6,Auto(S7),18,28,21,N,SC,Supercharged,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/5/11,\r2012,Audi,Audi,S4,ADX,39,3,6,Manual(M6),18,27,21,N,SC,Supercharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/5/11,\r2012,Bentley,Bentley Motors Ltd.,Continental GT,BEX,14,6,12,Auto(S6),12,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,2/15/11,N\r2012,BMW,BMW,328i,BMX,300,2,4,Auto(A8),24,36,28,N,TC,Turbocharged,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,328i,BMX,301,2,4,Manual(M6),23,34,27,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,335i,BMX,335,3,6,Auto(S8),23,33,26,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,335i,BMX,336,3,6,Manual(M6),20,30,23,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,640i Convertible,BMX,641,3,6,Auto(S8),21,31,25,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,10/12/11,\r2012,BMW,BMW,640i Coupe,BMX,640,3,6,Auto(S8),23,33,26,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,650i Convertible,BMX,654,4.4,8,Auto(S8),15,23,18,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,BMW,650i Convertible,BMX,655,4.4,8,Manual(M6),15,22,17,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,BMW,650i Coupe,BMX,650,4.4,8,Auto(S8),15,23,18,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,BMW,650i Coupe,BMX,651,4.4,8,Manual(M6),15,22,17,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,BMW,650i Coupe xDrive,BMX,652,4.4,8,Auto(S8),15,20,17,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,9/27/11,\r2012,General Motors,Buick,VERANO,GMX,141,2.4,4,Auto(S6),21,32,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,11/11/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,98,3.6,6,Auto(A6),19,30,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/15/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,46,3.6,6,Auto(S6),18,29,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/14/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,113,3.6,6,Manual(M6),17,28,20,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/14/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,47,6.2,8,Auto(S6),12,18,14,Y,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,11/30/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,78,6.2,8,Auto(S6),15,24,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/24/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,50,6.2,8,Manual(M6),16,24,19,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/2/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,137,6.2,8,Manual(M6),14,19,16,Y,SC,Supercharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,12/21/11,\r2012,General Motors,Chevrolet,SONIC,GMX,260,1.4,4,Auto(S6),27,37,31,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,2/14/12,\r2012,General Motors,Chevrolet,SONIC,GMX,100,1.4,4,Manual(M6),29,40,33,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/12/11,\r2012,General Motors,Chevrolet,SONIC,GMX,33,1.8,4,Auto(S6),25,35,28,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/11/11,\r2012,General Motors,Chevrolet,SONIC,GMX,34,1.8,4,Manual(M5),26,35,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/11/11,\r2012,Chrysler Group LLC,Chrysler,200 Convertible,CRX,205,2.4,4,Auto(A6),18,29,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/1/11,N\r2012,Chrysler Group LLC,Chrysler,200 Convertible,CRX,211,3.6,6,Auto(A6),19,29,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/1/11,\r2012,Ford Motor Company,Ford Division,FOCUS FWD,FMX,46,2,4,Auto(AM6),28,38,31,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,1/25/11,\r2012,Ford Motor Company,Ford Division,FOCUS FWD,FMX,6,2,4,Auto(AM-S6),27,37,31,N,NA,Naturally Aspirated,OT,Other,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,2/25/11,\r2012,Ford Motor Company,Ford Division,FOCUS FWD,FMX,5,2,4,Manual(M5),26,36,30,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,2/24/11,\r2012,Ford Motor Company,Ford Division,Focus FWD FFV,FMX,193,2,4,Auto(AM6),28,38,31,N,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/5/12,\r2012,Ford Motor Company,Ford Division,Focus FWD FFV,FMX,32,2,4,Manual(M5),26,36,30,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/5/12,\r2012,Ford Motor Company,Ford Division,Focus SFE FWD,FMX,10,2,4,Auto(AM6),28,40,33,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,2/24/11,\r2012,Ford Motor Company,Ford Division,Focus SFE FWD FFV,FMX,194,2,4,Auto(AM6),28,40,33,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/5/12,\r2012,Honda,Honda,ACCORD 2DR COUPE,HNX,18,2.4,4,Auto(A5),22,33,26,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 2DR COUPE,HNX,17,2.4,4,Manual(M5),23,32,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 2DR COUPE,HNX,26,3.5,6,Auto(S5),19,29,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 2DR COUPE,HNX,23,3.5,6,Manual(M6),17,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,N\r2012,Honda,Honda,CIVIC,HNX,12,1.8,4,Auto(A5),28,39,32,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,4/20/11,N\r2012,Honda,Honda,CIVIC,HNX,11,1.8,4,Manual(M5),28,36,31,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,4/20/11,N\r2012,Honda,Honda,CIVIC,HNX,14,2.4,4,Manual(M6),22,31,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/23/11,N\r2012,Honda,Honda,CIVIC HF,HNX,13,1.8,4,Auto(A5),29,41,33,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,4/20/11,N\r2012,Honda,Honda,CIVIC HYBRID,HNX,2,1.5,4,Auto(AV),44,44,44,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,4/20/11,N\r2012,Honda,Honda,INSIGHT,HNX,3,1.3,4,Auto(AV),41,44,42,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/29/11,N\r2012,Honda,Honda,INSIGHT,HNX,4,1.3,4,Auto(AV-S7),41,44,42,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),7,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/29/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ACCENT,HYX,3,1.6,4,Auto(A6),30,40,33,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/18/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ACCENT,HYX,4,1.6,4,Manual(M6),30,40,34,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/18/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,VELOSTER,HYX,33,1.6,4,Auto(AM6),29,38,32,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/20/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,VELOSTER,HYX,32,1.6,4,Manual(M6),28,40,32,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE KOUP,KMX,22,2,4,Auto(A6),25,34,29,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE KOUP,KMX,23,2,4,Manual(M6),24,33,28,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE KOUP,KMX,24,2.4,4,Auto(A6),23,31,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE KOUP,KMX,25,2.4,4,Manual(M6),22,32,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,RIO,KMX,32,1.6,4,Auto(A6),30,40,33,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/17/11,\r2012,Kia,KIA MOTORS CORPORATION,RIO,KMX,33,1.6,4,Manual(M6),30,40,34,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,12/20/11,\r2012,Toyota,LEXUS,CT 200h,TYX,12,1.8,4,Auto(AV),43,40,42,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/1/11,N\r2012,Toyota,LEXUS,HS 250h,TYX,21,2.4,4,Auto(AV),35,34,35,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/1/11,N\r2012,MAZDA,MAZDA,MAZDA2,TKX,17,1.5,4,Auto(A4),28,34,30,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/11/11,\r2012,MAZDA,MAZDA,MAZDA2,TKX,16,1.5,4,Manual(M5),29,35,32,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/11/11,\r2012,MAZDA,MAZDA,MAZDA3,TKX,11,2,4,Auto(S5),24,33,27,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/15/11,\r2012,MAZDA,MAZDA,MAZDA3,TKX,10,2,4,Manual(M5),25,33,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/15/11,\r2012,MAZDA,MAZDA,MAZDA3,TKX,13,2.5,4,Auto(S5),22,29,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/12/11,\r2012,MAZDA,MAZDA,MAZDA3,TKX,12,2.5,4,Manual(M6),20,28,23,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/12/11,\r2012,MAZDA,MAZDA,MAZDA3 DI 4-Door,TKX,19,2,4,Auto(S6),28,40,33,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/26/11,\r2012,MAZDA,MAZDA,MAZDA3 DI 4-Door,TKX,18,2,4,Manual(M6),27,39,31,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/26/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 250,MBX,101,1.8,4,Auto(A7),21,31,25,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,10/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 300 4MATIC,MBX,25,3,6,Auto(A7),17,24,20,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 300 4MATIC,MBX,26,3,6,Auto(A7),18,25,20,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/9/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 350,MBX,103,3.5,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 350,MBX,103,3.5,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 350,MBX,817,3.5,6,Auto(A7),19,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,2/14/12,\r2012,Mercedes-Benz,Mercedes-Benz,C 63 AMG,MBX,108,6.2,8,Auto(A7),13,19,15,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,CL 550 4MATIC,MBX,213,4.7,8,Auto(A7),15,24,18,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/20/11,\r2012,Mercedes-Benz,Mercedes-Benz,CL 600,MBX,214,5.5,12,Auto(A5),12,18,14,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,CL 63 AMG,MBX,215,5.5,8,Auto(A7),15,22,18,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,CL 65 AMG,MBX,218,6,12,Auto(A5),12,18,14,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,CLS 550,MBX,319,4.7,8,Auto(A7),17,25,20,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,6/2/11,\r2012,Mercedes-Benz,Mercedes-Benz,CLS 550 4MATIC,MBX,320,4.7,8,Auto(A7),16,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,CLS 63 AMG,MBX,321,5.5,8,Auto(A7),16,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,6/2/11,\r2012,BMW,Mini,Mini Cooper Countryman,BMX,30,1.6,4,Auto(S6),25,30,27,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Countryman,BMX,31,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Countryman,BMX,34,1.6,4,Auto(S6),25,32,28,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Countryman,BMX,35,1.6,4,Manual(M6),26,32,29,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Countryman All4,BMX,36,1.6,4,Auto(S6),23,30,26,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Countryman All4,BMX,37,1.6,4,Manual(M6),25,31,28,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,115,2,4,Auto(AM6),18,25,20,N,TC,Turbocharged,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,112,2,4,Auto(AV-S6),26,34,29,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,111,2,4,Manual(M5),25,34,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,114,2.4,4,Auto(AV-S6),23,30,26,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,113,2.4,4,Manual(M5),22,31,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER AWD,MTX,116,2.4,4,Auto(AV-S6),22,29,25,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER EVOLUTION,MTX,132,2,4,Auto(AM6),17,22,19,N,TC,Turbocharged,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER EVOLUTION,MTX,131,2,4,Manual(M5),17,23,19,N,TC,Turbocharged,M,Manual,5,N,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,10/5/11,\r2012,Nissan,NISSAN,VERSA,NSX,101,1.6,4,Auto(AV),30,38,33,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/5/11,\r2012,Nissan,NISSAN,VERSA,NSX,102,1.6,4,Manual(M5),27,36,30,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/5/11,\r2012,Nissan,NISSAN,VERSA,NSX,2,1.8,4,Auto(A4),24,32,27,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/17/11,\r2012,Nissan,NISSAN,VERSA,NSX,1,1.8,4,Auto(AV),28,34,30,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/17/11,\r2012,Nissan,NISSAN,VERSA,NSX,3,1.8,4,Manual(M6),26,31,28,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/17/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Phantom Coupe,RRG,4,6.7,12,Auto(S6),11,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Phantom Drophead Coupe,RRG,3,6.7,12,Auto(S6),11,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,Saab Cars North America,Saab,9-3 CONVERTIBLE,SAX,72,2,4,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3 CONVERTIBLE,SAX,73,2,4,Manual(M6),20,33,25,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3 SEDAN AWD,SAX,68,2,4,Auto(S6),18,29,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/11/11,\r2012,Saab Cars North America,Saab,9-3 SEDAN AWD,SAX,69,2,4,Manual(M6),20,30,24,N,TC,Turbocharged,M,Manual,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/11/11,\r2012,Saab Cars North America,Saab,9-3 SPORT SEDAN,SAX,64,2,4,Auto(S6),19,29,23,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3 SPORT SEDAN,SAX,65,2,4,Manual(M6),20,33,25,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/6/11,\r2012,Toyota,SCION,tC,TYX,9,2.5,4,Auto(S6),23,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/25/11,\r2012,Toyota,SCION,tC,TYX,8,2.5,4,Manual(M6),23,31,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/25/11,\r2012,Subaru,Subaru,IMPREZA AWD,FJX,3,2,4,Auto(AV),27,36,30,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/11/11,\r2012,Subaru,Subaru,IMPREZA AWD,FJX,1,2,4,Manual(M5),25,34,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/11/11,\r2012,Subaru,Subaru,IMPREZA AWD,FJX,12,2.5,4,Manual(M5),19,25,21,N,TC,Turbocharged,M,Manual,5,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/5/11,\r2012,Subaru,Subaru,IMPREZA AWD,FJX,14,2.5,4,Manual(M6),17,23,19,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/5/11,\r2012,Suzuki,Suzuki,KIZASHI,SKX,62,2.4,4,Auto(AV),23,30,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI,SKX,64,2.4,4,Manual(M6),20,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI AWD,SKX,66,2.4,4,Auto(AV),22,29,25,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI S,SKX,61,2.4,4,Auto(AV),23,31,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI S,SKX,63,2.4,4,Manual(M6),21,31,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI S AWD,SKX,65,2.4,4,Auto(AV),23,30,25,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 SEDAN,SKX,54,2,4,Auto(AV),25,32,28,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 SEDAN,SKX,53,2,4,Manual(M6),23,33,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 Sport,SKX,58,2,4,Auto(AV),23,30,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 Sport,SKX,57,2,4,Manual(M6),23,32,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Toyota,TOYOTA,COROLLA,TYX,68,1.8,4,Auto(A4),26,34,29,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA,TYX,69,1.8,4,Manual(M5),27,34,30,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,12/8/11,\r2012,Toyota,TOYOTA,PRIUS c,TYX,84,1.5,4,Auto(AV),53,46,50,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,1/16/12,N\r2012,Toyota,TOYOTA,YARIS,TYX,4,1.5,4,Auto(A4),30,35,32,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/20/11,\r2012,Toyota,TOYOTA,YARIS,TYX,5,1.5,4,Manual(M5),30,38,33,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/20/11,\r2012,Audi,Volkswagen,CC,ADX,3,2,4,Auto(S6),22,31,25,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,1/21/11,N\r2012,Audi,Volkswagen,CC,ADX,4,2,4,Manual(M6),21,31,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,1/21/11,N\r2012,Volkswagen,Volkswagen,CC 4MOTION,VWX,58,3.6,6,Auto(S6),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/13/11,N\r2012,Volkswagen,Volkswagen,GOLF,VWX,51,2,4,Auto(S6),30,42,34,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Compact Cars,car,6/3/11,N\r2012,Volkswagen,Volkswagen,GOLF,VWX,55,2,4,Manual(M6),30,42,34,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Compact Cars,car,6/3/11,N\r2012,Volkswagen,Volkswagen,GOLF,VWX,24,2.5,5,Auto(S6),24,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/9/11,\r2012,Volkswagen,Volkswagen,GOLF,VWX,28,2.5,5,Manual(M5),23,33,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/4/11,\r2012,Audi,Volkswagen,Golf R,ADX,89,2,4,Manual(M6),19,27,22,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,1/25/12,N\r2012,Audi,Volkswagen,GTI,ADX,44,2,4,Auto(S6),24,33,27,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/9/11,N\r2012,Volkswagen,Volkswagen,GTI,VWX,46,2,4,Manual(M6),21,31,25,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/9/11,N\r2012,Volkswagen,Volkswagen,Jetta,VWX,17,2,4,Auto(S6),24,32,27,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,3/25/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,50,2,4,Auto(S6),30,42,34,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Compact Cars,car,5/12/11,N\r2012,Volkswagen,Volkswagen,Jetta,VWX,78,2,4,Auto(S6),23,29,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/10/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,79,2,4,Manual(M5),24,34,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/13/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,18,2,4,Manual(M6),22,33,26,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,3/25/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,54,2,4,Manual(M6),30,42,34,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Compact Cars,car,6/3/11,N\r2012,Volkswagen,Volkswagen,Jetta,VWX,23,2.5,5,Auto(S6),24,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/9/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,27,2.5,5,Manual(M5),23,33,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/4/11,\r2012,Volvo,\"Volvo Cars of North America, LLC\",C30 FWD,VVX,70,2.5,5,Auto(S5),21,30,24,N,TC,Turbocharged,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",C30 FWD,VVX,73,2.5,5,Manual(M6),21,29,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/6/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",S60 AWD,VVX,23,3,6,Auto(S6),18,26,21,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,1/3/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",S60 FWD,VVX,74,2.5,5,Auto(S6),20,30,23,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,1/3/11,N\r2012,Honda,Acura,RL,HNX,32,3.7,6,Auto(S6),17,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/20/11,N\r2012,Honda,Acura,TL 2WD,HNX,22,3.5,6,Auto(S6),20,29,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/18/11,N\r2012,Honda,Acura,TL 4WD,HNX,31,3.7,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/18/11,\r2012,Honda,Acura,TL 4WD,HNX,30,3.7,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/18/11,\r2012,Audi,Audi,A6,ADX,9,2,4,Auto(AV),25,33,28,N,TC,Turbocharged,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/14/11,\r2012,Audi,Audi,A6 quattro,ADX,11,3,6,Auto(S8),19,28,22,N,SC,Supercharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,2/10/11,\r2012,Audi,Audi,A7 quattro,ADX,10,3,6,Auto(S8),18,28,22,N,SC,Supercharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,2/10/11,\r2012,Audi,Audi,A8,ADX,61,4.2,8,Auto(S8),18,28,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,6/8/11,\r2012,Bentley,Bentley Motors Ltd.,Continental Flying Spur,BEX,12,6,12,Auto(S6),11,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,2/15/11,N\r2012,Bentley,Bentley Motors Ltd.,Mulsanne,BEX,8,6.8,8,Auto(S8),11,18,13,Y,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/5/11,\r2012,BMW,BMW,528i,BMX,528,2,4,Auto(A8),23,34,27,N,TC,Turbocharged,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/25/11,\r2012,BMW,BMW,528i xDrive,BMX,530,2,4,Auto(A8),22,32,26,N,TC,Turbocharged,A,Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/28/11,\r2012,BMW,BMW,535i,BMX,535,3,6,Auto(S8),21,31,25,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,10/12/11,\r2012,BMW,BMW,535i,BMX,536,3,6,Manual(M6),20,30,23,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,12/9/11,\r2012,BMW,BMW,535i xDrive,BMX,537,3,6,Auto(S8),21,30,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,10/12/11,\r2012,BMW,BMW,550i,BMX,550,4.4,8,Auto(S8),15,23,18,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,8/29/11,\r2012,BMW,BMW,550i,BMX,551,4.4,8,Manual(M6),15,22,17,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,8/29/11,\r2012,BMW,BMW,550i xDrive,BMX,552,4.4,8,Auto(S8),15,20,17,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/27/11,\r2012,BMW,BMW,ActiveHybrid 7,BMX,758,4.4,8,Auto(S8),17,24,20,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/30/11,N\r2012,General Motors,Buick,LACROSSE,GMX,97,2.4,4,Auto(S6),25,36,29,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/8/11,N\r2012,General Motors,Buick,LACROSSE,GMX,7,3.6,6,Auto(S6),17,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/7/11,\r2012,General Motors,Buick,LACROSSE,GMX,9,3.6,6,Auto(S6),17,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,11/10/11,\r2012,General Motors,Buick,LACROSSE AWD,GMX,8,3.6,6,Auto(S6),16,26,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/16/11,\r2012,General Motors,Buick,REGAL,GMX,1,2,4,Auto(S6),18,29,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,General Motors,Buick,REGAL,GMX,2,2,4,Auto(S6),19,27,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,12/21/11,\r2012,General Motors,Buick,REGAL,GMX,5,2,4,Manual(M6),20,32,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,General Motors,Buick,REGAL,GMX,6,2,4,Manual(M6),19,27,22,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/15/11,\r2012,General Motors,Buick,REGAL,GMX,96,2.4,4,Auto(S6),25,36,29,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/8/11,N\r2012,General Motors,Buick,REGAL,GMX,116,2.4,4,Auto(S6),19,31,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,General Motors,Buick,REGAL,GMX,117,2.4,4,Auto(S6),19,31,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/19/11,\r2012,General Motors,Cadillac,CTS,GMX,11,3.6,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/14/11,\r2012,General Motors,Cadillac,CTS,GMX,14,3.6,6,Manual(M6),16,26,19,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/17/11,\r2012,General Motors,Cadillac,CTS,GMX,12,6.2,8,Auto(S6),12,18,14,Y,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,6/16/11,\r2012,General Motors,Cadillac,CTS,GMX,13,6.2,8,Manual(M6),14,19,16,Y,SC,Supercharged,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,6/14/11,\r2012,General Motors,Cadillac,CTS AWD,GMX,83,3,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/17/11,\r2012,General Motors,Cadillac,CTS AWD,GMX,124,3.6,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,11/21/11,\r2012,General Motors,Chevrolet,CRUZE,GMX,28,1.4,4,Auto(S6),26,38,30,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/26/11,\r2012,General Motors,Chevrolet,CRUZE,GMX,30,1.4,4,Manual(M6),26,38,30,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/7/11,\r2012,General Motors,Chevrolet,CRUZE,GMX,29,1.8,4,Auto(S6),22,35,27,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/26/11,\r2012,General Motors,Chevrolet,CRUZE,GMX,31,1.8,4,Manual(M6),25,36,29,N,NA,Naturally Aspirated,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,General Motors,Chevrolet,CRUZE ECO,GMX,94,1.4,4,Auto(A6),26,39,31,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/13/11,\r2012,General Motors,Chevrolet,CRUZE ECO,GMX,54,1.4,4,Manual(M6),28,42,33,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/3/11,\r2012,General Motors,Chevrolet,MALIBU,GMX,37,2.4,4,Auto(S6),22,33,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/17/11,\r2012,General Motors,Chevrolet,MALIBU,GMX,39,2.4,4,Auto(S6),22,33,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/7/11,\r2012,General Motors,Chevrolet,MALIBU,GMX,38,3.6,6,Auto(S6),17,26,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/17/11,\r2012,General Motors,Chevrolet,SONIC 5,GMX,261,1.4,4,Auto(S6),27,37,31,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,2/14/12,\r2012,Chrysler Group LLC,Chrysler,200,CRX,200,2.4,4,Auto(A4),21,30,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,N\r2012,Chrysler Group LLC,Chrysler,200,CRX,203,2.4,4,Auto(A6),20,31,24,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Chrysler Group LLC,Chrysler,200,CRX,209,3.6,6,Auto(A6),19,29,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Chrysler Group LLC,Dodge,Avenger,CRX,201,2.4,4,Auto(A4),21,30,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,N\r2012,Chrysler Group LLC,Dodge,Avenger,CRX,204,2.4,4,Auto(A6),20,31,24,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Chrysler Group LLC,Dodge,Avenger,CRX,210,3.6,6,Auto(A6),19,29,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Chrysler Group LLC,Dodge,Challenger,CRX,100,3.6,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/27/11,\r2012,Chrysler Group LLC,Dodge,Challenger,CRX,105,5.7,8,Auto(A5),16,25,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),Midsize Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Challenger,CRX,103,5.7,8,Manual(M6),15,23,18,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Challenger SRT8,CRX,122,6.4,8,Auto(A5),14,23,17,Y,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Challenger SRT8,CRX,109,6.4,8,Manual(M6),14,23,17,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Ford Motor Company,Ford Division,FUSION AWD,FMX,72,3.5,6,Auto(S6),17,25,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION AWD FFV,FMX,73,3,6,Auto(S6),18,26,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD,FMX,78,2.5,4,Auto(A6),23,33,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD,FMX,79,2.5,4,Auto(S6),22,30,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD,FMX,71,2.5,4,Manual(M6),22,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD,FMX,80,3.5,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD FFV,FMX,81,3,6,Auto(S6),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION HYBRID FWD,FMX,74,2.5,4,Auto(AV),41,36,39,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,N\r2012,Ford Motor Company,Ford Division,FUSION S FWD,FMX,75,2.5,4,Manual(M6),22,32,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ELANTRA,HYX,7,1.8,4,Auto(A6),29,40,33,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,4/18/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ELANTRA,HYX,8,1.8,4,Manual(M6),29,40,33,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,4/18/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SONATA HYBRID,HYX,34,2.4,4,Auto(A6),35,40,37,N,NA,Naturally Aspirated,A,Automatic,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,12/1/11,N\r2012,Nissan,INFINITI,G25,NSX,131,2.5,6,Auto(S7),20,29,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,G25x,NSX,132,2.5,6,Auto(S7),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37,NSX,51,3.7,6,Auto(S7),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37,NSX,52,3.7,6,Manual(M6),17,25,19,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37x,NSX,53,3.7,6,Auto(S7),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,M35h,NSX,141,3.5,6,Auto(S7),27,32,29,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/16/11,N\r2012,Nissan,INFINITI,M37,NSX,151,3.7,6,Auto(S7),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,3/2/11,\r2012,Nissan,INFINITI,M37x,NSX,152,3.7,6,Auto(S7),17,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,3/2/11,\r2012,Nissan,INFINITI,M56,NSX,111,5.6,8,Auto(S7),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,3/2/11,N\r2012,Nissan,INFINITI,M56x,NSX,112,5.6,8,Auto(S7),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,3/2/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XF,JCX,1,5,8,Auto(S6),15,21,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XF,JCX,5,5,8,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,7/14/11,N\r2012,Kia,KIA MOTORS CORPORATION,FORTE,KMX,17,2,4,Auto(A6),26,36,29,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE,KMX,18,2,4,Manual(M6),25,34,29,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE,KMX,20,2.4,4,Auto(A6),23,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/11/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE,KMX,21,2.4,4,Manual(M6),22,32,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/11/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE ECO,KMX,19,2,4,Auto(A6),27,37,30,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,OPTIMA,KMX,34,2,4,Auto(A6),22,34,26,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/1/11,\r2012,Kia,KIA MOTORS CORPORATION,OPTIMA,KMX,35,2.4,4,Auto(A6),24,35,28,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/1/11,\r2012,Kia,KIA MOTORS CORPORATION,OPTIMA,KMX,36,2.4,4,Manual(M6),24,35,28,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/1/11,\r2012,Kia,KIA MOTORS CORPORATION,OPTIMA HYBRID,KMX,37,2.4,4,Auto(A6),35,40,37,N,NA,Naturally Aspirated,A,Automatic,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,12/1/11,N\r2012,Toyota,LEXUS,ES 350,TYX,22,3.5,6,Auto(S6),19,28,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 460,TYX,28,4.6,8,Auto(S8),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 460 AWD,TYX,29,4.6,8,Auto(S8),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 460 L,TYX,30,4.6,8,Auto(S8),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 460 L AWD,TYX,31,4.6,8,Auto(S8),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 600h L,TYX,33,5,8,Auto(AV-S8),19,23,20,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),8,N,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,N\r2012,Ford Motor Company,Lincoln Truck,MKZ AWD,FMX,76,3.5,6,Auto(S6),17,25,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Lincoln Truck,MKZ FWD,FMX,82,3.5,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Lincoln Truck,MKZ HYBRID FWD,FMX,77,2.5,4,Auto(AV),41,36,39,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,N\r2012,MAZDA,MAZDA,MAZDA3 DI 5-Door,TKX,21,2,4,Auto(S6),28,39,32,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/26/11,\r2012,MAZDA,MAZDA,MAZDA3 DI 5-Door,TKX,20,2,4,Manual(M6),27,38,31,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/26/11,\r2012,MAZDA,MAZDA,MAZDA6,TKX,4,2.5,4,Auto(S5),22,31,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/13/11,N\r2012,MAZDA,MAZDA,MAZDA6,TKX,3,2.5,4,Manual(M6),21,30,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/13/11,N\r2012,MAZDA,MAZDA,MAZDA6,TKX,5,3.7,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/16/11,N\r2012,MAZDA,MAZDA,MAZDASPEED3,TKX,9,2.3,4,Manual(M6),18,25,21,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 350,MBX,301,3.5,6,Auto(A7),20,30,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,1/31/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 4MATIC,MBX,306,3.5,6,Auto(A7),19,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,1/31/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 BLUETEC,MBX,303,3,6,Auto(A7),21,32,25,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",DU,Diesel,Midsize Cars,car,10/6/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 550 4MATIC,MBX,307,4.7,8,Auto(A7),16,26,20,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 63 AMG,MBX,322,5.5,8,Auto(A7),16,24,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,9/26/11,\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,GALANT,DSX,331,2.4,4,Auto(S4),21,30,24,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/18/11,\r2012,Nissan,NISSAN,ALTIMA,NSX,23,2.5,4,Auto(AV-S6),23,32,27,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/27/11,N\r2012,Nissan,NISSAN,ALTIMA,NSX,41,3.5,6,Auto(AV-S6),20,27,23,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/24/11,N\r2012,Nissan,NISSAN,MAXIMA,NSX,45,3.5,6,Auto(AV-S6),19,26,22,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,8/30/11,\r2012,Nissan,NISSAN,SENTRA,NSX,11,2,4,Auto(AV),27,34,30,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/21/11,N\r2012,Nissan,NISSAN,SENTRA,NSX,12,2,4,Manual(M6),24,31,27,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/21/11,N\r2012,Nissan,NISSAN,SENTRA,NSX,21,2.5,4,Auto(AV-S6),24,30,26,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/21/11,N\r2012,Nissan,NISSAN,SENTRA,NSX,22,2.5,4,Manual(M6),21,28,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,6/21/11,N\r2012,Saab Cars North America,Saab,9-5 SEDAN,SAX,74,2,4,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-5 SEDAN,SAX,75,2,4,Manual(M6),20,33,25,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-5 SEDAN AWD,SAX,131,2.8,6,Auto(S6),17,27,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/18/11,\r2012,Subaru,Subaru,LEGACY AWD,FJX,7,2.5,4,Auto(AV),23,31,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/25/11,\r2012,Subaru,Subaru,LEGACY AWD,FJX,5,2.5,4,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/25/11,\r2012,Subaru,Subaru,LEGACY AWD,FJX,11,2.5,4,Manual(M6),18,25,21,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,7/25/11,\r2012,Subaru,Subaru,LEGACY AWD,FJX,17,3.6,6,Auto(S5),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/25/11,\r2012,Toyota,TOYOTA,CAMRY,TYX,7,2.5,4,Auto(S6),25,35,28,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/18/11,\r2012,Toyota,TOYOTA,CAMRY,TYX,10,3.5,6,Auto(S6),21,30,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/18/11,\r2012,Toyota,TOYOTA,CAMRY HYBRID LE,TYX,66,2.5,4,Auto(AV),43,39,41,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,10/27/11,N\r2012,Toyota,TOYOTA,CAMRY HYBRID XLE,TYX,67,2.5,4,Auto(AV),40,38,40,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,10/27/11,N\r2012,Toyota,TOYOTA,PRIUS,TYX,65,1.8,4,Auto(AV),51,48,50,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/7/11,N\r2012,Volkswagen,Volkswagen,Passat,VWX,76,2,4,Auto(S6),30,40,34,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Midsize Cars,car,6/15/11,\r2012,Volkswagen,Volkswagen,Passat,VWX,48,2,4,Manual(M6),31,43,35,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Midsize Cars,car,5/11/11,\r2012,Volkswagen,Volkswagen,Passat,VWX,1,2.5,5,Auto(S6),22,31,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/15/11,\r2012,Volkswagen,Volkswagen,Passat,VWX,2,2.5,5,Manual(M5),22,32,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/15/11,\r2012,Volkswagen,Volkswagen,Passat,VWX,19,3.6,6,Auto(S6),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,8/15/11,\r2012,Volvo,\"Volvo Cars of North America, LLC\",S80 AWD,VVX,20,3,6,Auto(S6),18,26,21,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",S80 FWD,VVX,11,3.2,6,Auto(S6),20,29,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/27/11,N\r2012,Audi,Audi,A8 L,ADX,60,4.2,8,Auto(S8),18,28,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/8/11,\r2012,Volkswagen,Audi,A8L,VWX,16,6.3,12,Auto(S8),14,21,16,Y,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,2/15/11,\r2012,BMW,BMW,535i Gran Turismo,BMX,540,3,6,Auto(S8),19,28,22,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/30/11,\r2012,BMW,BMW,535i xDrive Gran Turismo,BMX,541,3,6,Auto(S8),18,27,21,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/29/11,\r2012,BMW,BMW,550i Gran Turismo,BMX,554,4.4,8,Auto(S8),15,22,18,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/29/11,\r2012,BMW,BMW,550i xDrive Gran Turismo,BMX,555,4.4,8,Auto(S8),15,19,17,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,10/10/11,\r2012,BMW,BMW,740i,BMX,740,3,6,Auto(S6),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,8/29/11,N\r2012,BMW,BMW,740Li,BMX,741,3,6,Auto(S6),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,8/29/11,N\r2012,BMW,BMW,750i,BMX,750,4.4,8,Auto(S6),15,22,17,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,750i xDrive,BMX,752,4.4,8,Auto(S6),14,20,16,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,750Li,BMX,751,4.4,8,Auto(S6),14,22,17,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,750Li xDrive,BMX,753,4.4,8,Auto(S6),14,20,16,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,760Li,BMX,760,6,12,Auto(S8),13,19,15,Y,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/23/11,N\r2012,BMW,BMW,ActiveHybrid 7L,BMX,759,4.4,8,Auto(S8),17,24,20,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/30/11,N\r2012,BMW,BMW,Alpina B7 LWB,BMX,755,4.4,8,Auto(S6),14,22,17,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,Alpina B7 LWB xDrive,BMX,757,4.4,8,Auto(S6),14,20,16,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,Alpina B7 SWB,BMX,754,4.4,8,Auto(S6),14,22,17,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,Alpina B7 SWB xDrive,BMX,756,4.4,8,Auto(S6),14,20,16,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,General Motors,Chevrolet,IMPALA,GMX,40,3.6,6,Auto(A6),18,30,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,6/16/11,\r2012,General Motors,Chevrolet,IMPALA,GMX,41,3.6,6,Auto(A6),18,30,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,9/6/11,\r2012,Chrysler Group LLC,Chrysler,300,CRX,102,3.6,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/27/11,\r2012,Chrysler Group LLC,Chrysler,300,CRX,114,3.6,6,Auto(A8),19,31,23,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/12/11,\r2012,Chrysler Group LLC,Chrysler,300,CRX,106,5.7,8,Auto(A5),16,25,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Chrysler,300 AWD,CRX,116,3.6,6,Auto(A8),18,27,21,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/12/11,\r2012,Chrysler Group LLC,Chrysler,300 AWD,CRX,107,5.7,8,Auto(A5),15,23,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Chrysler,300 SRT8,CRX,120,6.4,8,Auto(A5),14,23,17,Y,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Charger,CRX,101,3.6,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/27/11,\r2012,Chrysler Group LLC,Dodge,Charger,CRX,113,3.6,6,Auto(A8),19,31,23,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/12/11,\r2012,Chrysler Group LLC,Dodge,Charger,CRX,104,5.7,8,Auto(A5),16,25,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Charger AWD,CRX,115,3.6,6,Auto(A8),18,27,21,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/12/11,\r2012,Chrysler Group LLC,Dodge,Charger AWD,CRX,108,5.7,8,Auto(A5),15,23,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Charger SRT8,CRX,121,6.4,8,Auto(A5),14,23,17,Y,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/29/11,\r2012,Ford Motor Company,Ford Division,TAURUS AWD,FMX,93,3.5,6,Auto(S6),17,26,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,TAURUS AWD,FMX,126,3.5,6,Auto(S6),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,TAURUS FWD,FMX,96,3.5,6,Auto(A6),18,28,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,TAURUS FWD,FMX,95,3.5,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Honda,Honda,ACCORD 4DR SEDAN,HNX,16,2.4,4,Auto(A5),23,34,27,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 4DR SEDAN,HNX,15,2.4,4,Manual(M5),23,34,27,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 4DR SEDAN,HNX,25,3.5,6,Auto(A5),20,30,24,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/17/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,AZERA,HYX,35,3.3,6,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,12/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,EQUUS,HYX,6,5,8,Auto(A8),15,23,18,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS,HYX,2,3.8,6,Auto(A8),19,29,22,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,3/15/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS,HYX,5,4.6,8,Auto(A8),17,26,20,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,5/2/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS,HYX,29,5,8,Auto(A8),17,26,20,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS R SPEC,HYX,1,5,8,Auto(A8),16,25,19,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/21/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SONATA,HYX,15,2,4,Auto(A6),22,34,26,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,6/1/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SONATA,HYX,16,2.4,4,Auto(A6),24,35,28,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,6/1/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SONATA,HYX,17,2.4,4,Manual(M6),24,35,28,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,6/1/11,\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XJ,JCX,7,5,8,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XJ,JCX,8,5,8,Auto(S6),15,21,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XJ,JCX,11,5,8,Auto(S6),15,21,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XJ LWB,JCX,9,5,8,Auto(S6),15,22,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/14/11,N\r2012,Ford Motor Company,Lincoln Truck,MKS AWD,FMX,125,3.5,6,Auto(S6),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Lincoln Truck,MKS AWD,FMX,92,3.7,6,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Lincoln Truck,MKS FWD,FMX,94,3.5,6,Auto(S6),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Maserati,MASERATI,QUATTROPORTE,MAX,16,4.7,8,Auto(A6),12,19,15,Y,NA,Naturally Aspirated,A,Automatic,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/22/11,N\r2012,Mercedes-Benz,Mercedes-Benz,S 350 BLUETEC 4MATIC,MBX,209,3,6,Auto(A7),21,31,25,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,DU,Diesel,Large Cars,car,9/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 550,MBX,202,4.7,8,Auto(A7),15,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/20/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 550 4MATIC,MBX,207,4.7,8,Auto(A7),15,24,18,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/20/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 600,MBX,204,5.5,12,Auto(A5),12,19,14,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/14/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 63 AMG,MBX,205,5.5,8,Auto(A7),15,23,18,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/2/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 65 AMG,MBX,208,6,12,Auto(A5),12,19,14,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,S400 HYBRID,MBX,203,3.5,6,Auto(A7),19,25,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/14/11,N\r2012,Porsche,Porsche,Panamera,PRX,90,3.6,6,Auto(A7),18,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera 4,PRX,91,3.6,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera 4S,PRX,93,4.8,8,Auto(A7),16,24,19,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera S,PRX,92,4.8,8,Auto(A7),16,24,19,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera S Hybrid,PRX,97,3,6,Auto(A8),22,30,25,N,SC,Supercharged,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/9/11,N\r2012,Porsche,Porsche,Panamera Turbo,PRX,95,4.8,8,Auto(A7),15,23,18,N,TC,Turbocharged,A,Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera Turbo S,PRX,96,4.8,8,Auto(A7),15,23,18,N,TC,Turbocharged,A,Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Ghost,RRG,5,6.6,12,Auto(S8),13,20,15,Y,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/23/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Ghost EWB,RRG,6,6.6,12,Auto(S8),13,20,15,Y,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/23/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Phantom,RRG,1,6.7,12,Auto(S6),11,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,8/29/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Phantom EWB,RRG,2,6.7,12,Auto(S6),11,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,8/29/11,\r2012,Toyota,TOYOTA,AVALON,TYX,75,3.5,6,Auto(S6),19,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,11/17/11,\r2012,Honda,Acura,TSX WAGON,HNX,21,2.4,4,Auto(S5),22,30,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,7/21/11,N\r2012,Audi,Audi,A3,ADX,68,2,4,Auto(S6),22,28,24,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,6/20/11,N\r2012,Volkswagen,Audi,A3,VWX,52,2,4,Auto(S6),30,42,34,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Small Station Wagons,car,6/3/11,N\r2012,Audi,Audi,A3,ADX,67,2,4,Manual(M6),21,30,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,6/20/11,N\r2012,Audi,Audi,A3 QUATTRO,ADX,69,2,4,Auto(S6),21,28,24,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,6/6/11,N\r2012,Audi,Audi,A4 AVANT QUATTRO,ADX,31,2,4,Auto(S8),21,29,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,5/4/11,\r2012,BMW,BMW,328i Sport Wagon,BMX,308,3,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,9/9/11,N\r2012,BMW,BMW,328i Sport Wagon,BMX,309,3,6,Manual(M6),17,26,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,9/9/11,N\r2012,BMW,BMW,328i xDrive Sport Wagon,BMX,310,3,6,Auto(S6),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,9/9/11,N\r2012,BMW,BMW,328i xDrive Sport Wagon,BMX,311,3,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,9/9/11,N\r2012,General Motors,Cadillac,CTS WAGON,GMX,16,3.6,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,6/14/11,\r2012,General Motors,Cadillac,CTS WAGON,GMX,17,6.2,8,Auto(S6),12,18,14,Y,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Small Station Wagons,car,6/16/11,\r2012,General Motors,Cadillac,CTS WAGON,GMX,18,6.2,8,Manual(M6),14,19,16,Y,SC,Supercharged,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Small Station Wagons,car,6/14/11,\r2012,General Motors,Cadillac,CTS WAGON AWD,GMX,84,3,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,6/17/11,\r2012,General Motors,Cadillac,CTS WAGON AWD,GMX,125,3.6,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,11/20/11,\r2012,Chrysler Group LLC,Dodge,Caliber,CRX,500,2,4,Auto(AV),23,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/1/11,N\r2012,Chrysler Group LLC,Dodge,Caliber,CRX,501,2,4,Manual(M5),24,32,27,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/1/11,\r2012,Chrysler Group LLC,Dodge,Caliber,CRX,503,2.4,4,Auto(AV),22,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/1/11,N\r2012,Honda,Honda,FIT,HNX,6,1.5,4,Auto(A5),28,35,31,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/19/11,\r2012,Honda,Honda,FIT,HNX,7,1.5,4,Auto(S5),27,33,30,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/19/11,\r2012,Honda,Honda,FIT,HNX,5,1.5,4,Manual(M5),27,33,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/19/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ELANTRA TOURING,HYX,27,2,4,Auto(A4),23,30,26,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ELANTRA TOURING,HYX,28,2,4,Manual(M5),23,31,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,6/27/11,\r2012,Nissan,INFINITI,EX35,NSX,46,3.5,6,Auto(S7),17,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,8/12/11,\r2012,Nissan,INFINITI,EX35 AWD,NSX,47,3.5,6,Auto(S7),17,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,8/12/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL,KMX,27,1.6,4,Auto(A6),27,35,30,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL,KMX,28,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL,KMX,30,2,4,Auto(A6),26,34,29,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL,KMX,31,2,4,Manual(M6),26,34,29,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL ECO,KMX,26,1.6,4,Auto(A6),29,36,32,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL ECO,KMX,29,2,4,Auto(A6),27,35,30,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Mercedes-Benz,Mercedes-Benz,F-Cell,MBX,500,0,,Auto(A1),52,53,53,N,,,A,Automatic,1,N,N,F,\"2-Wheel Drive, Front\",H,Hydrogen,Small Station Wagons,car,7/4/11,Y\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER SPORTBACK,MTX,122,2,4,Auto(AV-S6),24,32,27,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER SPORTBACK,MTX,124,2.4,4,Auto(AV-S6),22,29,25,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,10/5/11,\r2012,Nissan,NISSAN,CUBE,NSX,4,1.8,4,Auto(AV),27,31,28,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,1/4/12,\r2012,Nissan,NISSAN,CUBE,NSX,5,1.8,4,Manual(M6),25,30,27,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,1/4/12,\r2012,Nissan,NISSAN,JUKE,NSX,121,1.6,4,Auto(AV-S6),27,32,29,N,TC,Turbocharged,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,10/20/11,\r2012,Nissan,NISSAN,JUKE,NSX,122,1.6,4,Manual(M6),25,31,27,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,10/20/11,\r2012,Nissan,NISSAN,JUKE AWD,NSX,123,1.6,4,Auto(AV-S6),25,30,27,N,TC,Turbocharged,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,10/20/11,\r2012,Saab Cars North America,Saab,9-3 SPORTCOMBI,SAX,66,2,4,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3 SPORTCOMBI,SAX,67,2,4,Manual(M6),20,33,25,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3X SPORTCOMBI AWD,SAX,70,2,4,Auto(S6),18,29,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,5/11/11,\r2012,Saab Cars North America,Saab,9-3X SPORTCOMBI AWD,SAX,71,2,4,Manual(M6),20,30,24,N,TC,Turbocharged,M,Manual,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,5/11/11,\r2012,Toyota,SCION,xB,TYX,2,2.4,4,Auto(S4),22,28,24,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,2/1/11,\r2012,Toyota,SCION,xB,TYX,1,2.4,4,Manual(M5),22,28,24,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,2/1/11,\r2012,Subaru,Subaru,IMPREZA WAGON/OUTBACK SPORT AWD,FJX,4,2,4,Auto(AV),27,36,30,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/11/11,\r2012,Subaru,Subaru,IMPREZA WAGON/OUTBACK SPORT AWD,FJX,2,2,4,Manual(M5),25,33,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/11/11,\r2012,Subaru,Subaru,IMPREZA WAGON/OUTBACK SPORT AWD,FJX,13,2.5,4,Manual(M5),19,25,21,N,TC,Turbocharged,M,Manual,5,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,7/5/11,\r2012,Subaru,Subaru,IMPREZA WAGON/OUTBACK SPORT AWD,FJX,15,2.5,4,Manual(M6),17,23,19,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,7/5/11,\r2012,Suzuki,Suzuki,SX4,SKX,56,2,4,Auto(AV),23,30,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,8/10/11,\r2012,Suzuki,Suzuki,SX4,SKX,55,2,4,Manual(M6),22,30,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,8/20/11,\r2012,Suzuki,Suzuki,SX4 AWD,SKX,52,2,4,Auto(AV),23,29,25,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 AWD,SKX,51,2,4,Manual(M6),22,30,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,8/10/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,70,1.8,4,Auto(A4),25,32,28,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,71,1.8,4,Manual(M5),26,32,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,72,2.4,4,Auto(A4),20,26,22,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,74,2.4,4,Auto(S5),21,29,24,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,73,2.4,4,Manual(M5),21,28,24,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Volkswagen,Volkswagen,JETTA SPORTWAGEN,VWX,49,2,4,Auto(S6),29,39,33,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Small Station Wagons,car,6/1/11,N\r2012,Volkswagen,Volkswagen,JETTA SPORTWAGEN,VWX,53,2,4,Manual(M6),30,42,34,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Small Station Wagons,car,6/3/11,N\r2012,Volkswagen,Volkswagen,JETTA SPORTWAGEN,VWX,22,2.5,5,Auto(S6),24,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,5/9/11,\r2012,Volkswagen,Volkswagen,JETTA SPORTWAGEN,VWX,26,2.5,5,Manual(M5),23,33,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,5/4/11,\r2012,Kia,KIA MOTORS CORPORATION,RONDO,KMX,7,2.4,4,Auto(A4),20,27,22,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Station Wagons,car,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,RONDO,KMX,8,2.7,6,Auto(A5),18,26,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Station Wagons,car,4/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 4Matic (Wagon),MBX,316,3.5,6,Auto(A7),19,27,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Station Wagons,car,10/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 63 AMG (station wagon),MBX,323,5.5,8,Auto(A7),15,23,18,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Station Wagons,car,10/5/11,\r2012,Toyota,TOYOTA,PRIUS v,TYX,6,1.8,4,Auto(AV),44,40,42,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Station Wagons,car,7/16/11,N\r2012,General Motors,Chevrolet,COLORADO 2WD,GMX,527,2.9,4,Auto(A4),18,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,Chevrolet,COLORADO 2WD,GMX,529,2.9,4,Manual(M5),18,25,21,N,NA,Naturally Aspirated,M,Manual,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/4/11,\r2012,General Motors,Chevrolet,COLORADO 2WD,GMX,526,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO 2WD,GMX,528,5.3,8,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,Chevrolet,COLORADO CAB CHASSIS INC 2WD,GMX,540,3.7,5,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 2WD,GMX,535,2.9,4,Auto(A4),18,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 2WD,GMX,537,2.9,4,Manual(M5),18,25,21,N,NA,Naturally Aspirated,M,Manual,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/4/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 2WD,GMX,534,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 2WD,GMX,536,5.3,8,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,GMC,CANYON 2WD,GMX,578,2.9,4,Auto(A4),18,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,GMC,CANYON 2WD,GMX,580,2.9,4,Manual(M5),18,25,21,N,NA,Naturally Aspirated,M,Manual,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/4/11,\r2012,General Motors,GMC,CANYON 2WD,GMX,577,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,GMC,CANYON 2WD,GMX,579,5.3,8,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,GMC,CANYON CAB CHASSIS INC 2WD,GMX,585,3.7,5,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,GMC,CANYON CREW CAB 2WD,GMX,587,2.9,4,Auto(A4),18,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,GMC,CANYON CREW CAB 2WD,GMX,589,2.9,4,Manual(M5),18,25,21,N,NA,Naturally Aspirated,M,Manual,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/4/11,\r2012,General Motors,GMC,CANYON CREW CAB 2WD,GMX,586,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,GMC,CANYON CREW CAB 2WD,GMX,588,5.3,8,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,Nissan,NISSAN,FRONTIER 2WD,NSX,83,2.5,4,Auto(A5),17,22,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,NISSAN,FRONTIER 2WD,NSX,84,2.5,4,Manual(M5),19,23,21,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,NISSAN,FRONTIER 2WD,NSX,181,4,6,Auto(A5),15,20,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,NISSAN,FRONTIER 2WD,NSX,182,4,6,Manual(M6),16,20,17,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,SUZUKI,Equator 2WD,NSX,85,2.5,4,Auto(A5),17,22,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,SUZUKI,Equator 2WD,NSX,86,2.5,4,Manual(M5),19,23,21,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,SUZUKI,Equator 2WD,NSX,481,4,6,Auto(A5),15,20,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Toyota,TOYOTA,TACOMA 2WD,TYX,39,2.7,4,Auto(A4),19,24,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 2WD,TYX,40,2.7,4,Manual(M5),21,25,22,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 2WD,TYX,49,4,6,Auto(A5),17,21,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 2WD,TYX,50,4,6,Manual(M6),16,21,18,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,9/1/11,\r2012,General Motors,Chevrolet,COLORADO 4WD,GMX,531,2.9,4,Auto(A4),17,23,20,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/4/11,\r2012,General Motors,Chevrolet,COLORADO 4WD,GMX,533,2.9,4,Manual(M5),18,24,20,N,NA,Naturally Aspirated,M,Manual,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/4/11,\r2012,General Motors,Chevrolet,COLORADO 4WD,GMX,530,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO 4WD,GMX,532,5.3,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/5/11,\r2012,General Motors,Chevrolet,COLORADO CAB CHASSIS INC 4WD,GMX,541,3.7,5,Auto(A4),16,21,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 4WD,GMX,538,3.7,5,Auto(A4),16,21,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 4WD,GMX,539,5.3,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/5/11,\r2012,General Motors,GMC,CANYON 4WD,GMX,582,2.9,4,Auto(A4),17,23,20,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/4/11,\r2012,General Motors,GMC,CANYON 4WD,GMX,584,2.9,4,Manual(M5),18,24,20,N,NA,Naturally Aspirated,M,Manual,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/4/11,\r2012,General Motors,GMC,CANYON 4WD,GMX,581,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,GMC,CANYON 4WD,GMX,583,5.3,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/5/11,\r2012,General Motors,GMC,CANYON CAB CHASSIS INC 4WD,GMX,592,3.7,5,Auto(A4),16,21,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,GMC,CANYON CREW CAB 4WD,GMX,590,3.7,5,Auto(A4),16,21,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,GMC,CANYON CREW CAB 4WD,GMX,591,5.3,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/5/11,\r2012,Nissan,NISSAN,FRONTIER 4WD,NSX,183,4,6,Auto(A5),14,19,16,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,2,9/7/11,\r2012,Nissan,NISSAN,FRONTIER 4WD,NSX,184,4,6,Manual(M6),15,20,17,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,2,9/7/11,\r2012,Nissan,SUZUKI,Equator 4WD,NSX,482,4,6,Auto(A5),15,19,16,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,2,9/7/11,\r2012,Toyota,TOYOTA,TACOMA 4WD,TYX,41,2.7,4,Auto(A4),18,21,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 4WD,TYX,42,2.7,4,Manual(M5),18,20,19,N,NA,Naturally Aspirated,M,Manual,5,N,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 4WD,TYX,51,4,6,Auto(A5),16,21,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 4WD,TYX,52,4,6,Manual(M6),15,19,17,N,NA,Naturally Aspirated,M,Manual,6,N,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,9/1/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD,GMX,546,4.3,6,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/15/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD,GMX,547,4.8,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD,GMX,544,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD,GMX,545,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD HYBRID,GMX,548,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,N\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD XFE,GMX,549,5.3,8,Auto(A6),15,22,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,Chrysler Group LLC,Dodge,Ram 1500 2WD,CRX,55,3.7,6,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,7/12/11,\r2012,Chrysler Group LLC,Dodge,Ram 1500 2WD,CRX,56,4.7,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,7/11/11,\r2012,Chrysler Group LLC,Dodge,Ram 1500 2WD,CRX,58,5.7,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),Standard Pick-up Trucks 2WD,2,7/1/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD,FMX,132,3.5,6,Auto(A6),16,22,18,N,TC,Turbocharged,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD,FMX,133,3.5,6,Auto(S6),16,22,18,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD,FMX,109,6.2,8,Auto(S6),13,18,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD FFV,FMX,117,3.7,6,Auto(A6),17,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD FFV,FMX,118,3.7,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD FFV,FMX,139,5,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD FFV,FMX,140,5,8,Auto(S6),15,21,17,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,General Motors,GMC,C15 SIERRA 2WD,GMX,598,4.3,6,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/15/11,\r2012,General Motors,GMC,C15 SIERRA 2WD,GMX,599,4.8,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,GMC,C15 SIERRA 2WD,GMX,596,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,GMC,C15 SIERRA 2WD,GMX,597,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,GMC,C15 SIERRA 2WD HYBRID,GMX,600,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,N\r2012,General Motors,GMC,C15 SIERRA 2WD XFE,GMX,595,5.3,8,Auto(A6),15,22,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,Nissan,NISSAN,TITAN 2WD,NSX,284,5.6,8,Auto(A5),13,18,15,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,8/25/11,\r2012,Nissan,NISSAN,TITAN 2WD,NSX,293,5.6,8,Auto(A5),13,18,15,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,8/25/11,\r2012,Toyota,TOYOTA,TUNDRA 2WD,TYX,53,4,6,Auto(S5),16,20,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,9/1/11,\r2012,Toyota,TOYOTA,TUNDRA 2WD,TYX,57,4.6,8,Auto(S6),15,20,17,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,9/1/11,\r2012,Toyota,TOYOTA,TUNDRA 2WD,TYX,61,5.7,8,Auto(S6),14,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,9/1/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD,GMX,552,4.3,6,Auto(A4),14,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/15/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD,GMX,553,4.8,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD,GMX,550,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD,GMX,551,6.2,8,Auto(A6),12,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD HYBRID,GMX,554,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,N\r2012,Chrysler Group LLC,Dodge,Ram 1500 4WD,CRX,57,4.7,8,Auto(A6),14,19,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,7/11/11,\r2012,Chrysler Group LLC,Dodge,Ram 1500 4WD,CRX,59,5.7,8,Auto(A6),13,19,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),Standard Pick-up Trucks 4WD,2,7/1/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD,FMX,137,3.5,6,Auto(A6),15,21,17,N,TC,Turbocharged,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD,FMX,135,3.5,6,Auto(S6),15,21,17,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD,FMX,112,6.2,8,Auto(S6),12,16,13,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD FFV,FMX,122,3.7,6,Auto(A6),16,21,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD FFV,FMX,123,3.7,6,Auto(S6),16,21,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD FFV,FMX,141,5,8,Auto(A6),14,19,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD FFV,FMX,142,5,8,Auto(S6),14,19,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 Raptor Pickup 4WD,FMX,111,6.2,8,Auto(S6),11,16,13,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,General Motors,GMC,K15 SIERRA 4WD,GMX,603,4.3,6,Auto(A4),14,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/15/11,\r2012,General Motors,GMC,K15 SIERRA 4WD,GMX,604,4.8,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,GMC,K15 SIERRA 4WD,GMX,601,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,GMC,K15 SIERRA 4WD,GMX,602,6.2,8,Auto(A6),12,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,GMC,K15 SIERRA 4WD HYBRID,GMX,605,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,N\r2012,General Motors,GMC,K15 SIERRA AWD,GMX,606,6.2,8,Auto(A6),12,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,Honda,Honda,RIDGELINE 4WD,HNX,38,3.5,6,Auto(A5),15,21,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/28/11,N\r2012,Nissan,NISSAN,TITAN 4WD,NSX,285,5.6,8,Auto(A5),12,17,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,8/25/11,\r2012,Nissan,NISSAN,TITAN 4WD,NSX,294,5.6,8,Auto(A5),12,17,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,8/25/11,\r2012,Toyota,TOYOTA,TUNDRA 4WD,TYX,58,4.6,8,Auto(S6),14,19,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,9/1/11,\r2012,Toyota,TOYOTA,TUNDRA 4WD,TYX,62,5.7,8,Auto(S6),13,17,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,9/1/11,\r2012,Toyota,TOYOTA,TUNDRA 4WD FFV,TYX,64,5.7,8,Auto(S6),13,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,8/31/11,\r2012,General Motors,Chevrolet,G1500 EXPRESS 2WD CARGO,GMX,621,4.3,6,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/15/11,\r2012,General Motors,Chevrolet,G1500 EXPRESS 2WD CARGO,GMX,514,5.3,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G1500 EXPRESS CONV 2WD CARGO,GMX,515,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G2500 EXPRESS 2WD CARGO MDPV,GMX,614,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G2500 EXPRESS CONV 2WD CARGO,GMX,610,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G3500 EXPRESS 2WD CARGO MDPV,GMX,615,6,8,Auto(A6),10,14,11,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,H1500 EXPRESS AWD CARGO,GMX,519,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,H1500 EXPRESS CONV AWD CARGO,GMX,517,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,Ford Motor Company,Ford Division,E150 VAN FFV,FMX,146,4.6,8,Auto(A4),13,17,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E150 VAN FFV,FMX,150,5.4,8,Auto(A4),12,16,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E250 VAN FFV,FMX,148,4.6,8,Auto(A4),13,17,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E250 VAN FFV,FMX,151,5.4,8,Auto(A4),12,16,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E350 VAN,FMX,20,6.8,10,Auto(A5),10,14,12,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E350 VAN FFV,FMX,153,5.4,8,Auto(A4),12,16,13,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,General Motors,GMC,G1500 SAVANA 2WD CARGO,GMX,622,4.3,6,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/15/11,\r2012,General Motors,GMC,G1500 SAVANA 2WD CARGO,GMX,562,5.3,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,G1500 SAVANA CONV 2WD CARGO,GMX,563,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,G2500 SAVANA 2WD CARGO MDPV,GMX,619,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,G2500 SAVANA CONV 2WD CARGO,GMX,616,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,G3500 SAVANA 2WD CARGO MDPV,GMX,620,6,8,Auto(A6),10,14,11,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,H1500 SAVANA AWD CARGO,GMX,566,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,H1500 SAVANA CONV AWD CARGO,GMX,567,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G1500 EXPRESS 2WD PASS,GMX,513,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,G2500 EXPRESS 2WD PASS MDPV,GMX,555,4.8,8,Auto(A6),11,17,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,G2500 EXPRESS 2WD PASS MDPV,GMX,612,6,8,Auto(A6),11,16,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,G3500 EXPRESS 2WD PASS MDPV,GMX,556,4.8,8,Auto(A6),11,17,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,G3500 EXPRESS 2WD PASS MDPV,GMX,613,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,H1500 EXPRESS AWD PASS,GMX,518,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,Ford Motor Company,Ford Division,E150 WAGON FFV,FMX,147,4.6,8,Auto(A4),13,16,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E150 WAGON FFV,FMX,152,5.4,8,Auto(A4),12,16,13,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E350 WAGON,FMX,21,6.8,10,Auto(A5),10,13,11,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E350 WAGON FFV,FMX,165,5.4,8,Auto(A4),11,15,13,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,8/1/11,\r2012,General Motors,GMC,G1500 SAVANA 2WD PASS,GMX,559,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,G2500 SAVANA 2WD PASS (MDPV),GMX,607,4.8,8,Auto(A6),11,17,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,G2500 SAVANA 2WD PASS (MDPV),GMX,617,6,8,Auto(A6),11,16,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,G3500 SAVANA 2WD PASS (MDPV,GMX,608,4.8,8,Auto(A6),11,17,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,G3500 SAVANA 2WD PASS (MDPV,GMX,618,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,H1500 SAVANA AWD PASS,GMX,565,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,Azure Dynamics Incorporated,Azure Dynamics Incorporated,Transit Connect Electric Van,AZD,1,0,,Auto(A1),62,62,62,N,,,A,Automatic,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Special Purpose Vehicle 2WD,,10/1/11,N\r2012,Azure Dynamics Incorporated,Azure Dynamics Incorporated,Transit Connect Electric Wagon,AZD,2,0,,Auto(A1),62,62,62,N,,,A,Automatic,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Special Purpose Vehicle 2WD,,10/1/11,N\r2012,Ford Motor Company,Ford Division,Transit Connect Van,FMX,90,2,4,Auto(A4),21,27,23,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Special Purpose Vehicle 2WD,,7/11/11,\r2012,Ford Motor Company,Ford Division,TRANSIT CONNECT WAGON FWD,FMX,70,2,4,Auto(A4),22,27,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Special Purpose Vehicle 2WD,,7/11/11,\r2012,VPG,The Vehicle Production Group LLC,MV-1,TVP,1,4.6,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Special Purpose Vehicle 2WD,,11/11/11,N\r2012,Chrysler Group LLC,Chrysler,Town & Country,CRX,540,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Dodge,Grand Caravan,CRX,541,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Dodge,Ram C/V,CRX,543,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,7/1/11,\r2012,Honda,Honda,ODYSSEY 2WD,HNX,39,3.5,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,9/8/11,N\r2012,Honda,Honda,ODYSSEY 2WD,HNX,40,3.5,6,Auto(A6),19,28,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,9/8/11,N\r2012,Kia,KIA MOTORS CORPORATION,SEDONA,KMX,9,3.5,6,Auto(A6),18,25,21,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,4/1/11,N\r2012,MAZDA,MAZDA,MAZDA 5,TKX,2,2.5,4,Auto(S5),21,28,24,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,1/31/11,N\r2012,MAZDA,MAZDA,MAZDA 5,TKX,1,2.5,4,Manual(M6),21,28,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,1/31/11,N\r2012,Nissan,NISSAN,QUEST,NSX,96,3.5,6,Auto(AV),19,24,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,10/18/11,\r2012,Toyota,TOYOTA,SIENNA,TYX,34,2.7,4,Auto(S6),19,24,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,9/9/11,\r2012,Toyota,TOYOTA,SIENNA,TYX,37,3.5,6,Auto(S6),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,9/9/11,\r2012,Chrysler Group LLC,Volkswagen,Routan,CRX,542,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,7/1/11,\r2012,Toyota,TOYOTA,SIENNA AWD,TYX,38,3.5,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 4WD\",1,9/9/11,\r2012,Honda,Acura,RDX 2WD,HNX,34,2.3,4,Auto(S5),19,24,21,N,TC,Turbocharged,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/15/11,N\r2012,General Motors,Buick,ENCLAVE FWD,GMX,500,3.6,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/2/11,\r2012,General Motors,Cadillac,ESCALADE 2WD,GMX,505,6.2,8,Auto(A6),14,18,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Cadillac,ESCALADE 2WD HYBRID,GMX,504,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,N\r2012,General Motors,Cadillac,ESCALADE ESV 2WD,GMX,506,6.2,8,Auto(A6),14,18,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Chevrolet,C1500 AVALANCHE 2WD,GMX,511,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Chevrolet,C1500 SUBURBAN 2WD,GMX,520,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Chevrolet,C1500 TAHOE 2WD,GMX,509,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Chevrolet,C1500 TAHOE 2WD HYBRID,GMX,512,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,N\r2012,General Motors,Chevrolet,C2500 SUBURBAN 2WD,GMX,521,6,8,Auto(A6),10,16,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/21/11,\r2012,General Motors,Chevrolet,CAPTIVA FWD,GMX,120,2.4,4,Auto(A6),20,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,10/28/11,\r2012,General Motors,Chevrolet,CAPTIVA FWD,GMX,51,3,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,11/4/11,\r2012,General Motors,Chevrolet,EQUINOX FWD,GMX,23,2.4,4,Auto(A6),22,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/6/11,\r2012,General Motors,Chevrolet,EQUINOX FWD,GMX,119,2.4,4,Auto(A6),22,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/27/11,\r2012,General Motors,Chevrolet,EQUINOX FWD,GMX,21,3,6,Auto(A6),17,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/3/11,\r2012,General Motors,Chevrolet,EQUINOX FWD,GMX,24,3,6,Auto(A6),17,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/6/11,\r2012,General Motors,Chevrolet,TRAVERSE FWD,GMX,542,3.6,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/2/11,\r2012,Chrysler Group LLC,Dodge,Durango 2WD,CRX,35,3.6,6,Auto(A5),16,23,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/5/11,\r2012,Chrysler Group LLC,Dodge,Durango 2WD,CRX,37,5.7,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Dodge,Journey FWD,CRX,530,2.4,4,Auto(A4),19,26,22,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/4/11,N\r2012,Chrysler Group LLC,Dodge,Journey FWD,CRX,531,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/4/11,\r2012,Ford Motor Company,Ford Division,EDGE FWD,FMX,8,2,4,Auto(A6),21,30,24,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,EDGE FWD,FMX,119,3.5,6,Auto(S6),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,EDGE FWD,FMX,120,3.7,6,Auto(S6),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,ESCAPE FWD,FMX,100,2.5,4,Auto(A6),21,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE FWD,FMX,101,2.5,4,Manual(M5),23,28,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE FWD FFV,FMX,99,3,6,Auto(A6),19,25,21,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE HYBRID FWD,FMX,88,2.5,4,Auto(AV),34,31,32,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,N\r2012,Ford Motor Company,Ford Division,EXPEDITION 2WD FFV,FMX,186,5.4,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/22/11,\r2012,Ford Motor Company,Ford Division,EXPLORER FWD,FMX,65,2,4,Auto(A6),20,28,23,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/18/11,\r2012,Ford Motor Company,Ford Division,EXPLORER FWD,FMX,160,3.5,6,Auto(S6),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/18/11,\r2012,Ford Motor Company,Ford Division,FLEX FWD,FMX,86,3.5,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,General Motors,GMC,ACADIA FWD,GMX,593,3.6,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/2/11,\r2012,General Motors,GMC,C1500 YUKON 2WD,GMX,560,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,GMC,C1500 YUKON 2WD,GMX,561,6.2,8,Auto(A6),14,18,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,GMC,C1500 YUKON 2WD HYBRID,GMX,564,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,N\r2012,General Motors,GMC,C1500 YUKON XL 2WD,GMX,568,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,GMC,C1500 YUKON XL 2WD,GMX,569,6.2,8,Auto(A6),14,18,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,GMC,C2500 YUKON XL 2WD,GMX,570,6,8,Auto(A6),10,16,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/21/11,\r2012,General Motors,GMC,TERRAIN FWD,GMX,59,2.4,4,Auto(A6),22,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/6/11,\r2012,General Motors,GMC,TERRAIN FWD,GMX,121,2.4,4,Auto(A6),22,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/27/11,\r2012,General Motors,GMC,TERRAIN FWD,GMX,57,3,6,Auto(A6),17,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/3/11,\r2012,General Motors,GMC,TERRAIN FWD,GMX,60,3,6,Auto(A6),17,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/6/11,\r2012,Honda,Honda,CROSSTOUR 2WD,HNX,28,3.5,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/3/11,N\r2012,Honda,Honda,CR-V 2WD,HNX,36,2.4,4,Auto(A5),23,31,26,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/28/11,N\r2012,Honda,Honda,PILOT 2WD,HNX,41,3.5,6,Auto(A5),18,25,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/31/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 2WD,HYX,23,2.4,4,Auto(A6),20,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 2WD,HYX,24,2.4,4,Manual(M6),19,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 2WD,HYX,26,3.5,6,Auto(A6),20,26,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 2WD,HYX,13,2,4,Auto(A6),23,31,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 2WD,HYX,14,2,4,Manual(M5),20,27,23,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 2WD,HYX,10,2.4,4,Auto(A6),22,32,25,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 2WD,HYX,12,2.4,4,Manual(M6),21,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,VERACRUZ 2WD,HYX,31,3.8,6,Auto(A6),17,22,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/1/11,\r2012,Nissan,INFINITI,FX35 RWD,NSX,93,3.5,6,Auto(S7),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,10/3/11,\r2012,Nissan,INFINITI,QX56 2WD,NSX,381,5.6,8,Auto(S7),14,20,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/16/11,\r2012,Chrysler Group LLC,Jeep,Compass 2WD,CRX,510,2,4,Auto(AV),23,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Compass 2WD,CRX,505,2,4,Manual(M5),23,29,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Compass 2WD,CRX,507,2.4,4,Auto(AV),21,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Compass 2WD,CRX,515,2.4,4,Manual(M5),23,28,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee 2WD,CRX,31,3.6,6,Auto(A5),17,23,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee 2WD,CRX,33,5.7,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/18/11,\r2012,Chrysler Group LLC,Jeep,Liberty 2WD,CRX,40,3.7,6,Auto(A4),16,22,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Jeep,Patriot 2WD,CRX,511,2,4,Auto(AV),23,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Patriot 2WD,CRX,506,2,4,Manual(M5),23,29,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Patriot 2WD,CRX,508,2.4,4,Auto(AV),21,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Patriot 2WD,CRX,516,2.4,4,Manual(M5),23,28,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 2WD,KMX,11,2.4,4,Auto(A6),21,29,24,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/25/11,N\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 2WD,KMX,16,2.4,4,Auto(A6),22,32,25,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/25/11,\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 2WD,KMX,12,2.4,4,Manual(M6),20,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/25/11,N\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 2WD,KMX,14,3.5,6,Auto(A6),20,26,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/25/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 2WD,KMX,6,2,4,Auto(A6),22,29,24,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 2WD,KMX,3,2.4,4,Auto(A6),22,32,25,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 2WD,KMX,4,2.4,4,Manual(M6),21,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/1/11,\r2012,Toyota,LEXUS,RX 350,TYX,35,3.5,6,Auto(S6),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Toyota,LEXUS,RX 450h,TYX,19,3.5,6,Auto(AV-S6),32,28,30,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Ford Motor Company,Lincoln Truck,MKT FWD,FMX,87,3.5,6,Auto(S6),17,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,Ford Motor Company,Lincoln Truck,MKX FWD,FMX,178,3.7,6,Auto(S6),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/8/11,\r2012,Ford Motor Company,Lincoln Truck,NAVIGATOR 2WD FFV,FMX,184,5.4,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/22/11,\r2012,MAZDA,MAZDA,CX-7 2WD,TKX,22,2.3,4,Auto(S6),18,24,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,10/28/11,\r2012,MAZDA,MAZDA,CX-7 2WD,TKX,24,2.5,4,Auto(S5),20,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,11/8/11,\r2012,MAZDA,MAZDA,CX-9 2WD,TKX,14,3.7,6,Auto(S6),17,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,GLK 350,MBX,802,3.5,6,Auto(A7),16,22,18,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 2WD\",1,7/1/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER 2WD,MTX,211,2.4,4,Auto(AV-S6),23,28,25,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER 2WD,MTX,213,3,6,Auto(S6),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER SPORT 2WD,MTX,222,2,4,Auto(AV-S6),25,31,27,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,11/1/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER SPORT 2WD,MTX,221,2,4,Manual(M5),24,31,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,11/1/11,\r2012,Nissan,NISSAN,ARMADA 2WD,NSX,282,5.6,8,Auto(A5),13,19,15,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/25/11,\r2012,Nissan,NISSAN,ARMADA 2WD,NSX,291,5.6,8,Auto(A5),12,19,15,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/25/11,\r2012,Nissan,NISSAN,MURANO FWD,NSX,91,3.5,6,Auto(AV),18,24,20,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/27/11,\r2012,Nissan,NISSAN,PATHFINDER 2WD,NSX,187,4,6,Auto(A5),15,22,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/7/11,\r2012,Nissan,NISSAN,ROGUE FWD,NSX,81,2.5,4,Auto(AV),23,28,25,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/16/11,N\r2012,Nissan,NISSAN,XTERRA 2WD,NSX,483,4,6,Auto(A5),16,22,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/7/11,\r2012,General Motors,Saab,9-4X FWD,GMX,77,3,6,Auto(S6),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/31/11,\r2012,Suzuki,Suzuki,GRAND VITARA,SKX,93,2.4,4,Auto(A4),19,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/10/11,\r2012,Suzuki,Suzuki,GRAND VITARA,SKX,91,2.4,4,Manual(M5),19,26,22,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/10/11,\r2012,Toyota,TOYOTA,4RUNNER 2WD,TYX,43,4,6,Auto(S5),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,10/22/11,\r2012,Toyota,TOYOTA,FJ CRUISER 2WD,TYX,46,4,6,Auto(A5),17,20,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/23/11,\r2012,Toyota,TOYOTA,HIGHLANDER 2WD,TYX,15,2.7,4,Auto(S6),20,25,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/4/11,\r2012,Toyota,TOYOTA,HIGHLANDER 2WD,TYX,16,3.5,6,Auto(S5),18,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/4/11,\r2012,Toyota,TOYOTA,RAV4 2WD,TYX,76,2.5,4,Auto(A4),22,28,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,12/20/11,\r2012,Toyota,TOYOTA,RAV4 2WD,TYX,78,3.5,6,Auto(A5),19,27,22,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,12/20/11,\r2012,Toyota,TOYOTA,SEQUOIA 2WD,TYX,55,4.6,8,Auto(S6),14,20,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/29/11,\r2012,Toyota,TOYOTA,SEQUOIA 2WD,TYX,59,5.7,8,Auto(S6),13,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/29/11,\r2012,Toyota,TOYOTA,VENZA,TYX,80,2.7,4,Auto(S6),21,27,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,12/8/11,\r2012,Toyota,TOYOTA,VENZA,TYX,82,3.5,6,Auto(S6),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,12/8/11,\r2012,Audi,Volkswagen,TIGUAN,ADX,83,2,4,Auto(S6),22,27,24,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/28/11,N\r2012,Audi,Volkswagen,TIGUAN,ADX,84,2,4,Manual(M6),18,26,21,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/28/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC60 FWD,VVX,13,3.2,6,Auto(S6),19,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC70 FWD,VVX,18,3.2,6,Auto(S6),19,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC90 FWD,VVX,40,3.2,6,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/27/11,N\r2012,Honda,Acura,MDX 4WD,HNX,43,3.7,6,Auto(S6),16,21,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/8/11,\r2012,Honda,Acura,RDX 4WD,HNX,35,2.3,4,Auto(S5),17,22,19,N,TC,Turbocharged,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/15/11,N\r2012,Honda,Acura,ZDX 4WD,HNX,33,3.7,6,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/24/11,N\r2012,Audi,Audi,Q5,ADX,35,2,4,Auto(S8),20,27,22,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/5/11,\r2012,Audi,Audi,Q5,ADX,35,2,4,Auto(S8),20,27,22,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/5/11,\r2012,Audi,Audi,Q5,ADX,36,3.2,6,Auto(S6),18,23,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/5/11,N\r2012,Audi,Audi,Q7,ADX,72,3,6,Auto(S8),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,6/27/11,N\r2012,Audi,Audi,Q7,ADX,77,3,6,Auto(S8),16,22,18,N,SC,Supercharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,N\r2012,BMW,BMW,X3 xDrive28i,BMX,370,3,6,Auto(S8),19,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/9/11,N\r2012,BMW,BMW,X3 xDrive35i,BMX,372,3,6,Auto(S8),19,26,21,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/24/11,\r2012,BMW,BMW,X5 xDrive35d,BMX,572,3,6,Auto(S6),19,26,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,9/24/11,\r2012,BMW,BMW,X5 xDrive35i,BMX,570,3,6,Auto(S8),16,23,19,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X5 xDrive50i,BMX,573,4.4,8,Auto(S8),14,20,16,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X5 xDriveM,BMX,574,4.4,8,Auto(S6),12,17,14,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X6 xDrive35i,BMX,671,3,6,Auto(S8),16,23,19,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X6 xDrive50i,BMX,672,4.4,8,Auto(S8),14,20,16,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X6 xDriveM,BMX,673,4.4,8,Auto(S6),12,17,14,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,General Motors,Buick,ENCLAVE AWD,GMX,501,3.6,6,Auto(A6),16,22,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/2/11,\r2012,General Motors,Cadillac,ESCALADE 4WD HYBRID,GMX,502,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/25/11,N\r2012,General Motors,Cadillac,ESCALADE AWD,GMX,503,6.2,8,Auto(A6),13,18,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Cadillac,ESCALADE ESV AWD,GMX,508,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Cadillac,ESCALADE EXT AWD,GMX,507,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Cadillac,SRX AWD,GMX,19,3.6,6,Auto(S6),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/6/11,\r2012,General Motors,Chevrolet,CAPTIVA AWD,GMX,130,3,6,Auto(A6),16,22,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/28/11,\r2012,General Motors,Chevrolet,EQUINOX AWD,GMX,26,2.4,4,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,General Motors,Chevrolet,EQUINOX AWD,GMX,122,2.4,4,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/27/11,\r2012,General Motors,Chevrolet,EQUINOX AWD,GMX,27,3,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/6/11,\r2012,General Motors,Chevrolet,EQUINOX AWD,GMX,90,3,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,General Motors,Chevrolet,K1500 AVALANCHE 4WD,GMX,510,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Chevrolet,K1500 SUBURBAN 4WD,GMX,524,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Chevrolet,K1500 TAHOE 4WD,GMX,522,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Chevrolet,K1500 TAHOE 4WD HYBRID,GMX,523,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,N\r2012,General Motors,Chevrolet,K2500 SUBURBAN 4WD,GMX,525,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/21/11,\r2012,General Motors,Chevrolet,TRAVERSE AWD,GMX,543,3.6,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/2/11,\r2012,Chrysler Group LLC,Dodge,Durango 4WD,CRX,36,3.6,6,Auto(A5),16,23,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/5/11,\r2012,Chrysler Group LLC,Dodge,Durango 4WD,CRX,38,5.7,8,Auto(A6),13,20,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,\r2012,Chrysler Group LLC,Dodge,Journey AWD,CRX,532,3.6,6,Auto(A6),16,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/4/11,N\r2012,Ford Motor Company,Ford Division,EDGE AWD,FMX,128,3.5,6,Auto(S6),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,EDGE AWD,FMX,114,3.7,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,ESCAPE AWD,FMX,131,2.5,4,Auto(A6),20,27,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE AWD FFV,FMX,98,3,6,Auto(A6),18,23,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE HYBRID AWD,FMX,89,2.5,4,Auto(AV),30,27,29,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,N\r2012,Ford Motor Company,Ford Division,EXPEDITION 4WD FFV,FMX,161,5.4,8,Auto(A6),13,18,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,Ford Motor Company,Ford Division,EXPLORER AWD,FMX,190,3.5,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/18/11,\r2012,Ford Motor Company,Ford Division,FLEX AWD,FMX,85,3.5,6,Auto(A6),16,23,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,FLEX AWD,FMX,67,3.5,6,Auto(S6),16,22,18,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/25/11,\r2012,General Motors,GMC,ACADIA AWD,GMX,594,3.6,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/2/11,\r2012,General Motors,GMC,K1500 YUKON 4WD,GMX,574,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,GMC,K1500 YUKON 4WD HYBRID,GMX,575,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,N\r2012,General Motors,GMC,K1500 YUKON DENALI AWD,GMX,573,6.2,8,Auto(A6),13,18,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,GMC,K1500 YUKON DENALI HYBRID 4WD,GMX,609,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/25/11,N\r2012,General Motors,GMC,K1500 YUKON XL 4WD,GMX,572,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,GMC,K1500 YUKON XL AWD,GMX,576,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,GMC,K2500 YUKON XL 4WD,GMX,571,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/21/11,\r2012,General Motors,GMC,TERRAIN AWD,GMX,62,2.4,4,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,General Motors,GMC,TERRAIN AWD,GMX,123,2.4,4,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/27/11,\r2012,General Motors,GMC,TERRAIN AWD,GMX,63,3,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/6/11,\r2012,General Motors,GMC,TERRAIN AWD,GMX,92,3,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Honda,Honda,CROSSTOUR 4WD,HNX,29,3.5,6,Auto(A5),18,26,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/3/11,N\r2012,Honda,Honda,CR-V 4WD,HNX,37,2.4,4,Auto(A5),22,30,25,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/28/11,N\r2012,Honda,Honda,PILOT 4WD,HNX,42,3.5,6,Auto(A5),17,24,20,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/31/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 4WD,HYX,22,2.4,4,Auto(A6),20,25,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 4WD,HYX,25,3.5,6,Auto(A6),20,26,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 4WD,HYX,9,2.4,4,Auto(A6),21,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 4WD,HYX,11,2.4,4,Manual(M6),20,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,VERACRUZ 4WD,HYX,30,3.8,6,Auto(A6),16,21,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,Nissan,INFINITI,FX35 AWD,NSX,94,3.5,6,Auto(S7),16,21,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/3/11,\r2012,Nissan,INFINITI,FX50 AWD,NSX,391,5,8,Auto(S7),14,20,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,10/3/11,\r2012,Nissan,INFINITI,QX56 4WD,NSX,382,5.6,8,Auto(S7),14,20,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/16/11,\r2012,Chrysler Group LLC,Jeep,Compass 4WD,CRX,517,2.4,4,Auto(AV),21,26,23,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Compass 4WD,CRX,520,2.4,4,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Compass 4WD,CRX,513,2.4,4,Manual(M5),22,28,24,N,NA,Naturally Aspirated,M,Manual,5,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee 4WD,CRX,32,3.6,6,Auto(A5),16,23,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/5/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee 4WD,CRX,34,5.7,8,Auto(A6),13,20,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee SRT8,CRX,39,6.4,8,Auto(A5),12,18,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Chrysler Group LLC,Jeep,Liberty 4WD,CRX,41,3.7,6,Auto(A4),15,21,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,\r2012,Chrysler Group LLC,Jeep,Patriot 4WD,CRX,518,2.4,4,Auto(AV),21,26,23,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Patriot 4WD,CRX,521,2.4,4,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Patriot 4WD,CRX,514,2.4,4,Manual(M5),22,28,24,N,NA,Naturally Aspirated,M,Manual,5,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Wrangler 4WD,CRX,75,3.6,6,Auto(A5),17,21,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Chrysler Group LLC,Jeep,Wrangler 4WD,CRX,77,3.6,6,Manual(M6),17,21,18,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Chrysler Group LLC,Jeep,Wrangler Unlimited 4WD,CRX,76,3.6,6,Auto(A5),16,20,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Chrysler Group LLC,Jeep,Wrangler Unlimited 4WD,CRX,78,3.6,6,Manual(M6),16,21,18,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 4WD,KMX,10,2.4,4,Auto(A6),21,27,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/25/11,N\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 4WD,KMX,15,2.4,4,Auto(A6),21,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/25/11,\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 4WD,KMX,13,3.5,6,Auto(A6),18,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/25/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 4WD,KMX,5,2,4,Auto(A6),21,26,23,N,TC,Turbocharged,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 4WD,KMX,1,2.4,4,Auto(A6),21,28,24,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 4WD,KMX,2,2.4,4,Manual(M6),20,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/1/11,\r2012,Land Rover,Land Rover,LR2,LRX,1,3.2,6,Auto(S6),15,22,17,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/30/11,\r2012,Land Rover,Land Rover,LR4,LRX,6,5,8,Auto(S6),12,17,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Land Rover,Land Rover,Range Rover,LRX,2,5,8,Auto(S6),12,18,14,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Land Rover,Land Rover,Range Rover,LRX,3,5,8,Auto(S6),12,18,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Land Rover,Land Rover,Range Rover Evoque,LRX,7,2,4,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,9/30/11,N\r2012,Land Rover,Land Rover,Range Rover sport,LRX,4,5,8,Auto(S6),12,17,14,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Land Rover,Land Rover,Range Rover sport,LRX,5,5,8,Auto(S6),13,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Toyota,LEXUS,GX 460,TYX,54,4.6,8,Auto(S6),15,20,17,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Toyota,LEXUS,RX 350 AWD,TYX,36,3.5,6,Auto(S6),18,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Toyota,LEXUS,RX 450h AWD,TYX,20,3.5,6,Auto(AV-S6),30,28,29,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,N\r2012,Ford Motor Company,Lincoln Truck,MKT AWD,FMX,68,3.5,6,Auto(S6),16,22,18,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/25/11,\r2012,Ford Motor Company,Lincoln Truck,MKX AWD,FMX,129,3.7,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/8/11,\r2012,Ford Motor Company,Lincoln Truck,NAVIGATOR 4WD FFV,FMX,162,5.4,8,Auto(A6),13,18,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,MAZDA,MAZDA,CX-7 4WD,TKX,23,2.3,4,Auto(S6),17,21,19,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/28/11,\r2012,MAZDA,MAZDA,CX-9 4WD,TKX,15,3.7,6,Auto(S6),16,22,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,G 550,MBX,435,5.5,8,Auto(A7),12,15,13,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,GL 350 BLUETEC 4MATIC,MBX,422,3,6,Auto(A7),17,21,19,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,7/27/11,\r2012,Mercedes-Benz,Mercedes-Benz,GL 450 4MATIC,MBX,421,4.7,8,Auto(A7),13,18,15,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,GL 550 4MATIC,MBX,423,5.5,8,Auto(A7),12,17,14,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,GLK 350 4MATIC,MBX,4,3.5,6,Auto(A7),16,21,18,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,ML 350 4MATIC,MBX,402,3.5,6,Auto(A7),17,22,19,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/27/11,\r2012,Mercedes-Benz,Mercedes-Benz,ML 350 BLUETEC 4MATIC,MBX,403,3,6,Auto(A7),20,27,22,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,9/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,ML 550 4MATIC,MBX,405,4.7,8,Auto(A7),15,20,17,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,2/16/12,\r2012,Mercedes-Benz,Mercedes-Benz,ML 63 AMG,MBX,406,5.5,8,Auto(A7),14,18,15,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,2/16/12,\r2012,Mercedes-Benz,Mercedes-Benz,R 350 4MATIC,MBX,412,3.5,6,Auto(A7),16,21,18,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/27/11,\r2012,Mercedes-Benz,Mercedes-Benz,R 350 BLUETEC 4MATIC,MBX,413,3,6,Auto(A7),18,23,20,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,10/4/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER 4WD,MTX,212,2.4,4,Auto(AV-S6),22,27,24,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER 4WD,MTX,214,3,6,Auto(S6),19,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER SPORT 4WD,MTX,224,2,4,Auto(AV-S6),23,28,25,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,11/1/11,\r2012,Nissan,NISSAN,ARMADA 4WD,NSX,283,5.6,8,Auto(A5),12,18,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/25/11,\r2012,Nissan,NISSAN,ARMADA 4WD,NSX,292,5.6,8,Auto(A5),12,18,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/25/11,\r2012,Nissan,NISSAN,MURANO AWD,NSX,92,3.5,6,Auto(AV),18,23,20,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/27/11,\r2012,Nissan,NISSAN,MURANO CrossCabriolet,NSX,95,3.5,6,Auto(AV),17,22,19,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/27/11,\r2012,Nissan,NISSAN,PATHFINDER 4WD,NSX,188,4,6,Auto(A5),14,20,16,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/7/11,\r2012,Nissan,NISSAN,PATHFINDER 4WD,NSX,281,5.6,8,Auto(S5),13,18,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/7/11,\r2012,Nissan,NISSAN,ROGUE AWD,NSX,82,2.5,4,Auto(AV),22,26,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/16/11,N\r2012,Nissan,NISSAN,XTERRA 4WD,NSX,185,4,6,Auto(A5),15,20,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/7/11,\r2012,Nissan,NISSAN,XTERRA 4WD,NSX,186,4,6,Manual(M6),16,20,17,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/17/11,\r2012,Porsche,Porsche,Cayenne,PRX,1,3.6,6,Auto(S8),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,Porsche,Porsche,Cayenne,PRX,2,3.6,6,Manual(M6),15,22,17,N,NA,Naturally Aspirated,M,Manual,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,Porsche,Porsche,Cayenne S,PRX,3,4.8,8,Auto(A8),16,22,18,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/6/11,\r2012,Porsche,Porsche,Cayenne S Hybrid,PRX,9,3,6,Auto(A8),20,24,21,N,SC,Supercharged,A,Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,N\r2012,Porsche,Porsche,Cayenne Turbo,PRX,7,4.8,8,Auto(A8),15,22,17,N,TC,Turbocharged,A,Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/6/11,\r2012,General Motors,Saab,9-4X AWD,GMX,99,2.8,6,Auto(S6),15,22,18,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/31/11,\r2012,General Motors,Saab,9-4X AWD,GMX,76,3,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/31/11,\r2012,Subaru,Subaru,FORESTER AWD,FJX,10,2.5,4,Auto(S4),21,27,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/16/11,\r2012,Subaru,Subaru,FORESTER AWD,FJX,16,2.5,4,Auto(S4),19,24,21,N,TC,Turbocharged,SA,Semi-Automatic,4,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/16/11,\r2012,Subaru,Subaru,FORESTER AWD,FJX,9,2.5,4,Manual(M5),21,27,23,N,NA,Naturally Aspirated,M,Manual,5,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/16/11,\r2012,Subaru,Subaru,OUTBACK WAGON AWD,FJX,8,2.5,4,Auto(AV),22,29,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/18/11,\r2012,Subaru,Subaru,OUTBACK WAGON AWD,FJX,6,2.5,4,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/18/11,\r2012,Subaru,Subaru,OUTBACK WAGON AWD,FJX,18,3.6,6,Auto(S5),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/18/11,\r2012,Subaru,Subaru,TRIBECA AWD,FJX,19,3.6,6,Auto(S5),16,21,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,Suzuki,Suzuki,GRAND VITARA 4WD,SKX,94,2.4,4,Auto(A4),19,23,20,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/10/11,\r2012,Toyota,TOYOTA,4RUNNER 4WD,TYX,44,4,6,Auto(S5),17,22,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/22/11,\r2012,Toyota,TOYOTA,4RUNNER 4WD,TYX,45,4,6,Auto(S5),17,22,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/22/11,\r2012,Toyota,TOYOTA,FJ CRUISER 4WD,TYX,47,4,6,Auto(A5),17,20,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/23/11,\r2012,Toyota,TOYOTA,FJ CRUISER 4WD,TYX,48,4,6,Manual(M6),15,18,16,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/23/11,\r2012,Toyota,TOYOTA,HIGHLANDER 4WD,TYX,17,3.5,6,Auto(S5),17,22,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/4/11,\r2012,Toyota,TOYOTA,HIGHLANDER HYBRID 4WD,TYX,18,3.5,6,Auto(AV),28,28,28,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/8/11,N\r2012,Toyota,TOYOTA,RAV4 4WD,TYX,77,2.5,4,Auto(A4),21,27,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,12/20/11,\r2012,Toyota,TOYOTA,RAV4 4WD,TYX,79,3.5,6,Auto(A5),19,26,22,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,12/20/11,\r2012,Toyota,TOYOTA,SEQUOIA 4WD,TYX,56,4.6,8,Auto(S6),13,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Toyota,TOYOTA,SEQUOIA 4WD,TYX,60,5.7,8,Auto(S6),13,17,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Toyota,TOYOTA,SEQUOIA 4WD FFV,TYX,63,5.7,8,Auto(S6),13,17,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Toyota,TOYOTA,VENZA AWD,TYX,81,2.7,4,Auto(S6),20,25,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,12/8/11,\r2012,Toyota,TOYOTA,VENZA AWD,TYX,83,3.5,6,Auto(S6),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,12/8/11,\r2012,Audi,Volkswagen,TIGUAN 4MOTION,ADX,82,2,4,Auto(S6),21,27,23,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/28/11,N\r2012,Audi,Volkswagen,TOUAREG,ADX,47,3,6,Auto(S8),19,28,22,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,5/11/11,N\r2012,Volkswagen,Volkswagen,TOUAREG,VWX,81,3.6,6,Auto(S8),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/21/11,\r2012,Volkswagen,Volkswagen,Touareg Hybrid,VWX,59,3,6,Auto(S8),20,24,21,N,SC,Supercharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/17/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC60 AWD,VVX,22,3,6,Auto(S6),17,23,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC60 AWD,VVX,43,3.2,6,Auto(S6),18,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC70 AWD,VVX,21,3,6,Auto(S6),17,23,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC70 AWD,VVX,42,3.2,6,Auto(S6),18,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC90 AWD,VVX,41,3.2,6,Auto(S6),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,GM,Chevrolet,VOLT,GMX,32,1.4,4,Auto(AV),35,40,37,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/21/11,N\r2012,Nissan,NISSAN,LEAF,NSX,901,0,,Auto(A1),106,92,99,N,,,A,Automatic,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Midsize Cars,car,10/4/11,N\r2012,Ford Motor Company,Ford Division,Focus FWD BEV,FMX,300,0,,Auto(AV),110,99,105,N,,,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Compact Cars,car,3/5/12,N\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH 57,MBX,240,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,11/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH 57 S,MBX,250,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,11/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH 62,MBX,245,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,12/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH 62 S,MBX,255,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,11/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH Landaulet,MBX,258,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,11/24/11,"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/fuel_eff_city/fuel_eff_city.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\nfuel_eff <- read.csv(\"fuel_eff.csv\")\n\n# select a small sample ---------------------------------------------\nman_rows <- which(fuel_eff$transmission == \"M\")\naut_rows <- which(fuel_eff$transmission == \"A\")\n\nset.seed(3583)\nman_rows_samp <- sample(man_rows, 26)\naut_rows_samp <- sample(aut_rows, 26)\n\nfuel_eff_samp <- fuel_eff[c(man_rows_samp,aut_rows_samp), ]\nfuel_eff_samp$transmission <- droplevels(fuel_eff_samp$transmission)\n\nlevels(fuel_eff_samp$transmission) <- c(\"automatic\", \"manual\")\n\n# plot --------------------------------------------------------------\nmyPDF(\"fuel_eff_city_box.pdf\", 3.5, mar = c(3.7,2,0.3,1), mgp = c(2.5,0.55,0))\nboxPlot(fuel_eff_samp$city_mpg, fact = fuel_eff_samp$transmission, ylim = c(10,37), \n        xlab = \"City MPG\", axes = FALSE, xlim=c(0.5, 2.5),\n        lwd = 1.5, lcol = COL[1], medianLwd = 2.5)\naxis(1, at = c(1,2), labels = c(\"automatic\", \"manual\"))\naxis(2, at = c(15,25,35))\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/fuel_eff_hway/fuel_eff.csv",
    "content": "model_yr,mfr_name,division,carline,mfr_code,model_type_index,engine_displacement,no_cylinders,transmission_speed,city_mpg,hwy_mpg,comb_mpg,guzzler,air_aspir_method,air_aspir_method_desc,transmission,transmission_desc,no_gears,trans_lockup,trans_creeper_gear,drive_sys,drive_desc,fuel_usage,fuel_usage_desc,class,car_truck,release_date,fuel_cell\r2012,aston martin,Aston Martin Lagonda Ltd,V12 Vantage,ASX,8,5.9,12,Manual(M6),11,17,13,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,V8 Vantage,ASX,2,4.7,8,Auto(AM6),14,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/11/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,V8 Vantage,ASX,11,4.7,8,Auto(AM7),14,21,16,Y,NA,Naturally Aspirated,AM,Automated Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/9/12,N\r2012,aston martin,Aston Martin Lagonda Ltd,V8 Vantage,ASX,1,4.7,8,Manual(M6),13,19,15,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/11/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,V8 Vantage S,ASX,3,4.7,8,Auto(AM7),14,21,16,Y,NA,Naturally Aspirated,AM,Automated Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/11/11,N\r2012,Audi,Audi,R8,ADX,73,4.2,8,Auto(AM6),13,21,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/26/11,\r2012,Audi,Audi,R8,ADX,75,4.2,8,Manual(M6),11,20,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,6/7/11,\r2012,Audi,Audi,R8,ADX,41,5.2,10,Auto(AM6),13,19,15,Y,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/6/11,\r2012,Audi,Audi,R8,ADX,43,5.2,10,Manual(M6),12,19,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/9/11,\r2012,Audi,Audi,R8 Spyder,ADX,66,4.2,8,Auto(AM6),13,21,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/26/11,\r2012,Audi,Audi,R8 Spyder,ADX,74,4.2,8,Manual(M6),11,20,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,6/7/11,\r2012,Audi,Audi,R8 Spyder,ADX,40,5.2,10,Auto(AM6),13,19,15,Y,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/6/11,\r2012,Audi,Audi,R8 Spyder,ADX,42,5.2,10,Manual(M6),12,19,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/9/11,\r2012,Audi,Audi,TT ROADSTER QUATTRO,ADX,71,2,4,Auto(S6),23,31,26,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,6/7/11,N\r2012,Bentley,Bentley Motors Ltd.,Continental Supersports,BEX,15,6,12,Auto(S6),12,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,2/15/11,N\r2012,BMW,BMW,Z4 sDrive28i,BMX,428,2,4,Auto(A8),24,33,27,N,TC,Turbocharged,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,9/28/11,\r2012,BMW,BMW,Z4 sDrive28i,BMX,429,2,4,Manual(M6),23,34,27,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,12/9/11,\r2012,BMW,BMW,Z4 sDrive35i,BMX,436,3,6,Auto(S7),17,24,19,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,9/24/11,\r2012,BMW,BMW,Z4 sDrive35i,BMX,435,3,6,Manual(M6),19,26,21,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,9/24/11,\r2012,BMW,BMW,Z4 sDrive35is,BMX,438,3,6,Auto(S7),17,24,19,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,9/24/11,\r2012,Bugatti,Bugatti,Veyron,BGT,85,8,16,Auto(S7),8,15,10,Y,TC,Turbocharged,SA,Semi-Automatic,7,N,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,7/8/11,\r2012,General Motors,Chevrolet,CORVETTE,GMX,42,6.2,8,Auto(S6),15,25,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Two Seaters,car,6/2/11,\r2012,General Motors,Chevrolet,CORVETTE,GMX,43,6.2,8,Manual(M6),16,26,19,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Two Seaters,car,6/2/11,\r2012,General Motors,Chevrolet,CORVETTE,GMX,44,6.2,8,Manual(M6),14,21,17,Y,SC,Supercharged,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,6/23/11,\r2012,General Motors,Chevrolet,CORVETTE,GMX,45,7,8,Manual(M6),15,24,18,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,6/23/11,\r2012,Honda,Honda,CR-Z,HNX,9,1.5,4,Auto(AV-S7),35,39,37,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),7,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Two Seaters,car,9/30/11,N\r2012,Honda,Honda,CR-Z,HNX,8,1.5,4,Manual(M6),31,37,34,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Two Seaters,car,9/30/11,N\r2012,Lamborghini,Lamborghini,Aventador Coupe,NLX,7,6.5,12,Auto(S7),11,17,13,Y,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,2/28/11,\r2012,Audi,Lamborghini,Gallardo Coupe,ADX,62,5.2,10,Auto(AM6),13,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/25/11,\r2012,Audi,Lamborghini,Gallardo Coupe,ADX,64,5.2,10,Manual(M6),12,20,15,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/25/11,\r2012,Audi,Lamborghini,Gallardo Spyder,ADX,63,5.2,10,Auto(AM6),13,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/25/11,\r2012,Lamborghini,Lamborghini,Gallardo Spyder,NLX,65,5.2,10,Manual(M6),12,20,14,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,5/25/11,\r2012,Toyota,LEXUS,LFA,TYX,3,4.8,10,Auto(S6),11,16,12,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,1/3/11,\r2012,MAZDA,MAZDA,MX-5,TKX,8,2,4,Auto(S6),21,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/12/11,\r2012,MAZDA,MAZDA,MX-5,TKX,6,2,4,Manual(M5),22,28,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/12/11,\r2012,MAZDA,MAZDA,MX-5,TKX,7,2,4,Manual(M6),21,28,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,SL 550,MBX,222,5.5,8,Auto(A7),14,22,17,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,6/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,SL 63 AMG,MBX,226,6.2,8,Auto(A7),12,19,14,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,7/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,SLK 250,MBX,232,1.8,4,Auto(A7),23,33,26,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,2/23/12,\r2012,Mercedes-Benz,Mercedes-Benz,SLK 250,MBX,233,1.8,4,Manual(M6),22,32,26,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,2/23/12,\r2012,Mercedes-Benz,Mercedes-Benz,SLK 350,MBX,236,3.5,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,5/30/11,\r2012,Mercedes-Benz,Mercedes-Benz,SLK 55 AMG,MBX,238,5.5,8,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,2/1/12,\r2012,Mercedes-Benz,Mercedes-Benz,SLS AMG,MBX,270,6.2,8,Auto(AM7),14,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,7/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,SLS AMG Roadster,MBX,271,6.2,8,Auto(AM7),14,20,16,Y,NA,Naturally Aspirated,AM,Automated Manual,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,8/31/11,\r2012,Mercedes-Benz,Mercedes-Benz,Smart fortwo (CABRIOLET),MBX,703,1,3,Auto(AM5),34,38,36,N,NA,Naturally Aspirated,AM,Automated Manual,5,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,10/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,Smart fortwo (COUPE),MBX,702,1,3,Auto(AM5),34,38,36,N,NA,Naturally Aspirated,AM,Automated Manual,5,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,10/10/11,\r2012,BMW,Mini,Mini Cooper Coupe,BMX,40,1.6,4,Auto(S6),28,36,31,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Coupe,BMX,41,1.6,4,Manual(M6),29,37,32,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Roadster,BMX,42,1.6,4,Auto(S6),27,35,30,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Roadster,BMX,43,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Coupe,BMX,44,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Coupe,BMX,45,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Roadster,BMX,46,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Roadster,BMX,47,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works Coupe,BMX,48,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works Roadster,BMX,49,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,8/29/11,\r2012,Nissan,NISSAN,370Z,NSX,56,3.7,6,Auto(S7),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,9/24/11,\r2012,Nissan,NISSAN,370Z,NSX,57,3.7,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,9/24/11,\r2012,Nissan,NISSAN,370Z ROADSTER,NSX,58,3.7,6,Auto(S7),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,10/4/11,\r2012,Nissan,NISSAN,370Z ROADSTER,NSX,59,3.7,6,Manual(M6),18,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Two Seaters,car,10/4/11,\r2012,Porsche,Porsche,911 Speedster,PRX,65,3.8,6,Auto(A7),19,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,2/11/11,\r2012,Porsche,Porsche,Boxster,PRX,31,2.9,6,Auto(A7),20,29,24,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,4/1/11,\r2012,Porsche,Porsche,Boxster,PRX,30,2.9,6,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,4/1/11,\r2012,Porsche,Porsche,Boxster S,PRX,36,3.4,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Boxster S,PRX,35,3.4,6,Manual(M6),19,26,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Boxster Spyder,PRX,40,3.4,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Boxster Spyder,PRX,39,3.4,6,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Cayman,PRX,33,2.9,6,Auto(A7),20,29,24,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,4/1/11,\r2012,Porsche,Porsche,Cayman,PRX,32,2.9,6,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,4/1/11,\r2012,Porsche,Porsche,Cayman R,PRX,42,3.4,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Cayman R,PRX,41,3.4,6,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Cayman S,PRX,38,3.4,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,Porsche,Porsche,Cayman S,PRX,37,3.4,6,Manual(M6),19,26,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Two Seaters,car,1/31/11,\r2012,aston martin,Aston Martin Lagonda Ltd,DB9,ASX,6,5.9,12,Auto(S6),13,20,15,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,DB9,ASX,10,5.9,12,Manual(M6),11,17,13,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,DBS,ASX,5,5.9,12,Auto(S6),12,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,DBS,ASX,4,5.9,12,Manual(M6),11,17,13,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,aston martin,Aston Martin Lagonda Ltd,Virage,ASX,9,5.9,12,Auto(S6),13,18,15,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,5/6/11,N\r2012,Chrysler Group LLC,FIAT,500,CRX,601,1.4,4,Auto(A6),27,34,30,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/21/11,\r2012,Chrysler Group LLC,FIAT,500,CRX,600,1.4,4,Manual(M5),30,38,33,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/3/11,\r2012,Chrysler Group LLC,FIAT,500 Abarth,CRX,603,1.4,4,Manual(M5),28,34,31,N,TC,Turbocharged,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/10/12,\r2012,Chrysler Group LLC,FIAT,500 Cabrio,CRX,602,1.4,4,Auto(A6),27,32,29,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XK,JCX,4,5,8,Auto(S6),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XK,JCX,10,5,8,Auto(S6),15,22,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XK Convertible,JCX,2,5,8,Auto(S6),15,22,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XK Convertible,JCX,3,5,8,Auto(S6),16,22,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,7/14/11,N\r2012,Lotus,Lotus Cars Ltd,Evora,LTX,5,3.5,6,Auto(S6),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/2/12,\r2012,Lotus,Lotus Cars Ltd,Evora,LTX,6,3.5,6,Auto(S6),19,28,22,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,11/18/11,\r2012,Lotus,Lotus Cars Ltd,Evora,LTX,3,3.5,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/2/12,\r2012,Lotus,Lotus Cars Ltd,Evora,LTX,4,3.5,6,Manual(M6),17,26,20,N,SC,Supercharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/2/12,\r2012,BMW,Mini,Mini Cooper,BMX,10,1.6,4,Auto(S6),28,36,31,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper,BMX,11,1.6,4,Manual(M6),29,37,32,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Convertible,BMX,14,1.6,4,Auto(S6),27,35,30,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Convertible,BMX,15,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S,BMX,16,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S,BMX,17,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Convertible,BMX,20,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Convertible,BMX,21,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works,BMX,23,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works Conv,BMX,24,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,8/29/11,\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE SPYDER,DSX,322,2.4,4,Auto(S4),20,27,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Minicompact Cars,car,1/24/11,N\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE SPYDER,DSX,324,3.8,6,Auto(S5),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,1/24/11,N\r2012,Porsche,Porsche,911 C4 GTS,PRX,67,3.8,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,6/10/11,\r2012,Porsche,Porsche,911 C4 GTS,PRX,66,3.8,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,6/10/11,\r2012,Porsche,Porsche,911 C4 GTS Cabriolet,PRX,69,3.8,6,Auto(A7),18,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,6/10/11,\r2012,Porsche,Porsche,911 C4 GTS Cabriolet,PRX,68,3.8,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,6/10/11,\r2012,Porsche,Porsche,911 Carrera,PRX,11,3.6,6,Auto(A7),19,27,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera,PRX,10,3.6,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4,PRX,19,3.6,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4,PRX,18,3.6,6,Manual(M6),18,24,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4 Cabriolet,PRX,21,3.6,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4 Cabriolet,PRX,20,3.6,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4 Targa,PRX,27,3.6,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4 Targa,PRX,26,3.6,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S,PRX,23,3.8,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S,PRX,22,3.8,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S Cabriolet,PRX,25,3.8,6,Auto(A7),18,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S Cabriolet,PRX,24,3.8,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S Targa,PRX,29,3.8,6,Auto(A7),18,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera 4S Targa,PRX,28,3.8,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera Cabriolet,PRX,13,3.6,6,Auto(A7),19,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera Cabriolet,PRX,12,3.6,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera S,PRX,15,3.8,6,Auto(A7),19,26,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera S,PRX,14,3.8,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera S Cabriolet,PRX,17,3.8,6,Auto(A7),19,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Carrera S Cabriolet,PRX,16,3.8,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 GTS,PRX,62,3.8,6,Auto(A7),19,26,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 GTS,PRX,61,3.8,6,Manual(M6),18,25,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 GTS Cabriolet,PRX,64,3.8,6,Auto(A7),19,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 GTS Cabriolet,PRX,63,3.8,6,Manual(M6),18,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo Cabriolet,PRX,51,3.8,6,Auto(A7),16,24,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo Cabriolet,PRX,55,3.8,6,Manual(M6),16,24,19,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo Coupe,PRX,50,3.8,6,Auto(A7),17,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo Coupe,PRX,54,3.8,6,Manual(M6),16,24,19,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo S Cabriolet,PRX,53,3.8,6,Auto(A7),16,24,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,911 Turbo S Coupe,PRX,52,3.8,6,Auto(A7),17,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,3/1/11,\r2012,Porsche,Porsche,New 911 Carrera,PRX,102,3.4,6,Auto(S7),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera,PRX,101,3.4,6,Manual(M7),19,27,22,N,NA,Naturally Aspirated,M,Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera Cabriolet,PRX,104,3.4,6,Auto(S7),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera Cabriolet,PRX,103,3.4,6,Manual(M7),19,27,22,N,NA,Naturally Aspirated,M,Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera S,PRX,106,3.8,6,Auto(S7),20,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera S,PRX,105,3.8,6,Manual(M7),19,27,22,N,NA,Naturally Aspirated,M,Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera S Cabriolet,PRX,108,3.8,6,Auto(S7),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Porsche,Porsche,New 911 Carrera S Cabriolet,PRX,107,3.8,6,Manual(M7),19,27,22,N,NA,Naturally Aspirated,M,Manual,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Minicompact Cars,car,2/1/12,\r2012,Toyota,SCION,iQ,TYX,11,1.3,4,Auto(AV),36,37,37,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Minicompact Cars,car,8/20/11,\r2012,aston martin,Aston Martin Lagonda Ltd,Rapide,ASX,7,5.9,12,Auto(S6),13,19,15,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/6/11,N\r2012,Audi,Audi,A5 Cabriolet,ADX,21,2,4,Auto(AV),22,30,25,N,TC,Turbocharged,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,4/18/11,\r2012,Audi,Audi,A5 Cabriolet quattro,ADX,32,2,4,Auto(S8),21,29,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/4/11,\r2012,Audi,Audi,A5 QUATTRO,ADX,30,2,4,Auto(S8),21,29,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/4/11,\r2012,Audi,Audi,A5 QUATTRO,ADX,34,2,4,Manual(M6),21,31,25,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/4/11,\r2012,Audi,Audi,S5,ADX,57,4.2,8,Auto(S6),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/13/11,N\r2012,Audi,Audi,S5,ADX,56,4.2,8,Manual(M6),14,22,17,Y,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/13/11,N\r2012,Audi,Audi,S5 Cabriolet,ADX,38,3,6,Auto(S7),17,26,20,N,SC,Supercharged,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/5/11,\r2012,Audi,Audi,TT COUPE QUATTRO,ADX,70,2,4,Auto(S6),23,31,26,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,6/7/11,N\r2012,Audi,Audi,TTRS COUPE,ADX,80,2.5,5,Manual(M6),18,25,20,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,6/13/11,N\r2012,Bentley,Bentley Motors Ltd.,Continental GTC,BEX,88,6,12,Auto(S6),11,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,10/10/11,N\r2012,Bentley,Bentley Motors Ltd.,Continental Supersports Convt,BEX,13,6,12,Auto(S6),12,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,2/15/11,N\r2012,BMW,BMW,128Ci Convertible,BMX,130,3,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,128Ci Convertible,BMX,131,3,6,Manual(M6),18,28,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,N\r2012,BMW,BMW,128i,BMX,128,3,6,Auto(S6),18,28,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,N\r2012,BMW,BMW,128i,BMX,129,3,6,Manual(M6),18,28,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,N\r2012,BMW,BMW,135i,BMX,135,3,6,Auto(S7),18,25,21,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,135i,BMX,136,3,6,Manual(M6),20,28,23,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,135i Convertible,BMX,137,3,6,Auto(S7),18,25,20,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,135i Convertible,BMX,138,3,6,Manual(M6),19,28,22,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,328Ci Convertible,BMX,312,3,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328Ci Convertible,BMX,313,3,6,Manual(M6),17,26,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328i Coupe,BMX,302,3,6,Auto(S6),18,28,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328i Coupe,BMX,303,3,6,Manual(M6),18,28,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328i Coupe xDrive,BMX,306,3,6,Auto(S6),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,328i Coupe xDrive,BMX,307,3,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/9/11,N\r2012,BMW,BMW,335Ci Convertible,BMX,347,3,6,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/23/11,\r2012,BMW,BMW,335Ci Convertible,BMX,348,3,6,Manual(M6),19,28,22,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/23/11,\r2012,BMW,BMW,335i Coupe,BMX,337,3,6,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/23/11,\r2012,BMW,BMW,335i Coupe,BMX,338,3,6,Manual(M6),19,28,22,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/23/11,\r2012,BMW,BMW,335i Coupe xDrive,BMX,341,3,6,Auto(S6),18,27,21,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335i Coupe xDrive,BMX,342,3,6,Manual(M6),19,27,22,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335is Convertible,BMX,345,3,6,Auto(S7),17,24,19,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335is Convertible,BMX,346,3,6,Manual(M6),18,26,21,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335is Coupe,BMX,343,3,6,Auto(S7),17,24,19,N,TC,Turbocharged,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,335is Coupe,BMX,344,3,6,Manual(M6),18,26,21,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,9/24/11,\r2012,BMW,BMW,M3 Convertible,BMX,365,4,8,Auto(S7),14,20,16,Y,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,M3 Convertible,BMX,364,4,8,Manual(M6),13,20,16,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,M3 Coupe,BMX,363,4,8,Auto(S7),14,20,16,Y,NA,Naturally Aspirated,SA,Semi-Automatic,7,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,BMW,M3 Coupe,BMX,362,4,8,Manual(M6),14,20,16,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,General Motors,Chevrolet,SONIC 5,GMX,101,1.4,4,Manual(M6),29,40,33,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,9/12/11,\r2012,General Motors,Chevrolet,SONIC 5,GMX,35,1.8,4,Auto(S6),25,35,28,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,7/11/11,\r2012,General Motors,Chevrolet,SONIC 5,GMX,36,1.8,4,Manual(M5),26,35,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,8/11/11,\r2012,Coda,CODA Automotive Inc,CODA,CDA,1,0,,Auto(A1),77,68,73,N,,,A,Automatic,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Subcompact Cars,car,2/27/12,N\r2012,Ford Motor Company,Ford Division,Fiesta FWD,FMX,1,1.6,4,Auto(AM6),29,39,33,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,Fiesta FWD,FMX,2,1.6,4,Manual(M5),29,38,33,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,Fiesta SFE FWD,FMX,189,1.6,4,Auto(AM6),29,40,33,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,27,3.7,6,Auto(A6),19,31,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,28,3.7,6,Manual(M6),19,29,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,25,5,8,Auto(A6),18,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,10/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,26,5,8,Manual(M6),17,26,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG,FMX,24,5.4,8,Manual(M6),15,23,17,N,SC,Supercharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Ford Motor Company,Ford Division,MUSTANG CONVERTIBLE,FMX,29,3.7,6,Auto(A6),19,30,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/18/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS COUPE,HYX,18,2,4,Auto(A5),20,30,23,N,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS COUPE,HYX,19,2,4,Manual(M6),21,30,24,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS COUPE,HYX,20,3.8,6,Auto(A6),17,27,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS COUPE,HYX,21,3.8,6,Manual(M6),17,26,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,6/24/11,\r2012,Nissan,INFINITI,G37 CONVERTIBLE,NSX,54,3.7,6,Auto(S7),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37 CONVERTIBLE,NSX,55,3.7,6,Manual(M6),16,24,19,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37 COUPE,NSX,73,3.7,6,Auto(S7),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37 COUPE,NSX,72,3.7,6,Manual(M6),17,25,19,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37x COUPE,NSX,74,3.7,6,Auto(S7),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/15/11,\r2012,Toyota,LEXUS,IS 250 AWD,TYX,25,2.5,6,Auto(S6),20,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS 250/IS 250C,TYX,27,2.5,6,Auto(S6),21,30,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS 250/IS 250C,TYX,26,2.5,6,Manual(M6),19,28,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS 350 AWD,TYX,23,3.5,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS 350/IS 350C,TYX,24,3.5,6,Auto(S6),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,9/1/11,\r2012,Toyota,LEXUS,IS F,TYX,32,5,8,Auto(S8),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,10/1/11,\r2012,Maserati,MASERATI,GRANTURISMO,MAX,21,4.7,8,Auto(A6),13,21,15,Y,NA,Naturally Aspirated,A,Automatic,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,7/22/11,N\r2012,Maserati,MASERATI,Granturismo Convertible,MAX,25,4.7,8,Auto(A6),13,20,15,Y,NA,Naturally Aspirated,A,Automatic,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,7/22/11,N\r2012,Mercedes-Benz,Mercedes-Benz,C 250 (Coupe),MBX,102,1.8,4,Auto(A7),21,31,25,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,10/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 350 (Coupe),MBX,112,3.5,6,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,8/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 63 AMG Coupe,MBX,69,6.2,8,Auto(A7),13,19,15,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,8/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 63 Black Series AMG Coupe,MBX,110,6.2,8,Auto(A7),13,19,15,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/25/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 (CONVERTIBLE),MBX,141,3.5,6,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,7/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 (CONVERTIBLE),MBX,818,3.5,6,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/1/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 (coupe),MBX,131,3.5,6,Auto(A7),19,29,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,1/31/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 (coupe),MBX,819,3.5,6,Auto(A7),20,28,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/1/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 4MATIC (coupe),MBX,133,3.5,6,Auto(A7),19,28,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,1/31/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 4MATIC (coupe),MBX,820,3.5,6,Auto(A7),19,27,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/1/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 550 (CONVERTIBLE),MBX,142,4.7,8,Auto(A7),16,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,10/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 550 (COUPE),MBX,132,4.7,8,Auto(A7),17,27,21,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,10/5/11,\r2012,BMW,Mini,Mini Cooper Clubman,BMX,12,1.6,4,Auto(S6),27,35,30,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Clubman,BMX,13,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Clubman,BMX,18,1.6,4,Auto(S6),26,34,29,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Clubman,BMX,19,1.6,4,Manual(M6),27,35,30,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,BMW,Mini,Mini John Cooper Works Clubman,BMX,22,1.6,4,Manual(M6),25,33,28,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,i-MiEV,MTX,141,0,,Auto(A1),126,99,112,N,,,A,Automatic,1,Y,N,R,\"2-Wheel Drive, Rear\",EL,Electricity,Subcompact Cars,car,10/17/11,N\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE,DSX,312,2.4,4,Auto(S4),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/24/11,N\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE,DSX,311,2.4,4,Manual(M5),20,28,23,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,1/24/11,N\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,ECLIPSE,DSX,314,3.8,6,Auto(S5),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,1/24/11,N\r2012,Nissan,NISSAN,ALTIMA COUPE,NSX,25,2.5,4,Auto(AV-S6),23,32,26,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/27/11,N\r2012,Nissan,NISSAN,ALTIMA COUPE,NSX,26,2.5,4,Manual(M6),23,31,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/27/11,N\r2012,Nissan,NISSAN,ALTIMA COUPE,NSX,43,3.5,6,Auto(AV-S6),20,27,23,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/24/11,N\r2012,Nissan,NISSAN,ALTIMA COUPE,NSX,44,3.5,6,Manual(M6),18,27,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/24/11,N\r2012,Nissan,NISSAN,GT-R,NSX,71,3.8,6,Auto(AM6),16,23,19,N,TC,Turbocharged,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,2/3/11,\r2012,Roush,\"Roush Industries, Inc.\",Roush Stage 3 Mustang,RII,2,5,8,Auto(A6),15,22,18,N,SC,Supercharged,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,11/30/11,\r2012,Roush,\"Roush Industries, Inc.\",Roush Stage 3 Mustang,RII,1,5,8,Manual(M6),14,21,16,Y,SC,Supercharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Subcompact Cars,car,5/2/11,N\r2012,Toyota,SCION,xD,TYX,13,1.8,4,Auto(A4),27,33,29,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,9/17/11,\r2012,Toyota,SCION,xD,TYX,14,1.8,4,Manual(M5),27,33,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,9/17/11,\r2012,Volkswagen,Volkswagen,BEETLE,VWX,45,2,4,Auto(S6),22,30,25,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,5/9/11,\r2012,Volkswagen,Volkswagen,BEETLE,VWX,86,2,4,Manual(M6),21,30,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,10/17/11,N\r2012,Volkswagen,Volkswagen,BEETLE,VWX,25,2.5,5,Auto(S6),22,29,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,5/9/11,\r2012,Volkswagen,Volkswagen,BEETLE,VWX,87,2.5,5,Manual(M5),22,31,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,10/17/11,\r2012,Volkswagen,Volkswagen,EOS,VWX,5,2,4,Auto(S6),22,30,25,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Subcompact Cars,car,1/21/11,\r2012,Volvo,\"Volvo Cars of North America, LLC\",C70 FWD,VVX,69,2.5,5,Auto(S5),18,28,21,N,TC,Turbocharged,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Subcompact Cars,car,6/20/11,N\r2012,Honda,Acura,TSX,HNX,20,2.4,4,Auto(S5),22,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/21/11,N\r2012,Honda,Acura,TSX,HNX,19,2.4,4,Manual(M6),21,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/21/11,N\r2012,Honda,Acura,TSX,HNX,24,3.5,6,Auto(S5),19,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/21/11,N\r2012,Audi,Audi,A4,ADX,20,2,4,Auto(AV),22,30,25,N,TC,Turbocharged,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,4/18/11,\r2012,Audi,Audi,A4 QUATTRO,ADX,29,2,4,Auto(S8),21,29,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/4/11,\r2012,Audi,Audi,A4 QUATTRO,ADX,33,2,4,Manual(M6),21,31,25,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/4/11,\r2012,Audi,Audi,S4,ADX,37,3,6,Auto(S7),18,28,21,N,SC,Supercharged,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/5/11,\r2012,Audi,Audi,S4,ADX,39,3,6,Manual(M6),18,27,21,N,SC,Supercharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/5/11,\r2012,Bentley,Bentley Motors Ltd.,Continental GT,BEX,14,6,12,Auto(S6),12,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,2/15/11,N\r2012,BMW,BMW,328i,BMX,300,2,4,Auto(A8),24,36,28,N,TC,Turbocharged,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,328i,BMX,301,2,4,Manual(M6),23,34,27,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,335i,BMX,335,3,6,Auto(S8),23,33,26,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,335i,BMX,336,3,6,Manual(M6),20,30,23,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,640i Convertible,BMX,641,3,6,Auto(S8),21,31,25,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,10/12/11,\r2012,BMW,BMW,640i Coupe,BMX,640,3,6,Auto(S8),23,33,26,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,12/9/11,\r2012,BMW,BMW,650i Convertible,BMX,654,4.4,8,Auto(S8),15,23,18,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,BMW,650i Convertible,BMX,655,4.4,8,Manual(M6),15,22,17,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,BMW,650i Coupe,BMX,650,4.4,8,Auto(S8),15,23,18,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,BMW,650i Coupe,BMX,651,4.4,8,Manual(M6),15,22,17,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,BMW,650i Coupe xDrive,BMX,652,4.4,8,Auto(S8),15,20,17,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,9/27/11,\r2012,General Motors,Buick,VERANO,GMX,141,2.4,4,Auto(S6),21,32,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,11/11/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,98,3.6,6,Auto(A6),19,30,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/15/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,46,3.6,6,Auto(S6),18,29,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/14/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,113,3.6,6,Manual(M6),17,28,20,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/14/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,47,6.2,8,Auto(S6),12,18,14,Y,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,11/30/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,78,6.2,8,Auto(S6),15,24,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/24/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,50,6.2,8,Manual(M6),16,24,19,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/2/11,\r2012,General Motors,Chevrolet,CAMARO,GMX,137,6.2,8,Manual(M6),14,19,16,Y,SC,Supercharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,12/21/11,\r2012,General Motors,Chevrolet,SONIC,GMX,260,1.4,4,Auto(S6),27,37,31,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,2/14/12,\r2012,General Motors,Chevrolet,SONIC,GMX,100,1.4,4,Manual(M6),29,40,33,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/12/11,\r2012,General Motors,Chevrolet,SONIC,GMX,33,1.8,4,Auto(S6),25,35,28,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/11/11,\r2012,General Motors,Chevrolet,SONIC,GMX,34,1.8,4,Manual(M5),26,35,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/11/11,\r2012,Chrysler Group LLC,Chrysler,200 Convertible,CRX,205,2.4,4,Auto(A6),18,29,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/1/11,N\r2012,Chrysler Group LLC,Chrysler,200 Convertible,CRX,211,3.6,6,Auto(A6),19,29,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/1/11,\r2012,Ford Motor Company,Ford Division,FOCUS FWD,FMX,46,2,4,Auto(AM6),28,38,31,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,1/25/11,\r2012,Ford Motor Company,Ford Division,FOCUS FWD,FMX,6,2,4,Auto(AM-S6),27,37,31,N,NA,Naturally Aspirated,OT,Other,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,2/25/11,\r2012,Ford Motor Company,Ford Division,FOCUS FWD,FMX,5,2,4,Manual(M5),26,36,30,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,2/24/11,\r2012,Ford Motor Company,Ford Division,Focus FWD FFV,FMX,193,2,4,Auto(AM6),28,38,31,N,NA,Naturally Aspirated,AM,Automated Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/5/12,\r2012,Ford Motor Company,Ford Division,Focus FWD FFV,FMX,32,2,4,Manual(M5),26,36,30,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/5/12,\r2012,Ford Motor Company,Ford Division,Focus SFE FWD,FMX,10,2,4,Auto(AM6),28,40,33,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,2/24/11,\r2012,Ford Motor Company,Ford Division,Focus SFE FWD FFV,FMX,194,2,4,Auto(AM6),28,40,33,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/5/12,\r2012,Honda,Honda,ACCORD 2DR COUPE,HNX,18,2.4,4,Auto(A5),22,33,26,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 2DR COUPE,HNX,17,2.4,4,Manual(M5),23,32,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 2DR COUPE,HNX,26,3.5,6,Auto(S5),19,29,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 2DR COUPE,HNX,23,3.5,6,Manual(M6),17,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,N\r2012,Honda,Honda,CIVIC,HNX,12,1.8,4,Auto(A5),28,39,32,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,4/20/11,N\r2012,Honda,Honda,CIVIC,HNX,11,1.8,4,Manual(M5),28,36,31,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,4/20/11,N\r2012,Honda,Honda,CIVIC,HNX,14,2.4,4,Manual(M6),22,31,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/23/11,N\r2012,Honda,Honda,CIVIC HF,HNX,13,1.8,4,Auto(A5),29,41,33,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,4/20/11,N\r2012,Honda,Honda,CIVIC HYBRID,HNX,2,1.5,4,Auto(AV),44,44,44,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,4/20/11,N\r2012,Honda,Honda,INSIGHT,HNX,3,1.3,4,Auto(AV),41,44,42,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/29/11,N\r2012,Honda,Honda,INSIGHT,HNX,4,1.3,4,Auto(AV-S7),41,44,42,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),7,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/29/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ACCENT,HYX,3,1.6,4,Auto(A6),30,40,33,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/18/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ACCENT,HYX,4,1.6,4,Manual(M6),30,40,34,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,3/18/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,VELOSTER,HYX,33,1.6,4,Auto(AM6),29,38,32,N,NA,Naturally Aspirated,AM,Automated Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/20/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,VELOSTER,HYX,32,1.6,4,Manual(M6),28,40,32,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/17/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE KOUP,KMX,22,2,4,Auto(A6),25,34,29,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE KOUP,KMX,23,2,4,Manual(M6),24,33,28,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE KOUP,KMX,24,2.4,4,Auto(A6),23,31,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE KOUP,KMX,25,2.4,4,Manual(M6),22,32,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,RIO,KMX,32,1.6,4,Auto(A6),30,40,33,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/17/11,\r2012,Kia,KIA MOTORS CORPORATION,RIO,KMX,33,1.6,4,Manual(M6),30,40,34,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,12/20/11,\r2012,Toyota,LEXUS,CT 200h,TYX,12,1.8,4,Auto(AV),43,40,42,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/1/11,N\r2012,Toyota,LEXUS,HS 250h,TYX,21,2.4,4,Auto(AV),35,34,35,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/1/11,N\r2012,MAZDA,MAZDA,MAZDA2,TKX,17,1.5,4,Auto(A4),28,34,30,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/11/11,\r2012,MAZDA,MAZDA,MAZDA2,TKX,16,1.5,4,Manual(M5),29,35,32,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/11/11,\r2012,MAZDA,MAZDA,MAZDA3,TKX,11,2,4,Auto(S5),24,33,27,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/15/11,\r2012,MAZDA,MAZDA,MAZDA3,TKX,10,2,4,Manual(M5),25,33,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/15/11,\r2012,MAZDA,MAZDA,MAZDA3,TKX,13,2.5,4,Auto(S5),22,29,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/12/11,\r2012,MAZDA,MAZDA,MAZDA3,TKX,12,2.5,4,Manual(M6),20,28,23,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/12/11,\r2012,MAZDA,MAZDA,MAZDA3 DI 4-Door,TKX,19,2,4,Auto(S6),28,40,33,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/26/11,\r2012,MAZDA,MAZDA,MAZDA3 DI 4-Door,TKX,18,2,4,Manual(M6),27,39,31,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/26/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 250,MBX,101,1.8,4,Auto(A7),21,31,25,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,10/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 300 4MATIC,MBX,25,3,6,Auto(A7),17,24,20,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 300 4MATIC,MBX,26,3,6,Auto(A7),18,25,20,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/9/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 350,MBX,103,3.5,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 350,MBX,103,3.5,6,Auto(A7),20,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,C 350,MBX,817,3.5,6,Auto(A7),19,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,2/14/12,\r2012,Mercedes-Benz,Mercedes-Benz,C 63 AMG,MBX,108,6.2,8,Auto(A7),13,19,15,Y,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,CL 550 4MATIC,MBX,213,4.7,8,Auto(A7),15,24,18,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/20/11,\r2012,Mercedes-Benz,Mercedes-Benz,CL 600,MBX,214,5.5,12,Auto(A5),12,18,14,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,CL 63 AMG,MBX,215,5.5,8,Auto(A7),15,22,18,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,CL 65 AMG,MBX,218,6,12,Auto(A5),12,18,14,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,CLS 550,MBX,319,4.7,8,Auto(A7),17,25,20,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,6/2/11,\r2012,Mercedes-Benz,Mercedes-Benz,CLS 550 4MATIC,MBX,320,4.7,8,Auto(A7),16,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,8/10/11,\r2012,Mercedes-Benz,Mercedes-Benz,CLS 63 AMG,MBX,321,5.5,8,Auto(A7),16,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,6/2/11,\r2012,BMW,Mini,Mini Cooper Countryman,BMX,30,1.6,4,Auto(S6),25,30,27,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper Countryman,BMX,31,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Countryman,BMX,34,1.6,4,Auto(S6),25,32,28,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Countryman,BMX,35,1.6,4,Manual(M6),26,32,29,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Countryman All4,BMX,36,1.6,4,Auto(S6),23,30,26,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,BMW,Mini,Mini Cooper S Countryman All4,BMX,37,1.6,4,Manual(M6),25,31,28,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,115,2,4,Auto(AM6),18,25,20,N,TC,Turbocharged,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,112,2,4,Auto(AV-S6),26,34,29,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,111,2,4,Manual(M5),25,34,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,114,2.4,4,Auto(AV-S6),23,30,26,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER,MTX,113,2.4,4,Manual(M5),22,31,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER AWD,MTX,116,2.4,4,Auto(AV-S6),22,29,25,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER EVOLUTION,MTX,132,2,4,Auto(AM6),17,22,19,N,TC,Turbocharged,AM,Automated Manual,6,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER EVOLUTION,MTX,131,2,4,Manual(M5),17,23,19,N,TC,Turbocharged,M,Manual,5,N,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,10/5/11,\r2012,Nissan,NISSAN,VERSA,NSX,101,1.6,4,Auto(AV),30,38,33,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/5/11,\r2012,Nissan,NISSAN,VERSA,NSX,102,1.6,4,Manual(M5),27,36,30,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/5/11,\r2012,Nissan,NISSAN,VERSA,NSX,2,1.8,4,Auto(A4),24,32,27,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/17/11,\r2012,Nissan,NISSAN,VERSA,NSX,1,1.8,4,Auto(AV),28,34,30,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/17/11,\r2012,Nissan,NISSAN,VERSA,NSX,3,1.8,4,Manual(M6),26,31,28,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/17/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Phantom Coupe,RRG,4,6.7,12,Auto(S6),11,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Phantom Drophead Coupe,RRG,3,6.7,12,Auto(S6),11,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,8/29/11,\r2012,Saab Cars North America,Saab,9-3 CONVERTIBLE,SAX,72,2,4,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3 CONVERTIBLE,SAX,73,2,4,Manual(M6),20,33,25,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3 SEDAN AWD,SAX,68,2,4,Auto(S6),18,29,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/11/11,\r2012,Saab Cars North America,Saab,9-3 SEDAN AWD,SAX,69,2,4,Manual(M6),20,30,24,N,TC,Turbocharged,M,Manual,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/11/11,\r2012,Saab Cars North America,Saab,9-3 SPORT SEDAN,SAX,64,2,4,Auto(S6),19,29,23,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3 SPORT SEDAN,SAX,65,2,4,Manual(M6),20,33,25,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,9/6/11,\r2012,Toyota,SCION,tC,TYX,9,2.5,4,Auto(S6),23,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/25/11,\r2012,Toyota,SCION,tC,TYX,8,2.5,4,Manual(M6),23,31,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/25/11,\r2012,Subaru,Subaru,IMPREZA AWD,FJX,3,2,4,Auto(AV),27,36,30,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/11/11,\r2012,Subaru,Subaru,IMPREZA AWD,FJX,1,2,4,Manual(M5),25,34,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,7/11/11,\r2012,Subaru,Subaru,IMPREZA AWD,FJX,12,2.5,4,Manual(M5),19,25,21,N,TC,Turbocharged,M,Manual,5,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/5/11,\r2012,Subaru,Subaru,IMPREZA AWD,FJX,14,2.5,4,Manual(M6),17,23,19,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,7/5/11,\r2012,Suzuki,Suzuki,KIZASHI,SKX,62,2.4,4,Auto(AV),23,30,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI,SKX,64,2.4,4,Manual(M6),20,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI AWD,SKX,66,2.4,4,Auto(AV),22,29,25,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI S,SKX,61,2.4,4,Auto(AV),23,31,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI S,SKX,63,2.4,4,Manual(M6),21,31,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,KIZASHI S AWD,SKX,65,2.4,4,Auto(AV),23,30,25,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 SEDAN,SKX,54,2,4,Auto(AV),25,32,28,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 SEDAN,SKX,53,2,4,Manual(M6),23,33,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 Sport,SKX,58,2,4,Auto(AV),23,30,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 Sport,SKX,57,2,4,Manual(M6),23,32,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/10/11,\r2012,Toyota,TOYOTA,COROLLA,TYX,68,1.8,4,Auto(A4),26,34,29,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA,TYX,69,1.8,4,Manual(M5),27,34,30,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,12/8/11,\r2012,Toyota,TOYOTA,PRIUS c,TYX,84,1.5,4,Auto(AV),53,46,50,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,1/16/12,N\r2012,Toyota,TOYOTA,YARIS,TYX,4,1.5,4,Auto(A4),30,35,32,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/20/11,\r2012,Toyota,TOYOTA,YARIS,TYX,5,1.5,4,Manual(M5),30,38,33,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,8/20/11,\r2012,Audi,Volkswagen,CC,ADX,3,2,4,Auto(S6),22,31,25,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,1/21/11,N\r2012,Audi,Volkswagen,CC,ADX,4,2,4,Manual(M6),21,31,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,1/21/11,N\r2012,Volkswagen,Volkswagen,CC 4MOTION,VWX,58,3.6,6,Auto(S6),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/13/11,N\r2012,Volkswagen,Volkswagen,GOLF,VWX,51,2,4,Auto(S6),30,42,34,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Compact Cars,car,6/3/11,N\r2012,Volkswagen,Volkswagen,GOLF,VWX,55,2,4,Manual(M6),30,42,34,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Compact Cars,car,6/3/11,N\r2012,Volkswagen,Volkswagen,GOLF,VWX,24,2.5,5,Auto(S6),24,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/9/11,\r2012,Volkswagen,Volkswagen,GOLF,VWX,28,2.5,5,Manual(M5),23,33,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/4/11,\r2012,Audi,Volkswagen,Golf R,ADX,89,2,4,Manual(M6),19,27,22,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,1/25/12,N\r2012,Audi,Volkswagen,GTI,ADX,44,2,4,Auto(S6),24,33,27,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/9/11,N\r2012,Volkswagen,Volkswagen,GTI,VWX,46,2,4,Manual(M6),21,31,25,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,5/9/11,N\r2012,Volkswagen,Volkswagen,Jetta,VWX,17,2,4,Auto(S6),24,32,27,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,3/25/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,50,2,4,Auto(S6),30,42,34,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Compact Cars,car,5/12/11,N\r2012,Volkswagen,Volkswagen,Jetta,VWX,78,2,4,Auto(S6),23,29,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/10/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,79,2,4,Manual(M5),24,34,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,6/13/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,18,2,4,Manual(M6),22,33,26,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Compact Cars,car,3/25/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,54,2,4,Manual(M6),30,42,34,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Compact Cars,car,6/3/11,N\r2012,Volkswagen,Volkswagen,Jetta,VWX,23,2.5,5,Auto(S6),24,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/9/11,\r2012,Volkswagen,Volkswagen,Jetta,VWX,27,2.5,5,Manual(M5),23,33,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/4/11,\r2012,Volvo,\"Volvo Cars of North America, LLC\",C30 FWD,VVX,70,2.5,5,Auto(S5),21,30,24,N,TC,Turbocharged,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",C30 FWD,VVX,73,2.5,5,Manual(M6),21,29,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,5/6/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",S60 AWD,VVX,23,3,6,Auto(S6),18,26,21,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,1/3/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",S60 FWD,VVX,74,2.5,5,Auto(S6),20,30,23,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Compact Cars,car,1/3/11,N\r2012,Honda,Acura,RL,HNX,32,3.7,6,Auto(S6),17,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/20/11,N\r2012,Honda,Acura,TL 2WD,HNX,22,3.5,6,Auto(S6),20,29,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/18/11,N\r2012,Honda,Acura,TL 4WD,HNX,31,3.7,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/18/11,\r2012,Honda,Acura,TL 4WD,HNX,30,3.7,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/18/11,\r2012,Audi,Audi,A6,ADX,9,2,4,Auto(AV),25,33,28,N,TC,Turbocharged,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/14/11,\r2012,Audi,Audi,A6 quattro,ADX,11,3,6,Auto(S8),19,28,22,N,SC,Supercharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,2/10/11,\r2012,Audi,Audi,A7 quattro,ADX,10,3,6,Auto(S8),18,28,22,N,SC,Supercharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,2/10/11,\r2012,Audi,Audi,A8,ADX,61,4.2,8,Auto(S8),18,28,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,6/8/11,\r2012,Bentley,Bentley Motors Ltd.,Continental Flying Spur,BEX,12,6,12,Auto(S6),11,19,14,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,2/15/11,N\r2012,Bentley,Bentley Motors Ltd.,Mulsanne,BEX,8,6.8,8,Auto(S8),11,18,13,Y,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/5/11,\r2012,BMW,BMW,528i,BMX,528,2,4,Auto(A8),23,34,27,N,TC,Turbocharged,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/25/11,\r2012,BMW,BMW,528i xDrive,BMX,530,2,4,Auto(A8),22,32,26,N,TC,Turbocharged,A,Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/28/11,\r2012,BMW,BMW,535i,BMX,535,3,6,Auto(S8),21,31,25,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,10/12/11,\r2012,BMW,BMW,535i,BMX,536,3,6,Manual(M6),20,30,23,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,12/9/11,\r2012,BMW,BMW,535i xDrive,BMX,537,3,6,Auto(S8),21,30,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,10/12/11,\r2012,BMW,BMW,550i,BMX,550,4.4,8,Auto(S8),15,23,18,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,8/29/11,\r2012,BMW,BMW,550i,BMX,551,4.4,8,Manual(M6),15,22,17,N,TC,Turbocharged,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,8/29/11,\r2012,BMW,BMW,550i xDrive,BMX,552,4.4,8,Auto(S8),15,20,17,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/27/11,\r2012,BMW,BMW,ActiveHybrid 7,BMX,758,4.4,8,Auto(S8),17,24,20,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/30/11,N\r2012,General Motors,Buick,LACROSSE,GMX,97,2.4,4,Auto(S6),25,36,29,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/8/11,N\r2012,General Motors,Buick,LACROSSE,GMX,7,3.6,6,Auto(S6),17,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/7/11,\r2012,General Motors,Buick,LACROSSE,GMX,9,3.6,6,Auto(S6),17,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,11/10/11,\r2012,General Motors,Buick,LACROSSE AWD,GMX,8,3.6,6,Auto(S6),16,26,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/16/11,\r2012,General Motors,Buick,REGAL,GMX,1,2,4,Auto(S6),18,29,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,General Motors,Buick,REGAL,GMX,2,2,4,Auto(S6),19,27,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,12/21/11,\r2012,General Motors,Buick,REGAL,GMX,5,2,4,Manual(M6),20,32,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,General Motors,Buick,REGAL,GMX,6,2,4,Manual(M6),19,27,22,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/15/11,\r2012,General Motors,Buick,REGAL,GMX,96,2.4,4,Auto(S6),25,36,29,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/8/11,N\r2012,General Motors,Buick,REGAL,GMX,116,2.4,4,Auto(S6),19,31,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,General Motors,Buick,REGAL,GMX,117,2.4,4,Auto(S6),19,31,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/19/11,\r2012,General Motors,Cadillac,CTS,GMX,11,3.6,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/14/11,\r2012,General Motors,Cadillac,CTS,GMX,14,3.6,6,Manual(M6),16,26,19,N,NA,Naturally Aspirated,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/17/11,\r2012,General Motors,Cadillac,CTS,GMX,12,6.2,8,Auto(S6),12,18,14,Y,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,6/16/11,\r2012,General Motors,Cadillac,CTS,GMX,13,6.2,8,Manual(M6),14,19,16,Y,SC,Supercharged,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,6/14/11,\r2012,General Motors,Cadillac,CTS AWD,GMX,83,3,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/17/11,\r2012,General Motors,Cadillac,CTS AWD,GMX,124,3.6,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,11/21/11,\r2012,General Motors,Chevrolet,CRUZE,GMX,28,1.4,4,Auto(S6),26,38,30,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/26/11,\r2012,General Motors,Chevrolet,CRUZE,GMX,30,1.4,4,Manual(M6),26,38,30,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/7/11,\r2012,General Motors,Chevrolet,CRUZE,GMX,29,1.8,4,Auto(S6),22,35,27,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/26/11,\r2012,General Motors,Chevrolet,CRUZE,GMX,31,1.8,4,Manual(M6),25,36,29,N,NA,Naturally Aspirated,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,General Motors,Chevrolet,CRUZE ECO,GMX,94,1.4,4,Auto(A6),26,39,31,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/13/11,\r2012,General Motors,Chevrolet,CRUZE ECO,GMX,54,1.4,4,Manual(M6),28,42,33,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/3/11,\r2012,General Motors,Chevrolet,MALIBU,GMX,37,2.4,4,Auto(S6),22,33,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/17/11,\r2012,General Motors,Chevrolet,MALIBU,GMX,39,2.4,4,Auto(S6),22,33,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/7/11,\r2012,General Motors,Chevrolet,MALIBU,GMX,38,3.6,6,Auto(S6),17,26,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/17/11,\r2012,General Motors,Chevrolet,SONIC 5,GMX,261,1.4,4,Auto(S6),27,37,31,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,2/14/12,\r2012,Chrysler Group LLC,Chrysler,200,CRX,200,2.4,4,Auto(A4),21,30,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,N\r2012,Chrysler Group LLC,Chrysler,200,CRX,203,2.4,4,Auto(A6),20,31,24,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Chrysler Group LLC,Chrysler,200,CRX,209,3.6,6,Auto(A6),19,29,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Chrysler Group LLC,Dodge,Avenger,CRX,201,2.4,4,Auto(A4),21,30,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,N\r2012,Chrysler Group LLC,Dodge,Avenger,CRX,204,2.4,4,Auto(A6),20,31,24,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Chrysler Group LLC,Dodge,Avenger,CRX,210,3.6,6,Auto(A6),19,29,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Chrysler Group LLC,Dodge,Challenger,CRX,100,3.6,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/27/11,\r2012,Chrysler Group LLC,Dodge,Challenger,CRX,105,5.7,8,Auto(A5),16,25,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),Midsize Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Challenger,CRX,103,5.7,8,Manual(M6),15,23,18,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Challenger SRT8,CRX,122,6.4,8,Auto(A5),14,23,17,Y,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Challenger SRT8,CRX,109,6.4,8,Manual(M6),14,23,17,Y,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,7/1/11,\r2012,Ford Motor Company,Ford Division,FUSION AWD,FMX,72,3.5,6,Auto(S6),17,25,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION AWD FFV,FMX,73,3,6,Auto(S6),18,26,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD,FMX,78,2.5,4,Auto(A6),23,33,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD,FMX,79,2.5,4,Auto(S6),22,30,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD,FMX,71,2.5,4,Manual(M6),22,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD,FMX,80,3.5,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION FWD FFV,FMX,81,3,6,Auto(S6),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Ford Division,FUSION HYBRID FWD,FMX,74,2.5,4,Auto(AV),41,36,39,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,N\r2012,Ford Motor Company,Ford Division,FUSION S FWD,FMX,75,2.5,4,Manual(M6),22,32,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ELANTRA,HYX,7,1.8,4,Auto(A6),29,40,33,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,4/18/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ELANTRA,HYX,8,1.8,4,Manual(M6),29,40,33,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,4/18/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SONATA HYBRID,HYX,34,2.4,4,Auto(A6),35,40,37,N,NA,Naturally Aspirated,A,Automatic,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,12/1/11,N\r2012,Nissan,INFINITI,G25,NSX,131,2.5,6,Auto(S7),20,29,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,G25x,NSX,132,2.5,6,Auto(S7),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37,NSX,51,3.7,6,Auto(S7),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37,NSX,52,3.7,6,Manual(M6),17,25,19,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,G37x,NSX,53,3.7,6,Auto(S7),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,9/15/11,\r2012,Nissan,INFINITI,M35h,NSX,141,3.5,6,Auto(S7),27,32,29,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,3/16/11,N\r2012,Nissan,INFINITI,M37,NSX,151,3.7,6,Auto(S7),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,3/2/11,\r2012,Nissan,INFINITI,M37x,NSX,152,3.7,6,Auto(S7),17,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,3/2/11,\r2012,Nissan,INFINITI,M56,NSX,111,5.6,8,Auto(S7),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,3/2/11,N\r2012,Nissan,INFINITI,M56x,NSX,112,5.6,8,Auto(S7),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,3/2/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XF,JCX,1,5,8,Auto(S6),15,21,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XF,JCX,5,5,8,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,7/14/11,N\r2012,Kia,KIA MOTORS CORPORATION,FORTE,KMX,17,2,4,Auto(A6),26,36,29,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE,KMX,18,2,4,Manual(M6),25,34,29,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE,KMX,20,2.4,4,Auto(A6),23,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/11/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE,KMX,21,2.4,4,Manual(M6),22,32,26,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/11/11,\r2012,Kia,KIA MOTORS CORPORATION,FORTE ECO,KMX,19,2,4,Auto(A6),27,37,30,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/13/11,\r2012,Kia,KIA MOTORS CORPORATION,OPTIMA,KMX,34,2,4,Auto(A6),22,34,26,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/1/11,\r2012,Kia,KIA MOTORS CORPORATION,OPTIMA,KMX,35,2.4,4,Auto(A6),24,35,28,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/1/11,\r2012,Kia,KIA MOTORS CORPORATION,OPTIMA,KMX,36,2.4,4,Manual(M6),24,35,28,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/1/11,\r2012,Kia,KIA MOTORS CORPORATION,OPTIMA HYBRID,KMX,37,2.4,4,Auto(A6),35,40,37,N,NA,Naturally Aspirated,A,Automatic,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,12/1/11,N\r2012,Toyota,LEXUS,ES 350,TYX,22,3.5,6,Auto(S6),19,28,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 460,TYX,28,4.6,8,Auto(S8),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 460 AWD,TYX,29,4.6,8,Auto(S8),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 460 L,TYX,30,4.6,8,Auto(S8),16,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 460 L AWD,TYX,31,4.6,8,Auto(S8),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,\r2012,Toyota,LEXUS,LS 600h L,TYX,33,5,8,Auto(AV-S8),19,23,20,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),8,N,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/1/11,N\r2012,Ford Motor Company,Lincoln Truck,MKZ AWD,FMX,76,3.5,6,Auto(S6),17,25,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Lincoln Truck,MKZ FWD,FMX,82,3.5,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,\r2012,Ford Motor Company,Lincoln Truck,MKZ HYBRID FWD,FMX,77,2.5,4,Auto(AV),41,36,39,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/6/11,N\r2012,MAZDA,MAZDA,MAZDA3 DI 5-Door,TKX,21,2,4,Auto(S6),28,39,32,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/26/11,\r2012,MAZDA,MAZDA,MAZDA3 DI 5-Door,TKX,20,2,4,Manual(M6),27,38,31,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/26/11,\r2012,MAZDA,MAZDA,MAZDA6,TKX,4,2.5,4,Auto(S5),22,31,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/13/11,N\r2012,MAZDA,MAZDA,MAZDA6,TKX,3,2.5,4,Manual(M6),21,30,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/13/11,N\r2012,MAZDA,MAZDA,MAZDA6,TKX,5,3.7,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/16/11,N\r2012,MAZDA,MAZDA,MAZDASPEED3,TKX,9,2.3,4,Manual(M6),18,25,21,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 350,MBX,301,3.5,6,Auto(A7),20,30,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,1/31/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 4MATIC,MBX,306,3.5,6,Auto(A7),19,29,23,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,1/31/12,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 BLUETEC,MBX,303,3,6,Auto(A7),21,32,25,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",DU,Diesel,Midsize Cars,car,10/6/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 550 4MATIC,MBX,307,4.7,8,Auto(A7),16,26,20,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,10/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 63 AMG,MBX,322,5.5,8,Auto(A7),16,24,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,9/26/11,\r2012,Mitsubishi Motors NA,Mitsubishi Motors North America,GALANT,DSX,331,2.4,4,Auto(S4),21,30,24,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/18/11,\r2012,Nissan,NISSAN,ALTIMA,NSX,23,2.5,4,Auto(AV-S6),23,32,27,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/27/11,N\r2012,Nissan,NISSAN,ALTIMA,NSX,41,3.5,6,Auto(AV-S6),20,27,23,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/24/11,N\r2012,Nissan,NISSAN,MAXIMA,NSX,45,3.5,6,Auto(AV-S6),19,26,22,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,8/30/11,\r2012,Nissan,NISSAN,SENTRA,NSX,11,2,4,Auto(AV),27,34,30,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/21/11,N\r2012,Nissan,NISSAN,SENTRA,NSX,12,2,4,Manual(M6),24,31,27,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/21/11,N\r2012,Nissan,NISSAN,SENTRA,NSX,21,2.5,4,Auto(AV-S6),24,30,26,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,6/21/11,N\r2012,Nissan,NISSAN,SENTRA,NSX,22,2.5,4,Manual(M6),21,28,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GPR,Gasoline (Premium Unleaded Required),Midsize Cars,car,6/21/11,N\r2012,Saab Cars North America,Saab,9-5 SEDAN,SAX,74,2,4,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-5 SEDAN,SAX,75,2,4,Manual(M6),20,33,25,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/6/11,\r2012,Saab Cars North America,Saab,9-5 SEDAN AWD,SAX,131,2.8,6,Auto(S6),17,27,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/18/11,\r2012,Subaru,Subaru,LEGACY AWD,FJX,7,2.5,4,Auto(AV),23,31,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/25/11,\r2012,Subaru,Subaru,LEGACY AWD,FJX,5,2.5,4,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/25/11,\r2012,Subaru,Subaru,LEGACY AWD,FJX,11,2.5,4,Manual(M6),18,25,21,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,7/25/11,\r2012,Subaru,Subaru,LEGACY AWD,FJX,17,3.6,6,Auto(S5),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,7/25/11,\r2012,Toyota,TOYOTA,CAMRY,TYX,7,2.5,4,Auto(S6),25,35,28,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/18/11,\r2012,Toyota,TOYOTA,CAMRY,TYX,10,3.5,6,Auto(S6),21,30,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/18/11,\r2012,Toyota,TOYOTA,CAMRY HYBRID LE,TYX,66,2.5,4,Auto(AV),43,39,41,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,10/27/11,N\r2012,Toyota,TOYOTA,CAMRY HYBRID XLE,TYX,67,2.5,4,Auto(AV),40,38,40,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,10/27/11,N\r2012,Toyota,TOYOTA,PRIUS,TYX,65,1.8,4,Auto(AV),51,48,50,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,9/7/11,N\r2012,Volkswagen,Volkswagen,Passat,VWX,76,2,4,Auto(S6),30,40,34,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Midsize Cars,car,6/15/11,\r2012,Volkswagen,Volkswagen,Passat,VWX,48,2,4,Manual(M6),31,43,35,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Midsize Cars,car,5/11/11,\r2012,Volkswagen,Volkswagen,Passat,VWX,1,2.5,5,Auto(S6),22,31,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/15/11,\r2012,Volkswagen,Volkswagen,Passat,VWX,2,2.5,5,Manual(M5),22,32,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,8/15/11,\r2012,Volkswagen,Volkswagen,Passat,VWX,19,3.6,6,Auto(S6),20,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Midsize Cars,car,8/15/11,\r2012,Volvo,\"Volvo Cars of North America, LLC\",S80 AWD,VVX,20,3,6,Auto(S6),18,26,21,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",S80 FWD,VVX,11,3.2,6,Auto(S6),20,29,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Cars,car,5/27/11,N\r2012,Audi,Audi,A8 L,ADX,60,4.2,8,Auto(S8),18,28,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/8/11,\r2012,Volkswagen,Audi,A8L,VWX,16,6.3,12,Auto(S8),14,21,16,Y,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,2/15/11,\r2012,BMW,BMW,535i Gran Turismo,BMX,540,3,6,Auto(S8),19,28,22,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/30/11,\r2012,BMW,BMW,535i xDrive Gran Turismo,BMX,541,3,6,Auto(S8),18,27,21,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/29/11,\r2012,BMW,BMW,550i Gran Turismo,BMX,554,4.4,8,Auto(S8),15,22,18,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/29/11,\r2012,BMW,BMW,550i xDrive Gran Turismo,BMX,555,4.4,8,Auto(S8),15,19,17,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,10/10/11,\r2012,BMW,BMW,740i,BMX,740,3,6,Auto(S6),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,8/29/11,N\r2012,BMW,BMW,740Li,BMX,741,3,6,Auto(S6),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,8/29/11,N\r2012,BMW,BMW,750i,BMX,750,4.4,8,Auto(S6),15,22,17,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,750i xDrive,BMX,752,4.4,8,Auto(S6),14,20,16,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,750Li,BMX,751,4.4,8,Auto(S6),14,22,17,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,750Li xDrive,BMX,753,4.4,8,Auto(S6),14,20,16,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,760Li,BMX,760,6,12,Auto(S8),13,19,15,Y,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/23/11,N\r2012,BMW,BMW,ActiveHybrid 7L,BMX,759,4.4,8,Auto(S8),17,24,20,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/30/11,N\r2012,BMW,BMW,Alpina B7 LWB,BMX,755,4.4,8,Auto(S6),14,22,17,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,Alpina B7 LWB xDrive,BMX,757,4.4,8,Auto(S6),14,20,16,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,Alpina B7 SWB,BMX,754,4.4,8,Auto(S6),14,22,17,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,BMW,BMW,Alpina B7 SWB xDrive,BMX,756,4.4,8,Auto(S6),14,20,16,Y,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/4/11,\r2012,General Motors,Chevrolet,IMPALA,GMX,40,3.6,6,Auto(A6),18,30,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,6/16/11,\r2012,General Motors,Chevrolet,IMPALA,GMX,41,3.6,6,Auto(A6),18,30,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,9/6/11,\r2012,Chrysler Group LLC,Chrysler,300,CRX,102,3.6,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/27/11,\r2012,Chrysler Group LLC,Chrysler,300,CRX,114,3.6,6,Auto(A8),19,31,23,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/12/11,\r2012,Chrysler Group LLC,Chrysler,300,CRX,106,5.7,8,Auto(A5),16,25,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Chrysler,300 AWD,CRX,116,3.6,6,Auto(A8),18,27,21,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/12/11,\r2012,Chrysler Group LLC,Chrysler,300 AWD,CRX,107,5.7,8,Auto(A5),15,23,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Chrysler,300 SRT8,CRX,120,6.4,8,Auto(A5),14,23,17,Y,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Charger,CRX,101,3.6,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/27/11,\r2012,Chrysler Group LLC,Dodge,Charger,CRX,113,3.6,6,Auto(A8),19,31,23,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/12/11,\r2012,Chrysler Group LLC,Dodge,Charger,CRX,104,5.7,8,Auto(A5),16,25,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Charger AWD,CRX,115,3.6,6,Auto(A8),18,27,21,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/12/11,\r2012,Chrysler Group LLC,Dodge,Charger AWD,CRX,108,5.7,8,Auto(A5),15,23,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),Large Cars,car,7/29/11,\r2012,Chrysler Group LLC,Dodge,Charger SRT8,CRX,121,6.4,8,Auto(A5),14,23,17,Y,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/29/11,\r2012,Ford Motor Company,Ford Division,TAURUS AWD,FMX,93,3.5,6,Auto(S6),17,26,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,TAURUS AWD,FMX,126,3.5,6,Auto(S6),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,TAURUS FWD,FMX,96,3.5,6,Auto(A6),18,28,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Ford Division,TAURUS FWD,FMX,95,3.5,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Honda,Honda,ACCORD 4DR SEDAN,HNX,16,2.4,4,Auto(A5),23,34,27,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 4DR SEDAN,HNX,15,2.4,4,Manual(M5),23,34,27,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/17/11,N\r2012,Honda,Honda,ACCORD 4DR SEDAN,HNX,25,3.5,6,Auto(A5),20,30,24,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,8/17/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,AZERA,HYX,35,3.3,6,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,12/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,EQUUS,HYX,6,5,8,Auto(A8),15,23,18,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS,HYX,2,3.8,6,Auto(A8),19,29,22,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,3/15/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS,HYX,5,4.6,8,Auto(A8),17,26,20,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,5/2/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS,HYX,29,5,8,Auto(A8),17,26,20,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,GENESIS R SPEC,HYX,1,5,8,Auto(A8),16,25,19,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,3/21/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SONATA,HYX,15,2,4,Auto(A6),22,34,26,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,6/1/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SONATA,HYX,16,2.4,4,Auto(A6),24,35,28,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,6/1/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SONATA,HYX,17,2.4,4,Manual(M6),24,35,28,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,6/1/11,\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XJ,JCX,7,5,8,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XJ,JCX,8,5,8,Auto(S6),15,21,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XJ,JCX,11,5,8,Auto(S6),15,21,17,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/14/11,N\r2012,Jaguar Cars,Jaguar Cars Ltd,Jaguar XJ LWB,JCX,9,5,8,Auto(S6),15,22,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,7/14/11,N\r2012,Ford Motor Company,Lincoln Truck,MKS AWD,FMX,125,3.5,6,Auto(S6),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Lincoln Truck,MKS AWD,FMX,92,3.7,6,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Ford Motor Company,Lincoln Truck,MKS FWD,FMX,94,3.5,6,Auto(S6),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,7/18/11,\r2012,Maserati,MASERATI,QUATTROPORTE,MAX,16,4.7,8,Auto(A6),12,19,15,Y,NA,Naturally Aspirated,A,Automatic,6,N,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/22/11,N\r2012,Mercedes-Benz,Mercedes-Benz,S 350 BLUETEC 4MATIC,MBX,209,3,6,Auto(A7),21,31,25,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,DU,Diesel,Large Cars,car,9/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 550,MBX,202,4.7,8,Auto(A7),15,25,19,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/20/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 550 4MATIC,MBX,207,4.7,8,Auto(A7),15,24,18,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/20/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 600,MBX,204,5.5,12,Auto(A5),12,19,14,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/14/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 63 AMG,MBX,205,5.5,8,Auto(A7),15,23,18,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/2/11,\r2012,Mercedes-Benz,Mercedes-Benz,S 65 AMG,MBX,208,6,12,Auto(A5),12,19,14,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/15/11,\r2012,Mercedes-Benz,Mercedes-Benz,S400 HYBRID,MBX,203,3.5,6,Auto(A7),19,25,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,7/14/11,N\r2012,Porsche,Porsche,Panamera,PRX,90,3.6,6,Auto(A7),18,27,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera 4,PRX,91,3.6,6,Auto(A7),18,26,21,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera 4S,PRX,93,4.8,8,Auto(A7),16,24,19,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera S,PRX,92,4.8,8,Auto(A7),16,24,19,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera S Hybrid,PRX,97,3,6,Auto(A8),22,30,25,N,SC,Supercharged,A,Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/9/11,N\r2012,Porsche,Porsche,Panamera Turbo,PRX,95,4.8,8,Auto(A7),15,23,18,N,TC,Turbocharged,A,Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Porsche,Porsche,Panamera Turbo S,PRX,96,4.8,8,Auto(A7),15,23,18,N,TC,Turbocharged,A,Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,6/6/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Ghost,RRG,5,6.6,12,Auto(S8),13,20,15,Y,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/23/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Ghost EWB,RRG,6,6.6,12,Auto(S8),13,20,15,Y,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,9/23/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Phantom,RRG,1,6.7,12,Auto(S6),11,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,8/29/11,\r2012,Rolls-Royce,Rolls-Royce Motor Cars Limited,Phantom EWB,RRG,2,6.7,12,Auto(S6),11,18,14,Y,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Large Cars,car,8/29/11,\r2012,Toyota,TOYOTA,AVALON,TYX,75,3.5,6,Auto(S6),19,28,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Large Cars,car,11/17/11,\r2012,Honda,Acura,TSX WAGON,HNX,21,2.4,4,Auto(S5),22,30,25,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,7/21/11,N\r2012,Audi,Audi,A3,ADX,68,2,4,Auto(S6),22,28,24,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,6/20/11,N\r2012,Volkswagen,Audi,A3,VWX,52,2,4,Auto(S6),30,42,34,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Small Station Wagons,car,6/3/11,N\r2012,Audi,Audi,A3,ADX,67,2,4,Manual(M6),21,30,24,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,6/20/11,N\r2012,Audi,Audi,A3 QUATTRO,ADX,69,2,4,Auto(S6),21,28,24,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,6/6/11,N\r2012,Audi,Audi,A4 AVANT QUATTRO,ADX,31,2,4,Auto(S8),21,29,24,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,5/4/11,\r2012,BMW,BMW,328i Sport Wagon,BMX,308,3,6,Auto(S6),18,27,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,9/9/11,N\r2012,BMW,BMW,328i Sport Wagon,BMX,309,3,6,Manual(M6),17,26,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,9/9/11,N\r2012,BMW,BMW,328i xDrive Sport Wagon,BMX,310,3,6,Auto(S6),17,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,9/9/11,N\r2012,BMW,BMW,328i xDrive Sport Wagon,BMX,311,3,6,Manual(M6),17,25,20,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,9/9/11,N\r2012,General Motors,Cadillac,CTS WAGON,GMX,16,3.6,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,6/14/11,\r2012,General Motors,Cadillac,CTS WAGON,GMX,17,6.2,8,Auto(S6),12,18,14,Y,SC,Supercharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Small Station Wagons,car,6/16/11,\r2012,General Motors,Cadillac,CTS WAGON,GMX,18,6.2,8,Manual(M6),14,19,16,Y,SC,Supercharged,M,Manual,6,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Small Station Wagons,car,6/14/11,\r2012,General Motors,Cadillac,CTS WAGON AWD,GMX,84,3,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,6/17/11,\r2012,General Motors,Cadillac,CTS WAGON AWD,GMX,125,3.6,6,Auto(S6),18,26,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,11/20/11,\r2012,Chrysler Group LLC,Dodge,Caliber,CRX,500,2,4,Auto(AV),23,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/1/11,N\r2012,Chrysler Group LLC,Dodge,Caliber,CRX,501,2,4,Manual(M5),24,32,27,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/1/11,\r2012,Chrysler Group LLC,Dodge,Caliber,CRX,503,2.4,4,Auto(AV),22,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/1/11,N\r2012,Honda,Honda,FIT,HNX,6,1.5,4,Auto(A5),28,35,31,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/19/11,\r2012,Honda,Honda,FIT,HNX,7,1.5,4,Auto(S5),27,33,30,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/19/11,\r2012,Honda,Honda,FIT,HNX,5,1.5,4,Manual(M5),27,33,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/19/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ELANTRA TOURING,HYX,27,2,4,Auto(A4),23,30,26,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,6/24/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,ELANTRA TOURING,HYX,28,2,4,Manual(M5),23,31,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,6/27/11,\r2012,Nissan,INFINITI,EX35,NSX,46,3.5,6,Auto(S7),17,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,8/12/11,\r2012,Nissan,INFINITI,EX35 AWD,NSX,47,3.5,6,Auto(S7),17,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,8/12/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL,KMX,27,1.6,4,Auto(A6),27,35,30,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL,KMX,28,1.6,4,Manual(M6),27,35,30,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL,KMX,30,2,4,Auto(A6),26,34,29,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL,KMX,31,2,4,Manual(M6),26,34,29,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL ECO,KMX,26,1.6,4,Auto(A6),29,36,32,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Kia,KIA MOTORS CORPORATION,SOUL ECO,KMX,29,2,4,Auto(A6),27,35,30,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/29/11,\r2012,Mercedes-Benz,Mercedes-Benz,F-Cell,MBX,500,0,,Auto(A1),52,53,53,N,,,A,Automatic,1,N,N,F,\"2-Wheel Drive, Front\",H,Hydrogen,Small Station Wagons,car,7/4/11,Y\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER SPORTBACK,MTX,122,2,4,Auto(AV-S6),24,32,27,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,10/5/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,LANCER SPORTBACK,MTX,124,2.4,4,Auto(AV-S6),22,29,25,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,10/5/11,\r2012,Nissan,NISSAN,CUBE,NSX,4,1.8,4,Auto(AV),27,31,28,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,1/4/12,\r2012,Nissan,NISSAN,CUBE,NSX,5,1.8,4,Manual(M6),25,30,27,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,1/4/12,\r2012,Nissan,NISSAN,JUKE,NSX,121,1.6,4,Auto(AV-S6),27,32,29,N,TC,Turbocharged,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,10/20/11,\r2012,Nissan,NISSAN,JUKE,NSX,122,1.6,4,Manual(M6),25,31,27,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,10/20/11,\r2012,Nissan,NISSAN,JUKE AWD,NSX,123,1.6,4,Auto(AV-S6),25,30,27,N,TC,Turbocharged,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,10/20/11,\r2012,Saab Cars North America,Saab,9-3 SPORTCOMBI,SAX,66,2,4,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3 SPORTCOMBI,SAX,67,2,4,Manual(M6),20,33,25,N,TC,Turbocharged,M,Manual,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,9/6/11,\r2012,Saab Cars North America,Saab,9-3X SPORTCOMBI AWD,SAX,70,2,4,Auto(S6),18,29,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,5/11/11,\r2012,Saab Cars North America,Saab,9-3X SPORTCOMBI AWD,SAX,71,2,4,Manual(M6),20,30,24,N,TC,Turbocharged,M,Manual,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,5/11/11,\r2012,Toyota,SCION,xB,TYX,2,2.4,4,Auto(S4),22,28,24,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,2/1/11,\r2012,Toyota,SCION,xB,TYX,1,2.4,4,Manual(M5),22,28,24,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,2/1/11,\r2012,Subaru,Subaru,IMPREZA WAGON/OUTBACK SPORT AWD,FJX,4,2,4,Auto(AV),27,36,30,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/11/11,\r2012,Subaru,Subaru,IMPREZA WAGON/OUTBACK SPORT AWD,FJX,2,2,4,Manual(M5),25,33,28,N,NA,Naturally Aspirated,M,Manual,5,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,7/11/11,\r2012,Subaru,Subaru,IMPREZA WAGON/OUTBACK SPORT AWD,FJX,13,2.5,4,Manual(M5),19,25,21,N,TC,Turbocharged,M,Manual,5,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,7/5/11,\r2012,Subaru,Subaru,IMPREZA WAGON/OUTBACK SPORT AWD,FJX,15,2.5,4,Manual(M6),17,23,19,N,TC,Turbocharged,M,Manual,6,N,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),Small Station Wagons,car,7/5/11,\r2012,Suzuki,Suzuki,SX4,SKX,56,2,4,Auto(AV),23,30,26,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,8/10/11,\r2012,Suzuki,Suzuki,SX4,SKX,55,2,4,Manual(M6),22,30,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,8/20/11,\r2012,Suzuki,Suzuki,SX4 AWD,SKX,52,2,4,Auto(AV),23,29,25,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,8/10/11,\r2012,Suzuki,Suzuki,SX4 AWD,SKX,51,2,4,Manual(M6),22,30,25,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,8/10/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,70,1.8,4,Auto(A4),25,32,28,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,71,1.8,4,Manual(M5),26,32,29,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,72,2.4,4,Auto(A4),20,26,22,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,74,2.4,4,Auto(S5),21,29,24,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Toyota,TOYOTA,COROLLA MATRIX,TYX,73,2.4,4,Manual(M5),21,28,24,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,12/8/11,\r2012,Volkswagen,Volkswagen,JETTA SPORTWAGEN,VWX,49,2,4,Auto(S6),29,39,33,N,TC,Turbocharged,SA,Semi-Automatic,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Small Station Wagons,car,6/1/11,N\r2012,Volkswagen,Volkswagen,JETTA SPORTWAGEN,VWX,53,2,4,Manual(M6),30,42,34,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",DU,Diesel,Small Station Wagons,car,6/3/11,N\r2012,Volkswagen,Volkswagen,JETTA SPORTWAGEN,VWX,22,2.5,5,Auto(S6),24,31,26,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,5/9/11,\r2012,Volkswagen,Volkswagen,JETTA SPORTWAGEN,VWX,26,2.5,5,Manual(M5),23,33,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Small Station Wagons,car,5/4/11,\r2012,Kia,KIA MOTORS CORPORATION,RONDO,KMX,7,2.4,4,Auto(A4),20,27,22,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Station Wagons,car,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,RONDO,KMX,8,2.7,6,Auto(A5),18,26,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Station Wagons,car,4/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 350 4Matic (Wagon),MBX,316,3.5,6,Auto(A7),19,27,22,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),Midsize Station Wagons,car,10/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,E 63 AMG (station wagon),MBX,323,5.5,8,Auto(A7),15,23,18,N,TC,Turbocharged,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Midsize Station Wagons,car,10/5/11,\r2012,Toyota,TOYOTA,PRIUS v,TYX,6,1.8,4,Auto(AV),44,40,42,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Midsize Station Wagons,car,7/16/11,N\r2012,General Motors,Chevrolet,COLORADO 2WD,GMX,527,2.9,4,Auto(A4),18,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,Chevrolet,COLORADO 2WD,GMX,529,2.9,4,Manual(M5),18,25,21,N,NA,Naturally Aspirated,M,Manual,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/4/11,\r2012,General Motors,Chevrolet,COLORADO 2WD,GMX,526,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO 2WD,GMX,528,5.3,8,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,Chevrolet,COLORADO CAB CHASSIS INC 2WD,GMX,540,3.7,5,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 2WD,GMX,535,2.9,4,Auto(A4),18,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 2WD,GMX,537,2.9,4,Manual(M5),18,25,21,N,NA,Naturally Aspirated,M,Manual,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/4/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 2WD,GMX,534,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 2WD,GMX,536,5.3,8,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,GMC,CANYON 2WD,GMX,578,2.9,4,Auto(A4),18,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,GMC,CANYON 2WD,GMX,580,2.9,4,Manual(M5),18,25,21,N,NA,Naturally Aspirated,M,Manual,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/4/11,\r2012,General Motors,GMC,CANYON 2WD,GMX,577,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,GMC,CANYON 2WD,GMX,579,5.3,8,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,GMC,CANYON CAB CHASSIS INC 2WD,GMX,585,3.7,5,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,GMC,CANYON CREW CAB 2WD,GMX,587,2.9,4,Auto(A4),18,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,General Motors,GMC,CANYON CREW CAB 2WD,GMX,589,2.9,4,Manual(M5),18,25,21,N,NA,Naturally Aspirated,M,Manual,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/4/11,\r2012,General Motors,GMC,CANYON CREW CAB 2WD,GMX,586,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,6/3/11,\r2012,General Motors,GMC,CANYON CREW CAB 2WD,GMX,588,5.3,8,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,5/5/11,\r2012,Nissan,NISSAN,FRONTIER 2WD,NSX,83,2.5,4,Auto(A5),17,22,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,NISSAN,FRONTIER 2WD,NSX,84,2.5,4,Manual(M5),19,23,21,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,NISSAN,FRONTIER 2WD,NSX,181,4,6,Auto(A5),15,20,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,NISSAN,FRONTIER 2WD,NSX,182,4,6,Manual(M6),16,20,17,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,SUZUKI,Equator 2WD,NSX,85,2.5,4,Auto(A5),17,22,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,SUZUKI,Equator 2WD,NSX,86,2.5,4,Manual(M5),19,23,21,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Nissan,SUZUKI,Equator 2WD,NSX,481,4,6,Auto(A5),15,20,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,2,9/7/11,\r2012,Toyota,TOYOTA,TACOMA 2WD,TYX,39,2.7,4,Auto(A4),19,24,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 2WD,TYX,40,2.7,4,Manual(M5),21,25,22,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 2WD,TYX,49,4,6,Auto(A5),17,21,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 2WD,TYX,50,4,6,Manual(M6),16,21,18,N,NA,Naturally Aspirated,M,Manual,6,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 2WD,1,9/1/11,\r2012,General Motors,Chevrolet,COLORADO 4WD,GMX,531,2.9,4,Auto(A4),17,23,20,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/4/11,\r2012,General Motors,Chevrolet,COLORADO 4WD,GMX,533,2.9,4,Manual(M5),18,24,20,N,NA,Naturally Aspirated,M,Manual,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/4/11,\r2012,General Motors,Chevrolet,COLORADO 4WD,GMX,530,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO 4WD,GMX,532,5.3,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/5/11,\r2012,General Motors,Chevrolet,COLORADO CAB CHASSIS INC 4WD,GMX,541,3.7,5,Auto(A4),16,21,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 4WD,GMX,538,3.7,5,Auto(A4),16,21,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,Chevrolet,COLORADO CREW CAB 4WD,GMX,539,5.3,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/5/11,\r2012,General Motors,GMC,CANYON 4WD,GMX,582,2.9,4,Auto(A4),17,23,20,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/4/11,\r2012,General Motors,GMC,CANYON 4WD,GMX,584,2.9,4,Manual(M5),18,24,20,N,NA,Naturally Aspirated,M,Manual,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/4/11,\r2012,General Motors,GMC,CANYON 4WD,GMX,581,3.7,5,Auto(A4),17,23,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,GMC,CANYON 4WD,GMX,583,5.3,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/5/11,\r2012,General Motors,GMC,CANYON CAB CHASSIS INC 4WD,GMX,592,3.7,5,Auto(A4),16,21,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,GMC,CANYON CREW CAB 4WD,GMX,590,3.7,5,Auto(A4),16,21,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,6/3/11,\r2012,General Motors,GMC,CANYON CREW CAB 4WD,GMX,591,5.3,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,5/5/11,\r2012,Nissan,NISSAN,FRONTIER 4WD,NSX,183,4,6,Auto(A5),14,19,16,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,2,9/7/11,\r2012,Nissan,NISSAN,FRONTIER 4WD,NSX,184,4,6,Manual(M6),15,20,17,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,2,9/7/11,\r2012,Nissan,SUZUKI,Equator 4WD,NSX,482,4,6,Auto(A5),15,19,16,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,2,9/7/11,\r2012,Toyota,TOYOTA,TACOMA 4WD,TYX,41,2.7,4,Auto(A4),18,21,19,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 4WD,TYX,42,2.7,4,Manual(M5),18,20,19,N,NA,Naturally Aspirated,M,Manual,5,N,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 4WD,TYX,51,4,6,Auto(A5),16,21,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,9/1/11,\r2012,Toyota,TOYOTA,TACOMA 4WD,TYX,52,4,6,Manual(M6),15,19,17,N,NA,Naturally Aspirated,M,Manual,6,N,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Small Pick-up Trucks 4WD,1,9/1/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD,GMX,546,4.3,6,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/15/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD,GMX,547,4.8,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD,GMX,544,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD,GMX,545,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD HYBRID,GMX,548,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,N\r2012,General Motors,Chevrolet,C15 SILVERADO 2WD XFE,GMX,549,5.3,8,Auto(A6),15,22,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,Chrysler Group LLC,Dodge,Ram 1500 2WD,CRX,55,3.7,6,Auto(A4),14,20,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,7/12/11,\r2012,Chrysler Group LLC,Dodge,Ram 1500 2WD,CRX,56,4.7,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,7/11/11,\r2012,Chrysler Group LLC,Dodge,Ram 1500 2WD,CRX,58,5.7,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),Standard Pick-up Trucks 2WD,2,7/1/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD,FMX,132,3.5,6,Auto(A6),16,22,18,N,TC,Turbocharged,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD,FMX,133,3.5,6,Auto(S6),16,22,18,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD,FMX,109,6.2,8,Auto(S6),13,18,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD FFV,FMX,117,3.7,6,Auto(A6),17,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD FFV,FMX,118,3.7,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD FFV,FMX,139,5,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 2WD FFV,FMX,140,5,8,Auto(S6),15,21,17,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,10/24/11,\r2012,General Motors,GMC,C15 SIERRA 2WD,GMX,598,4.3,6,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/15/11,\r2012,General Motors,GMC,C15 SIERRA 2WD,GMX,599,4.8,8,Auto(A4),14,19,16,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,GMC,C15 SIERRA 2WD,GMX,596,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,GMC,C15 SIERRA 2WD,GMX,597,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,General Motors,GMC,C15 SIERRA 2WD HYBRID,GMX,600,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,N\r2012,General Motors,GMC,C15 SIERRA 2WD XFE,GMX,595,5.3,8,Auto(A6),15,22,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,6/1/11,\r2012,Nissan,NISSAN,TITAN 2WD,NSX,284,5.6,8,Auto(A5),13,18,15,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,8/25/11,\r2012,Nissan,NISSAN,TITAN 2WD,NSX,293,5.6,8,Auto(A5),13,18,15,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,8/25/11,\r2012,Toyota,TOYOTA,TUNDRA 2WD,TYX,53,4,6,Auto(S5),16,20,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,9/1/11,\r2012,Toyota,TOYOTA,TUNDRA 2WD,TYX,57,4.6,8,Auto(S6),15,20,17,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,9/1/11,\r2012,Toyota,TOYOTA,TUNDRA 2WD,TYX,61,5.7,8,Auto(S6),14,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 2WD,2,9/1/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD,GMX,552,4.3,6,Auto(A4),14,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/15/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD,GMX,553,4.8,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD,GMX,550,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD,GMX,551,6.2,8,Auto(A6),12,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,Chevrolet,K15 SILVERADO 4WD HYBRID,GMX,554,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,N\r2012,Chrysler Group LLC,Dodge,Ram 1500 4WD,CRX,57,4.7,8,Auto(A6),14,19,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,7/11/11,\r2012,Chrysler Group LLC,Dodge,Ram 1500 4WD,CRX,59,5.7,8,Auto(A6),13,19,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),Standard Pick-up Trucks 4WD,2,7/1/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD,FMX,137,3.5,6,Auto(A6),15,21,17,N,TC,Turbocharged,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD,FMX,135,3.5,6,Auto(S6),15,21,17,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD,FMX,112,6.2,8,Auto(S6),12,16,13,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD FFV,FMX,122,3.7,6,Auto(A6),16,21,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD FFV,FMX,123,3.7,6,Auto(S6),16,21,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD FFV,FMX,141,5,8,Auto(A6),14,19,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 PICKUP 4WD FFV,FMX,142,5,8,Auto(S6),14,19,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,Ford Motor Company,Ford Division,F150 Raptor Pickup 4WD,FMX,111,6.2,8,Auto(S6),11,16,13,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/24/11,\r2012,General Motors,GMC,K15 SIERRA 4WD,GMX,603,4.3,6,Auto(A4),14,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/15/11,\r2012,General Motors,GMC,K15 SIERRA 4WD,GMX,604,4.8,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,GMC,K15 SIERRA 4WD,GMX,601,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,GMC,K15 SIERRA 4WD,GMX,602,6.2,8,Auto(A6),12,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,General Motors,GMC,K15 SIERRA 4WD HYBRID,GMX,605,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,N\r2012,General Motors,GMC,K15 SIERRA AWD,GMX,606,6.2,8,Auto(A6),12,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,6/1/11,\r2012,Honda,Honda,RIDGELINE 4WD,HNX,38,3.5,6,Auto(A5),15,21,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,10/28/11,N\r2012,Nissan,NISSAN,TITAN 4WD,NSX,285,5.6,8,Auto(A5),12,17,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,8/25/11,\r2012,Nissan,NISSAN,TITAN 4WD,NSX,294,5.6,8,Auto(A5),12,17,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,8/25/11,\r2012,Toyota,TOYOTA,TUNDRA 4WD,TYX,58,4.6,8,Auto(S6),14,19,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,9/1/11,\r2012,Toyota,TOYOTA,TUNDRA 4WD,TYX,62,5.7,8,Auto(S6),13,17,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,9/1/11,\r2012,Toyota,TOYOTA,TUNDRA 4WD FFV,TYX,64,5.7,8,Auto(S6),13,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),Standard Pick-up Trucks 4WD,2,8/31/11,\r2012,General Motors,Chevrolet,G1500 EXPRESS 2WD CARGO,GMX,621,4.3,6,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/15/11,\r2012,General Motors,Chevrolet,G1500 EXPRESS 2WD CARGO,GMX,514,5.3,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G1500 EXPRESS CONV 2WD CARGO,GMX,515,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G2500 EXPRESS 2WD CARGO MDPV,GMX,614,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G2500 EXPRESS CONV 2WD CARGO,GMX,610,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G3500 EXPRESS 2WD CARGO MDPV,GMX,615,6,8,Auto(A6),10,14,11,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,H1500 EXPRESS AWD CARGO,GMX,519,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,H1500 EXPRESS CONV AWD CARGO,GMX,517,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,Ford Motor Company,Ford Division,E150 VAN FFV,FMX,146,4.6,8,Auto(A4),13,17,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E150 VAN FFV,FMX,150,5.4,8,Auto(A4),12,16,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E250 VAN FFV,FMX,148,4.6,8,Auto(A4),13,17,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E250 VAN FFV,FMX,151,5.4,8,Auto(A4),12,16,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E350 VAN,FMX,20,6.8,10,Auto(A5),10,14,12,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E350 VAN FFV,FMX,153,5.4,8,Auto(A4),12,16,13,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,8/1/11,\r2012,General Motors,GMC,G1500 SAVANA 2WD CARGO,GMX,622,4.3,6,Auto(A4),15,20,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/15/11,\r2012,General Motors,GMC,G1500 SAVANA 2WD CARGO,GMX,562,5.3,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,G1500 SAVANA CONV 2WD CARGO,GMX,563,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,G2500 SAVANA 2WD CARGO MDPV,GMX,619,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,G2500 SAVANA CONV 2WD CARGO,GMX,616,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,G3500 SAVANA 2WD CARGO MDPV,GMX,620,6,8,Auto(A6),10,14,11,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,H1500 SAVANA AWD CARGO,GMX,566,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,GMC,H1500 SAVANA CONV AWD CARGO,GMX,567,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Cargo Types\",2,6/1/11,\r2012,General Motors,Chevrolet,G1500 EXPRESS 2WD PASS,GMX,513,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,G2500 EXPRESS 2WD PASS MDPV,GMX,555,4.8,8,Auto(A6),11,17,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,G2500 EXPRESS 2WD PASS MDPV,GMX,612,6,8,Auto(A6),11,16,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,G3500 EXPRESS 2WD PASS MDPV,GMX,556,4.8,8,Auto(A6),11,17,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,G3500 EXPRESS 2WD PASS MDPV,GMX,613,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,Chevrolet,H1500 EXPRESS AWD PASS,GMX,518,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,Ford Motor Company,Ford Division,E150 WAGON FFV,FMX,147,4.6,8,Auto(A4),13,16,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E150 WAGON FFV,FMX,152,5.4,8,Auto(A4),12,16,13,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E350 WAGON,FMX,21,6.8,10,Auto(A5),10,13,11,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,8/1/11,\r2012,Ford Motor Company,Ford Division,E350 WAGON FFV,FMX,165,5.4,8,Auto(A4),11,15,13,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,8/1/11,\r2012,General Motors,GMC,G1500 SAVANA 2WD PASS,GMX,559,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,G2500 SAVANA 2WD PASS (MDPV),GMX,607,4.8,8,Auto(A6),11,17,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,G2500 SAVANA 2WD PASS (MDPV),GMX,617,6,8,Auto(A6),11,16,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,G3500 SAVANA 2WD PASS (MDPV,GMX,608,4.8,8,Auto(A6),11,17,13,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,G3500 SAVANA 2WD PASS (MDPV,GMX,618,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,General Motors,GMC,H1500 SAVANA AWD PASS,GMX,565,5.3,8,Auto(A4),13,17,14,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Vans, Passenger Type\",2,6/1/11,\r2012,Azure Dynamics Incorporated,Azure Dynamics Incorporated,Transit Connect Electric Van,AZD,1,0,,Auto(A1),62,62,62,N,,,A,Automatic,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Special Purpose Vehicle 2WD,,10/1/11,N\r2012,Azure Dynamics Incorporated,Azure Dynamics Incorporated,Transit Connect Electric Wagon,AZD,2,0,,Auto(A1),62,62,62,N,,,A,Automatic,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Special Purpose Vehicle 2WD,,10/1/11,N\r2012,Ford Motor Company,Ford Division,Transit Connect Van,FMX,90,2,4,Auto(A4),21,27,23,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Special Purpose Vehicle 2WD,,7/11/11,\r2012,Ford Motor Company,Ford Division,TRANSIT CONNECT WAGON FWD,FMX,70,2,4,Auto(A4),22,27,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),Special Purpose Vehicle 2WD,,7/11/11,\r2012,VPG,The Vehicle Production Group LLC,MV-1,TVP,1,4.6,8,Auto(A4),13,18,15,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),Special Purpose Vehicle 2WD,,11/11/11,N\r2012,Chrysler Group LLC,Chrysler,Town & Country,CRX,540,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Dodge,Grand Caravan,CRX,541,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Dodge,Ram C/V,CRX,543,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,7/1/11,\r2012,Honda,Honda,ODYSSEY 2WD,HNX,39,3.5,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,9/8/11,N\r2012,Honda,Honda,ODYSSEY 2WD,HNX,40,3.5,6,Auto(A6),19,28,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,9/8/11,N\r2012,Kia,KIA MOTORS CORPORATION,SEDONA,KMX,9,3.5,6,Auto(A6),18,25,21,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,4/1/11,N\r2012,MAZDA,MAZDA,MAZDA 5,TKX,2,2.5,4,Auto(S5),21,28,24,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,1/31/11,N\r2012,MAZDA,MAZDA,MAZDA 5,TKX,1,2.5,4,Manual(M6),21,28,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,1/31/11,N\r2012,Nissan,NISSAN,QUEST,NSX,96,3.5,6,Auto(AV),19,24,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,10/18/11,\r2012,Toyota,TOYOTA,SIENNA,TYX,34,2.7,4,Auto(S6),19,24,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,9/9/11,\r2012,Toyota,TOYOTA,SIENNA,TYX,37,3.5,6,Auto(S6),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,9/9/11,\r2012,Chrysler Group LLC,Volkswagen,Routan,CRX,542,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 2WD\",1,7/1/11,\r2012,Toyota,TOYOTA,SIENNA AWD,TYX,38,3.5,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, minivan 4WD\",1,9/9/11,\r2012,Honda,Acura,RDX 2WD,HNX,34,2.3,4,Auto(S5),19,24,21,N,TC,Turbocharged,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/15/11,N\r2012,General Motors,Buick,ENCLAVE FWD,GMX,500,3.6,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/2/11,\r2012,General Motors,Cadillac,ESCALADE 2WD,GMX,505,6.2,8,Auto(A6),14,18,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Cadillac,ESCALADE 2WD HYBRID,GMX,504,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,N\r2012,General Motors,Cadillac,ESCALADE ESV 2WD,GMX,506,6.2,8,Auto(A6),14,18,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Chevrolet,C1500 AVALANCHE 2WD,GMX,511,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Chevrolet,C1500 SUBURBAN 2WD,GMX,520,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Chevrolet,C1500 TAHOE 2WD,GMX,509,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,Chevrolet,C1500 TAHOE 2WD HYBRID,GMX,512,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,N\r2012,General Motors,Chevrolet,C2500 SUBURBAN 2WD,GMX,521,6,8,Auto(A6),10,16,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/21/11,\r2012,General Motors,Chevrolet,CAPTIVA FWD,GMX,120,2.4,4,Auto(A6),20,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,10/28/11,\r2012,General Motors,Chevrolet,CAPTIVA FWD,GMX,51,3,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,11/4/11,\r2012,General Motors,Chevrolet,EQUINOX FWD,GMX,23,2.4,4,Auto(A6),22,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/6/11,\r2012,General Motors,Chevrolet,EQUINOX FWD,GMX,119,2.4,4,Auto(A6),22,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/27/11,\r2012,General Motors,Chevrolet,EQUINOX FWD,GMX,21,3,6,Auto(A6),17,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/3/11,\r2012,General Motors,Chevrolet,EQUINOX FWD,GMX,24,3,6,Auto(A6),17,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/6/11,\r2012,General Motors,Chevrolet,TRAVERSE FWD,GMX,542,3.6,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/2/11,\r2012,Chrysler Group LLC,Dodge,Durango 2WD,CRX,35,3.6,6,Auto(A5),16,23,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/5/11,\r2012,Chrysler Group LLC,Dodge,Durango 2WD,CRX,37,5.7,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Dodge,Journey FWD,CRX,530,2.4,4,Auto(A4),19,26,22,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/4/11,N\r2012,Chrysler Group LLC,Dodge,Journey FWD,CRX,531,3.6,6,Auto(A6),17,25,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/4/11,\r2012,Ford Motor Company,Ford Division,EDGE FWD,FMX,8,2,4,Auto(A6),21,30,24,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,EDGE FWD,FMX,119,3.5,6,Auto(S6),19,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,EDGE FWD,FMX,120,3.7,6,Auto(S6),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,ESCAPE FWD,FMX,100,2.5,4,Auto(A6),21,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE FWD,FMX,101,2.5,4,Manual(M5),23,28,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE FWD FFV,FMX,99,3,6,Auto(A6),19,25,21,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE HYBRID FWD,FMX,88,2.5,4,Auto(AV),34,31,32,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,N\r2012,Ford Motor Company,Ford Division,EXPEDITION 2WD FFV,FMX,186,5.4,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/22/11,\r2012,Ford Motor Company,Ford Division,EXPLORER FWD,FMX,65,2,4,Auto(A6),20,28,23,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/18/11,\r2012,Ford Motor Company,Ford Division,EXPLORER FWD,FMX,160,3.5,6,Auto(S6),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/18/11,\r2012,Ford Motor Company,Ford Division,FLEX FWD,FMX,86,3.5,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,General Motors,GMC,ACADIA FWD,GMX,593,3.6,6,Auto(A6),17,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/2/11,\r2012,General Motors,GMC,C1500 YUKON 2WD,GMX,560,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,GMC,C1500 YUKON 2WD,GMX,561,6.2,8,Auto(A6),14,18,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,GMC,C1500 YUKON 2WD HYBRID,GMX,564,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,N\r2012,General Motors,GMC,C1500 YUKON XL 2WD,GMX,568,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,GMC,C1500 YUKON XL 2WD,GMX,569,6.2,8,Auto(A6),14,18,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/1/11,\r2012,General Motors,GMC,C2500 YUKON XL 2WD,GMX,570,6,8,Auto(A6),10,16,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/21/11,\r2012,General Motors,GMC,TERRAIN FWD,GMX,59,2.4,4,Auto(A6),22,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/6/11,\r2012,General Motors,GMC,TERRAIN FWD,GMX,121,2.4,4,Auto(A6),22,32,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/27/11,\r2012,General Motors,GMC,TERRAIN FWD,GMX,57,3,6,Auto(A6),17,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/3/11,\r2012,General Motors,GMC,TERRAIN FWD,GMX,60,3,6,Auto(A6),17,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/6/11,\r2012,Honda,Honda,CROSSTOUR 2WD,HNX,28,3.5,6,Auto(A5),18,27,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/3/11,N\r2012,Honda,Honda,CR-V 2WD,HNX,36,2.4,4,Auto(A5),23,31,26,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/28/11,N\r2012,Honda,Honda,PILOT 2WD,HNX,41,3.5,6,Auto(A5),18,25,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/31/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 2WD,HYX,23,2.4,4,Auto(A6),20,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 2WD,HYX,24,2.4,4,Manual(M6),19,26,21,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 2WD,HYX,26,3.5,6,Auto(A6),20,26,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 2WD,HYX,13,2,4,Auto(A6),23,31,26,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 2WD,HYX,14,2,4,Manual(M5),20,27,23,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 2WD,HYX,10,2.4,4,Auto(A6),22,32,25,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 2WD,HYX,12,2.4,4,Manual(M6),21,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,VERACRUZ 2WD,HYX,31,3.8,6,Auto(A6),17,22,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/1/11,\r2012,Nissan,INFINITI,FX35 RWD,NSX,93,3.5,6,Auto(S7),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,10/3/11,\r2012,Nissan,INFINITI,QX56 2WD,NSX,381,5.6,8,Auto(S7),14,20,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/16/11,\r2012,Chrysler Group LLC,Jeep,Compass 2WD,CRX,510,2,4,Auto(AV),23,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Compass 2WD,CRX,505,2,4,Manual(M5),23,29,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Compass 2WD,CRX,507,2.4,4,Auto(AV),21,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Compass 2WD,CRX,515,2.4,4,Manual(M5),23,28,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee 2WD,CRX,31,3.6,6,Auto(A5),17,23,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee 2WD,CRX,33,5.7,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",GM,Gasoline (Mid Grade Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/18/11,\r2012,Chrysler Group LLC,Jeep,Liberty 2WD,CRX,40,3.7,6,Auto(A4),16,22,18,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,7/1/11,\r2012,Chrysler Group LLC,Jeep,Patriot 2WD,CRX,511,2,4,Auto(AV),23,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Patriot 2WD,CRX,506,2,4,Manual(M5),23,29,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Patriot 2WD,CRX,508,2.4,4,Auto(AV),21,27,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Patriot 2WD,CRX,516,2.4,4,Manual(M5),23,28,25,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 2WD,KMX,11,2.4,4,Auto(A6),21,29,24,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/25/11,N\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 2WD,KMX,16,2.4,4,Auto(A6),22,32,25,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/25/11,\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 2WD,KMX,12,2.4,4,Manual(M6),20,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/25/11,N\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 2WD,KMX,14,3.5,6,Auto(A6),20,26,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/25/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 2WD,KMX,6,2,4,Auto(A6),22,29,24,N,TC,Turbocharged,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 2WD,KMX,3,2.4,4,Auto(A6),22,32,25,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 2WD,KMX,4,2.4,4,Manual(M6),21,29,24,N,NA,Naturally Aspirated,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,4/1/11,\r2012,Toyota,LEXUS,RX 350,TYX,35,3.5,6,Auto(S6),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,\r2012,Toyota,LEXUS,RX 450h,TYX,19,3.5,6,Auto(AV-S6),32,28,30,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 2WD\",1,9/1/11,N\r2012,Ford Motor Company,Lincoln Truck,MKT FWD,FMX,87,3.5,6,Auto(S6),17,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/13/11,\r2012,Ford Motor Company,Lincoln Truck,MKX FWD,FMX,178,3.7,6,Auto(S6),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/8/11,\r2012,Ford Motor Company,Lincoln Truck,NAVIGATOR 2WD FFV,FMX,184,5.4,8,Auto(A6),14,20,16,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/22/11,\r2012,MAZDA,MAZDA,CX-7 2WD,TKX,22,2.3,4,Auto(S6),18,24,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,10/28/11,\r2012,MAZDA,MAZDA,CX-7 2WD,TKX,24,2.5,4,Auto(S5),20,27,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,11/8/11,\r2012,MAZDA,MAZDA,CX-9 2WD,TKX,14,3.7,6,Auto(S6),17,24,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,GLK 350,MBX,802,3.5,6,Auto(A7),16,22,18,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 2WD\",1,7/1/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER 2WD,MTX,211,2.4,4,Auto(AV-S6),23,28,25,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER 2WD,MTX,213,3,6,Auto(S6),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER SPORT 2WD,MTX,222,2,4,Auto(AV-S6),25,31,27,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,11/1/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER SPORT 2WD,MTX,221,2,4,Manual(M5),24,31,26,N,NA,Naturally Aspirated,M,Manual,5,N,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,11/1/11,\r2012,Nissan,NISSAN,ARMADA 2WD,NSX,282,5.6,8,Auto(A5),13,19,15,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/25/11,\r2012,Nissan,NISSAN,ARMADA 2WD,NSX,291,5.6,8,Auto(A5),12,19,15,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/25/11,\r2012,Nissan,NISSAN,MURANO FWD,NSX,91,3.5,6,Auto(AV),18,24,20,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/27/11,\r2012,Nissan,NISSAN,PATHFINDER 2WD,NSX,187,4,6,Auto(A5),15,22,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/7/11,\r2012,Nissan,NISSAN,ROGUE FWD,NSX,81,2.5,4,Auto(AV),23,28,25,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/16/11,N\r2012,Nissan,NISSAN,XTERRA 2WD,NSX,483,4,6,Auto(A5),16,22,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,9/7/11,\r2012,General Motors,Saab,9-4X FWD,GMX,77,3,6,Auto(S6),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/31/11,\r2012,Suzuki,Suzuki,GRAND VITARA,SKX,93,2.4,4,Auto(A4),19,25,21,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/10/11,\r2012,Suzuki,Suzuki,GRAND VITARA,SKX,91,2.4,4,Manual(M5),19,26,22,N,NA,Naturally Aspirated,M,Manual,5,N,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/10/11,\r2012,Toyota,TOYOTA,4RUNNER 2WD,TYX,43,4,6,Auto(S5),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,10/22/11,\r2012,Toyota,TOYOTA,FJ CRUISER 2WD,TYX,46,4,6,Auto(A5),17,20,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/23/11,\r2012,Toyota,TOYOTA,HIGHLANDER 2WD,TYX,15,2.7,4,Auto(S6),20,25,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/4/11,\r2012,Toyota,TOYOTA,HIGHLANDER 2WD,TYX,16,3.5,6,Auto(S5),18,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/4/11,\r2012,Toyota,TOYOTA,RAV4 2WD,TYX,76,2.5,4,Auto(A4),22,28,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,12/20/11,\r2012,Toyota,TOYOTA,RAV4 2WD,TYX,78,3.5,6,Auto(A5),19,27,22,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,12/20/11,\r2012,Toyota,TOYOTA,SEQUOIA 2WD,TYX,55,4.6,8,Auto(S6),14,20,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/29/11,\r2012,Toyota,TOYOTA,SEQUOIA 2WD,TYX,59,5.7,8,Auto(S6),13,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,R,\"2-Wheel Drive, Rear\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,8/29/11,\r2012,Toyota,TOYOTA,VENZA,TYX,80,2.7,4,Auto(S6),21,27,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,12/8/11,\r2012,Toyota,TOYOTA,VENZA,TYX,82,3.5,6,Auto(S6),19,26,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,12/8/11,\r2012,Audi,Volkswagen,TIGUAN,ADX,83,2,4,Auto(S6),22,27,24,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/28/11,N\r2012,Audi,Volkswagen,TIGUAN,ADX,84,2,4,Manual(M6),18,26,21,N,TC,Turbocharged,M,Manual,6,N,N,F,\"2-Wheel Drive, Front\",GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,6/28/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC60 FWD,VVX,13,3.2,6,Auto(S6),19,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC70 FWD,VVX,18,3.2,6,Auto(S6),19,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC90 FWD,VVX,40,3.2,6,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,F,\"2-Wheel Drive, Front\",G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 2WD\",1,5/27/11,N\r2012,Honda,Acura,MDX 4WD,HNX,43,3.7,6,Auto(S6),16,21,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/8/11,\r2012,Honda,Acura,RDX 4WD,HNX,35,2.3,4,Auto(S5),17,22,19,N,TC,Turbocharged,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/15/11,N\r2012,Honda,Acura,ZDX 4WD,HNX,33,3.7,6,Auto(S6),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/24/11,N\r2012,Audi,Audi,Q5,ADX,35,2,4,Auto(S8),20,27,22,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/5/11,\r2012,Audi,Audi,Q5,ADX,35,2,4,Auto(S8),20,27,22,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/5/11,\r2012,Audi,Audi,Q5,ADX,36,3.2,6,Auto(S6),18,23,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/5/11,N\r2012,Audi,Audi,Q7,ADX,72,3,6,Auto(S8),17,25,20,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,6/27/11,N\r2012,Audi,Audi,Q7,ADX,77,3,6,Auto(S8),16,22,18,N,SC,Supercharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,N\r2012,BMW,BMW,X3 xDrive28i,BMX,370,3,6,Auto(S8),19,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/9/11,N\r2012,BMW,BMW,X3 xDrive35i,BMX,372,3,6,Auto(S8),19,26,21,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/24/11,\r2012,BMW,BMW,X5 xDrive35d,BMX,572,3,6,Auto(S6),19,26,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,9/24/11,\r2012,BMW,BMW,X5 xDrive35i,BMX,570,3,6,Auto(S8),16,23,19,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X5 xDrive50i,BMX,573,4.4,8,Auto(S8),14,20,16,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X5 xDriveM,BMX,574,4.4,8,Auto(S6),12,17,14,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X6 xDrive35i,BMX,671,3,6,Auto(S8),16,23,19,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X6 xDrive50i,BMX,672,4.4,8,Auto(S8),14,20,16,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,BMW,BMW,X6 xDriveM,BMX,673,4.4,8,Auto(S6),12,17,14,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,N\r2012,General Motors,Buick,ENCLAVE AWD,GMX,501,3.6,6,Auto(A6),16,22,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/2/11,\r2012,General Motors,Cadillac,ESCALADE 4WD HYBRID,GMX,502,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/25/11,N\r2012,General Motors,Cadillac,ESCALADE AWD,GMX,503,6.2,8,Auto(A6),13,18,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Cadillac,ESCALADE ESV AWD,GMX,508,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Cadillac,ESCALADE EXT AWD,GMX,507,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Cadillac,SRX AWD,GMX,19,3.6,6,Auto(S6),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/6/11,\r2012,General Motors,Chevrolet,CAPTIVA AWD,GMX,130,3,6,Auto(A6),16,22,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/28/11,\r2012,General Motors,Chevrolet,EQUINOX AWD,GMX,26,2.4,4,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,General Motors,Chevrolet,EQUINOX AWD,GMX,122,2.4,4,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/27/11,\r2012,General Motors,Chevrolet,EQUINOX AWD,GMX,27,3,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/6/11,\r2012,General Motors,Chevrolet,EQUINOX AWD,GMX,90,3,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,General Motors,Chevrolet,K1500 AVALANCHE 4WD,GMX,510,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Chevrolet,K1500 SUBURBAN 4WD,GMX,524,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Chevrolet,K1500 TAHOE 4WD,GMX,522,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,Chevrolet,K1500 TAHOE 4WD HYBRID,GMX,523,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,N\r2012,General Motors,Chevrolet,K2500 SUBURBAN 4WD,GMX,525,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/21/11,\r2012,General Motors,Chevrolet,TRAVERSE AWD,GMX,543,3.6,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/2/11,\r2012,Chrysler Group LLC,Dodge,Durango 4WD,CRX,36,3.6,6,Auto(A5),16,23,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/5/11,\r2012,Chrysler Group LLC,Dodge,Durango 4WD,CRX,38,5.7,8,Auto(A6),13,20,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,\r2012,Chrysler Group LLC,Dodge,Journey AWD,CRX,532,3.6,6,Auto(A6),16,24,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/4/11,N\r2012,Ford Motor Company,Ford Division,EDGE AWD,FMX,128,3.5,6,Auto(S6),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,EDGE AWD,FMX,114,3.7,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/8/11,\r2012,Ford Motor Company,Ford Division,ESCAPE AWD,FMX,131,2.5,4,Auto(A6),20,27,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE AWD FFV,FMX,98,3,6,Auto(A6),18,23,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,ESCAPE HYBRID AWD,FMX,89,2.5,4,Auto(AV),30,27,29,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,N\r2012,Ford Motor Company,Ford Division,EXPEDITION 4WD FFV,FMX,161,5.4,8,Auto(A6),13,18,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,Ford Motor Company,Ford Division,EXPLORER AWD,FMX,190,3.5,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/18/11,\r2012,Ford Motor Company,Ford Division,FLEX AWD,FMX,85,3.5,6,Auto(A6),16,23,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/13/11,\r2012,Ford Motor Company,Ford Division,FLEX AWD,FMX,67,3.5,6,Auto(S6),16,22,18,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/25/11,\r2012,General Motors,GMC,ACADIA AWD,GMX,594,3.6,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/2/11,\r2012,General Motors,GMC,K1500 YUKON 4WD,GMX,574,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,GMC,K1500 YUKON 4WD HYBRID,GMX,575,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,N\r2012,General Motors,GMC,K1500 YUKON DENALI AWD,GMX,573,6.2,8,Auto(A6),13,18,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,GMC,K1500 YUKON DENALI HYBRID 4WD,GMX,609,6,8,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/25/11,N\r2012,General Motors,GMC,K1500 YUKON XL 4WD,GMX,572,5.3,8,Auto(A6),15,21,17,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,GMC,K1500 YUKON XL AWD,GMX,576,6.2,8,Auto(A6),13,18,14,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,General Motors,GMC,K2500 YUKON XL 4WD,GMX,571,6,8,Auto(A6),10,15,12,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/21/11,\r2012,General Motors,GMC,TERRAIN AWD,GMX,62,2.4,4,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,General Motors,GMC,TERRAIN AWD,GMX,123,2.4,4,Auto(A6),20,29,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/27/11,\r2012,General Motors,GMC,TERRAIN AWD,GMX,63,3,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/6/11,\r2012,General Motors,GMC,TERRAIN AWD,GMX,92,3,6,Auto(A6),16,23,19,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Honda,Honda,CROSSTOUR 4WD,HNX,29,3.5,6,Auto(A5),18,26,21,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/3/11,N\r2012,Honda,Honda,CR-V 4WD,HNX,37,2.4,4,Auto(A5),22,30,25,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/28/11,N\r2012,Honda,Honda,PILOT 4WD,HNX,42,3.5,6,Auto(A5),17,24,20,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/31/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 4WD,HYX,22,2.4,4,Auto(A6),20,25,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,SANTA FE 4WD,HYX,25,3.5,6,Auto(A6),20,26,22,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/15/11,\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 4WD,HYX,9,2.4,4,Auto(A6),21,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,TUCSON 4WD,HYX,11,2.4,4,Manual(M6),20,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/1/11,N\r2012,Hyundai,HYUNDAI MOTOR COMPANY,VERACRUZ 4WD,HYX,30,3.8,6,Auto(A6),16,21,18,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,Nissan,INFINITI,FX35 AWD,NSX,94,3.5,6,Auto(S7),16,21,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/3/11,\r2012,Nissan,INFINITI,FX50 AWD,NSX,391,5,8,Auto(S7),14,20,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,10/3/11,\r2012,Nissan,INFINITI,QX56 4WD,NSX,382,5.6,8,Auto(S7),14,20,16,N,NA,Naturally Aspirated,SA,Semi-Automatic,7,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/16/11,\r2012,Chrysler Group LLC,Jeep,Compass 4WD,CRX,517,2.4,4,Auto(AV),21,26,23,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Compass 4WD,CRX,520,2.4,4,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Compass 4WD,CRX,513,2.4,4,Manual(M5),22,28,24,N,NA,Naturally Aspirated,M,Manual,5,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee 4WD,CRX,32,3.6,6,Auto(A5),16,23,19,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/5/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee 4WD,CRX,34,5.7,8,Auto(A6),13,20,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,GM,Gasoline (Mid Grade Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,\r2012,Chrysler Group LLC,Jeep,Grand Cherokee SRT8,CRX,39,6.4,8,Auto(A5),12,18,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Chrysler Group LLC,Jeep,Liberty 4WD,CRX,41,3.7,6,Auto(A4),15,21,17,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,\r2012,Chrysler Group LLC,Jeep,Patriot 4WD,CRX,518,2.4,4,Auto(AV),21,26,23,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Patriot 4WD,CRX,521,2.4,4,Auto(AV),20,23,21,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,N\r2012,Chrysler Group LLC,Jeep,Patriot 4WD,CRX,514,2.4,4,Manual(M5),22,28,24,N,NA,Naturally Aspirated,M,Manual,5,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Chrysler Group LLC,Jeep,Wrangler 4WD,CRX,75,3.6,6,Auto(A5),17,21,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Chrysler Group LLC,Jeep,Wrangler 4WD,CRX,77,3.6,6,Manual(M6),17,21,18,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Chrysler Group LLC,Jeep,Wrangler Unlimited 4WD,CRX,76,3.6,6,Auto(A5),16,20,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Chrysler Group LLC,Jeep,Wrangler Unlimited 4WD,CRX,78,3.6,6,Manual(M6),16,21,18,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,N\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 4WD,KMX,10,2.4,4,Auto(A6),21,27,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/25/11,N\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 4WD,KMX,15,2.4,4,Auto(A6),21,28,23,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/25/11,\r2012,Kia,KIA MOTORS CORPORATION,SORENTO 4WD,KMX,13,3.5,6,Auto(A6),18,24,20,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/25/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 4WD,KMX,5,2,4,Auto(A6),21,26,23,N,TC,Turbocharged,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 4WD,KMX,1,2.4,4,Auto(A6),21,28,24,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/1/11,\r2012,Kia,KIA MOTORS CORPORATION,SPORTAGE 4WD,KMX,2,2.4,4,Manual(M6),20,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,4/1/11,\r2012,Land Rover,Land Rover,LR2,LRX,1,3.2,6,Auto(S6),15,22,17,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/30/11,\r2012,Land Rover,Land Rover,LR4,LRX,6,5,8,Auto(S6),12,17,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Land Rover,Land Rover,Range Rover,LRX,2,5,8,Auto(S6),12,18,14,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Land Rover,Land Rover,Range Rover,LRX,3,5,8,Auto(S6),12,18,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Land Rover,Land Rover,Range Rover Evoque,LRX,7,2,4,Auto(S6),18,28,22,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,9/30/11,N\r2012,Land Rover,Land Rover,Range Rover sport,LRX,4,5,8,Auto(S6),12,17,14,N,SC,Supercharged,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Land Rover,Land Rover,Range Rover sport,LRX,5,5,8,Auto(S6),13,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/11/11,N\r2012,Toyota,LEXUS,GX 460,TYX,54,4.6,8,Auto(S6),15,20,17,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Toyota,LEXUS,RX 350 AWD,TYX,36,3.5,6,Auto(S6),18,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,\r2012,Toyota,LEXUS,RX 450h AWD,TYX,20,3.5,6,Auto(AV-S6),30,28,29,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,A,All Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,9/1/11,N\r2012,Ford Motor Company,Lincoln Truck,MKT AWD,FMX,68,3.5,6,Auto(S6),16,22,18,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/25/11,\r2012,Ford Motor Company,Lincoln Truck,MKX AWD,FMX,129,3.7,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/8/11,\r2012,Ford Motor Company,Lincoln Truck,NAVIGATOR 4WD FFV,FMX,162,5.4,8,Auto(A6),13,18,15,N,NA,Naturally Aspirated,A,Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,MAZDA,MAZDA,CX-7 4WD,TKX,23,2.3,4,Auto(S6),17,21,19,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/28/11,\r2012,MAZDA,MAZDA,CX-9 4WD,TKX,15,3.7,6,Auto(S6),16,22,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,G 550,MBX,435,5.5,8,Auto(A7),12,15,13,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,GL 350 BLUETEC 4MATIC,MBX,422,3,6,Auto(A7),17,21,19,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,7/27/11,\r2012,Mercedes-Benz,Mercedes-Benz,GL 450 4MATIC,MBX,421,4.7,8,Auto(A7),13,18,15,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,GL 550 4MATIC,MBX,423,5.5,8,Auto(A7),12,17,14,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/12/11,\r2012,Mercedes-Benz,Mercedes-Benz,GLK 350 4MATIC,MBX,4,3.5,6,Auto(A7),16,21,18,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,ML 350 4MATIC,MBX,402,3.5,6,Auto(A7),17,22,19,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/27/11,\r2012,Mercedes-Benz,Mercedes-Benz,ML 350 BLUETEC 4MATIC,MBX,403,3,6,Auto(A7),20,27,22,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,9/5/11,\r2012,Mercedes-Benz,Mercedes-Benz,ML 550 4MATIC,MBX,405,4.7,8,Auto(A7),15,20,17,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,2/16/12,\r2012,Mercedes-Benz,Mercedes-Benz,ML 63 AMG,MBX,406,5.5,8,Auto(A7),14,18,15,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,2/16/12,\r2012,Mercedes-Benz,Mercedes-Benz,R 350 4MATIC,MBX,412,3.5,6,Auto(A7),16,21,18,N,NA,Naturally Aspirated,A,Automatic,7,Y,N,4,4-Wheel Drive,GPR,Gasoline (Premium Unleaded Required),\"Special Purpose Vehicle, SUV 4WD\",1,7/27/11,\r2012,Mercedes-Benz,Mercedes-Benz,R 350 BLUETEC 4MATIC,MBX,413,3,6,Auto(A7),18,23,20,N,TC,Turbocharged,A,Automatic,7,Y,N,4,4-Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,10/4/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER 4WD,MTX,212,2.4,4,Auto(AV-S6),22,27,24,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER 4WD,MTX,214,3,6,Auto(S6),19,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Mitsubishi Motors Co,Mitsubishi Motors Corporation,OUTLANDER SPORT 4WD,MTX,224,2,4,Auto(AV-S6),23,28,25,N,NA,Naturally Aspirated,SCV,Selectable Continuously Variable (e.g. CVT with paddles),6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,11/1/11,\r2012,Nissan,NISSAN,ARMADA 4WD,NSX,283,5.6,8,Auto(A5),12,18,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/25/11,\r2012,Nissan,NISSAN,ARMADA 4WD,NSX,292,5.6,8,Auto(A5),12,18,14,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/25/11,\r2012,Nissan,NISSAN,MURANO AWD,NSX,92,3.5,6,Auto(AV),18,23,20,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/27/11,\r2012,Nissan,NISSAN,MURANO CrossCabriolet,NSX,95,3.5,6,Auto(AV),17,22,19,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/27/11,\r2012,Nissan,NISSAN,PATHFINDER 4WD,NSX,188,4,6,Auto(A5),14,20,16,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/7/11,\r2012,Nissan,NISSAN,PATHFINDER 4WD,NSX,281,5.6,8,Auto(S5),13,18,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,4,4-Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/7/11,\r2012,Nissan,NISSAN,ROGUE AWD,NSX,82,2.5,4,Auto(AV),22,26,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/16/11,N\r2012,Nissan,NISSAN,XTERRA 4WD,NSX,185,4,6,Auto(A5),15,20,17,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/7/11,\r2012,Nissan,NISSAN,XTERRA 4WD,NSX,186,4,6,Manual(M6),16,20,17,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,9/17/11,\r2012,Porsche,Porsche,Cayenne,PRX,1,3.6,6,Auto(S8),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,Porsche,Porsche,Cayenne,PRX,2,3.6,6,Manual(M6),15,22,17,N,NA,Naturally Aspirated,M,Manual,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,\r2012,Porsche,Porsche,Cayenne S,PRX,3,4.8,8,Auto(A8),16,22,18,N,NA,Naturally Aspirated,A,Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/6/11,\r2012,Porsche,Porsche,Cayenne S Hybrid,PRX,9,3,6,Auto(A8),20,24,21,N,SC,Supercharged,A,Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/1/11,N\r2012,Porsche,Porsche,Cayenne Turbo,PRX,7,4.8,8,Auto(A8),15,22,17,N,TC,Turbocharged,A,Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/6/11,\r2012,General Motors,Saab,9-4X AWD,GMX,99,2.8,6,Auto(S6),15,22,18,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/31/11,\r2012,General Motors,Saab,9-4X AWD,GMX,76,3,6,Auto(S6),17,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/31/11,\r2012,Subaru,Subaru,FORESTER AWD,FJX,10,2.5,4,Auto(S4),21,27,23,N,NA,Naturally Aspirated,SA,Semi-Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/16/11,\r2012,Subaru,Subaru,FORESTER AWD,FJX,16,2.5,4,Auto(S4),19,24,21,N,TC,Turbocharged,SA,Semi-Automatic,4,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/16/11,\r2012,Subaru,Subaru,FORESTER AWD,FJX,9,2.5,4,Manual(M5),21,27,23,N,NA,Naturally Aspirated,M,Manual,5,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/16/11,\r2012,Subaru,Subaru,OUTBACK WAGON AWD,FJX,8,2.5,4,Auto(AV),22,29,24,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/18/11,\r2012,Subaru,Subaru,OUTBACK WAGON AWD,FJX,6,2.5,4,Manual(M6),19,27,22,N,NA,Naturally Aspirated,M,Manual,6,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/18/11,\r2012,Subaru,Subaru,OUTBACK WAGON AWD,FJX,18,3.6,6,Auto(S5),18,25,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,7/18/11,\r2012,Subaru,Subaru,TRIBECA AWD,FJX,19,3.6,6,Auto(S5),16,21,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/1/11,\r2012,Suzuki,Suzuki,GRAND VITARA 4WD,SKX,94,2.4,4,Auto(A4),19,23,20,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/10/11,\r2012,Toyota,TOYOTA,4RUNNER 4WD,TYX,44,4,6,Auto(S5),17,22,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/22/11,\r2012,Toyota,TOYOTA,4RUNNER 4WD,TYX,45,4,6,Auto(S5),17,22,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,10/22/11,\r2012,Toyota,TOYOTA,FJ CRUISER 4WD,TYX,47,4,6,Auto(A5),17,20,18,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/23/11,\r2012,Toyota,TOYOTA,FJ CRUISER 4WD,TYX,48,4,6,Manual(M6),15,18,16,N,NA,Naturally Aspirated,M,Manual,6,N,N,4,4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/23/11,\r2012,Toyota,TOYOTA,HIGHLANDER 4WD,TYX,17,3.5,6,Auto(S5),17,22,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/4/11,\r2012,Toyota,TOYOTA,HIGHLANDER HYBRID 4WD,TYX,18,3.5,6,Auto(AV),28,28,28,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,N,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/8/11,N\r2012,Toyota,TOYOTA,RAV4 4WD,TYX,77,2.5,4,Auto(A4),21,27,24,N,NA,Naturally Aspirated,A,Automatic,4,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,12/20/11,\r2012,Toyota,TOYOTA,RAV4 4WD,TYX,79,3.5,6,Auto(A5),19,26,22,N,NA,Naturally Aspirated,A,Automatic,5,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,12/20/11,\r2012,Toyota,TOYOTA,SEQUOIA 4WD,TYX,56,4.6,8,Auto(S6),13,18,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Toyota,TOYOTA,SEQUOIA 4WD,TYX,60,5.7,8,Auto(S6),13,17,14,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Toyota,TOYOTA,SEQUOIA 4WD FFV,TYX,63,5.7,8,Auto(S6),13,17,15,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,P,Part-time 4-Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,8/29/11,\r2012,Toyota,TOYOTA,VENZA AWD,TYX,81,2.7,4,Auto(S6),20,25,22,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,12/8/11,\r2012,Toyota,TOYOTA,VENZA AWD,TYX,83,3.5,6,Auto(S6),18,25,21,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,12/8/11,\r2012,Audi,Volkswagen,TIGUAN 4MOTION,ADX,82,2,4,Auto(S6),21,27,23,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/28/11,N\r2012,Audi,Volkswagen,TOUAREG,ADX,47,3,6,Auto(S8),19,28,22,N,TC,Turbocharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,DU,Diesel,\"Special Purpose Vehicle, SUV 4WD\",1,5/11/11,N\r2012,Volkswagen,Volkswagen,TOUAREG,VWX,81,3.6,6,Auto(S8),16,23,19,N,NA,Naturally Aspirated,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,6/21/11,\r2012,Volkswagen,Volkswagen,Touareg Hybrid,VWX,59,3,6,Auto(S8),20,24,21,N,SC,Supercharged,SA,Semi-Automatic,8,Y,N,A,All Wheel Drive,GP,Gasoline (Premium Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/17/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC60 AWD,VVX,22,3,6,Auto(S6),17,23,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC60 AWD,VVX,43,3.2,6,Auto(S6),18,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC70 AWD,VVX,21,3,6,Auto(S6),17,23,20,N,TC,Turbocharged,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC70 AWD,VVX,42,3.2,6,Auto(S6),18,24,20,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,Volvo,\"Volvo Cars of North America, LLC\",XC90 AWD,VVX,41,3.2,6,Auto(S6),16,23,18,N,NA,Naturally Aspirated,SA,Semi-Automatic,6,Y,N,A,All Wheel Drive,G,Gasoline (Regular Unleaded Recommended),\"Special Purpose Vehicle, SUV 4WD\",1,5/27/11,N\r2012,GM,Chevrolet,VOLT,GMX,32,1.4,4,Auto(AV),35,40,37,N,NA,Naturally Aspirated,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",GPR,Gasoline (Premium Unleaded Required),Compact Cars,car,7/21/11,N\r2012,Nissan,NISSAN,LEAF,NSX,901,0,,Auto(A1),106,92,99,N,,,A,Automatic,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Midsize Cars,car,10/4/11,N\r2012,Ford Motor Company,Ford Division,Focus FWD BEV,FMX,300,0,,Auto(AV),110,99,105,N,,,CVT,Continuously Variable,1,Y,N,F,\"2-Wheel Drive, Front\",EL,Electricity,Compact Cars,car,3/5/12,N\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH 57,MBX,240,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,11/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH 57 S,MBX,250,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,11/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH 62,MBX,245,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,12/1/11,\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH 62 S,MBX,255,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,11/24/11,\r2012,Mercedes-Benz,Mercedes-Benz,MAYBACH Landaulet,MBX,258,5.5,12,Auto(A5),10,16,12,Y,TC,Turbocharged,A,Automatic,5,Y,N,R,\"2-Wheel Drive, Rear\",GPR,Gasoline (Premium Unleaded Required),Large Cars,car,11/24/11,"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/fuel_eff_hway/fuel_eff_hway.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\nfuel_eff <- read.csv(\"fuel_eff.csv\")\n\n# select a small sample ---------------------------------------------\nman_rows <- which(fuel_eff$transmission == \"M\")\naut_rows <- which(fuel_eff$transmission == \"A\")\n\nset.seed(3583)\nman_rows_samp <- sample(man_rows, 26)\naut_rows_samp <- sample(aut_rows, 26)\n\nfuel_eff_samp <- fuel_eff[c(man_rows_samp,aut_rows_samp), ]\nfuel_eff_samp$transmission <- droplevels(fuel_eff_samp$transmission)\n\nlevels(fuel_eff_samp$transmission) <- c(\"automatic\", \"manual\")\n\n# plot --------------------------------------------------------------\nmyPDF(\"fuel_eff_hway_box.pdf\", 3.5, mar = c(3.7,2,0.3,1), mgp = c(2.5,0.55,0))\nboxPlot(fuel_eff_samp$hwy_mpg, fact = fuel_eff_samp$transmission, ylim = c(10, 37), \n        xlab = \"Hwy MPG\", axes = FALSE, xlim = c(0.5, 2.5),\n        lcol = COL[1], lwd = 1.5, medianLwd = 2.5)\naxis(1, at = c(1,2), labels = c(\"automatic\",\"manual\"))\naxis(2, at = c(15,25,35))\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/gifted_children/gifted_children.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(gifted)\n\n# histogram of IQ scores --------------------------------------------\nmyPDF(\"gifted_children_IQ_hist.pdf\", 5.5, 1.55, mar = c(3, 2, 0.2, 1), \n      mgp=c(1.8, 0.55, 0), mfrow = c(1,3))\n\nhistPlot(gifted$motheriq, col = COL[1], \n         xlab = \"Mother's IQ\", ylab = \"\", \n         axes = FALSE, xlim = c(100,140), ylim = c(0,12))\naxis(1, at = seq(100,140,20))\naxis(2, at = seq(0,12,4))\nhistPlot(gifted$fatheriq, col = COL[1], \n         xlab = \"Father's IQ\", ylab = \"\", \n         axes = FALSE, xlim = c(110,130), ylim = c(0,12))\naxis(1, at = seq(100,130,10))\naxis(2, at = seq(0,12,4))\nhistPlot(gifted$motheriq - gifted$fatheriq, col = COL[1], \n         xlab = \"Diff.\", ylab = \"\", \n         axes = FALSE, xlim = c(-20,20), ylim = c(0,12))\naxis(1, at = seq(-20,20,20))\naxis(2, at = seq(0,12,4))\n\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/gifted_children_ht/gifted_children_ht.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(gifted)\n\n# plot mom's IQ -----------------------------------------------------\npdf(\"gifted_children_ht_momIQ_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(gifted$motheriq, col = COL[1], \n         xlab = \"Mother's IQ\", ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at = c(0,4,8,12))\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/gifted_children_intro/gifted_children_intro.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(gifted)\n\n# plot counts -------------------------------------------------------\npdf(\"gifted_children_ht_count_hist.pdf\", height = 3, width = 6)\npar(mar=c(3.7,2.2,0.5,0.5), las=1, mgp=c(2.5,0.7,0), cex.lab = 1.5)\nhistPlot(gifted$count, col = COL[1], \n         xlab = \"Age child first counted to 10 (in months)\", ylab = \"\", \n         axes = FALSE)\naxis(1)\naxis(2, at = c(0,3,6))\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/global_warming_v2_1/global_warming_v2_1.R",
    "content": "library(openintro)\nd <- climate70$dx90_2018 - climate70$dx90_1948\nmean(d)\nsd(d)\nlength(d)\nt.test(d)\n\n\nmyPDF(\"global_warming_v2_1_diffs.pdf\", 4, 3,\n    mar = c(3.9, 2, 0.5, 0.5))\nhistPlot(d, col = COL[1], \n    xlab = \"Differences in Number of Days\",\n    ylab = \"\")\naxis(1)\ndev.off()"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/gpa_major/gpa_major.R",
    "content": "library(openintro)\nlibrary(xtable)\n\nsurvey <- read.csv(\"survey.csv\")\n\n# subset for meaningful gpa -----------------------------------------\nsurvey <- subset(survey,\n    !is.na(survey$gpa) &\n    !is.na(survey$major) &\n    survey$gpa <= 4)\n\n# set major level names ---------------------------------------------\nlevels(survey$major) <- c(\n    \"Arts and Humanities\",\n    \"Natural Sciences\",\n    \"Social Sciences\")\n\n# boxplot -----------------------------------------------------------\nmyPDF(\"gpa_major.pdf\", 7.2, 2.7,\n    mar = c(2.2,4.7,0.5,1),\n    mgp = c(3.5,0.7,0),\n    cex.lab = 1.25,\n    cex.axis = 1.25)\n\nboxPlot(survey$gpa, fact = survey$major, col = COL[1], \n        ylab = \"GPA\", axes = FALSE,\n        xlim = c(0.6, 3.4),\n        ylim = c(2.5, 4),\n        lcol = COL[1], lwd = 1.5, medianLwd = 2.5)\naxis(1, at = c(1,2,3), \n     labels = c(\"Arts and Humanities\", \"Natural Sciences\", \"Social Sciences\"))\naxis(2, at = seq(2.5, 4, 0.5))\ndev.off()\n\n# anova output ------------------------------------------------------\nxtable(anova(lm(survey$gpa ~ survey$major)), digits = 2)\n\n# summary stats -----------------------------------------------------\nround(by(survey$gpa, survey$major, mean),2)\nround(by(survey$gpa, survey$major, sd),2)\nby(survey$gpa, survey$major, length)"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/gpa_major/survey.csv",
    "content": "\"gpa\",\"major\"\n4,\"social sciences\"\n3.8,\"social sciences\"\n3.93,\"social sciences\"\n3.4,\"natural sciences\"\nNA,\"natural sciences\"\n3.9,\"social sciences\"\nNA,\"natural sciences\"\n3.69,\"social sciences\"\n3.2,\"social sciences\"\n3.2,\"social sciences\"\n3.52,\"social sciences\"\n3.68,\"social sciences\"\n3.4,\"social sciences\"\n3.7,\"arts and humanities\"\nNA,\"social sciences\"\n3.75,\"natural sciences\"\n3.3,\"arts and humanities\"\n3.425,\"social sciences\"\n3.795,\"social sciences\"\n3.5,NA\n3.83,\"natural sciences\"\n3.3,\"social sciences\"\n3.75,\"social sciences\"\n4.3,\"social sciences\"\n3.15,\"natural sciences\"\n3.7,\"social sciences\"\n3.8,\"natural sciences\"\n3.63,NA\n3.9,\"arts and humanities\"\n3.294,\"social sciences\"\n3.7,\"arts and humanities\"\n3.4,\"natural sciences\"\n4,\"natural sciences\"\n3.4,\"arts and humanities\"\n3.7,\"natural sciences\"\n3.8,\"social sciences\"\n3.4,\"natural sciences\"\n3.4,\"social sciences\"\nNA,\"natural sciences\"\n3.4,\"social sciences\"\n3,\"social sciences\"\n3.6,\"social sciences\"\n3.567,\"social sciences\"\n3.3,\"natural sciences\"\n3.4,\"social sciences\"\n3.6,\"arts and humanities\"\n3.67,\"social sciences\"\n3.82,\"social sciences\"\n2.9,\"social sciences\"\n3.9,\"social sciences\"\n3.4,\"social sciences\"\n3.6,\"social sciences\"\n3.1,\"social sciences\"\n3.4,\"social sciences\"\n3.8,\"natural sciences\"\n3.7,\"arts and humanities\"\n3.7,\"social sciences\"\n3.8,\"arts and humanities\"\n3.9,\"arts and humanities\"\n3.92,\"social sciences\"\n3.8,\"social sciences\"\n3.868,\"natural sciences\"\n3.35,\"social sciences\"\n3.85,\"arts and humanities\"\n3.55,NA\n3.7,\"social sciences\"\n3.65,\"natural sciences\"\n3.125,\"arts and humanities\"\n4,\"natural sciences\"\n3.25,\"arts and humanities\"\n3.86,\"arts and humanities\"\n3.5,\"social sciences\"\n3.45,\"social sciences\"\n3.6,\"natural sciences\"\nNA,\"arts and humanities\"\n3.866,\"social sciences\"\n3.82,\"social sciences\"\n3.2,\"arts and humanities\"\n3.5,\"arts and humanities\"\n3.8,\"natural sciences\"\n3.8,\"social sciences\"\n3.7,\"natural sciences\"\n3.75,\"social sciences\"\n3.3,\"natural sciences\"\n3.875,\"social sciences\"\n3.7,\"social sciences\"\n3.5,\"social sciences\"\nNA,\"natural sciences\"\n3.2,\"natural sciences\"\n3.566,\"social sciences\"\n3.5,\"social sciences\"\n4.3,\"natural sciences\"\n3.6,\"natural sciences\"\n3.2,\"social sciences\"\nNA,\"natural sciences\"\n3.825,\"social sciences\"\n3.85,\"social sciences\"\n3.75,\"natural sciences\"\n4,\"social sciences\"\n3.4,\"social sciences\"\n3.9,\"social sciences\"\n3.825,\"arts and humanities\"\n3.7,\"social sciences\"\n3.8,\"social sciences\"\n2.91,\"social sciences\"\n3.559,\"natural sciences\"\n3.69,\"social sciences\"\n3.3,\"natural sciences\"\n3.75,\"arts and humanities\"\n3.9,\"social sciences\"\n3.65,\"social sciences\"\n3.5,\"natural sciences\"\n3.6,\"social sciences\"\n3.675,\"social sciences\"\n3.9,\"natural sciences\"\n3.6,\"social sciences\"\n3.675,\"social sciences\"\n3.7,\"social sciences\"\n3.66,\"social sciences\"\n3.733,\"natural sciences\"\n3.7,\"social sciences\"\n2.6,\"social sciences\"\n4,\"arts and humanities\"\n3.2,\"arts and humanities\"\n3.16,\"social sciences\"\n3.7,NA\n3.5,\"natural sciences\"\n3.65,\"natural sciences\"\n3.9,\"social sciences\"\n3.785,\"social sciences\"\n3.1,\"social sciences\"\n3.15,\"social sciences\"\n3.61,\"natural sciences\"\n3.3,\"natural sciences\"\nNA,\"social sciences\"\n3.7,\"arts and humanities\"\n3.7,\"arts and humanities\"\n3.75,\"arts and humanities\"\nNA,\"social sciences\"\n3.4,\"natural sciences\"\n3.6,\"arts and humanities\"\n3.5,\"social sciences\"\n3.8,\"natural sciences\"\n3.7,\"social sciences\"\n3.925,\"social sciences\"\n3.84,\"natural sciences\"\n3.85,\"arts and humanities\"\n3.41,\"arts and humanities\"\n3.825,\"natural sciences\"\n2.95,\"natural sciences\"\n3.925,\"natural sciences\"\n3.3,\"natural sciences\"\n3.3,\"arts and humanities\"\n3.6,\"natural sciences\"\nNA,\"arts and humanities\"\n4,\"social sciences\"\nNA,\"social sciences\"\n3.3,\"arts and humanities\"\n3.89,\"natural sciences\"\n3.2,\"social sciences\"\n3.97,\"natural sciences\"\n3.3,\"social sciences\"\n3.3,\"arts and humanities\"\n3.86,\"social sciences\"\n3.76,\"natural sciences\"\n3.8,\"social sciences\"\n3.5,\"social sciences\"\nNA,\"natural sciences\"\n3.6,\"social sciences\"\n3.55,\"arts and humanities\"\n3.97,\"natural sciences\"\n3.925,\"social sciences\"\n3.68,\"natural sciences\"\n3.25,\"social sciences\"\n3.56,\"social sciences\"\n2.85,\"social sciences\"\n3.6,\"social sciences\"\n3.45,\"natural sciences\"\n3.5,\"social sciences\"\n3.15,\"natural sciences\"\n3.35,\"social sciences\"\n3.5,\"social sciences\"\n3.79,\"arts and humanities\"\n3.022,\"social sciences\"\n3.46,\"social sciences\"\n3.55,\"social sciences\"\n3.97,\"social sciences\"\n3.925,\"social sciences\"\n3.2,\"social sciences\"\n3.4,\"natural sciences\"\n3.9,\"natural sciences\"\nNA,\"natural sciences\"\n3.6,\"arts and humanities\"\n3.83,\"social sciences\"\n3.8,\"natural sciences\"\n4,\"social sciences\"\n3.5,\"social sciences\"\n3.3,\"arts and humanities\"\n4,\"social sciences\"\n3.1,\"social sciences\"\n3.5,\"social sciences\"\n3.62,\"social sciences\"\n3.6,\"natural sciences\"\n3.8,\"social sciences\"\n3.2,\"social sciences\"\n3.925,\"social sciences\"\n3.84,\"social sciences\"\n3.1,\"arts and humanities\"\n4,\"natural sciences\"\n3.33,NA\n3.35,\"natural sciences\"\n3.925,\"social sciences\"\n3,\"natural sciences\"\n3.6,\"social sciences\"\n3.7,\"social sciences\"\n3.84,\"social sciences\"\n3.8,\"social sciences\"\n3.1,\"social sciences\"\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/hs_beyond_1/hs_beyond.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(hsb2)\n\n# create variables from data ----------------------------------------\nscores <- c(hsb2$read, hsb2$write)\ngp <- c(rep('read', nrow(hsb2)), rep('write', nrow(hsb2)))\n\n# read vs. write side-by-side box plot ------------------------------\npdf(\"hs_beyond_read_write_box.pdf\", height = 3, width = 5)\npar(mar = c(3, 4, 0.5, 0.5), las = 1, mgp = c(2.8, 0.7, 0), \n    cex.axis = 1.1, cex.lab = 1.1)\nopenintro::dotPlot(scores, gp, vertical = TRUE, ylab = \"scores\", \n                   at=1:2+0.13, col = COL[1,3], \n                   xlim = c(0.5,2.5), ylim = c(20, 80), \n                   axes = FALSE, cex.lab = 1.25, cex.axis = 1.25)\naxis(1, at = c(1,2), labels = c(\"read\",\"write\"), cex.lab = 1.25, cex.axis = 1.25)\naxis(2, at = seq(20, 80, 20), cex.axis = 1.25)\nboxplot(scores ~ gp, add = TRUE, axes = FALSE, col = NA)\ndev.off()\n\n# histogram of differences of read and write ------------------------\npdf(\"hs_beyond_diff_hist.pdf\", height = 3, width = 5.5)\npar(mar=c(3.3, 2, 0.5, 0.5), las = 1, mgp = c(2.1, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\nhistPlot(hsb2$read - hsb2$write, col = COL[1], \n         xlab = \"Differences in scores (read - write)\", ylab = \"\")\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/oscar_winners/oscar_winners.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(oscars)\n\n# plot of oscar winner women and men ages ---------------------------\np <- oscars %>%\n  mutate(award = ifelse(gender == \"female\", \"Best Actress\", \"Best Actor\")) %>%\n  ggplot(aes(x = age)) +\n    geom_histogram(binwidth = 10, fill = COL[1,1], color = COL[5,1], size = 0.3) +\n    facet_wrap(~award, nrow = 2) +\n    theme_minimal() +\n    labs(x = \"Age (in years)\", y = \"\")\n\nggsave(p, file = \"ch_inference_for_means/oscar_winners/figures/oscars_winners_hist.pdf\",\n       height = 6, width = 8)\n\n# summary stats -----------------------------------------------------\noscars %>%\n  group_by(gender) %>%\n  summarise(\n    mean = mean(age),\n    sd = sd(age),\n    n = n()\n  )\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/prison_isolation_T/prison_isolation.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\nprison <- read.csv(\"prison_isolation.csv\")\n\n# calculate diffs ---------------------------------------------------\n\ndiff1 = prison$PreTrt1 - prison$PostTrt1\ndiff2 = prison$PreTrt2 - prison$PostTrt2\ndiff3 = prison$PreTrt3 - prison$PostTrt3\n\ndiff = c(diff1, diff2, diff3)\ntr = c(rep(\"Tr 1\", 14), rep(\"Tr 2\", 14), rep(\"Tr 3\", 14))\n\n# hists ------------------------------------------\nH <- function(x, xlab) {\n  tmp <- hist(x,\n      col = COL[1],\n      xlab = xlab,\n      ylab = \"\",\n      main = \"\",\n      axes = FALSE)\n  axis(1, at = pretty(tmp$breaks, n = 3))\n  axis(2, at = pretty(c(0, max(tmp$counts)), n = 3))\n  # rug(x)\n  return(tmp)\n}\n\nmyPDF(\"prison_isolation_hist.pdf\", 9, 2,\n    mar = c(4, 2.5, 0.5, 2.5),\n    mgp = c(2.9, 0.7, 0),\n    mfrow = c(1,3),\n    cex.lab = 1.25)\nfor (i in 1:3) {\n  H(diff[tr == paste(\"Tr\", i)], paste(\"Treatment\", i))\n}\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/prison_isolation_T/prison_isolation.csv",
    "content": "PreTrt1,PostTrt1,PreTrt2,PostTrt2,PreTrt3,PostTrt3\r67,74,88,79,86,90\r86,50,79,81,53,53\r64,64,67,83,81,102\r69,76,83,74,69,67\r67,64,79,76,81,76\r79,81,76,69,76,81\r67,74,71,71,74,69\r67,50,67,75,60,60\r69,60,69,64,67,69\r57,57,67,64,86,83\r76,62,67,64,86,107\r90,76,74,71,74,71\r71,71,81,74,71,71\r93,76,81,64,71,81"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/prius_fuel_efficiency/prius_fuel_efficiency.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\nprius <- c(54.6, 59.5, 49.5, 44.3, 63.3, 52.3, 55.4, \n           50.3, 60.3, 53.2, 52.6, 46.6, 52.1, 52.0)\n\n# histogram ---------------------------------------------------------\npdf(\"prius_fuel_efficiency_hist.pdf\", height = 3, width = 6)\n\npar(mar = c(4,2,0,0), las = 1, mgp = c(2.5,1,0),\n    cex.lab = 1.25, cex.axis = 1.25)\n\nhistPlot(prius, ylab = \"\",xlab = \"Mileage (in MPG)\", col = COL[1], axes = FALSE)\naxis(1, at = seq(40, 65, 5))\naxis(2, at = seq(0, 6, 2))\n\ndev.off()\n\n# summary stats -----------------------------------------------------\nround(mean(prius), 1)\nround(sd(prius), 1)\nlength(prius)"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/prius_fuel_efficiency_update/prius_fuel_efficiency.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\nprius <- c(54.6, 59.5, 49.5, 44.3, 63.3, 52.3, 55.4, \n           50.3, 60.3, 53.2, 52.6, 46.6, 52.1, 52.0)\n\n# histogram ---------------------------------------------------------\npdf(\"prius_fuel_efficiency_hist.pdf\", height = 3, width = 6)\n\npar(mar = c(4,2,0,0), las = 1, mgp = c(2.5,1,0),\n    cex.lab = 1.25, cex.axis = 1.25)\n\nhistPlot(prius, ylab = \"\",xlab = \"Mileage (in MPG)\", col = COL[1], axes = FALSE)\naxis(1, at = seq(40, 65, 5))\naxis(2, at = seq(0, 6, 2))\n\ndev.off()\n\n# summary stats -----------------------------------------------------\nround(mean(prius), 1)\nround(sd(prius), 1)\nlength(prius)"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/t_distribution/t_distribution.R",
    "content": "# plot --------------------------------------------------------------\npdf('t_distribution.pdf', 4.3, 2.3)\n\npar(mar=c(2, 0, 0, 0), mgp=c(5, 0.6, 0))\nplot(c(-4.2, 4.2), c(0, dnorm(0)), type='n', axes=FALSE, xlab = \"\", ylab = \"\")\naxis(1)\nabline(h=0)\n\nX <- seq(-5, 5, 0.01)\nY <- dnorm(X)\nlines(X, Y, lwd = 0.7)\n\nY <- dt(X, 1)\nlines(X, Y, lty=3)\n\nZ <- dt(X, 5)\nlines(X, Z, lty=5)\n\nlegend(\"topright\", lty = c(1,5,3), c(\"solid\",\"dashed\",\"dotted\"), inset = 0.01, box.col = \"white\")\n\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/torque_on_rusty_bolt/torque_on_rusty_bolt (Autosaved).R",
    "content": "library(openintro)\nlibrary(xtable)\n\nd <- penetrating_oil\n\nmyPDF(\"torque_on_rusty_bolt_dot_plot.pdf\", 7, 3.2,\n    mar = c(3.5, 6.5, 0.1, 0.3),\n    mgp = c(2.3, 0.55, 0))\ndotPlot(d$torque, d$treatment,\n    pch = 19, col = COL[1, 2], cex = 2,\n    vertical = FALSE,\n    xlab = paste(\n        \"Torque Required to Loosen Rusty Bolt,\",\n        \"in Foot-Pounds\"),\n    ylab = \"\")\nabline(h = 1:8, col = COL[5, 7])\ndev.off()\n\nm <- lm(torque ~ treatment, data = penetrating_oil)\nanova(m)\nxtable(anova(m))\n\nxbar <- tapply(penetrating_oil$torque, penetrating_oil$treatment, mean)\nn <- table(penetrating_oil$treatment)\nstopifnot(all(names(xbar) == names(n)))\ns <- summary(m)$sigma\ndf <- summary(m)$df[2]\n\np <- matrix(\"\", length(n), length(n))\nN <- length(n)\nK <- N * (N - 1) / 2\nfor (i in 1:(N - 1)) {\n  for (j in (i + 1):N) {\n    diff <- xbar[i] - xbar[j]\n    se <- s * sqrt(1 / n[i] + 1 / n[j])\n    p[i, j] <- round(2 * pt(-abs(diff / se), df), 4)\n  }\n}\nrownames(p) <-\n    colnames(p) <-\n    names(xbar)\nxtable(p[1:7, 2:8])\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/torque_on_rusty_bolt/torque_on_rusty_bolt.R",
    "content": "library(openintro)\nlibrary(xtable)\n\nd <- penetrating_oil\n\nmyPDF(\"torque_on_rusty_bolt_dot_plot.pdf\", 7, 3.2,\n    mar = c(3.5, 6.5, 0.1, 0.3),\n    mgp = c(2.3, 0.55, 0))\ndotPlot(d$torque, d$treatment,\n    pch = 19, col = COL[1, 2], cex = 2,\n    vertical = FALSE,\n    xlab = paste(\n        \"Torque Required to Loosen Rusty Bolt,\",\n        \"in Foot-Pounds\"),\n    ylab = \"\")\nabline(h = 1:8, col = COL[5, 7])\ndev.off()\n\nm <- lm(torque ~ treatment, data = penetrating_oil)\nanova(m)\nxtable(anova(m))\n\nxbar <- tapply(penetrating_oil$torque, penetrating_oil$treatment, mean)\nn <- table(penetrating_oil$treatment)\nstopifnot(all(names(xbar) == names(n)))\ns <- summary(m)$sigma\ndf <- summary(m)$df[2]\n\np <- matrix(\"\", length(n), length(n))\nN <- length(n)\nK <- N * (N - 1) / 2\nfor (i in 1:(N - 1)) {\n  for (j in (i + 1):N) {\n    diff <- xbar[i] - xbar[j]\n    se <- s * sqrt(1 / n[i] + 1 / n[j])\n    tmp <- round(2 * pt(-abs(diff / se), df), 4)\n    p[i, j] <- format(c(tmp, 0.0001), scientific = FALSE)[1]\n  }\n}\nrownames(p) <-\n    colnames(p) <-\n    names(xbar)\nxtable(p[1:7, 2:8])\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/eoce/work_hours_education/work_hours_education.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\nload(\"gss2010.Rda\")\ngss <- gss2010\n\n# subset & clean data -----------------------------------------------\ngss_sub <- gss[which(!is.na(gss$hrs1) & !is.na(gss$degree)), ]\ngss_sub <- gss_sub[, which(names(gss_sub) == \"degree\" | names(gss_sub) == \"hrs1\")]\nlevels(gss_sub$degree) <- c(\"Less than HS\",\"HS\",\"Jr Coll\",\"Bachelor's\",\"Graduate\")\n\n# plot --------------------------------------------------------------\n\npdf(\"work_hours_education.pdf\", height = 2.5, width = 8)\n\npar(mar = c(2,3.5,0.5,.5), mgp = c(2.3,0.7,0), las = 1)\n\nboxPlot(gss_sub$hrs1, fact = gss_sub$degree, \n        col = COL[1,2], ylab = \"Hours worked per week\", xlim=c(0.6, 5.4),\n        lcol = COL[1], lwd = 1.5, medianLwd = 2.5)\ndev.off()\n\n# summary stats -----------------------------------------------------\n\nround(by(gss_sub$hrs1, gss_sub$degree, mean),2)\nround(by(gss_sub$hrs1, gss_sub$degree, sd),2)\nby(gss_sub$hrs1, gss_sub$degree, length)\n\n# anova -------------------------------------------------------------\n\nxtable(anova(lm(gss_sub$hrs1 ~ gss_sub$degree)), digits = 2)\n"
  },
  {
    "path": "ch_inference_for_means/figures/fDist2And423/fDist2And423.R",
    "content": "rm(list = ls())\nlibrary(openintro)\n\nX <- seq(0, 8, len = 300)\nY <- df(X, 2.00001, 423)\n\nmyPDF(\"fDist2And423.pdf\", 5, 2.4,\n      mar = c(2.8, 0.5, 0, 0.5),\n      mgp = c(1.8, 0.4, 0))\nplot(X, Y,\n     type = \"l\",\n     xlab = \"F\",\n     ylab = \"\",\n     axes = FALSE,\n     lwd = 1.5)\nlines(c(0, 8), rep(0, 2))\naxis(1)\ndev.off()\n\n\nmyPDF(\"fDist2And423Shaded.pdf\", 5, 1.8,\n      mar = c(1.6, 3.1, 0.5, 0.5),\n      mgp = c(2, 0.5, 0))\nplot(X, Y,\n     type = \"l\",\n     xlab = \"F\",\n     ylab = \"\",\n     axes = FALSE)\nlines(c(0, 8), rep(0, 2))\naxis(1)\ntemp <- which(X > 5.077)\nx    <- X[c(temp, rev(temp), temp[1])]\ny    <- c(Y[temp], rep(0, length(temp)), Y[temp[1]])\npolygon(x, y, col = COL[4], border = COL[4], lwd = 2)\narrows(6.3, 0.3, 6.5, 0.05, length = 0.05)\ntext(6.3, 0.3, \"Small tail area\", pos = 3)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/fDist3And323/fDist3And323.R",
    "content": "rm(list = ls())\nlibrary(openintro)\n\nX <- seq(0, 6, len = 300)\nY <- df(X, 3, 323)\n\nmyPDF(\"fDist3And323.pdf\", 5, 2.4,\n      mar = c(2.8, 0.5, 0, 0.5),\n      mgp = c(1.8, 0.4, 0))\nplot(X, Y,\n     type = \"l\",\n     xlab = \"F\",\n     ylab = \"\",\n     axes = FALSE,\n     lwd = 1.5)\nabline(h = 0)\naxis(1)\ndev.off()\n\n\nmyPDF(\"fDist3And323Shaded.pdf\", 5, 1.8,\n      mar = c(1.6, 3.1, 0.5, 0.5),\n      mgp = c(2, 0.5, 0))\nplot(X, Y,\n     type = \"l\",\n     xlab = \"F\",\n     ylab = \"\",\n     axes = FALSE)\nabline(h = 0)\naxis(1)\ntemp <- which(X > 1.994)\nx    <- X[c(temp, rev(temp), temp[1])]\ny    <- c(Y[temp], rep(0, length(temp)), Y[temp[1]])\npolygon(x, y, col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/mlbANOVA/mlbANOVA.R",
    "content": "rm(list = ls())\nlibrary(xtable)\nlibrary(openintro)\nlibrary(dplyr)\nd   <- subset(mlb_players_18, AB >= 100)\nd   <- subset(d, !position %in% c(\"P\", \"DH\"))\npos <- list(c(\"LF\", \"CF\", \"RF\"), c(\"1B\", \"2B\", \"3B\", \"SS\"), \"DH\", \"C\")\nPOS <- c(\"OF\", \"IF\", \"DH\", \"C\")\n\nfor (i in 1:length(pos)) {\n  these <- which(d$position %in% pos[[i]])\n  cat(length(these), \"\\n\")\n  d$position[these] <- POS[i]\n}\nd <- select(d, name, team, position, AB, H, HR, RBI, AVG, OBP)\nd <- d[order(d$name, d$team), ]\nrownames(d) <- NULL\nxtable(rbind.data.frame(head(d, 3), tail(d, 3)), digits = 3)\n\n\nmod <- lm(OBP ~ position, data = d)\nsummary(mod)\nanova(mod)\nxtable(summary(mod))\nxtable(anova(mod), digits = 4)\n\n\nmyPDF(\"mlbANOVABoxPlot.pdf\", 5.4, 3,\n      mar = c(2.8, 4, 0, 1))\nkey <- POS[c(1, 2, 4)]\nboxPlot(d$OBP, d$position,\n        xlab = \"\",\n        ylab = \"On-Base Percentage\",\n        axes = FALSE,\n        pchCex = 1,\n        key = key,\n        col = COL[1, 3],\n        lcol = COL[1])\nmtext(\"Position\", 1, 1.5)\naxis(1, 1:3, key)\naxis(2)\ndev.off()\n\n\ntab <- rbind(\n    by(d$OBP, d$position, length),\n    by(d$OBP, d$position, mean),\n    by(d$OBP, d$position, sd))[, c(\"OF\", \"IF\", \"C\")]\nxtable(tab, digits = 3)\n\n\ng <- rep(1:3, c(10, 1000, 1000))\nx <- c()\nfor (i in 1:3) {\n  n <- sum(g == i)\n  x <- c(x, rnorm(n))\n}\nsum(by(x, g, length) * (by(x, g, mean) - mean(x))^2) / 2\nanova(lm(x ~ as.factor(g)))\n\n\n# uTeams <- unique(mlb_players_18$team)\n# nTeams <- length(uTeams)\n# myPDF(\"mlbANOVADiagIndepOfTeam.pdf\", 5, 4)\n# dotPlot(mod$res, mlbBat10$team,\n        # key = uTeams,\n        # ylim = c(0, nTeams),\n        # axes = FALSE,\n        # ylab = \"Teams\",\n        # xlab = \"Residuals\",\n        # col = COL[1])\n# axis(1)\n# axis(2, 1:nTeams, uTeams, cex.axis = 0.5)\n# abline(h = 1:nTeams, col = COL[7], lwd = 0.5)\n# abline(h = seq(1, nTeams, 5), col = COL[6], lwd = 1.5)\n# dev.off()\n\n\nmyPDF(\"mlbANOVADiagNormality.pdf\", 5, 4,\n      mar = c(3.5, 4.4, 0.5, 0.5))\nqqnorm(mod$res,\n       main = \"\",\n       xlab = \"\",\n       ylab = \"\",\n       pch = 20,\n       cex = 0.7,\n       col = COL[1,3])\nmtext(\"Theoretical Quantiles\", 1, 2.2)\npar(las = 0)\nmtext(\"Residuals\", 2, line = 3.3)\ndev.off()\n\n\nmyPDF(\"mlbANOVADiagNormalityGroups.pdf\", 6, 1.7,\n      mar = c(3.4, 3.4, 2, 0.5),\n      mgp = c(2.2, 0.55, 0),\n      mfrow = c(1, 3))\nxlim <- range(d$OBP)\nat <- pretty(xlim, 3)\nbreaks <- pretty(xlim, 15)\nHistOfOBP <- function(x, main) {\n  histPlot(x,\n      main = main,\n      xlim = xlim,\n      breaks = breaks,\n      xlab = \"On-Base Percentage\",\n      ylab = \"Frequency\",\n      col = COL[1],\n      axes = FALSE)\n  axis(1, at)\n  axis(2)\n}\nHistOfOBP(d$OBP[d$position == \"OF\"], \"Outfielders\")\nHistOfOBP(d$OBP[d$position == \"IF\"], \"Infielders\")\nHistOfOBP(d$OBP[d$position == \"C\"], \"Catchers\")\ndev.off()\n\n\nmyPDF(\"mlbANOVADiagConstantVar.pdf\", 5, 4,\n      mar = c(3.5, 4.4, 0.5, 0.5))\nboxPlot(mod$res, d$position,\n        main = \"\",\n        xlab = \"\",\n        ylab = \"\",\n        pch = 20,\n        cex = 0.7,\n        col = COL[1, 3],\n        lcol = COL[1])\nmtext(\"Position\", 1, 2.2)\npar(las = 0)\nmtext(\"Residuals\", 2, line = 3.3)\ndev.off()\n\n\nanova(lm(OBP ~ team + position, d))\nanova(lm(OBP ~ position + team, d))\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/outliers_and_ss_condition/outliers_and_ss_condition.R",
    "content": "library(openintro)\nset.seed(2)\n\nd1 <- rnorm(15, 3, 2)\nd2 <- c(exp(rnorm(49, 0, 0.7)), 22)\n\nmyPDF('outliers_and_ss_condition.pdf', 8, 2.5,\n      mar = c(3, 3, 0.5, 2),\n      mgp = c(1.8, 0.5, 0),\n      mfrow = c(1, 2))\n\nhistPlot(d1, axes = FALSE, # breaks = 20,\n         xlab = \"Sample 1 Observations (n = 15)\",\n         ylab = \"\",\n         col = COL[1])\naxis(1, at = seq(-10, 10, 2))\naxis(2)\npar(las = 0)\nmtext(\"Frequency\", 2, 1.8)\n\npar(las = 1, mar = c(3, 4, 0.5, 0.5))\nhistPlot(d2, axes = FALSE, breaks = 20,\n         xlab = \"Sample 2 Observations (n = 50)\",\n         ylab = \"\",\n         col = COL[1])\naxis(1, at = seq(-10, 30, 10))\naxis(2)\npar(las = 0)\nmtext(\"Frequency\", 2, 2)\n\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/pValueOfTwoTailAreaOfExamVersionsWhereDFIs26/pValueOfTwoTailAreaOfExamVersionsWhereDFIs26.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('pValueOfTwoTailAreaOfExamVersionsWhereDFIs26.pdf',\n      4.8, 1.7,\n      mar = c(1.6, 1, 0, 1),\n      mgp = c(0, 0.45, 0))\nnormTail(0, 1,\n         L = -1.15,\n         U = 1.15,\n         df = 26,\n         col = COL[1])\nlines(c(1.16, 1.16),\n      c(dt(1.16, 26), 0.25),\n      lty = 3,\n      cex = 2)\ntext(1.55, 0.24, \"T = 1.15\",\n     pos = 3)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/pValueShownForSATHTOfOver100PtGain/pValueShownForSATHTOfOver100PtGain.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('pValueShownForSATHTOfOver100PtGain.pdf', 4, 2,\n      mar = c(1.5, 1, 0.2, 1),\n      mgp = c(0, 0.45, 0))\nnormTail(0, 1,\n         U = 2.39,\n         df = 20,\n         col = COL[1])\nlines(c(2.4, 2.4),\n      c(dt(2.4, 20), 0.1),\n      lty = 3,\n      lwd = 2)\ntext(2.73, 0.088, \"T = 2.39\",\n     pos = 3,\n     cex = 0.8)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/power_best_sample_size/power_best_sample_size.R",
    "content": "library(openintro)\ndata(COL)\n\nBuildNull <- function() {\n  normTail(0, 1.07, L = -1000, U = 1000,\n           df = 50, lwd = 2.5, axes = FALSE,\n           curveColor = COL[1],\n           xlim = c(-10, 10))\n  axis(1, at = seq(-15, 15, 3))\n  mtext(expression(bar(x)[trmt] - bar(x)[ctrl]),\n        side = 1, line = 1.5)\n  text(0.6, 0.3, \"Null distribution\", col = COL[1], pos = 4)\n  lines(rep(0, 2), c(0, dnorm(0, 0, 1.07)),\n        col = COL[1,4], lwd = 0.5)\n}\n\n# _____ Null Distribution + Alternative At -3 _____ #\nmyPDF('power_best_sample_size.pdf',\n      7, 1.5,\n      mar = c(2.5, 0, 0, 0),\n      mgp = c(0, 0.45, 0))\nBuildNull()\nnormTail(-3, 1.07, L = -2.10, U = 1000,\n         df = 50, lwd = 2, add = TRUE,\n         curveColor = COL[2, 2],\n         col = COL[2, 2], border = COL[2])\nlines(rep(-3, 2), c(0, dnorm(0, 0, 1.07)),\n      col = COL[2,4], lwd = 0.5)\nsegments(2.1 * c(-1, 1), rep(0, 2), y1 = rep(0.2, 2),\n         col = COL[4, 4], lty = 3, lwd = 3)\nsegments(2.1 * c(-1, 1), rep(0, 2), y1 = rep(0.2, 2),\n         col = COL[4, 4], lty = 3, lwd = 1.5)\ntext(rep(-6, 2), 1.5 * c(0.21, 0.15),\n     c(\"Distribution with\",\n       expression(mu[trmt] - mu[ctrl]*\" = -3\")),\n     col = COL[2])\narrows(-3, 0.02, -2.15, 0.02,\n       col = COL[3], lwd = 2,\n       length = 0.05, code = 3)\ntext(-2.85, 0.01, \"0.84 SE\", pos = 3, col = COL[3], cex = 0.75)\nrect(-1.5, 0.005, 0.5, 0.1, col = \"#ffffffAA\", border = \"#00000000\")\narrows(-2.05, 0.02, 0, 0.02,\n       col = COL[4], lwd = 2,\n       length = 0.05, code = 3)\ntext(-1, 0.007, \"1.96 SE\", pos = 3, col = COL[4], cex = 0.75)\ndev.off()\n\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/power_curve/power_curve.R",
    "content": "library(openintro)\ndata(COL)\n\nn <- c(10:500, seq(510, 2000, 10), seq(2100, 10000, 100))\nse <- sapply(n, function(x) sqrt(2 * 12^2 / x))\nleft.reject <- qt(0.025, n - 1) * se\nx <- (left.reject - (-3)) / se\np <- pt(x, 2 * n - 2)\n\nmyPDF('power_curve_neg-3.pdf', 7, 3)\nplot(n, p,\n     xlab = \"Sample Size Per Group\",\n     ylab = \"Power\",\n     xlim = c(20, 5000),\n     ylim = 0:1,\n     type = \"n\",\n     log = \"x\",\n     axes = FALSE)\naxis(1)\naxis(2)\nabline(h = 0:1, lty = 2, col = COL[6,2])\nlines(n, p, col = COL[1], lwd = 3)\ndev.off()\n\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/power_null_0_0-76/power_null_0_0-76.R",
    "content": "library(openintro)\ndata(COL)\n\nBuildNull <- function() {\n  normTail(0, 0.8, L = -1000, U = 1000,\n           df = 50, lwd = 2.5, axes = FALSE,\n           curveColor = COL[1],\n           xlim = c(-10, 10))\n  axis(1, at = seq(-15, 15, 3))\n  mtext(expression(bar(x)[trmt] - bar(x)[ctrl]),\n        side = 1, line = 1.5)\n  text(0.6, 0.4, \"Null distribution\", col = COL[1], pos = 4)\n  lines(rep(0, 2), c(0, dnorm(0, 0, 0.8)),\n        col = COL[1,4], lwd = 0.5)\n}\n\n# _____ Null Distribution + Alternative At -3 _____ #\nmyPDF('power_null_0_0-76_with_alt_at_3_and_shaded.pdf',\n      7, 1.4,\n      mar = c(2.5, 0, 0, 0),\n      mgp = c(0, 0.45, 0))\nBuildNull()\nnormTail(-3, 0.8, L = -1.49, U = 1000,\n         df = 50, lwd = 2.5, add = TRUE,\n         curveColor = COL[2],\n         col = COL[2, 3], border = COL[2])\nlines(rep(-3, 2), c(0, dnorm(0, 0, 0.8)),\n      col = COL[2,4], lwd = 0.5)\nsegments(1.5 * c(-1, 1), rep(0, 2), y1 = rep(0.3, 2),\n         col = COL[4], lty = 3, lwd = 3)\nsegments(1.5 * c(-1, 1), rep(0, 2), y1 = rep(0.3, 2),\n         col = COL[4], lty = 3, lwd = 1.5)\ntext(rep(-5.8, 2), 2 * c(0.21, 0.15),\n     c(\"Distribution with\",\n       expression(mu[trmt] - mu[ctrl]*\" = -3\")),\n     col = COL[2])\ndev.off()\n\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/power_null_0_1-7/power_null_0_1-7.R",
    "content": "library(openintro)\ndata(COL)\n\nBuildNull <- function(xlim = c(-10, 10)) {\n  normTail(0, 1.70, L = -1000, U = 1000,\n           df = 50, lwd = 2.5, axes = FALSE,\n           curveColor = COL[1],\n           xlim = xlim)\n  axis(1, at = seq(-15, 15, 3))\n  mtext(expression(bar(x)[trmt] - bar(x)[ctrl]),\n        side = 1, line = 1.8)\n  text(1.2, 0.2, \"Null distribution\", col = COL[1], pos = 4)\n  lines(rep(0, 2), c(0, dnorm(0, 0, 1.70)),\n        col = COL[1,4], lwd = 0.5)\n}\n\n# _____ Null Distribution Only _____ #\nmyPDF('power_null_A_0_1-7.pdf',\n      7, 1.9,\n      mar = c(2.8, 0, 0, 0),\n      mgp = c(0, 0.45, 0))\nBuildNull()\ndev.off()\n\n\n\n# _____ Null Distribution + Rejection Regions _____ #\nmyPDF('power_null_B_0_1-7_with_rejection_region.pdf',\n      7, 1.9,\n      mar = c(2.8, 0, 0, 0),\n      mgp = c(0, 0.45, 0))\nBuildNull()\nsegments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),\n         col = COL[4], lty = 3, lwd = 3)\nsegments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),\n         col = COL[4], lty = 3, lwd = 1.5)\ntext(c(-6, 0, 0, 6), c(0.07, 0.09, 0.05, 0.07),\n     c(expression(\"Reject \" * H[0]),\n       \"Do not\",\n       expression(\"reject \" * H[0]),\n       expression(\"Reject \" * H[0])),\n     col = COL[4])\ndev.off()\n\n\n\n# _____ Null Distribution + Alternative At -3 _____ #\nmyPDF('power_null_C_0_1-7_with_alt_at_3.pdf',\n      7, 1.9,\n      mar = c(2.8, 0, 0, 0),\n      mgp = c(0, 0.45, 0))\nBuildNull(xlim = c(-8.8, 10))\nnormTail(-3, 1.70, L = -1000, U = 1000,\n         df = 50, lwd = 2.5, add = TRUE,\n         curveColor = COL[2])\nlines(rep(-3, 2), c(0, dnorm(0, 0, 1.70)),\n      col = COL[2,4], lwd = 0.5)\nsegments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),\n         col = COL[4], lty = 3, lwd = 3)\nsegments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),\n         col = COL[4], lty = 3, lwd = 1.5)\ntext(rep(-6.5, 2), c(0.21, 0.175),\n     c(\"Distribution with\",\n       expression(mu[trmt] - mu[ctrl]*\" = -3\")),\n     col = COL[2])\ndev.off()\n\n\n\n# _____ Null Distribution + Alternative At -3 + Shaded _____ #\nmyPDF('power_null_D_0_1-7_with_alt_at_3_and_shaded.pdf',\n      7, 1.9,\n      mar = c(2.8, 0, 0, 0),\n      mgp = c(0, 0.45, 0))\nBuildNull()\nnormTail(-3, 1.70, L = -3.332, U = 1000,\n         df = 50, lwd = 2.5, add = TRUE,\n         curveColor = COL[2],\n         border = COL[2],\n         col = COL[2,3])\nlines(rep(-3, 2), c(0, dnorm(0, 0, 1.70)),\n      col = COL[2,4], lwd = 0.5)\nsegments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),\n         col = COL[4], lty = 3, lwd = 3)\nsegments(3.3 * c(-1, 1), rep(0, 2), y1 = rep(0.15, 2),\n         col = COL[4], lty = 3, lwd = 1.5)\ntext(rep(-6.5, 2), c(0.21, 0.175),\n     c(\"Distribution with\",\n       expression(mu[trmt] - mu[ctrl]*\" = -3\")),\n     col = COL[2])\ndev.off()\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/rissosDolphin/ReadMe.txt",
    "content": "\nPhoto by Mike Baird (http://www.bairdphotos.com/). Image was licensed under Creative Commons Attribution 2.0 Generic.\n"
  },
  {
    "path": "ch_inference_for_means/figures/run10SampTimeHistogram/run10SampTimeHistogram.R",
    "content": "library(openintro)\ndata(COL)\n\n\ndata(run10Samp)\nd <- run10Samp\n\nmyPDF(\"run10SampTimeHistogram.pdf\", 5, 2.8,\n      mar = c(3.5, 3.5, 0.5, 1),\n      mgp = c(2.2, 0.55, 0))\nhistPlot(d$time,\n         main = \"\",\n         xlab = \"Time (Minutes)\",\n         ylab = \"Frequency\",\n         col = COL[1])\ndev.off()\n\n\nset.seed(1)\nrun17 <- subset(run17, event == \"10 Mile\")\nmean(run17$net_sec / 60)\nd <- run17[sample(nrow(run17), 100), ]\nmyPDF(\"run17SampTimeHistogram.pdf\", 5, 2.8,\n      mar = c(3.5, 3.5, 0.5, 1),\n      mgp = c(2.2, 0.55, 0))\nhistPlot(d$net_sec / 60,\n         main = \"\",\n         xlab = \"Time (Minutes)\",\n         ylab = \"Frequency\",\n         col = COL[1])\ndev.off()\nt.test(d$net_sec / 60, mu = 93.29)\nmean(d$net_sec / 60)\nsd(d$net_sec / 60)\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/satImprovementHTDataHistogram/satImprovementHTDataHistogram.R",
    "content": "library(openintro)\ndata(COL)\n\nset.seed(2)\nx <- round(rnorm(30, 120, 70))\nt.test(x - 100)\nmean(x)\nsd(x)\n\nmyPDF('satImprovementHTDataHistogram.pdf', 3.9, 2.2,\n      mar = c(1.6, 2, 0.2, 1),\n      mgp = c(0, 0.45, 0))\nhistPlot(x,\n         xlab = '',\n         ylab = '',\n         main = '',\n         axes = FALSE,\n         col = COL[1])\naxis(1)\naxis(2, at = seq(0, 10, 5))\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/stemCellTherapyForHearts/stemCellTherapyForHearts.R",
    "content": "library(openintro)\ndata(COL)\ndata(stem.cells)\nd <- stem.cells\n\nchange <- d$after - d$before\nt.test(change ~ d[,1])\n\nmyPDF('stemCellTherapyForHearts.pdf', 4.8, 4.2,\n      mar=c(3.2, 1.8, 1.7, 0.7),\n      mgp=c(2, 0.3, 0),\n      mfrow=c(2, 1))\nhistPlot(change[d[,1] == 'esc'],\n         xlim=c(-10, 15),\n         axes=FALSE,\n         xlab='',\n         main='',\n         breaks=seq(-10, 15, 2.5),\n         col=COL[1])\nx.axis.at <- seq(-10, 15, 5)\nx.axis.labels <- paste0(seq(-10, 15, 5), \"%\")\ncex.axis <- 0.85\naxis(1, x.axis.at, x.axis.labels, cex.axis=cex.axis)\nmtext('Embryonic stem cell transplant', line=0.5, cex=1.1)\nmtext('Change in heart pumping function', 1, line=1.3, cex = 0.9)\npar(mgp=c(2, 0.6, 0))\naxis(2, at=0:3, cex.axis=0.925)\n\npar(mar=c(2.4, 1.8, 2, 0.7),\n    mgp=c(2, 0.3, 0))\nhistPlot(change[d[,1] == 'ctrl'],\n         xlim=c(-10, 15),\n         axes=FALSE,\n         xlab='',\n         main='',\n         breaks=seq(-10, 15, 2.5),\n         col=COL[1])\naxis(1, x.axis.at, x.axis.labels, cex.axis=cex.axis)\npar(mgp=c(2, 0.6, 0))\naxis(2, at=0:3, cex.axis=0.925)\nmtext('Control (no treatment)', line=0.5, cex=1.1)\nmtext('Change in heart pumping function', 1, line=1.3, cex = 0.9)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/stemCellTherapyForHeartsPValue/stemCellTherapyForHeartsPValue.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('stemCellTherapyForHeartsPValue.pdf', 3.9, 2.3,\n      mar = c(1.75, 1, 1, 1),\n      mgp = c(2, 0.6, 0))\n\nnormTail(U = 4.03,\n         xlim = c(-3, 5.2),\n         df = 3,\n         lwd = 1.5,\n         border = COL[4],\n         col = COL[4],\n         axes = FALSE)\ntext(7.5 - 4, 0.23, \"Area representing\\np-value\", col = COL[4])\narrows(7.5 - 4, 0.17, 4.3, 0.02, length = 0.1, col = COL[4])\naxis(1, at = seq(-8,12,2)) #, rep(\"\", 11), tcl = -0.2)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/tDistAppendixTwoEx/tDistAppendixTwoEx.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('tDistAppendixTwoEx.pdf', 6.8, 1.9,\n      mar = c(1.6, 1, 0.05, 1),\n      mgp = c(5, 0.45, 0),\n      mfrow = c(1, 2))\nnormTail(U = 1.65,\n         df = 12,\n         xlim = c(-4, 4),\n         col = COL[1],\n         axes = FALSE)\naxis(1)\nnormTail(L = -2,\n         U = 2,\n         df = 475,\n         xlim = c(-4.5, 4.5),\n         col = COL[1],\n         axes = FALSE)\naxis(1)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/tDistCompareToNormalDist/tDistCompareToNormalDist.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('tDistCompareToNormalDist.pdf', 5, 2.3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(5, 0.6, 0))\nplot(c(-5, 5),\n     c(0, dnorm(0)),\n     type = 'n',\n     axes = FALSE)\naxis(1, seq(-6, 6, 2))\nabline(h = 0)\n\nxleg <- 2\nyleg <- 0.35\nyleg.line.offset <- -0.07\nline.leg.width <- 0.55\nlines(\n    c(xleg, xleg + line.leg.width),\n    rep(yleg, 2),\n    col = COL[4], lty = 3, lwd = 2.5)\nlines(\n    c(xleg, xleg + line.leg.width),\n    rep(yleg + yleg.line.offset, 2),\n    col = COL[1], lty = 1, lwd = 1.8)\ntext(xleg + line.leg.width, yleg,\n    \"Normal\",\n    col = COL[4], pos = 4)\ntext(xleg + line.leg.width, yleg + yleg.line.offset,\n    \"t-distribution\",\n    col = COL[1], pos = 4)\n\nX <- seq(-6, 6, 0.01)\nY <- dnorm(X)\nlines(X, Y, lty = 3, lwd = 2.5, col = COL[4])\n\nY <- dt(X, 2)\nlines(X, Y, lwd = 1.8, col = COL[1])\n\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/tDistConvergeToNormalDist/tDistConvergeToNormalDist.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('tDistConvergeToNormalDist.pdf', 5.94, 2.53,\n      mar = c(2, 1, 1, 1),\n      mgp = c(5, 0.6, 0))\nplot(c(-5, 5),\n     c(0, dnorm(0)),\n     type = 'n',\n     axes = FALSE)\nat <- seq(-10, 10, 2)\naxis(1, at)\naxis(1, at - 1, rep(\"\", length(at)), tcl = -0.1)\nabline(h = 0)\n\nCOL. <- fadeColor(COL[1], c(\"FF\", \"89\", \"68\", \"4C\", \"33\"))\nCOLt <- fadeColor(COL[1], c(\"FF\", \"AA\", \"85\", \"60\", \"45\"))\nDF   <- c('normal', 8, 4, 2, 1)\n\nX <- seq(-10, 10, 0.02)\nY <- dnorm(X)\nlines(X, Y, col = COL.[1])\n\nfor (i in 2:5) {\n  Y <- dt(X, as.numeric(DF[i]))\n  lines(X, Y, col = COL.[i], lwd = 1.5)\n}\n\nlegend(2.5, 0.4,\n       legend = c(DF[1],\n       paste('t, df = ', DF[2:5], sep = '')),\n       col = COL.,\n       text.col = COLt,\n       lty = rep(1, 5),\n       lwd = 1.5)\n\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/tDistDF18LeftTail2Point10/tDistDF18LeftTail2Point10.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('tDistDF18LeftTail2Point10.pdf', 4, 1.8,\n      mar = c(1.6, 1, 0.1, 1),\n      mgp = c(5, 0.45, 0))\nnormTail(L = -2.10,\n         df = 10,\n         xlim = c(-4, 4),\n         col = COL[1],\n         axes = FALSE)\naxis(1)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/tDistDF20RightTail1Point65/tDistDF20RightTail1Point65.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('tDistDF20RightTail1Point65.pdf', 6.8, 1.9,\n      mar = c(1.6, 1, 0.05, 1),\n      mgp = c(5, 0.45, 0),\n      mfrow = c(1, 2))\nnormTail(U = 1.65,\n         df = 12,\n         xlim = c(-4, 4),\n         col = COL[1],\n         axes = FALSE)\naxis(1)\nnormTail(L = -3,\n         U = 3,\n         df = 2.3,\n         xlim = c(-4.5, 4.5),\n         col = COL[1],\n         axes = FALSE)\naxis(1)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/textbooksF18/diffInTextbookPricesF18.R",
    "content": "library(openintro)\ndata(textbooks)\ndata(COL)\n\nd <- as.numeric(na.omit(ucla_textbooks_f18$bookstore_new -\n    ucla_textbooks_f18$amazon_new))\n\nmyPDF('diffInTextbookPricesF18.pdf', 5, 2.5,\n      mar = c(3, 3.5, 0.5, 0.5),\n      mgp = c(1.8, 0.5, 0))\nhistPlot(d, axes = FALSE, # breaks = 20,\n         xlab = \"UCLA Bookstore Price - Amazon Price (USD)\",\n         ylab = \"\",\n         col = COL[1])\nAxisInDollars(1, at = pretty(d), tck = -0.03)\naxis(2, at = seq(0, 30, 10), tck = -0.02)\n# axis(2, at = seq(0, 15, 5), tck = -0.02)\npar(las = 0)\nmtext(\"Frequency\", 2, 2.4)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/textbooksF18/textbooksF18HTTails.R",
    "content": "library(openintro)\ndata(textbooks)\ndata(COL)\nd <- as.numeric(na.omit(ucla_textbooks_f18$bookstore_new -\n    ucla_textbooks_f18$amazon_new))\n(m <- mean(d))\n(s <- sd(d))\n(se <- s / sqrt(length(d)))\n(z <- m / se)\n\nmyPDF('textbooksF18HTTails.pdf', 4, 1.3,\n      mar = c(1.7, 0, 0, 0),\n      mgp = c(3, 0.5, 0))\nnormTail(L = -abs(m),\n         U = abs(m),\n         s = se,\n         df = 20,\n         # xlim = 5 * c(-1, 1),\n         col = COL[1],\n         # border = COL[4],\n         axes = FALSE)\nat <- c(-100, 0, m, 100)\nlabels <- expression(0, mu[0]*' = 0', bar(x)[diff]*\" = 3.58\", 0)\naxis(1, at, labels, cex.axis = 0.9)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/textbooksS10/diffInTextbookPricesS10.R",
    "content": "library(openintro)\ndata(textbooks)\ndata(COL)\n\nd <- textbooks\n\nmyPDF('diffInTextbookPricesS10.pdf', 6, 3,\n      mar = c(3, 3.2, 0.5, 0.5),\n      mgp = c(1.8, 0.5, 0))\nhistPlot(d$diff, axes = FALSE, # breaks = 20,\n         xlim = c(-20, 80),\n         xlab = \"UCLA price - Amazon price (USD)\",\n         ylab = \"\",\n         col = COL[1])\nmtext(\"Frequency\", 2, 2.1, las = 0)\naxis(1, tck = -0.03)\naxis(2, at = seq(0, 30, 10), tck = -0.02)\n# axis(2, at = seq(0, 15, 5), tck = -0.02)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/textbooksS10/textbooksS10HTTails.R",
    "content": "library(openintro)\ndata(textbooks)\ndata(COL)\nd <- textbooks\n\nmyPDF('textbooksS10HTTails.pdf', 5, 1.6,\n      mar = c(1.7, 0, 0, 0),\n      mgp = c(3, 0.5, 0))\nnormTail(L = -6.5,\n         U = 6.5,\n         df = 20,\n         xlim = c(-8, 8),\n         col = COL[4],\n         border = COL[4],\n         axes = FALSE)\nat <- c(-10, 0, 6.5, 10)\nlabels <- expression(0, mu[0]*' = 0', bar(x)[diff]*\" = 12.76\", 0)\naxis(1, at, labels, cex.axis = 0.9)\nsegments(c(-9, 9), rep(0, 2),\n         c(-6.5, 6.5), rep(0, 2),\n         col = COL[4, 2], lwd = 4)\narrows(c(-7, 7), rep(0.1, 2),\n       c(-7, 7), rep(0.015, 2),\n       length = 0.08,\n       col = COL[4])\ntext(c(-7, 7), rep(0.1, 2),\n     c(\"left tail\", \"right tail\"),\n     pos = 3,\n     col = COL[4])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_means/figures/textbooks_scatter/textbooks_scatter.R",
    "content": "library(openintro)\nlibrary(xtable)\nlibrary(dplyr)\n\nd <- select(ucla_textbooks_f18,\n    subject, course_num, bookstore_new, amazon_new)\nd$price_diff <- d$bookstore_new - d$amazon_new\nd <- subset(d, !is.na(bookstore_new) & !is.na(amazon_new))\nrownames(d) <- NULL\n\nmyPDF('textbooks_scatter.pdf', 6, 4,\n      mar = c(3.7, 4.1, 0.5, 0.5),\n      mgp = c(2.6, 0.55, 0))\nplot(d$bookstore_new, d$amazon_new,\n     pch = 19,\n     col = COL[1, 2],\n     cex = 1.2,\n     xlab = 'UCLA Bookstore Price',\n     ylab = '',\n     axes = FALSE)\nm <- lm(amazon_new ~ bookstore_new, d)\nabline(m)\nAxisInDollars(1, seq(0, 300, 50))\nAxisInDollars(2, seq(0, 300, 50))\npar(las = 0)\nmtext(\"Amazon Bookstore Price\", 2, line = 3)\ndev.off()\n\n\nm <- lm(amazon_new ~ bookstore_new, d)\nmyPDF('textbooks_scatter_residuals.pdf', 6, 4,\n      mar = c(3.7, 4.1, 0.5, 0.5),\n      mgp = c(2.6, 0.55, 0))\nplot(d$bookstore_new, m$residuals,\n     pch = 19,\n     col = COL[1, 2],\n     cex = 1.2,\n     xlab = 'UCLA Bookstore Price',\n     ylab = '',\n     axes = FALSE,\n     ylim = range(m$residuals) + c(-10, 20))\nAxisInDollars(1, seq(0, 300, 50))\nAxisInDollars(2, seq(-100, 100, 20))\npar(las = 0)\nmtext(\"Residuals\", 2, line = 3)\ndev.off()\n\n\nxtable(m)\n\n"
  },
  {
    "path": "ch_inference_for_means/figures/toyANOVA/toyANOVA.R",
    "content": "library(xtable)\nlibrary(openintro)\n\nby(toy_anova$outcome, toy_anova$group, mean)\n\n\nmyPDF(\"toyANOVA.pdf\",\n      mar = c(1.7, 3.1, 0.5, 0.5),\n      mgp = c(2, 0.5, 0))\nplot(toy_anova$outcome,\n     xlim = c(0.5, 6.5),\n     type = \"n\",\n     axes = FALSE,\n     xlab = \"\",\n     ylab = \"Outcome\")\nrect(-100, -100,\n     100, 100,\n     col = COL[7,3])\nabline(h = seq(-10, 10, 2), col = \"#FFFFFF\", lwd = 3)\nabline(h = seq(-10, 10, 1), col = \"#FFFFFF\", lwd = 0.8)\nthese <- toy_anova$group %in% c(\"I\", \"II\", \"III\")\ndotPlot(toy_anova$outcome[these], toy_anova$group[these],\n        vertical = TRUE,\n        at = 1:3,\n        add = TRUE,\n        col = COL[1, 3],\n        cex = 0.9, pch = 19)\ndotPlot(toy_anova$outcome[!these], toy_anova$group[!these],\n        vertical = TRUE,\n        at = 1:3 + 3,\n        add = TRUE,\n        col = COL[4, 3],\n        cex = 0.9,\n        pch = 19)\nabline(v = 3.5, col = COL[7], lwd = 5.5)\nabline(v = 3.5, col = \"#AAAAAA\", lwd = 3)\nabline(v = 3.5, col = \"#333333\", lwd = 0.8)\naxis(2)\npar(mgp = c(2, 0.45, 0.1))\naxis(1, at = 1:3, c(\"I\", \"II\", \"III\"))\naxis(1, at = 4:6, c(\"IV\", \"V\", \"VI\"))\nbox()\ndev.off()\n\nxtable(anova(lm(outcome ~ group, toy_anova[these, ])))\nxtable(anova(lm(outcome ~ group, toy_anova[!these, ])))\n"
  },
  {
    "path": "ch_inference_for_props/TeX/ch_inference_for_props.tex",
    "content": "\\begin{chapterpage}{Inference for categorical data}\n  \\chaptertitle{Inference for categorical data}\n  \\label{inferenceForCategoricalData}\n  \\label{ch_inference_for_props}\n  \\chaptersection{singleProportion}\n  \\chaptersection{differenceOfTwoProportions}\n  \\chaptersection{oneWayChiSquare}\n  \\chaptersection{twoWayTablesAndChiSquare}\n\\end{chapterpage}\n\\renewcommand{\\chapterfolder}{ch_inference_for_props}\n\n\\chapterintro{In this chapter,\n  we apply the methods and ideas from\n  Chapter~\\ref{ch_foundations_for_inf}\n  in several contexts for categorical data.\n  We'll start by revisiting what we learned for a single\n  proportion, where the normal distribution can be used\n  to model the uncertainty in the sample proportion.\n  Next, we apply these same ideas to analyze the difference\n  of two proportions using the normal model.\n  Later in the chapter, we apply inference techniques\n  to contingency tables;\n  while we will use a different\n  distribution in this context, the core\n  ideas of hypothesis testing remain the same.}\n\n\n%__________________\n\\section{Inference for a single proportion}\n\\label{singleProportion}\n\nWe encountered inference methods for a single proportion\nin Chapter~\\ref{ch_foundations_for_inf},\nexploring point estimates, confidence intervals,\nand hypothesis tests.\nIn this section, we'll do a review of these topics\nand also how to choose an appropriate sample size\nwhen collecting data for single proportion contexts.\n\n\n\\subsection{Identifying when the sample proportion is nearly normal}\n\nA sample proportion $\\hat{p}$ can be modeled using\na normal distribution when the sample observations\nare independent and the sample size is sufficiently\nlarge.\n\n\n%A sample proportion can be described as a sample mean. If we represent each ``success'' as a 1 and each ``failure'' as a 0, then the sample proportion is the mean of these numerical outcomes:\n%\\begin{eqnarray*}\n%\\hat{p} = \\frac{\\ 0 + 1 + 1 + \\cdots + 0\\ }{1042} = 0.82\n%\\end{eqnarray*}\n%The distribution of $\\hat{p}$ is nearly normal when the distribution of 0's and 1's is not too strongly skewed for the sample size. The most common guideline for sample size and skew when working with proportions is to ensure that we expect to observe a minimum number of successes (1's) and failures (0's), typically at least 10 of each. The labels \\term{success} and \\term{failure} need not mean something positive or negative. These terms are just convenient words that are frequently used when discussing proportions.\n\n\\begin{onebox}{Sampling distribution of\n    $\\pmb{\\hat{\\MakeLowercase{p}}}$}\n  The sampling distribution for $\\hat{p}$ based on\n  a sample of size $n$ from a population with a true\n  proportion $p$ is nearly normal when:\n  \\begin{enumerate}\n  \\setlength{\\itemsep}{0mm}\n  \\item The sample's observations are independent,\n      e.g. are from a simple random sample.\n  \\item We expected to see at least 10 successes and\n      10 failures in the sample, i.e. $np\\geq10$ and\n      $n(1-p)\\geq10$.\n      This is called the \\term{success-failure condition}.\n  \\end{enumerate}\n  When these conditions are met, then the sampling\n  distribution of $\\hat{p}$ is nearly normal with mean\n  $p$ and standard error\n  \\index{standard error (SE)!single proportion}%\n  $SE = \\sqrt{\\frac{\\ p(1-p)\\ }{n}}$.\n\\end{onebox}\n\nTypically we don't know the true proportion $p$,\nso we substitute some value to check conditions\nand estimate the standard error.\nFor confidence intervals, the sample proportion\n$\\hat{p}$ is used to check the success-failure condition\nand compute the standard error.\nFor hypothesis tests, typically the null value --\nthat is, the proportion claimed in the null hypothesis --\nis used in place of $p$.\n\n\n\\subsection{Confidence intervals for a proportion}\n\\label{confIntForPropSection}\n\n\\index{point estimate!single proportion}\n\nA confidence interval provides a range of\nplausible values for the parameter $p$,\nand when $\\hat{p}$ can be modeled using a\nnormal distribution, the confidence interval\nfor $p$ takes the form\n\\begin{align*}\n\\hat{p} \\pm z^{\\star} \\times SE\n\\end{align*}\n\n\\index{data!Payday regulation poll|(}\n\n\\newcommand{\\paydayN}{826}\n\\newcommand{\\paydayNHalf}{413}\n\\newcommand{\\paydayRegPerc}{70\\%}\n\\newcommand{\\paydayRegProp}{0.70}\n\\newcommand{\\paydayRegSE}{0.016}\n\\newcommand{\\paydayRegSEPerc}{1.6\\%}\n\\newcommand{\\paydayRegLower}{0.669}\n\\newcommand{\\paydayRegUpper}{0.731}\n\\newcommand{\\paydayRegLowerPerc}{66.9\\%}\n\\newcommand{\\paydayRegUpperPerc}{73.1\\%}\n% https://www.pewtrusts.org/-/media/assets/2017/04/payday-loan-customers-want-more-protections-methodology.pdf\n\n\\begin{examplewrap}\n\\begin{nexample}{A simple random sample of \\paydayN{}\n    payday loan borrowers was surveyed to better\n    understand their interests around regulation and costs.\n    \\paydayRegPerc{} of the responses supported new\n    regulations on payday lenders.\n    Is it reasonable to model $\\hat{p} = \\paydayRegProp{}$\n    using a normal distribution?}\n  The data are a random sample, so the observations are\n  independent and representative of the population of\n  interest.\n\n  We also must check the success-failure condition,\n  which we do using $\\hat{p}$ in place\n  of $p$ when computing a confidence interval:\n  \\begin{align*}\n  \\text{Support: }\n      n p &\n          \\approx \\paydayN{} \\times \\paydayRegProp{}\n      = 578\n  &\\text{Not: }\n      n (1 - p) &\n        \\approx \\paydayN{} \\times (1 - \\paydayRegProp{})\n      = 248\n  \\end{align*}\n  Since both values are at least 10, we can use the normal\n  distribution to model $\\hat{p}$.\n\\end{nexample}\n\\end{examplewrap}\n\n\n\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{seOfPropOfPDBorrowersSupportReg}\nEstimate the standard error of $\\hat{p} = \\paydayRegProp{}$.\nBecause $p$ is unknown and the standard error is for\na confidence interval, use $\\hat{p}$ in place of $p$\nin the formula.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$SE = \\sqrt{\\frac{p(1-p)}{n}} \\approx\n    \\sqrt{\\frac{\\paydayRegProp{} (1 - \\paydayRegProp{})}\n        {\\paydayN{}}} = \\paydayRegSE{}$.}\n\n\\begin{examplewrap}\n\\begin{nexample}{Construct a 95\\% confidence interval for $p$,\n    the proportion of payday borrowers who support increased\n    regulation for payday lenders.}\n  Using\n  the point estimate \\paydayRegProp{},\n  $z^{\\star} = 1.96$ for a 95\\% confidence interval,\n  and\n  the standard error $SE = \\paydayRegSE{}$ from\n  Guided Practice~\\ref{seOfPropOfPDBorrowersSupportReg},\n  the confidence interval is\n  \\begin{eqnarray*}\n  \\text{point estimate} \\ \\pm\\ z^{\\star} \\times SE\n      \\quad\\to\\quad\n      \\paydayRegProp{} \\ \\pm\\ 1.96 \\times \\paydayRegSE{}\n      \\quad\\to\\quad\n      (\\paydayRegLower{}, \\paydayRegUpper{})\n  \\end{eqnarray*}\n  We are 95\\% confident that the true proportion of\n  payday borrowers who supported regulation at the time\n  of the poll was between \\paydayRegLower{} and\n  \\paydayRegUpper{}.\n\\end{nexample}\n\\end{examplewrap}\n\n\\onepropconfintsummary{}\n%\\begin{onebox}{Constructing a confidence interval for a proportion}\n%  There are three steps to constructing a confidence\n%  interval for $p$.\n%  \\begin{itemize}\n%  \\setlength{\\itemsep}{0mm}\n%  \\item Check independence and the success-failure condition\n%      using $\\hat{p}$.\n%      If the conditions are met, the sampling distribution\n%      of $\\hat{p}$ may be well-approximated by the normal model.\n%  \\item Construct the standard error using $\\hat{p}$\n%      in place of $p$ in the standard error formula.\n%  \\item Apply the general confidence interval formula.\n%  \\end{itemize}\n%\\end{onebox}\n\n\\noindent%\nFor additional one-proportion confidence interval examples,\nsee Section~\\ref{confidenceIntervals}.\n\n\n\\subsection{Hypothesis testing for a proportion}\n\\label{htForPropSection}\n\n\\newcommand{\\paydayCCPerc}{51\\%}\n\\newcommand{\\paydayCCProp}{0.51}\n\\newcommand{\\paydayCCSE}{0.017}\n\\newcommand{\\paydayCCSEPerc}{1.7\\%}\n\\newcommand{\\paydayCCZ}{0.59}\n\\newcommand{\\paydayCCOneTail}{0.2776}\n\\newcommand{\\paydayCCPvalue}{0.5552}\n\nOne possible regulation for payday lenders is that they\nwould be required to do a credit check and evaluate debt\npayments against the borrower's finances.\nWe would like to know: would borrowers support this form\nof regulation?\n\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{paydayCC_hypotheses_gp}%\nSet up hypotheses to evaluate whether borrowers\nhave a majority support or majority opposition for this\ntype of regulation.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$H_0$: $p = 0.50$. $H_A$: $p \\neq 0.50$.}\n\nTo apply the normal distribution framework in the context\nof a hypothesis test for a proportion, the independence\nand success-failure conditions must be satisfied.\nIn a hypothesis test, the success-failure condition is\nchecked using the null proportion:\nwe verify $np_0$ and $n(1-p_0)$ are at least 10,\nwhere $p_0$ is the null value.\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{paydayCC_conditions_gp}%\nDo payday loan borrowers support a regulation\nthat would\nrequire lenders to pull their credit report\nand evaluate their debt payments?\nFrom a random sample of \\paydayN{} borrowers,\n\\paydayCCPerc{} said they would support such\na regulation.\nIs it reasonable to model $\\hat{p} = \\paydayCCProp{}$\nusing a normal distribution\nfor a hypothesis test here?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Independence holds since the poll\n    is based on a random sample.\n    The success-failure condition also holds,\n    which is checked\n    using the null value ($p_0 = 0.5$) from $H_0$:\n    $np_0 = \\paydayN{} \\times 0.5 = \\paydayNHalf$,\n    $n(1 - p_0) = \\paydayN{} \\times 0.5 = \\paydayNHalf$.}\n    \n\\begin{examplewrap}\n\\begin{nexample}{Using the hypotheses and data from\n    Guided Practice~\\ref{paydayCC_hypotheses_gp}\n    and~\\ref{paydayCC_conditions_gp},\n    evaluate whether the poll provides convincing evidence\n    that a majority of payday loan borrowers support\n    a new regulation that would\n    require lenders to pull credit reports\n    and evaluate debt payments.}\n  With hypotheses already set up and conditions checked,\n  we can move onto calculations.\n  The standard error in the context of a one-proportion\n  hypothesis test is computed using the null value, $p_0$:\n  \\begin{align*}\n  SE = \\sqrt{\\frac{p_0 (1 - p_0)}{n}}\n      = \\sqrt{\\frac{0.5 (1 - 0.5)}{\\paydayN{}}}\n      = \\paydayCCSE{}\n  \\end{align*}\n  A picture of the normal model is shown below\n  with the p-value represented by the shaded region.\n  \\begin{center}\n  \\Figure[A normal distribution is shown with a center of 0.5 and a standard deviation of 0.017. Two tails are shaded: The region above 0.51 and a region in the corresponding lower tail. Visually, it looks like a little over half of the area under the normal curve is shaded.]{0.5}{paydayCC_norm_pvalue}\n  \\end{center}\n  Based on the normal model, the test statistic can be\n  computed as the Z-score of the point estimate:\n  \\begin{align*}\n  Z = \\frac{\\text{point estimate} - \\text{null value}}{SE}\n      = \\frac{\\paydayCCProp{} - 0.50}{\\paydayCCSE{}}\n      = \\paydayCCZ{}\n  \\end{align*}\n  The single tail area is \\paydayCCOneTail{}, and the p-value,\n  represented by both tail areas together, is \\paydayCCPvalue{}.\n  Because the p-value is larger than 0.05,\n  we do not reject $H_0$.\n  The poll does not provide convincing evidence that\n  a majority of payday loan borrowers support or oppose\n  regulations around credit checks and evaluation of\n  debt payments.\n\\end{nexample}\n\\end{examplewrap}\n\n\\oneprophtsummary{}\n\n%\\begin{onebox}{Hypothesis test for a proportion}\n%Set up hypotheses and verify the conditions using the null value, $p_0$, to ensure $\\hat{p}$ is nearly normal under $H_0$. If the conditions hold, construct the standard error, again using $p_0$, and show the p-value in a drawing. Lastly, compute the p-value and evaluate the hypotheses.\n%\\end{onebox}\n\n\\noindent%\nFor additional one-proportion hypothesis test examples,\nsee Section~\\ref{hypothesisTesting}.\n\n\\index{data!Payday regulation poll|)}\n\n\\CalculatorVideos{confidence intervals and hypothesis tests for a single proportion}\n\n\n\\D{\\newpage}\n\n\\subsection{When one or more conditions aren't met}\n\nWe've spent a lot of time discussing conditions for when\n$\\hat{p}$ can be reasonably modeled by a normal distribution.\nWhat happens when the success-failure condition fails?\nWhat about when the independence condition fails?\nIn either case, the general ideas of confidence intervals\nand hypothesis tests remain the same, but the strategy\nor technique used to generate the interval or p-value\nchange.\n\nWhen the success-failure condition isn't met\nfor a hypothesis test, we can simulate the null distribution\nof $\\hat{p}$ using the null value, $p_0$.\nThe simulation concept is similar to the ideas used\nin the malaria case study presented in\nSection~\\ref{caseStudyMalariaVaccine},\nand an online section outlines this strategy:\n\\begin{center}\n\\oiRedirect{stat_sim_prop_ht}\n    {www.openintro.org/r?go=stat\\_sim\\_prop\\_ht}\n\\end{center}\nFor a confidence interval when the success-failure condition\nisn't met, we can use what's called\nthe \\term{Clopper-Pearson interval}.\nThe details are beyond the scope of this book.\nHowever, there are many internet resources covering\nthis topic.\n\nThe independence condition is a more nuanced requirement.\nWhen it isn't met, it is important to understand how and why\nit isn't met.\nFor example, if we took a cluster sample\n(see Section~\\ref{section_obs_data_sampling}),\nsuitable statistical methods are available but would\nbe beyond the scope of even most second or third courses\nin statistics.\nOn the other hand, we'd be stretched to find any method\nthat we could confidently apply to correct the inherent biases\nof data from a convenience sample.\n\nWhile this book is scoped to well-constrained statistical\nproblems, do remember that this is just the first\nbook in what is a large library of statistical methods that\nare suitable for a very wide range of data and contexts.\n\n\n\\D{\\newpage}\n\n\\subsection{Choosing a sample size when estimating a proportion}\n\n\\index{margin of error|(}\n\nWhen collecting data, we choose a sample size suitable\nfor the purpose of the study.\nOften times this means choosing a sample size large\nenough that the \\term{margin of error} --\nwhich is the part we add and subtract from the point\nestimate in a confidence interval --\nis sufficiently small that the sample is useful.\nFor example, our task might be to find a sample size\n$n$ so that the sample proportion is within $\\pm 0.04$\nof the actual proportion in a 95\\% confidence interval.\n\n% For example, the margin of error for a point estimate using 95\\% confidence can be written as $1.96\\times SE$. We set up a general equation to represent the problem:\n%\\begin{align*}\n%ME = z^{\\star} \\times SE \\leq m\n%\\end{align*}\n%where $ME$ represented the actual margin of error and $z^{\\star}$ was chosen to correspond to the confidence level. The standard error formula is specified to correspond to the particular setting. For instance, in the case of means, the standard error was given as $\\sigma / \\sqrt{n}$. In the case of a single proportion, we use $\\sqrt{p(1-p) / n\\ }$ for the standard error.\n\n\\index{data!Student football stadium|(}\n\n\\begin{examplewrap}\n\\begin{nexample}{A university newspaper is conducting\n    a survey to determine what fraction of students\n    support a \\$200 per year increase in fees to pay\n    for a new football stadium.\n    How big of a sample is required to ensure the\n    margin of error is smaller than 0.04 using a\n    95\\% confidence level?}\n  The margin of error for a sample proportion is\n  \\begin{align*}\n  z^{\\star} \\sqrt{\\frac{p (1 - p)}{n}}\n  \\end{align*}\n  Our goal is to find the smallest sample size $n$\n  so that this margin of error is smaller than $0.04$.\n  For a 95\\% confidence level, the value $z^{\\star}$\n  corresponds to 1.96:\n  \\begin{align*}\n  1.96\\times \\sqrt{\\frac{p(1-p)}{n}} \\ < \\ 0.04\n  \\end{align*}\n  There are two unknowns in the equation: $p$ and $n$.\n  If we have an estimate of $p$, perhaps from a prior\n  survey, we could enter in that value and solve for~$n$.\n  If we have no such estimate, we must use some other\n  value for~$p$.\n  It turns out that the margin of error is largest\n  when $p$ is 0.5, so we typically use this\n  \\emph{worst case value} if no estimate of the\n  proportion is available:\n  \\begin{align*}\n\t1.96\\times \\sqrt{\\frac{0.5(1-0.5)}{n}} &\\ < \\ 0.04 \\\\\n\t1.96^2\\times \\frac{0.5(1-0.5)}{n} &\\ < \\ 0.04^2 \\\\\n\t1.96^2\\times \\frac{0.5(1-0.5)}{0.04^2} &\\ < \\ n \\\\\n\t600.25 &\\ < \\  n\n  \\end{align*}\n  We would need over 600.25 participants, which means\n  we need 601 participants or more, to ensure the\n  sample proportion is within 0.04 of the true proportion\n  with 95\\% confidence.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!Student football stadium|)}\n\nWhen an estimate of the proportion is available, we use it in place of the worst case proportion value,~0.5.\n\n\\D{\\newpage}\n\n\\index{data!Tire failure rate|(}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{tire_failure_rate_3_samp_size_calc}%\nA manager is about to oversee the mass\nproduction of a new tire model in her factory,\nand she would like to estimate what proportion of\nthese tires will be rejected through quality control.\nThe quality control team has monitored the last three\ntire models produced by the factory,\nfailing 1.7\\% of tires in the first model,\n6.2\\% of the second model,\nand 1.3\\% of the third model.\nThe manager would like to examine enough tires\nto estimate the failure rate of the new tire model\nto within about 1\\% with a 90\\% confidence level.\nThere are three different failure rates to choose from.\nPerform the sample size computation for each separately,\nand identify three sample sizes to consider.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{For a 90\\% confidence interval, $z^{\\star} = 1.6449$,\n  and since an estimate of the proportion 0.017 is available,\n  we'll use it in the margin of error formula:\n  \\begin{align*}\n  1.6449\\times \\sqrt{\\frac{0.017(1-0.017)}{n}} &\\ < \\ 0.01\n    \\qquad\\to\\qquad\n      \\frac{0.017(1-0.017)}{n} \\ < \\ \n          \\left(\\frac{0.01}{1.6449}\\right)^2\n    \\qquad\\to\\qquad\n      452.15 \\ < \\ n\n  \\end{align*}\n  For sample size calculations, we always round up,\n  so the first tire model suggests 453 tires would\n  be sufficient.\n\n  A similar computation can be accomplished using 0.062\n  and 0.013 for $p$, and you should verify that using these\n  proportions results in minimum sample sizes of 1574\n  and~348 tires, respectively.}\n\n\\begin{examplewrap}\n\\begin{nexample}{The sample sizes vary widely in\n    Guided Practice~\\ref{tire_failure_rate_3_samp_size_calc}.\n    Which of the three would you suggest using?\n    What would influence your choice?}\n  We could examine which of the old models is most\n  like the new model, then choose the corresponding sample\n  size.\n  Or if two of the previous estimates are based on small\n  samples while the other is based on a larger sample,\n  we might consider the value corresponding to the larger\n  sample.\n  There are also other reasonable approaches.\n\n  Also observe that the success-failure\n  condition would need to be checked in the final sample.\n  For instance, if we sampled $n = 1584$ tires and found\n  a failure rate of 0.5\\%, the normal approximation would\n  not be reasonable, and we would require more advanced\n  statistical methods for creating the confidence interval.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!Tire failure rate|)}\n\\index{data!Payday regulation poll|(}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nSuppose we want to continually track the support\nof payday borrowers for regulation on lenders,\nwhere we would conduct a new poll every month.\nRunning such frequent polls is expensive, so we decide\na wider margin of error of 5\\% for each individual survey\nwould be acceptable.\nBased on the original sample of borrowers where\n\\paydayRegPerc{} supported some form of regulation,\nhow big should our monthly sample be for a margin\nof error of 0.05 with 95\\% confidence?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We complete the same computations as before,\n   except now we use $\\paydayRegProp{}$ instead of $0.5$\n   for $p$:\n   \\begin{align*}\n   1.96\\times \\sqrt{\\frac{p(1-p)}{n}}\n       \\approx 1.96\\times\n           \\sqrt{\\frac{\\paydayRegProp{}(1-\\paydayRegProp{})}\n               {n}}\n       &\\leq 0.05\n     \\qquad\\to\\qquad\n       n \\geq 322.7\n  \\end{align*}\n  A sample size of 323 or more would be reasonable.\n  (Reminder: always round up for sample size calculations!)\n  Given that we plan to track this poll over time,\n  we also may want to periodically repeat these calculations\n  to ensure that we're being thoughtful in our sample\n  size recommendations in case the baseline rate fluctuates.}\n\n\\index{data!Payday regulation poll|)}\n\\index{margin of error|)}\n\n{\\input{ch_inference_for_props/TeX/inference_for_a_single_proportion.tex}}\n\n\n\n\n\n%__________________\n\\section{Difference of two proportions}\n\\label{differenceOfTwoProportions}\n\nWe would like to extend the methods from\nSection~\\ref{singleProportion}\nto apply confidence intervals and hypothesis tests\nto differences in population proportions:\n\\mbox{$p_1 - p_2$}.\n%We~consider three examples.\n%In the first, we compare the utility of a blood thinner\n%for heart attack patients.\n%In the second application, we examine the efficacy of\n%mammograms in reducing deaths from breast cancer.\n%In the last example, a quadcopter company weighs whether\n%to switch to a higher quality manufacturer of rotor blades.\nIn our investigations, we'll identify a reasonable\npoint estimate of $p_1 - p_2$ based on the sample,\nand you may have already guessed its form:\n$\\hat{p}_1 - \\hat{p}_2$.\n\\index{point estimate!difference of proportions}%\nNext, we'll apply the same processes we used in\nthe single-proportion context:\nwe verify that the point estimate\ncan be modeled using a normal distribution,\nwe compute the estimate's standard error, and\nwe apply our inferential framework.\n\n\n\\subsection{Sampling distribution of the difference\n    of two proportions}\n\nLike with $\\hat{p}$, the difference of two sample\nproportions $\\hat{p}_1 - \\hat{p}_2$ can be modeled\nusing a normal distribution when certain conditions\nare met.\nFirst, we require a broader independence condition,\nand secondly,\nthe success-failure condition must be met by both groups.\n\n\\begin{onebox}{Conditions for the\n    sampling distribution of\n    $\\pmb{\\hat{\\MakeLowercase{p}}_1 -\n        \\hat{\\MakeLowercase{p}}_2}$\n    to be normal}\n  The difference $\\hat{p}_1 - \\hat{p}_2$ can be modeled\n  using a normal distribution when\n  \\begin{itemize}\n  \\setlength{\\itemsep}{0mm}\n  \\item \\emph{Independence, extended.}\n    The data are independent within and between\n    the two groups.\n    Generally this is satisfied if the data come\n    from two independent random samples\n    or if the data come from a randomized experiment.\n  \\item \\emph{Success-failure condition.}\n    The success-failure condition holds for both\n    groups, where we check successes and failures\n    in each group separately.\n  \\end{itemize}\n  When these conditions are satisfied,\n  the standard error of $\\hat{p}_1 - \\hat{p}_2$ is\n  \\index{standard error (SE)!difference in proportions}\n  \\begin{eqnarray*}\n  SE %_{\\hat{p}_1 - \\hat{p}_2}\n    %= \\sqrt{SE_{\\hat{p}_1}^2 + SE_{\\hat{p}_2}^2}\n    = \\sqrt{\\frac{p_1(1-p_1)}{n_1} + \\frac{p_2(1-p_2)}{n_2}}\n  \\label{seForDiffOfProp}\n  \\end{eqnarray*}\n  where $p_1$ and $p_2$ represent the population proportions,\n  and $n_1$ and $n_2$ represent the sample~sizes.\n\\end{onebox}\n\n%\\noindent%\n%Ultimately, we can check the two conditions by\n%thinking of it as a broader independence check\n%along with a check on the success-failure condition\n%for each group:\n%\\begin{description}\n%\\item[Independence, extended.]\n%    The data are independent within and between\n%    the two groups.\n%    Generally this is satisfied if the data come\n%    from two independent random samples\n%    or if the data come from a randomized experiment.\n%\\item[Success-failure condition.]\n%    The success-failure condition holds for both\n%    groups, where we check successes and failures\n%    in each group separately.\n%\\end{description}\n\n%For the difference in two means, the standard error formula took the following form:\n%\\begin{eqnarray*}\n%SE_{\\bar{x}_{1} - \\bar{x}_{2}} = \\sqrt{SE_{\\bar{x}_1}^2 + SE_{\\bar{x}_2}^2}\n%\\end{eqnarray*}\n%The standard error for the difference in two proportions takes a similar form. The reasons behind this similarity are rooted in the probability theory of Section~\\ref{randomVariablesSection}, which is described for this context in Guided Practice~\\vref{derivingSEForDiffOfTwoMeansExercise}.\n\n\n%\\D{\\newpage}\n\\subsection[Confidence intervals for $p_1 - p_2$]\n    {Confidence intervals for $\\pmb{p_1 - p_2}$}\n\n\\index{data!CPR and blood thinner|(}\n\n%In the setting of confidence intervals for a difference\n%of two proportions, the two sample proportions are used\n%to verify the success-failure condition and also compute\n%the standard error, just as was the case with a single\n%proportion.\n\\noindent%\nWe can apply the generic confidence interval formula\nfor a difference of two proportions,\nwhere we use $\\hat{p}_1 - \\hat{p}_2$ as the point\nestimate and substitute the $SE$ formula:\n\\begin{align*}\n&\\text{point estimate} \\ \\pm\\  z^{\\star} \\times SE\n&&\\to\n&&\\hat{p}_1 - \\hat{p}_2 \\ \\pm\\ \n    z^{\\star} \\times\n   \\sqrt{\\frac{p_1(1-p_1)}{n_1} + \\frac{p_2(1-p_2)}{n_2}}\n\\end{align*}\nWe can also follow the same\nPrepare, Check, Calculate, Conclude steps for\ncomputing a confidence interval\nor completing a hypothesis test.\nThe details change a little,\nbut the general approach remain the same.\nThink about these steps when you apply statistical methods.\n\n\\begin{examplewrap}\n\\begin{nexample}{We consider an experiment for patients\n    who underwent cardiopulmonary resuscitation (CPR)\n    for a heart attack and were\n    subsequently admitted to a\n    hospital.\n    These patients were randomly divided into a treatment\n    group where they received a blood thinner or the control\n    group where they did not receive a blood thinner.\n    The outcome variable of interest was whether the\n    patients survived for at least 24 hours.\n    The results are shown in\n    Figure~\\ref{resultsForCPRStudyInSmallSampleSection}.\n    Check whether we can model the difference in\n    sample proportions using the normal distribution.}\n\n  We first check for independence:\n  since this is a randomized experiment,\n  this condition is satisfied.\n  \n  Next, we check the success-failure condition for\n  each group.\n  We have at least 10 successes and 10 failures in\n  each experiment arm (11, 14, 39, 26),\n  so this condition is also satisfied.\n\n  With both conditions satisfied,\n  the difference in sample proportions can be\n  reasonably modeled using a normal distribution\n  for these data.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{lccccc}\n\\hline\n\t\t\t&& Survived \t& Died \t&& Total \\\\\n\\hline\nControl\t\t&& 11\t\t& 39\t\t&& 50 \\\\\nTreatment\t\t&& 14\t\t& 26\t\t&& 40 \\\\\n\\hline\nTotal\t\t\t&& 25\t\t& 65\t\t&& 90 \\\\\n\\hline\n\\end{tabular}\n\\caption{Results for the CPR study.\n    Patients in the treatment group were given\n    a blood thinner, and patients in the control\n    group were not.}\n\\label{resultsForCPRStudyInSmallSampleSection}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{\n    Create and interpret a 90\\% confidence interval of the\n    difference for the survival rates in the CPR study.}\n\n  We'll use $p_t$ for the survival\n  rate in the treatment group and $p_c$ for the control\n  group:\n  \\begin{align*}\n  \\hat{p}_{t} - \\hat{p}_{c}\n    = \\frac{14}{40} - \\frac{11}{50}\n    = 0.35 - 0.22\n    = 0.13\n  \\end{align*}\n  We use the standard error formula provided on\n  page~\\pageref{seForDiffOfProp}.\n  As with the one-sample proportion case,\n  we use the sample estimates of each proportion\n  in the formula in the confidence interval context:\n  \\begin{align*}\n  SE \\approx \\sqrt{\\frac{0.35 (1 - 0.35)}{40} +\n      \\frac{0.22 (1 - 0.22)}{50}}\n    = 0.095\n  \\end{align*}\n  For a 90\\% confidence interval, we use $z^{\\star} = 1.6449$:\n  \\begin{align*}\n  \\text{point estimate} \\ \\pm\\ z^{\\star} \\times SE\n    \\quad \\to \\quad 0.13 \\ \\pm\\ 1.6449 \\times  0.095\n    \\quad \\to \\quad (-0.026, 0.286)\n  \\end{align*}\n  We are 90\\% confident that blood thinners have\n  a difference of -2.6\\% to +28.6\\% percentage point\n  impact on survival rate for patients who are like\n  those in the study.\n  Because 0\\% is contained in the interval,\n  we do not have enough information to say\n  whether blood thinners help or harm\n  heart attack patients who have been admitted after\n  they have undergone CPR.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!CPR and blood thinner|)}\n\n%\\begin{onebox}{Confidence interval for a difference\n%    of two proportions}\n%  Once you've determined a confidence interval for the\n%  difference of two proportions would be helpful for an\n%  application, there are four steps to constructing the interval:\n%  \\begin{description}\n%  \\item[Prepare.]\n%      Identify the sample proportions and sample sizes\n%      for each of the two groups,\n%      determine what confidence level you wish to use.\n%  \\item[Check.]\n%      Verify the conditions to ensure each sample\n%      proportion is nearly normal.\n%      The success-failure condition should be checked\n%      for each group.\n%  \\item[Calculate.]\n%      If the conditions hold, compute $SE$,\n%      find $z^{\\star}$, and construct the interval.\n%  \\item[Conclude.]\n%      Interpret the confidence interval in the context\n%      of the problem.\n%  \\end{description}\n%\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nA 5-year experiment\nwas conducted to evaluate the effectiveness\nof fish oils on reducing cardiovascular events,\nwhere each subject was randomized into one of two\ntreatment groups.\nWe'll consider heart attack outcomes in these patients:\n\\begin{center}\n\\begin{tabular}{l ccc}\n  \\hline\n  & heart attack &\n      no event & Total \\\\\n  \\hline\n  fish oil & 145 & 12788 & 12933 \\\\\n  placebo & 200 & 12738 & 12938 \\\\\n  \\hline\n\\end{tabular}\n\\end{center}\n% library(openintro); library(xtable); xtable(fish_oil_18[[3]], digits = 0)\nCreate a 95\\% confidence interval for the effect of fish oils\non heart attacks for patients who are well-represented by\nthose in the study.\nAlso interpret the interval in the context of the\nstudy.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{\n  Because the patients were randomized,\n  the subjects are independent, both within and between\n  the two groups.\n  The success-failure condition is also met for both\n  groups as all counts are at least~10.\n  This satisfies the conditions necessary to model\n  the difference in proportions using a normal distribution.\n\n  Compute the sample proportions\n  ($\\hat{p}_{\\text{fish oil}} = 0.0112$,\n    $\\hat{p}_{\\text{placebo}} = 0.0155$),\n  point estimate of the difference ($0.0112 - 0.0155 = -0.0043$),\n  and standard error\n  ($SE = \\sqrt{\\frac{0.0112 \\times 0.9888}{12933} +\n      \\frac{0.0155 \\times 0.9845}{12938}}\n    = 0.00145$).\n  Next, plug the values into the general formula for\n  a confidence interval, where we'll use a 95\\%\n  confidence level with $z^{\\star} = 1.96$:\n  \\begin{align*}\n  -0.0043 \\pm 1.96 \\times 0.00145\n      \\quad \\to \\quad\n      (-0.0071, -0.0015)\n  \\end{align*}\n  We are 95\\% confident that fish oils decreases\n  heart attacks by\n  0.15 to 0.71 percentage points\n  (off of a baseline of about 1.55\\%)\n  over a 5-year period for subjects who are similar\n  to those in the study.\n  Because the interval is entirely below~0,\n  the data provide strong evidence\n  that fish oil supplements reduce heart attacks\n  in patients like those in the~study.}\n\n\n\\subsection%[Hypothesis tests for $p_1 - p_2$]\n    {Hypothesis tests for the difference of two proportions}\n\n\\index{data!mammography|(}\n\\index{data!breast cancer|(}\n\n%We'll explore an experiment evaluating the benefits\n%of mammograms using a hypothesis test.\nA mammogram is an X-ray procedure used to check for\nbreast cancer.\nWhether mammograms should be used is part of a\ncontroversial discussion, and it's the topic of our\nnext example where we learn about 2-proportion\nhypothesis tests when $H_0$~is~$p_1 - p_2 = 0$\n(or equivalently, $p_1 = p_2$).\n\nA 30-year study was conducted with nearly 90,000 female participants. During a 5-year screening period, each woman was randomized to one of two groups: in the first group, women received regular mammograms to screen for breast cancer, and in the second group, women received regular non-mammogram breast cancer exams. No intervention was made during the following 25 years of the study, and we'll consider death resulting from breast cancer over the full 30-year period. Results from the study are summarized in Figure~\\ref{mammogramStudySummaryTable}.\n\nIf mammograms are much more effective than non-mammogram breast cancer exams, then we would expect to see additional deaths from breast cancer in the control group. On~the other hand, if mammograms are not as effective as regular breast cancer exams, we~would expect to see an increase in breast cancer deaths in the mammogram group.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{rrcc}\n\t& \\multicolumn{3}{c}{Death from breast cancer?} \\\\\n  \\cline{2-4}\n & \\ \\hspace{3mm}\\ & Yes & No \\\\\n  \\hline\nMammogram && 500 & 44,425 \\\\\nControl && 505 & 44,405 \\\\\n   \\hline\n\\end{tabular}\n\\caption{Summary results for breast cancer study.}\n\\label{mammogramStudySummaryTable}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIs this study an experiment or an observational study?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{This is an experiment. Patients were randomized\n    to receive mammograms or a standard breast cancer exam.\n    We will be able to make causal conclusions based on this study.}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{htFormammogramStudySummaryTable}\nSet up hypotheses to test whether there was a difference\nin breast cancer deaths in the mammogram and control groups.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$H_0$: the breast cancer death rate for patients\n    screened using mammograms is the same as the breast cancer\n    death rate for patients in the control,\n    $p_{mgm} - p_{ctrl} = 0$. \\\\\n    $H_A$: the breast cancer death rate for patients screened\n    using mammograms is different than the breast cancer death\n    rate for patients in the control,\n    $p_{mgm} - p_{ctrl} \\neq 0$.}\n\nIn Example~\\ref{condFormammogramStudySummaryTableNormalInference},\nwe will check the conditions for using a normal distribution to\nanalyze the results of the study.\nThe details are very similar to that of confidence intervals.\nHowever, when the null hypothesis is that $p_1 - p_2 = 0$,\nwe use a special proportion called the\n\\term{pooled proportion} to check the success-failure condition:\n\\begin{align*}\n\\hat{p}_{\\textit{pooled}}\n    &= \\frac\n        {\\text{\\# of patients who died from breast cancer in the\n            entire study}}\n        {\\text{\\# of patients in the entire study}} \\\\\n\t&= \\frac{500 + 505}{500 + \\text{44,425} + 505 + \\text{44,405}} \\\\\n\t&= 0.0112\n\\end{align*}\nThis proportion is an estimate of the breast cancer death rate\nacross the entire study, and it's our best estimate of the\nproportions $p_{mgm}$ and $p_{ctrl}$\n\\emph{if the null hypothesis is true that $p_{mgm} = p_{ctrl}$}.\nWe~will also use this pooled proportion when computing\nthe standard error.\n\n\\begin{examplewrap}\n\\begin{nexample}{Is it reasonable to model the difference\n    in proportions using a normal distribution in this\n    study?}\n  \\label{condFormammogramStudySummaryTableNormalInference}%\n  Because the patients are randomized, they can be treated\n  as independent, both within and between groups.\n  We also must check the success-failure condition for each group.\n  Under the null hypothesis, the proportions $p_{mgm}$\n  and $p_{ctrl}$ are equal, so we check the success-failure\n  condition with our best estimate of these values under $H_0$,\n  the \\hiddenterm{pooled proportion} from the two samples,\n  $\\hat{p}_{\\textit{pooled}} = 0.0112$:\n  \\begin{align*}\n  \\hat{p}_{\\textit{pooled}} \\times n_{mgm}\n      &= 0.0112 \\times \\text{44,925} = 503\n    & (1 - \\hat{p}_{\\textit{pooled}}) \\times n_{mgm}\n      &= 0.9888 \\times \\text{44,925} = \\text{44,422} \\\\\n  \\hat{p}_{\\textit{pooled}} \\times n_{ctrl}\n      &= 0.0112 \\times \\text{44,910} = 503\n    & (1 - \\hat{p}_{\\textit{pooled}}) \\times n_{ctrl}\n      &= 0.9888 \\times \\text{44,910} = \\text{44,407}\n  \\end{align*}\n  The success-failure condition is satisfied since\n  all values are at least 10.\n  With both conditions satisfied, we can safely model\n  the difference in proportions using a normal\n  distribution.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{Use the pooled proportion when\n    $\\pmb{H_0}$ is $\\pmb{\\MakeLowercase{p_1 - p_2 = 0}}$}\n  When the null hypothesis is that the proportions are equal,\n  use the pooled proportion ($\\hat{p}_{\\textit{pooled}}$)\n  to verify the\n  success-failure condition and estimate the standard error:\n  \\begin{eqnarray*}\n  \\hat{p}_{\\textit{pooled}}\n    = \\frac{\\text{number of ``successes''}}\n      {\\text{number of cases}}\n    = \\frac{\\hat{p}_1 n_1 + \\hat{p}_2 n_2}{n_1 + n_2}\n  \\end{eqnarray*}\n  Here $\\hat{p}_1 n_1$ represents the number of successes in\n  sample 1 since\n  \\begin{eqnarray*}\n  \\hat{p}_1\n    = \\frac{\\text{number of successes in sample 1}}{n_1}\n  \\end{eqnarray*}\n  Similarly, $\\hat{p}_2 n_2$ represents the number\n  of successes in sample~2.\n\\end{onebox}\n\nIn Example~\\ref{condFormammogramStudySummaryTableNormalInference},\nthe pooled proportion was used to check the success-failure\ncondition.\\footnote{For an example of a two-proportion\n  hypothesis test that does not require the\n  success-failure condition to be met, see\n  Section~\\ref{caseStudyMalariaVaccine}.}\nIn the next example, we see the second place where the pooled\nproportion comes into play: the standard error calculation.\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{Compute the point estimate of the difference\n    in breast cancer death rates in the two groups,\n    and use the pooled proportion\n    $\\hat{p}_{\\textit{pooled}} = 0.0112$ to calculate\n    the standard error.}\n  The point estimate of the difference in breast cancer death\n  rates is\n  \\begin{align*}\n  \\hat{p}_{mgm} - \\hat{p}_{ctrl}\n    &= \\frac{500}{500 + 44,425} - \\frac{505}{505 + 44,405} \\\\\n    &= 0.01113 - 0.01125 \\\\\n    &= -0.00012\n  \\end{align*}\n  The breast cancer death rate in the mammogram group\n  was 0.012\\% less than in the control group.\n  Next, the standard error is calculated\n  \\emph{using the pooled proportion},~$\\hat{p}_{\\textit{pooled}}$:\n\\begin{align*}\nSE = \\sqrt{\n      \\frac{\\hat{p}_{\\textit{pooled}}(1-\\hat{p}_{\\textit{pooled}})}\n          {n_{mgm}}\n      + \\frac{\\hat{p}_{\\textit{pooled}}(1-\\hat{p}_{\\textit{pooled}})}\n          {n_{ctrl}}\n    }\n\t= 0.00070\n\\end{align*}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Using the point estimate $\\hat{p}_{mgm} - \\hat{p}_{ctrl} = -0.00012$ and standard error $SE = 0.00070$, calculate a p-value for the hypothesis test and write a conclusion.}\nJust like in past tests, we first compute a test statistic and draw a picture:\n\\begin{align*}\nZ = \\frac{\\text{point estimate} - \\text{null value}}{SE}\n\t= \\frac{-0.00012 - 0}{0.00070}\n\t= -0.17\n\\end{align*}\n\\begin{center}\n\\Figures[A normal distribution is shown centered at 0 with a standard deviation of 0.0007. The lower tail is shaded below -0.00012 and the upper tail is shaded above 0.00012. Visually, it looks like very roughly 90\\% of the area under the normal distribution is shaded.]{0.45}{mammograms}{mammogramPValue}\n\\end{center}\nThe lower tail area is 0.4325, which we double to get the p-value:~0.8650. Because this p-value is larger than 0.05, we do not reject the null hypothesis. That is, the difference in breast cancer death rates is reasonably explained by chance, and we do not observe benefits or harm from mammograms relative to a regular breast exam.\n\\end{nexample}\n\\end{examplewrap}\n\nCan we conclude that mammograms have no benefits or harm?\nHere are a few considerations to keep in mind when reviewing\nthe mammogram study as well as any other medical study:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item\n    We do not reject the null hypothesis, which means\n    we don't have sufficient evidence to conclude that\n    mammograms reduce or increase breast cancer deaths.\n\\item\n    If mammograms are helpful or harmful, the data\n    suggest the effect isn't very large.\n\\item\n    Are mammograms more or less expensive than\n    a non-mammogram breast exam?\n    If~one option is much more expensive than the\n    other and doesn't offer clear benefits,\n    then we should lean towards the less expensive\n    option.\n\\item\n    The study's authors also found that mammograms\n    led to overdiagnosis of breast cancer,\n    which means some breast cancers were found\n    (or thought to be found) but that these cancers\n    would not cause symptoms during patients' lifetimes.\n    That is, something else would kill the patient\n    before breast cancer symptoms appeared.\n    This means some patients may have been treated\n    for breast cancer unnecessarily, and this\n    treatment is another cost to consider.\n    It is also important to recognize that\n    overdiagnosis can cause unnecessary physical\n    or emotional harm to patients.\n\\end{itemize}\nThese considerations highlight the complexity around medical care and treatment recommendations. Experts and medical boards who study medical treatments use considerations like those above to provide their best recommendation based on the current evidence.\n\n\\index{data!breast cancer|)}\n\\index{data!mammography|)}\n\n%\\begin{onebox}{Hypothesis testing when $\\mathbf{H_0}$ is\n%    $\\mathbf{p_1 - p_2 = 0}$}\n%  Once you've determined a hypothesis test for the difference\n%  of two proportions is the correct procedure, there are four\n%  steps to completing the test:\n%  \\begin{description}\n%  \\item[Prepare.]\n%      Identify the parameter of interest,\n%      list out hypotheses,\n%      identify the significance level,\n%      and compute summary statistics for each group.\n%  \\item[Check.]\n%      Verify the conditions to ensure\n%      $\\hat{p}_1 - \\hat{p}_2$ is nearly normal under $H_0$.\n%      When the null hypothesis is that the difference is~0,\n%      use a pooled proportion to check the success-failure\n%      condition for each group.\n%  \\item[Calculate.]\n%      If the conditions hold, compute the standard\n%      error, again using the pooled proportion,\n%      compute the Z-score, and identify the p-value.\n%  \\item[Conclude.]\n%      Evaluate the hypothesis test by comparing the p-value\n%      to $\\alpha$, and provide a conclusion in the context\n%      of the problem.\n%  \\end{description}\n%\\end{onebox}\n\n\n\n\\D{\\newpage}\n\n\\subsection{More on 2-proportion hypothesis tests (special topic)}\n\nWhen we conduct a 2-proportion hypothesis test,\nusually $H_0$ is $p_1 - p_2 = 0$. However, there are rare\nsituations where we want to check for some difference in\n$p_1$ and $p_2$ that is some value other than 0.\nFor example, maybe we care about checking a null hypothesis\nwhere $p_1 - p_2 = 0.1$. %\\footnote{We can\n%  also encounter a similar situation with a difference of\n%  two means, though no such example is given in\n%  Chapter~\\ref{inferenceForNumericalData} since the methods\n%  remain exactly the same in the context of sample means.\n%  On the other hand, the success-failure condition and the\n%  calculation of the standard error vary slightly in different\n%  proportion contexts.}\nIn contexts like these, we generally use $\\hat{p}_1$ and\n$\\hat{p}_2$ to check the success-failure condition and\nconstruct the standard error.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{carWheelBladeManufacturer}%\nA quadcopter company is considering a new manufacturer\nfor rotor blades.\nThe new manufacturer would be more expensive,\nbut they claim\ntheir higher-quality blades are more reliable,\nwith 3\\% more blades passing inspection than their\ncompetitor.\nSet up appropriate hypotheses for the test.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$H_0$: The higher-quality blades will pass\n  inspection 3\\% more frequently than the standard-quality blades.\n  $p_{highQ} - p_{standard} = 0.03$.\n  $H_A$: The higher-quality blades will pass inspection\n  some amount different than 3\\% more often than the\n  standard-quality blades.\n  $p_{highQ} - p_{standard} \\neq 0.03$.}\n\n\\captionsetup{width=85mm}\n\n\\begin{figure}[h]\n\\centering\n\\Figures[A photo of a Phantom quadcopter drone.]{0.6}{quadcopter}{quadcopter_david_j}\n\\caption{A Phantom quadcopter.\\vspace{-1mm} \\\\\n   -----------------------------\\vspace{-2mm}\\\\\n   {\\footnotesize Photo by David J\n   (\\oiRedirect{textbook-quadcopter_david_j}\n       {http://flic.kr/p/oiWLNu}).\n   \\oiRedirect{textbook-CC_BY_2}{CC-BY 2.0 license.}\n   This photo has been cropped and a border has been added.}}\n\\label{quadcopter_david_j}\n\\end{figure}\n\n\\captionsetup{width=\\mycaptionwidth}\n\n\\D{\\newpage}\n\n%\\Add{In Guided Practice~\\ref{qualityCtrlEngHypothesisEval}, the null difference is 0.03. However, in the vast majority of applications for differences in means or proportions, the null difference is~0. While the details for a difference of means does not change if the null difference is zero or non-zero, that is not the case for a difference in proportions. As we'll see in Section~\\ref{}, a hypothesis test for a difference in proportions where the null value is 0 requires additional~care.}\n\n\\begin{examplewrap}\n\\begin{nexample}{The quality control engineer from\n    Guided Practice~\\ref{carWheelBladeManufacturer}\n    collects a sample of blades, examining 1000 blades\n    from each company, and she finds that 899 blades pass\n    inspection from the current supplier and 958 pass\n    inspection from the prospective supplier.\n    Using these data, evaluate the hypotheses from\n    Guided Practice~\\ref{carWheelBladeManufacturer}\n    with a significance level of 5\\%.}\n  \\label{qualityCtrlEngHypothesisEval}%\n  First, we check the conditions.\n  The sample is not necessarily random, so to proceed\n  we must assume the blades are all independent;\n  for this sample we will suppose this assumption\n  is reasonable, but the engineer would be more knowledgeable\n  as to whether this assumption is appropriate.\n  The success-failure condition also holds for each sample.\n  Thus, the difference in sample proportions,\n  $0.958 - 0.899 = 0.059$, can be said to come from a nearly\n  normal distribution.\n\n  The standard error is computed using the two sample\n  proportions since we do not use a pooled proportion\n  for this context:\n  \\begin{align*}\n  SE\n    = \\sqrt{\\frac{0.958(1-0.958)}{1000} +\n        \\frac{0.899(1-0.899)}{1000}}\n    = 0.0114\n  \\end{align*}\n  In this hypothesis test, because the null is that\n  $p_1 - p_2 = 0.03$, the sample proportions were used\n  for the standard error calculation rather than a pooled\n  proportion.\n\n  Next, we compute the test statistic and use it to find the\n  p-value, which is depicted in\n  Figure~\\ref{bladesTwoSampleHTPValueQC}.\n  \\begin{align*}\n  Z = \\frac{\\text{point estimate} - \\text{null value}}{SE}\n    = \\frac{0.059 - 0.03}{0.0114} = 2.54\n  \\end{align*}\n  Using a standard normal distribution for this test statistic,\n  we identify the right tail area as 0.006,\n  and we double it to get the p-value: 0.012.\n  We reject the null hypothesis because 0.012 is less than 0.05.\n  Since we observed a larger-than-3\\% increase in blades\n  that pass inspection, we have statistically significant\n  evidence that the higher-quality blades pass inspection\n  \\emph{more than} 3\\% as often as the currently used blades,\n  exceeding the company's claims.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A normal distribution is shown that is centered at 0.03 with a standard deviation of 0.0114. Small tail areas on each side are shaded. On the upper end, the tail area above 0.059 is shaded, and this area is annotated with a value signifying the area of that upper tail as 0.006. The corresponding lower tail is also shaded.]{0.45}{bladesTwoSampleHTPValueQC}\n  \\caption{Distribution of the test statistic if the null\n      hypothesis was true.\n      The p-value is represented by the shaded areas.}\n  \\label{bladesTwoSampleHTPValueQC}\n\\end{figure}\n\n\n\\D{\\newpage}\n\n\\subsection{Examining the standard error formula\n    (special topic)}\n\nThis subsection covers more theoretical topics\nthat offer deeper insights into the origins of the\nstandard error formula for the difference of two\nproportions.\nUltimately, all of the standard error formulas\nwe encounter in this chapter and in\nChapter~\\ref{ch_inference_for_means}\ncan be derived from the probability principles of\nSection~\\ref{randomVariablesSection}.\n\nThe formula for the standard error of the difference\nin two proportions can be deconstructed into the formulas\nfor the standard errors of the individual sample proportions.\nRecall that the standard error of the individual\nsample proportions $\\hat{p}_1$ and $\\hat{p}_2$ are\n\\begin{align*}\n&SE_{\\hat{p}_1} = \\sqrt{\\frac{{p}_1 (1 - {p}_1)}{n_1}}\n&&SE_{\\hat{p}_2} = \\sqrt{\\frac{{p}_2 (1 - {p}_2)}{n_2}}\n\\end{align*}\nThe standard error of the difference of two sample proportions\ncan be deconstructed from the standard errors of the separate\nsample proportions:\n\\begin{align*}\nSE_{\\hat{p}_{1} - \\hat{p}_{2}}\n\t= \\sqrt{SE_{\\hat{p}_1}^2 + SE_{\\hat{p}_2}^2}\n\t= \\sqrt{\\frac{{p}_1 (1 - {p}_1)}{n_1}\n\t    + \\frac{{p}_2 (1 - {p}_2)}{n_2}}\n\\end{align*}\nThis special relationship follows from probability theory.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{derivingSEForDiffOfTwoMeansExercise}%\nPrerequisite: Section~\\ref{randomVariablesSection}.\nWe can rewrite the equation above in a different way:\n\\begin{align*}\nSE_{\\hat{p}_{1} - \\hat{p}_{2}}^2\n  = SE_{\\hat{p}_1}^2 + SE_{\\hat{p}_2}^2\n\\end{align*}\nExplain where this formula comes from using\nthe formula for the variability of the sum of\ntwo random variables.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The standard error squared represents\n  the variance of the estimate.\n  If $X$ and $Y$ are two random variables with variances\n  $\\sigma_x^2$ and $\\sigma_y^2$,\n  then the variance of $X - Y$ is $\\sigma_x^2 + \\sigma_y^2$.\n  Likewise, the variance corresponding to $\\hat{p}_1 - \\hat{p}_2$ is $\\sigma_{\\hat{p}_1}^2 + \\sigma_{\\hat{p}_2}^2$. Because $\\sigma_{\\hat{p}_1}^2$ and $\\sigma_{\\hat{p}_2}^2$ are just another way of writing $SE_{\\hat{p}_1}^2$ and  $SE_{\\hat{p}_2}^2$, the variance associated with $\\hat{p}_1 - \\hat{p}_2$ may be written as $SE_{\\hat{p}_1}^2 + SE_{\\hat{p}_2}^2$.}\n\n\n\n%%__________________\n%\\section{Determining a sample size for an experiment}\n%\\label{SampleSizeFor2Proportions}\n%\n%So far we've been focused on controlling the Type~1 Error rate for hypothesis tests. However, when planning an experiment, we often are interested in determining if there is an effect.\\footnote{Similar planning is also appropriate for a} There are often two competing considerations:\n%\\begin{itemize}\n%\\setlength{\\itemsep}{0mm}\n%\\item We want to collect enough data that we can detect important effects.\n%\\item In many contexts, collecting data is expensive, so we don't want to collect more than what we need to detect effects we care about.\n%\\end{itemize}\n%The first point is relatively simple: the more data we collect, the more precise our estimates will be, and so we'll be able to detect smaller effects. The second point is more subtle, since we need to determine the size of effects that we care about.\n%\n%\\begin{examplewrap}\n%\\begin{nexample}{Alzheimer's disease is a neurological disease. It affects patients mildly at the beginning and eventually leads to dementia. If an Alzheimer's patient lives long enough, the disease will begin affecting bodily functions and ultimately lead to death. It's an extremely serious condition that millions of people, has no cure, and is very expensive to research, partially due to its slow progression. A group of researchers is }\n%\\end{nexample}\n%\\end{examplewrap}\n%\n%\n%, even large ones, are difficult to detect with small samples, so we should want to collect a larger sample to detect such effects. If we take a very large sample, we might find a statistically significant difference but the magnitude might be so small that it is of no practical value. In this section we describe techniques for selecting an appropriate sample size based on these considerations.\n\n\n{\\input{ch_inference_for_props/TeX/difference_of_two_proportions.tex}}\n\n\n\n\n\n\n%__________________\n\\section{Testing for goodness of fit using chi-square}\n\\label{oneWayChiSquare}\n\nIn this section, we develop a method for assessing a null model when the data are binned.\nThis technique is commonly used in two circumstances:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item Given a sample of cases that can be classified into several groups, determine if the sample is representative of the general population.\n\\item Evaluate whether data resemble a particular distribution, such as a normal distribution or a geometric distribution.\n\\end{itemize}\nEach of these scenarios can be addressed using the same statistical test: a chi-square test.\n\n\\index{data!racial make-up of jury|(}\n\nIn the first case, we consider data from a random sample of 275 jurors in a small county. Jurors identified their racial group, as shown in Figure~\\ref{juryRepresentationAndCityRepresentationForRace}, and we would like to determine if these jurors are racially representative of the population.  If the jury is representative of the population, then the proportions in the sample should roughly reflect the population of eligible jurors, i.e. registered voters.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{ll ccc c ll}\n\\hline\nRace\t & \\hspace{2mm} & White & Black & Hispanic & Other & \\hspace{2mm} & Total \\\\\n\\hline\nRepresentation in juries &\t& 205 & 26 & 25 & 19 & & 275 \\\\\nRegistered voters\t & \t\t& 0.72 & 0.07 & 0.12 & 0.09 & & 1.00 \\\\\n\\hline\n\\end{tabular}\n\\caption{Representation by race in a city's juries and population.}\n\\label{juryRepresentationAndCityRepresentationForRace}\n\\end{figure}\n\nWhile the proportions in the juries do not precisely represent the population proportions, it is unclear whether these data provide convincing evidence that the sample is not representative. If the jurors really were randomly sampled from the registered voters, we might expect small differences due to chance. However, unusually large differences may provide convincing evidence that the juries were not representative.\n\nA second application, assessing the fit of a distribution, is presented at the end of this section. Daily stock returns from the S\\&P500 for 25 years are used to assess whether stock activity each day is independent of the stock's behavior on previous days.\n\nIn these problems, we would like to examine all bins simultaneously, not simply compare one or two bins at a time, which will require us to develop a new test statistic.\n\n\n\\subsection{Creating a test statistic for one-way tables}\n\n\\begin{examplewrap}\n\\begin{nexample}{Of the people in the city, 275 served on a jury. If the individuals are randomly selected to serve on a jury, about how many of the 275 people would we expect to be White? How many would we expect to be Black?}\nAbout 72\\% of the population is White, so we would expect about 72\\% of the jurors to be White: $0.72\\times 275 = 198$.\n\nSimilarly, we would expect about 7\\% of the jurors to be Black, which would correspond to about $0.07\\times 275 = 19.25$ Black jurors.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nTwelve percent of the population is Hispanic and 9\\% represent other races. How many of the 275 jurors would we expect to be Hispanic or from another race? Answers can be found in Figure~\\ref{expectedJuryRepresentationIfNoBias}.\n\\end{nexercise}\n\\end{exercisewrap}\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{ll ccc c ll}\n\\hline\nRace\t & \\hspace{2mm} & White & Black & Hispanic & Other & \\hspace{2mm} & Total \\\\\n\\hline\nObserved data\t\t\t&\t& 205 & 26\t& 25 & 19\t&\t& 275 \\\\\nExpected counts\t &\t& 198 & 19.25 & 33 & 24.75 & & 275 \\\\\n\\hline\n\\end{tabular}\n\\caption{Actual and expected make-up of the jurors.}\n\\label{expectedJuryRepresentationIfNoBias}\n\\end{figure}\n\nThe sample proportion represented from each race among the 275 jurors was not a precise match for any ethnic group. While some sampling variation is expected, we would expect the sample proportions to be fairly similar to the population proportions if there is no bias on juries. We need to test whether the differences are strong enough to provide convincing evidence that the jurors are not a random sample. These ideas can be organized into hypotheses:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[$H_0$:] The jurors are a random sample, i.e. there is no racial bias in who serves on a jury, and the observed counts reflect natural sampling fluctuation.\n\\item[$H_A$:] The jurors are not randomly sampled, i.e. there is racial bias in juror selection.\n\\end{itemize}\nTo evaluate these hypotheses, we quantify how different the observed counts are from the expected counts. Strong evidence for the alternative hypothesis would come in the form of unusually large deviations in the groups from what would be expected based on sampling variation alone.\n\n\n\\subsection{The chi-square test statistic}\n\\label{chiSquareTestStatistic}\n\nIn previous hypothesis tests, we constructed a test statistic of the following form:\n\\begin{align*}\n\\frac{\\text{point estimate} - \\text{null value}}\n    {\\text{SE of point estimate}}\n\\end{align*}\nThis construction was based on (1) identifying the difference between a point estimate and an expected value if the null hypothesis was true, and (2) standardizing that difference using the standard error of the point estimate. These two ideas will help in the construction of an appropriate test statistic for count data.\n\nOur strategy will be to first compute the difference between the observed counts and the counts we would expect if the null hypothesis was true, then we will standardize the difference:\n\\begin{align*}\nZ_{1} = \\frac{\\text{observed White count} - \\text{null White count}}\n\t\t\t\t{\\text{SE of observed White count}}\n\\end{align*}\nThe standard error for the point estimate of the count in binned data is the square root of the count under the null.\\footnote{Using some of the rules learned in earlier chapters, we might think that the standard error would be $np(1-p)$, where $n$ is the sample size and $p$ is the proportion in the population. This would be correct if we were looking only at one count. However, we are computing many standardized differences and adding them together. It can be shown -- though not here -- that the square root of the count is a better way to standardize the count differences.} Therefore:\n\\begin{align*}\nZ_1 = \\frac{205 - 198}{\\sqrt{198}} = 0.50\n\\end{align*}\nThe fraction is very similar to previous test statistics: first compute a difference, then standardize it. These computations should also be completed for the Black, Hispanic, and other groups:\n\\begin{align*}\n&Black && Hispanic\t&&Other \\\\\n& Z_2 = \\frac{26-19.25}{\\sqrt{19.25}}=1.54\\ \\ \\ \\ \n\t&& Z_3 = \\frac{25-33}{\\sqrt{33}}=-1.39\\ \\ \\ \\ \n\t&& Z_4 = \\frac{19-24.75}{\\sqrt{24.75}}=-1.16 \\\\\n\\end{align*}\nWe would like to use a single test statistic to determine if these four standardized differences are irregularly far from zero. That is, $Z_1$, $Z_2$, $Z_3$, and $Z_4$ must be combined somehow to help determine if they -- as a group -- tend to be unusually far from zero. A first thought might be to take the absolute value of these four standardized differences and add them~up:\n\\begin{align*}\n|Z_1| + |Z_2| + |Z_3| + |Z_4| = 4.58\n\\end{align*}\nIndeed, this does give one number summarizing how far the actual counts are from what was expected. However, it is more common to add the squared values:\n\\begin{align*}\nZ_1^2 + Z_2^2 + Z_3^2 + Z_4^2 = 5.89\n\\end{align*}\nSquaring each standardized difference before adding them together does two things:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item Any standardized difference that is squared will now be positive.\n\\item Differences that already look unusual -- e.g. a standardized difference of 2.5 -- will become much larger after being squared.\n\\end{itemize}\nThe test statistic $X^2$,\\index{chi-square statistic} which is the sum of the $Z^2$ values, is generally used for these reasons. We can also write an equation for $X^2$ using the observed counts and null counts:\n\\index{data!racial make-up of jury|)}\n\\begin{align*}\nX^2 &=\n\t\\frac\n\t{\\text{\\footnotesize$(\\text{observed count}_1 - \\text{null count}_1)^2$}}\n\t{\\text{\\footnotesize$\\text{null count}_1$}}\n\t+ \\dots + \\frac\n\t{\\text{\\footnotesize$(\\text{observed count}_4 - \\text{null count}_4)^2$}}\n\t{\\text{\\footnotesize$\\text{null count}_4$}}\n\\end{align*}\nThe final number $X^2$ summarizes how strongly the observed counts tend to deviate from the null counts. In Section~\\ref{pValueForAChiSquareTest}, we will see that if the null hypothesis is true, then $X^2$ follows a new distribution called a \\emph{chi-square distribution}. Using this distribution, we will be able to obtain a p-value to evaluate the hypotheses.\n\n\n\\subsection{The chi-square distribution and finding areas}\n\nThe \\term{chi-square distribution} is sometimes used to\ncharacterize data sets and statistics that are always positive\nand typically right skewed. Recall a normal distribution had\ntwo parameters -- mean and standard deviation -- that could be\nused to describe its exact characteristics.\nThe chi-square distribution has just one parameter called\n\\termsub{degrees of freedom (df)}{degrees of freedom (df)!chi-square},\nwhich influences the shape, center, and spread of the distribution.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{exerChiSquareDistributionDescriptionWithMoreDOF}%\nFigure~\\ref{chiSquareDistributionWithInceasingDF} shows three chi-square distributions. \\\\\n(a) How does the center of the distribution change when the degrees of freedom is larger? \\\\\n(b) What about the variability (spread)? \\\\\n(c) How does the shape change?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~The center becomes larger. If took a careful look, we could see that the mean of each distribution is equal to the distribution's degrees of freedom. (b)~The variability increases as the degrees of freedom increases. (c)~The distribution is very strongly skewed for $df=2$, and then the distributions become more symmetric for the larger degrees of freedom $df=4$ and $df=9$. We would see this trend continue if we examined distributions with even more larger degrees of freedom.}\n\n\\begin{figure}[h]\n\\centering\n\\Figure[Three chi-square distributions are shown with degrees of freedom 2, 4, and 9 on the same plot. The horizontal axis ranges from 0 to 25 -- recall that the chi-square distributions never take values smaller than 0. The chi-square distribution with 2 degrees of freedom starts at a peak at zero and then quickly declines more than halfway by the value of 2 and trails off after a value of about 5. The chi-square distribution with 4 degrees of freedom starts at 0 and quickly rises to a peak at about 2, before gradually declining and then more steeply declining starting at 3, before starting to flatten at about 5 or 6. The distribution has fallen very close to the horizontal axis by a value of 10. The chi-square distribution with 9 degrees of freedom starts at zero before gradually rising up to a peak at about 7 before declining again and trailing off between at around 15.]{0.8}{chiSquareDistributionWithInceasingDF}\n%\\includegraphics[width=0.8\\textwidth]{ch_inference_for_props/figures/chiSquareDistributionWithInceasingDF/chiSquareDistributionWithInceasingDF}\n\\caption{Three chi-square distributions with varying degrees of freedom.}\n\\label{chiSquareDistributionWithInceasingDF}\n\\end{figure}\n\n\\D{\\newpage}\n\nFigure~\\ref{chiSquareDistributionWithInceasingDF} and Guided Practice~\\ref{exerChiSquareDistributionDescriptionWithMoreDOF} demonstrate three general properties of chi-square distributions as the degrees of freedom increases: the distribution becomes more symmetric, the center moves to the right, and the variability inflates.\n\nOur principal interest in the chi-square distribution\nis the calculation of p-values, which (as we have seen before)\nis related to finding the relevant area in the tail of\na distribution.\nThe most common ways to do this are using computer software,\nusing a graphing calculator, or using a table.\nFor folks wanting to use the table option,\nwe provide an outline of how to read the chi-square table in\nAppendix~\\ref{chiSquareProbabilityTable},\nwhich is also where you may find the table.\n%\\Comment{If giving some \\R{} in the text, then put \\R{} code\n%  in the examples / exercises below.}\nFor the examples below, use your preferred approach\nto confirm you get the same answers.\n\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{chiSquareAreaAbove6Point25WithDF3}\n    shows a chi-square distribution with 3 degrees of freedom\n    and an upper shaded tail starting at 6.25.\n    Find the shaded area.}\n  Using statistical software or a graphing calculator,\n  we can find that the upper tail area for a chi-square\n  distribution with 3 degrees of freedom ($df$)\n  and a cutoff of 6.25 is 0.1001.\n  That is, the shaded upper tail of\n  Figure~\\ref{chiSquareAreaAbove6Point25WithDF3}\n  has area 0.1.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}\n\\centering\n\\subfigure[]{\n\\Figures[A chi-square distribution with 3 degrees of freedom is shown, with the area above 6.25 shaded. This region appears to be about 10\\% of the area under the curve.]{0.475}{arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove6Point25WithDF3}{chiSquareAreaAbove6Point25WithDF3}\n\\label{chiSquareAreaAbove6Point25WithDF3}\n}\n\\subfigure[]{\n\\Figures[A chi-square distribution with 2 degrees of freedom is shown, with the area above 4.3 shaded. This region appears to be about 10\\% of the area under the curve.]{0.475}{arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove4Point3WithDF2}{chiSquareAreaAbove4Point3WithDF2}\n\\label{chiSquareAreaAbove4Point3WithDF2}\n}\n\\subfigure[]{\n\\Figures[A chi-square distribution with 5 degrees of freedom is shown, with the area above 5.1 shaded. This region appears to be very roughly 50\\% of the area under the curve.]{0.475}{arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove5Point1WithDF5}{chiSquareAreaAbove5Point1WithDF5}\n\\label{chiSquareAreaAbove5Point1WithDF5}\n}\n\\subfigure[]{\n\\Figures[A chi-square distribution with 7 degrees of freedom is shown, with the area above 11.7 shaded. This region appears to be about 15\\% of the area under the curve.]{0.475}{arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove11Point7WithDF7}{chiSquareAreaAbove11Point7WithDF7}\n\\label{chiSquareAreaAbove11Point7WithDF7}\n}\n\\subfigure[]{\n\\Figures[A chi-square distribution with 4 degrees of freedom is shown, with the area above 10 shaded. This region appears to be about 5\\% of the area under the curve.]{0.475}{arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove10WithDF4}{chiSquareAreaAbove10WithDF4}\n\\label{chiSquareAreaAbove10WithDF4}\n}\n\\subfigure[]{\n\\Figures[A chi-square distribution with 3 degrees of freedom is shown, with the area above 9.21 shaded. This region appears to be about 3\\% of the area under the curve.]{0.475}{arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove9Point21WithDF3}{chiSquareAreaAbove9Point21WithDF3}\n\\label{chiSquareAreaAbove9Point21WithDF3}\n}\n\\caption{\n\\textbf{\\subref{chiSquareAreaAbove6Point25WithDF3}}~Chi-square distribution with 3~degrees of freedom, area above 6.25 shaded.\n\\textbf{\\subref{chiSquareAreaAbove4Point3WithDF2}}~2~degrees of freedom, area above 4.3 shaded.\n\\textbf{\\subref{chiSquareAreaAbove5Point1WithDF5}}~5~degrees of freedom, area above 5.1 shaded.\n\\textbf{\\subref{chiSquareAreaAbove11Point7WithDF7}}~7~degrees of freedom, area above 11.7 shaded.\n\\textbf{\\subref{chiSquareAreaAbove10WithDF4}}~4~degrees of freedom, area above 10 shaded.\n\\textbf{\\subref{chiSquareAreaAbove9Point21WithDF3}}~3~degrees of freedom, area above 9.21 shaded.\n}\n\\label{arrayOfFigureAreasForChiSquareDistribution}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{chiSquareAreaAbove4Point3WithDF2}\n    shows the upper tail of a chi-square distribution with\n    2~degrees of freedom.\n    The bound for this upper tail is at 4.3.\n    Find the tail area.}\n  Using software, we can find that the tail area shaded in\n  Figure~\\ref{chiSquareAreaAbove4Point3WithDF2}\n  to be 0.1165.\n  If using a table, we would only be able to find\n  a range of values for the tail area:\n  between 0.1 and 0.2.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{chiSquareAreaAbove5Point1WithDF5}\n    shows an upper tail for a chi-square distribution with\n    5~degrees of freedom and a cutoff of 5.1.\n    Find the tail area.}\n  Using software, we would obtain a tail area of 0.4038.\n  If using the table in Appendix~\\ref{chiSquareProbabilityTable},\n  we would have identified that the tail area is larger than 0.3\n  but not be able to give the precise value.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nFigure~\\ref{chiSquareAreaAbove11Point7WithDF7} shows a cutoff\nof 11.7 on a chi-square distribution with 7 degrees of freedom.\nFind the area of the upper tail.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{\n  The area is 0.1109.\n  If using a table, we would identify that it falls\n  between 0.1 and 0.2.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nFigure~\\ref{chiSquareAreaAbove10WithDF4} shows a cutoff\nof 10 on a chi-square distribution with 4 degrees of freedom.\nFind the area of the upper tail.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Precise value: 0.0404.\n  If using the table: between 0.02 and 0.05.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nFigure~\\ref{chiSquareAreaAbove9Point21WithDF3} shows a cutoff\nof 9.21 with a chi-square distribution with 3 df.\nFind the area of the upper tail.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Precise value: 0.0266.\n  If using the table: between 0.02 and 0.05.}\n\n\n\\D{\\newpage}\n\n\\subsection{Finding a p-value for a chi-square distribution}\n\\label{pValueForAChiSquareTest}\n\n\\index{data!racial make-up of jury|(}\nIn Section~\\ref{chiSquareTestStatistic}, we identified a new test statistic ($X^2$) within the context of assessing whether there was evidence of racial bias in how jurors were sampled. The null hypothesis represented the claim that jurors were randomly sampled and there was no racial bias. The alternative hypothesis was that there was racial bias in how the jurors were sampled.\n\nWe determined that a large $X^2$ value would suggest strong evidence favoring the alternative hypothesis: that there was racial bias. However, we could not quantify what the chance was of observing such a large test statistic ($X^2=5.89$) if the null hypothesis actually was true. This is where the chi-square distribution becomes useful. If the null hypothesis was true and there was no racial bias, then $X^2$ would follow a chi-square distribution, with three degrees of freedom in this case. Under certain conditions, the statistic $X^2$ follows a chi-square distribution with $k - 1$ degrees of freedom, where $k$ is the number of bins.\n\n\\begin{examplewrap}\n\\begin{nexample}{How many categories were there in the juror example? How many degrees of freedom should be associated with the chi-square distribution used for $X^2$?}\nIn the jurors example, there were $k=4$ categories: White, Black, Hispanic, and other. According to the rule above, the test statistic $X^2$ should then follow a chi-square distribution with $k-1 = 3$ degrees of freedom if $H_0$ is true.\n\\end{nexample}\n\\end{examplewrap}\n\nJust like we checked sample size conditions to use a normal\ndistribution in earlier sections, we must also check a sample\nsize condition to safely apply the chi-square distribution\nfor~$X^2$.\nEach expected count must be at least 5. In the juror example,\nthe expected counts were 198, 19.25, 33, and 24.75, all easily\nabove~5, so we can apply the chi-square model to the test\nstatistic, $X^2=5.89$.\n\n\\begin{examplewrap}\n\\begin{nexample}{If the null hypothesis is true,\n    the test statistic $X^2=5.89$ would be closely\n    associated with a chi-square distribution with\n    three degrees of freedom.\n    Using this distribution and test statistic,\n    identify the p-value.}\n  The chi-square distribution and p-value are shown in\n  Figure~\\ref{jurorHTPValueShown}.\n  Because larger chi-square values correspond to stronger\n  evidence against the null hypothesis, we shade the upper\n  tail to represent the p-value.\n  Using statistical software (or the table in\n  Appendix~\\ref{chiSquareProbabilityTable}),\n  we can determine that the area is 0.1171.\n  Generally we do not reject the null hypothesis\n  with such a large p-value.\n  In other words, the data do not provide convincing evidence\n  of racial bias in the juror selection.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n\\centering\n\\Figure[A chi-square distribution with 3 degrees of freedom is shown, with the area above 5.89 shaded. This region appears to be about 10\\% of the area under the curve.]{0.55}{jurorHTPValueShown}\n\\caption{The p-value for the juror hypothesis test is shaded in the chi-square distribution with $df=3$.}\n\\label{jurorHTPValueShown}\n\\end{figure}\n\n\\index{data!racial make-up of jury|)}\n\n\\begin{onebox}{Chi-square test for one-way table}\n  Suppose we are to evaluate whether there is convincing\n  evidence that a set of observed counts $O_1$, $O_2$, ...,\n  $O_k$ in $k$ categories are unusually different from what\n  might be expected under a null hypothesis.\n  Call the \\emph{expected counts} that are based on the null\n  hypothesis $E_1$, $E_2$, ..., $E_k$.\n  If each expected count is at least 5 and the null hypothesis\n  is true, then the test statistic below follows a chi-square\n  distribution with $k-1$ degrees of freedom:\n  \\begin{align*}\n  X^2\n    = \\frac{(O_1 - E_1)^2}{E_1} +\n        \\frac{(O_2 - E_2)^2}{E_2} +\n        \\cdots +\n        \\frac{(O_k - E_k)^2}{E_k}\n  \\end{align*}\n  The p-value for this test statistic is found by looking\n  at the upper tail of this chi-square distribution.\n  We consider the upper tail because larger values of $X^2$\n  would provide greater evidence against the null hypothesis.\n\\end{onebox}\n\n\\begin{onebox}{Conditions for the chi-square test}\n  There are two conditions that must be checked before\n  performing a chi-square test:\\vspace{-1mm}\n  \\begin{description}\n  \\setlength{\\itemsep}{0mm}\n  \\item[Independence.] Each case that contributes a count to\n      the table must be independent of all the other cases in\n      the table.\n  \\item[Sample size / distribution.] Each particular scenario\n      (i.e. cell count) must have at least 5~expected cases.\n  \\end{description}\n  Failing to check conditions may affect the test's error rates.\n\\end{onebox}\n\n%\\begin{onebox}{Chi-square test for one-way table}\n%  Suppose we are to evaluate whether there is convincing\n%  evidence that a set of observed counts $O_1$, $O_2$, ...,\n%  $O_k$ in $k$ categories are unusually different from what\n%  might be expected under a null hypothesis.\n%  \\begin{description}\n%  \\item[Prepare.]\n%      List out hypotheses and identify the significance level.\n%  \\item[Check.]\n%      Verify the conditions are met,\n%      which will include finding the expected value\n%      for each of the $k$ cells based on the null hypothesis,\n%      which we'll label as $E_1$, $E_2$, ..., $E_k$.\n%  \\item[Calculate.]\n%      Compute the degrees of freedom $df = k - 1$ and\n%      the test statistic using the expected values\n%      against the observed values $O_1, ..., O_k$:\n%      \\begin{align*}\n%      X^2\n%        = \\frac{(O_1 - E_1)^2}{E_1} +\n%            \\frac{(O_2 - E_2)^2}{E_2} +\n%            \\cdots +\n%            \\frac{(O_k - E_k)^2}{E_k}\n%      \\end{align*}\n%      Identify the p-value as the upper tail in the chi-square\n%      distribution using the test statistic as a cutoff.\n%  \\item[Conclude.]\n%      Evaluate the hypothesis test by comparing the p-value\n%      to $\\alpha$, and provide a conclusion in the context\n%      of the problem.\n%  \\end{description}\n%\\end{onebox}\n\nWhen examining a table with just two bins,\npick a single bin and use the one-proportion methods\nintroduced in Section~\\ref{singleProportion}.\n\n\n\\D{\\newpage}\n\n\\subsection{Evaluating goodness of fit for a distribution}\n\nSection~\\ref{geomDist} would be useful background reading\nfor this example, but it is not a prerequisite.\n\n\\index{data!S\\&P500 stock data|(}\n\n\\newcommand{\\spyears}{10}\n\\newcommand{\\spdays}{1362}\n\\newcommand{\\spdaysA}{717}\n\\newcommand{\\spdaysB}{369}\n\\newcommand{\\spdaysC}{155}\n\\newcommand{\\spdaysD}{69}\n\\newcommand{\\spdaysE}{28}\n\\newcommand{\\spdaysF}{14}\n\\newcommand{\\spdaysG}{10}\n\\newcommand{\\spdaysEA}{743}\n\\newcommand{\\spdaysEB}{338}\n\\newcommand{\\spdaysEC}{154}\n\\newcommand{\\spdaysED}{70}\n\\newcommand{\\spdaysEE}{32}\n\\newcommand{\\spdaysEF}{14}\n\\newcommand{\\spdaysEG}{12}\n\\newcommand{\\spdaysEProp}{0.1128}\n\\newcommand{\\spdaysEPerc}{11.28\\%}\n\\newcommand{\\spUpProp}{0.545}\n\\newcommand{\\spUpPerc}{54.5\\%}\n\\newcommand{\\spDownProp}{0.455}\n\\newcommand{\\spDownPerc}{45.5\\%}\n\\newcommand{\\spdaysXSq}{4.61}\n\\newcommand{\\spdaysN}{7}\n\\newcommand{\\spdaysDF}{6}\n\\newcommand{\\spdaysPvalue}{0.5951}\n\nWe can apply the chi-square testing framework to the\nsecond problem in this section:\nevaluating whether a certain statistical model fits\na data set.\nDaily stock returns from the S\\&P500 for \\spyears{} can be used to assess whether stock activity each day is independent of the stock's behavior on previous days. This sounds like a very complex question, and it is, but a chi-square test can be used to study the problem. We will label each day as \\resp{Up} or \\resp{Down} (\\resp{D}) depending on whether the market was up or down that day. For example, consider the following changes in price, their new labels of up and down, and then the number of days that must be observed before each \\resp{Up} day:\n\\begin{center}\\footnotesize\n\\begin{tabular}{lc ccc ccc ccc cc}\nChange in price\t\t&\\hspace{-1mm}\t& \\footnotesize2.52 &\n\t\\footnotesize-1.46 & \\footnotesize 0.51 &\n\t\\footnotesize-4.07 & \\footnotesize3.36 &\n\t\\footnotesize1.10 &\n\t\\footnotesize-5.46 & \\footnotesize-1.03 & \\footnotesize-2.99 & \\footnotesize1.71 \\\\\nOutcome\t & \\hspace{-1mm} &\n\tUp &\n\tD & Up &\n\tD & Up &\n\tUp &\n\tD & D & D & Up \\\\\n\\footnotesize Days to Up & \\hspace{-1mm} & 1 & - & 2 & - & 2 & 1 & - & - & - & 4 \\\\\n\\end{tabular}\n\\end{center}\nIf the days really are independent, then the number of days until a positive trading day should follow a geometric distribution. The geometric distribution describes the probability of waiting for the $k^{th}$ trial to observe the first success. Here each up day (Up) represents a success, and down (D) days represent failures. In the data above, it took only one day until the market was up, so the first wait time was 1 day. It took two more days before we observed our next \\resp{Up} trading day, and two more for the third \\resp{Up} day. We would like to determine if these counts (1, 2, 2, 1, 4, and so on) follow the geometric distribution. Figure~\\ref{sAndP500TimeToPosTrade} shows the number of waiting days for a positive trading day during \\spyears{} years for the S\\&P500.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{ll ccc ccc c ll}\n  \\hline\n  Days\t & \\hspace{2mm} & 1 & 2 & 3 & 4 & 5 & 6 & 7+ &\n      \\hspace{2mm} & Total \\\\\n  Observed &\t\t& \\spdaysA{} & \\spdaysB{} & \\spdaysC{} &\n      \\spdaysD{} & \\spdaysE{} & \\spdaysF{} & \\spdaysG{} & &\n      \\spdays{} \\\\\n  \\hline\n\\end{tabular}\n\\caption{Observed distribution of the waiting time until\n    a positive trading day for the S\\&P500.}\n\\label{sAndP500TimeToPosTrade}\n\\end{figure}\n\nWe consider how many days one must wait until observing an \\resp{Up} day on the S\\&P500 stock index. If the stock activity was independent from one day to the next and the probability of a positive trading day was constant, then we would expect this waiting time to follow a \\emph{geometric distribution}. We can organize this into a hypothesis framework:\n\\begin{itemize}\n\\item[$H_0$:] The stock market being up or down on a given day is independent from all other days. We will consider the number of days that pass until an \\resp{Up} day is observed. Under this hypothesis, the number of days until an \\resp{Up} day should follow a geometric distribution.\n\\item[$H_A$:] The stock market being up or down on a given day is not independent from all other days. Since we know the number of days until an \\resp{Up} day would follow a geometric distribution under the null, we look for deviations from the geometric distribution, which would support the alternative hypothesis.\n\\end{itemize}\nThere are important implications in our result for stock traders: if information from past trading days is useful in telling what will happen today, that information may provide an advantage over other traders.\n\nWe consider data for the S\\&P500 and summarize the waiting times in Figure~\\ref{sAndP500TimeToPosTrade2} and Figure~\\ref{geomFitEvaluationForSP500}. The S\\&P500 was positive on \\spUpPerc{} of those days.\n\n\\begin{figure}\n\\centering\n\\begin{tabular}{ll ccc ccc c ll}\n  \\hline\n  Days & \\hspace{1mm} &\n      1 & 2 & 3 & 4 & 5 & 6 & 7+ & \\hspace{1mm} & Total \\\\\n  \\hline\n  Observed & & \\spdaysA{} & \\spdaysB{} & \\spdaysC{} &\n      \\spdaysD{} & \\spdaysE{} & \\spdaysF{} & \\spdaysG{} & &\n      \\spdays{} \\\\\n  Geometric Model & & \\spdaysEA{} & \\spdaysEB{} & \\spdaysEC{} &\n      \\spdaysED{} & \\spdaysEE{} & \\spdaysEF{} & \\spdaysEG{} & &\n      \\spdays{} \\\\\n  \\hline\n\\end{tabular}\n\\caption{Distribution of the waiting time until a positive\n    trading day.\n    The expected counts based on the geometric model are\n    shown in the last row.\n    To find each expected count, we identify the probability\n    of waiting $D$ days based on the geometric model\n    ($P(D) = (1-\\spUpProp{})^{D-1}(\\spUpProp{})$)\n    and multiply by the total number of streaks, \\spdays{}.\n    For example, waiting for three days occurs under the\n    geometric model about\n    $\\spDownProp{}^2\\times \\spUpProp{} = \\spdaysEPerc{}$\n    of the time, which corresponds to\n    $\\spdaysEProp{} \\times \\spdays{} = \\spdaysEC$ streaks.}\n\\label{sAndP500TimeToPosTrade2}\n\\end{figure}\n\n\\begin{figure}\n  \\centering\n  \\Figure[A side-by-side bar plot is shown for the variable \"Wait Until Positive Day\", where the two groups shown for the bars are \"Observed counts\" and \"Expected counts\". The horizontal axis shows values 1, 2, 3, 4, 5, 6, and \"7+\". The bar heights highest for \"1\" at roughly 715 for Observed and 740 for Expected. The bar heights for \"2\" are about half as high as at \"1\", with values of about 370 for Observed and 340 for Expected. The bar heights for \"3\" are about another half has high at about 150 for each for observed and expected. The values at 5, 6, and 7+ are all relatively small, at or below about 30.]{0.85}{geomFitEvaluationForSP500}\n  \\caption{Side-by-side bar plot of the observed\n      and expected counts for each waiting time.}\n  \\label{geomFitEvaluationForSP500}\n\\end{figure}\n\nBecause applying the chi-square framework requires expected counts\nto be at least~5, we have \\emph{binned} together all the cases\nwhere the waiting time was at least \\spdaysN{} days to ensure each\nexpected count is well above this minimum.\nThe actual data, shown in the \\emph{Observed} row in\nFigure~\\ref{sAndP500TimeToPosTrade2}, can be compared to the\nexpected counts from the \\emph{Geometric Model} row.\nThe method for computing expected counts is discussed in\nFigure~\\ref{sAndP500TimeToPosTrade2}.\nIn general, the expected counts are determined by\n(1)~identifying the null proportion associated with each bin,\nthen (2)~multiplying each null proportion by the total count\nto obtain the expected counts.\nThat is, this strategy identifies what proportion of the total\ncount we would expect to be in each bin.\n\n\\begin{examplewrap}\n\\begin{nexample}{Do you notice any unusually large deviations\n    in the graph?\n    Can you tell if these deviations are due to chance just\n    by looking?}\n  It is not obvious whether differences in the observed counts\n  and the expected counts from the geometric distribution are\n  significantly different.\n  That is, it is not clear whether these deviations might be\n  due to chance or whether they are so strong that the data\n  provide convincing evidence against the null hypothesis.\n  However, we can perform a chi-square test using the counts\n  in Figure~\\ref{sAndP500TimeToPosTrade2}.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nFigure~\\ref{sAndP500TimeToPosTrade2}\nprovides a set of count data for waiting times\n($O_1=\\spdaysA{}$, $O_2=\\spdaysB{}$, ...)\nand expected counts under the geometric distribution\n($E_1=\\spdaysEA{}$, $E_2=\\spdaysEB{}$, ...).\nCompute the chi-square test statistic, $X^2$.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$X^2 =\n      \\frac{(\\spdaysA{}-\\spdaysEA{})^2}{\\spdaysEA{}} +\n      \\frac{(\\spdaysB{}-\\spdaysEB{})^2}{\\spdaysEB{}} +\n      \\cdots +\n      \\frac{(\\spdaysG{}-\\spdaysEG{})^2}{\\spdaysEG{}}\n    = \\spdaysXSq{}$}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nBecause the expected counts are all at least~5,\nwe can safely apply the chi-square distribution to $X^2$.\nHowever, how many degrees of freedom should\nwe~use?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{There are $k = \\spdaysN{}$ groups, so we use\n    $df = k - 1 = \\spdaysDF{}$.}\n\n\\begin{examplewrap}\n\\begin{nexample}{If the observed counts follow the\n    geometric model, then the chi-square test statistic\n    $X^2 = \\spdaysXSq{}$ would closely follow a chi-square\n    distribution with $df = \\spdaysDF{}$.\n    Using this information, compute a p-value.} \n  \\label{DNRejectGeomModelForSP500}%\n  Figure~\\ref{geomFitPValueForSP500} shows the\n  chi-square distribution, cutoff, and the shaded p-value.\n  % We could look up $X^2 = \\spdaysXSq{}$ in\n  % Appendix~\\ref{chiSquareProbabilityTable} to determine\n  % that the p-value is greater than 0.3.\n  Using software, we can find the p-value: \\spdaysPvalue{}.\n  Ultimately, we do not have sufficient evidence to reject\n  the notion that the wait times follow a geometric\n  distribution for the last \\spyears{} years of data\n  for the S\\&P500,\n  i.e. we cannot reject the notion that trading days\n  are independent.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A chi-square distribution with 6 degrees of freedom is shown, with the area above 4.61 shaded. This region appears to be about 60\\% of the area under the curve.]{0.74}{geomFitPValueForSP500}\n  \\caption{Chi-square distribution with \\spdaysDF{}\n      degrees of freedom.\n      The p-value for the stock analysis is shaded.}\n  \\label{geomFitPValueForSP500}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{In\n    Example~\\ref{DNRejectGeomModelForSP500},\n    we did not reject the null hypothesis that the trading days\n    are independent during the last \\spyears{} of data.\n    Why is this so important?}\n  It may be tempting to think the market is ``due'' for\n  an \\resp{Up} day if there have been several consecutive\n  days where it has been down.\n  However, we haven't found strong evidence that there's\n  any such property where the market is ``due'' for\n  a correction.\n  At the very least, the analysis suggests any dependence\n  between days is very weak.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!S\\&P500 stock data|)}\n\n\\CalculatorVideos{the chi-square goodness of fit test}\n\n\n{\\input{ch_inference_for_props/TeX/testing_for_goodness_of_fit_using_chi-square.tex}}\n\n\n\n\n\n%__________________\n\\section{Testing for independence in two-way tables}\n\\label{twoWayTablesAndChiSquare}\n\n\\index{data!iPod|(}\n\n\\newcommand{\\iPodAA}{2}\n\\newcommand{\\iPodAB}{23}\n\\newcommand{\\iPodAC}{36}\n\\newcommand{\\iPodAD}{61}\n\\newcommand{\\iPodAFraction}{0.2785}\n\\newcommand{\\iPodAExpected}{20.33}\n\\newcommand{\\iPodBA}{71}\n\\newcommand{\\iPodBB}{50}\n\\newcommand{\\iPodBC}{37}\n\\newcommand{\\iPodBD}{158}\n\\newcommand{\\iPodBFraction}{0.7215}\n\\newcommand{\\iPodBExpected}{52.67}\n\\newcommand{\\iPodDA}{73}\n\\newcommand{\\iPodDB}{73}\n\\newcommand{\\iPodDC}{73}\n\\newcommand{\\iPodDD}{219}\n\\newcommand{\\iPodN}{\\iPodDD}\n\nWe all buy used products --\ncars, computers, textbooks, and so on --\nand we sometimes assume the sellers of those products\nwill be forthright about any underlying problems with\nwhat they're selling.\nThis is not something we should take for granted.\nResearchers recruited \\iPodN{} participants in a study where they\nwould sell a used iPod\\footnote{For readers not as old as\n  the authors, an iPod is basically an iPhone without\n  any cellular service, assuming it was one of the later\n  generations. Earlier generations were more basic.}\nthat was known to have frozen twice in the past.\nThe participants were incentivized to get as much money\nas they could for the iPod since they would receive a 5\\%\ncut of the sale on top of \\$10 for participating.\nThe researchers wanted to understand what types of questions\nwould elicit the seller to disclose the freezing issue.\n\nUnbeknownst to the participants who were the sellers\nin the study,\nthe buyers were collaborating with the researchers\nto evaluate the influence of different questions\non the likelihood of getting the sellers to disclose\nthe past issues with the iPod.\nThe scripted buyers started with\n``Okay, I guess I'm supposed to go first.\n  So you've had the iPod for 2 years ...''\nand ended with one of three questions:\n\\begin{itemize}\n\\item General: What can you tell me about it?\n\\item Positive Assumption: It doesn't have any problems, does it?\n\\item Negative Assumption: What problems does it have?\n\\end{itemize}\nThe question is the treatment given to the sellers,\nand the response is whether the question prompted them\nto disclose the freezing issue with the iPod.\nThe results are shown in Figure~\\ref{ipod_ask_data_summary},\nand the data suggest that asking the,\n\\emph{What problems does it have?},\nwas the most effective at getting the seller to disclose\nthe past freezing issues.\nHowever, you should also be asking yourself:\ncould we see these results due to chance alone,\nor is this in fact evidence that some questions\nare more effective for getting at the truth?\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l ccc l}\n  \\hline\n  & General & Positive Assumption &\n      Negative Assumption & Total \\\\ \n  \\hline\n  Disclose Problem & \\iPodAA{} &  \\iPodAB{} &\n      \\iPodAC{} & \\iPodAD{} \\\\ \n  Hide Problem &  \\iPodBA{} &  \\iPodBB{} &\n      \\iPodBC{} & \\iPodBD{} \\\\ \n  \\hline\n  Total & \\iPodDA{} & \\iPodDB{} &\n      \\iPodDC{} & \\iPodDD{} \\\\\n  \\hline\n\\end{tabular}\n\\caption{Summary of the iPod study, where a question was\n  posed to the study participant who acted}\n\\label{ipod_ask_data_summary}\n\\end{figure}\n\n\\begin{onebox}{Differences of one-way tables vs two-way tables}\n  A one-way table describes counts for each outcome in a single\n  variable.\n  A two-way table describes counts for \\emph{combinations}\n  of outcomes for two variables.\n  When we consider a two-way table, we often would like to know,\n  are these variables related in any way?\n  That is, are they dependent (versus independent)?\n\\end{onebox}\n\nThe hypothesis test for the iPod experiment is really about\nassessing whether there is statistically significant evidence\nthat the success each question had on getting the participant\nto disclose the problem with the iPod.\nIn other words, the goal is to check whether the buyer's\nquestion was independent of whether the seller disclosed\na problem.\n\n\n\\D{\\newpage}\n\n\\subsection{Expected counts in two-way tables}\n\n\\noindent%\nLike with one-way tables, we will need to compute\nestimated counts for each cell in a two-way table.\n\n\\begin{examplewrap}\n\\begin{nexample}{From the experiment,\n    we can compute the proportion of all sellers who disclosed\n    the freezing problem as $\\iPodAD{}/\\iPodDD = \\iPodAFraction{}$.\n    If there really is no difference among the questions\n    and 27.85\\% of sellers were going to disclose the freezing\n    problem no matter the question that was put to them,\n    how many of the \\iPodDA{} people in the \\resp{General}\n    group would we have expected to disclose the freezing\n    problem?} \\label{iPodExComputeExpAA}\n  We would predict that $\\iPodAFraction{} \\times \\iPodDA{} = \\iPodAExpected{}$\n  sellers would disclose the problem.\n  Obviously we observed fewer than this, though it is not\n  yet clear if that is due to chance variation or whether\n  that is because the questions vary in how effective they\n  are at getting to the truth.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{iPodExComputeExpBB}\nIf the questions were actually equally effective,\nmeaning about 27.85\\% of respondents would disclose the\nfreezing issue regardless of what question they were asked,\nabout how many sellers would we expect to \\emph{hide} the\nfreezing problem from the Positive Assumption\ngroup?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We would expect\n    $(1 - \\iPodAFraction{}) \\times \\iPodDA{} = \\iPodBExpected{}$.\n    It is okay that this result,\n    like the result from Example~\\ref{iPodExComputeExpAA},\n    is a fraction.}\n\nWe can compute the expected number of sellers who we would\nexpect to disclose or hide the freezing issue for all groups,\nif the questions had no impact on what they disclosed,\nusing the same strategy employed in\nExample~\\ref{iPodExComputeExpAA} and\nGuided Practice~\\ref{iPodExComputeExpBB}.\nThese expected counts were used to construct Figure~\\ref{ipod_ask_data_summary_expected},\nwhich is the same as Figure~\\ref{ipod_ask_data_summary},\nexcept now the expected counts have been added in parentheses.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l lll l}\n  \\hline\n  & General & Positive Assumption &\n      Negative Assumption & Total \\\\ \n  \\hline\n  Disclose Problem &\n      \\iPodAA{} \\ \\highlightO{\\footnotesize(\\iPodAExpected{})} &\n      \\iPodAB{} \\highlightO{\\footnotesize(\\iPodAExpected{})} &\n      \\iPodAC{} \\highlightO{\\footnotesize(\\iPodAExpected{})} &\n      \\iPodAD{} \\\\ \n  Hide Problem &\n      \\iPodBA{} \\highlightO{\\footnotesize(\\iPodBExpected{})} &\n      \\iPodBB{} \\highlightO{\\footnotesize(\\iPodBExpected{})} &\n      \\iPodBC{} \\highlightO{\\footnotesize(\\iPodBExpected{})} &\n      \\iPodBD{} \\\\ \n  \\hline\n  Total & \\iPodDA{} & \\iPodDB{} &\n      \\iPodDC{} & \\iPodDD{} \\\\\n  \\hline\n\\end{tabular}\n\\caption{The observed counts and the\n    \\highlightO{(expected counts)}.}\n\\label{ipod_ask_data_summary_expected}\n\\end{figure}\n\nThe examples and exercises above provided some help\nin computing expected counts.\nIn general, expected counts for a two-way table may\nbe computed using the row totals, column totals,\nand the table total.\nFor instance, if there was no difference between the groups,\nthen about 27.85\\% of each column should be in the first row:\n\\begin{align*}\n\\iPodAFraction{}\\times (\\text{column 1 total}) &= \\iPodAExpected{} \\\\\n\\iPodAFraction{}\\times (\\text{column 2 total}) &= \\iPodAExpected{} \\\\\n\\iPodAFraction{}\\times (\\text{column 3 total}) &= \\iPodAExpected{}\n\\end{align*}\nLooking back to how \\iPodAFraction{} was computed --\nas the fraction of sellers who disclosed the freezing issue\n($\\iPodBD{}/\\iPodDD{}$) --\nthese three expected counts could have been computed as\n\\begin{align*}\n\\left(\\frac{\\text{row 1 total}}{\\text{table total}}\\right)\n    \\text{(column 1 total)} &= \\iPodAExpected{} \\\\\n\\left(\\frac{\\text{row 1 total}}{\\text{table total}}\\right)\n    \\text{(column 2 total)} &= \\iPodAExpected{} \\\\\n\\left(\\frac{\\text{row 1 total}}{\\text{table total}}\\right)\n    \\text{(column 3 total)} &= \\iPodAExpected{}\n\\end{align*}\nThis leads us to a general formula for computing expected\ncounts in a two-way table when we would like to test whether\nthere is strong evidence of an association between the column\nvariable and row variable.\n\n\\D{\\newpage}\n\n\\begin{onebox}{Computing expected counts in a two-way table}\n  To identify the expected count for the $i^{th}$ row\n  and $j^{th}$ column, compute\n  \\begin{align*}\n  \\text{Expected Count}_{\\text{row }i,\\text{ col }j}\n    = \\frac{(\\text{row $i$ total}) \\times\n        (\\text{column $j$ total})}{\\text{table total}}\\vspace{2mm}\n  \\end{align*}\n\\end{onebox}\n\n\n\\subsection{The chi-square test for two-way tables}\n\nThe chi-square test statistic for a two-way table is found\nthe same way it is found for a one-way table.\nFor each table count, compute\n\\begin{align*}\n&\\text{General formula} &&\n    \\frac{(\\text{observed count } - \\text{expected count})^2}\n        {\\text{expected count}} \\\\\n&\\text{Row 1, Col 1} &&\n    \\frac{(\\iPodAA - \\iPodAExpected)^2}{\\iPodAExpected} = 16.53 \\\\\n&\\text{Row 1, Col 2} &&\n    \\frac{(\\iPodAB - \\iPodAExpected)^2}{\\iPodAExpected} = 0.35 \\\\\n& \\hspace{9mm}\\vdots &&\n    \\hspace{13mm}\\vdots \\\\\n&\\text{Row 2, Col 3} &&\n    \\frac{(\\iPodBC - \\iPodBExpected)^2}{\\iPodBExpected} = 4.66\n\\end{align*}\nAdding the computed value for each cell gives the chi-square test statistic $X^2$:\n\\begin{align*}\nX^2 = 16.53 + 0.35 + \\dots + 4.66 = 40.13\n\\end{align*}\nJust like before, this test statistic follows a chi-square distribution. However, the degrees of freedom are computed a little differently for a two-way table.\\footnote{Recall: in the one-way table, the degrees of freedom was the number of cells minus 1.} For two way tables, the degrees of freedom is equal to\n\\begin{align*}\ndf = \\text{(number of rows minus 1)}\\times \\text{(number of columns minus 1)}\n\\end{align*}\nIn our example, the degrees of freedom parameter is\n\\begin{align*}\ndf = (2-1)\\times (3-1) = 2\n\\end{align*}\nIf the null hypothesis is true\n(i.e. the questions had no impact on the sellers in\n    the experiment),\nthen the test statistic $X^2 = 40.13$ closely follows\na chi-square distribution with 2 degrees of freedom.\nUsing this information, we can compute the p-value for\nthe test, which is depicted in\nFigure~\\ref{iPodChiSqTail}.\n\n\\begin{onebox}{Computing degrees of freedom for a two-way table}\n  When applying the chi-square test to a two-way table,\n  we use\n  \\begin{align*}\n  df = (R-1)\\times (C-1)\n  \\end{align*}\n  where $R$ is the number of rows in the table\n  and $C$ is the number of columns.\n\\end{onebox}\n\nWhen analyzing 2-by-2 contingency tables, one guideline\nis to use the two-proportion methods introduced in\nSection~\\ref{differenceOfTwoProportions}.\n\n\\D{\\newpage}\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.65\\textwidth]{ch_inference_for_props/figures/iPodChiSqTail/iPodChiSqTail}\n\\caption{Visualization of the p-value for $X^2 = 40.13$\n    when $df = 2$.}\n\\label{iPodChiSqTail}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Find the p-value and draw a conclusion\n    about whether the question affects the sellers likelihood\n    of reporting the freezing problem.}\n  % Looking in Appendix~\\ref{chiSquareProbabilityTable}\n  % on page~\\pageref{chiSquareProbabilityTable},\n  % we examine the row corresponding to 2 degrees of freedom.\n  % The test statistic, $X^2 = 40.13$,\n  % is larger than the value in the last column,\n  % meaning the tail area and p-value are smaller than 0.001.\n  Using a computer, we can compute a very precise value\n  for the tail area above $X^2 = 40.13$ for a chi-square\n  distribution with 2 degrees of freedom:\n  0.000000002.\n  (If using the table in\n    Appendix~\\ref{chiSquareProbabilityTable},\n    we would identify the p-value is smaller\n    than 0.001.)\n  Using a significance level of $\\alpha=0.05$,\n  the null hypothesis is rejected since the p-value is smaller.\n  That is, the data provide convincing evidence that the\n  question asked did affect a seller's likelihood to tell\n  the truth about problems with the iPod.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!iPod|)}\n\n\\index{data!diabetes|(}\n\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{diabetes2ExpMetRosiLifestyleSummary}\n    summarizes the results of an experiment evaluating\n    three treatments for Type~2 Diabetes in patients\n    aged 10-17 who were being treated with metformin.\n    The three treatments considered were\n    continued treatment with metformin (\\resp{met}),\n    treatment with metformin combined with rosiglitazone\n    (\\resp{rosi}),\n    or a lifestyle intervention program.\n    Each patient had a primary outcome, which was either lacked\n    glycemic control (failure)\n    or did not lack that control (success).\n    What are appropriate hypotheses for this test?}\n  \\label{diabetes2ExpMetRosiLifestyleIntroExample}\n  \\begin{itemize}\n  \\item[$H_0$:] There is no difference in the effectiveness\n      of the three treatments.\n  \\item[$H_A$:] There is some difference in effectiveness\n      between the three treatments, e.g. perhaps the\n      \\resp{rosi} treatment performed better than\n      \\resp{lifestyle}.\n  \\end{itemize}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l ccc l}\n\\hline\n & Failure & Success & Total \\\\ \n\\hline\n\\resp{lifestyle} & 109 & 125 & 234 \\\\ \n\\resp{met} & 120 & 112 & 232 \\\\ \n\\resp{rosi} &  90 & 143 & 233 \\\\ \n\\hline\nTotal & 319 & 380 & 699 \\\\\n\\hline\n\\end{tabular}\n\\caption{Results for the Type~2 Diabetes study.}\n\\label{diabetes2ExpMetRosiLifestyleSummary}\n\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nA chi-square test for a two-way table may be used to test\nthe hypotheses in\nExample~\\ref{diabetes2ExpMetRosiLifestyleIntroExample}.\nAs a first step, compute the expected values for each of the\nsix table cells.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The expected count for\n    row one / column one is found by multiplying the\n    row one total (234) and column one total (319),\n    then dividing by the table total (699):\n    $\\frac{234\\times 319}{699} = 106.8$.\n    Similarly for the second column and the first row:\n    $\\frac{234\\times 380}{699} = 127.2$.\n    Row 2: 105.9 and 126.1.\n    Row 3: 106.3 and 126.7.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nCompute the chi-square test statistic for the data in\nFigure~\\ref{diabetes2ExpMetRosiLifestyleSummary}.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{For each cell,\n    compute $\\frac{(\\text{obs} - \\text{exp})^2}{exp}$.\n    For instance, the first row and first column:\n    $\\frac{(109-106.8)^2}{106.8} = 0.05$.\n    Adding the results of each cell gives the\n    chi-square test statistic:\n    {\\scriptsize$X^2 = 0.05 + \\cdots + 2.11 = 8.16$}.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nBecause there are 3 rows and 2 columns,\nthe degrees of freedom for the test is\n$df = (3 - 1) \\times (2 - 1) = 2$.\nUse $X^2 = 8.16$, $df = 2$, evaluate whether\nto reject the null hypothesis using a significance level\nof~0.05.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{\n    If using a computer, we can identify the p-value\n    as 0.017.\n    That is, we reject the null hypothesis because\n    the p-value is less than 0.05, and we conclude\n    that at least one of the treatments is more or\n    less effective than the others at treating\n    Type~2 Diabetes for glycemic control.}\n\n\\index{data!diabetes|)}\n\n\\CalculatorVideos{the chi-square test for independence}\n\n\n{\\input{ch_inference_for_props/TeX/testing_for_independence_in_two-way_tables.tex}}\n"
  },
  {
    "path": "ch_inference_for_props/TeX/difference_of_two_proportions.tex",
    "content": "\\exercisesheader{}\n\n% 17\n\n\\eoce{\\qt{Social experiment, Part I\\label{social_experiment_conditions}} A ``social \nexperiment\" conducted by a TV program questioned what people do when they see \na very obviously bruised woman getting picked on by her boyfriend. On two \ndifferent occasions at the same restaurant, the same couple was depicted. In \none scenario the woman was dressed ``provocatively'' and in the other \nscenario the woman was dressed ``conservatively''. The table below shows how \nmany restaurant diners were present under each scenario, and whether or not \nthey intervened.\n\\begin{center}\n\\begin{tabular}{ll cc c} \n            &               & \\multicolumn{2}{c}{\\textit{Scenario}} \\\\\n\\cline{3-4}\n                            &       & Provocative   & Conservative  & Total \\\\\n\\cline{2-5}\n\\multirow{2}{*}{\\textit{Intervene}} &Yes & 5   & 15  & 20    \\\\\n                            &No     & 15      & 10  & 25 \\\\\n\\cline{2-5}\n                            &Total  & 20      & 25  & 45 \\\\\n\\end{tabular}\n\\end{center}\nExplain why the sampling distribution of the difference between the \nproportions of interventions under provocative and conservative scenarios \ndoes not follow an approximately normal distribution.\n}{}\n\n% 18\n\n\\eoce{\\qt{Heart transplant success\\label{heart_transplant_conditions}} The Stanford \nUniversity Heart Transplant Study was conducted to determine whether an \nexperimental heart transplant program increased lifespan. Each patient \nentering the program was officially designated a heart transplant candidate, \nmeaning that he was gravely ill and might benefit from a new heart. Patients \nwere randomly assigned into treatment and control groups. Patients in the \ntreatment group received a transplant, and those in the control group did \nnot. The table below displays how many patients survived and died in each \ngroup. \\footfullcite{Turnbull+Brown+Hu:1974}\\vspace{-2mm}\n\\begin{center}\n\\begin{tabular}{rcc}\n\\hline\n            & control   & treatment \\\\ \n\\hline\nalive       & 4         & 24 \\\\ \ndead        & 30        & 45 \\\\ \n\\hline\n\\end{tabular}\n\\end{center}\nSuppose we are interested in estimating the difference in survival rate between \nthe control and treatment groups using a confidence interval.\nExplain why we cannot construct such an interval using the normal \napproximation. What might go wrong if we constructed the confidence interval \ndespite this problem?\n}{}\n\n% 19\n\n\\eoce{\\qt{Gender and color preference\\label{gender_color_preference_CI_concept}} \nA study asked 1,924 male and 3,666 female undergraduate college students \ntheir favorite color.\nA 95\\% confidence interval for the difference between \nthe proportions of males and females whose favorite color is black \n$(p_{male} - p_{female})$ was calculated to be (0.02, 0.06).\nBased on this \ninformation, determine if the following statements about\nundergraduate college students are true or false, and \nexplain your reasoning for each statement you identify as false.\n\\footfullcite{Ellis:2001}\n\\begin{parts}\n\\item We are 95\\% confident that the true proportion of males whose favorite \ncolor is black is 2\\% lower to 6\\% higher than the true proportion of females \nwhose favorite color is black.\n\\item We are 95\\% confident that the true proportion of males whose favorite \ncolor is black is 2\\% to 6\\% higher than the true proportion of females whose \nfavorite color is black.\n\\item 95\\% of random samples will produce 95\\% confidence intervals that \ninclude the true difference between the population proportions of males and \nfemales whose favorite color is black.\n\\item We can conclude that there is a significant difference between the \nproportions of males and females whose favorite color is black and that the \ndifference between the two sample proportions is too large to plausibly be \ndue to chance.\n\\item The 95\\% confidence interval for $(p_{female} - p_{male})$ cannot be \ncalculated with only the information given in this exercise.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 20\n\n\\eoce{\\qt{Government shutdown\\label{government_shutdown_CI_concept}}\nThe United States federal government shutdown of 2018–2019 occurred\nfrom December 22, 2018 until January 25, 2019, a span of 35 days.\nA~Survey USA poll of 614 randomly sampled Americans during this time\nperiod reported that 48\\% of those who make less than \\$40,000 per\nyear and 55\\% of those who make \\$40,000 or more per year said the\ngovernment shutdown has not at all affected them personally.\nA~95\\% confidence interval for $(p_\\text{$<$40K} - p_\\text{$\\ge$40K})$,\nwhere $p$ is the proportion of those who said the government shutdown\nhas not at all affected them personally, is (-0.16, 0.02).\nBased on this information, determine if the following statements are \ntrue or false, and explain your reasoning if you identify the statement\nas false.\\footfullcite{data:govt_shuthown}\n\\begin{parts}\n\\item\n    At the 5\\% significance level, the data provide convincing\n    evidence of a real difference in the proportion who are\n    not affected personally between Americans who make less than\n    \\$40,000 annually and Americans who make \\$40,000 annually.\n\\item\n    We are 95\\% confident that 16\\% more to 2\\% fewer Americans\n    who make less than \\$40,000 per year are not at all personally\n    affected by the government shutdown compared to those who make\n    \\$40,000 or more per year.\n\\item\n    A 90\\% confidence interval for\n    $(p_\\text{$<$40K} - p_\\text{$\\ge$40K})$\n    would be wider than the $(-0.16, 0.02)$ interval.\n\\item\n    A 95\\% confidence interval for\n    $(p_\\text{$\\ge$40K} - p_\\text{$<$40K})$\n    is (-0.02, 0.16).\n\\end{parts}\n% p1 = 0.48\n% p2 = 0.55\n% n1 = 162\n% n2 = 452\n% ((p1 - p2) + c(-1,1) * 1.96 * sqrt( (p1*(1-p1)/n1) + (p2*(1-p2)/n2)) ) %>% round(2)\n% (-0.16, 0.02)\n}{}\n\n% 21\n\n\\eoce{\\qt{National Health Plan,\n    Part III\\label{national_health_plan_CI_replaced}}\nExercise~\\ref{national_health_plan_HT}\npresents the results of a poll evaluating support for\na generically branded ``National Health Plan''\nin the United States.\n79\\% of 347 Democrats and 55\\% of 617 Independents\nsupport a National Health Plan.\n\\begin{parts}\n\\item\n    Calculate a 95\\% confidence interval for the\n    difference between the proportion of Democrats\n    and Independents who support a National\n    Health Plan $(p_{D} - p_{I})$, and interpret\n    it in this context.\n    We have already checked conditions for you.\n\\item\n    True or false:\n    If we had picked a random Democrat and a random\n    Independent at the time of this poll, it is more\n    likely that the Democrat would support the National\n    Health Plan than the Independent.\n\\end{parts}\n}{}\n\n% 22\n\n\\eoce{\\qt{Sleep deprivation, CA vs. OR, Part I\\label{sleep_OR_CA_CI}} According to \na report on sleep deprivation by the Centers for Disease Control and Prevention, \nthe proportion of California residents who reported insufficient rest or sleep \nduring each of the preceding 30 days is 8.0\\%, while this proportion is 8.8\\% \nfor Oregon residents. These data are based on simple random samples of 11,545 \nCalifornia and 4,691 Oregon residents. Calculate a 95\\% confidence interval \nfor the difference between the proportions of Californians and Oregonians who \nare sleep deprived and interpret it in context of the data.\\footfullcite{data:sleepCAandOR}\n}{}\n\n% 23\n\n\\eoce{\\qt{Offshore drilling, Part I\\label{offshore_drill_edu_dontknow_HT}}\nA survey asked 827 randomly sampled registered voters in California\n``Do you support? Or do you oppose? Drilling for oil and natural gas\noff the Coast of California? Or do you not know enough to say?''\nBelow is the distribution of \nresponses, separated based on whether or not the respondent graduated from \ncollege. \\footfullcite{data:prop19_and_offshoreDrill} \\\\[1.3mm]\n\n\\noindent\\begin{minipage}[c]{0.6\\textwidth}\n\\begin{parts}\n\\item What percent of college graduates and what percent of the non-college \ngraduates in this sample do not know enough to have an opinion on drilling \nfor oil and natural gas off the Coast of California?\n\\item Conduct a hypothesis test to determine if the data provide strong \nevidence that the proportion of college graduates who do not have an opinion \non this issue is different than that of non-college graduates.\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.4\\textwidth}\n\\begin{center}\n\\begin{tabular}{l c c}\n\t\t\t& \\multicolumn{2}{c}{\\textit{College Grad}} \\\\\n\\cline{2-3}\n\t\t\t& Yes\t\t& No\t\\\\\n\\cline{1-3}\nSupport\t\t& 154\t\t& 132\t\\\\\nOppose\t\t& 180\t\t& 126\t\\\\\nDo not know\t& 104\t\t& 131\t\\\\\n\\cline{1-3}\n Total\t\t& 438\t\t& 389\t\t\n\\end{tabular}\n\\end{center}\n\\end{minipage}\n}{}\n\n% 24\n\n\\eoce{\\qt{Sleep deprivation, CA vs. OR, Part II\\label{sleep_OR_CA_HT}} \nExercise~\\ref{sleep_OR_CA_CI} provides data on sleep deprivation rates of \nCalifornians and Oregonians. The proportion of California residents who \nreported insufficient rest or sleep during each of the preceding 30 days is \n8.0\\%, while this proportion is 8.8\\% for Oregon residents. These data are \nbased on simple random samples of 11,545 California and 4,691 Oregon \nresidents. \n\\begin{parts}\n\\item Conduct a hypothesis test to determine if these data provide strong \nevidence the rate of sleep deprivation is different for the two states. \n(Reminder: Check conditions)\n\\item It is possible the conclusion of the test in part (a) is incorrect. If \nthis is the case, what type of error was made?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 25\n\n\\eoce{\\qt{Offshore drilling, Part II\\label{offshore_drill_edu_support_HT}}\nResults of a poll evaluating support for drilling for oil\nand natural gas off the coast of California were introduced\nin Exercise~\\ref{offshore_drill_edu_dontknow_HT}.\n\\begin{center}\n\\begin{tabular}{l c c}\n\t\t\t\t& \\multicolumn{2}{c}{\\textit{College Grad}} \\\\\n\\cline{2-3}\n\t\t\t\t\t\t& Yes\t\t& No\t\t\t\t\\\\\n\\cline{1-3}\nSupport\t\t& 154\t\t& 132\t\t\t\\\\\nOppose\t\t& 180\t\t& 126\t\t\t\\\\\nDo not know\t& 104\t\t& 131\t\t\t\\\\\n\\cline{1-3}\n Total\t\t& 438\t\t& 389\t\t\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item What percent of college graduates and what percent of the non-college \ngraduates in this sample support drilling for oil and natural gas off the Coast \nof California?\n\\item Conduct a hypothesis test to determine if the data provide strong evidence \nthat the proportion of college graduates who support off-shore drilling in California \nis different than that of non-college graduates.\n\\end{parts}\n}{}\n\n% 26\n\n\\eoce{\\qt{Full body scan, Part I\\label{full_body_scan_HT_Error}} A news article \nreports that ``Americans have differing views on two potentially inconvenient \nand invasive practices that airports could implement to uncover potential \nterrorist attacks.\" This news piece was based on a survey conducted among a \nrandom sample of 1,137 adults nationwide, where one of the questions on the \nsurvey was ``Some airports are \nnow using `full-body' digital x-ray machines to electronically screen \npassengers in airport security lines. Do you think these new x-ray machines \nshould or should not be used at airports?\" Below is a summary of responses \nbased on party affiliation. \\footfullcite{news:fullBodyScan}\n\\begin{center}\n\\begin{tabular}{ll  cc c} \n            &   & \\multicolumn{3}{c}{\\textit{Party Affiliation}} \\\\\n\\cline{3-5}\n                                &           & Republican & Democrat & Independent   \\\\\n\\cline{2-5}\n\\multirow{3}{*}{\\textit{Answer}}& Should    & 264        & 299      & 351 \\\\\n                                & Should not& 38         & 55       & 77 \\\\\n                                & Don't know/No answer & 16 & 15    & 22 \\\\\n\\cline{2-5}\n                                & Total      & 318       & 369      & 450\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Conduct an appropriate hypothesis test evaluating whether there is a \ndifference in the proportion of Republicans and Democrats who think the full-\nbody scans should be applied in airports. Assume that all relevant conditions \nare met.\n\\item The conclusion of the test in part (a) may be incorrect, meaning a \ntesting error was made. If an error was made, was it a Type~1 or a Type~2 \nError? Explain.\n\\end{parts}\n}{}\n\n% 27\n\n\\eoce{\\qt{Sleep deprived transportation workers\\label{sleep_deprived_driver_HT}} \nThe National Sleep Foundation conducted a survey on the sleep habits of \nrandomly sampled transportation workers and a control sample of non-transportation \nworkers. The results of the survey are shown below.\n\\footfullcite{data:sleepTransport}\\vspace{-1.8mm}\n\\begin{center}\n\\begin{tabular}{l c c c c c }\n\t\t\t\t\t\t& \t\t\t& \\multicolumn{4}{c}{\\textit{Transportation Professionals}} \\\\\n\\cline{3-6}\n\t\t\t& \t\t\t& \t\t& Truck\t& Train\t\t& Bus/Taxi/Limo\t\t\\\\\n\t\t\t& \\textit{Control}& Pilots\t& Drivers\t& Operators\t& Drivers\t\\\\\n\\cline{1-6}\nLess than 6 hours of sleep\t& 35\t\t& 19\t\t& 35\t& 29\t& 21\t\\\\\n6 to 8 hours of sleep\t\t& 193\t\t& 132\t    & 117\t& 119\t& 131\t\\\\\nMore than 8 hours\t\t\t& 64\t\t& 51\t\t& 51\t& 32\t& 58\t\\\\\n\\cline{1-6}\nTotal\t\t\t\t\t\t& 292\t\t& 202\t    & 203\t& 180\t& 210\t\t\n\\end{tabular}\n\\end{center}\\vspace{-1.2mm}\nConduct a hypothesis test to evaluate if these data provide evidence of a \ndifference between the proportions of truck drivers and non-transportation \nworkers (the control group) who get less than 6 hours of sleep per day, i.e. \nare considered sleep deprived.\n}{}\n\n\\D{\\newpage}\n\n% 28\n\n\\eoce{\\qt{Prenatal vitamins and Autism\\label{prenatal_vitamin_autism_HT}} \nResearchers studying the link between prenatal vitamin use and autism \nsurveyed the mothers of a random sample of children aged 24 - 60 months with \nautism and conducted another separate random sample for children with typical \ndevelopment. The table below shows the number of mothers in each group who \ndid and did not use prenatal vitamins during the three months before \npregnancy (periconceptional period).\\footfullcite{Schmidt:2011}\\vspace{-1.8mm}\n\\begin{center}\n\\begin{tabular}{l l c c c}\n\t\t&\t\t\t& \\multicolumn{2}{c}{\\textit{Autism}}\t&\t\t\\\\\n\\cline{3-4}\n\t\t&\t\t\t& Autism\t\t& Typical development\t\t& Total\t\\\\\n\\cline{2-5}\n\\textit{Periconceptional}\t& No vitamin\t& 111\t& 70\t\t& 181\t\\\\\n\\textit{prenatal vitamin}\t& Vitamin\t& 143\t\t& 159\t\t& 302\t\\\\\n\\cline{2-5}\n\t\t\t\t\t\t\t& Total\t\t& 254\t\t& 229\t\t& 483\n\\end{tabular}\n\\end{center}\\vspace{-4.2mm}\n\\begin{parts}\n\\item State appropriate hypotheses to test for independence of use of \nprenatal vitamins during the three months before pregnancy and autism.\n\\item Complete the hypothesis test and state an appropriate conclusion. \n(Reminder: Verify any necessary conditions for the test.)\n\\item A New York Times article reporting on this study was titled ``Prenatal \nVitamins May Ward Off Autism\". Do you find the title of this article to be \nappropriate? Explain your answer. Additionally, propose an alternative title.\n\\footfullcite{news:prenatalVitAutism}\n\\end{parts}\n}{}\n\n% 29\n\n\\eoce{\\qt{HIV in sub-Saharan Africa\\label{hiv_africa_HT}}\nIn July 2008 the US National Institutes of Health announced\nthat it was stopping a clinical study early because of unexpected\nresults.\nThe study population consisted of HIV-infected women in sub-Saharan\nAfrica who had been given single dose Nevaripine (a treatment for HIV)\nwhile giving birth, to prevent transmission of HIV to the infant.\nThe study was a randomized comparison of continued\ntreatment of a woman (after successful childbirth)\nwith Nevaripine vs Lopinavir, a second drug used to treat HIV.\n240 women participated in the study;\n120 were randomized to each of the two treatments.\nTwenty-four weeks after starting the study treatment,\neach woman was tested to determine if the HIV infection\nwas becoming worse (an outcome called \\textit{virologic failure}).\nTwenty-six of the 120 women treated with Nevaripine experienced\nvirologic failure, while 10 of the 120 women treated with the\nother drug experienced virologic failure.\\footfullcite{Lockman:2007}\n\\begin{parts}\n\\item Create a two-way table presenting the results of this study.\n\\item State appropriate hypotheses to test for difference in virologic failure\nrates between treatment groups.\n\\item Complete the hypothesis test and state an appropriate conclusion. \n(Reminder: Verify any necessary conditions for the test.)\n\\end{parts}\n}{}\n\n% 30\n\n\\eoce{\\qt{An apple a day keeps the doctor\n    away\\label{apple_doctor_HT_concept}}\nA physical education teacher at a high school wanting\nto increase awareness on issues of nutrition and health\nasked her students at the beginning of the semester\nwhether they believed the expression\n``an apple a day keeps the doctor away'',\nand 40\\% of the students responded yes.\nThroughout the semester she started each class with\na brief discussion of a study highlighting positive\neffects of eating more fruits and vegetables.\nShe conducted the same apple-a-day survey at the end\nof the semester, and this time 60\\% of the students\nresponded yes.\nCan she used a two-proportion method from this section\nfor this analysis?\nExplain your reasoning.\n}{}\n"
  },
  {
    "path": "ch_inference_for_props/TeX/inference_for_a_single_proportion.tex",
    "content": "\\exercisesheader{}\n\n% 1\n\n\\eoce{\\qt{Vegetarian college students\\label{veg_coll_students_CLT}} Suppose that 8\\% \nof college students are vegetarians. Determine if the following statements are \ntrue or false, and explain your reasoning.\n\\begin{parts}\n\\item The distribution of the sample proportions of vegetarians in random \nsamples of size 60 is approximately normal since $n \\ge 30$. \n\\item The distribution of the sample proportions of vegetarian college \nstudents in random samples of size 50 is right skewed.\n\\item A random sample of 125 college students where 12\\% are vegetarians \nwould be considered unusual. \n\\item A random sample of 250 college students where 12\\% are vegetarians \nwould be considered unusual.\n\\item The standard error would be reduced by one-half if we increased the \nsample size from 125 to~250.\n\\end{parts}\n \n}{}\n\n% 2\n\n\\eoce{\\qt{Young Americans, Part I\\label{young_americans_CLT_1}} About 77\\% of \nyoung adults think they can achieve the American dream. Determine if the \nfollowing statements are true or false, and explain your reasoning.\n\\footfullcite{news:youngAmericans1}\n\\begin{parts}\n\\item The distribution of sample proportions of young Americans who think \nthey can achieve the American dream in samples of size 20 is left skewed.\n\\item The distribution of sample proportions of young Americans who think \nthey can achieve the American dream in random samples of size 40 is \napproximately normal since $n \\ge 30$. \n\\item A random sample of 60 young Americans where 85\\% think they can achieve \nthe American dream would be considered unusual.\n\\item A random sample of 120 young Americans where 85\\% think they can \nachieve the American dream would be considered unusual.\n\\end{parts}\n}{}\n\n% 3\n\n\\eoce{\\qt{Orange tabbies\\label{orange_tabbies_CLT}} Suppose that 90\\% of orange\ntabby cats are male. Determine if the following statements are true or false, \nand explain your reasoning.\n\\begin{parts}\n\\item The distribution of sample proportions of random samples of size 30 is \nleft skewed.\n\\item Using a sample size that is 4 times as large will reduce the standard \nerror of the sample proportion by one-half.\n\\item The distribution of sample proportions of random samples of size 140 is \napproximately normal.\n\\item The distribution of sample proportions of random samples of size 280 is \napproximately normal.\n\\end{parts}\n}{}\n\n% 4\n\n\\eoce{\\qt{Young Americans, Part II\\label{young_americans_CLT_2}} About 25\\% of \nyoung Americans have delayed starting a family due to the continued economic \nslump. Determine if the following statements are true or false, and explain \nyour reasoning.\\footfullcite{news:youngAmericans2}\n\\begin{parts}\n\\item The distribution of sample proportions of young Americans who have \ndelayed starting a family due to the continued economic slump in random \nsamples of size 12 is right skewed.\n\\item In order for the distribution of sample proportions of young Americans \nwho have delayed starting a family due to the continued economic slump to be \napproximately normal, we need random samples where the sample size is at \nleast 40.\n\\item A random sample of 50 young Americans where 20\\% have delayed starting \na family due to the continued economic slump would be considered unusual.\n\\item A random sample of 150 young Americans where 20\\% have delayed \nstarting a family due to the continued economic slump would be considered \nunusual.\n\\item Tripling the sample size will reduce the standard error of the sample \nproportion by one-third.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 5\n\n\\eoce{\\qt{Gender equality\\label{gender_equality}}\nThe General Social Survey asked a random sample of\n1,390 Americans the following question:\n``On the whole, do you think it should or should not be\nthe government's responsibility to  promote equality\nbetween men and women?''\n82\\% of the respondents said it ``should be''.\nAt a 95\\% confidence level, this sample has 2\\% margin of error.\nBased on this information, determine if the following statements\nare true or false, and explain your reasoning.\\footfullcite{data:gss}\n\\begin{parts}\n\\item We are 95\\% confident that between 80\\% and 84\\% of Americans in this \nsample think it's the government's responsibility to promote equality between \nmen and women.\n\\item We are 95\\% confident that between 80\\% and 84\\% of all Americans \nthink it's the government's responsibility to promote equality between \nmen and women.\n\\item If we considered many random samples of 1,390 Americans, and we calculated \n95\\% confidence intervals for each, 95\\% of these intervals would include the \ntrue population proportion of Americans who think it's the government's \nresponsibility to promote equality between men and women.\n\\item In order to decrease the margin of error to 1\\%, we would need to \nquadruple (multiply by 4) the sample size.\n\\item Based on this confidence interval, there is sufficient evidence to \nconclude that a majority of Americans think it's the government's responsibility \nto promote equality between men and women.\n\\end{parts}\n\n% n = 1390\n% should be: 1142\n% p = 1142/1390 = 0.82\n% me = sqrt(.82*.08/1390)*1.96 = 0.02\n}{}\n\n% 6\n\n\\eoce{\\qt{Elderly drivers\\label{elderly_drivers_CI_concept}} \nThe Marist Poll published a report stating that 66\\% of adults nationally \nthink licensed drivers should be required to retake their road test once \nthey reach 65 years of age. It was also reported that interviews were \nconducted on 1,018 American adults, and that the margin of error was 3\\% \nusing a 95\\% confidence level. \\footfullcite{data:elderlyDriving}\n\\begin{parts}\n\\item Verify the margin of error reported by The Marist Poll. \n\\item Based on a 95\\% confidence interval, does the poll provide convincing \nevidence that \\textit{more than} 70\\% of the population think that licensed \ndrivers should be required to retake their road test once they turn 65?\n\\end{parts}\n}{}\n\n% 7\n\n\\eoce{\\qt{Fireworks on July 4$^{\\text{th}}$\\label{fireworks_CI_concept}} A local \nnews outlet reported that 56\\% of 600 randomly sampled Kansas residents planned \nto set off fireworks on July~$4^{th}$. Determine the margin of error for the \n56\\% point estimate using a 95\\% confidence level.\\footfullcite{data:july4}\n}{}\n\n% 8\n\n\\eoce{\\qt{Life rating in Greece\\label{greece_life_rating_CI}} Greece has faced a \nsevere economic crisis since the end of 2009. A Gallup poll surveyed 1,000 \nrandomly sampled Greeks in 2011 and found that 25\\% of them said they would \nrate their lives poorly enough to be considered ``suffering''.\\footfullcite{data:suffering}\n\\begin{parts}\n\\item Describe the population parameter of interest. What is the value of the \npoint estimate of this parameter?\n\\item Check if the conditions required for constructing a confidence interval \nbased on these data are met.\n\\item Construct a 95\\% confidence interval for the proportion of Greeks who \nare ``suffering\".\n\\item Without doing any calculations, describe what would happen to the \nconfidence interval if we decided to use a higher confidence level.\n\\item Without doing any calculations, describe what would happen to the \nconfidence interval if we used a larger sample.\n\\end{parts}\n}{}\n\n% 9\n\n\\eoce{\\qt{Study abroad\\label{study_abroad_CI_decision}}\nA survey on 1,509 high school seniors who took the SAT\nand who completed an optional web survey shows that\n55\\% of high school seniors are fairly certain that\nthey will participate in a study abroad program in \ncollege.\\footfullcite{data:studyAbroad}\n\\begin{parts}\n\\item\n    Is this sample a representative sample from the population\n    of all high school seniors in the US?\n    Explain your reasoning.\n\\item\n    Let's suppose the conditions for inference are met.\n    Even if your answer to part (a) indicated that this approach\n    would not be reliable, this analysis may still be interesting\n    to carry out (though not report).\n    Construct a 90\\% confidence interval for the proportion of high\n    school seniors (of those who took the SAT) who are fairly certain\n    they will participate in a study abroad program in college,\n    and interpret this interval in context.\n\\item\n    What does ``90\\% confidence\" mean?\n\\item\n    Based on this interval, would it be appropriate to claim that\n    the majority of high school seniors are fairly certain that they\n    will participate in a study abroad program in college?\n\\end{parts}\n}{}\n\n% 10\n\n\\eoce{\\qt{Legalization of marijuana, Part I\\label{legalize_marijuana_CI_decision}} \nThe General Social Survey asked 1,578 US residents:\n``Do you think the use of marijuana should be made legal, or not?''\n61\\% of the respondents said \nit should be made legal.\\footfullcite{data:gss}\n\\begin{parts}\n\\item Is 61\\% a sample statistic or a population parameter? Explain.\n\\item Construct a 95\\% confidence interval for the proportion of US \nresidents who think marijuana should be made legal, and interpret it in the \ncontext of the data.\n\\item A critic points out that this 95\\% confidence interval is only \naccurate if the statistic follows a normal distribution, or if the normal \nmodel is a good approximation. Is this true for these data? Explain.\n\\item A news piece on this survey's findings states, ``Majority of Americans \nthink marijuana should be legalized.'' Based on your confidence \ninterval, is this news piece's statement justified? \n\\end{parts}\n\n% 2348 surveyed\n% 770 not asked question\n% 2348 - 770 = 1578 asked question\n% 968 said legalize\n% 968 / 1578 = 0.61\n}{}\n\n% 11\n\n\\eoce{\\qt{National Health Plan, Part I\\label{national_health_plan_HT}}\nA \\textit{Kaiser Family Foundation} poll for US adults\nin 2019 found that 79\\% of Democrats, 55\\% of Independents,\nand 24\\% of Republicans supported a generic ``National Health Plan''.\nThere were 347 Democrats, 298 Republicans, and 617 Independents\nsurveyed.\\footfullcite{data:KFF2019_nat_health_plan}\n\\begin{parts}\n\\item\n    A political pundit on TV claims that a majority of Independents\n    support a National Health Plan.\n    Do these data provide strong evidence to support this type\n    of statement?\n\\item\n    Would you expect a confidence interval for the proportion\n    of Independents who oppose the public option plan to\n    include 0.5?\n    Explain.\n\\end{parts}\n}{}\n\n% 12\n\n\\eoce{\\qt{Is college worth it? Part I\\label{college_worth_it_HT_CI}} Among a simple \nrandom sample of 331 American adults who do not have a four-year college degree \nand are not currently enrolled in school, 48\\% said they decided not to go to \ncollege because they could not afford school. \\footfullcite{data:collegeWorthIt}\n\\begin{parts}\n\\item A newspaper article states that only a minority of the Americans who \ndecide not to go to college do so because they cannot afford it and uses the \npoint estimate from this survey as evidence. Conduct a hypothesis test to \ndetermine if these data provide strong evidence supporting this statement.\n\\item Would you expect a confidence interval for the proportion of American \nadults who decide not to go to college because they cannot afford it to \ninclude 0.5? Explain.\n\\end{parts}\n}{}\n\n% 13\n\n\\eoce{\\qt{Taste test\\label{taste_test_HT_2_sided}}\nSome people claim that they can tell the \ndifference between a diet soda and a regular soda\nin the first sip.\nA researcher wanting to test this claim randomly sampled 80 such people.\nHe then filled 80 \nplain white cups with soda, half diet and half regular\nthrough random assignment, \nand asked each person to take one sip from their cup\nand identify the soda as \ndiet or regular.\n53 participants correctly identified the soda.\n\\begin{parts}\n\\item Do these data provide strong evidence that these\n  people are any better or worse than random guessing at\n  telling the difference between diet and regular soda?\n\\item Interpret the p-value in this context.\n\\end{parts}\n}{}\n\n% 14\n\n\\eoce{\\qt{Is college worth it? Part II\\label{college_worth_it_CI_sample_size}} \nExercise~\\ref{college_worth_it_HT_CI} presents the results of a poll where \n48\\% of 331 Americans who decide to not go to college do so because they \ncannot afford it.\n\\begin{parts}\n\\item Calculate a 90\\% confidence interval for the proportion of Americans \nwho decide to not go to college because they cannot afford it, and interpret \nthe interval in context.\n\\item Suppose we wanted the margin of error for the 90\\% confidence level to \nbe about 1.5\\%. How large of a survey would you recommend?\n\\end{parts}\n}{}\n\n% 15\n\n\\eoce{\\qt{National Health Plan,\n    Part II\\label{national_health_plan_CI_sample_size_replaced}} \nExercise~\\ref{national_health_plan_HT} presents the results\nof a poll evaluating support for a generic\n``National Health Plan'' in the US in 2019,\nreporting that 55\\% of Independents are supportive.\nIf we wanted to estimate this number to within 1\\% with\n90\\% confidence, what would be an appropriate sample size?\n}{}\n\n% 16\n\n\\eoce{\\qt{Legalize Marijuana, Part II\\label{legalize_marijuana_CI_sample_size}} As \ndiscussed in Exercise~\\ref{legalize_marijuana_CI_decision},\nthe General Social Survey reported a sample where about\n61\\% of US residents thought marijuana should be made legal.\nIf we wanted to limit the margin of error of \na 95\\% confidence interval to 2\\%, about how many\nAmericans would we need to survey?\n}{}\n"
  },
  {
    "path": "ch_inference_for_props/TeX/review_exercises.tex",
    "content": "\\reviewexercisesheader{}\n\n% 39\n\n\\eoce{\\qt{Active learning\\label{active_learning_HT_concept}} A teacher wanting to \nincrease the active learning component of her course is concerned about \nstudent reactions to changes she is planning to make. She conducts a survey \nin her class, asking students whether they believe more active learning in \nthe classroom (hands on exercises) instead of traditional lecture will helps \nimprove their learning. She does this at the beginning and end of the \nsemester and wants to evaluate whether students' opinions have changed over \nthe semester. Can she used the methods we learned in this chapter for this \nanalysis? Explain your reasoning.\n}{}\n\n% 40\n\n\\eoce{\\qt{Website experiment\\label{web_ctr_exp_chisq}}\nThe OpenIntro website occasionally experiments with design\nand link placement.\nWe conducted one experiment testing three different placements\nof a download link for this textbook on the book's main page\nto see which location, if any, led to the most downloads.\nThe number of site visitors included in the experiment\nwas~701 and is captured in one of the response combinations\nin the following table:\n\\begin{center}\n\\begin{tabular}{r cc}\n  \\hline\n            & Download & No Download \\\\ \n  \\hline\nPosition 1  & 13.8\\%   & 18.3\\% \\\\ \nPosition 2  & 14.6\\%   & 18.5\\% \\\\ \nPosition 3  & 12.1\\%   & 22.7\\% \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n% x <- matrix(c(97, 102, 85, 128, 130, 159), 3, 2)\n\\begin{parts}\n\\item\n    Calculate the actual number of site visitors in each\n    of the six response categories.\n\\item\n    Each individual in the experiment had an equal chance of\n    being in any of the three experiment groups.\n    However, we see that there are slightly different\n    totals for the groups.\n    Is there any evidence that the groups were actually imbalanced?\n    Make sure to clearly state hypotheses, check conditions,\n    calculate the appropriate test statistic and the p-value,\n    and make your conclusion in context of the data.\n\\item\n   Complete an appropriate hypothesis test to check whether\n   there is evidence that there is a higher rate of site visitors\n   clicking on the textbook link in any of the three groups.\n\\end{parts}\n}{}\n\n% 41\n\n\\eoce{\\qt{Shipping holiday gifts\\label{ship_gifts_chisq_indep_conditions}} A \nlocal news survey asked 500 randomly sampled Los Angeles residents \nwhich shipping carrier they prefer to use for shipping holiday gifts. \nThe table below shows the distribution of responses by age group as \nwell as the expected counts for each cell (shown in parentheses).\n\\begin{center}\n\\begin{tabular}{l l | c c | c c | c c | c }\n\t\t&\t\t& \\multicolumn{6}{c|}{\\textit{Age}}\t&\t\t\\\\\n\\cline{3-8}\n\t\t&\t\t& \\multicolumn{2}{c|}{18-34}\t\t& \\multicolumn{2}{c|}{35-54}\t& \\multicolumn{2}{c|}{55+}\t& Total\t\\\\\n\\cline{2-9}\n\\multirow{5}{*}{\\textit{Shipping Method}}\t& USPS\t\t& 72\t& \\ec{81}\t& 97\t& \\ec{102}\t& 76 \t& \\ec{62}\t\t& 245 \\\\\n\t\t& UPS\t\t& 52\t& \\ec{53}\t& 76\t& \\ec{68}\t& 34\t& \\ec{41}\t\t& 162 \\\\\n\t\t& FedEx\t\t& 31\t& \\ec{21}\t& 24\t& \\ec{27}\t& 9\t& \\ec{16}\t\t& 64 \\\\\n\t\t& Something else\t& 7 & \\ec{5}\t& 6\t& \\ec{7}\t& 3\t& \\ec{4}\t\t& 16 \\\\\n\t\t& Not sure\t& 3\t& \\ec{5}\t& 6\t& \\ec{5}\t& 4\t& \\ec{3}\t\t& 13 \\\\\n\\cline{2-9}\n\t\t& Total\t\t& \\multicolumn{2}{c|}{165}\t\t& \\multicolumn{2}{c|}{209}\t\t& \\multicolumn{2}{c|}{126}\t\t& 500\n\\end{tabular}\n\\end{center} \n\\begin{parts}\n\\item State the null and alternative hypotheses for testing for independence \nof age and preferred shipping method for holiday gifts among Los Angeles residents.\n\\item Are the conditions for inference using a chi-square test satisfied?\n\\end{parts}\n}{}\n\n% 42\n\n\\eoce{\\qt{The Civil War\\label{civil_war_HT_CI_2_sided}}\nA national survey conducted \namong a simple random sample of 1,507 adults shows that 56\\% of\nAmericans think the Civil War is still relevant to American politics and \npolitical life.%\n\\footfullcite{data:civilWar} \n\\begin{parts}\n\\item Conduct a hypothesis test to determine if these data provide strong \nevidence that the majority of the Americans think the Civil War is still \nrelevant.\n\\item Interpret the p-value in this context.\n\\item Calculate a 90\\% confidence interval for the proportion of Americans \nwho think the Civil War is still relevant. Interpret the interval in this \ncontext, and comment on whether or not the confidence interval agrees with \nthe conclusion of the hypothesis test.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 43\n\n\\eoce{\\qt{College smokers\\label{college_smokers_CI_sample_size}} We are interested \nin estimating the proportion of students at a university who smoke. Out of a \nrandom sample of 200 students from this university, 40 students smoke.\n\\begin{parts}\n\\item Calculate a 95\\% confidence interval for the proportion of students at \nthis university who smoke, and interpret this interval in context. \n(Reminder: Check conditions.)\n\\item If we wanted the margin of error to be no larger than 2\\% at a 95\\% \nconfidence level for the proportion of students who smoke, how big of a \nsample would we need? \n\\end{parts}\n}{}\n\n% 44\n\n\\eoce{\\qt{Acetaminophen and liver damage\\label{acetaminophen_CI_sample_size}} It \nis believed that large doses of acetaminophen (the active ingredient in over \nthe counter pain relievers like Tylenol) may cause damage to the liver. A \nresearcher wants to conduct a study to estimate the proportion of \nacetaminophen users who have liver damage. For participating in this study, \nhe will pay each subject \\$20 and provide a free medical consultation if the \npatient has liver damage.\n\\begin{parts}\n\\item If he wants to limit the margin of error of his 98\\% confidence \ninterval to 2\\%, what is the minimum amount of money he needs to set aside \nto pay his subjects?\n\\item The amount you calculated in part (a) is substantially over his budget \nso he decides to use fewer subjects. How will this affect the width of his \nconfidence interval?\n\\end{parts}\n}{}\n\n% 45\n\n\\eoce{\\qt{Life after college\\label{life_after_college_CI}} We are interested in \nestimating the proportion of graduates at a mid-sized university who found \na job within one year of completing their undergraduate degree. Suppose we \nconduct a survey and find out that 348 of the 400 randomly sampled graduates \nfound jobs. The graduating class under consideration included over 4500 students.\n\\begin{parts}\n\\item Describe the population parameter of interest. What is the value of \nthe point estimate of this parameter?\n\\item Check if the conditions for constructing a confidence interval based \non these data are met.\n\\item Calculate a 95\\% confidence interval for the proportion of graduates \nwho found a job within one year of completing their undergraduate degree at \nthis university, and interpret it in the context of the data.\n\\item What does ``95\\% confidence\" mean?\n\\item Now calculate a 99\\% confidence interval for the same parameter and \ninterpret it in the context of the data.\n\\item Compare the widths of the 95\\% and 99\\% confidence intervals. Which \none is wider? Explain.\n\\end{parts}\n}{}\n\n% 46\n\n\\eoce{\\qt{Diabetes and unemployment\\label{diabetes_unemp_effect_size}} A \nGallup poll surveyed Americans about their employment status and whether or \nnot they have diabetes. The survey results indicate that 1.5\\% of the 47,774 \nemployed (full or part time) and 2.5\\% of the 5,855 unemployed 18-29 year \nolds have diabetes.\\footfullcite{data:employmentDiabetes}\n\\begin{parts}\n\\item Create a two-way table presenting the results of this study.\n\\item State appropriate hypotheses to test for difference in proportions of \ndiabetes between employed and unemployed Americans.\n\\item The sample difference is about 1\\%. If we completed the hypothesis \ntest, we would find that the p-value is very small (about 0), meaning the \ndifference is statistically significant. Use this result to explain the \ndifference between statistically significant and practically significant \nfindings.\n\\end{parts}\n}{}\n\n% 47\n\n\\eoce{\\qt{Rock-paper-scissors\\label{rps_chisq_GOF}} Rock-paper-scissors is a hand \ngame played by two or more people where players choose to sign either rock, \npaper, or scissors with their hands. For your statistics class project, \nyou want to evaluate whether players choose between these three options \nrandomly, or if certain options are favored above others. You ask two friends \nto play rock-paper-scissors and count the times each option is played. The \nfollowing table summarizes the data:\n\\begin{center}\n\\begin{tabular}{c c c}\nRock\t& Paper\t& Scissors \t \\\\\n\\hline\n43\t\t& 21\t& 35\t\n\\end{tabular}\n\\end{center}\nUse these data to evaluate whether players choose between these three options \nrandomly, or if certain options are favored above others. Make sure to clearly \noutline each step of your analysis, and interpret your results in context of \nthe data and the research question.\n}{}\n\n\\D{\\newpage}\n\n% 48\n\n\\eoce{\\qt{2010 Healthcare Law\\label{healthcare_CI_concept}} On June 28, 2012 the \nU.S. Supreme Court upheld the much debated 2010 healthcare law, declaring it \nconstitutional. A Gallup poll released the day after this decision indicates \nthat 46\\% of 1,012 Americans agree with this decision. At a 95\\% confidence \nlevel, this sample has a 3\\% margin of error. Based on this information, \ndetermine if the following statements are true or false, and explain your \nreasoning.\\footfullcite{data:healthcare2010}\n\\begin{parts}\n\\item We are 95\\% confident that between  43\\% and 49\\% of Americans in this \nsample support the decision of the U.S. Supreme Court on the 2010 healthcare \nlaw.\n\\item We are 95\\% confident that between 43\\% and 49\\% of Americans support \nthe decision of the U.S. Supreme Court on the 2010 healthcare law.\n\\item If we considered many random samples of 1,012 Americans, and we \ncalculated the sample proportions of those who support the decision of the \nU.S. Supreme Court, 95\\% of those sample proportions will be between 43\\% and \n49\\%.\n\\item The margin of error at a 90\\% confidence level would be higher than 3\\%.\n\\end{parts}\n}{}\n\n% 49\n\n\\eoce{\\qt{Browsing on the mobile device\\label{mobile_browsing_HT_CI}} A \nsurvey of 2,254 American adults indicates that 17\\% of cell phone owners\nbrowse the internet exclusively on their phone rather than a computer\nor other device.\n\\footfullcite{data:mobileBrowse}\n\\begin{parts}\n\\item  According to an online article, a report from a mobile research \ncompany indicates that 38 percent of Chinese mobile web users only access \nthe internet through their cell phones.\n\\footfullcite{news:mobileBrowseChinese} Conduct a hypothesis test to \ndetermine if these data provide strong evidence that the proportion of \nAmericans who only use their cell phones to access the internet is different \nthan the Chinese proportion of 38\\%.\n\\item Interpret the p-value in this context.\n\\item Calculate a 95\\% confidence interval for the proportion of Americans \nwho access the internet on their cell phones, and interpret the interval in \nthis context.\n\\end{parts}\n}{}\n\n% 50\n\n\\eoce{\\qt{Coffee and Depression\\label{coffee_depression_chisq_indep}} \nResearchers conducted a study investigating the relationship between \ncaffeinated coffee consumption and risk of depression in women. They \ncollected data on 50,739 women free of depression symptoms at the start \nof the study in the year 1996, and these women were followed through \n2006. The researchers used questionnaires to collect data on \ncaffeinated coffee consumption, asked each individual about physician-\ndiagnosed depression, and also asked about the use of antidepressants. \nThe table below shows the distribution of incidences of depression by \namount of caffeinated coffee consumption.\\footfullcite{Lucas:2011}\n\\begin{adjustwidth}{-4em}{-4em}\n{\\small\n\\begin{center}\n\\begin{tabular}{l  l rrrrrr}\n\t&  \\multicolumn{1}{c}{}\t\t& \\multicolumn{5}{c}{\\textit{Caffeinated coffee consumption}} \\\\\n\\cline{3-7}\n\t&\t\t& $\\le$ 1\t& 2-6\t& 1\t& 2-3\t& $\\ge$ 4\t&   \\\\\n\t&\t\t& cup/week\t& cups/week\t& cup/day\t& cups/day\t& cups/day\t& Total  \\\\\n\\cline{2-8}\n\\textit{Clinical} & Yes\t& 670 & \\fbox{\\textcolor{oiB}{373}}\t& 905\t& 564\t& 95 \t& 2,607 \\\\\n\\textit{depression}\t& No& 11,545\t& 6,244\t& 16,329\t& 11,726\t& 2,288 \t& 48,132 \\\\\n\\cline{2-8}\n\t\t\t\t& Total\t& 12,215\t& 6,617 & 17,234\t& 12,290\t& 2,383 \t& 50,739 \\\\\n\\cline{2-8}\n\\end{tabular}\n\\end{center}\n}\n\\end{adjustwidth}\n\\begin{parts}\n\\item What type of test is appropriate for evaluating if there is an \nassociation between coffee intake and depression?\n\\item Write the hypotheses for the test you identified in part (a).\n\\item Calculate the overall proportion of women who do and do not \nsuffer from depression.\n\\item Identify the expected count for the highlighted cell, and \ncalculate the contribution of this cell to the test statistic, i.e. \n$(Observed-Expected)^2/Expected$.\n\\item The test statistic is $\\chi^2=20.93$. What is the p-value?\n\\item What is the conclusion of the hypothesis test?\n\\item One of the authors of this study was quoted on the NYTimes as \nsaying it was ``too early to recommend that women load up on extra \ncoffee\" based on just this study.\\footfullcite{news:coffeeDepression} \nDo you agree with this statement? Explain your reasoning.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_inference_for_props/TeX/testing_for_goodness_of_fit_using_chi-square.tex",
    "content": "\\exercisesheader{}\n\n% 31\n\n\\eoce{\\qt{True or false, Part I\\label{tf_chisq_1}} Determine if the statements below \nare true or false. For each false statement, suggest an alternative wording to \nmake it a true statement.\n\\begin{parts}\n\\item The chi-square distribution, just like the normal distribution, has two \nparameters, mean and standard deviation.\n\\item The chi-square distribution is always right skewed, regardless of the \nvalue of the degrees of freedom parameter.\n\\item The chi-square statistic is always positive.\n\\item As the degrees of freedom increases, the shape of the chi-square \ndistribution becomes more skewed.\n\\end{parts}\n}{}\n\n% 32\n\n\\eoce{\\qt{True or false, Part II\\label{tf_chisq_2}} Determine if the statements below \nare true or false. For each false statement, suggest an alternative wording to \nmake it a true statement.\n\\begin{parts}\n\\item As the degrees of freedom increases, the mean of the chi-square \ndistribution increases.\n\\item If you found $\\chi^2 = 10$ with $df = 5$ you would fail to reject $H_0$ \nat the 5\\% significance level.\n\\item When finding the p-value of a chi-square test, we always shade the tail \nareas in both tails.\n\\item As the degrees of freedom increases, the variability of the chi-square \ndistribution decreases.\n\\end{parts}\n}{}\n\n% 33\n\n\\eoce{\\qt{Open source textbook\\label{opensource_text_chisq_GOF}} A professor using \nan open source introductory statistics book predicts that 60\\% of the \nstudents will purchase a hard copy of the book, 25\\% will print it out from \nthe web, and 15\\% will read it online. At the end of the semester he asks his \nstudents to complete a survey where they indicate what format of the book \nthey used. Of the 126 students, 71 said they bought a hard copy of the book, \n30 said they printed it out from the web, and 25 said they read it online.\n\\begin{parts}\n\\item State the hypotheses for testing if the professor's predictions were \ninaccurate.\n\\item How many students did the professor expect to buy the book, print the \nbook, and read the book exclusively online?\n\\item This is an appropriate setting for a chi-square test. List the \nconditions required for a test and verify they are satisfied.\n\\item Calculate the chi-squared statistic, the degrees of freedom associated \nwith it, and the p-value.\n\\item Based on the p-value calculated in part (d), what is the conclusion of \nthe hypothesis test? Interpret your conclusion in this context.\n\\end{parts}\n}{}\n\n% 34\n\n\\eoce{\\qt{Barking deer\\label{barking_deer_chisq_GOF}}\nMicrohabitat factors associated with forage and bed sites\nof barking deer in Hainan Island, China were examined.\nIn this region woods make up 4.8\\% of the land,\ncultivated grass plot makes up 14.7\\%, and deciduous forests\nmake up 39.6\\%.\nOf the 426 sites where the deer forage, 4 were categorized\nas woods, 16 as cultivated grassplot, and 61 as deciduous forests.\nThe table below summarizes these data.\\footfullcite{Teng:2004}\n\\begin{center}\n\\begin{tabular}{c c c c c}\nWoods\t& Cultivated grassplot\t& Deciduous forests\t & Other & Total \\\\\n\\hline \n4\t\t& 16\t\t\t\t\t& 61\t\t\t     & 345\t & 426 \\\\\n\\end{tabular}\n\\end{center}\n\n\\noindent \\begin{minipage}[c]{0.7\\textwidth}\n\\begin{parts}\n\\item Write the hypotheses for testing if barking deer prefer to forage in \ncertain habitats over others.\n\\item What type of test can we use to answer this research question?\n\\item Check if the assumptions and conditions required for this test are \nsatisfied.\n\\item Do these data provide convincing evidence that barking deer prefer to \nforage in certain habitats over others? Conduct an appropriate hypothesis \ntest to answer this research question.\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.03\\textwidth}\n$\\:$ \\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.28\\textwidth}\n\\begin{center}\n\\Figures[A photo of a barking deer, which has pronged horns and a reddish brown color, looking out through leaves and foliage.]{0.7}{eoce/barking_deer_chisq_GOF}{barking_deer.jpg} \\\\\n{\\footnotesize Photo by Shrikant Rao (\\oiRedirect{textbook-flickr_shrikant_rao_barking_deer}{http://flic.kr/p/4Xjdkk}) \\oiRedirect{textbook-CC_BY_2}{CC~BY~2.0~license}}\n\\end{center}\n\\end{minipage}\n}{}\n"
  },
  {
    "path": "ch_inference_for_props/TeX/testing_for_independence_in_two-way_tables.tex",
    "content": "\\exercisesheader{}\n\n% 35\n\n\\eoce{\\qt{Quitters\\label{quitters_chisq_independence}} Does being part of a \nsupport group affect the ability of people to quit smoking? A county \nhealth department enrolled 300 smokers in a randomized experiment. 150 \nparticipants were assigned to a group that used a nicotine patch and \nmet weekly with a support group; the other 150 received the patch and \ndid not meet with a support group. At the end of the study, 40 of the \nparticipants in the patch plus support group had quit smoking while \nonly 30 smokers had  quit in the other group.\n\\begin{parts}\n\\item Create a two-way table presenting the results of this study.\n\\item Answer each of the following questions under the null hypothesis \nthat being part of a support group does not affect the ability of \npeople to quit smoking, and indicate whether the expected values are \nhigher or lower than the observed values.\n\\begin{subparts}\n\\item How many subjects in the ``patch + support\" group would you \nexpect to quit?\n\\item How many subjects in the ``patch only\" group would you expect to \nnot quit?\n\\end{subparts}\n\\end{parts}\n}{}\n\n% 36\n\n\\eoce{\\qt{Full body scan, Part II\\label{full_body_scan_chisq_indep}} The \ntable below summarizes a data set we first encountered in \nExercise~\\ref{full_body_scan_HT_Error} regarding views on full-body \nscans and political affiliation. The differences in each political \ngroup may be due to chance. Complete the following computations under \nthe null hypothesis of independence between an individual's party \naffiliation and his support of full-body scans. It may be useful to \nfirst add on an extra column for row totals before proceeding with the \ncomputations.\n\\begin{center}\n\\begin{tabular}{ll  cc c} \n            &   & \\multicolumn{3}{c}{\\textit{Party Affiliation}} \\\\\n\\cline{3-5}\n                                &           & Republican & Democrat & Independent   \\\\\n\\cline{2-5}\n\\multirow{3}{*}{\\textit{Answer}}& Should    & 264        & 299      & 351 \\\\\n                                & Should not& 38         & 55       & 77 \\\\\n                                & Don't know/No answer & 16 & 15    & 22 \\\\\n\\cline{2-5}\n                                & Total      & 318       & 369      & 450\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item How many Republicans would you expect to not support the use of \nfull-body scans?\n\\item How many Democrats would you expect to support the use of full-\nbody scans?\n\\item How many Independents would you expect to not know or not answer?\n\\end{parts}\n}{}\n\n% 37\n\n\\eoce{\\qt{Offshore drilling, Part III\\label{offshore_drilling_chisq_indep}} \nThe table below summarizes a data set we first encountered in \nExercise~\\ref{offshore_drill_edu_dontknow_HT} that examines the \nresponses of a random sample of college graduates and non-graduates on \nthe topic of oil drilling. Complete a chi-square test for these data to \ncheck whether there is a statistically significant difference in \nresponses from college graduates and non-graduates.\n\\begin{center}\n\\begin{tabular}{l c c}\n\t\t\t& \\multicolumn{2}{c}{\\textit{College Grad}} \\\\\n\\cline{2-3}\n\t\t\t& Yes\t\t& No\t\t\t\t\\\\\n\\cline{1-3}\nSupport\t\t& 154\t\t& 132\t\t\t\\\\\nOppose\t\t& 180\t\t& 126\t\t\t\\\\\nDo not know\t& 104\t\t& 131\t\t\t\\\\\n\\cline{1-3}\n Total\t\t& 438\t\t& 389\t\t\n\\end{tabular}\n\\end{center}\n}{}\n\n% 38\n\n\\eoce{\\qt{Parasitic worm\\label{parasitic_worm_chisq}}\nLymphatic filariasis is a disease caused by a parasitic worm.\nComplications of the disease can lead to extreme swelling\nand other complications.\nHere we consider results from a randomized experiment\nthat compared three\ndifferent drug treatment options to clear people of the\nthis parasite, which people are working to eliminate entirely.\nThe results for the second year of the study are\ngiven below:\\footfullcite{King_Suamani_2018}\n\\begin{center}\n\\begin{tabular}{l cc}\n  \\hline\n  & Clear at Year 2 & Not Clear at Year 2 \\\\ \n  \\hline\n  Three drugs & 52 & 2 \\\\ \n  Two drugs & 31 & 24 \\\\ \n  Two drugs annually & 42 & 14 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item\\label{parasitic_worm_chisq_hyp}\n    Set up hypotheses for evaluating\n    whether there is any difference in the\n    performance of the treatments,\n    and also check conditions.\n\\item\n    Statistical software was used to run\n    a chi-square test, which output:\n    \\begin{align*}\n    &X^2 = 23.7\n    &&df = 2\n    &&\\text{p-value} = \\text{7.2e-6}\n    \\end{align*}\n    Use these results to evaluate the hypotheses\n    from part~(\\ref{parasitic_worm_chisq_hyp}),\n    and provide a conclusion\n    in the context of the problem.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove10WithDF4/chiSquareAreaAbove10WithDF4.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove10WithDF4.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(10,\n              4,\n              c(0, 18),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove11Point7WithDF7/chiSquareAreaAbove11Point7WithDF7.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove11Point7WithDF7.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(11.7,\n              7,\n              c(0, 25),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove4Point3WithDF2/chiSquareAreaAbove4WithDF2.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove4Point3WithDF2.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(4.3,\n              2,\n              c(0, 15),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove5Point1WithDF5/chiSquareAreaAbove5Point1WithDF5.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove5Point1WithDF5.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(5.1,\n              5,\n              c(0, 25),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove6Point25WithDF3/chiSquareAreaAbove6Point25WithDF3.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove6Point25WithDF3.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(6.25,\n              3,\n              c(0, 15),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove9Point21WithDF3/chiSquareAreaAbove9Point21WithDF3.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareAreaAbove9Point21WithDF3.pdf', 5, 3,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.6, 0))\nChiSquareTail(9.21,\n              3,\n              c(0, 15),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/bladesTwoSampleHTPValueQC/bladesTwoSampleHTPValueQC.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('bladesTwoSampleHTPValueQC.pdf', 3.04, 1.56,\n      mar = c(2.4, 0, 0.5, 0),\n      mgp = c(3, 0.45, 0))\nnormTail(U = 2.3, L = -2.3, col = COL[1], axes = FALSE)\nat <- c(-5, 0, 2.3, 5)\nlabels <- c(0, 0.03, 0.059, 0)\naxis(1, at, labels, cex.axis = 0.9)\npar(mgp = c(5, 1.3, 0))\naxis(1, at = 0, '(null value)', cex.axis = 0.7)\narrows(2.5, 0.19,\n       2.5, 0.05,\n       length = 0.1,\n       col = COL[1])\ntext(2.5, 0.18, \"0.006\",\n     pos = 3,\n     cex = 0.8,\n     col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/chiSquareDistributionWithInceasingDF/chiSquareDistributionWithInceasingDF.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareDistributionWithInceasingDF.pdf', 6.5, 3,\n      mar = c(2, 0.5, 0.25, 0.5),\n      mgp = c(2.1, 0.7, 0))\nx <- c(0, seq(0.0000001, 40, 0.05))\nDF <- c(2.0000001, 4, 9)\ny <- list()\nfor (i in 1:length(DF)) {\n  y[[i]] <- dchisq(x, DF[i])\n}\nplot(0, 0,\n     type = 'n',\n     xlim = c(0, 25),\n     ylim = range(c(y, recursive = TRUE)),\n     axes = FALSE)\nfor (i in 1:length(DF)) {\n  lines(x, y[[i]],\n        lty = i,\n        col = COL[ifelse(i == 3, 4, i)],\n        lwd = 1.5 + i / 2)\n}\nabline(h = 0)\naxis(1)\nlegend('topright',\n       lwd = 0.3 + 1:4 / 1.25,\n       col = COL[c(1, 2, 4)],\n       lty = 1:4,\n       legend = paste(round(DF)),\n       title = 'Degrees of Freedom',\n       cex = 1)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/eoce/assisted_reproduction_one_sample_randomization/assisted_reproduction_one_sample_randomization.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# set sample size and number of simulations -------------------------\nn = 25\nN = 10^4\n\n# randomize ---------------------------------------------------------\n\nset.seed(15)\n\np <- 0.31\n\npHat <- rbinom(N, n, p)/n\nM    <- max(pHat)*n\n\npHatObs <- 0.4\n\nsum(pHat >= pHatObs)/N\n\n# plot randomization dist for question ------------------------------\n\npdf(\"assisted_reproduction_one_sample_randomization.pdf\", height = 3, width = 6)\n\npar(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))\n\nhistPlot(pHat, breaks = (-1:(2*M)+0.75)/2/n, \n         xlab = expression(hat(p)[sim]*\"    \"), \n         col = COL[7,3], ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at = (0:3)*N/20, labels=c(\"0\",\"0.05\",\"0.10\",\"0.15\"))\nabline(h = 0)\n\nabline(h = seq(250, 1500, 250), lty = 3, lwd = 2, col = COL[7])\n\ndev.off()\n\n# plot randomization dist for solution ------------------------------\n\npdf(\"assisted_reproduction_one_sample_randomization_soln.pdf\", height = 3, width = 6)\n\npar(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))\n\nhistPlot(pHat, breaks = (-1:(2*M)+0.75)/2/n, \n         xlab = expression(hat(p)[sim]*\"    \"), \n         col = COL[7,3], ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at = (0:3)*N/20, labels=c(\"0\",\"0.05\",\"0.10\",\"0.15\"))\nabline(h = 0)\n\nhistPlot(pHat[pHat >= pHatObs], breaks = (-1:(2*M)+0.75)/2/n, \n         col = COL[1], add = TRUE)\n\nlines(rep(pHatObs, 2), c(0, 3)*N/22, lty=3, lwd=1.7)\ntext(x = pHatObs, y = 3*N/22, as.character(pHatObs), pos=3, cex=1.25)\n\ndev.off()"
  },
  {
    "path": "ch_inference_for_props/figures/eoce/egypt_revolution_one_sample_randomization/egypt_revolution_one_sample_randomization.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# set sample size and number of simulations -------------------------\nn = 20\nN = 10^4\n\n# randomize ---------------------------------------------------------\n\nset.seed(5)\n\npHat <- rbinom(N, n, 0.69)/n\nM    <- max(pHat)*n\n\npHatObs <- 0.57\n\nsum(pHat <= pHatObs)/N\n\n# plot randomization dist for question ------------------------------\n\npdf(\"egypt_revolution_one_sample_randomization.pdf\", height = 3, width = 6)\n\npar(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))\n\nhistPlot(pHat, breaks = (11:(2*M)+0.75)/2/n, \n         xlab = expression(hat(p)[sim]*\"    \"), \n         col = COL[7,3], ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at=(0:3)*N/20, labels=c(\"0\",\"0.05\",\"0.10\",\"0.15\"))\nabline(h = 0)\n\nabline(h = seq(250,1500,250), lty = 3, lwd = 2, col = COL[7])\n\ndev.off()\n\n# plot randomization dist for solution ------------------------------\n\npdf(\"egypt_revolution_one_sample_randomization_soln.pdf\", height = 3, width = 6)\n\npar(mar=c(4,4,0,0), las=1, mgp=c(2.5,1,0))\n\nhistPlot(pHat, breaks = (11:(2*M)+0.75)/2/n, \n         xlab = expression(hat(p)[sim]*\"    \"), \n         col = COL[7,3], ylab = \"\", axes = FALSE)\naxis(1)\naxis(2, at=(0:3)*N/20, labels=c(\"0\",\"0.05\",\"0.10\",\"0.15\"))\nabline(h = 0)\n\nhistPlot(pHat[pHat <= pHatObs], breaks = (-1:(2*M)+0.75)/2/n, \n         col = COL[1], add = TRUE)\n\nlines(rep(pHatObs, 2), c(0, 3)*N/22, lty=3, lwd=1.7)\ntext(x = pHatObs, y = 3*N/22, as.character(pHatObs), pos=3, cex=1.25)\n\ndev.off()"
  },
  {
    "path": "ch_inference_for_props/figures/eoce/social_experiment_two_sample_randomization/social_experiment_two_sample_randomization.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# set number of simulations -----------------------------------------\nN = 10^4\n\n# randomize ---------------------------------------------------------\n\npHatObs = -0.35\n\nset.seed(3)\n\nsc <- c(rep(\"p\", 20), rep(\"c\",25))\nint <- c(rep(c(\"y\", \"n\"), c(5, 15)), rep(c(\"y\", \"n\"), c(15, 10)))\n\nd <- rep(NA, N)\nfor(i in 1:N){\n\tscf  <- sample(sc)\n\tp1   <- sum(int[scf == \"p\"] == \"y\") / 20\n\tp2   <- sum(int[scf == \"c\"] == \"y\") / 25\n\td[i] <- p1 - p2\n}\nsum((d) <= pHatObs) / N\n\n# plot randomization dist for question ------------------------------\n\npdf(\"social_experiment_two_sample_randomization.pdf\", height = 3, width = 6)\n\npar(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))\n\ntemp1 <- sort(unique(d))\ntemp2 <- diff(temp1[1:2])/2\nbr    <- seq(temp1[1]-temp2/2, tail(temp1,1)+temp2/2, temp2)\n\nhistPlot(d, breaks = br, col=COL[7,4], \n         main=\"\", xlab=expression(hat(p)[pr_sim] - hat(p)[con_sim]*\"    \"), \n         ylab=\"\", axes=FALSE)\naxis(1, seq(-0.4, 0.4, 0.2))\naxis(2, at=(0:4)*N/20, labels=c(0, NA, 2, NA, 4)/20)\nabline(h = 0)\n\nabline(h = c((1:4)*N/20), lty = 3, lwd = 2, col = COL[7])\n\ndev.off()\n\n# plot randomization dist for solution ------------------------------\n\npdf(\"social_experiment_two_sample_randomization_soln.pdf\", height = 3, width = 6)\n\npar(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))\n\ntemp1 <- sort(unique(d))\ntemp2 <- diff(temp1[1:2])/2\nbr    <- seq(temp1[1]-temp2/2, tail(temp1,1)+temp2/2, temp2)\n\nhistPlot(d, breaks = br, col=COL[7,4], \n         main=\"\", xlab=expression(hat(p)[pr_sim] - hat(p)[con_sim]*\"    \"), \n         ylab=\"\", axes=FALSE)\naxis(1, seq(-0.4, 0.4, 0.2))\naxis(2, at=(0:4)*N/20, labels=c(0, NA, 2, NA, 4)/20)\nabline(h = 0)\n\nhistPlot(d[d <= pHatObs], breaks=br, col=COL[1], add=TRUE)\nabline(h=0)\nlines(rep(pHatObs, 2), c(0, 3)*N/25, lty=3, lwd=1.7)\ntext(pHatObs, 3*N/25, as.character(pHatObs), pos=3, cex=1.25)\n\ndev.off()"
  },
  {
    "path": "ch_inference_for_props/figures/eoce/yawning_two_sample_randomization/yawning_two_sample_randomization.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# set number of simulations -----------------------------------------\nN = 10^4\n\n# randomize ---------------------------------------------------------\n\npHatObs = 0.04\n\nset.seed(29)\n\ngr <- c(rep(\"trtmt\", 34), rep(\"ctrl\",16))\nyawn <- c(rep(c(\"y\", \"n\"), c(10, 24)), rep(c(\"y\", \"n\"), c(4, 12)))\n\nd <- rep(NA, N)\nfor(i in 1:N){\n  grf  <- sample(gr)\n  p1   <- sum(yawn[grf == \"trtmt\"] == \"y\") / 34\n  p2   <- sum(yawn[grf == \"ctrl\"] == \"y\") / 16\n  d[i] <- p2 - p1\n}\nsum((d) >= pHatObs) / N\n\n# plot randomization dist for question ------------------------------\n\npdf(\"yawning_two_sample_randomization.pdf\", height = 3.5, width = 6.7)\n\npar(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))\n\nhistPlot(d, breaks=seq(-0.6, 0.7, 0.02), col=COL[7,4], \n         main=\"\", xlab=expression(hat(p)[trtmt] - hat(p)[ctrl]*\"    \"), \n         ylab=\"\", axes=FALSE)\naxis(1)\naxis(2, at=(0:5)*N/20, labels=c(0, NA, 2, NA, 4, NA)/20)\nabline(h = 0)\n\nabline(h = c((1:5)*N/20), lty = 3, lwd = 2, col = COL[7])\n\ndev.off()\n\n# plot randomization dist for solution ------------------------------\n\npdf(\"yawning_two_sample_randomization_soln.pdf\", height = 3.5, width = 6.7)\n\npar(mar=c(4,2,0,0), las=1, mgp=c(2.8,0.55,0))\n\nhistPlot(d, breaks=seq(-0.6, 0.7, 0.02), col=COL[7,4], \n         main=\"\", xlab=expression(hat(p)[trtmt] - hat(p)[ctrl]*\"    \"), \n         ylab=\"\", axes=FALSE)\naxis(1)\naxis(2, at=(0:5)*N/20, labels=c(0, NA, 2, NA, 4, NA)/20)\nabline(h = 0)\n\nhistPlot(d[d >= pHatObs], breaks=seq(-0.6, 0.7, 0.02), col=COL[1], add=TRUE)\nabline(h=0)\nlines(rep(pHatObs, 2), c(0, 6.1)*N/25, lty=3, lwd=1.7)\ntext(pHatObs, 6*N/25, as.character(pHatObs), pos=3, cex=1.25)\n\ndev.off()"
  },
  {
    "path": "ch_inference_for_props/figures/geomFitEvaluationForSP500/geomFitEvaluationForSP500.R",
    "content": "library(openintro)\nd <- sp500_1950_2018  # read.csv(\"sp500_1950_2018.csv\")\nd <- subset(d, \"2009-01-01\" <= as.Date(Date) & as.Date(Date) <= \"2018-12-31\")\nd. <- diff(d$Adj.Close)\nmean(d. > 0)\n\n# Not worrying about case where d. == 0.\nR  <- ifelse(d. > 0, 1, 0)\nCC <- table(diff(which(R == 1)))\nCC[names(CC) == 7] <- sum(CC[names(CC) %in% 7:100])\nCC <- CC[- which(names(CC) %in% 8:100)]\np  <- mean(R)\npr <- p * (1 - p)^(0:5)\npr <- append(pr, 1 - sum(pr))\n\np\n(CC <- c(CC))\nsum(CC)\nC  <- rep(1:7, CC)\n(EE <- round(pr * sum(CC)))\nE  <- rep(1:7, EE)\n(X2 <- sum((CC - EE)^2 / EE))\npchisq(X2, length(CC) - 1, lower.tail = FALSE)\n\nmyPDF('geomFitEvaluationForSP500.pdf', 7, 3.5,\n      mar = c(3.2, 4.2, 0.2, 1),\n      mgp = c(2.1, 0.7, 0))\nylim <- c(0, round(max(CC, EE) + 50, -2))\nhistPlot(C - 0.13,\n         breaks = seq(0, 8, 0.25),\n         xlim = c(0.5, 7.5),\n         ylim = ylim,\n         xlab = 'Wait Until Positive Day',\n         ylab = '',\n         axes = FALSE,\n         col = COL[1])\nhistPlot(E + 0.13,\n         breaks = seq(0, 8, 0.25),\n         add = TRUE,\n         col = COL[3])\naxis(1, 1:7, c(1:6, \"7+\"))\naxis(2, at = seq(0, ylim[2], 200))\npar(las = 0)\nmtext('Frequency', 2, line = 3)\nlegend('topright',\n       fill = COL[c(1, 3)],\n       legend = c('Observed', 'Expected'))\ndev.off()\n\n"
  },
  {
    "path": "ch_inference_for_props/figures/geomFitEvaluationForSP500/sp500_1950_2018.csv",
    "content": "Date,Open,High,Low,Close,Adj Close,Volume\n1950-01-03,16.660000,16.660000,16.660000,16.660000,16.660000,1260000\n1950-01-04,16.850000,16.850000,16.850000,16.850000,16.850000,1890000\n1950-01-05,16.930000,16.930000,16.930000,16.930000,16.930000,2550000\n1950-01-06,16.980000,16.980000,16.980000,16.980000,16.980000,2010000\n1950-01-09,17.080000,17.080000,17.080000,17.080000,17.080000,2520000\n1950-01-10,17.030001,17.030001,17.030001,17.030001,17.030001,2160000\n1950-01-11,17.090000,17.090000,17.090000,17.090000,17.090000,2630000\n1950-01-12,16.760000,16.760000,16.760000,16.760000,16.760000,2970000\n1950-01-13,16.670000,16.670000,16.670000,16.670000,16.670000,3330000\n1950-01-16,16.719999,16.719999,16.719999,16.719999,16.719999,1460000\n1950-01-17,16.860001,16.860001,16.860001,16.860001,16.860001,1790000\n1950-01-18,16.850000,16.850000,16.850000,16.850000,16.850000,1570000\n1950-01-19,16.870001,16.870001,16.870001,16.870001,16.870001,1170000\n1950-01-20,16.900000,16.900000,16.900000,16.900000,16.900000,1440000\n1950-01-23,16.920000,16.920000,16.920000,16.920000,16.920000,1340000\n1950-01-24,16.860001,16.860001,16.860001,16.860001,16.860001,1250000\n1950-01-25,16.740000,16.740000,16.740000,16.740000,16.740000,1700000\n1950-01-26,16.730000,16.730000,16.730000,16.730000,16.730000,1150000\n1950-01-27,16.820000,16.820000,16.820000,16.820000,16.820000,1250000\n1950-01-30,17.020000,17.020000,17.020000,17.020000,17.020000,1640000\n1950-01-31,17.049999,17.049999,17.049999,17.049999,17.049999,1690000\n1950-02-01,17.049999,17.049999,17.049999,17.049999,17.049999,1810000\n1950-02-02,17.230000,17.230000,17.230000,17.230000,17.230000,2040000\n1950-02-03,17.290001,17.290001,17.290001,17.290001,17.290001,2210000\n1950-02-06,17.320000,17.320000,17.320000,17.320000,17.320000,1490000\n1950-02-07,17.230000,17.230000,17.230000,17.230000,17.230000,1360000\n1950-02-08,17.209999,17.209999,17.209999,17.209999,17.209999,1470000\n1950-02-09,17.280001,17.280001,17.280001,17.280001,17.280001,1810000\n1950-02-10,17.240000,17.240000,17.240000,17.240000,17.240000,1790000\n1950-02-14,17.059999,17.059999,17.059999,17.059999,17.059999,2210000\n1950-02-15,17.059999,17.059999,17.059999,17.059999,17.059999,1730000\n1950-02-16,16.990000,16.990000,16.990000,16.990000,16.990000,1920000\n1950-02-17,17.150000,17.150000,17.150000,17.150000,17.150000,1940000\n1950-02-20,17.200001,17.200001,17.200001,17.200001,17.200001,1420000\n1950-02-21,17.170000,17.170000,17.170000,17.170000,17.170000,1260000\n1950-02-23,17.209999,17.209999,17.209999,17.209999,17.209999,1310000\n1950-02-24,17.280001,17.280001,17.280001,17.280001,17.280001,1710000\n1950-02-27,17.280001,17.280001,17.280001,17.280001,17.280001,1410000\n1950-02-28,17.219999,17.219999,17.219999,17.219999,17.219999,1310000\n1950-03-01,17.240000,17.240000,17.240000,17.240000,17.240000,1410000\n1950-03-02,17.230000,17.230000,17.230000,17.230000,17.230000,1340000\n1950-03-03,17.290001,17.290001,17.290001,17.290001,17.290001,1520000\n1950-03-06,17.320000,17.320000,17.320000,17.320000,17.320000,1470000\n1950-03-07,17.200001,17.200001,17.200001,17.200001,17.200001,1590000\n1950-03-08,17.190001,17.190001,17.190001,17.190001,17.190001,1360000\n1950-03-09,17.070000,17.070000,17.070000,17.070000,17.070000,1330000\n1950-03-10,17.090000,17.090000,17.090000,17.090000,17.090000,1260000\n1950-03-13,17.120001,17.120001,17.120001,17.120001,17.120001,1060000\n1950-03-14,17.250000,17.250000,17.250000,17.250000,17.250000,1140000\n1950-03-15,17.450001,17.450001,17.450001,17.450001,17.450001,1830000\n1950-03-16,17.490000,17.490000,17.490000,17.490000,17.490000,2060000\n1950-03-17,17.450001,17.450001,17.450001,17.450001,17.450001,1600000\n1950-03-20,17.440001,17.440001,17.440001,17.440001,17.440001,1430000\n1950-03-21,17.450001,17.450001,17.450001,17.450001,17.450001,1400000\n1950-03-22,17.549999,17.549999,17.549999,17.549999,17.549999,2010000\n1950-03-23,17.559999,17.559999,17.559999,17.559999,17.559999,2020000\n1950-03-24,17.559999,17.559999,17.559999,17.559999,17.559999,1570000\n1950-03-27,17.459999,17.459999,17.459999,17.459999,17.459999,1930000\n1950-03-28,17.530001,17.530001,17.530001,17.530001,17.530001,1780000\n1950-03-29,17.440001,17.440001,17.440001,17.440001,17.440001,2090000\n1950-03-30,17.299999,17.299999,17.299999,17.299999,17.299999,2370000\n1950-03-31,17.290001,17.290001,17.290001,17.290001,17.290001,1880000\n1950-04-03,17.530001,17.530001,17.530001,17.530001,17.530001,1570000\n1950-04-04,17.549999,17.549999,17.549999,17.549999,17.549999,2010000\n1950-04-05,17.629999,17.629999,17.629999,17.629999,17.629999,1430000\n1950-04-06,17.780001,17.780001,17.780001,17.780001,17.780001,2000000\n1950-04-10,17.850000,17.850000,17.850000,17.850000,17.850000,2070000\n1950-04-11,17.750000,17.750000,17.750000,17.750000,17.750000,2010000\n1950-04-12,17.940001,17.940001,17.940001,17.940001,17.940001,2010000\n1950-04-13,17.980000,17.980000,17.980000,17.980000,17.980000,2410000\n1950-04-14,17.959999,17.959999,17.959999,17.959999,17.959999,2750000\n1950-04-17,17.879999,17.879999,17.879999,17.879999,17.879999,2520000\n1950-04-18,18.030001,18.030001,18.030001,18.030001,18.030001,3320000\n1950-04-19,18.049999,18.049999,18.049999,18.049999,18.049999,2950000\n1950-04-20,17.930000,17.930000,17.930000,17.930000,17.930000,2590000\n1950-04-21,17.959999,17.959999,17.959999,17.959999,17.959999,2710000\n1950-04-24,17.830000,17.830000,17.830000,17.830000,17.830000,2310000\n1950-04-25,17.830000,17.830000,17.830000,17.830000,17.830000,1830000\n1950-04-26,17.760000,17.760000,17.760000,17.760000,17.760000,1880000\n1950-04-27,17.860001,17.860001,17.860001,17.860001,17.860001,2070000\n1950-04-28,17.959999,17.959999,17.959999,17.959999,17.959999,2190000\n1950-05-01,18.219999,18.219999,18.219999,18.219999,18.219999,2390000\n1950-05-02,18.110001,18.110001,18.110001,18.110001,18.110001,2250000\n1950-05-03,18.270000,18.270000,18.270000,18.270000,18.270000,2120000\n1950-05-04,18.120001,18.120001,18.120001,18.120001,18.120001,2150000\n1950-05-05,18.219999,18.219999,18.219999,18.219999,18.219999,1800000\n1950-05-08,18.270000,18.270000,18.270000,18.270000,18.270000,1680000\n1950-05-09,18.270000,18.270000,18.270000,18.270000,18.270000,1720000\n1950-05-10,18.290001,18.290001,18.290001,18.290001,18.290001,1880000\n1950-05-11,18.290001,18.290001,18.290001,18.290001,18.290001,1750000\n1950-05-12,18.180000,18.180000,18.180000,18.180000,18.180000,1790000\n1950-05-15,18.260000,18.260000,18.260000,18.260000,18.260000,1220000\n1950-05-16,18.440001,18.440001,18.440001,18.440001,18.440001,1730000\n1950-05-17,18.520000,18.520000,18.520000,18.520000,18.520000,2020000\n1950-05-18,18.559999,18.559999,18.559999,18.559999,18.559999,5240000\n1950-05-19,18.680000,18.680000,18.680000,18.680000,18.680000,2110000\n1950-05-22,18.600000,18.600000,18.600000,18.600000,18.600000,1620000\n1950-05-23,18.709999,18.709999,18.709999,18.709999,18.709999,1460000\n1950-05-24,18.690001,18.690001,18.690001,18.690001,18.690001,1850000\n1950-05-25,18.690001,18.690001,18.690001,18.690001,18.690001,1480000\n1950-05-26,18.670000,18.670000,18.670000,18.670000,18.670000,1330000\n1950-05-29,18.719999,18.719999,18.719999,18.719999,18.719999,1110000\n1950-05-31,18.780001,18.780001,18.780001,18.780001,18.780001,1530000\n1950-06-01,18.770000,18.770000,18.770000,18.770000,18.770000,1580000\n1950-06-02,18.790001,18.790001,18.790001,18.790001,18.790001,1450000\n1950-06-05,18.600000,18.600000,18.600000,18.600000,18.600000,1630000\n1950-06-06,18.879999,18.879999,18.879999,18.879999,18.879999,2250000\n1950-06-07,18.930000,18.930000,18.930000,18.930000,18.930000,1750000\n1950-06-08,19.139999,19.139999,19.139999,19.139999,19.139999,1780000\n1950-06-09,19.260000,19.260000,19.260000,19.260000,19.260000,2130000\n1950-06-12,19.400000,19.400000,19.400000,19.400000,19.400000,1790000\n1950-06-13,19.250000,19.250000,19.250000,19.250000,19.250000,1790000\n1950-06-14,18.980000,18.980000,18.980000,18.980000,18.980000,1650000\n1950-06-15,18.930000,18.930000,18.930000,18.930000,18.930000,1530000\n1950-06-16,18.969999,18.969999,18.969999,18.969999,18.969999,1180000\n1950-06-19,18.920000,18.920000,18.920000,18.920000,18.920000,1290000\n1950-06-20,18.830000,18.830000,18.830000,18.830000,18.830000,1470000\n1950-06-21,19.000000,19.000000,19.000000,19.000000,19.000000,1750000\n1950-06-22,19.160000,19.160000,19.160000,19.160000,19.160000,1830000\n1950-06-23,19.139999,19.139999,19.139999,19.139999,19.139999,1700000\n1950-06-26,18.110001,18.110001,18.110001,18.110001,18.110001,3950000\n1950-06-27,17.910000,17.910000,17.910000,17.910000,17.910000,4860000\n1950-06-28,18.110001,18.110001,18.110001,18.110001,18.110001,2600000\n1950-06-29,17.440001,17.440001,17.440001,17.440001,17.440001,3040000\n1950-06-30,17.690001,17.690001,17.690001,17.690001,17.690001,2660000\n1950-07-03,17.639999,17.639999,17.639999,17.639999,17.639999,1550000\n1950-07-05,17.809999,17.809999,17.809999,17.809999,17.809999,1400000\n1950-07-06,17.910000,17.910000,17.910000,17.910000,17.910000,1570000\n1950-07-07,17.670000,17.670000,17.670000,17.670000,17.670000,1870000\n1950-07-10,17.590000,17.590000,17.590000,17.590000,17.590000,1960000\n1950-07-11,17.320000,17.320000,17.320000,17.320000,17.320000,3250000\n1950-07-12,16.870001,16.870001,16.870001,16.870001,16.870001,3200000\n1950-07-13,16.690001,16.690001,16.690001,16.690001,16.690001,2660000\n1950-07-14,16.870001,16.870001,16.870001,16.870001,16.870001,1900000\n1950-07-17,16.680000,16.680000,16.680000,16.680000,16.680000,1520000\n1950-07-18,17.059999,17.059999,17.059999,17.059999,17.059999,1820000\n1950-07-19,17.360001,17.360001,17.360001,17.360001,17.360001,2430000\n1950-07-20,17.610001,17.610001,17.610001,17.610001,17.610001,3160000\n1950-07-21,17.590000,17.590000,17.590000,17.590000,17.590000,2810000\n1950-07-24,17.480000,17.480000,17.480000,17.480000,17.480000,2300000\n1950-07-25,17.230000,17.230000,17.230000,17.230000,17.230000,2770000\n1950-07-26,17.270000,17.270000,17.270000,17.270000,17.270000,2460000\n1950-07-27,17.500000,17.500000,17.500000,17.500000,17.500000,2300000\n1950-07-28,17.690001,17.690001,17.690001,17.690001,17.690001,2050000\n1950-07-31,17.840000,17.840000,17.840000,17.840000,17.840000,1590000\n1950-08-01,18.020000,18.020000,18.020000,18.020000,18.020000,1970000\n1950-08-02,17.950001,17.950001,17.950001,17.950001,17.950001,1980000\n1950-08-03,17.990000,17.990000,17.990000,17.990000,17.990000,1660000\n1950-08-04,18.139999,18.139999,18.139999,18.139999,18.139999,1600000\n1950-08-07,18.410000,18.410000,18.410000,18.410000,18.410000,1850000\n1950-08-08,18.459999,18.459999,18.459999,18.459999,18.459999,2180000\n1950-08-09,18.610001,18.610001,18.610001,18.610001,18.610001,1760000\n1950-08-10,18.480000,18.480000,18.480000,18.480000,18.480000,1870000\n1950-08-11,18.280001,18.280001,18.280001,18.280001,18.280001,1680000\n1950-08-14,18.290001,18.290001,18.290001,18.290001,18.290001,1280000\n1950-08-15,18.320000,18.320000,18.320000,18.320000,18.320000,1330000\n1950-08-16,18.340000,18.340000,18.340000,18.340000,18.340000,1770000\n1950-08-17,18.540001,18.540001,18.540001,18.540001,18.540001,2170000\n1950-08-18,18.680000,18.680000,18.680000,18.680000,18.680000,1780000\n1950-08-21,18.700001,18.700001,18.700001,18.700001,18.700001,1840000\n1950-08-22,18.680000,18.680000,18.680000,18.680000,18.680000,1550000\n1950-08-23,18.820000,18.820000,18.820000,18.820000,18.820000,1580000\n1950-08-24,18.790001,18.790001,18.790001,18.790001,18.790001,1620000\n1950-08-25,18.540001,18.540001,18.540001,18.540001,18.540001,1610000\n1950-08-28,18.530001,18.530001,18.530001,18.530001,18.530001,1300000\n1950-08-29,18.540001,18.540001,18.540001,18.540001,18.540001,1890000\n1950-08-30,18.430000,18.430000,18.430000,18.430000,18.430000,1490000\n1950-08-31,18.420000,18.420000,18.420000,18.420000,18.420000,1140000\n1950-09-01,18.549999,18.549999,18.549999,18.549999,18.549999,1290000\n1950-09-05,18.680000,18.680000,18.680000,18.680000,18.680000,1250000\n1950-09-06,18.540001,18.540001,18.540001,18.540001,18.540001,1300000\n1950-09-07,18.590000,18.590000,18.590000,18.590000,18.590000,1340000\n1950-09-08,18.750000,18.750000,18.750000,18.750000,18.750000,1960000\n1950-09-11,18.610001,18.610001,18.610001,18.610001,18.610001,1860000\n1950-09-12,18.870001,18.870001,18.870001,18.870001,18.870001,1680000\n1950-09-13,19.090000,19.090000,19.090000,19.090000,19.090000,2600000\n1950-09-14,19.180000,19.180000,19.180000,19.180000,19.180000,2350000\n1950-09-15,19.290001,19.290001,19.290001,19.290001,19.290001,2410000\n1950-09-18,19.370001,19.370001,19.370001,19.370001,19.370001,2040000\n1950-09-19,19.309999,19.309999,19.309999,19.309999,19.309999,1590000\n1950-09-20,19.209999,19.209999,19.209999,19.209999,19.209999,2100000\n1950-09-21,19.370001,19.370001,19.370001,19.370001,19.370001,1650000\n1950-09-22,19.440001,19.440001,19.440001,19.440001,19.440001,2510000\n1950-09-25,19.420000,19.420000,19.420000,19.420000,19.420000,2020000\n1950-09-26,19.139999,19.139999,19.139999,19.139999,19.139999,2280000\n1950-09-27,19.410000,19.410000,19.410000,19.410000,19.410000,2360000\n1950-09-28,19.420000,19.420000,19.420000,19.420000,19.420000,2200000\n1950-09-29,19.450001,19.450001,19.450001,19.450001,19.450001,1800000\n1950-10-02,19.690001,19.690001,19.690001,19.690001,19.690001,2200000\n1950-10-03,19.660000,19.660000,19.660000,19.660000,19.660000,2480000\n1950-10-04,20.000000,20.000000,20.000000,20.000000,20.000000,2920000\n1950-10-05,19.889999,19.889999,19.889999,19.889999,19.889999,2490000\n1950-10-06,20.120001,20.120001,20.120001,20.120001,20.120001,2360000\n1950-10-09,20.000000,20.000000,20.000000,20.000000,20.000000,2330000\n1950-10-10,19.780001,19.780001,19.780001,19.780001,19.780001,1870000\n1950-10-11,19.860001,19.860001,19.860001,19.860001,19.860001,2200000\n1950-10-13,19.850000,19.850000,19.850000,19.850000,19.850000,2030000\n1950-10-16,19.709999,19.709999,19.709999,19.709999,19.709999,1630000\n1950-10-17,19.889999,19.889999,19.889999,19.889999,19.889999,2010000\n1950-10-18,20.010000,20.010000,20.010000,20.010000,20.010000,2410000\n1950-10-19,20.020000,20.020000,20.020000,20.020000,20.020000,2250000\n1950-10-20,19.959999,19.959999,19.959999,19.959999,19.959999,1840000\n1950-10-23,19.959999,19.959999,19.959999,19.959999,19.959999,1850000\n1950-10-24,20.080000,20.080000,20.080000,20.080000,20.080000,1790000\n1950-10-25,20.049999,20.049999,20.049999,20.049999,20.049999,1930000\n1950-10-26,19.610001,19.610001,19.610001,19.610001,19.610001,3000000\n1950-10-27,19.770000,19.770000,19.770000,19.770000,19.770000,1800000\n1950-10-30,19.610001,19.610001,19.610001,19.610001,19.610001,1790000\n1950-10-31,19.530001,19.530001,19.530001,19.530001,19.530001,2010000\n1950-11-01,19.559999,19.559999,19.559999,19.559999,19.559999,1780000\n1950-11-02,19.730000,19.730000,19.730000,19.730000,19.730000,1580000\n1950-11-03,19.850000,19.850000,19.850000,19.850000,19.850000,1560000\n1950-11-06,19.360001,19.360001,19.360001,19.360001,19.360001,2580000\n1950-11-08,19.559999,19.559999,19.559999,19.559999,19.559999,1850000\n1950-11-09,19.790001,19.790001,19.790001,19.790001,19.790001,1760000\n1950-11-10,19.940001,19.940001,19.940001,19.940001,19.940001,1640000\n1950-11-13,20.010000,20.010000,20.010000,20.010000,20.010000,1630000\n1950-11-14,19.860001,19.860001,19.860001,19.860001,19.860001,1780000\n1950-11-15,19.820000,19.820000,19.820000,19.820000,19.820000,1620000\n1950-11-16,19.719999,19.719999,19.719999,19.719999,19.719999,1760000\n1950-11-17,19.860001,19.860001,19.860001,19.860001,19.860001,2130000\n1950-11-20,19.930000,19.930000,19.930000,19.930000,19.930000,2250000\n1950-11-21,19.879999,19.879999,19.879999,19.879999,19.879999,2010000\n1950-11-22,20.160000,20.160000,20.160000,20.160000,20.160000,2730000\n1950-11-24,20.320000,20.320000,20.320000,20.320000,20.320000,2620000\n1950-11-27,20.180000,20.180000,20.180000,20.180000,20.180000,1740000\n1950-11-28,19.559999,19.559999,19.559999,19.559999,19.559999,2970000\n1950-11-29,19.370001,19.370001,19.370001,19.370001,19.370001,2770000\n1950-11-30,19.510000,19.510000,19.510000,19.510000,19.510000,2080000\n1950-12-01,19.660000,19.660000,19.660000,19.660000,19.660000,1870000\n1950-12-04,19.000000,19.000000,19.000000,19.000000,19.000000,2510000\n1950-12-05,19.309999,19.309999,19.309999,19.309999,19.309999,1940000\n1950-12-06,19.450001,19.450001,19.450001,19.450001,19.450001,2010000\n1950-12-07,19.400000,19.400000,19.400000,19.400000,19.400000,1810000\n1950-12-08,19.400000,19.400000,19.400000,19.400000,19.400000,2310000\n1950-12-11,19.719999,19.719999,19.719999,19.719999,19.719999,2600000\n1950-12-12,19.680000,19.680000,19.680000,19.680000,19.680000,2140000\n1950-12-13,19.670000,19.670000,19.670000,19.670000,19.670000,2030000\n1950-12-14,19.430000,19.430000,19.430000,19.430000,19.430000,2660000\n1950-12-15,19.330000,19.330000,19.330000,19.330000,19.330000,2420000\n1950-12-18,19.850000,19.850000,19.850000,19.850000,19.850000,4500000\n1950-12-19,19.959999,19.959999,19.959999,19.959999,19.959999,3650000\n1950-12-20,19.969999,19.969999,19.969999,19.969999,19.969999,3510000\n1950-12-21,19.980000,19.980000,19.980000,19.980000,19.980000,3990000\n1950-12-22,20.070000,20.070000,20.070000,20.070000,20.070000,2720000\n1950-12-26,19.920000,19.920000,19.920000,19.920000,19.920000,2660000\n1950-12-27,20.299999,20.299999,20.299999,20.299999,20.299999,2940000\n1950-12-28,20.379999,20.379999,20.379999,20.379999,20.379999,3560000\n1950-12-29,20.430000,20.430000,20.430000,20.430000,20.430000,3440000\n1951-01-02,20.770000,20.770000,20.770000,20.770000,20.770000,3030000\n1951-01-03,20.690001,20.690001,20.690001,20.690001,20.690001,3370000\n1951-01-04,20.870001,20.870001,20.870001,20.870001,20.870001,3390000\n1951-01-05,20.870001,20.870001,20.870001,20.870001,20.870001,3390000\n1951-01-08,21.000000,21.000000,21.000000,21.000000,21.000000,2780000\n1951-01-09,21.120001,21.120001,21.120001,21.120001,21.120001,3800000\n1951-01-10,20.850000,20.850000,20.850000,20.850000,20.850000,3270000\n1951-01-11,21.190001,21.190001,21.190001,21.190001,21.190001,3490000\n1951-01-12,21.110001,21.110001,21.110001,21.110001,21.110001,2950000\n1951-01-15,21.299999,21.299999,21.299999,21.299999,21.299999,2830000\n1951-01-16,21.459999,21.459999,21.459999,21.459999,21.459999,3740000\n1951-01-17,21.549999,21.549999,21.549999,21.549999,21.549999,3880000\n1951-01-18,21.400000,21.400000,21.400000,21.400000,21.400000,3490000\n1951-01-19,21.360001,21.360001,21.360001,21.360001,21.360001,3170000\n1951-01-22,21.180000,21.180000,21.180000,21.180000,21.180000,2570000\n1951-01-23,21.260000,21.260000,21.260000,21.260000,21.260000,2080000\n1951-01-24,21.160000,21.160000,21.160000,21.160000,21.160000,1990000\n1951-01-25,21.030001,21.030001,21.030001,21.030001,21.030001,2520000\n1951-01-26,21.260000,21.260000,21.260000,21.260000,21.260000,2230000\n1951-01-29,21.670000,21.670000,21.670000,21.670000,21.670000,2630000\n1951-01-30,21.740000,21.740000,21.740000,21.740000,21.740000,2480000\n1951-01-31,21.660000,21.660000,21.660000,21.660000,21.660000,2340000\n1951-02-01,21.770000,21.770000,21.770000,21.770000,21.770000,2380000\n1951-02-02,21.959999,21.959999,21.959999,21.959999,21.959999,3030000\n1951-02-05,22.200001,22.200001,22.200001,22.200001,22.200001,2680000\n1951-02-06,22.120001,22.120001,22.120001,22.120001,22.120001,2370000\n1951-02-07,21.990000,21.990000,21.990000,21.990000,21.990000,2020000\n1951-02-08,22.090000,22.090000,22.090000,22.090000,22.090000,2120000\n1951-02-09,22.170000,22.170000,22.170000,22.170000,22.170000,2550000\n1951-02-13,22.180000,22.180000,22.180000,22.180000,22.180000,2400000\n1951-02-14,22.120001,22.120001,22.120001,22.120001,22.120001,2050000\n1951-02-15,22.000000,22.000000,22.000000,22.000000,22.000000,1700000\n1951-02-16,22.129999,22.129999,22.129999,22.129999,22.129999,1860000\n1951-02-19,21.830000,21.830000,21.830000,21.830000,21.830000,1910000\n1951-02-20,21.790001,21.790001,21.790001,21.790001,21.790001,2010000\n1951-02-21,21.860001,21.860001,21.860001,21.860001,21.860001,1670000\n1951-02-23,21.920000,21.920000,21.920000,21.920000,21.920000,1540000\n1951-02-26,21.930000,21.930000,21.930000,21.930000,21.930000,1650000\n1951-02-27,21.760000,21.760000,21.760000,21.760000,21.760000,1680000\n1951-02-28,21.799999,21.799999,21.799999,21.799999,21.799999,1640000\n1951-03-01,21.850000,21.850000,21.850000,21.850000,21.850000,1610000\n1951-03-02,21.930000,21.930000,21.930000,21.930000,21.930000,1570000\n1951-03-05,21.790001,21.790001,21.790001,21.790001,21.790001,1690000\n1951-03-06,21.790001,21.790001,21.790001,21.790001,21.790001,1490000\n1951-03-07,21.860001,21.860001,21.860001,21.860001,21.860001,1770000\n1951-03-08,21.950001,21.950001,21.950001,21.950001,21.950001,1440000\n1951-03-09,21.950001,21.950001,21.950001,21.950001,21.950001,1610000\n1951-03-12,21.700001,21.700001,21.700001,21.700001,21.700001,1640000\n1951-03-13,21.410000,21.410000,21.410000,21.410000,21.410000,2330000\n1951-03-14,21.250000,21.250000,21.250000,21.250000,21.250000,2110000\n1951-03-15,21.290001,21.290001,21.290001,21.290001,21.290001,2070000\n1951-03-16,21.639999,21.639999,21.639999,21.639999,21.639999,1660000\n1951-03-19,21.559999,21.559999,21.559999,21.559999,21.559999,1120000\n1951-03-20,21.520000,21.520000,21.520000,21.520000,21.520000,1020000\n1951-03-21,21.639999,21.639999,21.639999,21.639999,21.639999,1310000\n1951-03-22,21.730000,21.730000,21.730000,21.730000,21.730000,1290000\n1951-03-26,21.530001,21.530001,21.530001,21.530001,21.530001,1230000\n1951-03-27,21.510000,21.510000,21.510000,21.510000,21.510000,1250000\n1951-03-28,21.260000,21.260000,21.260000,21.260000,21.260000,1770000\n1951-03-29,21.330000,21.330000,21.330000,21.330000,21.330000,1300000\n1951-03-30,21.480000,21.480000,21.480000,21.480000,21.480000,1150000\n1951-04-02,21.320000,21.320000,21.320000,21.320000,21.320000,1280000\n1951-04-03,21.260000,21.260000,21.260000,21.260000,21.260000,1220000\n1951-04-04,21.400000,21.400000,21.400000,21.400000,21.400000,1300000\n1951-04-05,21.690001,21.690001,21.690001,21.690001,21.690001,1790000\n1951-04-06,21.719999,21.719999,21.719999,21.719999,21.719999,1450000\n1951-04-09,21.680000,21.680000,21.680000,21.680000,21.680000,1110000\n1951-04-10,21.650000,21.650000,21.650000,21.650000,21.650000,1280000\n1951-04-11,21.639999,21.639999,21.639999,21.639999,21.639999,1420000\n1951-04-12,21.830000,21.830000,21.830000,21.830000,21.830000,1530000\n1951-04-13,22.090000,22.090000,22.090000,22.090000,22.090000,2120000\n1951-04-16,22.040001,22.040001,22.040001,22.040001,22.040001,1730000\n1951-04-17,22.090000,22.090000,22.090000,22.090000,22.090000,1470000\n1951-04-18,22.129999,22.129999,22.129999,22.129999,22.129999,1780000\n1951-04-19,22.040001,22.040001,22.040001,22.040001,22.040001,1520000\n1951-04-20,22.040001,22.040001,22.040001,22.040001,22.040001,940000\n1951-04-23,22.049999,22.049999,22.049999,22.049999,22.049999,1160000\n1951-04-24,21.959999,21.959999,21.959999,21.959999,21.959999,1420000\n1951-04-25,21.969999,21.969999,21.969999,21.969999,21.969999,1520000\n1951-04-26,22.160000,22.160000,22.160000,22.160000,22.160000,1800000\n1951-04-27,22.389999,22.389999,22.389999,22.389999,22.389999,2120000\n1951-04-30,22.430000,22.430000,22.430000,22.430000,22.430000,1790000\n1951-05-01,22.530001,22.530001,22.530001,22.530001,22.530001,1760000\n1951-05-02,22.620001,22.620001,22.620001,22.620001,22.620001,1900000\n1951-05-03,22.809999,22.809999,22.809999,22.809999,22.809999,2060000\n1951-05-04,22.770000,22.770000,22.770000,22.770000,22.770000,2050000\n1951-05-07,22.629999,22.629999,22.629999,22.629999,22.629999,1580000\n1951-05-08,22.610001,22.610001,22.610001,22.610001,22.610001,1600000\n1951-05-09,22.639999,22.639999,22.639999,22.639999,22.639999,1960000\n1951-05-10,22.510000,22.510000,22.510000,22.510000,22.510000,1660000\n1951-05-11,22.330000,22.330000,22.330000,22.330000,22.330000,1640000\n1951-05-14,22.180000,22.180000,22.180000,22.180000,22.180000,1250000\n1951-05-15,21.760000,21.760000,21.760000,21.760000,21.760000,2020000\n1951-05-16,21.690001,21.690001,21.690001,21.690001,21.690001,1660000\n1951-05-17,21.910000,21.910000,21.910000,21.910000,21.910000,1370000\n1951-05-18,21.510000,21.510000,21.510000,21.510000,21.510000,1660000\n1951-05-21,21.459999,21.459999,21.459999,21.459999,21.459999,1580000\n1951-05-22,21.360001,21.360001,21.360001,21.360001,21.360001,1440000\n1951-05-23,21.160000,21.160000,21.160000,21.160000,21.160000,1540000\n1951-05-24,21.049999,21.049999,21.049999,21.049999,21.049999,2580000\n1951-05-25,21.030001,21.030001,21.030001,21.030001,21.030001,1210000\n1951-05-28,21.209999,21.209999,21.209999,21.209999,21.209999,1240000\n1951-05-29,21.350000,21.350000,21.350000,21.350000,21.350000,1190000\n1951-05-31,21.520000,21.520000,21.520000,21.520000,21.520000,1220000\n1951-06-01,21.480000,21.480000,21.480000,21.480000,21.480000,9810000\n1951-06-04,21.240000,21.240000,21.240000,21.240000,21.240000,1100000\n1951-06-05,21.330000,21.330000,21.330000,21.330000,21.330000,1180000\n1951-06-06,21.480000,21.480000,21.480000,21.480000,21.480000,1200000\n1951-06-07,21.559999,21.559999,21.559999,21.559999,21.559999,1340000\n1951-06-08,21.490000,21.490000,21.490000,21.490000,21.490000,1000000\n1951-06-11,21.610001,21.610001,21.610001,21.610001,21.610001,1220000\n1951-06-12,21.520000,21.520000,21.520000,21.520000,21.520000,1200000\n1951-06-13,21.549999,21.549999,21.549999,21.549999,21.549999,1060000\n1951-06-14,21.840000,21.840000,21.840000,21.840000,21.840000,1300000\n1951-06-15,22.040001,22.040001,22.040001,22.040001,22.040001,1370000\n1951-06-18,22.049999,22.049999,22.049999,22.049999,22.049999,1050000\n1951-06-19,22.020000,22.020000,22.020000,22.020000,22.020000,1100000\n1951-06-20,21.910000,21.910000,21.910000,21.910000,21.910000,1120000\n1951-06-21,21.780001,21.780001,21.780001,21.780001,21.780001,1100000\n1951-06-22,21.549999,21.549999,21.549999,21.549999,21.549999,1340000\n1951-06-25,21.290001,21.290001,21.290001,21.290001,21.290001,2440000\n1951-06-26,21.299999,21.299999,21.299999,21.299999,21.299999,1260000\n1951-06-27,21.370001,21.370001,21.370001,21.370001,21.370001,1360000\n1951-06-28,21.100000,21.100000,21.100000,21.100000,21.100000,1940000\n1951-06-29,20.959999,20.959999,20.959999,20.959999,20.959999,1730000\n1951-07-02,21.100000,21.100000,21.100000,21.100000,21.100000,1350000\n1951-07-03,21.230000,21.230000,21.230000,21.230000,21.230000,1250000\n1951-07-05,21.639999,21.639999,21.639999,21.639999,21.639999,1410000\n1951-07-06,21.639999,21.639999,21.639999,21.639999,21.639999,1170000\n1951-07-09,21.730000,21.730000,21.730000,21.730000,21.730000,1110000\n1951-07-10,21.629999,21.629999,21.629999,21.629999,21.629999,990000\n1951-07-11,21.680000,21.680000,21.680000,21.680000,21.680000,970000\n1951-07-12,21.799999,21.799999,21.799999,21.799999,21.799999,1050000\n1951-07-13,21.980000,21.980000,21.980000,21.980000,21.980000,1320000\n1951-07-16,21.730000,21.730000,21.730000,21.730000,21.730000,1200000\n1951-07-17,21.920000,21.920000,21.920000,21.920000,21.920000,1280000\n1951-07-18,21.879999,21.879999,21.879999,21.879999,21.879999,1370000\n1951-07-19,21.840000,21.840000,21.840000,21.840000,21.840000,1120000\n1951-07-20,21.879999,21.879999,21.879999,21.879999,21.879999,1390000\n1951-07-23,22.100000,22.100000,22.100000,22.100000,22.100000,1320000\n1951-07-24,22.440001,22.440001,22.440001,22.440001,22.440001,1740000\n1951-07-25,22.320000,22.320000,22.320000,22.320000,22.320000,1870000\n1951-07-26,22.469999,22.469999,22.469999,22.469999,22.469999,1480000\n1951-07-27,22.530001,22.530001,22.530001,22.530001,22.530001,1450000\n1951-07-30,22.629999,22.629999,22.629999,22.629999,22.629999,1600000\n1951-07-31,22.400000,22.400000,22.400000,22.400000,22.400000,1550000\n1951-08-01,22.510000,22.510000,22.510000,22.510000,22.510000,1680000\n1951-08-02,22.820000,22.820000,22.820000,22.820000,22.820000,2130000\n1951-08-03,22.850000,22.850000,22.850000,22.850000,22.850000,1570000\n1951-08-06,23.010000,23.010000,23.010000,23.010000,23.010000,1600000\n1951-08-07,23.030001,23.030001,23.030001,23.030001,23.030001,1810000\n1951-08-08,22.930000,22.930000,22.930000,22.930000,22.930000,1410000\n1951-08-09,22.840000,22.840000,22.840000,22.840000,22.840000,1500000\n1951-08-10,22.790001,22.790001,22.790001,22.790001,22.790001,1260000\n1951-08-13,22.799999,22.799999,22.799999,22.799999,22.799999,1320000\n1951-08-14,22.700001,22.700001,22.700001,22.700001,22.700001,1180000\n1951-08-15,22.790001,22.790001,22.790001,22.790001,22.790001,1340000\n1951-08-16,22.870001,22.870001,22.870001,22.870001,22.870001,1750000\n1951-08-17,22.940001,22.940001,22.940001,22.940001,22.940001,1620000\n1951-08-20,22.930000,22.930000,22.930000,22.930000,22.930000,1130000\n1951-08-21,22.830000,22.830000,22.830000,22.830000,22.830000,1400000\n1951-08-22,22.750000,22.750000,22.750000,22.750000,22.750000,1130000\n1951-08-23,22.900000,22.900000,22.900000,22.900000,22.900000,1230000\n1951-08-24,22.879999,22.879999,22.879999,22.879999,22.879999,1210000\n1951-08-27,22.850000,22.850000,22.850000,22.850000,22.850000,1080000\n1951-08-28,22.900000,22.900000,22.900000,22.900000,22.900000,1280000\n1951-08-29,23.080000,23.080000,23.080000,23.080000,23.080000,1520000\n1951-08-30,23.240000,23.240000,23.240000,23.240000,23.240000,1950000\n1951-08-31,23.280001,23.280001,23.280001,23.280001,23.280001,1530000\n1951-09-04,23.280001,23.280001,23.280001,23.280001,23.280001,1520000\n1951-09-05,23.420000,23.420000,23.420000,23.420000,23.420000,1850000\n1951-09-06,23.469999,23.469999,23.469999,23.469999,23.469999,2150000\n1951-09-07,23.530001,23.530001,23.530001,23.530001,23.530001,1970000\n1951-09-10,23.620001,23.620001,23.620001,23.620001,23.620001,2190000\n1951-09-11,23.500000,23.500000,23.500000,23.500000,23.500000,2040000\n1951-09-12,23.600000,23.600000,23.600000,23.600000,23.600000,2180000\n1951-09-13,23.709999,23.709999,23.709999,23.709999,23.709999,2350000\n1951-09-14,23.690001,23.690001,23.690001,23.690001,23.690001,2170000\n1951-09-17,23.620001,23.620001,23.620001,23.620001,23.620001,1800000\n1951-09-18,23.590000,23.590000,23.590000,23.590000,23.590000,2030000\n1951-09-19,23.590000,23.590000,23.590000,23.590000,23.590000,2070000\n1951-09-20,23.570000,23.570000,23.570000,23.570000,23.570000,2100000\n1951-09-21,23.400000,23.400000,23.400000,23.400000,23.400000,2180000\n1951-09-24,23.299999,23.299999,23.299999,23.299999,23.299999,1630000\n1951-09-25,23.379999,23.379999,23.379999,23.379999,23.379999,1740000\n1951-09-26,23.400000,23.400000,23.400000,23.400000,23.400000,1520000\n1951-09-27,23.270000,23.270000,23.270000,23.270000,23.270000,1540000\n1951-09-28,23.260000,23.260000,23.260000,23.260000,23.260000,1390000\n1951-10-01,23.469999,23.469999,23.469999,23.469999,23.469999,1330000\n1951-10-02,23.639999,23.639999,23.639999,23.639999,23.639999,1870000\n1951-10-03,23.790001,23.790001,23.790001,23.790001,23.790001,2780000\n1951-10-04,23.719999,23.719999,23.719999,23.719999,23.719999,1810000\n1951-10-05,23.780001,23.780001,23.780001,23.780001,23.780001,2080000\n1951-10-08,23.750000,23.750000,23.750000,23.750000,23.750000,1860000\n1951-10-09,23.650000,23.650000,23.650000,23.650000,23.650000,1750000\n1951-10-10,23.610001,23.610001,23.610001,23.610001,23.610001,1320000\n1951-10-11,23.700001,23.700001,23.700001,23.700001,23.700001,1760000\n1951-10-15,23.850000,23.850000,23.850000,23.850000,23.850000,1720000\n1951-10-16,23.770000,23.770000,23.770000,23.770000,23.770000,1730000\n1951-10-17,23.690001,23.690001,23.690001,23.690001,23.690001,1460000\n1951-10-18,23.670000,23.670000,23.670000,23.670000,23.670000,1450000\n1951-10-19,23.320000,23.320000,23.320000,23.320000,23.320000,1990000\n1951-10-22,22.750000,22.750000,22.750000,22.750000,22.750000,2690000\n1951-10-23,22.840000,22.840000,22.840000,22.840000,22.840000,2110000\n1951-10-24,23.030001,23.030001,23.030001,23.030001,23.030001,1670000\n1951-10-25,22.959999,22.959999,22.959999,22.959999,22.959999,1360000\n1951-10-26,22.809999,22.809999,22.809999,22.809999,22.809999,1710000\n1951-10-29,22.690001,22.690001,22.690001,22.690001,22.690001,1780000\n1951-10-30,22.660000,22.660000,22.660000,22.660000,22.660000,1530000\n1951-10-31,22.940001,22.940001,22.940001,22.940001,22.940001,1490000\n1951-11-01,23.100000,23.100000,23.100000,23.100000,23.100000,1430000\n1951-11-02,22.930000,22.930000,22.930000,22.930000,22.930000,1230000\n1951-11-05,22.820000,22.820000,22.820000,22.820000,22.820000,1130000\n1951-11-07,22.490000,22.490000,22.490000,22.490000,22.490000,1490000\n1951-11-08,22.469999,22.469999,22.469999,22.469999,22.469999,1410000\n1951-11-09,22.750000,22.750000,22.750000,22.750000,22.750000,1470000\n1951-11-13,22.790001,22.790001,22.790001,22.790001,22.790001,1160000\n1951-11-14,22.850000,22.850000,22.850000,22.850000,22.850000,1220000\n1951-11-15,22.840000,22.840000,22.840000,22.840000,22.840000,1200000\n1951-11-16,22.820000,22.820000,22.820000,22.820000,22.820000,1140000\n1951-11-19,22.730000,22.730000,22.730000,22.730000,22.730000,1030000\n1951-11-20,22.680000,22.680000,22.680000,22.680000,22.680000,1130000\n1951-11-21,22.639999,22.639999,22.639999,22.639999,22.639999,1090000\n1951-11-23,22.400000,22.400000,22.400000,22.400000,22.400000,1210000\n1951-11-26,22.430000,22.430000,22.430000,22.430000,22.430000,1180000\n1951-11-27,22.660000,22.660000,22.660000,22.660000,22.660000,1310000\n1951-11-28,22.610001,22.610001,22.610001,22.610001,22.610001,1150000\n1951-11-29,22.670000,22.670000,22.670000,22.670000,22.670000,1070000\n1951-11-30,22.879999,22.879999,22.879999,22.879999,22.879999,1530000\n1951-12-03,23.010000,23.010000,23.010000,23.010000,23.010000,1220000\n1951-12-04,23.139999,23.139999,23.139999,23.139999,23.139999,1280000\n1951-12-05,23.070000,23.070000,23.070000,23.070000,23.070000,1330000\n1951-12-06,23.340000,23.340000,23.340000,23.340000,23.340000,1840000\n1951-12-07,23.379999,23.379999,23.379999,23.379999,23.379999,1990000\n1951-12-10,23.420000,23.420000,23.420000,23.420000,23.420000,1340000\n1951-12-11,23.299999,23.299999,23.299999,23.299999,23.299999,1360000\n1951-12-12,23.370001,23.370001,23.370001,23.370001,23.370001,1280000\n1951-12-13,23.389999,23.389999,23.389999,23.389999,23.389999,1380000\n1951-12-14,23.370001,23.370001,23.370001,23.370001,23.370001,1360000\n1951-12-17,23.410000,23.410000,23.410000,23.410000,23.410000,1220000\n1951-12-18,23.490000,23.490000,23.490000,23.490000,23.490000,1290000\n1951-12-19,23.570000,23.570000,23.570000,23.570000,23.570000,1510000\n1951-12-20,23.570000,23.570000,23.570000,23.570000,23.570000,1340000\n1951-12-21,23.510000,23.510000,23.510000,23.510000,23.510000,1250000\n1951-12-24,23.540001,23.540001,23.540001,23.540001,23.540001,680000\n1951-12-26,23.440001,23.440001,23.440001,23.440001,23.440001,1520000\n1951-12-27,23.650000,23.650000,23.650000,23.650000,23.650000,1460000\n1951-12-28,23.690001,23.690001,23.690001,23.690001,23.690001,1470000\n1951-12-31,23.770000,23.770000,23.770000,23.770000,23.770000,1440000\n1952-01-02,23.799999,23.799999,23.799999,23.799999,23.799999,1070000\n1952-01-03,23.879999,23.879999,23.879999,23.879999,23.879999,1220000\n1952-01-04,23.920000,23.920000,23.920000,23.920000,23.920000,1480000\n1952-01-07,23.910000,23.910000,23.910000,23.910000,23.910000,1540000\n1952-01-08,23.820000,23.820000,23.820000,23.820000,23.820000,1390000\n1952-01-09,23.740000,23.740000,23.740000,23.740000,23.740000,1370000\n1952-01-10,23.860001,23.860001,23.860001,23.860001,23.860001,1520000\n1952-01-11,23.980000,23.980000,23.980000,23.980000,23.980000,1760000\n1952-01-14,24.160000,24.160000,24.160000,24.160000,24.160000,1510000\n1952-01-15,24.059999,24.059999,24.059999,24.059999,24.059999,1340000\n1952-01-16,24.090000,24.090000,24.090000,24.090000,24.090000,1430000\n1952-01-17,24.200001,24.200001,24.200001,24.200001,24.200001,1590000\n1952-01-18,24.250000,24.250000,24.250000,24.250000,24.250000,1740000\n1952-01-21,24.459999,24.459999,24.459999,24.459999,24.459999,1730000\n1952-01-22,24.660000,24.660000,24.660000,24.660000,24.660000,1920000\n1952-01-23,24.540001,24.540001,24.540001,24.540001,24.540001,1680000\n1952-01-24,24.559999,24.559999,24.559999,24.559999,24.559999,1570000\n1952-01-25,24.549999,24.549999,24.549999,24.549999,24.549999,1650000\n1952-01-28,24.610001,24.610001,24.610001,24.610001,24.610001,1590000\n1952-01-29,24.570000,24.570000,24.570000,24.570000,24.570000,1730000\n1952-01-30,24.230000,24.230000,24.230000,24.230000,24.230000,1880000\n1952-01-31,24.139999,24.139999,24.139999,24.139999,24.139999,1810000\n1952-02-01,24.299999,24.299999,24.299999,24.299999,24.299999,1350000\n1952-02-04,24.120001,24.120001,24.120001,24.120001,24.120001,1640000\n1952-02-05,24.110001,24.110001,24.110001,24.110001,24.110001,1590000\n1952-02-06,24.180000,24.180000,24.180000,24.180000,24.180000,1310000\n1952-02-07,24.110001,24.110001,24.110001,24.110001,24.110001,1170000\n1952-02-08,24.240000,24.240000,24.240000,24.240000,24.240000,1350000\n1952-02-11,24.110001,24.110001,24.110001,24.110001,24.110001,1140000\n1952-02-13,23.920000,23.920000,23.920000,23.920000,23.920000,1300000\n1952-02-14,23.870001,23.870001,23.870001,23.870001,23.870001,1340000\n1952-02-15,23.860001,23.860001,23.860001,23.860001,23.860001,1200000\n1952-02-18,23.740000,23.740000,23.740000,23.740000,23.740000,1140000\n1952-02-19,23.360001,23.360001,23.360001,23.360001,23.360001,1630000\n1952-02-20,23.090000,23.090000,23.090000,23.090000,23.090000,1970000\n1952-02-21,23.160000,23.160000,23.160000,23.160000,23.160000,1360000\n1952-02-25,23.230000,23.230000,23.230000,23.230000,23.230000,1200000\n1952-02-26,23.150000,23.150000,23.150000,23.150000,23.150000,1080000\n1952-02-27,23.180000,23.180000,23.180000,23.180000,23.180000,1260000\n1952-02-28,23.290001,23.290001,23.290001,23.290001,23.290001,1150000\n1952-02-29,23.260000,23.260000,23.260000,23.260000,23.260000,1000000\n1952-03-03,23.290001,23.290001,23.290001,23.290001,23.290001,1020000\n1952-03-04,23.680000,23.680000,23.680000,23.680000,23.680000,1570000\n1952-03-05,23.709999,23.709999,23.709999,23.709999,23.709999,1380000\n1952-03-06,23.690001,23.690001,23.690001,23.690001,23.690001,1210000\n1952-03-07,23.719999,23.719999,23.719999,23.719999,23.719999,1410000\n1952-03-10,23.600000,23.600000,23.600000,23.600000,23.600000,1170000\n1952-03-11,23.620001,23.620001,23.620001,23.620001,23.620001,1210000\n1952-03-12,23.730000,23.730000,23.730000,23.730000,23.730000,1310000\n1952-03-13,23.750000,23.750000,23.750000,23.750000,23.750000,1270000\n1952-03-14,23.750000,23.750000,23.750000,23.750000,23.750000,1350000\n1952-03-17,23.920000,23.920000,23.920000,23.920000,23.920000,1150000\n1952-03-18,23.870001,23.870001,23.870001,23.870001,23.870001,1170000\n1952-03-19,23.820000,23.820000,23.820000,23.820000,23.820000,1090000\n1952-03-20,23.889999,23.889999,23.889999,23.889999,23.889999,1240000\n1952-03-21,23.930000,23.930000,23.930000,23.930000,23.930000,1290000\n1952-03-24,23.930000,23.930000,23.930000,23.930000,23.930000,1040000\n1952-03-25,23.790001,23.790001,23.790001,23.790001,23.790001,1060000\n1952-03-26,23.780001,23.780001,23.780001,23.780001,23.780001,1030000\n1952-03-27,23.990000,23.990000,23.990000,23.990000,23.990000,1370000\n1952-03-28,24.180000,24.180000,24.180000,24.180000,24.180000,1560000\n1952-03-31,24.370001,24.370001,24.370001,24.370001,24.370001,1680000\n1952-04-01,24.180000,24.180000,24.180000,24.180000,24.180000,1720000\n1952-04-02,24.120001,24.120001,24.120001,24.120001,24.120001,1260000\n1952-04-03,24.120001,24.120001,24.120001,24.120001,24.120001,1280000\n1952-04-04,24.020000,24.020000,24.020000,24.020000,24.020000,1190000\n1952-04-07,23.799999,23.799999,23.799999,23.799999,23.799999,1230000\n1952-04-08,23.910000,23.910000,23.910000,23.910000,23.910000,1090000\n1952-04-09,23.940001,23.940001,23.940001,23.940001,23.940001,980000\n1952-04-10,24.110001,24.110001,24.110001,24.110001,24.110001,1130000\n1952-04-14,23.950001,23.950001,23.950001,23.950001,23.950001,1790000\n1952-04-15,23.650000,23.650000,23.650000,23.650000,23.650000,1720000\n1952-04-16,23.580000,23.580000,23.580000,23.580000,23.580000,1400000\n1952-04-17,23.410000,23.410000,23.410000,23.410000,23.410000,1620000\n1952-04-18,23.500000,23.500000,23.500000,23.500000,23.500000,1240000\n1952-04-21,23.690001,23.690001,23.690001,23.690001,23.690001,1110000\n1952-04-22,23.580000,23.580000,23.580000,23.580000,23.580000,1240000\n1952-04-23,23.480000,23.480000,23.480000,23.480000,23.480000,1090000\n1952-04-24,23.430000,23.430000,23.430000,23.430000,23.430000,1580000\n1952-04-25,23.540001,23.540001,23.540001,23.540001,23.540001,1240000\n1952-04-28,23.549999,23.549999,23.549999,23.549999,23.549999,980000\n1952-04-29,23.490000,23.490000,23.490000,23.490000,23.490000,1170000\n1952-04-30,23.320000,23.320000,23.320000,23.320000,23.320000,1000000\n1952-05-01,23.170000,23.170000,23.170000,23.170000,23.170000,1400000\n1952-05-02,23.559999,23.559999,23.559999,23.559999,23.559999,1300000\n1952-05-05,23.660000,23.660000,23.660000,23.660000,23.660000,860000\n1952-05-06,23.670000,23.670000,23.670000,23.670000,23.670000,1120000\n1952-05-07,23.809999,23.809999,23.809999,23.809999,23.809999,1120000\n1952-05-08,23.860001,23.860001,23.860001,23.860001,23.860001,1230000\n1952-05-09,23.840000,23.840000,23.840000,23.840000,23.840000,960000\n1952-05-12,23.750000,23.750000,23.750000,23.750000,23.750000,800000\n1952-05-13,23.780001,23.780001,23.780001,23.780001,23.780001,890000\n1952-05-14,23.680000,23.680000,23.680000,23.680000,23.680000,950000\n1952-05-15,23.600000,23.600000,23.600000,23.600000,23.600000,1050000\n1952-05-16,23.559999,23.559999,23.559999,23.559999,23.559999,910000\n1952-05-19,23.610001,23.610001,23.610001,23.610001,23.610001,780000\n1952-05-20,23.740000,23.740000,23.740000,23.740000,23.740000,1150000\n1952-05-21,23.780001,23.780001,23.780001,23.780001,23.780001,1210000\n1952-05-22,23.910000,23.910000,23.910000,23.910000,23.910000,1360000\n1952-05-23,23.889999,23.889999,23.889999,23.889999,23.889999,1150000\n1952-05-26,23.940001,23.940001,23.940001,23.940001,23.940001,940000\n1952-05-27,23.879999,23.879999,23.879999,23.879999,23.879999,1040000\n1952-05-28,23.840000,23.840000,23.840000,23.840000,23.840000,1130000\n1952-05-29,23.860001,23.860001,23.860001,23.860001,23.860001,1100000\n1952-06-02,23.799999,23.799999,23.799999,23.799999,23.799999,1190000\n1952-06-03,23.780001,23.780001,23.780001,23.780001,23.780001,940000\n1952-06-04,23.950001,23.950001,23.950001,23.950001,23.950001,1200000\n1952-06-05,24.100000,24.100000,24.100000,24.100000,24.100000,1410000\n1952-06-06,24.260000,24.260000,24.260000,24.260000,24.260000,1520000\n1952-06-09,24.370001,24.370001,24.370001,24.370001,24.370001,1270000\n1952-06-10,24.230000,24.230000,24.230000,24.230000,24.230000,1220000\n1952-06-11,24.309999,24.309999,24.309999,24.309999,24.309999,1190000\n1952-06-12,24.309999,24.309999,24.309999,24.309999,24.309999,1370000\n1952-06-13,24.370001,24.370001,24.370001,24.370001,24.370001,1130000\n1952-06-16,24.299999,24.299999,24.299999,24.299999,24.299999,980000\n1952-06-17,24.330000,24.330000,24.330000,24.330000,24.330000,920000\n1952-06-18,24.430000,24.430000,24.430000,24.430000,24.430000,1270000\n1952-06-19,24.510000,24.510000,24.510000,24.510000,24.510000,1320000\n1952-06-20,24.590000,24.590000,24.590000,24.590000,24.590000,1190000\n1952-06-23,24.559999,24.559999,24.559999,24.559999,24.559999,1200000\n1952-06-24,24.600000,24.600000,24.600000,24.600000,24.600000,1200000\n1952-06-25,24.660000,24.660000,24.660000,24.660000,24.660000,1230000\n1952-06-26,24.750000,24.750000,24.750000,24.750000,24.750000,1190000\n1952-06-27,24.830000,24.830000,24.830000,24.830000,24.830000,1210000\n1952-06-30,24.959999,24.959999,24.959999,24.959999,24.959999,1380000\n1952-07-01,25.120001,25.120001,25.120001,25.120001,25.120001,1450000\n1952-07-02,25.059999,25.059999,25.059999,25.059999,25.059999,1320000\n1952-07-03,25.049999,25.049999,25.049999,25.049999,25.049999,1150000\n1952-07-07,24.969999,24.969999,24.969999,24.969999,24.969999,1080000\n1952-07-08,24.959999,24.959999,24.959999,24.959999,24.959999,850000\n1952-07-09,24.860001,24.860001,24.860001,24.860001,24.860001,1120000\n1952-07-10,24.809999,24.809999,24.809999,24.809999,24.809999,1010000\n1952-07-11,24.980000,24.980000,24.980000,24.980000,24.980000,1040000\n1952-07-14,25.030001,25.030001,25.030001,25.030001,25.030001,1090000\n1952-07-15,25.160000,25.160000,25.160000,25.160000,25.160000,1220000\n1952-07-16,25.160000,25.160000,25.160000,25.160000,25.160000,1120000\n1952-07-17,25.049999,25.049999,25.049999,25.049999,25.049999,1010000\n1952-07-18,24.850000,24.850000,24.850000,24.850000,24.850000,1020000\n1952-07-21,24.950001,24.950001,24.950001,24.950001,24.950001,780000\n1952-07-22,25.000000,25.000000,25.000000,25.000000,25.000000,910000\n1952-07-23,25.110001,25.110001,25.110001,25.110001,25.110001,1020000\n1952-07-24,25.240000,25.240000,25.240000,25.240000,25.240000,1270000\n1952-07-25,25.160000,25.160000,25.160000,25.160000,25.160000,1130000\n1952-07-28,25.200001,25.200001,25.200001,25.200001,25.200001,1030000\n1952-07-29,25.260000,25.260000,25.260000,25.260000,25.260000,1010000\n1952-07-30,25.370001,25.370001,25.370001,25.370001,25.370001,1240000\n1952-07-31,25.400000,25.400000,25.400000,25.400000,25.400000,1230000\n1952-08-01,25.450001,25.450001,25.450001,25.450001,25.450001,1050000\n1952-08-04,25.430000,25.430000,25.430000,25.430000,25.430000,950000\n1952-08-05,25.459999,25.459999,25.459999,25.459999,25.459999,1050000\n1952-08-06,25.440001,25.440001,25.440001,25.440001,25.440001,1140000\n1952-08-07,25.520000,25.520000,25.520000,25.520000,25.520000,1180000\n1952-08-08,25.549999,25.549999,25.549999,25.549999,25.549999,1170000\n1952-08-11,25.520000,25.520000,25.520000,25.520000,25.520000,1160000\n1952-08-12,25.309999,25.309999,25.309999,25.309999,25.309999,1110000\n1952-08-13,25.280001,25.280001,25.280001,25.280001,25.280001,990000\n1952-08-14,25.280001,25.280001,25.280001,25.280001,25.280001,930000\n1952-08-15,25.200001,25.200001,25.200001,25.200001,25.200001,890000\n1952-08-18,24.940001,24.940001,24.940001,24.940001,24.940001,1090000\n1952-08-19,24.889999,24.889999,24.889999,24.889999,24.889999,980000\n1952-08-20,24.950001,24.950001,24.950001,24.950001,24.950001,960000\n1952-08-21,24.980000,24.980000,24.980000,24.980000,24.980000,800000\n1952-08-22,24.990000,24.990000,24.990000,24.990000,24.990000,910000\n1952-08-25,24.870001,24.870001,24.870001,24.870001,24.870001,840000\n1952-08-26,24.830000,24.830000,24.830000,24.830000,24.830000,890000\n1952-08-27,24.940001,24.940001,24.940001,24.940001,24.940001,930000\n1952-08-28,24.969999,24.969999,24.969999,24.969999,24.969999,980000\n1952-08-29,25.030001,25.030001,25.030001,25.030001,25.030001,890000\n1952-09-02,25.150000,25.150000,25.150000,25.150000,25.150000,970000\n1952-09-03,25.250000,25.250000,25.250000,25.250000,25.250000,1200000\n1952-09-04,25.240000,25.240000,25.240000,25.240000,25.240000,1120000\n1952-09-05,25.209999,25.209999,25.209999,25.209999,25.209999,1040000\n1952-09-08,25.110001,25.110001,25.110001,25.110001,25.110001,1170000\n1952-09-09,24.860001,24.860001,24.860001,24.860001,24.860001,1310000\n1952-09-10,24.690001,24.690001,24.690001,24.690001,24.690001,1590000\n1952-09-11,24.719999,24.719999,24.719999,24.719999,24.719999,970000\n1952-09-12,24.709999,24.709999,24.709999,24.709999,24.709999,1040000\n1952-09-15,24.450001,24.450001,24.450001,24.450001,24.450001,1100000\n1952-09-16,24.530001,24.530001,24.530001,24.530001,24.530001,1140000\n1952-09-17,24.580000,24.580000,24.580000,24.580000,24.580000,1000000\n1952-09-18,24.510000,24.510000,24.510000,24.510000,24.510000,1030000\n1952-09-19,24.570000,24.570000,24.570000,24.570000,24.570000,1150000\n1952-09-22,24.590000,24.590000,24.590000,24.590000,24.590000,1160000\n1952-09-23,24.700001,24.700001,24.700001,24.700001,24.700001,1240000\n1952-09-24,24.790001,24.790001,24.790001,24.790001,24.790001,1390000\n1952-09-25,24.809999,24.809999,24.809999,24.809999,24.809999,1210000\n1952-09-26,24.730000,24.730000,24.730000,24.730000,24.730000,1180000\n1952-09-29,24.680000,24.680000,24.680000,24.680000,24.680000,970000\n1952-09-30,24.540001,24.540001,24.540001,24.540001,24.540001,1120000\n1952-10-01,24.480000,24.480000,24.480000,24.480000,24.480000,1060000\n1952-10-02,24.520000,24.520000,24.520000,24.520000,24.520000,1040000\n1952-10-03,24.500000,24.500000,24.500000,24.500000,24.500000,980000\n1952-10-06,24.440001,24.440001,24.440001,24.440001,24.440001,1070000\n1952-10-07,24.400000,24.400000,24.400000,24.400000,24.400000,950000\n1952-10-08,24.580000,24.580000,24.580000,24.580000,24.580000,1260000\n1952-10-09,24.570000,24.570000,24.570000,24.570000,24.570000,1090000\n1952-10-10,24.549999,24.549999,24.549999,24.549999,24.549999,1070000\n1952-10-14,24.480000,24.480000,24.480000,24.480000,24.480000,1130000\n1952-10-15,24.059999,24.059999,24.059999,24.059999,24.059999,1730000\n1952-10-16,23.910000,23.910000,23.910000,23.910000,23.910000,1730000\n1952-10-17,24.200001,24.200001,24.200001,24.200001,24.200001,1360000\n1952-10-20,24.129999,24.129999,24.129999,24.129999,24.129999,1050000\n1952-10-21,24.070000,24.070000,24.070000,24.070000,24.070000,990000\n1952-10-22,23.799999,23.799999,23.799999,23.799999,23.799999,1160000\n1952-10-23,23.870001,23.870001,23.870001,23.870001,23.870001,1260000\n1952-10-24,24.030001,24.030001,24.030001,24.030001,24.030001,1060000\n1952-10-27,24.090000,24.090000,24.090000,24.090000,24.090000,1000000\n1952-10-28,24.129999,24.129999,24.129999,24.129999,24.129999,1080000\n1952-10-29,24.150000,24.150000,24.150000,24.150000,24.150000,1020000\n1952-10-30,24.150000,24.150000,24.150000,24.150000,24.150000,1090000\n1952-10-31,24.520000,24.520000,24.520000,24.520000,24.520000,1760000\n1952-11-03,24.600000,24.600000,24.600000,24.600000,24.600000,1670000\n1952-11-05,24.670000,24.670000,24.670000,24.670000,24.670000,2030000\n1952-11-06,24.770000,24.770000,24.770000,24.770000,24.770000,1390000\n1952-11-07,24.780001,24.780001,24.780001,24.780001,24.780001,1540000\n1952-11-10,24.770000,24.770000,24.770000,24.770000,24.770000,1360000\n1952-11-12,24.650000,24.650000,24.650000,24.650000,24.650000,1490000\n1952-11-13,24.709999,24.709999,24.709999,24.709999,24.709999,1330000\n1952-11-14,24.750000,24.750000,24.750000,24.750000,24.750000,1700000\n1952-11-17,24.799999,24.799999,24.799999,24.799999,24.799999,1490000\n1952-11-18,25.160000,25.160000,25.160000,25.160000,25.160000,2250000\n1952-11-19,25.330000,25.330000,25.330000,25.330000,25.330000,2350000\n1952-11-20,25.280001,25.280001,25.280001,25.280001,25.280001,1740000\n1952-11-21,25.270000,25.270000,25.270000,25.270000,25.270000,1760000\n1952-11-24,25.420000,25.420000,25.420000,25.420000,25.420000,2100000\n1952-11-25,25.360001,25.360001,25.360001,25.360001,25.360001,1930000\n1952-11-26,25.520000,25.520000,25.520000,25.520000,25.520000,1920000\n1952-11-28,25.660000,25.660000,25.660000,25.660000,25.660000,2160000\n1952-12-01,25.680000,25.680000,25.680000,25.680000,25.680000,2100000\n1952-12-02,25.740000,25.740000,25.740000,25.740000,25.740000,1610000\n1952-12-03,25.709999,25.709999,25.709999,25.709999,25.709999,1610000\n1952-12-04,25.610001,25.610001,25.610001,25.610001,25.610001,1570000\n1952-12-05,25.620001,25.620001,25.620001,25.620001,25.620001,1510000\n1952-12-08,25.760000,25.760000,25.760000,25.760000,25.760000,1790000\n1952-12-09,25.930000,25.930000,25.930000,25.930000,25.930000,2120000\n1952-12-10,25.980000,25.980000,25.980000,25.980000,25.980000,1880000\n1952-12-11,25.959999,25.959999,25.959999,25.959999,25.959999,1790000\n1952-12-12,26.040001,26.040001,26.040001,26.040001,26.040001,2030000\n1952-12-15,26.040001,26.040001,26.040001,26.040001,26.040001,1940000\n1952-12-16,26.070000,26.070000,26.070000,26.070000,26.070000,1980000\n1952-12-17,26.040001,26.040001,26.040001,26.040001,26.040001,1700000\n1952-12-18,26.030001,26.030001,26.030001,26.030001,26.030001,1860000\n1952-12-19,26.150000,26.150000,26.150000,26.150000,26.150000,2050000\n1952-12-22,26.299999,26.299999,26.299999,26.299999,26.299999,2100000\n1952-12-23,26.190001,26.190001,26.190001,26.190001,26.190001,2100000\n1952-12-24,26.209999,26.209999,26.209999,26.209999,26.209999,1510000\n1952-12-26,26.250000,26.250000,26.250000,26.250000,26.250000,1290000\n1952-12-29,26.400000,26.400000,26.400000,26.400000,26.400000,1820000\n1952-12-30,26.590000,26.590000,26.590000,26.590000,26.590000,2070000\n1952-12-31,26.570000,26.570000,26.570000,26.570000,26.570000,2050000\n1953-01-02,26.540001,26.540001,26.540001,26.540001,26.540001,1450000\n1953-01-05,26.660000,26.660000,26.660000,26.660000,26.660000,2130000\n1953-01-06,26.480000,26.480000,26.480000,26.480000,26.480000,2080000\n1953-01-07,26.370001,26.370001,26.370001,26.370001,26.370001,1760000\n1953-01-08,26.330000,26.330000,26.330000,26.330000,26.330000,1780000\n1953-01-09,26.080000,26.080000,26.080000,26.080000,26.080000,2080000\n1953-01-12,25.860001,25.860001,25.860001,25.860001,25.860001,1500000\n1953-01-13,26.020000,26.020000,26.020000,26.020000,26.020000,1680000\n1953-01-14,26.080000,26.080000,26.080000,26.080000,26.080000,1370000\n1953-01-15,26.129999,26.129999,26.129999,26.129999,26.129999,1450000\n1953-01-16,26.020000,26.020000,26.020000,26.020000,26.020000,1710000\n1953-01-19,26.010000,26.010000,26.010000,26.010000,26.010000,1360000\n1953-01-20,26.139999,26.139999,26.139999,26.139999,26.139999,1490000\n1953-01-21,26.090000,26.090000,26.090000,26.090000,26.090000,1300000\n1953-01-22,26.120001,26.120001,26.120001,26.120001,26.120001,1380000\n1953-01-23,26.070000,26.070000,26.070000,26.070000,26.070000,1340000\n1953-01-26,26.020000,26.020000,26.020000,26.020000,26.020000,1420000\n1953-01-27,26.049999,26.049999,26.049999,26.049999,26.049999,1550000\n1953-01-28,26.129999,26.129999,26.129999,26.129999,26.129999,1640000\n1953-01-29,26.200001,26.200001,26.200001,26.200001,26.200001,1830000\n1953-01-30,26.379999,26.379999,26.379999,26.379999,26.379999,1760000\n1953-02-02,26.510000,26.510000,26.510000,26.510000,26.510000,1890000\n1953-02-03,26.540001,26.540001,26.540001,26.540001,26.540001,1560000\n1953-02-04,26.420000,26.420000,26.420000,26.420000,26.420000,1660000\n1953-02-05,26.150000,26.150000,26.150000,26.150000,26.150000,1900000\n1953-02-06,26.510000,26.510000,26.510000,26.510000,26.510000,1870000\n1953-02-09,25.690001,25.690001,25.690001,25.690001,25.690001,1780000\n1953-02-10,25.620001,25.620001,25.620001,25.620001,25.620001,1350000\n1953-02-11,25.639999,25.639999,25.639999,25.639999,25.639999,1240000\n1953-02-13,25.740000,25.740000,25.740000,25.740000,25.740000,1350000\n1953-02-16,25.650000,25.650000,25.650000,25.650000,25.650000,1330000\n1953-02-17,25.500000,25.500000,25.500000,25.500000,25.500000,1290000\n1953-02-18,25.480000,25.480000,25.480000,25.480000,25.480000,1220000\n1953-02-19,25.570000,25.570000,25.570000,25.570000,25.570000,1390000\n1953-02-20,25.629999,25.629999,25.629999,25.629999,25.629999,1400000\n1953-02-24,25.750000,25.750000,25.750000,25.750000,25.750000,2300000\n1953-02-25,25.910000,25.910000,25.910000,25.910000,25.910000,2360000\n1953-02-26,25.950001,25.950001,25.950001,25.950001,25.950001,2290000\n1953-02-27,25.900000,25.900000,25.900000,25.900000,25.900000,1990000\n1953-03-02,25.930000,25.930000,25.930000,25.930000,25.930000,1760000\n1953-03-03,26.000000,26.000000,26.000000,26.000000,26.000000,1850000\n1953-03-04,25.780001,25.780001,25.780001,25.780001,25.780001,2010000\n1953-03-05,25.790001,25.790001,25.790001,25.790001,25.790001,1540000\n1953-03-06,25.840000,25.840000,25.840000,25.840000,25.840000,1690000\n1953-03-09,25.830000,25.830000,25.830000,25.830000,25.830000,1600000\n1953-03-10,25.910000,25.910000,25.910000,25.910000,25.910000,1530000\n1953-03-11,26.120001,26.120001,26.120001,26.120001,26.120001,1890000\n1953-03-12,26.129999,26.129999,26.129999,26.129999,26.129999,1780000\n1953-03-13,26.180000,26.180000,26.180000,26.180000,26.180000,1760000\n1953-03-16,26.219999,26.219999,26.219999,26.219999,26.219999,1770000\n1953-03-17,26.330000,26.330000,26.330000,26.330000,26.330000,2110000\n1953-03-18,26.240000,26.240000,26.240000,26.240000,26.240000,2110000\n1953-03-19,26.219999,26.219999,26.219999,26.219999,26.219999,1840000\n1953-03-20,26.180000,26.180000,26.180000,26.180000,26.180000,1730000\n1953-03-23,26.020000,26.020000,26.020000,26.020000,26.020000,1750000\n1953-03-24,26.170000,26.170000,26.170000,26.170000,26.170000,1970000\n1953-03-25,26.100000,26.100000,26.100000,26.100000,26.100000,2320000\n1953-03-26,25.950001,25.950001,25.950001,25.950001,25.950001,2000000\n1953-03-27,25.990000,25.990000,25.990000,25.990000,25.990000,1640000\n1953-03-30,25.610001,25.610001,25.610001,25.610001,25.610001,2740000\n1953-03-31,25.290001,25.290001,25.290001,25.290001,25.290001,3120000\n1953-04-01,25.250000,25.250000,25.250000,25.250000,25.250000,2240000\n1953-04-02,25.230000,25.230000,25.230000,25.230000,25.230000,1720000\n1953-04-06,24.610001,24.610001,24.610001,24.610001,24.610001,3050000\n1953-04-07,24.709999,24.709999,24.709999,24.709999,24.709999,2500000\n1953-04-08,24.930000,24.930000,24.930000,24.930000,24.930000,1860000\n1953-04-09,24.879999,24.879999,24.879999,24.879999,24.879999,1520000\n1953-04-10,24.820000,24.820000,24.820000,24.820000,24.820000,1360000\n1953-04-13,24.770000,24.770000,24.770000,24.770000,24.770000,1280000\n1953-04-14,24.860001,24.860001,24.860001,24.860001,24.860001,1480000\n1953-04-15,24.959999,24.959999,24.959999,24.959999,24.959999,1580000\n1953-04-16,24.910000,24.910000,24.910000,24.910000,24.910000,1310000\n1953-04-17,24.620001,24.620001,24.620001,24.620001,24.620001,1430000\n1953-04-20,24.730000,24.730000,24.730000,24.730000,24.730000,1520000\n1953-04-21,24.670000,24.670000,24.670000,24.670000,24.670000,1250000\n1953-04-22,24.459999,24.459999,24.459999,24.459999,24.459999,1390000\n1953-04-23,24.190001,24.190001,24.190001,24.190001,24.190001,1920000\n1953-04-24,24.200001,24.200001,24.200001,24.200001,24.200001,1780000\n1953-04-27,24.340000,24.340000,24.340000,24.340000,24.340000,1400000\n1953-04-28,24.520000,24.520000,24.520000,24.520000,24.520000,1330000\n1953-04-29,24.680000,24.680000,24.680000,24.680000,24.680000,1310000\n1953-04-30,24.620001,24.620001,24.620001,24.620001,24.620001,1140000\n1953-05-01,24.730000,24.730000,24.730000,24.730000,24.730000,1200000\n1953-05-04,25.000000,25.000000,25.000000,25.000000,25.000000,1520000\n1953-05-05,25.030001,25.030001,25.030001,25.030001,25.030001,1290000\n1953-05-06,25.000000,25.000000,25.000000,25.000000,25.000000,1110000\n1953-05-07,24.900000,24.900000,24.900000,24.900000,24.900000,1110000\n1953-05-08,24.900000,24.900000,24.900000,24.900000,24.900000,1220000\n1953-05-11,24.910000,24.910000,24.910000,24.910000,24.910000,1010000\n1953-05-12,24.740000,24.740000,24.740000,24.740000,24.740000,1080000\n1953-05-13,24.709999,24.709999,24.709999,24.709999,24.709999,1120000\n1953-05-14,24.850000,24.850000,24.850000,24.850000,24.850000,1210000\n1953-05-15,24.840000,24.840000,24.840000,24.840000,24.840000,1200000\n1953-05-18,24.750000,24.750000,24.750000,24.750000,24.750000,1080000\n1953-05-19,24.700001,24.700001,24.700001,24.700001,24.700001,1120000\n1953-05-20,24.930000,24.930000,24.930000,24.930000,24.930000,1690000\n1953-05-21,25.059999,25.059999,25.059999,25.059999,25.059999,1590000\n1953-05-22,25.030001,25.030001,25.030001,25.030001,25.030001,1350000\n1953-05-25,24.990000,24.990000,24.990000,24.990000,24.990000,1180000\n1953-05-26,24.870001,24.870001,24.870001,24.870001,24.870001,1160000\n1953-05-27,24.639999,24.639999,24.639999,24.639999,24.639999,1330000\n1953-05-28,24.459999,24.459999,24.459999,24.459999,24.459999,1240000\n1953-05-29,24.540001,24.540001,24.540001,24.540001,24.540001,920000\n1953-06-01,24.150000,24.150000,24.150000,24.150000,24.150000,1490000\n1953-06-02,24.219999,24.219999,24.219999,24.219999,24.219999,1450000\n1953-06-03,24.180000,24.180000,24.180000,24.180000,24.180000,1050000\n1953-06-04,24.030001,24.030001,24.030001,24.030001,24.030001,1400000\n1953-06-05,24.090000,24.090000,24.090000,24.090000,24.090000,1160000\n1953-06-08,24.010000,24.010000,24.010000,24.010000,24.010000,1000000\n1953-06-09,23.600000,23.600000,23.600000,23.600000,23.600000,2200000\n1953-06-10,23.540001,23.540001,23.540001,23.540001,23.540001,1960000\n1953-06-11,23.750000,23.750000,23.750000,23.750000,23.750000,1220000\n1953-06-12,23.820000,23.820000,23.820000,23.820000,23.820000,920000\n1953-06-15,23.620001,23.620001,23.620001,23.620001,23.620001,1090000\n1953-06-16,23.549999,23.549999,23.549999,23.549999,23.549999,1370000\n1953-06-17,23.850000,23.850000,23.850000,23.850000,23.850000,1150000\n1953-06-18,23.840000,23.840000,23.840000,23.840000,23.840000,1010000\n1953-06-19,23.840000,23.840000,23.840000,23.840000,23.840000,890000\n1953-06-22,23.959999,23.959999,23.959999,23.959999,23.959999,1030000\n1953-06-23,24.120001,24.120001,24.120001,24.120001,24.120001,1050000\n1953-06-24,24.090000,24.090000,24.090000,24.090000,24.090000,1030000\n1953-06-25,24.190001,24.190001,24.190001,24.190001,24.190001,1160000\n1953-06-26,24.209999,24.209999,24.209999,24.209999,24.209999,830000\n1953-06-29,24.139999,24.139999,24.139999,24.139999,24.139999,800000\n1953-06-30,24.139999,24.139999,24.139999,24.139999,24.139999,820000\n1953-07-01,24.240000,24.240000,24.240000,24.240000,24.240000,910000\n1953-07-02,24.309999,24.309999,24.309999,24.309999,24.309999,1030000\n1953-07-03,24.360001,24.360001,24.360001,24.360001,24.360001,830000\n1953-07-06,24.379999,24.379999,24.379999,24.379999,24.379999,820000\n1953-07-07,24.510000,24.510000,24.510000,24.510000,24.510000,1030000\n1953-07-08,24.500000,24.500000,24.500000,24.500000,24.500000,950000\n1953-07-09,24.430000,24.430000,24.430000,24.430000,24.430000,910000\n1953-07-10,24.410000,24.410000,24.410000,24.410000,24.410000,860000\n1953-07-13,24.170000,24.170000,24.170000,24.170000,24.170000,1120000\n1953-07-14,24.080000,24.080000,24.080000,24.080000,24.080000,1030000\n1953-07-15,24.150000,24.150000,24.150000,24.150000,24.150000,840000\n1953-07-16,24.180000,24.180000,24.180000,24.180000,24.180000,790000\n1953-07-17,24.350000,24.350000,24.350000,24.350000,24.350000,840000\n1953-07-20,24.219999,24.219999,24.219999,24.219999,24.219999,830000\n1953-07-21,24.160000,24.160000,24.160000,24.160000,24.160000,850000\n1953-07-22,24.190001,24.190001,24.190001,24.190001,24.190001,900000\n1953-07-23,24.230000,24.230000,24.230000,24.230000,24.230000,1000000\n1953-07-24,24.230000,24.230000,24.230000,24.230000,24.230000,890000\n1953-07-27,24.070000,24.070000,24.070000,24.070000,24.070000,1210000\n1953-07-28,24.110001,24.110001,24.110001,24.110001,24.110001,1080000\n1953-07-29,24.260000,24.260000,24.260000,24.260000,24.260000,1000000\n1953-07-30,24.490000,24.490000,24.490000,24.490000,24.490000,1200000\n1953-07-31,24.750000,24.750000,24.750000,24.750000,24.750000,1320000\n1953-08-03,24.840000,24.840000,24.840000,24.840000,24.840000,1160000\n1953-08-04,24.780001,24.780001,24.780001,24.780001,24.780001,1000000\n1953-08-05,24.680000,24.680000,24.680000,24.680000,24.680000,1080000\n1953-08-06,24.799999,24.799999,24.799999,24.799999,24.799999,1200000\n1953-08-07,24.780001,24.780001,24.780001,24.780001,24.780001,950000\n1953-08-10,24.750000,24.750000,24.750000,24.750000,24.750000,1090000\n1953-08-11,24.719999,24.719999,24.719999,24.719999,24.719999,940000\n1953-08-12,24.780001,24.780001,24.780001,24.780001,24.780001,990000\n1953-08-13,24.730000,24.730000,24.730000,24.730000,24.730000,1040000\n1953-08-14,24.620001,24.620001,24.620001,24.620001,24.620001,1000000\n1953-08-17,24.559999,24.559999,24.559999,24.559999,24.559999,910000\n1953-08-18,24.459999,24.459999,24.459999,24.459999,24.459999,1030000\n1953-08-19,24.309999,24.309999,24.309999,24.309999,24.309999,1400000\n1953-08-20,24.290001,24.290001,24.290001,24.290001,24.290001,860000\n1953-08-21,24.350000,24.350000,24.350000,24.350000,24.350000,850000\n1953-08-24,24.090000,24.090000,24.090000,24.090000,24.090000,1320000\n1953-08-25,23.930000,23.930000,23.930000,23.930000,23.930000,1470000\n1953-08-26,23.860001,23.860001,23.860001,23.860001,23.860001,1060000\n1953-08-27,23.790001,23.790001,23.790001,23.790001,23.790001,1290000\n1953-08-28,23.740000,23.740000,23.740000,23.740000,23.740000,1060000\n1953-08-31,23.320000,23.320000,23.320000,23.320000,23.320000,2190000\n1953-09-01,23.420000,23.420000,23.420000,23.420000,23.420000,1580000\n1953-09-02,23.559999,23.559999,23.559999,23.559999,23.559999,1110000\n1953-09-03,23.510000,23.510000,23.510000,23.510000,23.510000,900000\n1953-09-04,23.570000,23.570000,23.570000,23.570000,23.570000,770000\n1953-09-08,23.610001,23.610001,23.610001,23.610001,23.610001,740000\n1953-09-09,23.650000,23.650000,23.650000,23.650000,23.650000,860000\n1953-09-10,23.410000,23.410000,23.410000,23.410000,23.410000,1010000\n1953-09-11,23.139999,23.139999,23.139999,23.139999,23.139999,1930000\n1953-09-14,22.709999,22.709999,22.709999,22.709999,22.709999,2550000\n1953-09-15,22.900000,22.900000,22.900000,22.900000,22.900000,2850000\n1953-09-16,23.010000,23.010000,23.010000,23.010000,23.010000,1570000\n1953-09-17,23.070000,23.070000,23.070000,23.070000,23.070000,1290000\n1953-09-18,22.950001,22.950001,22.950001,22.950001,22.950001,1190000\n1953-09-21,22.879999,22.879999,22.879999,22.879999,22.879999,1070000\n1953-09-22,23.200001,23.200001,23.200001,23.200001,23.200001,1300000\n1953-09-23,23.230000,23.230000,23.230000,23.230000,23.230000,1240000\n1953-09-24,23.240000,23.240000,23.240000,23.240000,23.240000,1020000\n1953-09-25,23.299999,23.299999,23.299999,23.299999,23.299999,910000\n1953-09-28,23.450001,23.450001,23.450001,23.450001,23.450001,1150000\n1953-09-29,23.490000,23.490000,23.490000,23.490000,23.490000,1170000\n1953-09-30,23.350000,23.350000,23.350000,23.350000,23.350000,940000\n1953-10-01,23.490000,23.490000,23.490000,23.490000,23.490000,940000\n1953-10-02,23.590000,23.590000,23.590000,23.590000,23.590000,890000\n1953-10-05,23.480000,23.480000,23.480000,23.480000,23.480000,930000\n1953-10-06,23.389999,23.389999,23.389999,23.389999,23.389999,1100000\n1953-10-07,23.580000,23.580000,23.580000,23.580000,23.580000,1010000\n1953-10-08,23.620001,23.620001,23.620001,23.620001,23.620001,960000\n1953-10-09,23.660000,23.660000,23.660000,23.660000,23.660000,900000\n1953-10-13,23.570000,23.570000,23.570000,23.570000,23.570000,1130000\n1953-10-14,23.680000,23.680000,23.680000,23.680000,23.680000,1290000\n1953-10-15,23.950001,23.950001,23.950001,23.950001,23.950001,1710000\n1953-10-16,24.139999,24.139999,24.139999,24.139999,24.139999,1620000\n1953-10-19,24.160000,24.160000,24.160000,24.160000,24.160000,1190000\n1953-10-20,24.170000,24.170000,24.170000,24.170000,24.170000,1280000\n1953-10-21,24.190001,24.190001,24.190001,24.190001,24.190001,1320000\n1953-10-22,24.299999,24.299999,24.299999,24.299999,24.299999,1330000\n1953-10-23,24.350000,24.350000,24.350000,24.350000,24.350000,1330000\n1953-10-26,24.309999,24.309999,24.309999,24.309999,24.309999,1340000\n1953-10-27,24.260000,24.260000,24.260000,24.260000,24.260000,1170000\n1953-10-28,24.290001,24.290001,24.290001,24.290001,24.290001,1260000\n1953-10-29,24.580000,24.580000,24.580000,24.580000,24.580000,1610000\n1953-10-30,24.540001,24.540001,24.540001,24.540001,24.540001,1400000\n1953-11-02,24.660000,24.660000,24.660000,24.660000,24.660000,1340000\n1953-11-04,24.510000,24.510000,24.510000,24.510000,24.510000,1480000\n1953-11-05,24.639999,24.639999,24.639999,24.639999,24.639999,1720000\n1953-11-06,24.610001,24.610001,24.610001,24.610001,24.610001,1700000\n1953-11-09,24.660000,24.660000,24.660000,24.660000,24.660000,1440000\n1953-11-10,24.370001,24.370001,24.370001,24.370001,24.370001,1340000\n1953-11-12,24.459999,24.459999,24.459999,24.459999,24.459999,1390000\n1953-11-13,24.540001,24.540001,24.540001,24.540001,24.540001,1540000\n1953-11-16,24.379999,24.379999,24.379999,24.379999,24.379999,1490000\n1953-11-17,24.250000,24.250000,24.250000,24.250000,24.250000,1250000\n1953-11-18,24.290001,24.290001,24.290001,24.290001,24.290001,1250000\n1953-11-19,24.400000,24.400000,24.400000,24.400000,24.400000,1420000\n1953-11-20,24.440001,24.440001,24.440001,24.440001,24.440001,1300000\n1953-11-23,24.360001,24.360001,24.360001,24.360001,24.360001,1410000\n1953-11-24,24.500000,24.500000,24.500000,24.500000,24.500000,1470000\n1953-11-25,24.520000,24.520000,24.520000,24.520000,24.520000,1540000\n1953-11-27,24.660000,24.660000,24.660000,24.660000,24.660000,1600000\n1953-11-30,24.760000,24.760000,24.760000,24.760000,24.760000,1960000\n1953-12-01,24.780001,24.780001,24.780001,24.780001,24.780001,1580000\n1953-12-02,24.950001,24.950001,24.950001,24.950001,24.950001,1850000\n1953-12-03,24.969999,24.969999,24.969999,24.969999,24.969999,1740000\n1953-12-04,24.980000,24.980000,24.980000,24.980000,24.980000,1390000\n1953-12-07,24.950001,24.950001,24.950001,24.950001,24.950001,1410000\n1953-12-08,24.870001,24.870001,24.870001,24.870001,24.870001,1390000\n1953-12-09,24.840000,24.840000,24.840000,24.840000,24.840000,1410000\n1953-12-10,24.780001,24.780001,24.780001,24.780001,24.780001,1420000\n1953-12-11,24.760000,24.760000,24.760000,24.760000,24.760000,1440000\n1953-12-14,24.690001,24.690001,24.690001,24.690001,24.690001,1540000\n1953-12-15,24.709999,24.709999,24.709999,24.709999,24.709999,1450000\n1953-12-16,24.959999,24.959999,24.959999,24.959999,24.959999,1880000\n1953-12-17,24.940001,24.940001,24.940001,24.940001,24.940001,1600000\n1953-12-18,24.990000,24.990000,24.990000,24.990000,24.990000,1550000\n1953-12-21,24.950001,24.950001,24.950001,24.950001,24.950001,1690000\n1953-12-22,24.760000,24.760000,24.760000,24.760000,24.760000,1720000\n1953-12-23,24.690001,24.690001,24.690001,24.690001,24.690001,1570000\n1953-12-24,24.799999,24.799999,24.799999,24.799999,24.799999,1270000\n1953-12-28,24.709999,24.709999,24.709999,24.709999,24.709999,1570000\n1953-12-29,24.549999,24.549999,24.549999,24.549999,24.549999,2140000\n1953-12-30,24.760000,24.760000,24.760000,24.760000,24.760000,2050000\n1953-12-31,24.809999,24.809999,24.809999,24.809999,24.809999,2490000\n1954-01-04,24.950001,24.950001,24.950001,24.950001,24.950001,1310000\n1954-01-05,25.100000,25.100000,25.100000,25.100000,25.100000,1520000\n1954-01-06,25.139999,25.139999,25.139999,25.139999,25.139999,1460000\n1954-01-07,25.059999,25.059999,25.059999,25.059999,25.059999,1540000\n1954-01-08,24.930000,24.930000,24.930000,24.930000,24.930000,1260000\n1954-01-11,24.799999,24.799999,24.799999,24.799999,24.799999,1220000\n1954-01-12,24.930000,24.930000,24.930000,24.930000,24.930000,1250000\n1954-01-13,25.070000,25.070000,25.070000,25.070000,25.070000,1420000\n1954-01-14,25.190001,25.190001,25.190001,25.190001,25.190001,1530000\n1954-01-15,25.430000,25.430000,25.430000,25.430000,25.430000,2180000\n1954-01-18,25.430000,25.430000,25.430000,25.430000,25.430000,1580000\n1954-01-19,25.680000,25.680000,25.680000,25.680000,25.680000,1840000\n1954-01-20,25.750000,25.750000,25.750000,25.750000,25.750000,1960000\n1954-01-21,25.790001,25.790001,25.790001,25.790001,25.790001,1780000\n1954-01-22,25.850000,25.850000,25.850000,25.850000,25.850000,1890000\n1954-01-25,25.930000,25.930000,25.930000,25.930000,25.930000,1860000\n1954-01-26,26.090000,26.090000,26.090000,26.090000,26.090000,2120000\n1954-01-27,26.010000,26.010000,26.010000,26.010000,26.010000,2020000\n1954-01-28,26.020000,26.020000,26.020000,26.020000,26.020000,1730000\n1954-01-29,26.080000,26.080000,26.080000,26.080000,26.080000,1950000\n1954-02-01,25.990000,25.990000,25.990000,25.990000,25.990000,1740000\n1954-02-02,25.920000,25.920000,25.920000,25.920000,25.920000,1420000\n1954-02-03,26.010000,26.010000,26.010000,26.010000,26.010000,1690000\n1954-02-04,26.200001,26.200001,26.200001,26.200001,26.200001,2040000\n1954-02-05,26.299999,26.299999,26.299999,26.299999,26.299999,2030000\n1954-02-08,26.230000,26.230000,26.230000,26.230000,26.230000,2180000\n1954-02-09,26.170000,26.170000,26.170000,26.170000,26.170000,1880000\n1954-02-10,26.139999,26.139999,26.139999,26.139999,26.139999,1790000\n1954-02-11,26.059999,26.059999,26.059999,26.059999,26.059999,1860000\n1954-02-12,26.120001,26.120001,26.120001,26.120001,26.120001,1730000\n1954-02-15,26.040001,26.040001,26.040001,26.040001,26.040001,2080000\n1954-02-16,25.809999,25.809999,25.809999,25.809999,25.809999,1870000\n1954-02-17,25.860001,25.860001,25.860001,25.860001,25.860001,1740000\n1954-02-18,25.860001,25.860001,25.860001,25.860001,25.860001,1500000\n1954-02-19,25.920000,25.920000,25.920000,25.920000,25.920000,1510000\n1954-02-23,25.830000,25.830000,25.830000,25.830000,25.830000,1470000\n1954-02-24,25.830000,25.830000,25.830000,25.830000,25.830000,1350000\n1954-02-25,25.910000,25.910000,25.910000,25.910000,25.910000,1470000\n1954-02-26,26.150000,26.150000,26.150000,26.150000,26.150000,1910000\n1954-03-01,26.250000,26.250000,26.250000,26.250000,26.250000,2040000\n1954-03-02,26.320000,26.320000,26.320000,26.320000,26.320000,1980000\n1954-03-03,26.320000,26.320000,26.320000,26.320000,26.320000,2240000\n1954-03-04,26.410000,26.410000,26.410000,26.410000,26.410000,1830000\n1954-03-05,26.520000,26.520000,26.520000,26.520000,26.520000,2030000\n1954-03-08,26.450001,26.450001,26.450001,26.450001,26.450001,1650000\n1954-03-09,26.510000,26.510000,26.510000,26.510000,26.510000,1630000\n1954-03-10,26.570000,26.570000,26.570000,26.570000,26.570000,1870000\n1954-03-11,26.690001,26.690001,26.690001,26.690001,26.690001,2050000\n1954-03-12,26.690001,26.690001,26.690001,26.690001,26.690001,1980000\n1954-03-15,26.570000,26.570000,26.570000,26.570000,26.570000,1680000\n1954-03-16,26.559999,26.559999,26.559999,26.559999,26.559999,1540000\n1954-03-17,26.620001,26.620001,26.620001,26.620001,26.620001,1740000\n1954-03-18,26.730000,26.730000,26.730000,26.730000,26.730000,2020000\n1954-03-19,26.809999,26.809999,26.809999,26.809999,26.809999,1930000\n1954-03-22,26.790001,26.790001,26.790001,26.790001,26.790001,1800000\n1954-03-23,26.600000,26.600000,26.600000,26.600000,26.600000,2180000\n1954-03-24,26.469999,26.469999,26.469999,26.469999,26.469999,1900000\n1954-03-25,26.420000,26.420000,26.420000,26.420000,26.420000,1720000\n1954-03-26,26.559999,26.559999,26.559999,26.559999,26.559999,1550000\n1954-03-29,26.660000,26.660000,26.660000,26.660000,26.660000,1870000\n1954-03-30,26.690001,26.690001,26.690001,26.690001,26.690001,2130000\n1954-03-31,26.940001,26.940001,26.940001,26.940001,26.940001,2690000\n1954-04-01,27.170000,27.170000,27.170000,27.170000,27.170000,2270000\n1954-04-02,27.209999,27.209999,27.209999,27.209999,27.209999,1830000\n1954-04-05,27.260000,27.260000,27.260000,27.260000,27.260000,1710000\n1954-04-06,27.010000,27.010000,27.010000,27.010000,27.010000,2120000\n1954-04-07,27.110001,27.110001,27.110001,27.110001,27.110001,1830000\n1954-04-08,27.379999,27.379999,27.379999,27.379999,27.379999,2300000\n1954-04-09,27.379999,27.379999,27.379999,27.379999,27.379999,2360000\n1954-04-12,27.570000,27.570000,27.570000,27.570000,27.570000,1790000\n1954-04-13,27.639999,27.639999,27.639999,27.639999,27.639999,2020000\n1954-04-14,27.850000,27.850000,27.850000,27.850000,27.850000,2330000\n1954-04-15,27.940001,27.940001,27.940001,27.940001,27.940001,2200000\n1954-04-19,27.760000,27.760000,27.760000,27.760000,27.760000,2430000\n1954-04-20,27.750000,27.750000,27.750000,27.750000,27.750000,1860000\n1954-04-21,27.639999,27.639999,27.639999,27.639999,27.639999,1870000\n1954-04-22,27.680000,27.680000,27.680000,27.680000,27.680000,1750000\n1954-04-23,27.780001,27.780001,27.780001,27.780001,27.780001,1990000\n1954-04-26,27.879999,27.879999,27.879999,27.879999,27.879999,2150000\n1954-04-27,27.709999,27.709999,27.709999,27.709999,27.709999,1970000\n1954-04-28,27.760000,27.760000,27.760000,27.760000,27.760000,2120000\n1954-04-29,28.180000,28.180000,28.180000,28.180000,28.180000,2150000\n1954-04-30,28.260000,28.260000,28.260000,28.260000,28.260000,2450000\n1954-05-03,28.209999,28.209999,28.209999,28.209999,28.209999,1870000\n1954-05-04,28.280001,28.280001,28.280001,28.280001,28.280001,1990000\n1954-05-05,28.290001,28.290001,28.290001,28.290001,28.290001,2020000\n1954-05-06,28.510000,28.510000,28.510000,28.510000,28.510000,1980000\n1954-05-07,28.650000,28.650000,28.650000,28.650000,28.650000,2070000\n1954-05-10,28.620001,28.620001,28.620001,28.620001,28.620001,1800000\n1954-05-11,28.490000,28.490000,28.490000,28.490000,28.490000,1770000\n1954-05-12,28.719999,28.719999,28.719999,28.719999,28.719999,2210000\n1954-05-13,28.559999,28.559999,28.559999,28.559999,28.559999,2340000\n1954-05-14,28.799999,28.799999,28.799999,28.799999,28.799999,1970000\n1954-05-17,28.840000,28.840000,28.840000,28.840000,28.840000,2040000\n1954-05-18,28.850000,28.850000,28.850000,28.850000,28.850000,2250000\n1954-05-19,28.719999,28.719999,28.719999,28.719999,28.719999,2170000\n1954-05-20,28.820000,28.820000,28.820000,28.820000,28.820000,2070000\n1954-05-21,28.990000,28.990000,28.990000,28.990000,28.990000,2620000\n1954-05-24,29.000000,29.000000,29.000000,29.000000,29.000000,2330000\n1954-05-25,28.930000,28.930000,28.930000,28.930000,28.930000,2050000\n1954-05-26,29.170000,29.170000,29.170000,29.170000,29.170000,2180000\n1954-05-27,29.049999,29.049999,29.049999,29.049999,29.049999,2230000\n1954-05-28,29.190001,29.190001,29.190001,29.190001,29.190001,1940000\n1954-06-01,29.190001,29.190001,29.190001,29.190001,29.190001,1850000\n1954-06-02,29.160000,29.160000,29.160000,29.160000,29.160000,1930000\n1954-06-03,29.150000,29.150000,29.150000,29.150000,29.150000,1810000\n1954-06-04,29.100000,29.100000,29.100000,29.100000,29.100000,1720000\n1954-06-07,28.990000,28.990000,28.990000,28.990000,28.990000,1520000\n1954-06-08,28.340000,28.340000,28.340000,28.340000,28.340000,2540000\n1954-06-09,28.150000,28.150000,28.150000,28.150000,28.150000,2360000\n1954-06-10,28.340000,28.340000,28.340000,28.340000,28.340000,1610000\n1954-06-11,28.580000,28.580000,28.580000,28.580000,28.580000,1630000\n1954-06-14,28.620001,28.620001,28.620001,28.620001,28.620001,1420000\n1954-06-15,28.830000,28.830000,28.830000,28.830000,28.830000,1630000\n1954-06-16,29.040001,29.040001,29.040001,29.040001,29.040001,2070000\n1954-06-17,28.959999,28.959999,28.959999,28.959999,28.959999,1810000\n1954-06-18,29.040001,29.040001,29.040001,29.040001,29.040001,1580000\n1954-06-21,29.059999,29.059999,29.059999,29.059999,29.059999,1820000\n1954-06-22,29.080000,29.080000,29.080000,29.080000,29.080000,2100000\n1954-06-23,29.129999,29.129999,29.129999,29.129999,29.129999,2090000\n1954-06-24,29.260000,29.260000,29.260000,29.260000,29.260000,2260000\n1954-06-25,29.200001,29.200001,29.200001,29.200001,29.200001,2060000\n1954-06-28,29.280001,29.280001,29.280001,29.280001,29.280001,1890000\n1954-06-29,29.430000,29.430000,29.430000,29.430000,29.430000,2580000\n1954-06-30,29.209999,29.209999,29.209999,29.209999,29.209999,1950000\n1954-07-01,29.209999,29.209999,29.209999,29.209999,29.209999,1860000\n1954-07-02,29.590000,29.590000,29.590000,29.590000,29.590000,1980000\n1954-07-06,29.920000,29.920000,29.920000,29.920000,29.920000,2560000\n1954-07-07,29.940001,29.940001,29.940001,29.940001,29.940001,2380000\n1954-07-08,29.940001,29.940001,29.940001,29.940001,29.940001,2080000\n1954-07-09,30.139999,30.139999,30.139999,30.139999,30.139999,2240000\n1954-07-12,30.120001,30.120001,30.120001,30.120001,30.120001,2330000\n1954-07-13,30.020000,30.020000,30.020000,30.020000,30.020000,2430000\n1954-07-14,30.090000,30.090000,30.090000,30.090000,30.090000,2520000\n1954-07-15,30.190001,30.190001,30.190001,30.190001,30.190001,3000000\n1954-07-16,30.059999,30.059999,30.059999,30.059999,30.059999,2540000\n1954-07-19,29.980000,29.980000,29.980000,29.980000,29.980000,2370000\n1954-07-20,29.840000,29.840000,29.840000,29.840000,29.840000,2580000\n1954-07-21,30.030001,30.030001,30.030001,30.030001,30.030001,2510000\n1954-07-22,30.270000,30.270000,30.270000,30.270000,30.270000,2890000\n1954-07-23,30.309999,30.309999,30.309999,30.309999,30.309999,2520000\n1954-07-26,30.340000,30.340000,30.340000,30.340000,30.340000,2110000\n1954-07-27,30.520000,30.520000,30.520000,30.520000,30.520000,2690000\n1954-07-28,30.580000,30.580000,30.580000,30.580000,30.580000,2740000\n1954-07-29,30.690001,30.690001,30.690001,30.690001,30.690001,2710000\n1954-07-30,30.879999,30.879999,30.879999,30.879999,30.879999,2800000\n1954-08-02,30.990000,30.990000,30.990000,30.990000,30.990000,2850000\n1954-08-03,30.930000,30.930000,30.930000,30.930000,30.930000,2970000\n1954-08-04,30.900000,30.900000,30.900000,30.900000,30.900000,3620000\n1954-08-05,30.770000,30.770000,30.770000,30.770000,30.770000,3150000\n1954-08-06,30.379999,30.379999,30.379999,30.379999,30.379999,3350000\n1954-08-09,30.120001,30.120001,30.120001,30.120001,30.120001,2280000\n1954-08-10,30.370001,30.370001,30.370001,30.370001,30.370001,2890000\n1954-08-11,30.719999,30.719999,30.719999,30.719999,30.719999,3440000\n1954-08-12,30.590000,30.590000,30.590000,30.590000,30.590000,2680000\n1954-08-13,30.719999,30.719999,30.719999,30.719999,30.719999,2500000\n1954-08-16,31.049999,31.049999,31.049999,31.049999,31.049999,2760000\n1954-08-17,31.120001,31.120001,31.120001,31.120001,31.120001,2900000\n1954-08-18,31.090000,31.090000,31.090000,31.090000,31.090000,2390000\n1954-08-19,31.160000,31.160000,31.160000,31.160000,31.160000,2320000\n1954-08-20,31.209999,31.209999,31.209999,31.209999,31.209999,2110000\n1954-08-23,31.000000,31.000000,31.000000,31.000000,31.000000,2020000\n1954-08-24,30.870001,30.870001,30.870001,30.870001,30.870001,2000000\n1954-08-25,30.650000,30.650000,30.650000,30.650000,30.650000,2280000\n1954-08-26,30.570000,30.570000,30.570000,30.570000,30.570000,2060000\n1954-08-27,30.660000,30.660000,30.660000,30.660000,30.660000,1740000\n1954-08-30,30.350000,30.350000,30.350000,30.350000,30.350000,1950000\n1954-08-31,29.830000,29.830000,29.830000,29.830000,29.830000,2640000\n1954-09-01,30.040001,30.040001,30.040001,30.040001,30.040001,1790000\n1954-09-02,30.270000,30.270000,30.270000,30.270000,30.270000,1600000\n1954-09-03,30.500000,30.500000,30.500000,30.500000,30.500000,1630000\n1954-09-07,30.660000,30.660000,30.660000,30.660000,30.660000,1860000\n1954-09-08,30.680000,30.680000,30.680000,30.680000,30.680000,1970000\n1954-09-09,30.730000,30.730000,30.730000,30.730000,30.730000,1700000\n1954-09-10,30.840000,30.840000,30.840000,30.840000,30.840000,1870000\n1954-09-13,31.120001,31.120001,31.120001,31.120001,31.120001,2030000\n1954-09-14,31.280001,31.280001,31.280001,31.280001,31.280001,2120000\n1954-09-15,31.290001,31.290001,31.290001,31.290001,31.290001,2110000\n1954-09-16,31.459999,31.459999,31.459999,31.459999,31.459999,1880000\n1954-09-17,31.709999,31.709999,31.709999,31.709999,31.709999,2250000\n1954-09-20,31.570000,31.570000,31.570000,31.570000,31.570000,2060000\n1954-09-21,31.790001,31.790001,31.790001,31.790001,31.790001,1770000\n1954-09-22,32.000000,32.000000,32.000000,32.000000,32.000000,2260000\n1954-09-23,32.180000,32.180000,32.180000,32.180000,32.180000,2340000\n1954-09-24,32.400002,32.400002,32.400002,32.400002,32.400002,2340000\n1954-09-27,32.529999,32.529999,32.529999,32.529999,32.529999,2190000\n1954-09-28,32.689999,32.689999,32.689999,32.689999,32.689999,1800000\n1954-09-29,32.500000,32.500000,32.500000,32.500000,32.500000,1810000\n1954-09-30,32.310001,32.310001,32.310001,32.310001,32.310001,1840000\n1954-10-01,32.290001,32.290001,32.290001,32.290001,32.290001,1850000\n1954-10-04,32.470001,32.470001,32.470001,32.470001,32.470001,2000000\n1954-10-05,32.630001,32.630001,32.630001,32.630001,32.630001,2300000\n1954-10-06,32.759998,32.759998,32.759998,32.759998,32.759998,2570000\n1954-10-07,32.689999,32.689999,32.689999,32.689999,32.689999,1810000\n1954-10-08,32.669998,32.669998,32.669998,32.669998,32.669998,2120000\n1954-10-11,32.410000,32.410000,32.410000,32.410000,32.410000,2100000\n1954-10-12,32.279999,32.279999,32.279999,32.279999,32.279999,1620000\n1954-10-13,32.270000,32.270000,32.270000,32.270000,32.270000,2070000\n1954-10-14,31.879999,31.879999,31.879999,31.879999,31.879999,2540000\n1954-10-15,31.709999,31.709999,31.709999,31.709999,31.709999,2250000\n1954-10-18,31.830000,31.830000,31.830000,31.830000,31.830000,1790000\n1954-10-19,31.910000,31.910000,31.910000,31.910000,31.910000,1900000\n1954-10-20,32.169998,32.169998,32.169998,32.169998,32.169998,2380000\n1954-10-21,32.130001,32.130001,32.130001,32.130001,32.130001,2320000\n1954-10-22,32.130001,32.130001,32.130001,32.130001,32.130001,2080000\n1954-10-25,31.959999,31.959999,31.959999,31.959999,31.959999,2340000\n1954-10-26,31.940001,31.940001,31.940001,31.940001,31.940001,2010000\n1954-10-27,32.020000,32.020000,32.020000,32.020000,32.020000,2030000\n1954-10-28,31.879999,31.879999,31.879999,31.879999,31.879999,2190000\n1954-10-29,31.680000,31.680000,31.680000,31.680000,31.680000,1900000\n1954-11-01,31.790001,31.790001,31.790001,31.790001,31.790001,1790000\n1954-11-03,32.439999,32.439999,32.439999,32.439999,32.439999,2700000\n1954-11-04,32.820000,32.820000,32.820000,32.820000,32.820000,3140000\n1954-11-05,32.709999,32.709999,32.709999,32.709999,32.709999,2950000\n1954-11-08,33.020000,33.020000,33.020000,33.020000,33.020000,3180000\n1954-11-09,33.150002,33.150002,33.150002,33.150002,33.150002,3240000\n1954-11-10,33.180000,33.180000,33.180000,33.180000,33.180000,2070000\n1954-11-11,33.470001,33.470001,33.470001,33.470001,33.470001,2960000\n1954-11-12,33.540001,33.540001,33.540001,33.540001,33.540001,3720000\n1954-11-15,33.470001,33.470001,33.470001,33.470001,33.470001,3080000\n1954-11-16,33.570000,33.570000,33.570000,33.570000,33.570000,3260000\n1954-11-17,33.630001,33.630001,33.630001,33.630001,33.630001,3830000\n1954-11-18,33.439999,33.439999,33.439999,33.439999,33.439999,3530000\n1954-11-19,33.450001,33.450001,33.450001,33.450001,33.450001,3130000\n1954-11-22,33.580002,33.580002,33.580002,33.580002,33.580002,3000000\n1954-11-23,34.029999,34.029999,34.029999,34.029999,34.029999,3690000\n1954-11-24,34.220001,34.220001,34.220001,34.220001,34.220001,3990000\n1954-11-26,34.549999,34.549999,34.549999,34.549999,34.549999,3010000\n1954-11-29,34.540001,34.540001,34.540001,34.540001,34.540001,3300000\n1954-11-30,34.240002,34.240002,34.240002,34.240002,34.240002,3440000\n1954-12-01,33.990002,33.990002,33.990002,33.990002,33.990002,3100000\n1954-12-02,34.180000,34.180000,34.180000,34.180000,34.180000,3190000\n1954-12-03,34.490002,34.490002,34.490002,34.490002,34.490002,3790000\n1954-12-06,34.759998,34.759998,34.759998,34.759998,34.759998,3960000\n1954-12-07,34.919998,34.919998,34.919998,34.919998,34.919998,3820000\n1954-12-08,34.860001,34.860001,34.860001,34.860001,34.860001,4150000\n1954-12-09,34.689999,34.689999,34.689999,34.689999,34.689999,3300000\n1954-12-10,34.560001,34.560001,34.560001,34.560001,34.560001,3250000\n1954-12-13,34.590000,34.590000,34.590000,34.590000,34.590000,2750000\n1954-12-14,34.349998,34.349998,34.349998,34.349998,34.349998,2650000\n1954-12-15,34.560001,34.560001,34.560001,34.560001,34.560001,2740000\n1954-12-16,34.930000,34.930000,34.930000,34.930000,34.930000,3390000\n1954-12-17,35.919998,35.919998,35.919998,35.919998,35.919998,3730000\n1954-12-20,35.330002,35.330002,35.330002,35.330002,35.330002,3770000\n1954-12-21,35.380001,35.380001,35.380001,35.380001,35.380001,3630000\n1954-12-22,35.340000,35.340000,35.340000,35.340000,35.340000,3460000\n1954-12-23,35.369999,35.369999,35.369999,35.369999,35.369999,3310000\n1954-12-27,35.070000,35.070000,35.070000,35.070000,35.070000,2970000\n1954-12-28,35.430000,35.430000,35.430000,35.430000,35.430000,3660000\n1954-12-29,35.740002,35.740002,35.740002,35.740002,35.740002,4430000\n1954-12-30,35.740002,35.740002,35.740002,35.740002,35.740002,3590000\n1954-12-31,35.980000,35.980000,35.980000,35.980000,35.980000,3840000\n1955-01-03,36.750000,36.750000,36.750000,36.750000,36.750000,4570000\n1955-01-04,36.419998,36.419998,36.419998,36.419998,36.419998,4420000\n1955-01-05,35.520000,35.520000,35.520000,35.520000,35.520000,4640000\n1955-01-06,35.040001,35.040001,35.040001,35.040001,35.040001,5300000\n1955-01-07,35.330002,35.330002,35.330002,35.330002,35.330002,4030000\n1955-01-10,35.790001,35.790001,35.790001,35.790001,35.790001,4300000\n1955-01-11,35.680000,35.680000,35.680000,35.680000,35.680000,3680000\n1955-01-12,35.580002,35.580002,35.580002,35.580002,35.580002,3400000\n1955-01-13,35.430000,35.430000,35.430000,35.430000,35.430000,3350000\n1955-01-14,35.279999,35.279999,35.279999,35.279999,35.279999,2630000\n1955-01-17,34.580002,34.580002,34.580002,34.580002,34.580002,3360000\n1955-01-18,34.799999,34.799999,34.799999,34.799999,34.799999,3020000\n1955-01-19,34.959999,34.959999,34.959999,34.959999,34.959999,2760000\n1955-01-20,35.130001,35.130001,35.130001,35.130001,35.130001,2210000\n1955-01-21,35.439999,35.439999,35.439999,35.439999,35.439999,2690000\n1955-01-24,35.520000,35.520000,35.520000,35.520000,35.520000,2910000\n1955-01-25,35.509998,35.509998,35.509998,35.509998,35.509998,3230000\n1955-01-26,35.950001,35.950001,35.950001,35.950001,35.950001,3860000\n1955-01-27,35.990002,35.990002,35.990002,35.990002,35.990002,3500000\n1955-01-28,36.189999,36.189999,36.189999,36.189999,36.189999,3290000\n1955-01-31,36.630001,36.630001,36.630001,36.630001,36.630001,3500000\n1955-02-01,36.720001,36.720001,36.720001,36.720001,36.720001,3320000\n1955-02-02,36.610001,36.610001,36.610001,36.610001,36.610001,3210000\n1955-02-03,36.439999,36.439999,36.439999,36.439999,36.439999,2890000\n1955-02-04,36.959999,36.959999,36.959999,36.959999,36.959999,3370000\n1955-02-07,36.959999,36.959999,36.959999,36.959999,36.959999,3610000\n1955-02-08,36.459999,36.459999,36.459999,36.459999,36.459999,3400000\n1955-02-09,36.750000,36.750000,36.750000,36.750000,36.750000,3360000\n1955-02-10,37.080002,37.080002,37.080002,37.080002,37.080002,3460000\n1955-02-11,37.150002,37.150002,37.150002,37.150002,37.150002,3260000\n1955-02-14,36.889999,36.889999,36.889999,36.889999,36.889999,2950000\n1955-02-15,36.889999,36.889999,36.889999,36.889999,36.889999,3510000\n1955-02-16,36.770000,36.770000,36.770000,36.770000,36.770000,3660000\n1955-02-17,36.840000,36.840000,36.840000,36.840000,36.840000,3030000\n1955-02-18,36.889999,36.889999,36.889999,36.889999,36.889999,3660000\n1955-02-21,36.849998,36.849998,36.849998,36.849998,36.849998,3010000\n1955-02-23,36.820000,36.820000,36.820000,36.820000,36.820000,3030000\n1955-02-24,36.619999,36.619999,36.619999,36.619999,36.619999,2920000\n1955-02-25,36.570000,36.570000,36.570000,36.570000,36.570000,2540000\n1955-02-28,36.759998,36.759998,36.759998,36.759998,36.759998,2620000\n1955-03-01,36.830002,36.830002,36.830002,36.830002,36.830002,2830000\n1955-03-02,37.150002,37.150002,37.150002,37.150002,37.150002,3370000\n1955-03-03,37.290001,37.290001,37.290001,37.290001,37.290001,3330000\n1955-03-04,37.520000,37.520000,37.520000,37.520000,37.520000,2770000\n1955-03-07,37.279999,37.279999,37.279999,37.279999,37.279999,2630000\n1955-03-08,36.580002,36.580002,36.580002,36.580002,36.580002,3160000\n1955-03-09,36.220001,36.220001,36.220001,36.220001,36.220001,3590000\n1955-03-10,36.450001,36.450001,36.450001,36.450001,36.450001,2760000\n1955-03-11,35.820000,35.820000,35.820000,35.820000,35.820000,3040000\n1955-03-14,34.959999,34.959999,34.959999,34.959999,34.959999,4220000\n1955-03-15,35.709999,35.709999,35.709999,35.709999,35.709999,3160000\n1955-03-16,35.980000,35.980000,35.980000,35.980000,35.980000,2900000\n1955-03-17,36.119999,36.119999,36.119999,36.119999,36.119999,2200000\n1955-03-18,36.180000,36.180000,36.180000,36.180000,36.180000,2050000\n1955-03-21,35.950001,35.950001,35.950001,35.950001,35.950001,2020000\n1955-03-22,36.169998,36.169998,36.169998,36.169998,36.169998,1910000\n1955-03-23,36.639999,36.639999,36.639999,36.639999,36.639999,2730000\n1955-03-24,36.930000,36.930000,36.930000,36.930000,36.930000,3170000\n1955-03-25,36.959999,36.959999,36.959999,36.959999,36.959999,2540000\n1955-03-28,36.830002,36.830002,36.830002,36.830002,36.830002,2540000\n1955-03-29,36.849998,36.849998,36.849998,36.849998,36.849998,2770000\n1955-03-30,36.520000,36.520000,36.520000,36.520000,36.520000,3410000\n1955-03-31,36.580002,36.580002,36.580002,36.580002,36.580002,2680000\n1955-04-01,36.950001,36.950001,36.950001,36.950001,36.950001,2660000\n1955-04-04,36.830002,36.830002,36.830002,36.830002,36.830002,2500000\n1955-04-05,36.980000,36.980000,36.980000,36.980000,36.980000,2100000\n1955-04-06,37.169998,37.169998,37.169998,37.169998,37.169998,2500000\n1955-04-07,37.340000,37.340000,37.340000,37.340000,37.340000,2330000\n1955-04-11,37.439999,37.439999,37.439999,37.439999,37.439999,2680000\n1955-04-12,37.660000,37.660000,37.660000,37.660000,37.660000,2770000\n1955-04-13,37.709999,37.709999,37.709999,37.709999,37.709999,2820000\n1955-04-14,37.790001,37.790001,37.790001,37.790001,37.790001,2890000\n1955-04-15,37.959999,37.959999,37.959999,37.959999,37.959999,3180000\n1955-04-18,38.270000,38.270000,38.270000,38.270000,38.270000,3080000\n1955-04-19,38.220001,38.220001,38.220001,38.220001,38.220001,2700000\n1955-04-20,38.279999,38.279999,38.279999,38.279999,38.279999,3090000\n1955-04-21,38.320000,38.320000,38.320000,38.320000,38.320000,2810000\n1955-04-22,38.009998,38.009998,38.009998,38.009998,38.009998,2800000\n1955-04-25,38.110001,38.110001,38.110001,38.110001,38.110001,2720000\n1955-04-26,38.310001,38.310001,38.310001,38.310001,38.310001,2720000\n1955-04-27,38.110001,38.110001,38.110001,38.110001,38.110001,2660000\n1955-04-28,37.680000,37.680000,37.680000,37.680000,37.680000,2550000\n1955-04-29,37.959999,37.959999,37.959999,37.959999,37.959999,2230000\n1955-05-02,38.040001,38.040001,38.040001,38.040001,38.040001,2220000\n1955-05-03,37.700001,37.700001,37.700001,37.700001,37.700001,2630000\n1955-05-04,37.639999,37.639999,37.639999,37.639999,37.639999,2220000\n1955-05-05,37.820000,37.820000,37.820000,37.820000,37.820000,2270000\n1955-05-06,37.889999,37.889999,37.889999,37.889999,37.889999,2250000\n1955-05-09,37.930000,37.930000,37.930000,37.930000,37.930000,2090000\n1955-05-10,37.849998,37.849998,37.849998,37.849998,37.849998,2150000\n1955-05-11,37.419998,37.419998,37.419998,37.419998,37.419998,2120000\n1955-05-12,37.200001,37.200001,37.200001,37.200001,37.200001,2830000\n1955-05-13,37.439999,37.439999,37.439999,37.439999,37.439999,1860000\n1955-05-16,37.020000,37.020000,37.020000,37.020000,37.020000,2160000\n1955-05-17,36.970001,36.970001,36.970001,36.970001,36.970001,1900000\n1955-05-18,37.279999,37.279999,37.279999,37.279999,37.279999,2010000\n1955-05-19,37.490002,37.490002,37.490002,37.490002,37.490002,2380000\n1955-05-20,37.740002,37.740002,37.740002,37.740002,37.740002,2240000\n1955-05-23,37.480000,37.480000,37.480000,37.480000,37.480000,1900000\n1955-05-24,37.459999,37.459999,37.459999,37.459999,37.459999,1650000\n1955-05-25,37.599998,37.599998,37.599998,37.599998,37.599998,2100000\n1955-05-26,37.849998,37.849998,37.849998,37.849998,37.849998,2260000\n1955-05-27,37.930000,37.930000,37.930000,37.930000,37.930000,2220000\n1955-05-31,37.910000,37.910000,37.910000,37.910000,37.910000,1990000\n1955-06-01,37.959999,37.959999,37.959999,37.959999,37.959999,2510000\n1955-06-02,38.009998,38.009998,38.009998,38.009998,38.009998,2610000\n1955-06-03,38.369999,38.369999,38.369999,38.369999,38.369999,2590000\n1955-06-06,39.689999,39.689999,39.689999,39.689999,39.689999,2560000\n1955-06-07,39.959999,39.959999,39.959999,39.959999,39.959999,3230000\n1955-06-08,39.220001,39.220001,39.220001,39.220001,39.220001,3300000\n1955-06-09,39.009998,39.009998,39.009998,39.009998,39.009998,2960000\n1955-06-10,39.250000,39.250000,39.250000,39.250000,39.250000,2470000\n1955-06-13,39.619999,39.619999,39.619999,39.619999,39.619999,2770000\n1955-06-14,39.669998,39.669998,39.669998,39.669998,39.669998,2860000\n1955-06-15,39.889999,39.889999,39.889999,39.889999,39.889999,2650000\n1955-06-16,39.959999,39.959999,39.959999,39.959999,39.959999,2760000\n1955-06-17,40.099998,40.099998,40.099998,40.099998,40.099998,2340000\n1955-06-20,40.139999,40.139999,40.139999,40.139999,40.139999,2490000\n1955-06-21,40.509998,40.509998,40.509998,40.509998,40.509998,2720000\n1955-06-22,40.599998,40.599998,40.599998,40.599998,40.599998,3010000\n1955-06-23,40.750000,40.750000,40.750000,40.750000,40.750000,2900000\n1955-06-24,40.959999,40.959999,40.959999,40.959999,40.959999,2410000\n1955-06-27,40.990002,40.990002,40.990002,40.990002,40.990002,2250000\n1955-06-28,40.770000,40.770000,40.770000,40.770000,40.770000,2180000\n1955-06-29,40.790001,40.790001,40.790001,40.790001,40.790001,2180000\n1955-06-30,41.029999,41.029999,41.029999,41.029999,41.029999,2370000\n1955-07-01,41.189999,41.189999,41.189999,41.189999,41.189999,2540000\n1955-07-05,41.689999,41.689999,41.689999,41.689999,41.689999,2680000\n1955-07-06,43.180000,43.180000,43.180000,43.180000,43.180000,3140000\n1955-07-07,42.580002,42.580002,42.580002,42.580002,42.580002,3300000\n1955-07-08,42.639999,42.639999,42.639999,42.639999,42.639999,2450000\n1955-07-11,42.750000,42.750000,42.750000,42.750000,42.750000,2420000\n1955-07-12,42.750000,42.750000,42.750000,42.750000,42.750000,2630000\n1955-07-13,42.240002,42.240002,42.240002,42.240002,42.240002,2360000\n1955-07-14,42.250000,42.250000,42.250000,42.250000,42.250000,1980000\n1955-07-15,42.400002,42.400002,42.400002,42.400002,42.400002,2230000\n1955-07-18,42.360001,42.360001,42.360001,42.360001,42.360001,2160000\n1955-07-19,42.099998,42.099998,42.099998,42.099998,42.099998,2300000\n1955-07-20,42.230000,42.230000,42.230000,42.230000,42.230000,2080000\n1955-07-21,42.639999,42.639999,42.639999,42.639999,42.639999,2530000\n1955-07-22,43.000000,43.000000,43.000000,43.000000,43.000000,2500000\n1955-07-25,43.480000,43.480000,43.480000,43.480000,43.480000,2500000\n1955-07-26,43.580002,43.580002,43.580002,43.580002,43.580002,2340000\n1955-07-27,43.759998,43.759998,43.759998,43.759998,43.759998,2170000\n1955-07-28,43.500000,43.500000,43.500000,43.500000,43.500000,2090000\n1955-07-29,43.520000,43.520000,43.520000,43.520000,43.520000,2070000\n1955-08-01,42.930000,42.930000,42.930000,42.930000,42.930000,2190000\n1955-08-02,43.029999,43.029999,43.029999,43.029999,43.029999,2260000\n1955-08-03,43.090000,43.090000,43.090000,43.090000,43.090000,2190000\n1955-08-04,42.360001,42.360001,42.360001,42.360001,42.360001,2210000\n1955-08-05,42.560001,42.560001,42.560001,42.560001,42.560001,1690000\n1955-08-08,42.310001,42.310001,42.310001,42.310001,42.310001,1730000\n1955-08-09,41.750000,41.750000,41.750000,41.750000,41.750000,2240000\n1955-08-10,41.740002,41.740002,41.740002,41.740002,41.740002,1580000\n1955-08-11,42.130001,42.130001,42.130001,42.130001,42.130001,1620000\n1955-08-12,42.209999,42.209999,42.209999,42.209999,42.209999,1530000\n1955-08-15,42.169998,42.169998,42.169998,42.169998,42.169998,1230000\n1955-08-16,41.860001,41.860001,41.860001,41.860001,41.860001,1520000\n1955-08-17,41.900002,41.900002,41.900002,41.900002,41.900002,1570000\n1955-08-18,41.840000,41.840000,41.840000,41.840000,41.840000,1560000\n1955-08-19,42.020000,42.020000,42.020000,42.020000,42.020000,1400000\n1955-08-22,41.980000,41.980000,41.980000,41.980000,41.980000,1430000\n1955-08-23,42.549999,42.549999,42.549999,42.549999,42.549999,1890000\n1955-08-24,42.610001,42.610001,42.610001,42.610001,42.610001,2140000\n1955-08-25,42.799999,42.799999,42.799999,42.799999,42.799999,2120000\n1955-08-26,42.990002,42.990002,42.990002,42.990002,42.990002,2200000\n1955-08-29,42.959999,42.959999,42.959999,42.959999,42.959999,1910000\n1955-08-30,42.919998,42.919998,42.919998,42.919998,42.919998,1740000\n1955-08-31,43.180000,43.180000,43.180000,43.180000,43.180000,1850000\n1955-09-01,43.369999,43.369999,43.369999,43.369999,43.369999,1860000\n1955-09-02,43.599998,43.599998,43.599998,43.599998,43.599998,1700000\n1955-09-06,43.860001,43.860001,43.860001,43.860001,43.860001,2360000\n1955-09-07,43.849998,43.849998,43.849998,43.849998,43.849998,2380000\n1955-09-08,43.880001,43.880001,43.880001,43.880001,43.880001,2470000\n1955-09-09,43.889999,43.889999,43.889999,43.889999,43.889999,2480000\n1955-09-12,44.189999,44.189999,44.189999,44.189999,44.189999,2520000\n1955-09-13,44.799999,44.799999,44.799999,44.799999,44.799999,2580000\n1955-09-14,44.990002,44.990002,44.990002,44.990002,44.990002,2570000\n1955-09-15,44.750000,44.750000,44.750000,44.750000,44.750000,2890000\n1955-09-16,45.090000,45.090000,45.090000,45.090000,45.090000,2540000\n1955-09-19,45.160000,45.160000,45.160000,45.160000,45.160000,2390000\n1955-09-20,45.130001,45.130001,45.130001,45.130001,45.130001,2090000\n1955-09-21,45.389999,45.389999,45.389999,45.389999,45.389999,2460000\n1955-09-22,45.389999,45.389999,45.389999,45.389999,45.389999,2550000\n1955-09-23,45.630001,45.630001,45.630001,45.630001,45.630001,2540000\n1955-09-26,42.610001,42.610001,42.610001,42.610001,42.610001,7720000\n1955-09-27,43.580002,43.580002,43.580002,43.580002,43.580002,5500000\n1955-09-28,44.310001,44.310001,44.310001,44.310001,44.310001,3780000\n1955-09-29,44.029999,44.029999,44.029999,44.029999,44.029999,2560000\n1955-09-30,43.669998,43.669998,43.669998,43.669998,43.669998,2140000\n1955-10-03,42.490002,42.490002,42.490002,42.490002,42.490002,2720000\n1955-10-04,42.820000,42.820000,42.820000,42.820000,42.820000,2020000\n1955-10-05,42.990002,42.990002,42.990002,42.990002,42.990002,1920000\n1955-10-06,42.700001,42.700001,42.700001,42.700001,42.700001,1690000\n1955-10-07,42.380001,42.380001,42.380001,42.380001,42.380001,2150000\n1955-10-10,41.150002,41.150002,41.150002,41.150002,41.150002,3100000\n1955-10-11,40.799999,40.799999,40.799999,40.799999,40.799999,3590000\n1955-10-12,41.520000,41.520000,41.520000,41.520000,41.520000,1900000\n1955-10-13,41.389999,41.389999,41.389999,41.389999,41.389999,1980000\n1955-10-14,41.220001,41.220001,41.220001,41.220001,41.220001,1640000\n1955-10-17,41.349998,41.349998,41.349998,41.349998,41.349998,1480000\n1955-10-18,41.650002,41.650002,41.650002,41.650002,41.650002,1550000\n1955-10-19,42.070000,42.070000,42.070000,42.070000,42.070000,1760000\n1955-10-20,42.590000,42.590000,42.590000,42.590000,42.590000,2160000\n1955-10-21,42.590000,42.590000,42.590000,42.590000,42.590000,1710000\n1955-10-24,42.910000,42.910000,42.910000,42.910000,42.910000,1820000\n1955-10-25,42.630001,42.630001,42.630001,42.630001,42.630001,1950000\n1955-10-26,42.290001,42.290001,42.290001,42.290001,42.290001,1660000\n1955-10-27,42.340000,42.340000,42.340000,42.340000,42.340000,1830000\n1955-10-28,42.369999,42.369999,42.369999,42.369999,42.369999,1720000\n1955-10-31,42.340000,42.340000,42.340000,42.340000,42.340000,1800000\n1955-11-01,42.279999,42.279999,42.279999,42.279999,42.279999,1590000\n1955-11-02,42.349998,42.349998,42.349998,42.349998,42.349998,1610000\n1955-11-03,43.240002,43.240002,43.240002,43.240002,43.240002,2260000\n1955-11-04,43.959999,43.959999,43.959999,43.959999,43.959999,2430000\n1955-11-07,44.150002,44.150002,44.150002,44.150002,44.150002,2230000\n1955-11-09,44.610001,44.610001,44.610001,44.610001,44.610001,2580000\n1955-11-10,44.720001,44.720001,44.720001,44.720001,44.720001,2550000\n1955-11-11,45.240002,45.240002,45.240002,45.240002,45.240002,2000000\n1955-11-14,46.410000,46.410000,46.410000,46.410000,46.410000,2760000\n1955-11-15,46.209999,46.209999,46.209999,46.209999,46.209999,2560000\n1955-11-16,45.910000,45.910000,45.910000,45.910000,45.910000,2460000\n1955-11-17,45.590000,45.590000,45.590000,45.590000,45.590000,2310000\n1955-11-18,45.540001,45.540001,45.540001,45.540001,45.540001,2320000\n1955-11-21,45.220001,45.220001,45.220001,45.220001,45.220001,1960000\n1955-11-22,45.660000,45.660000,45.660000,45.660000,45.660000,2270000\n1955-11-23,45.720001,45.720001,45.720001,45.720001,45.720001,2550000\n1955-11-25,45.680000,45.680000,45.680000,45.680000,45.680000,2190000\n1955-11-28,45.380001,45.380001,45.380001,45.380001,45.380001,2460000\n1955-11-29,45.560001,45.560001,45.560001,45.560001,45.560001,2370000\n1955-11-30,45.509998,45.509998,45.509998,45.509998,45.509998,2900000\n1955-12-01,45.349998,45.349998,45.349998,45.349998,45.349998,2370000\n1955-12-02,45.439999,45.439999,45.439999,45.439999,45.439999,2400000\n1955-12-05,45.700001,45.700001,45.700001,45.700001,45.700001,2440000\n1955-12-06,45.700001,45.700001,45.700001,45.700001,45.700001,2540000\n1955-12-07,45.549999,45.549999,45.549999,45.549999,45.549999,2480000\n1955-12-08,45.820000,45.820000,45.820000,45.820000,45.820000,2970000\n1955-12-09,45.889999,45.889999,45.889999,45.889999,45.889999,2660000\n1955-12-12,45.419998,45.419998,45.419998,45.419998,45.419998,2510000\n1955-12-13,45.450001,45.450001,45.450001,45.450001,45.450001,2430000\n1955-12-14,45.070000,45.070000,45.070000,45.070000,45.070000,2670000\n1955-12-15,45.060001,45.060001,45.060001,45.060001,45.060001,2260000\n1955-12-16,45.130001,45.130001,45.130001,45.130001,45.130001,2310000\n1955-12-19,45.020000,45.020000,45.020000,45.020000,45.020000,2380000\n1955-12-20,44.950001,44.950001,44.950001,44.950001,44.950001,2280000\n1955-12-21,45.840000,45.840000,45.840000,45.840000,45.840000,2540000\n1955-12-22,45.410000,45.410000,45.410000,45.410000,45.410000,2650000\n1955-12-23,45.500000,45.500000,45.500000,45.500000,45.500000,2090000\n1955-12-27,45.220001,45.220001,45.220001,45.220001,45.220001,2010000\n1955-12-28,45.049999,45.049999,45.049999,45.049999,45.049999,1990000\n1955-12-29,45.150002,45.150002,45.150002,45.150002,45.150002,2190000\n1955-12-30,45.480000,45.480000,45.480000,45.480000,45.480000,2820000\n1956-01-03,45.160000,45.160000,45.160000,45.160000,45.160000,2390000\n1956-01-04,45.000000,45.000000,45.000000,45.000000,45.000000,2290000\n1956-01-05,44.950001,44.950001,44.950001,44.950001,44.950001,2110000\n1956-01-06,45.139999,45.139999,45.139999,45.139999,45.139999,2570000\n1956-01-09,44.509998,44.509998,44.509998,44.509998,44.509998,2700000\n1956-01-10,44.160000,44.160000,44.160000,44.160000,44.160000,2640000\n1956-01-11,44.380001,44.380001,44.380001,44.380001,44.380001,2310000\n1956-01-12,44.750000,44.750000,44.750000,44.750000,44.750000,2330000\n1956-01-13,44.669998,44.669998,44.669998,44.669998,44.669998,2120000\n1956-01-16,44.139999,44.139999,44.139999,44.139999,44.139999,2260000\n1956-01-17,44.470001,44.470001,44.470001,44.470001,44.470001,2050000\n1956-01-18,44.169998,44.169998,44.169998,44.169998,44.169998,2110000\n1956-01-19,43.720001,43.720001,43.720001,43.720001,43.720001,2500000\n1956-01-20,43.220001,43.220001,43.220001,43.220001,43.220001,2430000\n1956-01-23,43.110001,43.110001,43.110001,43.110001,43.110001,2720000\n1956-01-24,43.650002,43.650002,43.650002,43.650002,43.650002,2160000\n1956-01-25,43.720001,43.720001,43.720001,43.720001,43.720001,1950000\n1956-01-26,43.459999,43.459999,43.459999,43.459999,43.459999,1840000\n1956-01-27,43.349998,43.349998,43.349998,43.349998,43.349998,1950000\n1956-01-30,43.500000,43.500000,43.500000,43.500000,43.500000,1830000\n1956-01-31,43.820000,43.820000,43.820000,43.820000,43.820000,1900000\n1956-02-01,44.029999,44.029999,44.029999,44.029999,44.029999,2010000\n1956-02-02,44.220001,44.220001,44.220001,44.220001,44.220001,1900000\n1956-02-03,44.779999,44.779999,44.779999,44.779999,44.779999,2110000\n1956-02-06,44.810001,44.810001,44.810001,44.810001,44.810001,2230000\n1956-02-07,44.599998,44.599998,44.599998,44.599998,44.599998,2060000\n1956-02-08,44.160000,44.160000,44.160000,44.160000,44.160000,2170000\n1956-02-09,43.660000,43.660000,43.660000,43.660000,43.660000,2080000\n1956-02-10,43.639999,43.639999,43.639999,43.639999,43.639999,1770000\n1956-02-13,43.580002,43.580002,43.580002,43.580002,43.580002,1420000\n1956-02-14,43.419998,43.419998,43.419998,43.419998,43.419998,1590000\n1956-02-15,44.040001,44.040001,44.040001,44.040001,44.040001,3000000\n1956-02-16,43.820000,43.820000,43.820000,43.820000,43.820000,1750000\n1956-02-17,44.520000,44.520000,44.520000,44.520000,44.520000,2840000\n1956-02-20,44.450001,44.450001,44.450001,44.450001,44.450001,2530000\n1956-02-21,44.560001,44.560001,44.560001,44.560001,44.560001,2240000\n1956-02-23,44.950001,44.950001,44.950001,44.950001,44.950001,2900000\n1956-02-24,45.320000,45.320000,45.320000,45.320000,45.320000,2890000\n1956-02-27,45.270000,45.270000,45.270000,45.270000,45.270000,2440000\n1956-02-28,45.430000,45.430000,45.430000,45.430000,45.430000,2540000\n1956-02-29,45.340000,45.340000,45.340000,45.340000,45.340000,3900000\n1956-03-01,45.540001,45.540001,45.540001,45.540001,45.540001,2410000\n1956-03-02,45.810001,45.810001,45.810001,45.810001,45.810001,2860000\n1956-03-05,46.060001,46.060001,46.060001,46.060001,46.060001,3090000\n1956-03-06,46.040001,46.040001,46.040001,46.040001,46.040001,2770000\n1956-03-07,46.009998,46.009998,46.009998,46.009998,46.009998,2380000\n1956-03-08,46.119999,46.119999,46.119999,46.119999,46.119999,2500000\n1956-03-09,46.700001,46.700001,46.700001,46.700001,46.700001,3430000\n1956-03-12,47.130001,47.130001,47.130001,47.130001,47.130001,3110000\n1956-03-13,47.060001,47.060001,47.060001,47.060001,47.060001,2790000\n1956-03-14,47.529999,47.529999,47.529999,47.529999,47.529999,3140000\n1956-03-15,47.990002,47.990002,47.990002,47.990002,47.990002,3270000\n1956-03-16,48.139999,48.139999,48.139999,48.139999,48.139999,3120000\n1956-03-19,48.590000,48.590000,48.590000,48.590000,48.590000,2570000\n1956-03-20,48.869999,48.869999,48.869999,48.869999,48.869999,2960000\n1956-03-21,48.230000,48.230000,48.230000,48.230000,48.230000,2930000\n1956-03-22,48.720001,48.720001,48.720001,48.720001,48.720001,2650000\n1956-03-23,48.830002,48.830002,48.830002,48.830002,48.830002,2980000\n1956-03-26,48.619999,48.619999,48.619999,48.619999,48.619999,2720000\n1956-03-27,48.250000,48.250000,48.250000,48.250000,48.250000,2540000\n1956-03-28,48.509998,48.509998,48.509998,48.509998,48.509998,2610000\n1956-03-29,48.480000,48.480000,48.480000,48.480000,48.480000,3480000\n1956-04-02,48.700001,48.700001,48.700001,48.700001,48.700001,3120000\n1956-04-03,48.529999,48.529999,48.529999,48.529999,48.529999,2760000\n1956-04-04,48.799999,48.799999,48.799999,48.799999,48.799999,2760000\n1956-04-05,48.570000,48.570000,48.570000,48.570000,48.570000,2950000\n1956-04-06,48.849998,48.849998,48.849998,48.849998,48.849998,2600000\n1956-04-09,48.610001,48.610001,48.610001,48.610001,48.610001,2760000\n1956-04-10,47.930000,47.930000,47.930000,47.930000,47.930000,2590000\n1956-04-11,48.310001,48.310001,48.310001,48.310001,48.310001,2440000\n1956-04-12,48.020000,48.020000,48.020000,48.020000,48.020000,2700000\n1956-04-13,47.950001,47.950001,47.950001,47.950001,47.950001,2450000\n1956-04-16,47.959999,47.959999,47.959999,47.959999,47.959999,2310000\n1956-04-17,47.930000,47.930000,47.930000,47.930000,47.930000,2330000\n1956-04-18,47.740002,47.740002,47.740002,47.740002,47.740002,2470000\n1956-04-19,47.570000,47.570000,47.570000,47.570000,47.570000,2210000\n1956-04-20,47.759998,47.759998,47.759998,47.759998,47.759998,2320000\n1956-04-23,47.650002,47.650002,47.650002,47.650002,47.650002,2440000\n1956-04-24,47.259998,47.259998,47.259998,47.259998,47.259998,2500000\n1956-04-25,47.090000,47.090000,47.090000,47.090000,47.090000,2270000\n1956-04-26,47.490002,47.490002,47.490002,47.490002,47.490002,2630000\n1956-04-27,47.990002,47.990002,47.990002,47.990002,47.990002,2760000\n1956-04-30,48.380001,48.380001,48.380001,48.380001,48.380001,2730000\n1956-05-01,48.160000,48.160000,48.160000,48.160000,48.160000,2500000\n1956-05-02,48.169998,48.169998,48.169998,48.169998,48.169998,2440000\n1956-05-03,48.340000,48.340000,48.340000,48.340000,48.340000,2640000\n1956-05-04,48.509998,48.509998,48.509998,48.509998,48.509998,2860000\n1956-05-07,48.220001,48.220001,48.220001,48.220001,48.220001,2550000\n1956-05-08,48.020000,48.020000,48.020000,48.020000,48.020000,2440000\n1956-05-09,47.939999,47.939999,47.939999,47.939999,47.939999,2550000\n1956-05-10,47.160000,47.160000,47.160000,47.160000,47.160000,2850000\n1956-05-11,47.119999,47.119999,47.119999,47.119999,47.119999,2450000\n1956-05-14,46.860001,46.860001,46.860001,46.860001,46.860001,2440000\n1956-05-15,46.369999,46.369999,46.369999,46.369999,46.369999,2650000\n1956-05-16,46.049999,46.049999,46.049999,46.049999,46.049999,2080000\n1956-05-17,46.610001,46.610001,46.610001,46.610001,46.610001,1970000\n1956-05-18,46.389999,46.389999,46.389999,46.389999,46.389999,2020000\n1956-05-21,45.990002,45.990002,45.990002,45.990002,45.990002,1940000\n1956-05-22,45.259998,45.259998,45.259998,45.259998,45.259998,2290000\n1956-05-23,45.020000,45.020000,45.020000,45.020000,45.020000,2140000\n1956-05-24,44.599998,44.599998,44.599998,44.599998,44.599998,2600000\n1956-05-25,44.619999,44.619999,44.619999,44.619999,44.619999,2570000\n1956-05-28,44.099998,44.099998,44.099998,44.099998,44.099998,2780000\n1956-05-29,45.110001,45.110001,45.110001,45.110001,45.110001,2430000\n1956-05-31,45.200001,45.200001,45.200001,45.200001,45.200001,2020000\n1956-06-01,45.580002,45.580002,45.580002,45.580002,45.580002,1440000\n1956-06-04,45.849998,45.849998,45.849998,45.849998,45.849998,1500000\n1956-06-05,45.860001,45.860001,45.860001,45.860001,45.860001,1650000\n1956-06-06,45.630001,45.630001,45.630001,45.630001,45.630001,1460000\n1956-06-07,45.990002,45.990002,45.990002,45.990002,45.990002,1630000\n1956-06-08,45.139999,45.139999,45.139999,45.139999,45.139999,3630000\n1956-06-11,45.709999,45.709999,45.709999,45.709999,45.709999,2000000\n1956-06-12,46.360001,46.360001,46.360001,46.360001,46.360001,1900000\n1956-06-13,46.419998,46.419998,46.419998,46.419998,46.419998,1760000\n1956-06-14,46.310001,46.310001,46.310001,46.310001,46.310001,1670000\n1956-06-15,46.369999,46.369999,46.369999,46.369999,46.369999,1550000\n1956-06-18,46.169998,46.169998,46.169998,46.169998,46.169998,1440000\n1956-06-19,46.220001,46.220001,46.220001,46.220001,46.220001,1430000\n1956-06-20,46.410000,46.410000,46.410000,46.410000,46.410000,1670000\n1956-06-21,46.730000,46.730000,46.730000,46.730000,46.730000,1820000\n1956-06-22,46.590000,46.590000,46.590000,46.590000,46.590000,1630000\n1956-06-25,46.410000,46.410000,46.410000,46.410000,46.410000,1500000\n1956-06-26,46.720001,46.720001,46.720001,46.720001,46.720001,1730000\n1956-06-27,47.070000,47.070000,47.070000,47.070000,47.070000,2090000\n1956-06-28,47.130001,47.130001,47.130001,47.130001,47.130001,1900000\n1956-06-29,46.970001,46.970001,46.970001,46.970001,46.970001,1780000\n1956-07-02,46.930000,46.930000,46.930000,46.930000,46.930000,1610000\n1956-07-03,47.320000,47.320000,47.320000,47.320000,47.320000,1840000\n1956-07-05,47.799999,47.799999,47.799999,47.799999,47.799999,2240000\n1956-07-06,48.040001,48.040001,48.040001,48.040001,48.040001,2180000\n1956-07-09,48.250000,48.250000,48.250000,48.250000,48.250000,2180000\n1956-07-10,48.540001,48.540001,48.540001,48.540001,48.540001,2450000\n1956-07-11,48.689999,48.689999,48.689999,48.689999,48.689999,2520000\n1956-07-12,48.580002,48.580002,48.580002,48.580002,48.580002,2180000\n1956-07-13,48.720001,48.720001,48.720001,48.720001,48.720001,2020000\n1956-07-16,49.139999,49.139999,49.139999,49.139999,49.139999,2260000\n1956-07-17,49.310001,49.310001,49.310001,49.310001,49.310001,2520000\n1956-07-18,49.299999,49.299999,49.299999,49.299999,49.299999,2530000\n1956-07-19,49.320000,49.320000,49.320000,49.320000,49.320000,1950000\n1956-07-20,49.349998,49.349998,49.349998,49.349998,49.349998,2020000\n1956-07-23,49.330002,49.330002,49.330002,49.330002,49.330002,1970000\n1956-07-24,49.330002,49.330002,49.330002,49.330002,49.330002,2040000\n1956-07-25,49.439999,49.439999,49.439999,49.439999,49.439999,2220000\n1956-07-26,49.480000,49.480000,49.480000,49.480000,49.480000,2060000\n1956-07-27,49.080002,49.080002,49.080002,49.080002,49.080002,2240000\n1956-07-30,49.000000,49.000000,49.000000,49.000000,49.000000,2100000\n1956-07-31,49.389999,49.389999,49.389999,49.389999,49.389999,2520000\n1956-08-01,49.619999,49.619999,49.619999,49.619999,49.619999,2230000\n1956-08-02,49.639999,49.639999,49.639999,49.639999,49.639999,2530000\n1956-08-03,49.639999,49.639999,49.639999,49.639999,49.639999,2210000\n1956-08-06,48.959999,48.959999,48.959999,48.959999,48.959999,2280000\n1956-08-07,49.160000,49.160000,49.160000,49.160000,49.160000,2180000\n1956-08-08,49.360001,49.360001,49.360001,49.360001,49.360001,2480000\n1956-08-09,49.320000,49.320000,49.320000,49.320000,49.320000,2550000\n1956-08-10,49.090000,49.090000,49.090000,49.090000,49.090000,2040000\n1956-08-13,48.580002,48.580002,48.580002,48.580002,48.580002,1730000\n1956-08-14,48.000000,48.000000,48.000000,48.000000,48.000000,1790000\n1956-08-15,48.990002,48.990002,48.990002,48.990002,48.990002,2000000\n1956-08-16,48.880001,48.880001,48.880001,48.880001,48.880001,1790000\n1956-08-17,48.820000,48.820000,48.820000,48.820000,48.820000,1720000\n1956-08-20,48.250000,48.250000,48.250000,48.250000,48.250000,1770000\n1956-08-21,47.889999,47.889999,47.889999,47.889999,47.889999,2440000\n1956-08-22,47.419998,47.419998,47.419998,47.419998,47.419998,1570000\n1956-08-23,48.000000,48.000000,48.000000,48.000000,48.000000,1590000\n1956-08-24,47.950001,47.950001,47.950001,47.950001,47.950001,1530000\n1956-08-27,47.660000,47.660000,47.660000,47.660000,47.660000,1420000\n1956-08-28,47.570000,47.570000,47.570000,47.570000,47.570000,1400000\n1956-08-29,47.360001,47.360001,47.360001,47.360001,47.360001,1530000\n1956-08-30,46.939999,46.939999,46.939999,46.939999,46.939999,2050000\n1956-08-31,47.509998,47.509998,47.509998,47.509998,47.509998,1620000\n1956-09-04,47.889999,47.889999,47.889999,47.889999,47.889999,1790000\n1956-09-05,48.020000,48.020000,48.020000,48.020000,48.020000,2130000\n1956-09-06,48.099998,48.099998,48.099998,48.099998,48.099998,1550000\n1956-09-07,47.810001,47.810001,47.810001,47.810001,47.810001,1690000\n1956-09-10,47.560001,47.560001,47.560001,47.560001,47.560001,1860000\n1956-09-11,47.380001,47.380001,47.380001,47.380001,47.380001,1920000\n1956-09-12,47.049999,47.049999,47.049999,47.049999,47.049999,1930000\n1956-09-13,46.090000,46.090000,46.090000,46.090000,46.090000,2000000\n1956-09-14,47.209999,47.209999,47.209999,47.209999,47.209999,2110000\n1956-09-17,47.099998,47.099998,47.099998,47.099998,47.099998,1940000\n1956-09-18,46.790001,46.790001,46.790001,46.790001,46.790001,2200000\n1956-09-19,46.240002,46.240002,46.240002,46.240002,46.240002,2040000\n1956-09-20,46.209999,46.209999,46.209999,46.209999,46.209999,2150000\n1956-09-21,46.580002,46.580002,46.580002,46.580002,46.580002,2110000\n1956-09-24,46.400002,46.400002,46.400002,46.400002,46.400002,1840000\n1956-09-25,45.750000,45.750000,45.750000,45.750000,45.750000,2100000\n1956-09-26,45.820000,45.820000,45.820000,45.820000,45.820000,2370000\n1956-09-27,45.599998,45.599998,45.599998,45.599998,45.599998,1770000\n1956-09-28,45.349998,45.349998,45.349998,45.349998,45.349998,1720000\n1956-10-01,44.700001,44.700001,44.700001,44.700001,44.700001,2600000\n1956-10-02,45.520000,45.520000,45.520000,45.520000,45.520000,2400000\n1956-10-03,46.279999,46.279999,46.279999,46.279999,46.279999,2180000\n1956-10-04,46.290001,46.290001,46.290001,46.290001,46.290001,1600000\n1956-10-05,46.450001,46.450001,46.450001,46.450001,46.450001,1580000\n1956-10-08,46.430000,46.430000,46.430000,46.430000,46.430000,1450000\n1956-10-09,46.200001,46.200001,46.200001,46.200001,46.200001,1220000\n1956-10-10,46.840000,46.840000,46.840000,46.840000,46.840000,1620000\n1956-10-11,46.810001,46.810001,46.810001,46.810001,46.810001,1760000\n1956-10-12,47.000000,47.000000,47.000000,47.000000,47.000000,1330000\n1956-10-15,46.860001,46.860001,46.860001,46.860001,46.860001,1610000\n1956-10-16,46.619999,46.619999,46.619999,46.619999,46.619999,1580000\n1956-10-17,46.259998,46.259998,46.259998,46.259998,46.259998,1640000\n1956-10-18,46.340000,46.340000,46.340000,46.340000,46.340000,1640000\n1956-10-19,46.240002,46.240002,46.240002,46.240002,46.240002,1720000\n1956-10-22,46.230000,46.230000,46.230000,46.230000,46.230000,1430000\n1956-10-23,46.119999,46.119999,46.119999,46.119999,46.119999,1390000\n1956-10-24,45.930000,45.930000,45.930000,45.930000,45.930000,1640000\n1956-10-25,45.849998,45.849998,45.849998,45.849998,45.849998,1580000\n1956-10-26,46.270000,46.270000,46.270000,46.270000,46.270000,1800000\n1956-10-29,46.400002,46.400002,46.400002,46.400002,46.400002,2420000\n1956-10-30,46.369999,46.369999,46.369999,46.369999,46.369999,1830000\n1956-10-31,45.580002,45.580002,45.580002,45.580002,45.580002,2280000\n1956-11-01,46.520000,46.520000,46.520000,46.520000,46.520000,1890000\n1956-11-02,46.980000,46.980000,46.980000,46.980000,46.980000,2180000\n1956-11-05,47.599998,47.599998,47.599998,47.599998,47.599998,2830000\n1956-11-07,47.110001,47.110001,47.110001,47.110001,47.110001,2650000\n1956-11-08,46.730000,46.730000,46.730000,46.730000,46.730000,1970000\n1956-11-09,46.340000,46.340000,46.340000,46.340000,46.340000,1690000\n1956-11-12,46.490002,46.490002,46.490002,46.490002,46.490002,1600000\n1956-11-13,46.270000,46.270000,46.270000,46.270000,46.270000,2140000\n1956-11-14,46.009998,46.009998,46.009998,46.009998,46.009998,2290000\n1956-11-15,45.720001,45.720001,45.720001,45.720001,45.720001,2210000\n1956-11-16,45.740002,45.740002,45.740002,45.740002,45.740002,1820000\n1956-11-19,45.290001,45.290001,45.290001,45.290001,45.290001,2560000\n1956-11-20,44.889999,44.889999,44.889999,44.889999,44.889999,2240000\n1956-11-21,44.669998,44.669998,44.669998,44.669998,44.669998,2310000\n1956-11-23,45.139999,45.139999,45.139999,45.139999,45.139999,1880000\n1956-11-26,44.869999,44.869999,44.869999,44.869999,44.869999,2230000\n1956-11-27,44.910000,44.910000,44.910000,44.910000,44.910000,2130000\n1956-11-28,44.430000,44.430000,44.430000,44.430000,44.430000,2190000\n1956-11-29,44.380001,44.380001,44.380001,44.380001,44.380001,2440000\n1956-11-30,45.080002,45.080002,45.080002,45.080002,45.080002,2300000\n1956-12-03,45.980000,45.980000,45.980000,45.980000,45.980000,2570000\n1956-12-04,45.840000,45.840000,45.840000,45.840000,45.840000,2180000\n1956-12-05,46.389999,46.389999,46.389999,46.389999,46.389999,2360000\n1956-12-06,46.810001,46.810001,46.810001,46.810001,46.810001,2470000\n1956-12-07,47.040001,47.040001,47.040001,47.040001,47.040001,2400000\n1956-12-10,46.799999,46.799999,46.799999,46.799999,46.799999,2600000\n1956-12-11,46.480000,46.480000,46.480000,46.480000,46.480000,2210000\n1956-12-12,46.130001,46.130001,46.130001,46.130001,46.130001,2180000\n1956-12-13,46.500000,46.500000,46.500000,46.500000,46.500000,2370000\n1956-12-14,46.540001,46.540001,46.540001,46.540001,46.540001,2450000\n1956-12-17,46.540001,46.540001,46.540001,46.540001,46.540001,2500000\n1956-12-18,46.540001,46.540001,46.540001,46.540001,46.540001,2370000\n1956-12-19,46.430000,46.430000,46.430000,46.430000,46.430000,1900000\n1956-12-20,46.070000,46.070000,46.070000,46.070000,46.070000,2060000\n1956-12-21,46.369999,46.369999,46.369999,46.369999,46.369999,2380000\n1956-12-26,46.389999,46.389999,46.389999,46.389999,46.389999,2440000\n1956-12-27,46.349998,46.349998,46.349998,46.349998,46.349998,2420000\n1956-12-28,46.560001,46.560001,46.560001,46.560001,46.560001,2790000\n1956-12-31,46.669998,46.669998,46.669998,46.669998,46.669998,3680000\n1957-01-02,46.200001,46.200001,46.200001,46.200001,46.200001,1960000\n1957-01-03,46.599998,46.599998,46.599998,46.599998,46.599998,2260000\n1957-01-04,46.660000,46.660000,46.660000,46.660000,46.660000,2710000\n1957-01-07,46.419998,46.419998,46.419998,46.419998,46.419998,2500000\n1957-01-08,46.250000,46.250000,46.250000,46.250000,46.250000,2230000\n1957-01-09,46.160000,46.160000,46.160000,46.160000,46.160000,2330000\n1957-01-10,46.270000,46.270000,46.270000,46.270000,46.270000,2470000\n1957-01-11,46.180000,46.180000,46.180000,46.180000,46.180000,2340000\n1957-01-14,45.860001,45.860001,45.860001,45.860001,45.860001,2350000\n1957-01-15,45.180000,45.180000,45.180000,45.180000,45.180000,2370000\n1957-01-16,45.230000,45.230000,45.230000,45.230000,45.230000,2210000\n1957-01-17,45.220001,45.220001,45.220001,45.220001,45.220001,2140000\n1957-01-18,44.639999,44.639999,44.639999,44.639999,44.639999,2400000\n1957-01-21,44.400002,44.400002,44.400002,44.400002,44.400002,2740000\n1957-01-22,44.529999,44.529999,44.529999,44.529999,44.529999,1920000\n1957-01-23,44.869999,44.869999,44.869999,44.869999,44.869999,1920000\n1957-01-24,45.029999,45.029999,45.029999,45.029999,45.029999,1910000\n1957-01-25,44.820000,44.820000,44.820000,44.820000,44.820000,2010000\n1957-01-28,44.490002,44.490002,44.490002,44.490002,44.490002,1700000\n1957-01-29,44.709999,44.709999,44.709999,44.709999,44.709999,1800000\n1957-01-30,44.910000,44.910000,44.910000,44.910000,44.910000,1950000\n1957-01-31,44.720001,44.720001,44.720001,44.720001,44.720001,1920000\n1957-02-01,44.619999,44.619999,44.619999,44.619999,44.619999,1680000\n1957-02-04,44.529999,44.529999,44.529999,44.529999,44.529999,1750000\n1957-02-05,43.889999,43.889999,43.889999,43.889999,43.889999,2610000\n1957-02-06,43.820000,43.820000,43.820000,43.820000,43.820000,2110000\n1957-02-07,43.619999,43.619999,43.619999,43.619999,43.619999,1840000\n1957-02-08,43.320000,43.320000,43.320000,43.320000,43.320000,2120000\n1957-02-11,42.570000,42.570000,42.570000,42.570000,42.570000,2740000\n1957-02-12,42.389999,42.389999,42.389999,42.389999,42.389999,2550000\n1957-02-13,43.040001,43.040001,43.040001,43.040001,43.040001,2380000\n1957-02-14,42.990002,42.990002,42.990002,42.990002,42.990002,2220000\n1957-02-15,43.509998,43.509998,43.509998,43.509998,43.509998,2060000\n1957-02-18,43.459999,43.459999,43.459999,43.459999,43.459999,1800000\n1957-02-19,43.490002,43.490002,43.490002,43.490002,43.490002,1670000\n1957-02-20,43.630001,43.630001,43.630001,43.630001,43.630001,1790000\n1957-02-21,43.480000,43.480000,43.480000,43.480000,43.480000,1680000\n1957-02-25,43.380001,43.380001,43.380001,43.380001,43.380001,1710000\n1957-02-26,43.450001,43.450001,43.450001,43.450001,43.450001,1580000\n1957-02-27,43.410000,43.410000,43.410000,43.410000,43.410000,1620000\n1957-02-28,43.259998,43.259998,43.259998,43.259998,43.259998,1620000\n1957-03-01,43.740002,43.740002,43.740002,43.740002,43.740002,1700000\n1957-03-04,44.060001,44.060001,44.060001,44.060001,44.060001,1890000\n1957-03-05,44.220001,44.220001,44.220001,44.220001,44.220001,1860000\n1957-03-06,44.230000,44.230000,44.230000,44.230000,44.230000,1840000\n1957-03-07,44.209999,44.209999,44.209999,44.209999,44.209999,1830000\n1957-03-08,44.070000,44.070000,44.070000,44.070000,44.070000,1630000\n1957-03-11,43.779999,43.779999,43.779999,43.779999,43.779999,1650000\n1957-03-12,43.750000,43.750000,43.750000,43.750000,43.750000,1600000\n1957-03-13,44.040001,44.040001,44.040001,44.040001,44.040001,1840000\n1957-03-14,44.070000,44.070000,44.070000,44.070000,44.070000,1580000\n1957-03-15,44.049999,44.049999,44.049999,44.049999,44.049999,1600000\n1957-03-18,43.849998,43.849998,43.849998,43.849998,43.849998,1450000\n1957-03-19,44.040001,44.040001,44.040001,44.040001,44.040001,1540000\n1957-03-20,44.099998,44.099998,44.099998,44.099998,44.099998,1830000\n1957-03-21,44.110001,44.110001,44.110001,44.110001,44.110001,1630000\n1957-03-22,44.060001,44.060001,44.060001,44.060001,44.060001,1610000\n1957-03-25,43.880001,43.880001,43.880001,43.880001,43.880001,1590000\n1957-03-26,43.910000,43.910000,43.910000,43.910000,43.910000,1660000\n1957-03-27,44.090000,44.090000,44.090000,44.090000,44.090000,1710000\n1957-03-28,44.180000,44.180000,44.180000,44.180000,44.180000,1930000\n1957-03-29,44.110001,44.110001,44.110001,44.110001,44.110001,1650000\n1957-04-01,44.139999,44.139999,44.139999,44.139999,44.139999,1620000\n1957-04-02,44.419998,44.419998,44.419998,44.419998,44.419998,2300000\n1957-04-03,44.540001,44.540001,44.540001,44.540001,44.540001,2160000\n1957-04-04,44.439999,44.439999,44.439999,44.439999,44.439999,1820000\n1957-04-05,44.490002,44.490002,44.490002,44.490002,44.490002,1830000\n1957-04-08,44.389999,44.389999,44.389999,44.389999,44.389999,1950000\n1957-04-09,44.790001,44.790001,44.790001,44.790001,44.790001,2400000\n1957-04-10,44.980000,44.980000,44.980000,44.980000,44.980000,2920000\n1957-04-11,44.980000,44.980000,44.980000,44.980000,44.980000,2350000\n1957-04-12,44.980000,44.980000,44.980000,44.980000,44.980000,2370000\n1957-04-15,44.950001,44.950001,44.950001,44.950001,44.950001,2010000\n1957-04-16,45.020000,45.020000,45.020000,45.020000,45.020000,1890000\n1957-04-17,45.080002,45.080002,45.080002,45.080002,45.080002,2290000\n1957-04-18,45.410000,45.410000,45.410000,45.410000,45.410000,2480000\n1957-04-22,45.480000,45.480000,45.480000,45.480000,45.480000,2560000\n1957-04-23,45.650002,45.650002,45.650002,45.650002,45.650002,2840000\n1957-04-24,45.720001,45.720001,45.720001,45.720001,45.720001,2990000\n1957-04-25,45.560001,45.560001,45.560001,45.560001,45.560001,2640000\n1957-04-26,45.500000,45.500000,45.500000,45.500000,45.500000,2380000\n1957-04-29,45.730000,45.730000,45.730000,45.730000,45.730000,2290000\n1957-04-30,45.740002,45.740002,45.740002,45.740002,45.740002,2200000\n1957-05-01,46.020000,46.020000,46.020000,46.020000,46.020000,2310000\n1957-05-02,46.389999,46.389999,46.389999,46.389999,46.389999,2860000\n1957-05-03,46.340000,46.340000,46.340000,46.340000,46.340000,2390000\n1957-05-06,46.270000,46.270000,46.270000,46.270000,46.270000,2210000\n1957-05-07,46.130001,46.130001,46.130001,46.130001,46.130001,2300000\n1957-05-08,46.310001,46.310001,46.310001,46.310001,46.310001,2590000\n1957-05-09,46.360001,46.360001,46.360001,46.360001,46.360001,2520000\n1957-05-10,46.590000,46.590000,46.590000,46.590000,46.590000,2430000\n1957-05-13,46.880001,46.880001,46.880001,46.880001,46.880001,2720000\n1957-05-14,46.669998,46.669998,46.669998,46.669998,46.669998,2580000\n1957-05-15,46.830002,46.830002,46.830002,46.830002,46.830002,2590000\n1957-05-16,47.020000,47.020000,47.020000,47.020000,47.020000,2690000\n1957-05-17,47.150002,47.150002,47.150002,47.150002,47.150002,2510000\n1957-05-20,47.349998,47.349998,47.349998,47.349998,47.349998,2300000\n1957-05-21,47.330002,47.330002,47.330002,47.330002,47.330002,2370000\n1957-05-22,47.139999,47.139999,47.139999,47.139999,47.139999,2060000\n1957-05-23,47.150002,47.150002,47.150002,47.150002,47.150002,2110000\n1957-05-24,47.209999,47.209999,47.209999,47.209999,47.209999,2340000\n1957-05-27,46.779999,46.779999,46.779999,46.779999,46.779999,2290000\n1957-05-28,46.689999,46.689999,46.689999,46.689999,46.689999,2070000\n1957-05-29,47.110001,47.110001,47.110001,47.110001,47.110001,2270000\n1957-05-31,47.430000,47.430000,47.430000,47.430000,47.430000,2050000\n1957-06-03,47.369999,47.369999,47.369999,47.369999,47.369999,2050000\n1957-06-04,47.279999,47.279999,47.279999,47.279999,47.279999,2200000\n1957-06-05,47.270000,47.270000,47.270000,47.270000,47.270000,1940000\n1957-06-06,47.799999,47.799999,47.799999,47.799999,47.799999,2300000\n1957-06-07,47.849998,47.849998,47.849998,47.849998,47.849998,2380000\n1957-06-10,47.900002,47.900002,47.900002,47.900002,47.900002,2050000\n1957-06-11,47.939999,47.939999,47.939999,47.939999,47.939999,2850000\n1957-06-12,48.049999,48.049999,48.049999,48.049999,48.049999,2600000\n1957-06-13,48.139999,48.139999,48.139999,48.139999,48.139999,2630000\n1957-06-14,48.150002,48.150002,48.150002,48.150002,48.150002,2090000\n1957-06-17,48.240002,48.240002,48.240002,48.240002,48.240002,2220000\n1957-06-18,48.040001,48.040001,48.040001,48.040001,48.040001,2440000\n1957-06-19,47.720001,47.720001,47.720001,47.720001,47.720001,2220000\n1957-06-20,47.430000,47.430000,47.430000,47.430000,47.430000,2050000\n1957-06-21,47.150002,47.150002,47.150002,47.150002,47.150002,1970000\n1957-06-24,46.779999,46.779999,46.779999,46.779999,46.779999,2040000\n1957-06-25,47.150002,47.150002,47.150002,47.150002,47.150002,2000000\n1957-06-26,47.090000,47.090000,47.090000,47.090000,47.090000,1870000\n1957-06-27,47.259998,47.259998,47.259998,47.259998,47.259998,1800000\n1957-06-28,47.369999,47.369999,47.369999,47.369999,47.369999,1770000\n1957-07-01,47.430000,47.430000,47.430000,47.430000,47.430000,1840000\n1957-07-02,47.900002,47.900002,47.900002,47.900002,47.900002,2450000\n1957-07-03,48.459999,48.459999,48.459999,48.459999,48.459999,2720000\n1957-07-05,48.689999,48.689999,48.689999,48.689999,48.689999,2240000\n1957-07-08,48.900002,48.900002,48.900002,48.900002,48.900002,2840000\n1957-07-09,48.900002,48.900002,48.900002,48.900002,48.900002,2450000\n1957-07-10,49.000000,49.000000,49.000000,49.000000,49.000000,2880000\n1957-07-11,48.860001,48.860001,48.860001,48.860001,48.860001,2830000\n1957-07-12,49.080002,49.080002,49.080002,49.080002,49.080002,2240000\n1957-07-15,49.130001,49.130001,49.130001,49.130001,49.130001,2480000\n1957-07-16,48.880001,48.880001,48.880001,48.880001,48.880001,2510000\n1957-07-17,48.580002,48.580002,48.580002,48.580002,48.580002,2060000\n1957-07-18,48.529999,48.529999,48.529999,48.529999,48.529999,2130000\n1957-07-19,48.580002,48.580002,48.580002,48.580002,48.580002,1930000\n1957-07-22,48.470001,48.470001,48.470001,48.470001,48.470001,1950000\n1957-07-23,48.560001,48.560001,48.560001,48.560001,48.560001,1840000\n1957-07-24,48.610001,48.610001,48.610001,48.610001,48.610001,1730000\n1957-07-25,48.610001,48.610001,48.610001,48.610001,48.610001,1800000\n1957-07-26,48.450001,48.450001,48.450001,48.450001,48.450001,1710000\n1957-07-29,47.919998,47.919998,47.919998,47.919998,47.919998,1990000\n1957-07-30,47.919998,47.919998,47.919998,47.919998,47.919998,1780000\n1957-07-31,47.910000,47.910000,47.910000,47.910000,47.910000,1830000\n1957-08-01,47.790001,47.790001,47.790001,47.790001,47.790001,1660000\n1957-08-02,47.680000,47.680000,47.680000,47.680000,47.680000,1610000\n1957-08-05,47.259998,47.259998,47.259998,47.259998,47.259998,1790000\n1957-08-06,46.669998,46.669998,46.669998,46.669998,46.669998,1910000\n1957-08-07,47.029999,47.029999,47.029999,47.029999,47.029999,2460000\n1957-08-08,46.900002,46.900002,46.900002,46.900002,46.900002,1690000\n1957-08-09,46.919998,46.919998,46.919998,46.919998,46.919998,1570000\n1957-08-12,46.330002,46.330002,46.330002,46.330002,46.330002,1650000\n1957-08-13,46.299999,46.299999,46.299999,46.299999,46.299999,1580000\n1957-08-14,45.730000,45.730000,45.730000,45.730000,45.730000,2040000\n1957-08-15,45.750000,45.750000,45.750000,45.750000,45.750000,2040000\n1957-08-16,45.830002,45.830002,45.830002,45.830002,45.830002,1470000\n1957-08-19,44.910000,44.910000,44.910000,44.910000,44.910000,2040000\n1957-08-20,45.290001,45.290001,45.290001,45.290001,45.290001,2700000\n1957-08-21,45.490002,45.490002,45.490002,45.490002,45.490002,1720000\n1957-08-22,45.160000,45.160000,45.160000,45.160000,45.160000,1500000\n1957-08-23,44.509998,44.509998,44.509998,44.509998,44.509998,1960000\n1957-08-26,43.889999,43.889999,43.889999,43.889999,43.889999,2680000\n1957-08-27,44.610001,44.610001,44.610001,44.610001,44.610001,2250000\n1957-08-28,44.639999,44.639999,44.639999,44.639999,44.639999,1840000\n1957-08-29,44.459999,44.459999,44.459999,44.459999,44.459999,1630000\n1957-08-30,45.220001,45.220001,45.220001,45.220001,45.220001,1600000\n1957-09-03,45.439999,45.439999,45.439999,45.439999,45.439999,1490000\n1957-09-04,45.049999,45.049999,45.049999,45.049999,45.049999,1260000\n1957-09-05,44.820000,44.820000,44.820000,44.820000,44.820000,1420000\n1957-09-06,44.680000,44.680000,44.680000,44.680000,44.680000,1320000\n1957-09-09,44.279999,44.279999,44.279999,44.279999,44.279999,1420000\n1957-09-10,43.869999,43.869999,43.869999,43.869999,43.869999,1870000\n1957-09-11,44.259998,44.259998,44.259998,44.259998,44.259998,2130000\n1957-09-12,44.820000,44.820000,44.820000,44.820000,44.820000,2010000\n1957-09-13,44.799999,44.799999,44.799999,44.799999,44.799999,1620000\n1957-09-16,44.580002,44.580002,44.580002,44.580002,44.580002,1290000\n1957-09-17,44.639999,44.639999,44.639999,44.639999,44.639999,1490000\n1957-09-18,44.689999,44.689999,44.689999,44.689999,44.689999,1540000\n1957-09-19,44.400002,44.400002,44.400002,44.400002,44.400002,1520000\n1957-09-20,43.689999,43.689999,43.689999,43.689999,43.689999,2340000\n1957-09-23,42.689999,42.689999,42.689999,42.689999,42.689999,3160000\n1957-09-24,42.980000,42.980000,42.980000,42.980000,42.980000,2840000\n1957-09-25,42.980000,42.980000,42.980000,42.980000,42.980000,2770000\n1957-09-26,42.570000,42.570000,42.570000,42.570000,42.570000,2130000\n1957-09-27,42.549999,42.549999,42.549999,42.549999,42.549999,1750000\n1957-09-30,42.419998,42.419998,42.419998,42.419998,42.419998,1520000\n1957-10-01,42.759998,42.759998,42.759998,42.759998,42.759998,1680000\n1957-10-02,43.099998,43.099998,43.099998,43.099998,43.099998,1760000\n1957-10-03,43.139999,43.139999,43.139999,43.139999,43.139999,1590000\n1957-10-04,42.790001,42.790001,42.790001,42.790001,42.790001,1520000\n1957-10-07,42.220001,42.220001,42.220001,42.220001,42.220001,2490000\n1957-10-08,41.950001,41.950001,41.950001,41.950001,41.950001,3190000\n1957-10-09,41.990002,41.990002,41.990002,41.990002,41.990002,2120000\n1957-10-10,40.959999,40.959999,40.959999,40.959999,40.959999,3300000\n1957-10-11,40.939999,40.939999,40.939999,40.939999,40.939999,4460000\n1957-10-14,41.240002,41.240002,41.240002,41.240002,41.240002,2770000\n1957-10-15,41.669998,41.669998,41.669998,41.669998,41.669998,2620000\n1957-10-16,41.330002,41.330002,41.330002,41.330002,41.330002,2050000\n1957-10-17,40.650002,40.650002,40.650002,40.650002,40.650002,3060000\n1957-10-18,40.330002,40.330002,40.330002,40.330002,40.330002,2670000\n1957-10-21,39.150002,39.150002,39.150002,39.150002,39.150002,4670000\n1957-10-22,38.980000,38.980000,38.980000,38.980000,38.980000,5090000\n1957-10-23,40.730000,40.730000,40.730000,40.730000,40.730000,4600000\n1957-10-24,40.709999,40.709999,40.709999,40.709999,40.709999,4030000\n1957-10-25,40.590000,40.590000,40.590000,40.590000,40.590000,2400000\n1957-10-28,40.419998,40.419998,40.419998,40.419998,40.419998,1800000\n1957-10-29,40.689999,40.689999,40.689999,40.689999,40.689999,1860000\n1957-10-30,41.020000,41.020000,41.020000,41.020000,41.020000,2060000\n1957-10-31,41.060001,41.060001,41.060001,41.060001,41.060001,2170000\n1957-11-01,40.439999,40.439999,40.439999,40.439999,40.439999,2060000\n1957-11-04,40.369999,40.369999,40.369999,40.369999,40.369999,2380000\n1957-11-06,40.430000,40.430000,40.430000,40.430000,40.430000,2550000\n1957-11-07,40.669998,40.669998,40.669998,40.669998,40.669998,2580000\n1957-11-08,40.189999,40.189999,40.189999,40.189999,40.189999,2140000\n1957-11-11,40.180000,40.180000,40.180000,40.180000,40.180000,1540000\n1957-11-12,39.599998,39.599998,39.599998,39.599998,39.599998,2050000\n1957-11-13,39.549999,39.549999,39.549999,39.549999,39.549999,2120000\n1957-11-14,39.439999,39.439999,39.439999,39.439999,39.439999,2450000\n1957-11-15,40.369999,40.369999,40.369999,40.369999,40.369999,3510000\n1957-11-18,40.040001,40.040001,40.040001,40.040001,40.040001,2110000\n1957-11-19,39.810001,39.810001,39.810001,39.810001,39.810001,2240000\n1957-11-20,39.919998,39.919998,39.919998,39.919998,39.919998,2400000\n1957-11-21,40.480000,40.480000,40.480000,40.480000,40.480000,2900000\n1957-11-22,40.869999,40.869999,40.869999,40.869999,40.869999,2850000\n1957-11-25,41.180000,41.180000,41.180000,41.180000,41.180000,2600000\n1957-11-26,40.090000,40.090000,40.090000,40.090000,40.090000,3650000\n1957-11-27,41.250000,41.250000,41.250000,41.250000,41.250000,3330000\n1957-11-29,41.720001,41.720001,41.720001,41.720001,41.720001,2740000\n1957-12-02,41.360001,41.360001,41.360001,41.360001,41.360001,2430000\n1957-12-03,41.369999,41.369999,41.369999,41.369999,41.369999,2060000\n1957-12-04,41.540001,41.540001,41.540001,41.540001,41.540001,2220000\n1957-12-05,41.520000,41.520000,41.520000,41.520000,41.520000,2020000\n1957-12-06,41.310001,41.310001,41.310001,41.310001,41.310001,2350000\n1957-12-09,40.919998,40.919998,40.919998,40.919998,40.919998,2230000\n1957-12-10,40.560001,40.560001,40.560001,40.560001,40.560001,2360000\n1957-12-11,40.509998,40.509998,40.509998,40.509998,40.509998,2240000\n1957-12-12,40.549999,40.549999,40.549999,40.549999,40.549999,2330000\n1957-12-13,40.730000,40.730000,40.730000,40.730000,40.730000,2310000\n1957-12-16,40.119999,40.119999,40.119999,40.119999,40.119999,2350000\n1957-12-17,39.419998,39.419998,39.419998,39.419998,39.419998,2820000\n1957-12-18,39.380001,39.380001,39.380001,39.380001,39.380001,2750000\n1957-12-19,39.799999,39.799999,39.799999,39.799999,39.799999,2740000\n1957-12-20,39.480000,39.480000,39.480000,39.480000,39.480000,2500000\n1957-12-23,39.480000,39.480000,39.480000,39.480000,39.480000,2790000\n1957-12-24,39.520000,39.520000,39.520000,39.520000,39.520000,2220000\n1957-12-26,39.919998,39.919998,39.919998,39.919998,39.919998,2280000\n1957-12-27,39.779999,39.779999,39.779999,39.779999,39.779999,2620000\n1957-12-30,39.580002,39.580002,39.580002,39.580002,39.580002,3750000\n1957-12-31,39.990002,39.990002,39.990002,39.990002,39.990002,5070000\n1958-01-02,40.330002,40.330002,40.330002,40.330002,40.330002,1800000\n1958-01-03,40.869999,40.869999,40.869999,40.869999,40.869999,2440000\n1958-01-06,40.680000,40.680000,40.680000,40.680000,40.680000,2500000\n1958-01-07,41.000000,41.000000,41.000000,41.000000,41.000000,2220000\n1958-01-08,40.990002,40.990002,40.990002,40.990002,40.990002,2230000\n1958-01-09,40.750000,40.750000,40.750000,40.750000,40.750000,2180000\n1958-01-10,40.369999,40.369999,40.369999,40.369999,40.369999,2010000\n1958-01-13,40.490002,40.490002,40.490002,40.490002,40.490002,1860000\n1958-01-14,40.669998,40.669998,40.669998,40.669998,40.669998,2010000\n1958-01-15,40.990002,40.990002,40.990002,40.990002,40.990002,2080000\n1958-01-16,41.060001,41.060001,41.060001,41.060001,41.060001,3950000\n1958-01-17,41.099998,41.099998,41.099998,41.099998,41.099998,2200000\n1958-01-20,41.349998,41.349998,41.349998,41.349998,41.349998,2310000\n1958-01-21,41.299999,41.299999,41.299999,41.299999,41.299999,2160000\n1958-01-22,41.200001,41.200001,41.200001,41.200001,41.200001,2390000\n1958-01-23,41.360001,41.360001,41.360001,41.360001,41.360001,1910000\n1958-01-24,41.709999,41.709999,41.709999,41.709999,41.709999,2830000\n1958-01-27,41.590000,41.590000,41.590000,41.590000,41.590000,2320000\n1958-01-28,41.630001,41.630001,41.630001,41.630001,41.630001,2030000\n1958-01-29,41.880001,41.880001,41.880001,41.880001,41.880001,2220000\n1958-01-30,41.680000,41.680000,41.680000,41.680000,41.680000,2150000\n1958-01-31,41.700001,41.700001,41.700001,41.700001,41.700001,2030000\n1958-02-03,42.040001,42.040001,42.040001,42.040001,42.040001,2490000\n1958-02-04,42.459999,42.459999,42.459999,42.459999,42.459999,2970000\n1958-02-05,42.189999,42.189999,42.189999,42.189999,42.189999,2480000\n1958-02-06,42.099998,42.099998,42.099998,42.099998,42.099998,2210000\n1958-02-07,41.730000,41.730000,41.730000,41.730000,41.730000,2220000\n1958-02-10,41.480000,41.480000,41.480000,41.480000,41.480000,1900000\n1958-02-11,41.110001,41.110001,41.110001,41.110001,41.110001,2110000\n1958-02-12,40.930000,40.930000,40.930000,40.930000,40.930000,2030000\n1958-02-13,40.939999,40.939999,40.939999,40.939999,40.939999,1880000\n1958-02-14,41.330002,41.330002,41.330002,41.330002,41.330002,2070000\n1958-02-17,41.110001,41.110001,41.110001,41.110001,41.110001,1700000\n1958-02-18,41.169998,41.169998,41.169998,41.169998,41.169998,1680000\n1958-02-19,41.150002,41.150002,41.150002,41.150002,41.150002,2070000\n1958-02-20,40.910000,40.910000,40.910000,40.910000,40.910000,2060000\n1958-02-21,40.880001,40.880001,40.880001,40.880001,40.880001,1700000\n1958-02-24,40.650002,40.650002,40.650002,40.650002,40.650002,1570000\n1958-02-25,40.610001,40.610001,40.610001,40.610001,40.610001,1920000\n1958-02-26,40.919998,40.919998,40.919998,40.919998,40.919998,1880000\n1958-02-27,40.680000,40.680000,40.680000,40.680000,40.680000,1670000\n1958-02-28,40.840000,40.840000,40.840000,40.840000,40.840000,1580000\n1958-03-03,41.130001,41.130001,41.130001,41.130001,41.130001,1810000\n1958-03-04,41.349998,41.349998,41.349998,41.349998,41.349998,2010000\n1958-03-05,41.470001,41.470001,41.470001,41.470001,41.470001,2020000\n1958-03-06,42.000000,42.000000,42.000000,42.000000,42.000000,2470000\n1958-03-07,42.070000,42.070000,42.070000,42.070000,42.070000,2130000\n1958-03-10,42.209999,42.209999,42.209999,42.209999,42.209999,1980000\n1958-03-11,42.509998,42.509998,42.509998,42.509998,42.509998,2640000\n1958-03-12,42.410000,42.410000,42.410000,42.410000,42.410000,2420000\n1958-03-13,42.459999,42.459999,42.459999,42.459999,42.459999,2830000\n1958-03-14,42.330002,42.330002,42.330002,42.330002,42.330002,2150000\n1958-03-17,42.040001,42.040001,42.040001,42.040001,42.040001,2130000\n1958-03-18,41.889999,41.889999,41.889999,41.889999,41.889999,2070000\n1958-03-19,42.090000,42.090000,42.090000,42.090000,42.090000,2410000\n1958-03-20,42.110001,42.110001,42.110001,42.110001,42.110001,2280000\n1958-03-21,42.419998,42.419998,42.419998,42.419998,42.419998,2430000\n1958-03-24,42.580002,42.580002,42.580002,42.580002,42.580002,2580000\n1958-03-25,42.439999,42.439999,42.439999,42.439999,42.439999,2210000\n1958-03-26,42.299999,42.299999,42.299999,42.299999,42.299999,1990000\n1958-03-27,42.169998,42.169998,42.169998,42.169998,42.169998,2140000\n1958-03-28,42.200001,42.200001,42.200001,42.200001,42.200001,1930000\n1958-03-31,42.099998,42.099998,42.099998,42.099998,42.099998,2050000\n1958-04-01,41.930000,41.930000,41.930000,41.930000,41.930000,2070000\n1958-04-02,41.599998,41.599998,41.599998,41.599998,41.599998,2390000\n1958-04-03,41.480000,41.480000,41.480000,41.480000,41.480000,2130000\n1958-04-07,41.330002,41.330002,41.330002,41.330002,41.330002,2090000\n1958-04-08,41.430000,41.430000,41.430000,41.430000,41.430000,2190000\n1958-04-09,41.650002,41.650002,41.650002,41.650002,41.650002,2040000\n1958-04-10,41.700001,41.700001,41.700001,41.700001,41.700001,2000000\n1958-04-11,41.740002,41.740002,41.740002,41.740002,41.740002,2060000\n1958-04-14,42.000000,42.000000,42.000000,42.000000,42.000000,2180000\n1958-04-15,42.430000,42.430000,42.430000,42.430000,42.430000,2590000\n1958-04-16,42.099998,42.099998,42.099998,42.099998,42.099998,2240000\n1958-04-17,42.250000,42.250000,42.250000,42.250000,42.250000,2500000\n1958-04-18,42.709999,42.709999,42.709999,42.709999,42.709999,2700000\n1958-04-21,42.930000,42.930000,42.930000,42.930000,42.930000,2550000\n1958-04-22,42.799999,42.799999,42.799999,42.799999,42.799999,2440000\n1958-04-23,42.799999,42.799999,42.799999,42.799999,42.799999,2720000\n1958-04-24,43.139999,43.139999,43.139999,43.139999,43.139999,2870000\n1958-04-25,43.360001,43.360001,43.360001,43.360001,43.360001,3020000\n1958-04-28,43.220001,43.220001,43.220001,43.220001,43.220001,2400000\n1958-04-29,43.000000,43.000000,43.000000,43.000000,43.000000,2190000\n1958-04-30,43.439999,43.439999,43.439999,43.439999,43.439999,2900000\n1958-05-01,43.540001,43.540001,43.540001,43.540001,43.540001,2630000\n1958-05-02,43.689999,43.689999,43.689999,43.689999,43.689999,2290000\n1958-05-05,43.790001,43.790001,43.790001,43.790001,43.790001,2670000\n1958-05-06,44.009998,44.009998,44.009998,44.009998,44.009998,3110000\n1958-05-07,43.930000,43.930000,43.930000,43.930000,43.930000,2770000\n1958-05-08,43.990002,43.990002,43.990002,43.990002,43.990002,2790000\n1958-05-09,44.090000,44.090000,44.090000,44.090000,44.090000,2760000\n1958-05-12,43.750000,43.750000,43.750000,43.750000,43.750000,2780000\n1958-05-13,43.619999,43.619999,43.619999,43.619999,43.619999,2940000\n1958-05-14,43.119999,43.119999,43.119999,43.119999,43.119999,3060000\n1958-05-15,43.340000,43.340000,43.340000,43.340000,43.340000,2470000\n1958-05-16,43.360001,43.360001,43.360001,43.360001,43.360001,2030000\n1958-05-19,43.240002,43.240002,43.240002,43.240002,43.240002,1910000\n1958-05-20,43.610001,43.610001,43.610001,43.610001,43.610001,2500000\n1958-05-21,43.549999,43.549999,43.549999,43.549999,43.549999,2580000\n1958-05-22,43.779999,43.779999,43.779999,43.779999,43.779999,2950000\n1958-05-23,43.869999,43.869999,43.869999,43.869999,43.869999,2570000\n1958-05-26,43.849998,43.849998,43.849998,43.849998,43.849998,2500000\n1958-05-27,43.790001,43.790001,43.790001,43.790001,43.790001,2180000\n1958-05-28,43.849998,43.849998,43.849998,43.849998,43.849998,2260000\n1958-05-29,44.090000,44.090000,44.090000,44.090000,44.090000,2350000\n1958-06-02,44.310001,44.310001,44.310001,44.310001,44.310001,2770000\n1958-06-03,44.459999,44.459999,44.459999,44.459999,44.459999,2780000\n1958-06-04,44.500000,44.500000,44.500000,44.500000,44.500000,2690000\n1958-06-05,44.549999,44.549999,44.549999,44.549999,44.549999,2600000\n1958-06-06,44.639999,44.639999,44.639999,44.639999,44.639999,2680000\n1958-06-09,44.570000,44.570000,44.570000,44.570000,44.570000,2380000\n1958-06-10,44.480000,44.480000,44.480000,44.480000,44.480000,2390000\n1958-06-11,44.490002,44.490002,44.490002,44.490002,44.490002,2570000\n1958-06-12,44.750000,44.750000,44.750000,44.750000,44.750000,2760000\n1958-06-13,45.020000,45.020000,45.020000,45.020000,45.020000,3100000\n1958-06-16,45.180000,45.180000,45.180000,45.180000,45.180000,2870000\n1958-06-17,44.939999,44.939999,44.939999,44.939999,44.939999,2950000\n1958-06-18,45.340000,45.340000,45.340000,45.340000,45.340000,2640000\n1958-06-19,44.610001,44.610001,44.610001,44.610001,44.610001,2690000\n1958-06-20,44.849998,44.849998,44.849998,44.849998,44.849998,2590000\n1958-06-23,44.689999,44.689999,44.689999,44.689999,44.689999,2340000\n1958-06-24,44.520000,44.520000,44.520000,44.520000,44.520000,2560000\n1958-06-25,44.630001,44.630001,44.630001,44.630001,44.630001,2720000\n1958-06-26,44.840000,44.840000,44.840000,44.840000,44.840000,2910000\n1958-06-27,44.900002,44.900002,44.900002,44.900002,44.900002,2800000\n1958-06-30,45.240002,45.240002,45.240002,45.240002,45.240002,2820000\n1958-07-01,45.279999,45.279999,45.279999,45.279999,45.279999,2600000\n1958-07-02,45.320000,45.320000,45.320000,45.320000,45.320000,2370000\n1958-07-03,45.470001,45.470001,45.470001,45.470001,45.470001,2630000\n1958-07-07,45.619999,45.619999,45.619999,45.619999,45.619999,2510000\n1958-07-08,45.400002,45.400002,45.400002,45.400002,45.400002,2430000\n1958-07-09,45.250000,45.250000,45.250000,45.250000,45.250000,2630000\n1958-07-10,45.419998,45.419998,45.419998,45.419998,45.419998,2510000\n1958-07-11,45.720001,45.720001,45.720001,45.720001,45.720001,2400000\n1958-07-14,45.139999,45.139999,45.139999,45.139999,45.139999,2540000\n1958-07-15,45.110001,45.110001,45.110001,45.110001,45.110001,3090000\n1958-07-16,45.250000,45.250000,45.250000,45.250000,45.250000,3240000\n1958-07-17,45.549999,45.549999,45.549999,45.549999,45.549999,3180000\n1958-07-18,45.770000,45.770000,45.770000,45.770000,45.770000,3350000\n1958-07-21,46.330002,46.330002,46.330002,46.330002,46.330002,3440000\n1958-07-22,46.410000,46.410000,46.410000,46.410000,46.410000,3420000\n1958-07-23,46.400002,46.400002,46.400002,46.400002,46.400002,3550000\n1958-07-24,46.650002,46.650002,46.650002,46.650002,46.650002,3740000\n1958-07-25,46.970001,46.970001,46.970001,46.970001,46.970001,4430000\n1958-07-28,47.150002,47.150002,47.150002,47.150002,47.150002,3940000\n1958-07-29,46.959999,46.959999,46.959999,46.959999,46.959999,3310000\n1958-07-30,47.090000,47.090000,47.090000,47.090000,47.090000,3680000\n1958-07-31,47.189999,47.189999,47.189999,47.189999,47.189999,4440000\n1958-08-01,47.490002,47.490002,47.490002,47.490002,47.490002,3380000\n1958-08-04,47.939999,47.939999,47.939999,47.939999,47.939999,4000000\n1958-08-05,47.750000,47.750000,47.750000,47.750000,47.750000,4210000\n1958-08-06,47.459999,47.459999,47.459999,47.459999,47.459999,3440000\n1958-08-07,47.770000,47.770000,47.770000,47.770000,47.770000,3200000\n1958-08-08,48.049999,48.049999,48.049999,48.049999,48.049999,3650000\n1958-08-11,48.160000,48.160000,48.160000,48.160000,48.160000,2870000\n1958-08-12,47.730000,47.730000,47.730000,47.730000,47.730000,2600000\n1958-08-13,47.810001,47.810001,47.810001,47.810001,47.810001,2790000\n1958-08-14,47.910000,47.910000,47.910000,47.910000,47.910000,3370000\n1958-08-15,47.500000,47.500000,47.500000,47.500000,47.500000,2960000\n1958-08-18,47.220001,47.220001,47.220001,47.220001,47.220001,2390000\n1958-08-19,47.299999,47.299999,47.299999,47.299999,47.299999,2250000\n1958-08-20,47.320000,47.320000,47.320000,47.320000,47.320000,2460000\n1958-08-21,47.630001,47.630001,47.630001,47.630001,47.630001,2500000\n1958-08-22,47.730000,47.730000,47.730000,47.730000,47.730000,2660000\n1958-08-25,47.740002,47.740002,47.740002,47.740002,47.740002,2610000\n1958-08-26,47.900002,47.900002,47.900002,47.900002,47.900002,2910000\n1958-08-27,47.910000,47.910000,47.910000,47.910000,47.910000,3250000\n1958-08-28,47.660000,47.660000,47.660000,47.660000,47.660000,2540000\n1958-08-29,47.750000,47.750000,47.750000,47.750000,47.750000,2260000\n1958-09-02,48.000000,48.000000,48.000000,48.000000,48.000000,2930000\n1958-09-03,48.180000,48.180000,48.180000,48.180000,48.180000,3240000\n1958-09-04,48.099998,48.099998,48.099998,48.099998,48.099998,3100000\n1958-09-05,47.970001,47.970001,47.970001,47.970001,47.970001,2520000\n1958-09-08,48.130001,48.130001,48.130001,48.130001,48.130001,3030000\n1958-09-09,48.459999,48.459999,48.459999,48.459999,48.459999,3480000\n1958-09-10,48.310001,48.310001,48.310001,48.310001,48.310001,2820000\n1958-09-11,48.639999,48.639999,48.639999,48.639999,48.639999,3300000\n1958-09-12,48.529999,48.529999,48.529999,48.529999,48.529999,3100000\n1958-09-15,48.959999,48.959999,48.959999,48.959999,48.959999,3040000\n1958-09-16,49.349998,49.349998,49.349998,49.349998,49.349998,3940000\n1958-09-17,49.349998,49.349998,49.349998,49.349998,49.349998,3790000\n1958-09-18,49.380001,49.380001,49.380001,49.380001,49.380001,3460000\n1958-09-19,49.400002,49.400002,49.400002,49.400002,49.400002,3880000\n1958-09-22,49.200001,49.200001,49.200001,49.200001,49.200001,3490000\n1958-09-23,49.560001,49.560001,49.560001,49.560001,49.560001,3950000\n1958-09-24,49.779999,49.779999,49.779999,49.779999,49.779999,3120000\n1958-09-25,49.570000,49.570000,49.570000,49.570000,49.570000,4490000\n1958-09-26,49.660000,49.660000,49.660000,49.660000,49.660000,3420000\n1958-09-29,49.869999,49.869999,49.869999,49.869999,49.869999,3680000\n1958-09-30,50.060001,50.060001,50.060001,50.060001,50.060001,4160000\n1958-10-01,49.980000,49.980000,49.980000,49.980000,49.980000,3780000\n1958-10-02,50.169998,50.169998,50.169998,50.169998,50.169998,3750000\n1958-10-03,50.369999,50.369999,50.369999,50.369999,50.369999,3830000\n1958-10-06,51.070000,51.070000,51.070000,51.070000,51.070000,3570000\n1958-10-07,51.070000,51.070000,51.070000,51.070000,51.070000,3570000\n1958-10-08,51.060001,51.060001,51.060001,51.060001,51.060001,3680000\n1958-10-09,51.049999,51.049999,51.049999,51.049999,51.049999,3670000\n1958-10-10,51.389999,51.389999,51.389999,51.389999,51.389999,4610000\n1958-10-13,51.619999,51.619999,51.619999,51.619999,51.619999,4550000\n1958-10-14,51.259998,51.259998,51.259998,51.259998,51.259998,5110000\n1958-10-15,50.580002,50.580002,50.580002,50.580002,50.580002,4810000\n1958-10-16,50.939999,50.939999,50.939999,50.939999,50.939999,4560000\n1958-10-17,51.459999,51.459999,51.459999,51.459999,51.459999,5360000\n1958-10-20,51.270000,51.270000,51.270000,51.270000,51.270000,4560000\n1958-10-21,51.270000,51.270000,51.270000,51.270000,51.270000,4010000\n1958-10-22,51.070000,51.070000,51.070000,51.070000,51.070000,3500000\n1958-10-23,50.970001,50.970001,50.970001,50.970001,50.970001,3610000\n1958-10-24,50.810001,50.810001,50.810001,50.810001,50.810001,3770000\n1958-10-27,50.419998,50.419998,50.419998,50.419998,50.419998,3980000\n1958-10-28,50.580002,50.580002,50.580002,50.580002,50.580002,3670000\n1958-10-29,51.070000,51.070000,51.070000,51.070000,51.070000,4790000\n1958-10-30,51.270000,51.270000,51.270000,51.270000,51.270000,4360000\n1958-10-31,51.330002,51.330002,51.330002,51.330002,51.330002,3920000\n1958-11-03,51.560001,51.560001,51.560001,51.560001,51.560001,3240000\n1958-11-05,52.029999,52.029999,52.029999,52.029999,52.029999,4080000\n1958-11-06,52.450001,52.450001,52.450001,52.450001,52.450001,4890000\n1958-11-07,52.259998,52.259998,52.259998,52.259998,52.259998,3700000\n1958-11-10,52.570000,52.570000,52.570000,52.570000,52.570000,3730000\n1958-11-11,52.980000,52.980000,52.980000,52.980000,52.980000,4040000\n1958-11-12,53.049999,53.049999,53.049999,53.049999,53.049999,4440000\n1958-11-13,52.830002,52.830002,52.830002,52.830002,52.830002,4200000\n1958-11-14,53.090000,53.090000,53.090000,53.090000,53.090000,4390000\n1958-11-17,53.240002,53.240002,53.240002,53.240002,53.240002,4540000\n1958-11-18,53.130001,53.130001,53.130001,53.130001,53.130001,3820000\n1958-11-19,53.200001,53.200001,53.200001,53.200001,53.200001,4090000\n1958-11-20,53.209999,53.209999,53.209999,53.209999,53.209999,4320000\n1958-11-21,52.700001,52.700001,52.700001,52.700001,52.700001,3950000\n1958-11-24,52.029999,52.029999,52.029999,52.029999,52.029999,4770000\n1958-11-25,51.020000,51.020000,51.020000,51.020000,51.020000,3940000\n1958-11-26,51.900002,51.900002,51.900002,51.900002,51.900002,4090000\n1958-11-28,52.480000,52.480000,52.480000,52.480000,52.480000,4120000\n1958-12-01,52.689999,52.689999,52.689999,52.689999,52.689999,3800000\n1958-12-02,52.459999,52.459999,52.459999,52.459999,52.459999,3320000\n1958-12-03,52.529999,52.529999,52.529999,52.529999,52.529999,3460000\n1958-12-04,52.549999,52.549999,52.549999,52.549999,52.549999,3630000\n1958-12-05,52.459999,52.459999,52.459999,52.459999,52.459999,3360000\n1958-12-08,52.459999,52.459999,52.459999,52.459999,52.459999,3590000\n1958-12-09,52.820000,52.820000,52.820000,52.820000,52.820000,3790000\n1958-12-10,53.459999,53.459999,53.459999,53.459999,53.459999,4340000\n1958-12-11,53.349998,53.349998,53.349998,53.349998,53.349998,4250000\n1958-12-12,53.220001,53.220001,53.220001,53.220001,53.220001,3140000\n1958-12-15,53.369999,53.369999,53.369999,53.369999,53.369999,3340000\n1958-12-16,53.570000,53.570000,53.570000,53.570000,53.570000,3970000\n1958-12-17,53.919998,53.919998,53.919998,53.919998,53.919998,3900000\n1958-12-18,54.150002,54.150002,54.150002,54.150002,54.150002,3900000\n1958-12-19,54.070000,54.070000,54.070000,54.070000,54.070000,3540000\n1958-12-22,53.709999,53.709999,53.709999,53.709999,53.709999,3030000\n1958-12-23,53.419998,53.419998,53.419998,53.419998,53.419998,2870000\n1958-12-24,54.110001,54.110001,54.110001,54.110001,54.110001,3050000\n1958-12-29,54.740002,54.740002,54.740002,54.740002,54.740002,3790000\n1958-12-30,54.930000,54.930000,54.930000,54.930000,54.930000,3900000\n1958-12-31,55.209999,55.209999,55.209999,55.209999,55.209999,3970000\n1959-01-02,55.439999,55.439999,55.439999,55.439999,55.439999,3380000\n1959-01-05,55.660000,55.660000,55.660000,55.660000,55.660000,4210000\n1959-01-06,55.590000,55.590000,55.590000,55.590000,55.590000,3690000\n1959-01-07,54.889999,54.889999,54.889999,54.889999,54.889999,4140000\n1959-01-08,55.400002,55.400002,55.400002,55.400002,55.400002,4030000\n1959-01-09,55.770000,55.770000,55.770000,55.770000,55.770000,4760000\n1959-01-12,55.779999,55.779999,55.779999,55.779999,55.779999,4320000\n1959-01-13,55.470001,55.470001,55.470001,55.470001,55.470001,3790000\n1959-01-14,55.619999,55.619999,55.619999,55.619999,55.619999,4090000\n1959-01-15,55.830002,55.830002,55.830002,55.830002,55.830002,4500000\n1959-01-16,55.810001,55.810001,55.810001,55.810001,55.810001,4300000\n1959-01-19,55.680000,55.680000,55.680000,55.680000,55.680000,3840000\n1959-01-20,55.720001,55.720001,55.720001,55.720001,55.720001,3680000\n1959-01-21,56.040001,56.040001,56.040001,56.040001,56.040001,3940000\n1959-01-22,55.970001,55.970001,55.970001,55.970001,55.970001,4250000\n1959-01-23,56.000000,56.000000,56.000000,56.000000,56.000000,3600000\n1959-01-26,55.770000,55.770000,55.770000,55.770000,55.770000,3980000\n1959-01-27,55.779999,55.779999,55.779999,55.779999,55.779999,3480000\n1959-01-28,55.160000,55.160000,55.160000,55.160000,55.160000,4190000\n1959-01-29,55.200001,55.200001,55.200001,55.200001,55.200001,3470000\n1959-01-30,55.450001,55.450001,55.450001,55.450001,55.450001,3600000\n1959-02-02,55.209999,55.209999,55.209999,55.209999,55.209999,3610000\n1959-02-03,55.279999,55.279999,55.279999,55.279999,55.279999,3220000\n1959-02-04,55.060001,55.060001,55.060001,55.060001,55.060001,3170000\n1959-02-05,54.810001,54.810001,54.810001,54.810001,54.810001,3140000\n1959-02-06,54.369999,54.369999,54.369999,54.369999,54.369999,3010000\n1959-02-09,53.580002,53.580002,53.580002,53.580002,53.580002,3130000\n1959-02-10,54.320000,54.320000,54.320000,54.320000,54.320000,2960000\n1959-02-11,54.349998,54.349998,54.349998,54.349998,54.349998,3000000\n1959-02-12,54.000000,54.000000,54.000000,54.000000,54.000000,2630000\n1959-02-13,54.419998,54.419998,54.419998,54.419998,54.419998,3070000\n1959-02-16,54.500000,54.500000,54.500000,54.500000,54.500000,3480000\n1959-02-17,54.290001,54.290001,54.290001,54.290001,54.290001,3190000\n1959-02-18,54.299999,54.299999,54.299999,54.299999,54.299999,3480000\n1959-02-19,55.500000,55.500000,55.500000,55.500000,55.500000,4160000\n1959-02-20,55.520000,55.520000,55.520000,55.520000,55.520000,4190000\n1959-02-24,55.480000,55.480000,55.480000,55.480000,55.480000,4340000\n1959-02-25,55.240002,55.240002,55.240002,55.240002,55.240002,3780000\n1959-02-26,55.340000,55.340000,55.340000,55.340000,55.340000,3930000\n1959-02-27,55.410000,55.410000,55.410000,55.410000,55.410000,4300000\n1959-03-02,55.730000,55.730000,55.730000,55.730000,55.730000,4210000\n1959-03-03,56.250000,56.250000,56.250000,56.250000,56.250000,4790000\n1959-03-04,56.349998,56.349998,56.349998,56.349998,56.349998,4150000\n1959-03-05,56.430000,56.430000,56.430000,56.430000,56.430000,3930000\n1959-03-06,56.209999,56.209999,56.209999,56.209999,56.209999,3930000\n1959-03-09,56.150002,56.150002,56.150002,56.150002,56.150002,3530000\n1959-03-10,56.310001,56.310001,56.310001,56.310001,56.310001,3920000\n1959-03-11,56.349998,56.349998,56.349998,56.349998,56.349998,4160000\n1959-03-12,56.599998,56.599998,56.599998,56.599998,56.599998,4690000\n1959-03-13,56.669998,56.669998,56.669998,56.669998,56.669998,4880000\n1959-03-16,56.060001,56.060001,56.060001,56.060001,56.060001,4420000\n1959-03-17,56.520000,56.520000,56.520000,56.520000,56.520000,4730000\n1959-03-18,56.389999,56.389999,56.389999,56.389999,56.389999,4530000\n1959-03-19,56.340000,56.340000,56.340000,56.340000,56.340000,4150000\n1959-03-20,56.389999,56.389999,56.389999,56.389999,56.389999,3770000\n1959-03-23,55.869999,55.869999,55.869999,55.869999,55.869999,3700000\n1959-03-24,55.959999,55.959999,55.959999,55.959999,55.959999,3000000\n1959-03-25,55.880001,55.880001,55.880001,55.880001,55.880001,3280000\n1959-03-26,55.759998,55.759998,55.759998,55.759998,55.759998,2900000\n1959-03-30,55.450001,55.450001,55.450001,55.450001,55.450001,2940000\n1959-03-31,55.439999,55.439999,55.439999,55.439999,55.439999,2820000\n1959-04-01,55.689999,55.689999,55.689999,55.689999,55.689999,2980000\n1959-04-02,56.000000,56.000000,56.000000,56.000000,56.000000,3220000\n1959-04-03,56.439999,56.439999,56.439999,56.439999,56.439999,3680000\n1959-04-06,56.599998,56.599998,56.599998,56.599998,56.599998,3510000\n1959-04-07,56.480000,56.480000,56.480000,56.480000,56.480000,3020000\n1959-04-08,56.209999,56.209999,56.209999,56.209999,56.209999,3260000\n1959-04-09,56.169998,56.169998,56.169998,56.169998,56.169998,2830000\n1959-04-10,56.220001,56.220001,56.220001,56.220001,56.220001,3000000\n1959-04-13,56.430000,56.430000,56.430000,56.430000,56.430000,3140000\n1959-04-14,56.709999,56.709999,56.709999,56.709999,56.709999,3320000\n1959-04-15,56.959999,56.959999,56.959999,56.959999,56.959999,3680000\n1959-04-16,57.430000,57.430000,57.430000,57.430000,57.430000,3790000\n1959-04-17,57.919998,57.919998,57.919998,57.919998,57.919998,3870000\n1959-04-20,58.169998,58.169998,58.169998,58.169998,58.169998,3610000\n1959-04-21,58.110001,58.110001,58.110001,58.110001,58.110001,3650000\n1959-04-22,57.730000,57.730000,57.730000,57.730000,57.730000,3430000\n1959-04-23,57.599998,57.599998,57.599998,57.599998,57.599998,3310000\n1959-04-24,57.959999,57.959999,57.959999,57.959999,57.959999,3790000\n1959-04-27,58.139999,58.139999,58.139999,58.139999,58.139999,3850000\n1959-04-28,57.919998,57.919998,57.919998,57.919998,57.919998,3920000\n1959-04-29,57.689999,57.689999,57.689999,57.689999,57.689999,3470000\n1959-04-30,57.590000,57.590000,57.590000,57.590000,57.590000,3510000\n1959-05-01,57.650002,57.650002,57.650002,57.650002,57.650002,3020000\n1959-05-04,57.650002,57.650002,57.650002,57.650002,57.650002,3060000\n1959-05-05,57.750000,57.750000,57.750000,57.750000,57.750000,3360000\n1959-05-06,57.610001,57.610001,57.610001,57.610001,57.610001,4110000\n1959-05-07,56.880001,56.880001,56.880001,56.880001,56.880001,4530000\n1959-05-08,57.320000,57.320000,57.320000,57.320000,57.320000,3930000\n1959-05-11,57.959999,57.959999,57.959999,57.959999,57.959999,3860000\n1959-05-12,57.959999,57.959999,57.959999,57.959999,57.959999,3550000\n1959-05-13,57.970001,57.970001,57.970001,57.970001,57.970001,3540000\n1959-05-14,58.369999,58.369999,58.369999,58.369999,58.369999,3660000\n1959-05-15,58.160000,58.160000,58.160000,58.160000,58.160000,3510000\n1959-05-18,58.150002,58.150002,58.150002,58.150002,58.150002,2970000\n1959-05-19,58.320000,58.320000,58.320000,58.320000,58.320000,3170000\n1959-05-20,58.090000,58.090000,58.090000,58.090000,58.090000,3550000\n1959-05-21,58.139999,58.139999,58.139999,58.139999,58.139999,3230000\n1959-05-22,58.330002,58.330002,58.330002,58.330002,58.330002,3030000\n1959-05-25,58.180000,58.180000,58.180000,58.180000,58.180000,3260000\n1959-05-26,58.090000,58.090000,58.090000,58.090000,58.090000,2910000\n1959-05-27,58.189999,58.189999,58.189999,58.189999,58.189999,2940000\n1959-05-28,58.389999,58.389999,58.389999,58.389999,58.389999,2970000\n1959-05-29,58.680000,58.680000,58.680000,58.680000,58.680000,2790000\n1959-06-01,58.630001,58.630001,58.630001,58.630001,58.630001,2730000\n1959-06-02,58.230000,58.230000,58.230000,58.230000,58.230000,3120000\n1959-06-03,58.250000,58.250000,58.250000,58.250000,58.250000,2910000\n1959-06-04,57.630001,57.630001,57.630001,57.630001,57.630001,3210000\n1959-06-05,57.509998,57.509998,57.509998,57.509998,57.509998,2800000\n1959-06-08,56.759998,56.759998,56.759998,56.759998,56.759998,2970000\n1959-06-09,56.360001,56.360001,56.360001,56.360001,56.360001,3490000\n1959-06-10,57.189999,57.189999,57.189999,57.189999,57.189999,3310000\n1959-06-11,57.250000,57.250000,57.250000,57.250000,57.250000,3120000\n1959-06-12,57.290001,57.290001,57.290001,57.290001,57.290001,2580000\n1959-06-15,56.990002,56.990002,56.990002,56.990002,56.990002,2410000\n1959-06-16,56.560001,56.560001,56.560001,56.560001,56.560001,2440000\n1959-06-17,57.090000,57.090000,57.090000,57.090000,57.090000,2850000\n1959-06-18,57.049999,57.049999,57.049999,57.049999,57.049999,3150000\n1959-06-19,57.130001,57.130001,57.130001,57.130001,57.130001,2260000\n1959-06-22,57.130001,57.130001,57.130001,57.130001,57.130001,2630000\n1959-06-23,57.119999,57.119999,57.119999,57.119999,57.119999,2600000\n1959-06-24,57.410000,57.410000,57.410000,57.410000,57.410000,3180000\n1959-06-25,57.730000,57.730000,57.730000,57.730000,57.730000,3250000\n1959-06-26,57.980000,57.980000,57.980000,57.980000,57.980000,3100000\n1959-06-29,58.369999,58.369999,58.369999,58.369999,58.369999,3000000\n1959-06-30,58.470001,58.470001,58.470001,58.470001,58.470001,3200000\n1959-07-01,58.970001,58.970001,58.970001,58.970001,58.970001,3150000\n1959-07-02,59.279999,59.279999,59.279999,59.279999,59.279999,3610000\n1959-07-06,59.650002,59.650002,59.650002,59.650002,59.650002,3720000\n1959-07-07,60.009998,60.009998,60.009998,60.009998,60.009998,3840000\n1959-07-08,60.029999,60.029999,60.029999,60.029999,60.029999,4010000\n1959-07-09,59.970001,59.970001,59.970001,59.970001,59.970001,3560000\n1959-07-10,59.910000,59.910000,59.910000,59.910000,59.910000,3600000\n1959-07-13,59.410000,59.410000,59.410000,59.410000,59.410000,3360000\n1959-07-14,59.549999,59.549999,59.549999,59.549999,59.549999,3230000\n1959-07-15,59.590000,59.590000,59.590000,59.590000,59.590000,3280000\n1959-07-16,59.410000,59.410000,59.410000,59.410000,59.410000,3170000\n1959-07-17,59.189999,59.189999,59.189999,59.189999,59.189999,2510000\n1959-07-20,58.910000,58.910000,58.910000,58.910000,58.910000,2500000\n1959-07-21,59.410000,59.410000,59.410000,59.410000,59.410000,2950000\n1959-07-22,59.610001,59.610001,59.610001,59.610001,59.610001,3310000\n1959-07-23,59.669998,59.669998,59.669998,59.669998,59.669998,3310000\n1959-07-24,59.650002,59.650002,59.650002,59.650002,59.650002,2720000\n1959-07-27,60.020000,60.020000,60.020000,60.020000,60.020000,2910000\n1959-07-28,60.320000,60.320000,60.320000,60.320000,60.320000,3190000\n1959-07-29,60.619999,60.619999,60.619999,60.619999,60.619999,3460000\n1959-07-30,60.500000,60.500000,60.500000,60.500000,60.500000,3240000\n1959-07-31,60.509998,60.509998,60.509998,60.509998,60.509998,2270000\n1959-08-03,60.709999,60.709999,60.709999,60.709999,60.709999,2410000\n1959-08-04,60.610001,60.610001,60.610001,60.610001,60.610001,2530000\n1959-08-05,60.299999,60.299999,60.299999,60.299999,60.299999,2630000\n1959-08-06,60.240002,60.240002,60.240002,60.240002,60.240002,2610000\n1959-08-07,59.869999,59.869999,59.869999,59.869999,59.869999,2580000\n1959-08-10,58.619999,58.619999,58.619999,58.619999,58.619999,4190000\n1959-08-11,59.389999,59.389999,59.389999,59.389999,59.389999,2980000\n1959-08-12,59.250000,59.250000,59.250000,59.250000,59.250000,2700000\n1959-08-13,59.150002,59.150002,59.150002,59.150002,59.150002,2020000\n1959-08-14,59.290001,59.290001,59.290001,59.290001,59.290001,1990000\n1959-08-17,59.169998,59.169998,59.169998,59.169998,59.169998,1980000\n1959-08-18,58.619999,58.619999,58.619999,58.619999,58.619999,2280000\n1959-08-19,58.270000,58.270000,58.270000,58.270000,58.270000,3050000\n1959-08-20,59.139999,59.139999,59.139999,59.139999,59.139999,2450000\n1959-08-21,59.080002,59.080002,59.080002,59.080002,59.080002,2000000\n1959-08-24,58.869999,58.869999,58.869999,58.869999,58.869999,1860000\n1959-08-25,58.990002,58.990002,58.990002,58.990002,58.990002,1960000\n1959-08-26,59.070000,59.070000,59.070000,59.070000,59.070000,2210000\n1959-08-27,59.580002,59.580002,59.580002,59.580002,59.580002,2550000\n1959-08-28,59.599998,59.599998,59.599998,59.599998,59.599998,1930000\n1959-08-31,59.599998,59.599998,59.599998,59.599998,59.599998,2140000\n1959-09-01,58.869999,58.869999,58.869999,58.869999,58.869999,2430000\n1959-09-02,58.919998,58.919998,58.919998,58.919998,58.919998,2370000\n1959-09-03,58.259998,58.259998,58.259998,58.259998,58.259998,2330000\n1959-09-04,58.540001,58.540001,58.540001,58.540001,58.540001,2300000\n1959-09-08,57.700001,57.700001,57.700001,57.700001,57.700001,2940000\n1959-09-09,57.290001,57.290001,57.290001,57.290001,57.290001,3030000\n1959-09-10,56.990002,56.990002,56.990002,56.990002,56.990002,2520000\n1959-09-11,57.410000,57.410000,57.410000,57.410000,57.410000,2640000\n1959-09-14,56.990002,56.990002,56.990002,56.990002,56.990002,2590000\n1959-09-15,56.680000,56.680000,56.680000,56.680000,56.680000,2830000\n1959-09-16,56.720001,56.720001,56.720001,56.720001,56.720001,2180000\n1959-09-17,56.410000,56.410000,56.410000,56.410000,56.410000,2090000\n1959-09-18,56.189999,56.189999,56.189999,56.189999,56.189999,2530000\n1959-09-21,55.270000,55.270000,55.270000,55.270000,55.270000,3240000\n1959-09-22,55.139999,55.139999,55.139999,55.139999,55.139999,3000000\n1959-09-23,55.820000,55.820000,55.820000,55.820000,55.820000,3010000\n1959-09-24,56.779999,56.779999,56.779999,56.779999,56.779999,3480000\n1959-09-25,56.730000,56.730000,56.730000,56.730000,56.730000,3280000\n1959-09-28,57.150002,57.150002,57.150002,57.150002,57.150002,2640000\n1959-09-29,57.509998,57.509998,57.509998,57.509998,57.509998,3220000\n1959-09-30,56.880001,56.880001,56.880001,56.880001,56.880001,2850000\n1959-10-01,56.939999,56.939999,56.939999,56.939999,56.939999,2660000\n1959-10-02,57.200001,57.200001,57.200001,57.200001,57.200001,2270000\n1959-10-05,57.139999,57.139999,57.139999,57.139999,57.139999,2100000\n1959-10-06,57.090000,57.090000,57.090000,57.090000,57.090000,2330000\n1959-10-07,56.939999,56.939999,56.939999,56.939999,56.939999,2380000\n1959-10-08,56.810001,56.810001,56.810001,56.810001,56.810001,2510000\n1959-10-09,57.000000,57.000000,57.000000,57.000000,57.000000,2540000\n1959-10-12,57.320000,57.320000,57.320000,57.320000,57.320000,1750000\n1959-10-13,57.160000,57.160000,57.160000,57.160000,57.160000,2530000\n1959-10-14,56.709999,56.709999,56.709999,56.709999,56.709999,2320000\n1959-10-15,56.869999,56.869999,56.869999,56.869999,56.869999,2190000\n1959-10-16,57.330002,57.330002,57.330002,57.330002,57.330002,2760000\n1959-10-19,57.009998,57.009998,57.009998,57.009998,57.009998,2470000\n1959-10-20,56.660000,56.660000,56.660000,56.660000,56.660000,2740000\n1959-10-21,56.549999,56.549999,56.549999,56.549999,56.549999,2730000\n1959-10-22,56.000000,56.000000,56.000000,56.000000,56.000000,3060000\n1959-10-23,56.560001,56.560001,56.560001,56.560001,56.560001,2880000\n1959-10-26,56.939999,56.939999,56.939999,56.939999,56.939999,3580000\n1959-10-27,57.419998,57.419998,57.419998,57.419998,57.419998,4160000\n1959-10-28,57.459999,57.459999,57.459999,57.459999,57.459999,3920000\n1959-10-29,57.410000,57.410000,57.410000,57.410000,57.410000,3890000\n1959-10-30,57.520000,57.520000,57.520000,57.520000,57.520000,3560000\n1959-11-02,57.410000,57.410000,57.410000,57.410000,57.410000,3320000\n1959-11-04,57.259998,57.259998,57.259998,57.259998,57.259998,3940000\n1959-11-05,57.320000,57.320000,57.320000,57.320000,57.320000,3170000\n1959-11-06,57.599998,57.599998,57.599998,57.599998,57.599998,3450000\n1959-11-09,57.500000,57.500000,57.500000,57.500000,57.500000,3700000\n1959-11-10,57.480000,57.480000,57.480000,57.480000,57.480000,3020000\n1959-11-11,57.490002,57.490002,57.490002,57.490002,57.490002,2820000\n1959-11-12,57.169998,57.169998,57.169998,57.169998,57.169998,3600000\n1959-11-13,56.849998,56.849998,56.849998,56.849998,56.849998,3050000\n1959-11-16,56.220001,56.220001,56.220001,56.220001,56.220001,3710000\n1959-11-17,56.380001,56.380001,56.380001,56.380001,56.380001,3570000\n1959-11-18,56.990002,56.990002,56.990002,56.990002,56.990002,3660000\n1959-11-19,56.939999,56.939999,56.939999,56.939999,56.939999,3230000\n1959-11-20,56.970001,56.970001,56.970001,56.970001,56.970001,2960000\n1959-11-23,57.080002,57.080002,57.080002,57.080002,57.080002,3400000\n1959-11-24,57.349998,57.349998,57.349998,57.349998,57.349998,3650000\n1959-11-25,57.439999,57.439999,57.439999,57.439999,57.439999,3550000\n1959-11-27,57.700001,57.700001,57.700001,57.700001,57.700001,3030000\n1959-11-30,58.279999,58.279999,58.279999,58.279999,58.279999,3670000\n1959-12-01,58.700001,58.700001,58.700001,58.700001,58.700001,3990000\n1959-12-02,58.599998,58.599998,58.599998,58.599998,58.599998,3490000\n1959-12-03,58.730000,58.730000,58.730000,58.730000,58.730000,3280000\n1959-12-04,58.849998,58.849998,58.849998,58.849998,58.849998,3590000\n1959-12-07,58.959999,58.959999,58.959999,58.959999,58.959999,3620000\n1959-12-08,59.340000,59.340000,59.340000,59.340000,59.340000,3870000\n1959-12-09,58.970001,58.970001,58.970001,58.970001,58.970001,3430000\n1959-12-10,59.020000,59.020000,59.020000,59.020000,59.020000,3170000\n1959-12-11,58.880001,58.880001,58.880001,58.880001,58.880001,2910000\n1959-12-14,59.040001,59.040001,59.040001,59.040001,59.040001,3100000\n1959-12-15,58.900002,58.900002,58.900002,58.900002,58.900002,3450000\n1959-12-16,58.970001,58.970001,58.970001,58.970001,58.970001,3270000\n1959-12-17,58.860001,58.860001,58.860001,58.860001,58.860001,3040000\n1959-12-18,59.139999,59.139999,59.139999,59.139999,59.139999,3230000\n1959-12-21,59.209999,59.209999,59.209999,59.209999,59.209999,3290000\n1959-12-22,59.139999,59.139999,59.139999,59.139999,59.139999,2930000\n1959-12-23,58.959999,58.959999,58.959999,58.959999,58.959999,2890000\n1959-12-24,59.000000,59.000000,59.000000,59.000000,59.000000,2320000\n1959-12-28,58.980000,58.980000,58.980000,58.980000,58.980000,2830000\n1959-12-29,59.299999,59.299999,59.299999,59.299999,59.299999,3020000\n1959-12-30,59.770000,59.770000,59.770000,59.770000,59.770000,3680000\n1959-12-31,59.889999,59.889999,59.889999,59.889999,59.889999,3810000\n1960-01-04,59.910000,59.910000,59.910000,59.910000,59.910000,3990000\n1960-01-05,60.389999,60.389999,60.389999,60.389999,60.389999,3710000\n1960-01-06,60.130001,60.130001,60.130001,60.130001,60.130001,3730000\n1960-01-07,59.689999,59.689999,59.689999,59.689999,59.689999,3310000\n1960-01-08,59.500000,59.500000,59.500000,59.500000,59.500000,3290000\n1960-01-11,58.770000,58.770000,58.770000,58.770000,58.770000,3470000\n1960-01-12,58.410000,58.410000,58.410000,58.410000,58.410000,3760000\n1960-01-13,58.080002,58.080002,58.080002,58.080002,58.080002,3470000\n1960-01-14,58.400002,58.400002,58.400002,58.400002,58.400002,3560000\n1960-01-15,58.380001,58.380001,58.380001,58.380001,58.380001,3400000\n1960-01-18,57.889999,57.889999,57.889999,57.889999,57.889999,3020000\n1960-01-19,57.270000,57.270000,57.270000,57.270000,57.270000,3100000\n1960-01-20,57.070000,57.070000,57.070000,57.070000,57.070000,2720000\n1960-01-21,57.209999,57.209999,57.209999,57.209999,57.209999,2700000\n1960-01-22,57.380001,57.380001,57.380001,57.380001,57.380001,2690000\n1960-01-25,56.779999,56.779999,56.779999,56.779999,56.779999,2790000\n1960-01-26,56.860001,56.860001,56.860001,56.860001,56.860001,3060000\n1960-01-27,56.720001,56.720001,56.720001,56.720001,56.720001,2460000\n1960-01-28,56.130001,56.130001,56.130001,56.130001,56.130001,2630000\n1960-01-29,55.610001,55.610001,55.610001,55.610001,55.610001,3060000\n1960-02-01,55.959999,55.959999,55.959999,55.959999,55.959999,2820000\n1960-02-02,56.820000,56.820000,56.820000,56.820000,56.820000,3080000\n1960-02-03,56.320000,56.320000,56.320000,56.320000,56.320000,3020000\n1960-02-04,56.270000,56.270000,56.270000,56.270000,56.270000,2600000\n1960-02-05,55.980000,55.980000,55.980000,55.980000,55.980000,2530000\n1960-02-08,55.320000,55.320000,55.320000,55.320000,55.320000,3350000\n1960-02-09,55.840000,55.840000,55.840000,55.840000,55.840000,2860000\n1960-02-10,55.490002,55.490002,55.490002,55.490002,55.490002,2440000\n1960-02-11,55.180000,55.180000,55.180000,55.180000,55.180000,2610000\n1960-02-12,55.459999,55.459999,55.459999,55.459999,55.459999,2230000\n1960-02-15,55.169998,55.169998,55.169998,55.169998,55.169998,2780000\n1960-02-16,54.730000,54.730000,54.730000,54.730000,54.730000,3270000\n1960-02-17,55.029999,55.029999,55.029999,55.029999,55.029999,4210000\n1960-02-18,55.799999,55.799999,55.799999,55.799999,55.799999,3800000\n1960-02-19,56.240002,56.240002,56.240002,56.240002,56.240002,3230000\n1960-02-23,55.939999,55.939999,55.939999,55.939999,55.939999,2960000\n1960-02-24,55.740002,55.740002,55.740002,55.740002,55.740002,2740000\n1960-02-25,55.930000,55.930000,55.930000,55.930000,55.930000,3600000\n1960-02-26,56.160000,56.160000,56.160000,56.160000,56.160000,3380000\n1960-02-29,56.119999,56.119999,56.119999,56.119999,56.119999,2990000\n1960-03-01,56.009998,56.009998,56.009998,56.009998,56.009998,2920000\n1960-03-02,55.619999,55.619999,55.619999,55.619999,55.619999,3110000\n1960-03-03,54.779999,54.779999,54.779999,54.779999,54.779999,3160000\n1960-03-04,54.570000,54.570000,54.570000,54.570000,54.570000,4060000\n1960-03-07,54.020000,54.020000,54.020000,54.020000,54.020000,2900000\n1960-03-08,53.470001,53.470001,53.470001,53.470001,53.470001,3370000\n1960-03-09,54.040001,54.040001,54.040001,54.040001,54.040001,3580000\n1960-03-10,53.830002,53.830002,53.830002,53.830002,53.830002,3350000\n1960-03-11,54.240002,54.240002,54.240002,54.240002,54.240002,2770000\n1960-03-14,54.320000,54.320000,54.320000,54.320000,54.320000,2530000\n1960-03-15,54.740002,54.740002,54.740002,54.740002,54.740002,2690000\n1960-03-16,55.040001,55.040001,55.040001,55.040001,55.040001,2960000\n1960-03-17,54.959999,54.959999,54.959999,54.959999,54.959999,2140000\n1960-03-18,55.009998,55.009998,55.009998,55.009998,55.009998,2620000\n1960-03-21,55.070000,55.070000,55.070000,55.070000,55.070000,2500000\n1960-03-22,55.290001,55.290001,55.290001,55.290001,55.290001,2490000\n1960-03-23,55.740002,55.740002,55.740002,55.740002,55.740002,3020000\n1960-03-24,55.980000,55.980000,55.980000,55.980000,55.980000,2940000\n1960-03-25,55.980000,55.980000,55.980000,55.980000,55.980000,2640000\n1960-03-28,55.860001,55.860001,55.860001,55.860001,55.860001,2500000\n1960-03-29,55.779999,55.779999,55.779999,55.779999,55.779999,2320000\n1960-03-30,55.660000,55.660000,55.660000,55.660000,55.660000,2450000\n1960-03-31,55.340000,55.340000,55.340000,55.340000,55.340000,2690000\n1960-04-01,55.430000,55.430000,55.430000,55.430000,55.430000,2260000\n1960-04-04,55.540001,55.540001,55.540001,55.540001,55.540001,2450000\n1960-04-05,55.369999,55.369999,55.369999,55.369999,55.369999,2840000\n1960-04-06,56.509998,56.509998,56.509998,56.509998,56.509998,3450000\n1960-04-07,56.520000,56.520000,56.520000,56.520000,56.520000,3070000\n1960-04-08,56.389999,56.389999,56.389999,56.389999,56.389999,2820000\n1960-04-11,56.169998,56.169998,56.169998,56.169998,56.169998,2520000\n1960-04-12,56.299999,56.299999,56.299999,56.299999,56.299999,2470000\n1960-04-13,56.299999,56.299999,56.299999,56.299999,56.299999,2730000\n1960-04-14,56.430000,56.430000,56.430000,56.430000,56.430000,2730000\n1960-04-18,56.590000,56.590000,56.590000,56.590000,56.590000,3200000\n1960-04-19,56.130001,56.130001,56.130001,56.130001,56.130001,3080000\n1960-04-20,55.439999,55.439999,55.439999,55.439999,55.439999,3150000\n1960-04-21,55.590000,55.590000,55.590000,55.590000,55.590000,2700000\n1960-04-22,55.419998,55.419998,55.419998,55.419998,55.419998,2850000\n1960-04-25,54.860001,54.860001,54.860001,54.860001,54.860001,2980000\n1960-04-26,55.040001,55.040001,55.040001,55.040001,55.040001,2940000\n1960-04-27,55.040001,55.040001,55.040001,55.040001,55.040001,3020000\n1960-04-28,54.560001,54.560001,54.560001,54.560001,54.560001,3190000\n1960-04-29,54.369999,54.369999,54.369999,54.369999,54.369999,2850000\n1960-05-02,54.130001,54.130001,54.130001,54.130001,54.130001,2930000\n1960-05-03,54.830002,54.830002,54.830002,54.830002,54.830002,2910000\n1960-05-04,55.040001,55.040001,55.040001,55.040001,55.040001,2870000\n1960-05-05,54.860001,54.860001,54.860001,54.860001,54.860001,2670000\n1960-05-06,54.750000,54.750000,54.750000,54.750000,54.750000,2560000\n1960-05-09,54.799999,54.799999,54.799999,54.799999,54.799999,2670000\n1960-05-10,54.419998,54.419998,54.419998,54.419998,54.419998,2870000\n1960-05-11,54.570000,54.570000,54.570000,54.570000,54.570000,2900000\n1960-05-12,54.849998,54.849998,54.849998,54.849998,54.849998,3220000\n1960-05-13,55.299999,55.299999,55.299999,55.299999,55.299999,3750000\n1960-05-16,55.250000,55.250000,55.250000,55.250000,55.250000,3530000\n1960-05-17,55.459999,55.459999,55.459999,55.459999,55.459999,4080000\n1960-05-18,55.439999,55.439999,55.439999,55.439999,55.439999,5240000\n1960-05-19,55.680000,55.680000,55.680000,55.680000,55.680000,3700000\n1960-05-20,55.730000,55.730000,55.730000,55.730000,55.730000,3170000\n1960-05-23,55.759998,55.759998,55.759998,55.759998,55.759998,2530000\n1960-05-24,55.700001,55.700001,55.700001,55.700001,55.700001,3240000\n1960-05-25,55.669998,55.669998,55.669998,55.669998,55.669998,3440000\n1960-05-26,55.709999,55.709999,55.709999,55.709999,55.709999,3720000\n1960-05-27,55.740002,55.740002,55.740002,55.740002,55.740002,3040000\n1960-05-31,55.830002,55.830002,55.830002,55.830002,55.830002,3750000\n1960-06-01,55.889999,55.889999,55.889999,55.889999,55.889999,3770000\n1960-06-02,56.130001,56.130001,56.130001,56.130001,56.130001,3730000\n1960-06-03,56.230000,56.230000,56.230000,56.230000,56.230000,3340000\n1960-06-06,56.889999,56.889999,56.889999,56.889999,56.889999,3220000\n1960-06-07,57.430000,57.430000,57.430000,57.430000,57.430000,3710000\n1960-06-08,57.889999,57.889999,57.889999,57.889999,57.889999,3800000\n1960-06-09,58.000000,58.000000,58.000000,58.000000,58.000000,3820000\n1960-06-10,57.970001,57.970001,57.970001,57.970001,57.970001,2940000\n1960-06-13,57.990002,57.990002,57.990002,57.990002,57.990002,3180000\n1960-06-14,57.910000,57.910000,57.910000,57.910000,57.910000,3430000\n1960-06-15,57.570000,57.570000,57.570000,57.570000,57.570000,3630000\n1960-06-16,57.500000,57.500000,57.500000,57.500000,57.500000,3540000\n1960-06-17,57.439999,57.439999,57.439999,57.439999,57.439999,3920000\n1960-06-20,57.160000,57.160000,57.160000,57.160000,57.160000,3970000\n1960-06-21,57.110001,57.110001,57.110001,57.110001,57.110001,3860000\n1960-06-22,57.279999,57.279999,57.279999,57.279999,57.279999,3600000\n1960-06-23,57.590000,57.590000,57.590000,57.590000,57.590000,3620000\n1960-06-24,57.680000,57.680000,57.680000,57.680000,57.680000,3220000\n1960-06-27,57.330002,57.330002,57.330002,57.330002,57.330002,2960000\n1960-06-28,56.939999,56.939999,56.939999,56.939999,56.939999,3120000\n1960-06-29,56.939999,56.939999,56.939999,56.939999,56.939999,3160000\n1960-06-30,56.919998,56.919998,56.919998,56.919998,56.919998,2940000\n1960-07-01,57.060001,57.060001,57.060001,57.060001,57.060001,2620000\n1960-07-05,57.020000,57.020000,57.020000,57.020000,57.020000,2780000\n1960-07-06,56.939999,56.939999,56.939999,56.939999,56.939999,2970000\n1960-07-07,57.240002,57.240002,57.240002,57.240002,57.240002,3050000\n1960-07-08,57.380001,57.380001,57.380001,57.380001,57.380001,3010000\n1960-07-11,56.869999,56.869999,56.869999,56.869999,56.869999,2920000\n1960-07-12,56.250000,56.250000,56.250000,56.250000,56.250000,2860000\n1960-07-13,56.099998,56.099998,56.099998,56.099998,56.099998,2590000\n1960-07-14,56.119999,56.119999,56.119999,56.119999,56.119999,2480000\n1960-07-15,56.049999,56.049999,56.049999,56.049999,56.049999,2140000\n1960-07-18,55.700001,55.700001,55.700001,55.700001,55.700001,2350000\n1960-07-19,55.700001,55.700001,55.700001,55.700001,55.700001,2490000\n1960-07-20,55.610001,55.610001,55.610001,55.610001,55.610001,2370000\n1960-07-21,55.099998,55.099998,55.099998,55.099998,55.099998,2510000\n1960-07-22,54.720001,54.720001,54.720001,54.720001,54.720001,2850000\n1960-07-25,54.180000,54.180000,54.180000,54.180000,54.180000,2840000\n1960-07-26,54.509998,54.509998,54.509998,54.509998,54.509998,2720000\n1960-07-27,54.169998,54.169998,54.169998,54.169998,54.169998,2560000\n1960-07-28,54.570000,54.570000,54.570000,54.570000,54.570000,3020000\n1960-07-29,55.509998,55.509998,55.509998,55.509998,55.509998,2730000\n1960-08-01,55.529999,55.529999,55.529999,55.529999,55.529999,2440000\n1960-08-02,55.040001,55.040001,55.040001,55.040001,55.040001,2090000\n1960-08-03,54.720001,54.720001,54.720001,54.720001,54.720001,2470000\n1960-08-04,54.889999,54.889999,54.889999,54.889999,54.889999,2840000\n1960-08-05,55.439999,55.439999,55.439999,55.439999,55.439999,3000000\n1960-08-08,55.520000,55.520000,55.520000,55.520000,55.520000,2960000\n1960-08-09,55.840000,55.840000,55.840000,55.840000,55.840000,2700000\n1960-08-10,56.070000,56.070000,56.070000,56.070000,56.070000,2810000\n1960-08-11,56.279999,56.279999,56.279999,56.279999,56.279999,3070000\n1960-08-12,56.660000,56.660000,56.660000,56.660000,56.660000,3160000\n1960-08-15,56.610001,56.610001,56.610001,56.610001,56.610001,2450000\n1960-08-16,56.720001,56.720001,56.720001,56.720001,56.720001,2710000\n1960-08-17,56.840000,56.840000,56.840000,56.840000,56.840000,3090000\n1960-08-18,56.810001,56.810001,56.810001,56.810001,56.810001,2890000\n1960-08-19,57.009998,57.009998,57.009998,57.009998,57.009998,2570000\n1960-08-22,57.189999,57.189999,57.189999,57.189999,57.189999,2760000\n1960-08-23,57.750000,57.750000,57.750000,57.750000,57.750000,3560000\n1960-08-24,58.070000,58.070000,58.070000,58.070000,58.070000,3500000\n1960-08-25,57.790001,57.790001,57.790001,57.790001,57.790001,2680000\n1960-08-26,57.599998,57.599998,57.599998,57.599998,57.599998,2780000\n1960-08-29,57.439999,57.439999,57.439999,57.439999,57.439999,2780000\n1960-08-30,56.840000,56.840000,56.840000,56.840000,56.840000,2890000\n1960-08-31,56.959999,56.959999,56.959999,56.959999,56.959999,3130000\n1960-09-01,57.090000,57.090000,57.090000,57.090000,57.090000,3460000\n1960-09-02,57.000000,57.000000,57.000000,57.000000,57.000000,2680000\n1960-09-06,56.490002,56.490002,56.490002,56.490002,56.490002,2580000\n1960-09-07,55.790001,55.790001,55.790001,55.790001,55.790001,2850000\n1960-09-08,55.740002,55.740002,55.740002,55.740002,55.740002,2670000\n1960-09-09,56.110001,56.110001,56.110001,56.110001,56.110001,2750000\n1960-09-12,55.720001,55.720001,55.720001,55.720001,55.720001,2160000\n1960-09-13,55.830002,55.830002,55.830002,55.830002,55.830002,2180000\n1960-09-14,55.439999,55.439999,55.439999,55.439999,55.439999,2530000\n1960-09-15,55.220001,55.220001,55.220001,55.220001,55.220001,2870000\n1960-09-16,55.110001,55.110001,55.110001,55.110001,55.110001,2340000\n1960-09-19,53.860001,53.860001,53.860001,53.860001,53.860001,3790000\n1960-09-20,54.009998,54.009998,54.009998,54.009998,54.009998,3660000\n1960-09-21,54.570000,54.570000,54.570000,54.570000,54.570000,2930000\n1960-09-22,54.360001,54.360001,54.360001,54.360001,54.360001,1970000\n1960-09-23,53.900002,53.900002,53.900002,53.900002,53.900002,2580000\n1960-09-26,53.060001,53.060001,53.060001,53.060001,53.060001,3930000\n1960-09-27,52.939999,52.939999,52.939999,52.939999,52.939999,3170000\n1960-09-28,52.480000,52.480000,52.480000,52.480000,52.480000,3520000\n1960-09-29,52.619999,52.619999,52.619999,52.619999,52.619999,2850000\n1960-09-30,53.520000,53.520000,53.520000,53.520000,53.520000,3370000\n1960-10-03,53.360001,53.360001,53.360001,53.360001,53.360001,2220000\n1960-10-04,52.990002,52.990002,52.990002,52.990002,52.990002,2270000\n1960-10-05,53.389999,53.389999,53.389999,53.389999,53.389999,2650000\n1960-10-06,53.720001,53.720001,53.720001,53.720001,53.720001,2510000\n1960-10-07,54.029999,54.029999,54.029999,54.029999,54.029999,2530000\n1960-10-10,54.139999,54.139999,54.139999,54.139999,54.139999,2030000\n1960-10-11,54.220001,54.220001,54.220001,54.220001,54.220001,2350000\n1960-10-12,54.150002,54.150002,54.150002,54.150002,54.150002,1890000\n1960-10-13,54.570000,54.570000,54.570000,54.570000,54.570000,2220000\n1960-10-14,54.860001,54.860001,54.860001,54.860001,54.860001,2470000\n1960-10-17,54.630001,54.630001,54.630001,54.630001,54.630001,2280000\n1960-10-18,54.349998,54.349998,54.349998,54.349998,54.349998,2220000\n1960-10-19,54.250000,54.250000,54.250000,54.250000,54.250000,2410000\n1960-10-20,53.860001,53.860001,53.860001,53.860001,53.860001,2910000\n1960-10-21,53.720001,53.720001,53.720001,53.720001,53.720001,3090000\n1960-10-24,52.700001,52.700001,52.700001,52.700001,52.700001,4420000\n1960-10-25,52.200001,52.200001,52.200001,52.200001,52.200001,3030000\n1960-10-26,53.049999,53.049999,53.049999,53.049999,53.049999,3020000\n1960-10-27,53.619999,53.619999,53.619999,53.619999,53.619999,2900000\n1960-10-28,53.410000,53.410000,53.410000,53.410000,53.410000,2490000\n1960-10-31,53.389999,53.389999,53.389999,53.389999,53.389999,2460000\n1960-11-01,53.939999,53.939999,53.939999,53.939999,53.939999,2600000\n1960-11-02,54.220001,54.220001,54.220001,54.220001,54.220001,2780000\n1960-11-03,54.430000,54.430000,54.430000,54.430000,54.430000,2580000\n1960-11-04,54.900002,54.900002,54.900002,54.900002,54.900002,3050000\n1960-11-07,55.110001,55.110001,55.110001,55.110001,55.110001,3540000\n1960-11-09,55.349998,55.349998,55.349998,55.349998,55.349998,3450000\n1960-11-10,56.430000,56.430000,56.430000,56.430000,56.430000,4030000\n1960-11-11,55.869999,55.869999,55.869999,55.869999,55.869999,2730000\n1960-11-14,55.590000,55.590000,55.590000,55.590000,55.590000,2660000\n1960-11-15,55.810001,55.810001,55.810001,55.810001,55.810001,2990000\n1960-11-16,55.700001,55.700001,55.700001,55.700001,55.700001,3110000\n1960-11-17,55.549999,55.549999,55.549999,55.549999,55.549999,2450000\n1960-11-18,55.820000,55.820000,55.820000,55.820000,55.820000,2760000\n1960-11-21,55.930000,55.930000,55.930000,55.930000,55.930000,3090000\n1960-11-22,55.720001,55.720001,55.720001,55.720001,55.720001,3430000\n1960-11-23,55.799999,55.799999,55.799999,55.799999,55.799999,3000000\n1960-11-25,56.130001,56.130001,56.130001,56.130001,56.130001,3190000\n1960-11-28,56.029999,56.029999,56.029999,56.029999,56.029999,3860000\n1960-11-29,55.830002,55.830002,55.830002,55.830002,55.830002,3630000\n1960-11-30,55.540001,55.540001,55.540001,55.540001,55.540001,3080000\n1960-12-01,55.299999,55.299999,55.299999,55.299999,55.299999,3090000\n1960-12-02,55.389999,55.389999,55.389999,55.389999,55.389999,3140000\n1960-12-05,55.310001,55.310001,55.310001,55.310001,55.310001,3290000\n1960-12-06,55.470001,55.470001,55.470001,55.470001,55.470001,3360000\n1960-12-07,56.020000,56.020000,56.020000,56.020000,56.020000,3660000\n1960-12-08,56.150002,56.150002,56.150002,56.150002,56.150002,3540000\n1960-12-09,56.650002,56.650002,56.650002,56.650002,56.650002,4460000\n1960-12-12,56.849998,56.849998,56.849998,56.849998,56.849998,3020000\n1960-12-13,56.880001,56.880001,56.880001,56.880001,56.880001,3500000\n1960-12-14,56.840000,56.840000,56.840000,56.840000,56.840000,3880000\n1960-12-15,56.680000,56.680000,56.680000,56.680000,56.680000,3660000\n1960-12-16,57.200001,57.200001,57.200001,57.200001,57.200001,3770000\n1960-12-19,57.130001,57.130001,57.130001,57.130001,57.130001,3630000\n1960-12-20,57.090000,57.090000,57.090000,57.090000,57.090000,3340000\n1960-12-21,57.549999,57.549999,57.549999,57.549999,57.549999,4060000\n1960-12-22,57.389999,57.389999,57.389999,57.389999,57.389999,3820000\n1960-12-23,57.439999,57.439999,57.439999,57.439999,57.439999,3580000\n1960-12-27,57.520000,57.520000,57.520000,57.520000,57.520000,3270000\n1960-12-28,57.779999,57.779999,57.779999,57.779999,57.779999,3620000\n1960-12-29,58.049999,58.049999,58.049999,58.049999,58.049999,4340000\n1960-12-30,58.110001,58.110001,58.110001,58.110001,58.110001,5300000\n1961-01-03,57.570000,57.570000,57.570000,57.570000,57.570000,2770000\n1961-01-04,58.360001,58.360001,58.360001,58.360001,58.360001,3840000\n1961-01-05,58.570000,58.570000,58.570000,58.570000,58.570000,4130000\n1961-01-06,58.400002,58.400002,58.400002,58.400002,58.400002,3620000\n1961-01-09,58.810001,58.810001,58.810001,58.810001,58.810001,4210000\n1961-01-10,58.970001,58.970001,58.970001,58.970001,58.970001,4840000\n1961-01-11,59.139999,59.139999,59.139999,59.139999,59.139999,4370000\n1961-01-12,59.320000,59.320000,59.320000,59.320000,59.320000,4270000\n1961-01-13,59.599998,59.599998,59.599998,59.599998,59.599998,4520000\n1961-01-16,59.580002,59.580002,59.580002,59.580002,59.580002,4510000\n1961-01-17,59.639999,59.639999,59.639999,59.639999,59.639999,3830000\n1961-01-18,59.680000,59.680000,59.680000,59.680000,59.680000,4390000\n1961-01-19,59.770000,59.770000,59.770000,59.770000,59.770000,4740000\n1961-01-20,59.959999,59.959999,59.959999,59.959999,59.959999,3270000\n1961-01-23,60.290001,60.290001,60.290001,60.290001,60.290001,4450000\n1961-01-24,60.450001,60.450001,60.450001,60.450001,60.450001,4280000\n1961-01-25,60.529999,60.529999,60.529999,60.529999,60.529999,4470000\n1961-01-26,60.619999,60.619999,60.619999,60.619999,60.619999,4110000\n1961-01-27,61.240002,61.240002,61.240002,61.240002,61.240002,4510000\n1961-01-30,61.970001,61.970001,61.970001,61.970001,61.970001,5190000\n1961-01-31,61.779999,61.779999,61.779999,61.779999,61.779999,4690000\n1961-02-01,61.900002,61.900002,61.900002,61.900002,61.900002,4380000\n1961-02-02,62.299999,62.299999,62.299999,62.299999,62.299999,4900000\n1961-02-03,62.220001,62.220001,62.220001,62.220001,62.220001,5210000\n1961-02-06,61.759998,61.759998,61.759998,61.759998,61.759998,3890000\n1961-02-07,61.650002,61.650002,61.650002,61.650002,61.650002,4020000\n1961-02-08,62.209999,62.209999,62.209999,62.209999,62.209999,4940000\n1961-02-09,62.020000,62.020000,62.020000,62.020000,62.020000,5590000\n1961-02-10,61.500000,61.500000,61.500000,61.500000,61.500000,4840000\n1961-02-13,61.139999,61.139999,61.139999,61.139999,61.139999,3560000\n1961-02-14,61.410000,61.410000,61.410000,61.410000,61.410000,4490000\n1961-02-15,61.919998,61.919998,61.919998,61.919998,61.919998,5200000\n1961-02-16,62.299999,62.299999,62.299999,62.299999,62.299999,5070000\n1961-02-17,62.099998,62.099998,62.099998,62.099998,62.099998,4640000\n1961-02-20,62.320000,62.320000,62.320000,62.320000,62.320000,4680000\n1961-02-21,62.360001,62.360001,62.360001,62.360001,62.360001,5070000\n1961-02-23,62.590000,62.590000,62.590000,62.590000,62.590000,5620000\n1961-02-24,62.840000,62.840000,62.840000,62.840000,62.840000,5330000\n1961-02-27,63.299999,63.299999,63.299999,63.299999,63.299999,5470000\n1961-02-28,63.439999,63.439999,63.439999,63.439999,63.439999,5830000\n1961-03-01,63.430000,63.430000,63.430000,63.430000,63.430000,4970000\n1961-03-02,63.849998,63.849998,63.849998,63.849998,63.849998,5300000\n1961-03-03,63.950001,63.950001,63.950001,63.950001,63.950001,5530000\n1961-03-06,64.050003,64.050003,64.050003,64.050003,64.050003,5650000\n1961-03-07,63.470001,63.470001,63.470001,63.470001,63.470001,5540000\n1961-03-08,63.439999,63.439999,63.439999,63.439999,63.439999,5910000\n1961-03-09,63.500000,63.500000,63.500000,63.500000,63.500000,6010000\n1961-03-10,63.480000,63.480000,63.480000,63.480000,63.480000,5950000\n1961-03-13,63.660000,63.660000,63.660000,63.660000,63.660000,5080000\n1961-03-14,63.380001,63.380001,63.380001,63.380001,63.380001,4900000\n1961-03-15,63.570000,63.570000,63.570000,63.570000,63.570000,4900000\n1961-03-16,64.209999,64.209999,64.209999,64.209999,64.209999,5610000\n1961-03-17,64.000000,64.000000,64.000000,64.000000,64.000000,5960000\n1961-03-20,64.860001,64.860001,64.860001,64.860001,64.860001,5780000\n1961-03-21,64.739998,64.739998,64.739998,64.739998,64.739998,5800000\n1961-03-22,64.699997,64.699997,64.699997,64.699997,64.699997,5840000\n1961-03-23,64.529999,64.529999,64.529999,64.529999,64.529999,2170000\n1961-03-24,64.419998,64.419998,64.419998,64.419998,64.419998,4390000\n1961-03-27,64.349998,64.349998,64.349998,64.349998,64.349998,4190000\n1961-03-28,64.379997,64.379997,64.379997,64.379997,64.379997,4630000\n1961-03-29,64.930000,64.930000,64.930000,64.930000,64.930000,5330000\n1961-03-30,65.059998,65.059998,65.059998,65.059998,65.059998,5610000\n1961-04-03,65.599998,65.599998,65.599998,65.599998,65.599998,6470000\n1961-04-04,65.660004,65.660004,65.660004,65.660004,65.660004,7080000\n1961-04-05,65.459999,65.459999,65.459999,65.459999,65.459999,5430000\n1961-04-06,65.610001,65.610001,65.610001,65.610001,65.610001,4910000\n1961-04-07,65.959999,65.959999,65.959999,65.959999,65.959999,5100000\n1961-04-10,66.529999,66.529999,66.529999,66.529999,66.529999,5550000\n1961-04-11,66.620003,66.620003,66.620003,66.620003,66.620003,5230000\n1961-04-12,66.309998,66.309998,66.309998,66.309998,66.309998,4870000\n1961-04-13,66.260002,66.260002,66.260002,66.260002,66.260002,4770000\n1961-04-14,66.370003,66.370003,66.370003,66.370003,66.370003,5240000\n1961-04-17,68.680000,68.680000,68.680000,68.680000,68.680000,5860000\n1961-04-18,66.199997,66.199997,66.199997,66.199997,66.199997,4830000\n1961-04-19,65.809998,65.809998,65.809998,65.809998,65.809998,4870000\n1961-04-20,65.820000,65.820000,65.820000,65.820000,65.820000,4810000\n1961-04-21,65.769997,65.769997,65.769997,65.769997,65.769997,4340000\n1961-04-24,64.400002,64.400002,64.400002,64.400002,64.400002,4590000\n1961-04-25,65.300003,65.300003,65.300003,65.300003,65.300003,4670000\n1961-04-26,65.550003,65.550003,65.550003,65.550003,65.550003,4980000\n1961-04-27,65.459999,65.459999,65.459999,65.459999,65.459999,4450000\n1961-04-28,65.309998,65.309998,65.309998,65.309998,65.309998,3710000\n1961-05-01,65.169998,65.169998,65.169998,65.169998,65.169998,3710000\n1961-05-02,65.639999,65.639999,65.639999,65.639999,65.639999,4110000\n1961-05-03,66.180000,66.180000,66.180000,66.180000,66.180000,4940000\n1961-05-04,66.440002,66.440002,66.440002,66.440002,66.440002,5350000\n1961-05-05,66.519997,66.519997,66.519997,66.519997,66.519997,4980000\n1961-05-08,66.410004,66.410004,66.410004,66.410004,66.410004,5170000\n1961-05-09,66.470001,66.470001,66.470001,66.470001,66.470001,5380000\n1961-05-10,66.410004,66.410004,66.410004,66.410004,66.410004,5450000\n1961-05-11,66.389999,66.389999,66.389999,66.389999,66.389999,5170000\n1961-05-12,66.500000,66.500000,66.500000,66.500000,66.500000,4840000\n1961-05-15,66.830002,66.830002,66.830002,66.830002,66.830002,4840000\n1961-05-16,67.080002,67.080002,67.080002,67.080002,67.080002,5110000\n1961-05-17,67.389999,67.389999,67.389999,67.389999,67.389999,5520000\n1961-05-18,66.989998,66.989998,66.989998,66.989998,66.989998,4610000\n1961-05-19,67.269997,67.269997,67.269997,67.269997,67.269997,4200000\n1961-05-22,66.849998,66.849998,66.849998,66.849998,66.849998,4070000\n1961-05-23,66.680000,66.680000,66.680000,66.680000,66.680000,3660000\n1961-05-24,66.260002,66.260002,66.260002,66.260002,66.260002,3970000\n1961-05-25,66.010002,66.010002,66.010002,66.010002,66.010002,3760000\n1961-05-26,66.430000,66.430000,66.430000,66.430000,66.430000,3780000\n1961-05-31,66.559998,66.559998,66.559998,66.559998,66.559998,4320000\n1961-06-01,66.559998,66.559998,66.559998,66.559998,66.559998,3770000\n1961-06-02,66.730003,66.730003,66.730003,66.730003,66.730003,3670000\n1961-06-05,67.080002,67.080002,67.080002,67.080002,67.080002,4150000\n1961-06-06,66.889999,66.889999,66.889999,66.889999,66.889999,4250000\n1961-06-07,65.639999,65.639999,65.639999,65.639999,65.639999,3980000\n1961-06-08,66.669998,66.669998,66.669998,66.669998,66.669998,3810000\n1961-06-09,66.660004,66.660004,66.660004,66.660004,66.660004,3520000\n1961-06-12,66.150002,66.150002,66.150002,66.150002,66.150002,3260000\n1961-06-13,65.800003,65.800003,65.800003,65.800003,65.800003,3030000\n1961-06-14,65.980003,65.980003,65.980003,65.980003,65.980003,3430000\n1961-06-15,65.690002,65.690002,65.690002,65.690002,65.690002,3220000\n1961-06-16,65.180000,65.180000,65.180000,65.180000,65.180000,3380000\n1961-06-19,64.580002,64.580002,64.580002,64.580002,64.580002,3980000\n1961-06-20,65.150002,65.150002,65.150002,65.150002,65.150002,3280000\n1961-06-21,65.139999,65.139999,65.139999,65.139999,65.139999,3210000\n1961-06-22,64.900002,64.900002,64.900002,64.900002,64.900002,2880000\n1961-06-23,65.160004,65.160004,65.160004,65.160004,65.160004,2720000\n1961-06-26,64.470001,64.470001,64.470001,64.470001,64.470001,2690000\n1961-06-27,64.470001,64.470001,64.470001,64.470001,64.470001,3090000\n1961-06-28,64.589996,64.589996,64.589996,64.589996,64.589996,2830000\n1961-06-29,64.519997,64.519997,64.519997,64.519997,64.519997,2560000\n1961-06-30,64.639999,64.639999,64.639999,64.639999,64.639999,2380000\n1961-07-03,65.209999,65.209999,65.209999,65.209999,65.209999,2180000\n1961-07-05,65.629997,65.629997,65.629997,65.629997,65.629997,3270000\n1961-07-06,65.809998,65.809998,65.809998,65.809998,65.809998,3470000\n1961-07-07,65.769997,65.769997,65.769997,65.769997,65.769997,3030000\n1961-07-10,65.709999,65.709999,65.709999,65.709999,65.709999,3180000\n1961-07-11,65.690002,65.690002,65.690002,65.690002,65.690002,3160000\n1961-07-12,65.320000,65.320000,65.320000,65.320000,65.320000,3070000\n1961-07-13,64.860001,64.860001,64.860001,64.860001,64.860001,2670000\n1961-07-14,65.279999,65.279999,65.279999,65.279999,65.279999,2760000\n1961-07-17,64.790001,64.790001,64.790001,64.790001,64.790001,2690000\n1961-07-18,64.410004,64.410004,64.410004,64.410004,64.410004,3010000\n1961-07-19,64.699997,64.699997,64.699997,64.699997,64.699997,2940000\n1961-07-20,64.709999,64.709999,64.709999,64.709999,64.709999,2530000\n1961-07-21,64.860001,64.860001,64.860001,64.860001,64.860001,2360000\n1961-07-24,64.870003,64.870003,64.870003,64.870003,64.870003,2490000\n1961-07-25,65.230003,65.230003,65.230003,65.230003,65.230003,3010000\n1961-07-26,65.839996,65.839996,65.839996,65.839996,65.839996,4070000\n1961-07-27,66.610001,66.610001,66.610001,66.610001,66.610001,4170000\n1961-07-28,66.709999,66.709999,66.709999,66.709999,66.709999,3610000\n1961-07-31,66.760002,66.760002,66.760002,66.760002,66.760002,3170000\n1961-08-01,67.370003,67.370003,67.370003,67.370003,67.370003,3990000\n1961-08-02,66.940002,66.940002,66.940002,66.940002,66.940002,4300000\n1961-08-03,67.290001,67.290001,67.290001,67.290001,67.290001,3650000\n1961-08-04,67.680000,67.680000,67.680000,67.680000,67.680000,3710000\n1961-08-07,67.669998,67.669998,67.669998,67.669998,67.669998,3560000\n1961-08-08,67.820000,67.820000,67.820000,67.820000,67.820000,4050000\n1961-08-09,67.739998,67.739998,67.739998,67.739998,67.739998,3710000\n1961-08-10,67.949997,67.949997,67.949997,67.949997,67.949997,3570000\n1961-08-11,68.059998,68.059998,68.059998,68.059998,68.059998,3260000\n1961-08-14,67.720001,67.720001,67.720001,67.720001,67.720001,3120000\n1961-08-15,67.550003,67.550003,67.550003,67.550003,67.550003,3320000\n1961-08-16,67.730003,67.730003,67.730003,67.730003,67.730003,3430000\n1961-08-17,68.110001,68.110001,68.110001,68.110001,68.110001,4130000\n1961-08-18,68.290001,68.290001,68.290001,68.290001,68.290001,4030000\n1961-08-21,68.430000,68.430000,68.430000,68.430000,68.430000,3880000\n1961-08-22,68.440002,68.440002,68.440002,68.440002,68.440002,3640000\n1961-08-23,67.980003,67.980003,67.980003,67.980003,67.980003,3550000\n1961-08-24,67.589996,67.589996,67.589996,67.589996,67.589996,3090000\n1961-08-25,67.669998,67.669998,67.669998,67.669998,67.669998,3050000\n1961-08-28,67.699997,67.699997,67.699997,67.699997,67.699997,3150000\n1961-08-29,67.550003,67.550003,67.550003,67.550003,67.550003,3160000\n1961-08-30,67.809998,67.809998,67.809998,67.809998,67.809998,3220000\n1961-08-31,68.070000,68.070000,68.070000,68.070000,68.070000,2920000\n1961-09-01,68.190002,68.190002,68.190002,68.190002,68.190002,2710000\n1961-09-05,67.959999,67.959999,67.959999,67.959999,67.959999,3000000\n1961-09-06,68.459999,68.459999,68.459999,68.459999,68.459999,3440000\n1961-09-07,68.349998,68.349998,68.349998,68.349998,68.349998,3900000\n1961-09-08,67.879997,67.879997,67.879997,67.879997,67.879997,3430000\n1961-09-11,67.279999,67.279999,67.279999,67.279999,67.279999,2790000\n1961-09-12,67.959999,67.959999,67.959999,67.959999,67.959999,2950000\n1961-09-13,68.010002,68.010002,68.010002,68.010002,68.010002,3110000\n1961-09-14,67.529999,67.529999,67.529999,67.529999,67.529999,2920000\n1961-09-15,67.650002,67.650002,67.650002,67.650002,67.650002,3130000\n1961-09-18,67.209999,67.209999,67.209999,67.209999,67.209999,3550000\n1961-09-19,66.080002,66.080002,66.080002,66.080002,66.080002,3260000\n1961-09-20,66.959999,66.959999,66.959999,66.959999,66.959999,2700000\n1961-09-21,66.989998,66.989998,66.989998,66.989998,66.989998,3340000\n1961-09-22,66.720001,66.720001,66.720001,66.720001,66.720001,3070000\n1961-09-25,65.769997,65.769997,65.769997,65.769997,65.769997,3700000\n1961-09-26,65.779999,65.779999,65.779999,65.779999,65.779999,3320000\n1961-09-27,66.470001,66.470001,66.470001,66.470001,66.470001,3440000\n1961-09-28,66.580002,66.580002,66.580002,66.580002,66.580002,3000000\n1961-09-29,66.730003,66.730003,66.730003,66.730003,66.730003,3060000\n1961-10-02,66.769997,66.769997,66.769997,66.769997,66.769997,2800000\n1961-10-03,66.730003,66.730003,66.730003,66.730003,66.730003,2680000\n1961-10-04,67.180000,67.180000,67.180000,67.180000,67.180000,3380000\n1961-10-05,67.769997,67.769997,67.769997,67.769997,67.769997,3920000\n1961-10-06,66.970001,66.970001,66.970001,66.970001,66.970001,3470000\n1961-10-09,67.940002,67.940002,67.940002,67.940002,67.940002,2920000\n1961-10-10,68.110001,68.110001,68.110001,68.110001,68.110001,3430000\n1961-10-11,68.169998,68.169998,68.169998,68.169998,68.169998,3670000\n1961-10-12,68.160004,68.160004,68.160004,68.160004,68.160004,3060000\n1961-10-13,68.040001,68.040001,68.040001,68.040001,68.040001,3090000\n1961-10-16,67.849998,67.849998,67.849998,67.849998,67.849998,2840000\n1961-10-17,67.870003,67.870003,67.870003,67.870003,67.870003,3110000\n1961-10-18,68.209999,68.209999,68.209999,68.209999,68.209999,3520000\n1961-10-19,68.449997,68.449997,68.449997,68.449997,68.449997,3850000\n1961-10-20,68.000000,68.000000,68.000000,68.000000,68.000000,3470000\n1961-10-23,68.059998,68.059998,68.059998,68.059998,68.059998,3440000\n1961-10-24,67.980003,67.980003,67.980003,67.980003,67.980003,3430000\n1961-10-25,68.339996,68.339996,68.339996,68.339996,68.339996,3590000\n1961-10-26,68.459999,68.459999,68.459999,68.459999,68.459999,3330000\n1961-10-27,68.339996,68.339996,68.339996,68.339996,68.339996,3200000\n1961-10-30,68.419998,68.419998,68.419998,68.419998,68.419998,3430000\n1961-10-31,68.620003,68.620003,68.620003,68.620003,68.620003,3350000\n1961-11-01,68.730003,68.730003,68.730003,68.730003,68.730003,3210000\n1961-11-02,69.110001,69.110001,69.110001,69.110001,69.110001,3890000\n1961-11-03,69.470001,69.470001,69.470001,69.470001,69.470001,4070000\n1961-11-06,70.010002,70.010002,70.010002,70.010002,70.010002,4340000\n1961-11-08,70.870003,70.870003,70.870003,70.870003,70.870003,6090000\n1961-11-09,70.769997,70.769997,70.769997,70.769997,70.769997,4680000\n1961-11-10,71.070000,71.070000,71.070000,71.070000,71.070000,4180000\n1961-11-13,71.269997,71.269997,71.269997,71.269997,71.269997,4540000\n1961-11-14,71.660004,71.660004,71.660004,71.660004,71.660004,4750000\n1961-11-15,71.669998,71.669998,71.669998,71.669998,71.669998,4660000\n1961-11-16,71.620003,71.620003,71.620003,71.620003,71.620003,3980000\n1961-11-17,71.620003,71.620003,71.620003,71.620003,71.620003,3960000\n1961-11-20,71.720001,71.720001,71.720001,71.720001,71.720001,4190000\n1961-11-21,71.779999,71.779999,71.779999,71.779999,71.779999,4890000\n1961-11-22,71.699997,71.699997,71.699997,71.699997,71.699997,4500000\n1961-11-24,71.839996,71.839996,71.839996,71.839996,71.839996,4020000\n1961-11-27,71.849998,71.849998,71.849998,71.849998,71.849998,4700000\n1961-11-28,71.750000,71.750000,71.750000,71.750000,71.750000,4360000\n1961-11-29,71.699997,71.699997,71.699997,71.699997,71.699997,4550000\n1961-11-30,71.320000,71.320000,71.320000,71.320000,71.320000,4210000\n1961-12-01,71.779999,71.779999,71.779999,71.779999,71.779999,4420000\n1961-12-04,72.010002,72.010002,72.010002,72.010002,72.010002,4560000\n1961-12-05,71.930000,71.930000,71.930000,71.930000,71.930000,4330000\n1961-12-06,71.989998,71.989998,71.989998,71.989998,71.989998,4200000\n1961-12-07,71.699997,71.699997,71.699997,71.699997,71.699997,3900000\n1961-12-08,72.040001,72.040001,72.040001,72.040001,72.040001,4010000\n1961-12-11,72.389999,72.389999,72.389999,72.389999,72.389999,4360000\n1961-12-12,72.639999,72.639999,72.639999,72.639999,72.639999,4680000\n1961-12-13,72.529999,72.529999,72.529999,72.529999,72.529999,4890000\n1961-12-14,71.980003,71.980003,71.980003,71.980003,71.980003,4350000\n1961-12-15,72.010002,72.010002,72.010002,72.010002,72.010002,3710000\n1961-12-18,71.760002,71.760002,71.760002,71.760002,71.760002,3810000\n1961-12-19,71.260002,71.260002,71.260002,71.260002,71.260002,3440000\n1961-12-20,71.120003,71.120003,71.120003,71.120003,71.120003,3640000\n1961-12-21,70.860001,70.860001,70.860001,70.860001,70.860001,3440000\n1961-12-22,70.910004,70.910004,70.910004,70.910004,70.910004,3390000\n1961-12-26,71.019997,71.019997,71.019997,71.019997,71.019997,3180000\n1961-12-27,71.650002,71.650002,71.650002,71.650002,71.650002,4170000\n1961-12-28,71.690002,71.690002,71.690002,71.690002,71.690002,4530000\n1961-12-29,71.550003,71.550003,71.550003,71.550003,71.550003,5370000\n1962-01-02,71.550003,71.959999,70.709999,70.959999,70.959999,3120000\n1962-01-03,70.959999,71.480003,70.379997,71.129997,71.129997,3590000\n1962-01-04,71.129997,71.620003,70.449997,70.639999,70.639999,4450000\n1962-01-05,70.639999,70.839996,69.349998,69.660004,69.660004,4630000\n1962-01-08,69.660004,69.839996,68.169998,69.120003,69.120003,4620000\n1962-01-09,69.120003,69.930000,68.830002,69.150002,69.150002,3600000\n1962-01-10,69.150002,69.580002,68.620003,68.959999,68.959999,3300000\n1962-01-11,68.959999,69.540001,68.570000,69.370003,69.370003,3390000\n1962-01-12,69.370003,70.169998,69.230003,69.610001,69.610001,3730000\n1962-01-15,69.610001,69.959999,69.059998,69.470001,69.470001,3450000\n1962-01-16,69.470001,69.610001,68.680000,69.070000,69.070000,3650000\n1962-01-17,69.070000,69.309998,68.129997,68.320000,68.320000,3780000\n1962-01-18,68.320000,68.730003,67.750000,68.389999,68.389999,3460000\n1962-01-19,68.389999,70.080002,68.139999,68.750000,68.750000,3800000\n1962-01-22,68.750000,69.370003,68.449997,68.809998,68.809998,3810000\n1962-01-23,68.809998,68.959999,68.000000,68.290001,68.290001,3350000\n1962-01-24,68.290001,68.680000,67.550003,68.400002,68.400002,3760000\n1962-01-25,68.400002,69.050003,68.099998,68.349998,68.349998,3560000\n1962-01-26,68.349998,68.669998,67.830002,68.129997,68.129997,3330000\n1962-01-29,68.129997,68.500000,67.550003,67.900002,67.900002,3050000\n1962-01-30,67.900002,68.650002,67.620003,68.169998,68.169998,3520000\n1962-01-31,68.169998,69.089996,68.120003,68.839996,68.839996,3840000\n1962-02-01,68.839996,69.650002,68.559998,69.260002,69.260002,4260000\n1962-02-02,69.260002,70.019997,69.019997,69.809998,69.809998,3950000\n1962-02-05,69.809998,70.300003,69.419998,69.879997,69.879997,3890000\n1962-02-06,69.879997,70.320000,69.410004,69.959999,69.959999,3650000\n1962-02-07,69.959999,70.669998,69.779999,70.419998,70.419998,4140000\n1962-02-08,70.419998,70.949997,70.160004,70.580002,70.580002,3810000\n1962-02-09,70.580002,70.830002,69.930000,70.480003,70.480003,3370000\n1962-02-12,70.480003,70.809998,70.139999,70.459999,70.459999,2620000\n1962-02-13,70.459999,70.889999,70.070000,70.449997,70.449997,3400000\n1962-02-14,70.449997,70.790001,70.029999,70.419998,70.419998,3630000\n1962-02-15,70.419998,71.059998,70.230003,70.739998,70.739998,3470000\n1962-02-16,70.739998,71.129997,70.269997,70.589996,70.589996,3700000\n1962-02-19,70.589996,70.959999,70.120003,70.410004,70.410004,3350000\n1962-02-20,70.410004,70.910004,70.129997,70.660004,70.660004,3300000\n1962-02-21,70.660004,70.970001,70.120003,70.320000,70.320000,3310000\n1962-02-23,70.320000,70.570000,69.730003,70.160004,70.160004,3230000\n1962-02-26,70.160004,70.330002,69.440002,69.760002,69.760002,2910000\n1962-02-27,69.760002,70.320000,69.480003,69.889999,69.889999,3110000\n1962-02-28,69.889999,70.419998,69.570000,69.959999,69.959999,3030000\n1962-03-01,69.959999,70.599998,69.760002,70.199997,70.199997,2960000\n1962-03-02,70.160004,70.160004,69.750000,70.160004,70.160004,2980000\n1962-03-05,70.160004,70.480003,69.650002,70.010002,70.010002,3020000\n1962-03-06,70.010002,70.239998,69.459999,69.779999,69.779999,2870000\n1962-03-07,69.779999,70.070000,69.370003,69.690002,69.690002,2890000\n1962-03-08,69.690002,70.370003,69.400002,70.190002,70.190002,3210000\n1962-03-09,70.190002,70.709999,70.000000,70.419998,70.419998,3340000\n1962-03-12,70.419998,70.760002,70.019997,70.400002,70.400002,3280000\n1962-03-13,70.400002,70.860001,70.059998,70.599998,70.599998,3200000\n1962-03-14,70.599998,71.250000,70.480003,70.910004,70.910004,3670000\n1962-03-15,70.910004,71.440002,70.589996,71.059998,71.059998,3250000\n1962-03-16,71.059998,71.339996,70.669998,70.940002,70.940002,3060000\n1962-03-19,70.940002,71.309998,70.529999,70.849998,70.849998,3220000\n1962-03-20,70.849998,71.080002,70.400002,70.660004,70.660004,3060000\n1962-03-21,70.660004,70.930000,70.160004,70.510002,70.510002,3360000\n1962-03-22,70.510002,70.839996,70.139999,70.400002,70.400002,3130000\n1962-03-23,70.400002,70.779999,70.120003,70.449997,70.449997,3050000\n1962-03-26,70.449997,70.629997,69.730003,69.889999,69.889999,3040000\n1962-03-27,69.889999,70.199997,69.410004,69.699997,69.699997,3090000\n1962-03-28,69.699997,70.330002,69.540001,70.040001,70.040001,2940000\n1962-03-29,70.040001,70.500000,69.809998,70.010002,70.010002,2870000\n1962-03-30,70.010002,70.089996,69.160004,69.550003,69.550003,2950000\n1962-04-02,69.550003,69.820000,69.129997,69.370003,69.370003,2790000\n1962-04-03,69.370003,69.529999,68.529999,68.809998,68.809998,3350000\n1962-04-04,68.809998,69.220001,68.330002,68.489998,68.489998,3290000\n1962-04-05,68.489998,69.089996,68.120003,68.910004,68.910004,3130000\n1962-04-06,68.910004,69.419998,68.580002,68.839996,68.839996,2730000\n1962-04-09,68.839996,69.019997,68.089996,68.309998,68.309998,3020000\n1962-04-10,68.309998,68.800003,67.940002,68.559998,68.559998,2880000\n1962-04-11,68.559998,69.260002,68.239998,68.410004,68.410004,3240000\n1962-04-12,68.410004,68.430000,67.470001,67.900002,67.900002,3320000\n1962-04-13,67.900002,68.110001,67.029999,67.900002,67.900002,3470000\n1962-04-16,67.900002,68.190002,67.209999,67.599998,67.599998,3070000\n1962-04-17,67.599998,68.199997,67.239998,67.900002,67.900002,2940000\n1962-04-18,67.900002,68.720001,67.830002,68.269997,68.269997,3350000\n1962-04-19,68.269997,68.900002,68.070000,68.589996,68.589996,3100000\n1962-04-23,68.589996,69.010002,68.169998,68.529999,68.529999,3240000\n1962-04-24,68.529999,68.910004,68.160004,68.459999,68.459999,3040000\n1962-04-25,68.459999,68.580002,67.529999,67.709999,67.709999,3340000\n1962-04-26,67.709999,67.970001,66.919998,67.050003,67.050003,3650000\n1962-04-27,67.050003,67.610001,65.989998,66.300003,66.300003,4140000\n1962-04-30,66.300003,66.900002,64.949997,65.239998,65.239998,4150000\n1962-05-01,65.239998,65.940002,63.759998,65.699997,65.699997,5100000\n1962-05-02,65.699997,66.669998,65.559998,65.989998,65.989998,3780000\n1962-05-03,65.989998,66.930000,65.809998,66.529999,66.529999,3320000\n1962-05-04,66.529999,66.800003,65.800003,66.239998,66.239998,3010000\n1962-05-07,66.239998,66.559998,65.660004,66.019997,66.019997,2530000\n1962-05-08,66.019997,66.129997,64.879997,65.169998,65.169998,3020000\n1962-05-09,65.169998,65.169998,64.019997,64.260002,64.260002,3670000\n1962-05-10,64.260002,64.389999,62.990002,63.570000,63.570000,4730000\n1962-05-11,63.570000,64.099998,62.439999,62.650002,62.650002,4510000\n1962-05-14,62.650002,63.310001,61.110001,63.099998,63.099998,5990000\n1962-05-15,63.410000,64.870003,63.410000,64.290001,64.290001,4780000\n1962-05-16,64.290001,64.879997,63.820000,64.269997,64.269997,3360000\n1962-05-17,64.269997,64.410004,63.380001,63.930000,63.930000,2950000\n1962-05-18,63.930000,64.139999,63.290001,63.820000,63.820000,2490000\n1962-05-21,63.820000,64.000000,63.209999,63.590000,63.590000,2260000\n1962-05-22,63.590000,63.689999,62.259998,62.340000,62.340000,3640000\n1962-05-23,62.340000,62.419998,60.900002,61.110001,61.110001,5450000\n1962-05-24,61.110001,61.790001,60.360001,60.619999,60.619999,5250000\n1962-05-25,60.619999,60.980000,59.000000,59.470001,59.470001,6380000\n1962-05-28,59.150002,59.150002,55.419998,55.500000,55.500000,9350000\n1962-05-29,55.500000,58.290001,53.130001,58.080002,58.080002,14750000\n1962-05-31,58.799999,60.820000,58.799999,59.630001,59.630001,10710000\n1962-06-01,59.630001,59.959999,58.520000,59.380001,59.380001,5760000\n1962-06-04,59.119999,59.119999,57.139999,57.270000,57.270000,5380000\n1962-06-05,57.270000,58.419998,56.330002,57.570000,57.570000,6140000\n1962-06-06,57.639999,59.169998,57.639999,58.389999,58.389999,4190000\n1962-06-07,58.389999,58.900002,58.000000,58.400002,58.400002,2760000\n1962-06-08,58.400002,58.970001,58.139999,58.450001,58.450001,2560000\n1962-06-11,58.450001,58.580002,57.509998,57.820000,57.820000,2870000\n1962-06-12,57.660000,57.660000,56.230000,56.340000,56.340000,4690000\n1962-06-13,56.340000,56.799999,55.240002,55.500000,55.500000,5850000\n1962-06-14,55.500000,56.000000,54.119999,54.330002,54.330002,6240000\n1962-06-15,54.330002,55.959999,53.660000,55.889999,55.889999,7130000\n1962-06-18,55.889999,56.529999,54.970001,55.740002,55.740002,4580000\n1962-06-19,55.740002,55.880001,54.980000,55.540001,55.540001,2680000\n1962-06-20,55.540001,55.919998,54.660000,54.779999,54.779999,3360000\n1962-06-21,54.779999,54.779999,53.500000,53.590000,53.590000,4560000\n1962-06-22,53.590000,53.779999,52.480000,52.680000,52.680000,5640000\n1962-06-25,52.680000,52.959999,51.349998,52.450001,52.450001,7090000\n1962-06-26,52.450001,53.580002,52.099998,52.320000,52.320000,4630000\n1962-06-27,52.320000,52.830002,51.770000,52.599998,52.599998,3890000\n1962-06-28,52.980000,54.639999,52.980000,54.410000,54.410000,5440000\n1962-06-29,54.410000,55.470001,54.200001,54.750000,54.750000,4720000\n1962-07-02,54.750000,56.020000,54.470001,55.860001,55.860001,3450000\n1962-07-03,55.860001,56.740002,55.570000,56.490002,56.490002,3920000\n1962-07-05,56.490002,57.099998,56.150002,56.810001,56.810001,3350000\n1962-07-06,56.730000,56.730000,55.639999,56.169998,56.169998,3110000\n1962-07-09,56.169998,56.730000,55.540001,56.549999,56.549999,2950000\n1962-07-10,56.990002,58.360001,56.990002,57.200001,57.200001,7120000\n1962-07-11,57.200001,57.950001,56.770000,57.730000,57.730000,4250000\n1962-07-12,57.730000,58.669998,57.590000,58.029999,58.029999,5370000\n1962-07-13,58.029999,58.180000,57.230000,57.830002,57.830002,3380000\n1962-07-16,57.830002,58.099998,57.180000,57.830002,57.830002,3130000\n1962-07-17,57.830002,57.959999,56.680000,56.779999,56.779999,3500000\n1962-07-18,56.779999,56.810001,55.860001,56.200001,56.200001,3620000\n1962-07-19,56.200001,56.950001,55.959999,56.419998,56.419998,3090000\n1962-07-20,56.419998,57.090000,56.270000,56.810001,56.810001,2610000\n1962-07-23,56.810001,57.320000,56.529999,56.799999,56.799999,2770000\n1962-07-24,56.799999,56.930000,56.139999,56.360001,56.360001,2560000\n1962-07-25,56.360001,56.669998,55.779999,56.459999,56.459999,2910000\n1962-07-26,56.459999,57.180000,56.160000,56.770000,56.770000,2790000\n1962-07-27,56.770000,57.360001,56.560001,57.200001,57.200001,2890000\n1962-07-30,57.200001,57.980000,57.080002,57.830002,57.830002,3200000\n1962-07-31,57.830002,58.580002,57.740002,58.230000,58.230000,4190000\n1962-08-01,58.230000,58.299999,57.509998,57.750000,57.750000,3100000\n1962-08-02,57.750000,58.200001,57.380001,57.980000,57.980000,3410000\n1962-08-03,57.980000,58.320000,57.630001,58.119999,58.119999,5990000\n1962-08-06,58.119999,58.349998,57.540001,57.750000,57.750000,3110000\n1962-08-07,57.750000,57.810001,57.070000,57.360001,57.360001,2970000\n1962-08-08,57.360001,57.639999,56.759998,57.509998,57.509998,3080000\n1962-08-09,57.509998,57.880001,57.189999,57.570000,57.570000,2670000\n1962-08-10,57.570000,57.849998,57.160000,57.549999,57.549999,2470000\n1962-08-13,57.549999,57.900002,57.220001,57.630001,57.630001,2670000\n1962-08-14,57.630001,58.430000,57.410000,58.250000,58.250000,3640000\n1962-08-15,58.250000,59.110001,58.220001,58.660000,58.660000,4880000\n1962-08-16,58.660000,59.110001,58.240002,58.639999,58.639999,4180000\n1962-08-17,58.639999,59.240002,58.430000,59.009998,59.009998,3430000\n1962-08-20,59.009998,59.720001,58.900002,59.369999,59.369999,4580000\n1962-08-21,59.369999,59.660000,58.900002,59.119999,59.119999,3730000\n1962-08-22,59.119999,59.930000,58.910000,59.779999,59.779999,4520000\n1962-08-23,59.779999,60.330002,59.470001,59.700001,59.700001,4770000\n1962-08-24,59.700001,59.919998,59.180000,59.580002,59.580002,2890000\n1962-08-27,59.580002,59.939999,59.240002,59.549999,59.549999,3140000\n1962-08-28,59.549999,59.610001,58.660000,58.790001,58.790001,3180000\n1962-08-29,58.790001,58.959999,58.169998,58.660000,58.660000,2900000\n1962-08-30,58.660000,59.060001,58.389999,58.680000,58.680000,2260000\n1962-08-31,58.680000,59.250000,58.450001,59.119999,59.119999,2830000\n1962-09-04,59.119999,59.490002,58.439999,58.560001,58.560001,2970000\n1962-09-05,58.560001,58.770000,57.950001,58.119999,58.119999,3050000\n1962-09-06,58.119999,58.599998,57.720001,58.360001,58.360001,3180000\n1962-09-07,58.360001,58.900002,58.090000,58.380001,58.380001,2890000\n1962-09-10,58.380001,58.639999,57.880001,58.450001,58.450001,2520000\n1962-09-11,58.450001,58.930000,58.169998,58.590000,58.590000,3040000\n1962-09-12,58.590000,59.060001,58.400002,58.840000,58.840000,3100000\n1962-09-13,58.840000,59.180000,58.459999,58.700001,58.700001,3100000\n1962-09-14,58.700001,59.139999,58.400002,58.889999,58.889999,2880000\n1962-09-17,58.889999,59.419998,58.650002,59.080002,59.080002,3330000\n1962-09-18,59.080002,59.540001,58.770000,59.029999,59.029999,3690000\n1962-09-19,59.029999,59.259998,58.590000,58.950001,58.950001,2950000\n1962-09-20,58.950001,59.290001,58.330002,58.540001,58.540001,3350000\n1962-09-21,58.540001,58.639999,57.430000,57.689999,57.689999,4280000\n1962-09-24,57.450001,57.450001,56.299999,56.630001,56.630001,5000000\n1962-09-25,56.630001,57.220001,56.119999,56.959999,56.959999,3620000\n1962-09-26,56.959999,57.290001,55.919998,56.150002,56.150002,3550000\n1962-09-27,56.150002,56.549999,55.529999,55.770000,55.770000,3540000\n1962-09-28,55.770000,56.580002,55.590000,56.270000,56.270000,2850000\n1962-10-01,56.270000,56.310001,55.259998,55.490002,55.490002,3090000\n1962-10-02,55.490002,56.459999,55.310001,56.099998,56.099998,3000000\n1962-10-03,56.099998,56.709999,55.840000,56.160000,56.160000,2610000\n1962-10-04,56.160000,56.840000,55.900002,56.700001,56.700001,2530000\n1962-10-05,56.700001,57.299999,56.549999,57.070000,57.070000,2730000\n1962-10-08,57.070000,57.410000,56.680000,57.070000,57.070000,1950000\n1962-10-09,57.070000,57.400002,56.709999,57.200001,57.200001,2340000\n1962-10-10,57.200001,57.830002,56.959999,57.240002,57.240002,3040000\n1962-10-11,57.240002,57.459999,56.779999,57.049999,57.049999,2460000\n1962-10-12,57.049999,57.209999,56.660000,56.950001,56.950001,2020000\n1962-10-15,56.950001,57.500000,56.660000,57.270000,57.270000,2640000\n1962-10-16,57.270000,57.630001,56.869999,57.080002,57.080002,2860000\n1962-10-17,57.080002,57.230000,56.369999,56.889999,56.889999,3240000\n1962-10-18,56.889999,57.020000,56.180000,56.340000,56.340000,3280000\n1962-10-19,56.340000,56.540001,55.340000,55.590000,55.590000,4650000\n1962-10-22,55.480000,55.480000,54.380001,54.959999,54.959999,5690000\n1962-10-23,54.959999,55.189999,53.240002,53.490002,53.490002,6110000\n1962-10-24,53.490002,55.439999,52.549999,55.209999,55.209999,6720000\n1962-10-25,55.169998,55.169998,53.820000,54.689999,54.689999,3950000\n1962-10-26,54.689999,54.959999,54.080002,54.540001,54.540001,2580000\n1962-10-29,55.340000,56.380001,55.340000,55.720001,55.720001,4280000\n1962-10-30,55.720001,56.840000,55.520000,56.540001,56.540001,3830000\n1962-10-31,56.540001,57.000000,56.189999,56.520000,56.520000,3090000\n1962-11-01,56.520000,57.310001,55.900002,57.119999,57.119999,3400000\n1962-11-02,57.119999,58.189999,56.779999,57.750000,57.750000,5470000\n1962-11-05,57.750000,58.700001,57.689999,58.349998,58.349998,4320000\n1962-11-07,58.349998,59.110001,57.759998,58.709999,58.709999,4580000\n1962-11-08,58.709999,59.119999,58.090000,58.320000,58.320000,4160000\n1962-11-09,58.320000,58.990002,57.900002,58.779999,58.779999,4340000\n1962-11-12,58.779999,60.000000,58.590000,59.590000,59.590000,5090000\n1962-11-13,59.590000,60.060001,59.060001,59.459999,59.459999,4550000\n1962-11-14,59.459999,60.410000,59.180000,60.160000,60.160000,5090000\n1962-11-15,60.160000,60.669998,59.740002,59.970001,59.970001,5050000\n1962-11-16,59.970001,60.459999,59.459999,60.160000,60.160000,4000000\n1962-11-19,60.160000,60.419998,59.459999,59.820000,59.820000,3410000\n1962-11-20,59.820000,60.630001,59.570000,60.450001,60.450001,4290000\n1962-11-21,60.450001,61.180000,60.189999,60.810001,60.810001,5100000\n1962-11-23,60.810001,62.029999,60.660000,61.540001,61.540001,5660000\n1962-11-26,61.540001,62.130001,60.950001,61.360001,61.360001,5650000\n1962-11-27,61.360001,62.040001,60.980000,61.730000,61.730000,5500000\n1962-11-28,61.730000,62.480000,61.509998,62.119999,62.119999,5980000\n1962-11-29,62.119999,62.720001,61.689999,62.410000,62.410000,5810000\n1962-11-30,62.410000,62.779999,61.779999,62.259998,62.259998,4570000\n1962-12-03,62.259998,62.450001,61.279999,61.939999,61.939999,3810000\n1962-12-04,61.939999,62.930000,61.770000,62.639999,62.639999,5210000\n1962-12-05,62.639999,63.500000,62.369999,62.389999,62.389999,6280000\n1962-12-06,62.389999,63.360001,62.279999,62.930000,62.930000,4600000\n1962-12-07,62.930000,63.430000,62.450001,63.060001,63.060001,3900000\n1962-12-10,63.060001,63.349998,61.959999,62.270000,62.270000,4270000\n1962-12-11,62.270000,62.580002,61.720001,62.320000,62.320000,3700000\n1962-12-12,62.320000,63.160000,62.130001,62.630001,62.630001,3760000\n1962-12-13,62.630001,63.070000,62.090000,62.419998,62.419998,3380000\n1962-12-14,62.419998,62.830002,61.959999,62.570000,62.570000,3280000\n1962-12-17,62.570000,62.950001,62.139999,62.369999,62.369999,3590000\n1962-12-18,62.369999,62.660000,61.779999,62.070000,62.070000,3620000\n1962-12-19,62.070000,62.810001,61.720001,62.580002,62.580002,4000000\n1962-12-20,62.580002,63.279999,62.439999,62.820000,62.820000,4220000\n1962-12-21,62.820000,63.130001,62.259998,62.639999,62.639999,3470000\n1962-12-24,62.639999,63.029999,62.189999,62.630001,62.630001,3180000\n1962-12-26,62.630001,63.320000,62.560001,63.020000,63.020000,3370000\n1962-12-27,63.020000,63.410000,62.669998,62.930000,62.930000,3670000\n1962-12-28,62.930000,63.250000,62.529999,62.959999,62.959999,4140000\n1962-12-31,62.959999,63.430000,62.680000,63.099998,63.099998,5420000\n1963-01-02,63.099998,63.389999,62.320000,62.689999,62.689999,2540000\n1963-01-03,62.689999,63.889999,62.669998,63.720001,63.720001,4570000\n1963-01-04,63.720001,64.449997,63.570000,64.129997,64.129997,5400000\n1963-01-07,64.129997,64.589996,63.669998,64.120003,64.120003,4440000\n1963-01-08,64.120003,64.980003,64.000000,64.739998,64.739998,5410000\n1963-01-09,64.739998,65.220001,64.320000,64.589996,64.589996,5110000\n1963-01-10,64.589996,65.160004,64.330002,64.709999,64.709999,4520000\n1963-01-11,64.709999,65.099998,64.309998,64.849998,64.849998,4410000\n1963-01-14,64.849998,65.500000,64.610001,65.199997,65.199997,5000000\n1963-01-15,65.199997,65.620003,64.820000,65.110001,65.110001,5930000\n1963-01-16,65.110001,65.250000,64.419998,64.669998,64.669998,4260000\n1963-01-17,64.669998,65.400002,64.349998,65.129997,65.129997,5230000\n1963-01-18,65.129997,65.699997,64.860001,65.180000,65.180000,4760000\n1963-01-21,65.180000,65.519997,64.639999,65.279999,65.279999,4090000\n1963-01-22,65.279999,65.800003,65.029999,65.440002,65.440002,4810000\n1963-01-23,65.440002,65.910004,65.230003,65.620003,65.620003,4820000\n1963-01-24,65.620003,66.089996,65.330002,65.750000,65.750000,4810000\n1963-01-25,65.750000,66.230003,65.379997,65.919998,65.919998,4770000\n1963-01-28,65.919998,66.589996,65.769997,66.239998,66.239998,4720000\n1963-01-29,66.239998,66.580002,65.830002,66.230003,66.230003,4360000\n1963-01-30,66.230003,66.330002,65.550003,65.849998,65.849998,3740000\n1963-01-31,65.849998,66.449997,65.510002,66.199997,66.199997,4270000\n1963-02-01,66.309998,66.309998,66.309998,66.309998,66.309998,4280000\n1963-02-04,66.309998,66.660004,65.889999,66.169998,66.169998,3670000\n1963-02-05,66.169998,66.349998,65.379997,66.110001,66.110001,4050000\n1963-02-06,66.110001,66.760002,65.879997,66.400002,66.400002,4340000\n1963-02-07,66.400002,66.809998,65.910004,66.169998,66.169998,4240000\n1963-02-08,66.169998,66.449997,65.650002,66.169998,66.169998,3890000\n1963-02-11,66.169998,66.410004,65.500000,65.760002,65.760002,3880000\n1963-02-12,65.760002,66.010002,65.160004,65.830002,65.830002,3710000\n1963-02-13,65.830002,66.529999,65.559998,66.150002,66.150002,4960000\n1963-02-14,66.150002,66.750000,65.930000,66.349998,66.349998,5640000\n1963-02-15,66.349998,66.739998,65.959999,66.410004,66.410004,4410000\n1963-02-18,66.410004,66.959999,66.099998,66.519997,66.519997,4700000\n1963-02-19,66.519997,66.669998,65.919998,66.199997,66.199997,4130000\n1963-02-20,66.199997,66.279999,65.440002,65.830002,65.830002,4120000\n1963-02-21,65.830002,66.230003,65.360001,65.919998,65.919998,3980000\n1963-02-25,65.919998,66.089996,65.239998,65.459999,65.459999,3680000\n1963-02-26,65.459999,65.860001,65.059998,65.470001,65.470001,3670000\n1963-02-27,65.470001,65.739998,64.860001,65.010002,65.010002,3680000\n1963-02-28,65.010002,65.139999,64.080002,64.290001,64.290001,4090000\n1963-03-01,64.290001,64.750000,63.799999,64.099998,64.099998,3920000\n1963-03-04,64.099998,65.080002,63.880001,64.720001,64.720001,3650000\n1963-03-05,64.720001,65.269997,64.410004,64.739998,64.739998,3280000\n1963-03-06,64.739998,65.059998,64.309998,64.849998,64.849998,3100000\n1963-03-07,64.849998,65.599998,64.809998,65.260002,65.260002,3350000\n1963-03-08,65.260002,65.739998,65.029999,65.330002,65.330002,3360000\n1963-03-11,65.330002,65.860001,65.110001,65.510002,65.510002,3180000\n1963-03-12,65.510002,65.970001,65.260002,65.669998,65.669998,3350000\n1963-03-13,65.669998,66.269997,65.540001,65.910004,65.910004,4120000\n1963-03-14,65.910004,66.209999,65.389999,65.599998,65.599998,3540000\n1963-03-15,65.599998,66.220001,65.389999,65.930000,65.930000,3400000\n1963-03-18,65.930000,66.169998,65.360001,65.610001,65.610001,3250000\n1963-03-19,65.610001,65.849998,65.190002,65.470001,65.470001,3180000\n1963-03-20,65.470001,66.150002,65.300003,65.949997,65.949997,3690000\n1963-03-21,65.949997,66.250000,65.599998,65.849998,65.849998,3220000\n1963-03-22,65.849998,66.440002,65.680000,66.190002,66.190002,3820000\n1963-03-25,66.190002,66.599998,65.919998,66.209999,66.209999,3700000\n1963-03-26,66.209999,66.730003,66.010002,66.400002,66.400002,4100000\n1963-03-27,66.400002,66.930000,66.209999,66.680000,66.680000,4270000\n1963-03-28,66.680000,67.010002,66.320000,66.580002,66.580002,3890000\n1963-03-29,66.580002,66.900002,66.230003,66.570000,66.570000,3390000\n1963-04-01,66.570000,67.180000,66.230003,66.849998,66.849998,3890000\n1963-04-02,66.849998,67.360001,66.510002,66.839996,66.839996,4360000\n1963-04-03,66.839996,67.550003,66.629997,67.360001,67.360001,4660000\n1963-04-04,67.360001,68.120003,67.279999,67.849998,67.849998,5300000\n1963-04-05,67.849998,68.459999,67.459999,68.279999,68.279999,5240000\n1963-04-08,68.279999,68.910004,68.050003,68.519997,68.519997,5940000\n1963-04-09,68.519997,68.839996,68.029999,68.449997,68.449997,5090000\n1963-04-10,68.449997,68.889999,67.660004,68.290001,68.290001,5880000\n1963-04-11,68.290001,69.070000,67.970001,68.769997,68.769997,5250000\n1963-04-15,68.769997,69.559998,68.580002,69.089996,69.089996,5930000\n1963-04-16,69.089996,69.610001,68.660004,69.139999,69.139999,5570000\n1963-04-17,69.139999,69.370003,68.470001,68.919998,68.919998,5220000\n1963-04-18,68.919998,69.339996,68.559998,68.889999,68.889999,4770000\n1963-04-19,68.889999,69.459999,68.599998,69.230003,69.230003,4660000\n1963-04-22,69.230003,69.820000,69.010002,69.300003,69.300003,5180000\n1963-04-23,69.300003,69.830002,68.949997,69.529999,69.529999,5220000\n1963-04-24,69.529999,70.120003,69.339996,69.720001,69.720001,5910000\n1963-04-25,69.720001,70.080002,69.250000,69.760002,69.760002,5070000\n1963-04-26,69.760002,70.110001,69.230003,69.699997,69.699997,4490000\n1963-04-29,69.699997,70.040001,69.260002,69.650002,69.650002,3980000\n1963-04-30,69.650002,70.180000,69.260002,69.800003,69.800003,4680000\n1963-05-01,69.800003,70.430000,69.610001,69.970001,69.970001,5060000\n1963-05-02,69.970001,70.500000,69.750000,70.169998,70.169998,4480000\n1963-05-03,70.169998,70.510002,69.779999,70.029999,70.029999,4760000\n1963-05-06,70.029999,70.309998,69.320000,69.529999,69.529999,4090000\n1963-05-07,69.529999,69.919998,69.029999,69.440002,69.440002,4140000\n1963-05-08,69.440002,70.239998,69.230003,70.010002,70.010002,5140000\n1963-05-09,70.010002,70.739998,69.860001,70.349998,70.349998,5600000\n1963-05-10,70.349998,70.809998,69.989998,70.519997,70.519997,5260000\n1963-05-13,70.519997,70.889999,70.110001,70.480003,70.480003,4920000\n1963-05-14,70.480003,70.730003,69.919998,70.209999,70.209999,4740000\n1963-05-15,70.209999,70.769997,69.870003,70.430000,70.430000,5650000\n1963-05-16,70.430000,70.809998,69.910004,70.250000,70.250000,5640000\n1963-05-17,70.250000,70.629997,69.830002,70.290001,70.290001,4410000\n1963-05-20,70.290001,70.480003,69.589996,69.959999,69.959999,4710000\n1963-05-21,69.959999,70.510002,69.620003,70.139999,70.139999,5570000\n1963-05-22,70.139999,70.680000,69.820000,70.139999,70.139999,5560000\n1963-05-23,70.139999,70.529999,69.790001,70.099998,70.099998,4400000\n1963-05-24,70.099998,70.440002,69.660004,70.019997,70.019997,4320000\n1963-05-27,70.019997,70.269997,69.480003,69.870003,69.870003,3760000\n1963-05-28,69.870003,70.410004,69.550003,70.010002,70.010002,3860000\n1963-05-29,70.010002,70.650002,69.860001,70.330002,70.330002,4320000\n1963-05-31,70.330002,71.139999,70.269997,70.800003,70.800003,4680000\n1963-06-03,70.800003,71.239998,70.389999,70.690002,70.690002,5400000\n1963-06-04,70.690002,71.080002,70.199997,70.699997,70.699997,5970000\n1963-06-05,70.699997,71.169998,70.169998,70.529999,70.529999,5860000\n1963-06-06,70.529999,70.949997,70.110001,70.580002,70.580002,4990000\n1963-06-07,70.580002,70.980003,70.099998,70.410004,70.410004,5110000\n1963-06-10,70.410004,70.510002,69.570000,69.940002,69.940002,4690000\n1963-06-11,69.940002,70.410004,69.580002,70.029999,70.029999,4390000\n1963-06-12,70.029999,70.809998,69.910004,70.410004,70.410004,5210000\n1963-06-13,70.410004,70.849998,69.980003,70.230003,70.230003,4690000\n1963-06-14,70.230003,70.599998,69.870003,70.250000,70.250000,3840000\n1963-06-17,69.949997,69.949997,69.949997,69.949997,69.949997,3510000\n1963-06-18,69.949997,70.430000,69.629997,70.019997,70.019997,3910000\n1963-06-19,70.019997,70.470001,69.750000,70.089996,70.089996,3970000\n1963-06-20,70.089996,70.360001,69.309998,70.010002,70.010002,4970000\n1963-06-21,70.010002,70.570000,69.790001,70.250000,70.250000,4190000\n1963-06-24,70.250000,70.669998,69.839996,70.199997,70.199997,3700000\n1963-06-25,70.199997,70.510002,69.750000,70.040001,70.040001,4120000\n1963-06-26,70.040001,70.099998,69.169998,69.410004,69.410004,4500000\n1963-06-27,69.410004,69.809998,68.779999,69.070000,69.070000,4540000\n1963-06-28,69.070000,69.680000,68.930000,69.370003,69.370003,3020000\n1963-07-01,69.370003,69.529999,68.580002,68.860001,68.860001,3360000\n1963-07-02,68.860001,69.720001,68.739998,69.459999,69.459999,3540000\n1963-07-03,69.459999,70.279999,69.419998,69.940002,69.940002,4030000\n1963-07-05,69.940002,70.480003,69.779999,70.220001,70.220001,2910000\n1963-07-08,70.220001,70.349998,69.470001,69.739998,69.739998,3290000\n1963-07-09,69.739998,70.389999,69.550003,70.040001,70.040001,3830000\n1963-07-10,70.040001,70.309998,69.559998,69.889999,69.889999,3730000\n1963-07-11,69.889999,70.300003,69.519997,69.760002,69.760002,4100000\n1963-07-12,69.760002,70.129997,69.360001,69.639999,69.639999,3660000\n1963-07-15,69.639999,69.730003,68.970001,69.199997,69.199997,3290000\n1963-07-16,69.199997,69.510002,68.849998,69.139999,69.139999,3000000\n1963-07-17,69.139999,69.529999,68.680000,68.930000,68.930000,3940000\n1963-07-18,68.930000,69.269997,68.339996,68.489998,68.489998,3710000\n1963-07-19,68.489998,68.699997,67.900002,68.349998,68.349998,3340000\n1963-07-22,68.349998,68.599998,67.540001,67.900002,67.900002,3700000\n1963-07-23,67.900002,68.570000,67.650002,67.910004,67.910004,3500000\n1963-07-24,67.910004,68.540001,67.760002,68.279999,68.279999,2810000\n1963-07-25,68.279999,68.919998,68.019997,68.260002,68.260002,3710000\n1963-07-26,68.260002,68.760002,68.029999,68.540001,68.540001,2510000\n1963-07-29,68.540001,68.959999,68.320000,68.669998,68.669998,2840000\n1963-07-30,68.669998,69.449997,68.580002,69.239998,69.239998,3550000\n1963-07-31,69.239998,69.830002,68.910004,69.129997,69.129997,3960000\n1963-08-01,69.129997,69.470001,68.639999,69.070000,69.070000,3410000\n1963-08-02,69.070000,69.559998,68.860001,69.300003,69.300003,2940000\n1963-08-05,69.300003,69.970001,69.199997,69.709999,69.709999,3370000\n1963-08-06,69.709999,70.400002,69.570000,70.169998,70.169998,3760000\n1963-08-07,70.169998,70.529999,69.690002,69.959999,69.959999,3790000\n1963-08-08,69.959999,70.309998,69.580002,70.019997,70.019997,3460000\n1963-08-09,70.019997,70.650002,69.830002,70.480003,70.480003,4050000\n1963-08-12,70.480003,71.000000,70.190002,70.589996,70.589996,4770000\n1963-08-13,70.589996,71.089996,70.320000,70.790001,70.790001,4450000\n1963-08-14,70.790001,71.320000,70.389999,71.070000,71.070000,4420000\n1963-08-15,71.070000,71.709999,70.809998,71.379997,71.379997,4980000\n1963-08-16,71.379997,71.949997,71.050003,71.489998,71.489998,4130000\n1963-08-19,71.489998,71.919998,71.150002,71.440002,71.440002,3650000\n1963-08-20,71.440002,71.910004,71.029999,71.379997,71.379997,3660000\n1963-08-21,71.379997,71.730003,71.000000,71.290001,71.290001,3820000\n1963-08-22,71.290001,71.809998,70.949997,71.540001,71.540001,4540000\n1963-08-23,71.540001,72.139999,71.330002,71.760002,71.760002,4880000\n1963-08-26,71.760002,72.300003,71.570000,71.910004,71.910004,4700000\n1963-08-27,71.910004,72.040001,71.269997,71.519997,71.519997,4080000\n1963-08-28,71.519997,72.389999,71.489998,72.040001,72.040001,5120000\n1963-08-29,72.040001,72.559998,71.830002,72.160004,72.160004,5110000\n1963-08-30,72.160004,72.709999,71.879997,72.500000,72.500000,4560000\n1963-09-03,72.500000,73.089996,72.300003,72.660004,72.660004,5570000\n1963-09-04,72.660004,73.180000,72.320000,72.639999,72.639999,6070000\n1963-09-05,72.639999,73.190002,72.150002,73.000000,73.000000,5700000\n1963-09-06,73.000000,73.510002,72.510002,72.839996,72.839996,7160000\n1963-09-09,72.839996,73.230003,72.260002,72.580002,72.580002,5020000\n1963-09-10,72.580002,73.269997,72.250000,72.989998,72.989998,5310000\n1963-09-11,72.989998,73.790001,72.830002,73.199997,73.199997,6670000\n1963-09-12,73.199997,73.599998,72.720001,73.150002,73.150002,5560000\n1963-09-13,73.150002,73.589996,72.820000,73.169998,73.169998,5230000\n1963-09-16,73.169998,73.629997,72.800003,73.070000,73.070000,4740000\n1963-09-17,73.070000,73.639999,72.790001,73.120003,73.120003,4950000\n1963-09-18,73.120003,73.440002,72.510002,72.800003,72.800003,5070000\n1963-09-19,72.800003,73.470001,72.610001,73.220001,73.220001,4080000\n1963-09-20,73.220001,73.709999,72.919998,73.300003,73.300003,5310000\n1963-09-23,73.300003,73.529999,72.620003,72.959999,72.959999,5140000\n1963-09-24,72.959999,73.669998,72.589996,73.300003,73.300003,5520000\n1963-09-25,73.300003,73.870003,72.580002,72.889999,72.889999,6340000\n1963-09-26,72.889999,73.070000,72.010002,72.269997,72.269997,5100000\n1963-09-27,72.269997,72.599998,71.599998,72.129997,72.129997,4350000\n1963-09-30,72.129997,72.370003,71.279999,71.699997,71.699997,3730000\n1963-10-01,71.699997,72.650002,71.570000,72.220001,72.220001,4420000\n1963-10-02,72.220001,72.669998,71.919998,72.300003,72.300003,3780000\n1963-10-03,72.300003,73.099998,72.099998,72.830002,72.830002,4510000\n1963-10-04,72.830002,73.190002,72.459999,72.849998,72.849998,5120000\n1963-10-07,72.849998,73.269997,72.389999,72.699997,72.699997,4050000\n1963-10-08,72.699997,73.139999,72.239998,72.599998,72.599998,4920000\n1963-10-09,71.980003,71.980003,71.599998,71.870003,71.870003,5520000\n1963-10-10,71.870003,72.519997,71.599998,72.199997,72.199997,4470000\n1963-10-11,72.199997,72.709999,71.870003,72.269997,72.269997,4740000\n1963-10-14,72.269997,72.430000,71.849998,72.300003,72.300003,4270000\n1963-10-15,72.300003,72.790001,71.989998,72.400002,72.400002,4550000\n1963-10-16,72.400002,73.199997,72.080002,72.970001,72.970001,5570000\n1963-10-17,72.970001,73.769997,72.839996,73.260002,73.260002,6790000\n1963-10-18,73.260002,73.739998,72.849998,73.320000,73.320000,5830000\n1963-10-21,73.320000,73.870003,73.029999,73.379997,73.379997,5450000\n1963-10-22,73.379997,73.550003,72.480003,72.959999,72.959999,6420000\n1963-10-23,72.959999,73.550003,72.589996,73.000000,73.000000,5830000\n1963-10-24,73.000000,73.730003,72.739998,73.279999,73.279999,6280000\n1963-10-25,73.279999,74.410004,73.059998,74.010002,74.010002,6390000\n1963-10-28,74.010002,75.150002,73.750000,74.480003,74.480003,7150000\n1963-10-29,74.480003,75.180000,73.970001,74.459999,74.459999,6100000\n1963-10-30,74.459999,74.589996,73.430000,73.800003,73.800003,5170000\n1963-10-31,73.800003,74.349998,73.250000,74.010002,74.010002,5030000\n1963-11-01,74.010002,74.440002,73.470001,73.830002,73.830002,5240000\n1963-11-04,73.830002,74.269997,73.089996,73.449997,73.449997,5440000\n1963-11-06,73.449997,73.470001,72.330002,72.809998,72.809998,5600000\n1963-11-07,72.809998,73.480003,72.580002,73.059998,73.059998,4320000\n1963-11-08,73.059998,73.660004,72.800003,73.360001,73.360001,4570000\n1963-11-11,73.519997,73.519997,73.519997,73.519997,73.519997,3970000\n1963-11-12,73.230003,73.230003,73.230003,73.230003,73.230003,4610000\n1963-11-13,73.230003,73.669998,72.889999,73.290001,73.290001,4710000\n1963-11-14,73.290001,73.529999,72.629997,72.949997,72.949997,4610000\n1963-11-15,72.949997,73.199997,72.089996,72.349998,72.349998,4790000\n1963-11-18,72.349998,72.519997,71.419998,71.830002,71.830002,4730000\n1963-11-19,71.830002,72.610001,71.419998,71.900002,71.900002,4430000\n1963-11-20,71.900002,73.139999,71.489998,72.559998,72.559998,5330000\n1963-11-21,72.559998,72.860001,71.400002,71.620003,71.620003,5670000\n1963-11-22,71.620003,72.169998,69.480003,69.610001,69.610001,6630000\n1963-11-26,71.400002,72.739998,71.400002,72.379997,72.379997,9320000\n1963-11-27,72.379997,72.779999,71.760002,72.250000,72.250000,5210000\n1963-11-29,72.250000,73.470001,72.050003,73.230003,73.230003,4810000\n1963-12-02,73.230003,74.080002,73.019997,73.660004,73.660004,4770000\n1963-12-03,73.660004,74.010002,73.139999,73.620003,73.620003,4520000\n1963-12-04,73.620003,74.180000,73.209999,73.800003,73.800003,4790000\n1963-12-05,73.800003,74.570000,73.449997,74.279999,74.279999,5190000\n1963-12-06,74.279999,74.629997,73.620003,74.000000,74.000000,4830000\n1963-12-09,74.000000,74.410004,73.559998,73.959999,73.959999,4430000\n1963-12-10,73.959999,74.480003,73.400002,73.989998,73.989998,4560000\n1963-12-11,73.989998,74.370003,73.580002,73.900002,73.900002,4400000\n1963-12-12,73.900002,74.309998,73.580002,73.910004,73.910004,4220000\n1963-12-13,73.910004,74.389999,73.680000,74.059998,74.059998,4290000\n1963-12-16,74.059998,74.660004,73.779999,74.300003,74.300003,4280000\n1963-12-17,74.300003,75.080002,74.070000,74.739998,74.739998,5140000\n1963-12-18,74.739998,75.209999,74.250000,74.629997,74.629997,6000000\n1963-12-19,74.629997,74.919998,74.080002,74.400002,74.400002,4410000\n1963-12-20,74.400002,74.750000,73.849998,74.279999,74.279999,4600000\n1963-12-23,74.279999,74.449997,73.489998,73.809998,73.809998,4540000\n1963-12-24,73.809998,74.480003,73.440002,73.970001,73.970001,3970000\n1963-12-26,73.970001,74.629997,73.739998,74.320000,74.320000,3700000\n1963-12-27,74.320000,74.910004,74.089996,74.440002,74.440002,4360000\n1963-12-30,74.440002,74.940002,74.129997,74.559998,74.559998,4930000\n1963-12-31,74.559998,75.360001,74.400002,75.019997,75.019997,6730000\n1964-01-02,75.019997,75.790001,74.820000,75.430000,75.430000,4680000\n1964-01-03,75.430000,76.040001,75.089996,75.500000,75.500000,5550000\n1964-01-06,75.500000,76.120003,75.180000,75.669998,75.669998,5480000\n1964-01-07,75.669998,76.239998,75.250000,75.690002,75.690002,5700000\n1964-01-08,75.690002,76.349998,75.389999,76.000000,76.000000,5380000\n1964-01-09,76.000000,76.639999,75.599998,76.279999,76.279999,5180000\n1964-01-10,76.279999,76.669998,75.739998,76.239998,76.239998,5260000\n1964-01-13,76.239998,76.709999,75.779999,76.220001,76.220001,5440000\n1964-01-14,76.220001,76.849998,75.879997,76.360001,76.360001,6500000\n1964-01-15,76.360001,77.059998,75.959999,76.639999,76.639999,6750000\n1964-01-16,76.639999,77.209999,76.050003,76.550003,76.550003,6200000\n1964-01-17,76.550003,77.089996,76.019997,76.559998,76.559998,5600000\n1964-01-20,76.559998,77.190002,76.019997,76.410004,76.410004,5570000\n1964-01-21,76.410004,76.989998,75.870003,76.620003,76.620003,4800000\n1964-01-22,76.620003,77.620003,76.449997,77.029999,77.029999,5430000\n1964-01-23,77.029999,77.620003,76.669998,77.089996,77.089996,5380000\n1964-01-24,77.089996,77.559998,76.580002,77.110001,77.110001,5080000\n1964-01-27,77.110001,77.779999,76.639999,77.080002,77.080002,5240000\n1964-01-28,77.080002,77.559998,76.629997,77.099998,77.099998,4720000\n1964-01-29,77.099998,77.360001,76.330002,76.629997,76.629997,4450000\n1964-01-30,76.629997,77.199997,76.260002,76.699997,76.699997,4230000\n1964-01-31,76.699997,77.370003,76.389999,77.040001,77.040001,4000000\n1964-02-03,77.040001,77.550003,76.529999,76.970001,76.970001,4140000\n1964-02-04,76.970001,77.309998,76.459999,76.879997,76.879997,4320000\n1964-02-05,76.879997,77.279999,76.360001,76.750000,76.750000,4010000\n1964-02-06,76.750000,77.260002,76.470001,76.930000,76.930000,4110000\n1964-02-07,76.930000,77.510002,76.660004,77.180000,77.180000,4710000\n1964-02-10,77.180000,77.769997,76.830002,77.050003,77.050003,4150000\n1964-02-11,77.050003,77.650002,76.809998,77.330002,77.330002,4040000\n1964-02-12,77.330002,77.879997,77.139999,77.570000,77.570000,4650000\n1964-02-13,77.570000,77.930000,77.099998,77.519997,77.519997,4820000\n1964-02-14,77.519997,77.820000,77.019997,77.480003,77.480003,4360000\n1964-02-17,77.480003,77.930000,77.040001,77.459999,77.459999,4780000\n1964-02-18,77.459999,77.900002,77.000000,77.470001,77.470001,4660000\n1964-02-19,77.470001,77.980003,77.129997,77.550003,77.550003,4280000\n1964-02-20,77.550003,77.989998,77.160004,77.620003,77.620003,4690000\n1964-02-24,77.620003,78.160004,77.269997,77.680000,77.680000,5630000\n1964-02-25,77.680000,78.309998,77.190002,77.680000,77.680000,5010000\n1964-02-26,77.680000,78.129997,77.330002,77.870003,77.870003,5350000\n1964-02-27,77.870003,78.290001,77.379997,77.620003,77.620003,5420000\n1964-02-28,77.620003,78.059998,77.199997,77.800003,77.800003,4980000\n1964-03-02,77.800003,78.379997,77.500000,77.970001,77.970001,5690000\n1964-03-03,77.970001,78.660004,77.690002,78.220001,78.220001,5350000\n1964-03-04,78.220001,78.699997,77.699997,78.070000,78.070000,5250000\n1964-03-05,78.070000,78.440002,77.580002,78.059998,78.059998,4680000\n1964-03-06,78.059998,78.599998,77.849998,78.309998,78.309998,4790000\n1964-03-09,78.309998,78.879997,77.949997,78.330002,78.330002,5510000\n1964-03-10,78.330002,78.900002,77.949997,78.589996,78.589996,5500000\n1964-03-11,78.589996,79.419998,78.449997,78.949997,78.949997,6180000\n1964-03-12,78.949997,79.410004,78.550003,79.080002,79.080002,5290000\n1964-03-13,79.080002,79.589996,78.739998,79.139999,79.139999,5660000\n1964-03-16,79.139999,79.599998,78.720001,79.139999,79.139999,5140000\n1964-03-17,79.139999,79.650002,78.769997,79.320000,79.320000,5480000\n1964-03-18,79.320000,79.889999,78.900002,79.379997,79.379997,5890000\n1964-03-19,79.379997,79.849998,78.940002,79.300003,79.300003,5670000\n1964-03-20,79.300003,79.349998,78.919998,78.919998,78.919998,5020000\n1964-03-23,78.919998,79.330002,78.449997,78.930000,78.930000,4940000\n1964-03-24,78.930000,79.339996,78.510002,78.790001,78.790001,5210000\n1964-03-25,78.790001,79.330002,78.169998,78.980003,78.980003,5420000\n1964-03-26,78.980003,79.580002,78.669998,79.190002,79.190002,5760000\n1964-03-30,79.190002,79.669998,78.750000,79.139999,79.139999,6060000\n1964-03-31,79.139999,79.510002,78.570000,78.980003,78.980003,5270000\n1964-04-01,78.980003,79.580002,78.669998,79.239998,79.239998,5510000\n1964-04-02,79.239998,80.089996,79.129997,79.699997,79.699997,6840000\n1964-04-03,79.699997,80.370003,79.449997,79.940002,79.940002,5990000\n1964-04-06,79.940002,80.449997,79.550003,80.019997,80.019997,5840000\n1964-04-07,80.019997,80.440002,79.410004,79.739998,79.739998,5900000\n1964-04-08,79.739998,80.169998,79.260002,79.750000,79.750000,5380000\n1964-04-09,79.750000,80.230003,79.360001,79.699997,79.699997,5300000\n1964-04-10,79.699997,80.260002,79.430000,79.849998,79.849998,4990000\n1964-04-13,79.849998,80.300003,79.419998,79.769997,79.769997,5330000\n1964-04-14,79.769997,80.370003,79.459999,79.989998,79.989998,5120000\n1964-04-15,79.989998,80.500000,79.629997,80.089996,80.089996,5270000\n1964-04-16,80.089996,80.620003,79.730003,80.199997,80.199997,5240000\n1964-04-17,80.199997,80.980003,79.989998,80.550003,80.550003,6030000\n1964-04-20,80.550003,81.040001,80.110001,80.500000,80.500000,5560000\n1964-04-21,80.500000,80.980003,80.050003,80.540001,80.540001,5750000\n1964-04-22,80.540001,80.919998,80.059998,80.489998,80.489998,5390000\n1964-04-23,80.489998,81.199997,80.089996,80.379997,80.379997,6690000\n1964-04-24,80.379997,80.620003,79.449997,79.750000,79.750000,5610000\n1964-04-27,79.750000,80.010002,78.900002,79.349998,79.349998,5070000\n1964-04-28,79.349998,80.260002,79.139999,79.900002,79.900002,4790000\n1964-04-29,79.900002,80.599998,79.290001,79.699997,79.699997,6200000\n1964-04-30,79.699997,80.080002,79.080002,79.459999,79.459999,5690000\n1964-05-01,79.459999,80.470001,79.459999,80.169998,80.169998,5990000\n1964-05-04,80.169998,81.010002,79.870003,80.470001,80.470001,5360000\n1964-05-05,80.470001,81.199997,79.989998,80.879997,80.879997,5340000\n1964-05-06,80.879997,81.570000,80.529999,81.059998,81.059998,5560000\n1964-05-07,81.059998,81.720001,80.669998,81.150002,81.150002,5600000\n1964-05-08,81.000000,81.000000,81.000000,81.000000,81.000000,4910000\n1964-05-11,81.000000,81.510002,80.580002,80.900002,80.900002,4490000\n1964-05-12,80.900002,81.809998,80.660004,81.160004,81.160004,5200000\n1964-05-13,81.160004,81.650002,80.660004,80.970001,80.970001,5890000\n1964-05-14,80.970001,81.279999,80.370003,80.860001,80.860001,4720000\n1964-05-15,80.860001,81.449997,80.489998,81.099998,81.099998,5070000\n1964-05-18,81.099998,81.470001,80.419998,80.720001,80.720001,4590000\n1964-05-19,80.720001,81.040001,79.959999,80.300003,80.300003,4360000\n1964-05-20,80.300003,81.019997,80.089996,80.660004,80.660004,4790000\n1964-05-21,80.660004,81.489998,80.360001,80.940002,80.940002,5350000\n1964-05-22,80.940002,81.150002,80.360001,80.730003,80.730003,4640000\n1964-05-25,80.730003,81.160004,80.209999,80.559998,80.559998,3990000\n1964-05-26,80.559998,80.940002,80.120003,80.389999,80.389999,4290000\n1964-05-27,80.389999,80.720001,79.779999,80.260002,80.260002,4450000\n1964-05-28,80.260002,80.750000,79.879997,80.370003,80.370003,4560000\n1964-06-01,80.370003,80.830002,79.830002,80.110001,80.110001,4300000\n1964-06-02,80.110001,80.599998,79.500000,79.699997,79.699997,4180000\n1964-06-03,79.699997,80.120003,79.269997,79.489998,79.489998,3990000\n1964-06-04,79.489998,79.750000,78.440002,78.669998,78.669998,4880000\n1964-06-05,78.669998,79.449997,78.500000,79.019997,79.019997,4240000\n1964-06-08,79.019997,79.440002,78.440002,78.639999,78.639999,4010000\n1964-06-09,78.639999,79.389999,78.150002,79.139999,79.139999,4470000\n1964-06-10,79.139999,79.839996,79.019997,79.440002,79.440002,4170000\n1964-06-11,79.440002,80.129997,79.239998,79.730003,79.730003,3620000\n1964-06-12,79.730003,80.050003,79.190002,79.599998,79.599998,3840000\n1964-06-15,79.599998,80.330002,79.389999,79.970001,79.970001,4110000\n1964-06-16,79.970001,80.720001,79.849998,80.400002,80.400002,4590000\n1964-06-17,80.400002,81.129997,80.220001,80.809998,80.809998,5340000\n1964-06-18,80.809998,81.339996,80.430000,80.790001,80.790001,4730000\n1964-06-19,80.790001,81.230003,80.389999,80.889999,80.889999,4050000\n1964-06-22,80.889999,81.540001,80.660004,81.110001,81.110001,4540000\n1964-06-23,81.110001,81.430000,80.500000,80.769997,80.769997,4060000\n1964-06-24,80.769997,81.449997,80.410004,81.059998,81.059998,4840000\n1964-06-25,81.059998,81.730003,80.750000,81.209999,81.209999,5010000\n1964-06-26,81.209999,81.779999,80.860001,81.459999,81.459999,4440000\n1964-06-29,81.459999,82.099998,81.099998,81.639999,81.639999,4380000\n1964-06-30,81.639999,82.070000,81.190002,81.690002,81.690002,4360000\n1964-07-01,81.690002,82.510002,81.459999,82.269997,82.269997,5320000\n1964-07-02,82.269997,82.980003,82.089996,82.599998,82.599998,5230000\n1964-07-06,82.599998,83.379997,82.370003,82.980003,82.980003,5080000\n1964-07-07,82.980003,83.529999,82.599998,83.120003,83.120003,5240000\n1964-07-08,83.120003,83.559998,82.580002,83.120003,83.120003,4760000\n1964-07-09,83.120003,83.639999,82.739998,83.220001,83.220001,5040000\n1964-07-10,83.220001,83.989998,82.870003,83.360001,83.360001,5420000\n1964-07-13,83.360001,83.860001,82.919998,83.309998,83.309998,4800000\n1964-07-14,83.309998,83.709999,82.720001,83.059998,83.059998,4760000\n1964-07-15,83.059998,83.669998,82.720001,83.339996,83.339996,4610000\n1964-07-16,83.339996,83.980003,83.059998,83.639999,83.639999,4640000\n1964-07-17,83.639999,84.330002,83.370003,84.010002,84.010002,4640000\n1964-07-20,84.010002,84.330002,83.440002,83.739998,83.739998,4390000\n1964-07-21,83.739998,83.989998,83.059998,83.540001,83.540001,4570000\n1964-07-22,83.540001,83.949997,82.959999,83.519997,83.519997,4570000\n1964-07-23,83.519997,83.910004,83.059998,83.480003,83.480003,4560000\n1964-07-24,83.480003,83.919998,83.070000,83.459999,83.459999,4210000\n1964-07-27,83.459999,83.820000,82.820000,83.080002,83.080002,4090000\n1964-07-28,83.080002,83.300003,82.400002,82.849998,82.849998,3860000\n1964-07-29,82.849998,83.300003,82.470001,82.919998,82.919998,4050000\n1964-07-30,82.919998,83.500000,82.629997,83.089996,83.089996,4530000\n1964-07-31,83.089996,83.570000,82.720001,83.180000,83.180000,4220000\n1964-08-03,83.180000,83.489998,82.650002,83.000000,83.000000,3780000\n1964-08-04,83.000000,83.019997,81.680000,81.959999,81.959999,4780000\n1964-08-05,81.959999,82.410004,80.800003,82.089996,82.089996,6160000\n1964-08-06,82.089996,82.449997,81.199997,81.339996,81.339996,3940000\n1964-08-07,81.339996,82.199997,81.190002,81.860001,81.860001,3190000\n1964-08-10,81.860001,82.230003,81.430000,81.779999,81.779999,3050000\n1964-08-11,81.779999,82.250000,81.449997,81.760002,81.760002,3450000\n1964-08-12,81.760002,82.529999,81.599998,82.169998,82.169998,4140000\n1964-08-13,82.169998,82.870003,81.980003,82.410004,82.410004,4600000\n1964-08-14,82.410004,82.830002,82.029999,82.349998,82.349998,4080000\n1964-08-17,82.349998,82.849998,82.019997,82.360001,82.360001,3780000\n1964-08-18,82.360001,82.790001,82.010002,82.400002,82.400002,4180000\n1964-08-19,82.400002,82.800003,81.989998,82.320000,82.320000,4160000\n1964-08-20,82.320000,82.570000,81.599998,81.940002,81.940002,3840000\n1964-08-21,81.940002,82.430000,81.639999,82.070000,82.070000,3620000\n1964-08-24,82.070000,82.480003,81.639999,81.910004,81.910004,3790000\n1964-08-25,81.910004,82.129997,81.199997,81.440002,81.440002,3780000\n1964-08-26,81.440002,81.739998,80.989998,81.320000,81.320000,3300000\n1964-08-27,81.320000,81.940002,81.070000,81.699997,81.699997,3560000\n1964-08-28,81.699997,82.290001,81.540001,81.989998,81.989998,3760000\n1964-08-31,81.989998,82.480003,81.459999,81.830002,81.830002,3340000\n1964-09-01,81.830002,82.500000,81.570000,82.180000,82.180000,4650000\n1964-09-02,82.180000,82.760002,81.949997,82.309998,82.309998,4800000\n1964-09-03,82.309998,82.830002,82.040001,82.559998,82.559998,4310000\n1964-09-04,82.559998,83.029999,82.309998,82.760002,82.760002,4210000\n1964-09-08,82.760002,83.239998,82.459999,82.870003,82.870003,4090000\n1964-09-09,82.870003,83.510002,82.540001,83.050003,83.050003,5690000\n1964-09-10,83.050003,83.500000,82.599998,83.099998,83.099998,5470000\n1964-09-11,83.099998,83.839996,82.790001,83.449997,83.449997,5630000\n1964-09-14,83.449997,83.889999,82.879997,83.220001,83.220001,5370000\n1964-09-15,83.220001,83.680000,82.690002,83.000000,83.000000,5690000\n1964-09-16,83.000000,83.519997,82.570000,83.239998,83.239998,4230000\n1964-09-17,83.239998,84.180000,83.169998,83.790001,83.790001,6380000\n1964-09-18,83.790001,84.290001,83.029999,83.480003,83.480003,6160000\n1964-09-21,83.480003,84.320000,83.410004,83.860001,83.860001,5310000\n1964-09-22,83.860001,84.440002,83.529999,83.889999,83.889999,5250000\n1964-09-23,83.889999,84.370003,83.449997,83.910004,83.910004,5920000\n1964-09-24,83.910004,84.430000,83.449997,84.000000,84.000000,5840000\n1964-09-25,84.000000,84.620003,83.559998,84.209999,84.209999,6170000\n1964-09-28,84.209999,84.730003,83.790001,84.279999,84.279999,4810000\n1964-09-29,84.279999,84.800003,83.839996,84.239998,84.239998,5070000\n1964-09-30,84.239998,84.660004,83.860001,84.180000,84.180000,4720000\n1964-10-01,84.180000,84.529999,83.739998,84.080002,84.080002,4470000\n1964-10-02,84.080002,84.639999,83.709999,84.360001,84.360001,4370000\n1964-10-05,84.360001,85.250000,84.199997,84.739998,84.739998,4850000\n1964-10-06,84.739998,85.239998,84.370003,84.790001,84.790001,4820000\n1964-10-07,84.790001,85.250000,84.419998,84.800003,84.800003,5090000\n1964-10-08,84.800003,85.400002,84.470001,85.040001,85.040001,5060000\n1964-10-09,85.040001,85.599998,84.720001,85.220001,85.220001,5290000\n1964-10-12,85.220001,85.580002,84.879997,85.239998,85.239998,4110000\n1964-10-13,85.239998,85.570000,84.629997,84.959999,84.959999,5400000\n1964-10-14,84.959999,85.290001,84.500000,84.790001,84.790001,4530000\n1964-10-15,84.790001,84.989998,83.650002,84.250000,84.250000,6500000\n1964-10-16,84.250000,85.099998,84.099998,84.830002,84.830002,5140000\n1964-10-19,84.830002,85.360001,84.470001,84.930000,84.930000,5010000\n1964-10-20,84.930000,85.570000,84.559998,85.180000,85.180000,5140000\n1964-10-21,85.180000,85.639999,84.769997,85.099998,85.099998,5170000\n1964-10-22,85.099998,85.440002,84.510002,84.940002,84.940002,4670000\n1964-10-23,84.940002,85.419998,84.570000,85.139999,85.139999,3830000\n1964-10-26,85.139999,85.699997,84.650002,85.000000,85.000000,5230000\n1964-10-27,85.000000,85.400002,84.610001,85.000000,85.000000,4470000\n1964-10-28,85.000000,85.370003,84.430000,84.690002,84.690002,4890000\n1964-10-29,84.690002,85.150002,84.360001,84.730003,84.730003,4390000\n1964-10-30,84.730003,85.220001,84.410004,84.860001,84.860001,4120000\n1964-11-02,84.860001,85.540001,84.510002,85.180000,85.180000,4430000\n1964-11-04,85.180000,85.900002,84.800003,85.139999,85.139999,4720000\n1964-11-05,85.139999,85.620003,84.720001,85.160004,85.160004,4380000\n1964-11-06,85.160004,85.550003,84.650002,85.230003,85.230003,4810000\n1964-11-09,85.230003,85.720001,84.930000,85.190002,85.190002,4560000\n1964-11-10,85.190002,85.550003,84.489998,84.839996,84.839996,5020000\n1964-11-11,84.839996,85.300003,84.489998,85.080002,85.080002,3790000\n1964-11-12,85.080002,85.629997,84.750000,85.190002,85.190002,5250000\n1964-11-13,85.190002,85.680000,84.760002,85.209999,85.209999,4860000\n1964-11-16,85.209999,85.940002,84.879997,85.650002,85.650002,4870000\n1964-11-17,85.650002,86.550003,85.480003,86.029999,86.029999,5920000\n1964-11-18,86.029999,86.800003,85.730003,86.220001,86.220001,6560000\n1964-11-19,86.220001,86.570000,85.599998,86.180000,86.180000,5570000\n1964-11-20,86.180000,86.800003,85.730003,86.279999,86.279999,5210000\n1964-11-23,86.279999,86.589996,85.480003,86.000000,86.000000,4860000\n1964-11-24,86.000000,86.120003,85.150002,85.730003,85.730003,5070000\n1964-11-25,85.730003,86.180000,85.099998,85.440002,85.440002,4800000\n1964-11-27,85.440002,85.680000,84.550003,85.160004,85.160004,4070000\n1964-11-30,85.160004,85.410004,84.099998,84.419998,84.419998,4890000\n1964-12-01,84.419998,84.559998,83.360001,83.550003,83.550003,4940000\n1964-12-02,83.550003,84.230003,83.120003,83.790001,83.790001,4930000\n1964-12-03,83.790001,84.739998,83.709999,84.180000,84.180000,4250000\n1964-12-04,84.349998,84.349998,84.349998,84.349998,84.349998,4340000\n1964-12-07,84.349998,85.029999,84.040001,84.330002,84.330002,4770000\n1964-12-08,84.330002,84.709999,83.690002,84.000000,84.000000,4990000\n1964-12-09,84.000000,84.239998,83.239998,83.459999,83.459999,5120000\n1964-12-10,83.459999,83.959999,82.980003,83.449997,83.449997,4790000\n1964-12-11,83.449997,84.050003,83.089996,83.660004,83.660004,4530000\n1964-12-14,83.660004,84.169998,83.099998,83.449997,83.449997,4340000\n1964-12-15,83.449997,83.790001,82.650002,83.220001,83.220001,5340000\n1964-12-16,83.220001,83.940002,83.000000,83.550003,83.550003,4610000\n1964-12-17,83.550003,84.239998,83.339996,83.900002,83.900002,4850000\n1964-12-18,83.900002,84.650002,83.730003,84.290001,84.290001,4630000\n1964-12-21,84.290001,84.910004,84.110001,84.379997,84.379997,4470000\n1964-12-22,84.379997,84.879997,83.940002,84.330002,84.330002,4520000\n1964-12-23,84.330002,84.760002,83.790001,84.150002,84.150002,4470000\n1964-12-24,84.150002,84.589996,83.739998,84.150002,84.150002,3600000\n1964-12-28,84.150002,84.580002,83.699997,84.070000,84.070000,3990000\n1964-12-29,84.070000,84.349998,83.379997,83.809998,83.809998,4450000\n1964-12-30,83.809998,84.629997,83.629997,84.300003,84.300003,5610000\n1964-12-31,84.300003,85.180000,84.180000,84.750000,84.750000,6470000\n1965-01-04,84.750000,85.150002,83.769997,84.230003,84.230003,3930000\n1965-01-05,84.230003,85.019997,84.019997,84.629997,84.629997,4110000\n1965-01-06,84.629997,85.379997,84.449997,84.889999,84.889999,4850000\n1965-01-07,84.889999,85.620003,84.660004,85.260002,85.260002,5080000\n1965-01-08,85.260002,85.839996,84.910004,85.370003,85.370003,5340000\n1965-01-11,85.370003,85.809998,84.900002,85.400002,85.400002,5440000\n1965-01-12,85.400002,85.980003,85.129997,85.610001,85.610001,5400000\n1965-01-13,85.610001,86.269997,85.349998,85.839996,85.839996,6160000\n1965-01-14,85.839996,86.379997,85.410004,85.839996,85.839996,5810000\n1965-01-15,85.839996,86.519997,85.599998,86.209999,86.209999,5340000\n1965-01-18,86.209999,87.150002,85.989998,86.489998,86.489998,5550000\n1965-01-19,86.489998,87.089996,86.150002,86.629997,86.629997,5550000\n1965-01-20,86.629997,87.099998,86.260002,86.599998,86.599998,5550000\n1965-01-21,86.599998,86.900002,86.019997,86.519997,86.519997,4780000\n1965-01-22,86.519997,87.150002,86.199997,86.739998,86.739998,5430000\n1965-01-25,86.739998,87.269997,86.389999,86.860001,86.860001,5370000\n1965-01-26,86.860001,87.449997,86.510002,86.940002,86.940002,5760000\n1965-01-27,86.940002,87.669998,86.699997,87.230003,87.230003,6010000\n1965-01-28,87.230003,87.879997,86.889999,87.480003,87.480003,6730000\n1965-01-29,87.480003,88.190002,87.180000,87.559998,87.559998,6940000\n1965-02-01,87.559998,88.010002,87.050003,87.580002,87.580002,5690000\n1965-02-02,87.580002,87.940002,87.029999,87.550003,87.550003,5460000\n1965-02-03,87.550003,88.010002,87.070000,87.629997,87.629997,6130000\n1965-02-04,87.629997,88.059998,87.059998,87.570000,87.570000,6230000\n1965-02-05,87.570000,87.980003,86.900002,87.290001,87.290001,5690000\n1965-02-08,87.000000,87.000000,85.949997,86.949997,86.949997,6010000\n1965-02-09,86.949997,87.639999,86.699997,87.239998,87.239998,5690000\n1965-02-10,87.239998,87.699997,86.199997,86.459999,86.459999,7210000\n1965-02-11,86.459999,86.889999,85.400002,85.540001,85.540001,5800000\n1965-02-12,85.540001,86.480003,85.540001,86.169998,86.169998,4960000\n1965-02-15,86.169998,86.860001,85.750000,86.070000,86.070000,5760000\n1965-02-16,86.070000,86.309998,85.330002,85.669998,85.669998,5000000\n1965-02-17,85.669998,86.250000,85.250000,85.769997,85.769997,5510000\n1965-02-18,85.769997,86.480003,85.470001,86.050003,86.050003,6060000\n1965-02-19,86.050003,86.669998,85.709999,86.209999,86.209999,5560000\n1965-02-23,86.209999,87.010002,86.029999,86.639999,86.639999,5880000\n1965-02-24,86.639999,87.720001,86.430000,87.169998,87.169998,7160000\n1965-02-25,87.169998,87.699997,86.699997,87.199997,87.199997,6680000\n1965-02-26,87.199997,87.839996,86.809998,87.430000,87.430000,5800000\n1965-03-01,87.430000,87.930000,86.919998,87.250000,87.250000,5780000\n1965-03-02,87.250000,87.790001,86.839996,87.400002,87.400002,5730000\n1965-03-03,87.400002,87.830002,86.879997,87.260002,87.260002,6600000\n1965-03-04,87.260002,87.720001,86.629997,86.980003,86.980003,7300000\n1965-03-05,86.980003,87.260002,86.000000,86.800003,86.800003,6120000\n1965-03-08,86.800003,87.279999,86.309998,86.830002,86.830002,5250000\n1965-03-09,86.830002,87.269997,86.330002,86.690002,86.690002,5210000\n1965-03-10,86.690002,87.070000,86.199997,86.540001,86.540001,5100000\n1965-03-11,86.540001,87.290001,86.169998,86.900002,86.900002,5770000\n1965-03-12,86.900002,87.650002,86.599998,87.209999,87.209999,6370000\n1965-03-15,87.209999,87.919998,86.820000,87.239998,87.239998,6000000\n1965-03-16,87.239998,87.610001,86.669998,87.129997,87.129997,5480000\n1965-03-17,87.129997,87.510002,86.629997,87.019997,87.019997,5120000\n1965-03-18,87.019997,87.480003,86.500000,86.809998,86.809998,4990000\n1965-03-19,86.809998,87.370003,86.430000,86.839996,86.839996,5040000\n1965-03-22,86.839996,87.339996,86.410004,86.830002,86.830002,4920000\n1965-03-23,86.830002,87.339996,86.449997,86.930000,86.930000,4820000\n1965-03-24,86.930000,87.550003,86.680000,87.089996,87.089996,5420000\n1965-03-25,87.089996,87.500000,86.550003,86.839996,86.839996,5460000\n1965-03-26,86.839996,87.059998,85.959999,86.199997,86.199997,5020000\n1965-03-29,86.199997,86.660004,85.650002,86.029999,86.029999,4590000\n1965-03-30,86.029999,86.529999,85.690002,86.199997,86.199997,4270000\n1965-03-31,86.199997,86.639999,85.830002,86.160004,86.160004,4470000\n1965-04-01,86.160004,86.730003,85.870003,86.320000,86.320000,4890000\n1965-04-02,86.320000,86.889999,86.080002,86.529999,86.529999,5060000\n1965-04-05,86.529999,87.080002,86.139999,86.529999,86.529999,4920000\n1965-04-06,86.529999,86.910004,86.080002,86.500000,86.500000,4610000\n1965-04-07,86.500000,86.879997,86.139999,86.550003,86.550003,4430000\n1965-04-08,86.550003,87.349998,86.339996,87.040001,87.040001,5770000\n1965-04-09,87.040001,87.870003,86.860001,87.559998,87.559998,6580000\n1965-04-12,87.559998,88.360001,87.309998,87.940002,87.940002,6040000\n1965-04-13,87.940002,88.480003,87.540001,88.040001,88.040001,6690000\n1965-04-14,88.040001,88.650002,87.709999,88.239998,88.239998,6580000\n1965-04-15,88.239998,88.629997,87.550003,88.150002,88.150002,5830000\n1965-04-19,88.150002,88.900002,87.900002,88.510002,88.510002,5700000\n1965-04-20,88.510002,89.070000,88.019997,88.459999,88.459999,6480000\n1965-04-21,88.459999,88.820000,87.699997,88.300003,88.300003,5590000\n1965-04-22,88.300003,89.129997,88.120003,88.779999,88.779999,5990000\n1965-04-23,88.779999,89.410004,88.480003,88.879997,88.879997,5860000\n1965-04-26,88.879997,89.290001,88.300003,88.889999,88.889999,5410000\n1965-04-27,88.889999,89.639999,88.709999,89.040001,89.040001,6310000\n1965-04-28,89.040001,89.480003,88.510002,89.000000,89.000000,5680000\n1965-04-29,89.000000,89.430000,88.470001,88.930000,88.930000,5510000\n1965-04-30,88.930000,89.440002,88.500000,89.110001,89.110001,5190000\n1965-05-03,89.110001,89.680000,88.620003,89.230003,89.230003,5340000\n1965-05-04,89.230003,89.889999,88.820000,89.510002,89.510002,5720000\n1965-05-05,89.510002,90.400002,89.139999,89.709999,89.709999,6350000\n1965-05-06,89.709999,90.570000,89.389999,89.919998,89.919998,6340000\n1965-05-07,89.919998,90.300003,89.330002,89.849998,89.849998,5820000\n1965-05-10,89.849998,90.220001,89.220001,89.660004,89.660004,5600000\n1965-05-11,89.660004,89.980003,89.050003,89.550003,89.550003,5150000\n1965-05-12,89.550003,90.309998,89.300003,89.940002,89.940002,6310000\n1965-05-13,89.940002,90.680000,89.680000,90.269997,90.269997,6460000\n1965-05-14,90.269997,90.660004,89.629997,90.099998,90.099998,5860000\n1965-05-17,90.099998,90.440002,89.239998,89.540001,89.540001,4980000\n1965-05-18,89.540001,89.839996,88.870003,89.459999,89.459999,5130000\n1965-05-19,89.459999,90.150002,89.169998,89.669998,89.669998,5860000\n1965-05-20,89.669998,89.860001,88.739998,89.180000,89.180000,5750000\n1965-05-21,89.180000,89.410004,88.400002,88.750000,88.750000,4660000\n1965-05-24,88.750000,88.889999,87.750000,88.089996,88.089996,4790000\n1965-05-25,88.089996,88.959999,87.820000,88.599998,88.599998,4950000\n1965-05-26,88.599998,89.220001,88.040001,88.300003,88.300003,5330000\n1965-05-27,88.300003,88.360001,87.239998,87.839996,87.839996,5520000\n1965-05-28,87.839996,88.680000,87.580002,88.419998,88.419998,4270000\n1965-06-01,88.419998,88.800003,87.879997,88.720001,88.720001,4830000\n1965-06-02,87.870003,87.870003,86.250000,87.089996,87.089996,6790000\n1965-06-03,87.089996,88.050003,86.580002,86.900002,86.900002,5720000\n1965-06-04,86.900002,87.459999,86.360001,87.110001,87.110001,4530000\n1965-06-07,87.110001,87.449997,86.040001,86.879997,86.879997,4680000\n1965-06-08,86.879997,87.099998,85.739998,85.930000,85.930000,4660000\n1965-06-09,85.930000,86.370003,84.750000,85.040001,85.040001,7070000\n1965-06-10,85.040001,85.820000,84.099998,84.730003,84.730003,7470000\n1965-06-11,84.730003,85.680000,84.500000,85.120003,85.120003,5350000\n1965-06-14,85.120003,85.680000,83.639999,84.010002,84.010002,5920000\n1965-06-15,84.010002,84.860001,83.010002,84.489998,84.489998,8450000\n1965-06-16,84.580002,85.790001,84.580002,85.199997,85.199997,6290000\n1965-06-17,85.199997,86.220001,84.980003,85.739998,85.739998,5220000\n1965-06-18,85.739998,86.099998,84.900002,85.339996,85.339996,4330000\n1965-06-21,85.339996,85.639999,84.529999,85.050003,85.050003,3280000\n1965-06-22,85.050003,85.699997,84.760002,85.209999,85.209999,3330000\n1965-06-23,85.209999,85.589996,84.519997,84.669998,84.669998,3580000\n1965-06-24,84.669998,84.730003,83.300003,83.559998,83.559998,5840000\n1965-06-25,83.559998,83.830002,82.599998,83.059998,83.059998,5790000\n1965-06-28,83.059998,83.339996,81.360001,81.599998,81.599998,7650000\n1965-06-29,81.599998,83.040001,80.730003,82.410004,82.410004,10450000\n1965-06-30,82.970001,84.629997,82.970001,84.120003,84.120003,6930000\n1965-07-01,84.120003,84.639999,83.570000,84.480003,84.480003,4520000\n1965-07-02,84.480003,85.400002,84.129997,85.160004,85.160004,4260000\n1965-07-06,85.160004,85.629997,84.570000,84.989998,84.989998,3400000\n1965-07-07,84.989998,85.139999,84.279999,84.669998,84.669998,3020000\n1965-07-08,84.669998,85.599998,84.290001,85.389999,85.389999,4380000\n1965-07-09,85.389999,86.110001,85.110001,85.709999,85.709999,4800000\n1965-07-12,85.709999,86.080002,85.239998,85.690002,85.690002,3690000\n1965-07-13,85.690002,86.010002,85.120003,85.589996,85.589996,3260000\n1965-07-14,85.589996,86.230003,85.180000,85.870003,85.870003,4100000\n1965-07-15,85.870003,86.470001,85.440002,85.720001,85.720001,4420000\n1965-07-16,85.720001,86.139999,85.260002,85.690002,85.690002,3520000\n1965-07-19,85.690002,86.040001,85.209999,85.629997,85.629997,3220000\n1965-07-20,85.629997,85.849998,84.389999,84.550003,84.550003,4670000\n1965-07-21,84.550003,84.839996,83.760002,84.070000,84.070000,4350000\n1965-07-22,84.070000,84.449997,83.529999,83.849998,83.849998,3310000\n1965-07-23,83.849998,84.519997,83.570000,84.070000,84.070000,3600000\n1965-07-26,84.070000,84.470001,83.489998,84.050003,84.050003,3790000\n1965-07-27,84.050003,84.589996,83.580002,83.870003,83.870003,4190000\n1965-07-28,83.870003,84.519997,83.300003,84.029999,84.029999,4760000\n1965-07-29,84.029999,85.000000,83.790001,84.680000,84.680000,4690000\n1965-07-30,84.680000,85.639999,84.639999,85.250000,85.250000,5200000\n1965-08-02,85.250000,85.870003,84.870003,85.419998,85.419998,4220000\n1965-08-03,85.419998,85.809998,84.800003,85.459999,85.459999,4640000\n1965-08-04,85.459999,86.120003,85.220001,85.790001,85.790001,4830000\n1965-08-05,85.790001,86.279999,85.430000,85.790001,85.790001,4920000\n1965-08-06,85.790001,86.400002,85.419998,86.070000,86.070000,4200000\n1965-08-09,86.070000,86.540001,85.519997,85.860001,85.860001,4540000\n1965-08-10,85.860001,86.309998,85.449997,85.870003,85.870003,4690000\n1965-08-11,85.870003,86.480003,85.639999,86.129997,86.129997,5030000\n1965-08-12,86.129997,86.750000,85.849998,86.379997,86.379997,5160000\n1965-08-13,86.379997,87.139999,86.089996,86.769997,86.769997,5430000\n1965-08-16,86.769997,87.430000,86.459999,86.870003,86.870003,5270000\n1965-08-17,86.870003,87.419998,86.480003,87.040001,87.040001,4520000\n1965-08-18,87.040001,87.570000,86.629997,86.989998,86.989998,5850000\n1965-08-19,86.989998,87.480003,86.489998,86.790001,86.790001,5000000\n1965-08-20,86.790001,87.139999,86.209999,86.690002,86.690002,4170000\n1965-08-23,86.690002,87.099998,86.220001,86.559998,86.559998,4470000\n1965-08-24,86.559998,87.190002,86.220001,86.709999,86.709999,4740000\n1965-08-25,86.709999,87.269997,86.330002,86.809998,86.809998,6240000\n1965-08-26,86.809998,87.519997,86.400002,87.139999,87.139999,6010000\n1965-08-27,87.139999,87.739998,86.809998,87.199997,87.199997,5570000\n1965-08-30,87.199997,87.639999,86.760002,87.209999,87.209999,4400000\n1965-08-31,87.209999,87.790001,86.779999,87.169998,87.169998,5170000\n1965-09-01,87.169998,87.629997,86.690002,87.169998,87.169998,5890000\n1965-09-02,87.169998,87.959999,86.980003,87.650002,87.650002,6470000\n1965-09-03,87.650002,88.410004,87.519997,88.059998,88.059998,6010000\n1965-09-07,88.059998,88.769997,87.760002,88.360001,88.360001,5750000\n1965-09-08,88.360001,89.080002,87.930000,88.660004,88.660004,6240000\n1965-09-09,88.660004,89.459999,88.349998,88.889999,88.889999,7360000\n1965-09-10,88.889999,89.849998,88.410004,89.120003,89.120003,6650000\n1965-09-13,89.120003,89.910004,88.769997,89.379997,89.379997,7020000\n1965-09-14,89.379997,90.010002,88.690002,89.029999,89.029999,7830000\n1965-09-15,89.029999,89.959999,88.709999,89.519997,89.519997,6220000\n1965-09-16,90.019997,90.019997,90.019997,90.019997,90.019997,7410000\n1965-09-17,90.019997,90.470001,89.320000,90.050003,90.050003,6610000\n1965-09-20,90.050003,90.669998,89.510002,90.080002,90.080002,7040000\n1965-09-21,90.080002,90.660004,89.430000,89.809998,89.809998,7750000\n1965-09-22,89.809998,90.669998,89.449997,90.220001,90.220001,8290000\n1965-09-23,90.220001,90.779999,89.430000,89.860001,89.860001,9990000\n1965-09-24,89.860001,90.470001,89.129997,90.019997,90.019997,7810000\n1965-09-27,90.650002,90.650002,90.650002,90.650002,90.650002,6820000\n1965-09-28,90.650002,91.129997,89.830002,90.430000,90.430000,8750000\n1965-09-29,90.430000,91.110001,89.559998,90.019997,90.019997,10600000\n1965-09-30,90.019997,90.709999,89.510002,89.959999,89.959999,8670000\n1965-10-01,89.959999,90.480003,89.300003,89.900002,89.900002,7470000\n1965-10-04,89.900002,90.559998,89.470001,90.080002,90.080002,5590000\n1965-10-05,90.080002,91.019997,89.919998,90.629997,90.629997,6980000\n1965-10-06,90.629997,90.940002,89.739998,90.540001,90.540001,6010000\n1965-10-07,90.540001,91.089996,90.089996,90.470001,90.470001,6670000\n1965-10-08,90.470001,91.309998,90.300003,90.849998,90.849998,7670000\n1965-10-11,90.849998,91.839996,90.730003,91.370003,91.370003,9600000\n1965-10-12,91.370003,91.940002,90.830002,91.349998,91.349998,9470000\n1965-10-13,91.349998,91.809998,90.730003,91.339996,91.339996,9470000\n1965-10-14,91.339996,91.900002,90.709999,91.190002,91.190002,8580000\n1965-10-15,91.190002,92.089996,90.760002,91.379997,91.379997,7470000\n1965-10-18,91.379997,92.279999,91.059998,91.680000,91.680000,8180000\n1965-10-19,91.680000,92.449997,91.349998,91.800003,91.800003,8620000\n1965-10-20,91.800003,92.260002,91.120003,91.779999,91.779999,8200000\n1965-10-21,91.779999,92.510002,91.419998,91.940002,91.940002,9170000\n1965-10-22,91.940002,92.739998,91.540001,91.980003,91.980003,8960000\n1965-10-25,91.980003,92.720001,91.339996,91.669998,91.669998,7090000\n1965-10-26,91.669998,92.629997,91.360001,92.199997,92.199997,6750000\n1965-10-27,92.199997,93.190002,91.949997,92.510002,92.510002,7670000\n1965-10-28,92.510002,92.949997,91.599998,92.209999,92.209999,7230000\n1965-10-29,92.209999,92.940002,91.830002,92.419998,92.419998,7240000\n1965-11-01,92.419998,92.919998,91.730003,92.230003,92.230003,6340000\n1965-11-03,92.230003,92.790001,91.620003,92.309998,92.309998,7520000\n1965-11-04,92.309998,93.070000,91.900002,92.459999,92.459999,8380000\n1965-11-05,92.459999,92.919998,91.779999,92.370003,92.370003,7310000\n1965-11-08,92.370003,92.970001,91.629997,92.230003,92.230003,7000000\n1965-11-09,92.230003,92.650002,91.470001,91.930000,91.930000,6680000\n1965-11-10,91.930000,92.400002,91.349998,91.830002,91.830002,4860000\n1965-11-11,91.830002,92.370003,91.309998,92.110001,92.110001,5430000\n1965-11-12,92.110001,93.070000,91.830002,92.550003,92.550003,7780000\n1965-11-15,92.550003,93.300003,92.040001,92.629997,92.629997,8310000\n1965-11-16,92.629997,93.129997,91.900002,92.410004,92.410004,8380000\n1965-11-17,92.410004,93.279999,91.849998,92.599998,92.599998,9120000\n1965-11-18,92.599998,92.940002,91.720001,92.220001,92.220001,7040000\n1965-11-19,92.220001,92.879997,91.730003,92.239998,92.239998,6850000\n1965-11-22,92.239998,92.480003,91.160004,91.639999,91.639999,6370000\n1965-11-23,91.639999,92.239998,91.150002,91.779999,91.779999,7150000\n1965-11-24,91.779999,92.500000,91.139999,91.940002,91.940002,7870000\n1965-11-26,91.940002,92.650002,91.389999,92.029999,92.029999,6970000\n1965-11-29,92.029999,92.599998,91.370003,91.800003,91.800003,8760000\n1965-11-30,91.800003,92.139999,90.809998,91.610001,91.610001,8990000\n1965-12-01,91.610001,92.260002,91.019997,91.500000,91.500000,10140000\n1965-12-02,91.500000,91.949997,90.690002,91.209999,91.209999,9070000\n1965-12-03,91.209999,91.800003,90.529999,91.269997,91.269997,8160000\n1965-12-06,91.199997,91.199997,89.199997,90.589996,90.589996,11440000\n1965-12-07,90.589996,92.000000,90.449997,91.389999,91.389999,9340000\n1965-12-08,91.389999,92.239998,90.839996,91.279999,91.279999,10120000\n1965-12-09,91.279999,92.059998,90.870003,91.559998,91.559998,9150000\n1965-12-10,91.559998,92.279999,91.139999,91.800003,91.800003,8740000\n1965-12-13,91.800003,92.449997,91.269997,91.830002,91.830002,8660000\n1965-12-14,91.830002,92.589996,91.349998,91.879997,91.879997,9920000\n1965-12-15,91.879997,92.669998,91.300003,92.019997,92.019997,9560000\n1965-12-16,92.019997,92.949997,91.529999,92.120003,92.120003,9950000\n1965-12-17,92.120003,92.760002,91.510002,92.080002,92.080002,9490000\n1965-12-20,92.080002,92.349998,91.089996,91.650002,91.650002,7350000\n1965-12-21,91.650002,92.589996,91.239998,92.010002,92.010002,8230000\n1965-12-22,92.010002,93.070000,91.529999,92.290001,92.290001,9720000\n1965-12-23,92.290001,92.889999,91.580002,92.190002,92.190002,6870000\n1965-12-27,92.190002,92.709999,91.279999,91.519997,91.519997,5950000\n1965-12-28,91.519997,92.129997,90.629997,91.529999,91.529999,7280000\n1965-12-29,91.529999,92.389999,91.139999,91.809998,91.809998,7610000\n1965-12-30,91.809998,92.680000,91.519997,92.199997,92.199997,7060000\n1965-12-31,92.199997,93.050003,91.820000,92.430000,92.430000,7240000\n1966-01-03,92.430000,92.870003,91.629997,92.180000,92.180000,5950000\n1966-01-04,92.180000,93.040001,91.680000,92.260002,92.260002,7540000\n1966-01-05,92.260002,93.330002,91.989998,92.849998,92.849998,9650000\n1966-01-06,92.849998,93.650002,92.510002,93.059998,93.059998,7880000\n1966-01-07,93.059998,93.639999,92.470001,93.139999,93.139999,7600000\n1966-01-10,93.139999,93.940002,92.750000,93.330002,93.330002,7720000\n1966-01-11,93.330002,94.050003,92.849998,93.410004,93.410004,8910000\n1966-01-12,93.410004,93.980003,92.800003,93.190002,93.190002,8530000\n1966-01-13,93.190002,94.000000,92.680000,93.360001,93.360001,8860000\n1966-01-14,93.360001,94.139999,92.980003,93.500000,93.500000,9210000\n1966-01-17,93.500000,94.459999,93.099998,93.769997,93.769997,9430000\n1966-01-18,93.769997,94.639999,93.230003,93.949997,93.949997,9790000\n1966-01-19,93.949997,94.620003,93.160004,93.690002,93.690002,10230000\n1966-01-20,93.690002,94.330002,92.870003,93.360001,93.360001,8670000\n1966-01-21,93.360001,93.970001,92.599998,93.470001,93.470001,9180000\n1966-01-24,93.470001,94.410004,93.070000,93.709999,93.709999,8780000\n1966-01-25,93.709999,94.559998,93.239998,93.849998,93.849998,9300000\n1966-01-26,93.849998,94.529999,93.180000,93.699997,93.699997,9910000\n1966-01-27,93.699997,94.339996,93.089996,93.669998,93.669998,8970000\n1966-01-28,93.669998,94.150002,92.839996,93.309998,93.309998,9000000\n1966-01-31,93.309998,93.769997,92.459999,92.879997,92.879997,7800000\n1966-02-01,92.879997,93.360001,91.610001,92.160004,92.160004,9090000\n1966-02-02,92.160004,92.910004,91.320000,92.529999,92.529999,8130000\n1966-02-03,92.529999,93.669998,92.110001,92.650002,92.650002,8160000\n1966-02-04,92.650002,93.699997,92.330002,93.260002,93.260002,7560000\n1966-02-07,93.260002,94.220001,92.849998,93.589996,93.589996,8000000\n1966-02-08,93.589996,94.290001,92.580002,93.550003,93.550003,10560000\n1966-02-09,93.550003,94.720001,93.290001,94.059998,94.059998,9760000\n1966-02-10,94.059998,94.699997,93.320000,93.830002,93.830002,9790000\n1966-02-11,93.830002,94.519997,93.250000,93.809998,93.809998,8150000\n1966-02-14,93.809998,94.400002,93.150002,93.529999,93.529999,8360000\n1966-02-15,93.529999,94.040001,92.669998,93.169998,93.169998,8750000\n1966-02-16,93.169998,93.739998,92.629997,93.160004,93.160004,9180000\n1966-02-17,93.160004,93.580002,92.110001,92.660004,92.660004,9330000\n1966-02-18,92.660004,93.139999,91.800003,92.410004,92.410004,8470000\n1966-02-21,92.410004,92.830002,91.349998,91.870003,91.870003,8510000\n1966-02-23,91.870003,92.209999,90.989998,91.480003,91.480003,8080000\n1966-02-24,91.480003,91.809998,90.449997,90.889999,90.889999,7860000\n1966-02-25,90.889999,91.879997,90.430000,91.139999,91.139999,8140000\n1966-02-28,91.139999,91.949997,90.650002,91.220001,91.220001,9910000\n1966-03-01,91.220001,91.650002,89.760002,90.059998,90.059998,11030000\n1966-03-02,90.059998,90.650002,88.699997,89.150002,89.150002,10470000\n1966-03-03,89.150002,90.029999,88.260002,89.470001,89.470001,9900000\n1966-03-04,89.470001,90.250000,88.720001,89.239998,89.239998,9000000\n1966-03-07,89.239998,89.389999,87.669998,88.040001,88.040001,9370000\n1966-03-08,88.040001,89.000000,87.169998,88.180000,88.180000,10120000\n1966-03-09,88.180000,89.209999,87.959999,88.959999,88.959999,7980000\n1966-03-10,88.959999,90.139999,88.360001,88.959999,88.959999,10310000\n1966-03-11,88.959999,89.629997,88.300003,88.849998,88.849998,7000000\n1966-03-14,88.849998,88.919998,87.559998,87.849998,87.849998,7400000\n1966-03-15,87.849998,88.199997,86.690002,87.349998,87.349998,9440000\n1966-03-16,87.349998,88.550003,87.089996,87.860001,87.860001,7330000\n1966-03-17,87.860001,88.599998,87.449997,88.169998,88.169998,5460000\n1966-03-18,88.169998,89.230003,87.820000,88.529999,88.529999,6450000\n1966-03-21,88.529999,89.730003,88.400002,89.199997,89.199997,7230000\n1966-03-22,89.199997,90.279999,89.010002,89.459999,89.459999,8910000\n1966-03-23,89.459999,89.800003,88.690002,89.129997,89.129997,6720000\n1966-03-24,89.129997,89.800003,88.680000,89.290001,89.290001,7880000\n1966-03-25,89.290001,90.139999,88.959999,89.540001,89.540001,7750000\n1966-03-28,89.540001,90.410004,89.150002,89.620003,89.620003,8640000\n1966-03-29,89.620003,90.040001,88.629997,89.269997,89.269997,8300000\n1966-03-30,89.269997,89.570000,88.309998,88.779999,88.779999,7980000\n1966-03-31,88.779999,89.699997,88.470001,89.230003,89.230003,6690000\n1966-04-01,89.230003,90.370003,88.959999,89.940002,89.940002,9050000\n1966-04-04,89.940002,91.330002,89.919998,90.760002,90.760002,9360000\n1966-04-05,90.760002,92.040001,90.470001,91.309998,91.309998,10560000\n1966-04-06,91.309998,92.099998,90.769997,91.559998,91.559998,9040000\n1966-04-07,91.559998,92.419998,90.989998,91.760002,91.760002,9650000\n1966-04-11,91.760002,92.599998,91.080002,91.790001,91.790001,9310000\n1966-04-12,91.790001,92.510002,90.919998,91.449997,91.449997,10500000\n1966-04-13,91.449997,92.809998,90.730003,91.540001,91.540001,10440000\n1966-04-14,91.540001,92.800003,91.120003,91.870003,91.870003,12980000\n1966-04-15,91.870003,92.750000,91.279999,91.989998,91.989998,10270000\n1966-04-18,91.989998,92.589996,91.089996,91.580002,91.580002,9150000\n1966-04-19,91.580002,92.309998,90.889999,91.570000,91.570000,8820000\n1966-04-20,91.570000,92.750000,91.339996,92.080002,92.080002,10530000\n1966-04-21,92.080002,93.019997,91.779999,92.419998,92.419998,9560000\n1966-04-22,92.419998,92.870003,91.599998,92.269997,92.269997,8650000\n1966-04-25,92.269997,92.860001,91.410004,92.080002,92.080002,7270000\n1966-04-26,92.080002,92.769997,91.470001,91.989998,91.989998,7540000\n1966-04-27,91.989998,92.489998,91.099998,91.760002,91.760002,7950000\n1966-04-28,91.760002,91.919998,90.239998,91.129997,91.129997,8310000\n1966-04-29,91.129997,91.860001,90.570000,91.059998,91.059998,7220000\n1966-05-02,91.059998,91.750000,90.430000,90.900002,90.900002,7070000\n1966-05-03,90.900002,91.099998,89.459999,89.849998,89.849998,8020000\n1966-05-04,89.849998,90.110001,88.540001,89.389999,89.389999,9740000\n1966-05-05,89.389999,89.769997,87.599998,87.930000,87.930000,10100000\n1966-05-06,87.930000,88.519997,86.239998,87.839996,87.839996,13110000\n1966-05-09,87.839996,87.959999,85.919998,86.320000,86.320000,9290000\n1966-05-10,86.320000,87.879997,86.120003,87.080002,87.080002,9050000\n1966-05-11,87.080002,88.379997,86.839996,87.230003,87.230003,7470000\n1966-05-12,87.230003,87.489998,85.720001,86.230003,86.230003,8210000\n1966-05-13,86.230003,86.309998,84.769997,85.470001,85.470001,8970000\n1966-05-16,85.470001,86.040001,83.900002,84.410004,84.410004,9260000\n1966-05-17,84.410004,85.029999,83.180000,83.629997,83.629997,9870000\n1966-05-18,83.720001,85.639999,83.720001,85.120003,85.120003,9310000\n1966-05-19,85.120003,86.330002,84.540001,85.019997,85.019997,8640000\n1966-05-20,85.019997,85.790001,84.209999,85.430000,85.430000,6430000\n1966-05-23,85.430000,86.910004,85.290001,86.199997,86.199997,7080000\n1966-05-24,86.199997,87.699997,86.190002,86.769997,86.769997,7210000\n1966-05-25,86.769997,87.480003,86.050003,87.070000,87.070000,5820000\n1966-05-26,87.070000,87.879997,86.540001,87.070000,87.070000,6080000\n1966-05-27,87.070000,87.419998,86.430000,87.330002,87.330002,4790000\n1966-05-31,87.330002,87.650002,85.800003,86.129997,86.129997,5770000\n1966-06-01,86.129997,86.650002,85.279999,86.099998,86.099998,5290000\n1966-06-02,86.099998,86.849998,85.550003,85.959999,85.959999,5080000\n1966-06-03,85.959999,86.550003,85.430000,86.059998,86.059998,4430000\n1966-06-06,86.059998,86.279999,85.029999,85.419998,85.419998,4260000\n1966-06-07,85.419998,85.540001,84.250000,84.830002,84.830002,5040000\n1966-06-08,84.830002,85.430000,84.309998,84.930000,84.930000,4580000\n1966-06-09,84.930000,85.980003,84.559998,85.500000,85.500000,5810000\n1966-06-10,85.500000,86.970001,85.320000,86.440002,86.440002,8240000\n1966-06-13,86.440002,87.589996,86.199997,86.830002,86.830002,7600000\n1966-06-14,86.830002,87.570000,86.019997,87.070000,87.070000,7600000\n1966-06-15,87.070000,87.739998,86.330002,86.730003,86.730003,8520000\n1966-06-16,86.730003,87.180000,85.879997,86.470001,86.470001,6870000\n1966-06-17,86.470001,87.110001,85.889999,86.510002,86.510002,6580000\n1966-06-20,86.510002,87.029999,85.839996,86.480003,86.480003,5940000\n1966-06-21,86.480003,87.279999,86.070000,86.709999,86.709999,6860000\n1966-06-22,86.709999,87.379997,86.150002,86.849998,86.849998,7800000\n1966-06-23,86.849998,87.730003,86.110001,86.500000,86.500000,7930000\n1966-06-24,86.500000,87.309998,85.680000,86.580002,86.580002,7140000\n1966-06-27,86.580002,87.309998,85.769997,86.080002,86.080002,5330000\n1966-06-28,86.080002,86.430000,85.000000,85.669998,85.669998,6280000\n1966-06-29,85.669998,85.980003,84.519997,84.860001,84.860001,6020000\n1966-06-30,84.860001,85.370003,83.750000,84.739998,84.739998,7250000\n1966-07-01,84.739998,86.080002,84.739998,85.610001,85.610001,5200000\n1966-07-05,85.610001,86.410004,85.089996,85.820000,85.820000,4610000\n1966-07-06,85.820000,87.379997,85.570000,87.059998,87.059998,6860000\n1966-07-07,87.059998,88.019997,86.669998,87.379997,87.379997,7200000\n1966-07-08,87.379997,88.040001,86.849998,87.610001,87.610001,6100000\n1966-07-11,87.610001,88.190002,86.970001,87.449997,87.449997,6200000\n1966-07-12,87.449997,87.779999,86.449997,86.879997,86.879997,5180000\n1966-07-13,86.879997,87.059998,85.830002,86.300003,86.300003,5580000\n1966-07-14,86.300003,87.339996,85.849998,86.820000,86.820000,5950000\n1966-07-15,86.820000,87.680000,86.440002,87.080002,87.080002,6090000\n1966-07-18,87.080002,87.589996,86.419998,86.989998,86.989998,5110000\n1966-07-19,86.989998,87.169998,85.750000,86.330002,86.330002,5960000\n1966-07-20,86.330002,86.639999,85.260002,85.510002,85.510002,5470000\n1966-07-21,85.510002,86.239998,84.769997,85.519997,85.519997,6200000\n1966-07-22,85.519997,86.110001,84.930000,85.410004,85.410004,6540000\n1966-07-25,85.410004,85.570000,83.559998,83.830002,83.830002,7050000\n1966-07-26,83.830002,84.669998,83.050003,83.699997,83.699997,7610000\n1966-07-27,83.699997,84.830002,83.500000,84.099998,84.099998,6070000\n1966-07-28,84.099998,84.760002,83.440002,83.769997,83.769997,5680000\n1966-07-29,83.769997,84.300003,83.099998,83.599998,83.599998,5150000\n1966-08-01,83.500000,83.500000,81.980003,82.309998,82.309998,5880000\n1966-08-02,82.309998,83.040001,81.769997,82.330002,82.330002,5710000\n1966-08-03,82.330002,83.709999,82.300003,83.150002,83.150002,6220000\n1966-08-04,83.150002,84.540001,83.070000,83.930000,83.930000,6880000\n1966-08-05,83.930000,84.699997,83.430000,84.000000,84.000000,5500000\n1966-08-08,84.000000,84.309998,82.970001,83.750000,83.750000,4900000\n1966-08-09,83.750000,84.360001,83.040001,83.489998,83.489998,6270000\n1966-08-10,83.489998,83.830002,82.690002,83.110001,83.110001,5290000\n1966-08-11,83.110001,83.529999,82.339996,83.019997,83.019997,5700000\n1966-08-12,83.019997,83.879997,82.570000,83.169998,83.169998,6230000\n1966-08-15,83.169998,83.690002,82.389999,82.739998,82.739998,5680000\n1966-08-16,82.709999,82.709999,81.260002,81.629997,81.629997,6130000\n1966-08-17,81.629997,81.900002,80.529999,81.180000,81.180000,6630000\n1966-08-18,81.180000,81.379997,79.599998,80.160004,80.160004,7000000\n1966-08-19,80.160004,80.779999,79.239998,79.620003,79.620003,7070000\n1966-08-22,79.620003,79.879997,77.580002,78.239998,78.239998,8690000\n1966-08-23,78.239998,79.239998,77.050003,78.110001,78.110001,9830000\n1966-08-24,78.110001,79.629997,77.919998,79.070000,79.070000,7050000\n1966-08-25,79.070000,79.790001,77.800003,78.059998,78.059998,6760000\n1966-08-26,77.849998,77.849998,76.099998,76.410004,76.410004,8190000\n1966-08-29,76.239998,76.239998,74.180000,74.529999,74.529999,10900000\n1966-08-30,74.529999,76.459999,73.910004,75.860001,75.860001,11230000\n1966-08-31,75.980003,78.059998,75.980003,77.099998,77.099998,8690000\n1966-09-01,77.099998,78.500000,76.660004,77.699997,77.699997,6250000\n1966-09-02,77.699997,78.199997,76.269997,77.419998,77.419998,6080000\n1966-09-06,77.419998,78.160004,76.550003,76.959999,76.959999,4350000\n1966-09-07,76.959999,77.260002,75.769997,76.370003,76.370003,5530000\n1966-09-08,76.370003,76.949997,75.029999,76.050003,76.050003,6660000\n1966-09-09,76.050003,76.940002,75.430000,76.290001,76.290001,5280000\n1966-09-12,76.470001,78.339996,76.470001,77.910004,77.910004,6780000\n1966-09-13,77.910004,79.160004,77.660004,78.320000,78.320000,6870000\n1966-09-14,78.320000,79.430000,77.730003,79.129997,79.129997,6250000\n1966-09-15,79.129997,80.599998,78.870003,80.080002,80.080002,6140000\n1966-09-16,80.080002,80.809998,79.330002,79.989998,79.989998,5150000\n1966-09-19,79.989998,80.500000,79.019997,79.589996,79.589996,4920000\n1966-09-20,79.589996,79.900002,78.570000,79.040001,79.040001,4560000\n1966-09-21,79.040001,79.150002,77.519997,77.709999,77.709999,5360000\n1966-09-22,77.709999,78.410004,76.809998,77.940002,77.940002,5760000\n1966-09-23,77.940002,78.430000,77.150002,77.669998,77.669998,4560000\n1966-09-26,77.669998,78.339996,76.879997,77.860001,77.860001,4960000\n1966-09-27,77.860001,79.099998,77.559998,78.099998,78.099998,6300000\n1966-09-28,78.099998,78.360001,76.699997,77.110001,77.110001,5990000\n1966-09-29,77.110001,77.279999,75.849998,76.309998,76.309998,6110000\n1966-09-30,76.309998,77.089996,75.449997,76.559998,76.559998,6170000\n1966-10-03,76.559998,76.980003,74.709999,74.900002,74.900002,6490000\n1966-10-04,74.900002,75.760002,73.910004,75.099998,75.099998,8910000\n1966-10-05,75.099998,76.099998,74.309998,74.690002,74.690002,5880000\n1966-10-06,74.690002,75.089996,73.470001,74.050003,74.050003,8110000\n1966-10-07,74.050003,74.669998,72.769997,73.199997,73.199997,8140000\n1966-10-10,73.199997,74.970001,72.279999,74.529999,74.529999,9630000\n1966-10-11,74.529999,76.199997,74.220001,74.910004,74.910004,8430000\n1966-10-12,74.910004,77.260002,74.370003,77.040001,77.040001,6910000\n1966-10-13,77.040001,78.449997,76.220001,76.889999,76.889999,8680000\n1966-10-14,76.889999,77.800003,76.010002,76.599998,76.599998,5610000\n1966-10-17,76.599998,78.410004,76.480003,77.470001,77.470001,5570000\n1966-10-18,77.470001,79.080002,77.349998,78.680000,78.680000,7180000\n1966-10-19,78.680000,79.339996,77.540001,78.050003,78.050003,6460000\n1966-10-20,78.050003,78.959999,77.260002,77.839996,77.839996,6840000\n1966-10-21,77.839996,78.620003,77.160004,78.190002,78.190002,5690000\n1966-10-24,78.190002,79.199997,77.730003,78.419998,78.419998,5780000\n1966-10-25,78.419998,79.220001,77.559998,78.900002,78.900002,6190000\n1966-10-26,78.900002,80.290001,78.699997,79.580002,79.580002,6760000\n1966-10-27,79.580002,80.720001,79.279999,80.230003,80.230003,6670000\n1966-10-28,80.230003,80.910004,79.489998,80.239998,80.239998,6420000\n1966-10-31,80.239998,80.820000,79.339996,80.199997,80.199997,5860000\n1966-11-01,80.199997,81.180000,79.790001,80.809998,80.809998,6480000\n1966-11-02,80.809998,81.680000,80.300003,80.879997,80.879997,6740000\n1966-11-03,80.879997,81.349998,79.980003,80.559998,80.559998,5860000\n1966-11-04,80.559998,81.209999,79.639999,80.809998,80.809998,6530000\n1966-11-07,80.809998,81.480003,80.160004,80.730003,80.730003,6120000\n1966-11-09,80.730003,81.900002,80.459999,81.379997,81.379997,8390000\n1966-11-10,81.379997,82.430000,81.000000,81.889999,81.889999,8870000\n1966-11-11,81.889999,82.360001,81.269997,81.940002,81.940002,6690000\n1966-11-14,81.940002,82.180000,80.809998,81.370003,81.370003,6540000\n1966-11-15,81.370003,82.070000,80.820000,81.690002,81.690002,7190000\n1966-11-16,81.690002,83.010002,81.550003,82.370003,82.370003,10350000\n1966-11-17,82.370003,82.800003,81.239998,81.800003,81.800003,8900000\n1966-11-18,81.800003,82.050003,80.790001,81.260002,81.260002,6900000\n1966-11-21,81.089996,81.089996,79.510002,80.089996,80.089996,7450000\n1966-11-22,80.089996,80.320000,78.889999,79.669998,79.669998,6430000\n1966-11-23,79.669998,80.849998,79.389999,80.209999,80.209999,7350000\n1966-11-25,80.209999,81.370003,79.830002,80.849998,80.849998,6810000\n1966-11-28,80.849998,81.379997,79.959999,80.709999,80.709999,7630000\n1966-11-29,80.709999,81.160004,79.940002,80.419998,80.419998,7320000\n1966-11-30,80.419998,80.900002,79.620003,80.449997,80.449997,7230000\n1966-12-01,80.449997,81.040001,79.660004,80.080002,80.080002,8480000\n1966-12-02,80.080002,81.290001,79.489998,80.129997,80.129997,6230000\n1966-12-05,80.129997,80.809998,79.599998,80.239998,80.239998,6470000\n1966-12-06,80.239998,81.290001,79.949997,80.839996,80.839996,7670000\n1966-12-07,80.839996,82.190002,80.589996,81.720001,81.720001,8980000\n1966-12-08,81.720001,82.720001,81.339996,82.050003,82.050003,8370000\n1966-12-09,82.050003,82.680000,81.330002,82.139999,82.139999,7650000\n1966-12-12,82.139999,83.540001,81.940002,83.000000,83.000000,9530000\n1966-12-13,83.000000,83.879997,82.279999,82.730003,82.730003,9650000\n1966-12-14,82.730003,83.349998,81.970001,82.639999,82.639999,7470000\n1966-12-15,82.639999,82.889999,81.199997,81.639999,81.639999,7150000\n1966-12-16,81.639999,82.209999,80.940002,81.580002,81.580002,6980000\n1966-12-19,81.580002,82.059998,80.559998,81.269997,81.269997,7340000\n1966-12-20,81.269997,81.690002,80.309998,80.959999,80.959999,6830000\n1966-12-21,80.959999,81.910004,80.419998,81.379997,81.379997,7690000\n1966-12-22,81.379997,82.339996,81.000000,81.690002,81.690002,8560000\n1966-12-23,81.690002,82.220001,80.970001,81.470001,81.470001,7350000\n1966-12-27,81.470001,81.839996,80.550003,81.000000,81.000000,6280000\n1966-12-28,81.000000,81.669998,80.290001,80.610001,80.610001,7160000\n1966-12-29,80.610001,81.080002,79.839996,80.370003,80.370003,7900000\n1966-12-30,80.370003,81.139999,79.660004,80.330002,80.330002,11330000\n1967-01-03,80.330002,81.610001,79.589996,80.379997,80.379997,6100000\n1967-01-04,80.379997,81.010002,79.430000,80.550003,80.550003,6150000\n1967-01-05,80.550003,81.930000,80.500000,81.599998,81.599998,7320000\n1967-01-06,81.599998,82.790001,81.320000,82.180000,82.180000,7830000\n1967-01-09,82.180000,83.309998,81.779999,82.809998,82.809998,9180000\n1967-01-10,82.809998,83.540001,82.220001,82.809998,82.809998,8120000\n1967-01-11,82.809998,83.919998,81.370003,83.470001,83.470001,13230000\n1967-01-12,83.470001,84.800003,83.110001,83.910004,83.910004,12830000\n1967-01-13,83.910004,84.900002,83.099998,84.529999,84.529999,10000000\n1967-01-16,84.529999,85.279999,83.730003,84.309998,84.309998,10280000\n1967-01-17,84.309998,85.809998,84.029999,85.239998,85.239998,11590000\n1967-01-18,85.239998,86.360001,84.900002,85.790001,85.790001,11390000\n1967-01-19,85.790001,86.610001,85.169998,85.820000,85.820000,10230000\n1967-01-20,85.820000,86.470001,85.070000,86.070000,86.070000,9530000\n1967-01-23,86.070000,88.169998,85.639999,86.389999,86.389999,10830000\n1967-01-24,86.389999,87.000000,85.290001,86.510002,86.510002,10430000\n1967-01-25,86.510002,87.019997,85.470001,85.849998,85.849998,10260000\n1967-01-26,85.849998,86.660004,84.870003,85.809998,85.809998,10630000\n1967-01-27,85.809998,86.760002,85.339996,86.160004,86.160004,9690000\n1967-01-30,86.160004,87.349998,85.839996,86.660004,86.660004,10250000\n1967-01-31,86.660004,87.459999,86.059998,86.610001,86.610001,11540000\n1967-02-01,86.610001,87.040001,85.680000,86.430000,86.430000,9580000\n1967-02-02,86.430000,87.309998,85.870003,86.730003,86.730003,10720000\n1967-02-03,86.730003,87.970001,86.510002,87.360001,87.360001,12010000\n1967-02-06,87.360001,87.980003,86.610001,87.180000,87.180000,10680000\n1967-02-07,87.180000,87.519997,86.480003,86.949997,86.949997,6400000\n1967-02-08,86.949997,88.250000,86.639999,87.720001,87.720001,11220000\n1967-02-09,87.720001,88.570000,86.989998,87.360001,87.360001,10970000\n1967-02-10,87.360001,88.190002,86.790001,87.629997,87.629997,8850000\n1967-02-13,87.629997,88.190002,86.949997,87.580002,87.580002,7570000\n1967-02-14,87.580002,88.739998,87.150002,88.169998,88.169998,9760000\n1967-02-15,88.169998,89.000000,87.620003,88.269997,88.269997,10480000\n1967-02-16,88.269997,88.800003,87.430000,87.860001,87.860001,8490000\n1967-02-17,87.860001,88.400002,87.250000,87.889999,87.889999,8530000\n1967-02-20,87.889999,88.129997,86.650002,87.400002,87.400002,8640000\n1967-02-21,87.400002,88.010002,86.800003,87.339996,87.339996,9030000\n1967-02-23,87.339996,88.000000,86.639999,87.449997,87.449997,10010000\n1967-02-24,87.449997,88.160004,86.760002,87.410004,87.410004,9830000\n1967-02-27,87.410004,87.610001,85.680000,86.459999,86.459999,10210000\n1967-02-28,86.459999,87.260002,85.610001,86.779999,86.779999,9970000\n1967-03-01,86.779999,88.360001,86.669998,87.680000,87.680000,11510000\n1967-03-02,87.680000,88.849998,87.389999,88.160004,88.160004,11900000\n1967-03-03,88.160004,89.000000,87.510002,88.290001,88.290001,11100000\n1967-03-06,88.290001,89.080002,87.459999,88.099998,88.099998,10400000\n1967-03-07,88.099998,88.739998,87.339996,88.160004,88.160004,9810000\n1967-03-08,88.160004,89.099998,87.690002,88.269997,88.269997,11070000\n1967-03-09,88.269997,89.040001,87.699997,88.529999,88.529999,10480000\n1967-03-10,88.529999,90.370003,88.459999,88.889999,88.889999,14900000\n1967-03-13,88.889999,89.410004,87.930000,88.430000,88.430000,9910000\n1967-03-14,88.430000,89.070000,87.580002,88.349998,88.349998,10260000\n1967-03-15,88.349998,89.599998,88.000000,89.190002,89.190002,10830000\n1967-03-16,89.190002,90.660004,89.089996,90.089996,90.089996,12170000\n1967-03-17,90.089996,90.839996,89.389999,90.250000,90.250000,10020000\n1967-03-20,90.250000,90.870003,89.349998,90.199997,90.199997,9040000\n1967-03-21,90.199997,91.050003,89.519997,90.000000,90.000000,9820000\n1967-03-22,90.000000,90.699997,89.169998,90.250000,90.250000,8820000\n1967-03-23,90.250000,91.510002,90.040001,90.940002,90.940002,9500000\n1967-03-27,90.940002,91.720001,90.190002,90.870003,90.870003,9260000\n1967-03-28,90.870003,91.620003,90.230003,90.910004,90.910004,8940000\n1967-03-29,90.910004,91.449997,90.169998,90.730003,90.730003,8430000\n1967-03-30,90.730003,91.320000,90.059998,90.699997,90.699997,8340000\n1967-03-31,90.699997,91.150002,89.750000,90.199997,90.199997,8130000\n1967-04-03,90.199997,90.370003,88.760002,89.239998,89.239998,8530000\n1967-04-04,89.239998,89.930000,88.449997,89.220001,89.220001,8750000\n1967-04-05,89.220001,90.309998,88.919998,89.790001,89.790001,8810000\n1967-04-06,89.790001,90.739998,89.440002,89.940002,89.940002,9470000\n1967-04-07,89.940002,90.599998,88.959999,89.360001,89.360001,9090000\n1967-04-10,89.320000,89.320000,87.860001,88.239998,88.239998,8110000\n1967-04-11,88.239998,89.339996,87.919998,88.879997,88.879997,7710000\n1967-04-12,88.879997,89.540001,88.360001,88.779999,88.779999,7750000\n1967-04-13,88.779999,89.860001,88.489998,89.459999,89.459999,7610000\n1967-04-14,89.459999,91.080002,89.260002,90.430000,90.430000,8810000\n1967-04-17,90.430000,91.779999,90.180000,91.070000,91.070000,9070000\n1967-04-18,91.070000,92.309998,90.699997,91.860001,91.860001,10500000\n1967-04-19,91.860001,92.730003,91.250000,91.940002,91.940002,10860000\n1967-04-20,91.940002,92.610001,91.209999,92.110001,92.110001,9690000\n1967-04-21,92.110001,92.900002,91.480003,92.300003,92.300003,10210000\n1967-04-24,92.300003,93.449997,91.779999,92.620003,92.620003,10250000\n1967-04-25,92.620003,93.570000,92.010002,93.110001,93.110001,10420000\n1967-04-26,93.110001,93.989998,92.440002,93.019997,93.019997,10560000\n1967-04-27,93.019997,94.250000,92.410004,93.809998,93.809998,10250000\n1967-04-28,93.809998,94.769997,93.330002,94.010002,94.010002,11200000\n1967-05-01,94.010002,94.599998,93.080002,93.839996,93.839996,9410000\n1967-05-02,93.839996,94.419998,93.059998,93.669998,93.669998,10260000\n1967-05-03,93.669998,94.480003,92.940002,93.910004,93.910004,11550000\n1967-05-04,93.910004,94.919998,93.410004,94.320000,94.320000,12850000\n1967-05-05,94.320000,95.139999,93.639999,94.440002,94.440002,10630000\n1967-05-08,94.440002,95.220001,93.709999,94.580002,94.580002,10330000\n1967-05-09,94.580002,95.250000,93.279999,93.599998,93.599998,10830000\n1967-05-10,93.599998,94.040001,92.510002,93.349998,93.349998,10410000\n1967-05-11,93.349998,94.370003,92.900002,93.750000,93.750000,10320000\n1967-05-12,93.750000,94.449997,92.940002,93.480003,93.480003,10470000\n1967-05-15,93.480003,93.750000,92.269997,92.709999,92.709999,8320000\n1967-05-16,92.709999,93.849998,92.190002,93.139999,93.139999,10700000\n1967-05-17,93.139999,93.750000,92.339996,92.779999,92.779999,9560000\n1967-05-18,92.779999,93.300003,91.980003,92.529999,92.529999,10290000\n1967-05-19,92.529999,92.860001,91.400002,92.070000,92.070000,10560000\n1967-05-22,92.070000,92.400002,90.830002,91.669998,91.669998,9600000\n1967-05-23,91.669998,92.070000,90.580002,91.230003,91.230003,9810000\n1967-05-24,91.230003,91.360001,89.680000,90.180000,90.180000,10290000\n1967-05-25,90.180000,91.839996,90.040001,91.190002,91.190002,8960000\n1967-05-26,91.190002,91.699997,90.339996,90.980003,90.980003,7810000\n1967-05-29,90.980003,91.220001,89.919998,90.489998,90.489998,6590000\n1967-05-31,90.389999,90.389999,88.709999,89.080002,89.080002,8870000\n1967-06-01,89.080002,90.760002,88.809998,90.230003,90.230003,9040000\n1967-06-02,90.230003,90.900002,89.269997,89.790001,89.790001,8070000\n1967-06-05,89.559998,89.559998,87.190002,88.430000,88.430000,11110000\n1967-06-06,88.480003,90.589996,88.480003,90.230003,90.230003,9230000\n1967-06-07,90.230003,91.750000,89.919998,90.910004,90.910004,10170000\n1967-06-08,90.910004,91.779999,90.239998,91.400002,91.400002,8300000\n1967-06-09,91.400002,92.260002,90.769997,91.559998,91.559998,9650000\n1967-06-12,91.559998,92.660004,91.120003,92.040001,92.040001,10230000\n1967-06-13,92.040001,93.269997,91.650002,92.620003,92.620003,11570000\n1967-06-14,92.620003,93.209999,91.809998,92.400002,92.400002,10960000\n1967-06-15,92.400002,93.260002,91.760002,92.489998,92.489998,11240000\n1967-06-16,92.489998,93.279999,91.980003,92.540001,92.540001,10740000\n1967-06-19,92.510002,92.510002,92.510002,92.510002,92.510002,8570000\n1967-06-20,92.480003,92.480003,92.480003,92.480003,92.480003,10350000\n1967-06-21,92.199997,92.199997,92.199997,92.199997,92.199997,9760000\n1967-06-22,91.970001,91.970001,91.970001,91.970001,91.970001,9550000\n1967-06-23,92.000000,92.000000,92.000000,92.000000,92.000000,9130000\n1967-06-26,91.639999,91.639999,91.639999,91.639999,91.639999,9040000\n1967-06-27,91.300003,91.300003,91.300003,91.300003,91.300003,8780000\n1967-06-28,91.309998,91.309998,91.309998,91.309998,91.309998,9310000\n1967-06-29,90.849998,90.849998,90.849998,90.849998,90.849998,9940000\n1967-06-30,90.639999,90.639999,90.639999,90.639999,90.639999,7850000\n1967-07-03,90.639999,91.320000,90.120003,90.910004,90.910004,6040000\n1967-07-05,90.910004,91.910004,90.559998,91.360001,91.360001,9170000\n1967-07-06,91.360001,92.029999,90.639999,91.320000,91.320000,10170000\n1967-07-07,91.320000,92.279999,90.760002,91.690002,91.690002,11540000\n1967-07-10,91.690002,92.800003,91.110001,92.050003,92.050003,12130000\n1967-07-11,92.050003,93.160004,91.580002,92.480003,92.480003,12400000\n1967-07-12,92.480003,93.099998,91.620003,92.400002,92.400002,11240000\n1967-07-13,92.400002,93.169998,91.820000,92.419998,92.419998,10730000\n1967-07-14,92.419998,93.349998,91.870003,92.739998,92.739998,10880000\n1967-07-17,92.739998,93.529999,92.099998,92.750000,92.750000,10390000\n1967-07-18,92.750000,94.050003,92.300003,93.500000,93.500000,12060000\n1967-07-19,93.500000,94.400002,92.830002,93.650002,93.650002,12850000\n1967-07-20,93.650002,94.489998,93.010002,93.849998,93.849998,11160000\n1967-07-21,93.849998,94.919998,93.239998,94.040001,94.040001,11710000\n1967-07-24,94.040001,94.680000,92.910004,93.730003,93.730003,9580000\n1967-07-25,93.730003,94.559998,93.029999,93.239998,93.239998,9890000\n1967-07-26,93.239998,94.709999,93.120003,94.059998,94.059998,11160000\n1967-07-27,94.059998,95.190002,93.510002,94.349998,94.349998,12400000\n1967-07-28,94.349998,95.230003,93.769997,94.489998,94.489998,10900000\n1967-07-31,94.489998,95.510002,94.010002,94.750000,94.750000,10330000\n1967-08-01,94.750000,95.839996,94.199997,95.370003,95.370003,12290000\n1967-08-02,95.370003,96.639999,95.029999,95.779999,95.779999,13510000\n1967-08-03,95.779999,96.360001,94.419998,95.660004,95.660004,13440000\n1967-08-04,95.660004,96.540001,95.150002,95.830002,95.830002,11130000\n1967-08-07,95.830002,96.430000,95.019997,95.580002,95.580002,10160000\n1967-08-08,95.580002,96.279999,95.040001,95.690002,95.690002,8970000\n1967-08-09,95.690002,96.470001,95.110001,95.779999,95.779999,10100000\n1967-08-10,95.779999,96.669998,95.050003,95.529999,95.529999,9040000\n1967-08-11,95.529999,95.980003,94.620003,95.150002,95.150002,8250000\n1967-08-14,95.150002,95.400002,94.019997,94.639999,94.639999,7990000\n1967-08-15,94.639999,95.540001,94.180000,94.769997,94.769997,8710000\n1967-08-16,94.769997,95.150002,93.930000,94.550003,94.550003,8220000\n1967-08-17,94.550003,95.330002,94.110001,94.629997,94.629997,8790000\n1967-08-18,94.629997,95.400002,94.160004,94.779999,94.779999,8250000\n1967-08-21,94.779999,95.220001,93.790001,94.250000,94.250000,8600000\n1967-08-22,94.250000,94.720001,93.349998,93.739998,93.739998,7940000\n1967-08-23,93.739998,94.150002,92.769997,93.610001,93.610001,8760000\n1967-08-24,93.610001,94.279999,92.769997,93.089996,93.089996,7740000\n1967-08-25,93.089996,93.379997,92.040001,92.699997,92.699997,7250000\n1967-08-28,92.699997,93.309998,92.010002,92.639999,92.639999,6270000\n1967-08-29,92.639999,93.580002,92.169998,92.879997,92.879997,6350000\n1967-08-30,92.879997,93.669998,92.430000,93.070000,93.070000,7200000\n1967-08-31,93.070000,94.190002,92.839996,93.639999,93.639999,8840000\n1967-09-01,93.639999,94.209999,93.000000,93.680000,93.680000,7460000\n1967-09-05,93.680000,94.699997,93.360001,94.209999,94.209999,8320000\n1967-09-06,94.209999,95.059998,93.720001,94.389999,94.389999,9550000\n1967-09-07,94.389999,94.949997,93.699997,94.330002,94.330002,8910000\n1967-09-08,94.330002,95.040001,93.699997,94.360001,94.360001,9300000\n1967-09-11,94.360001,95.260002,93.879997,94.540001,94.540001,9170000\n1967-09-12,94.540001,95.480003,94.010002,94.989998,94.989998,9930000\n1967-09-13,94.989998,96.620003,94.800003,95.989998,95.989998,12400000\n1967-09-14,95.989998,97.400002,95.589996,96.199997,96.199997,12220000\n1967-09-15,96.199997,96.940002,95.470001,96.269997,96.269997,10270000\n1967-09-18,96.269997,97.309998,95.730003,96.529999,96.529999,11620000\n1967-09-19,96.529999,97.349998,95.839996,96.169998,96.169998,11540000\n1967-09-20,96.169998,96.839996,95.389999,96.129997,96.129997,10980000\n1967-09-21,96.129997,97.500000,95.669998,96.750000,96.750000,11290000\n1967-09-22,96.750000,97.610001,96.110001,97.000000,97.000000,11160000\n1967-09-25,97.000000,98.309998,96.739998,97.589996,97.589996,10910000\n1967-09-26,97.589996,98.199997,96.400002,96.760002,96.760002,10940000\n1967-09-27,96.760002,97.540001,96.000000,96.790001,96.790001,8810000\n1967-09-28,96.790001,97.589996,96.190002,96.790001,96.790001,10470000\n1967-09-29,96.790001,97.370003,96.059998,96.709999,96.709999,9710000\n1967-10-02,96.709999,97.250000,95.820000,96.320000,96.320000,9240000\n1967-10-03,96.320000,97.230003,95.750000,96.650002,96.650002,10320000\n1967-10-04,96.650002,97.470001,95.940002,96.430000,96.430000,11520000\n1967-10-05,96.430000,97.250000,95.889999,96.669998,96.669998,8490000\n1967-10-06,96.669998,97.830002,96.339996,97.260002,97.260002,9830000\n1967-10-09,97.260002,98.250000,96.699997,97.510002,97.510002,11180000\n1967-10-10,97.510002,98.150002,96.379997,96.839996,96.839996,12000000\n1967-10-11,96.839996,97.339996,95.699997,96.370003,96.370003,11230000\n1967-10-12,96.370003,96.699997,95.320000,95.750000,95.750000,7770000\n1967-10-13,95.750000,96.690002,95.160004,96.000000,96.000000,9040000\n1967-10-16,96.000000,96.550003,94.849998,95.250000,95.250000,9080000\n1967-10-17,95.250000,95.919998,94.190002,95.000000,95.000000,10290000\n1967-10-18,95.000000,95.820000,94.339996,95.250000,95.250000,10500000\n1967-10-19,95.250000,96.459999,94.860001,95.430000,95.430000,11620000\n1967-10-20,95.430000,96.120003,94.620003,95.379997,95.379997,9510000\n1967-10-23,95.379997,95.690002,93.919998,94.959999,94.959999,9680000\n1967-10-24,94.959999,95.980003,94.050003,94.419998,94.419998,11110000\n1967-10-25,94.419998,95.180000,93.470001,94.519997,94.519997,10300000\n1967-10-26,94.519997,95.559998,93.989998,94.940002,94.940002,9920000\n1967-10-27,94.940002,95.790001,94.309998,94.959999,94.959999,9880000\n1967-10-30,94.959999,95.669998,94.139999,94.790001,94.790001,10250000\n1967-10-31,94.790001,95.250000,93.290001,93.300003,93.300003,12020000\n1967-11-01,93.300003,94.209999,92.449997,92.709999,92.709999,10930000\n1967-11-02,92.709999,93.690002,91.849998,92.339996,92.339996,10760000\n1967-11-03,92.339996,92.900002,91.330002,91.779999,91.779999,8800000\n1967-11-06,91.779999,92.230003,90.389999,91.480003,91.480003,10320000\n1967-11-08,91.480003,93.070000,90.800003,91.139999,91.139999,12630000\n1967-11-09,91.139999,92.250000,90.610001,91.589996,91.589996,8890000\n1967-11-10,91.589996,92.839996,91.290001,92.209999,92.209999,9960000\n1967-11-13,92.209999,93.230003,91.459999,91.970001,91.970001,10130000\n1967-11-14,91.970001,92.489998,90.809998,91.389999,91.389999,10350000\n1967-11-15,91.389999,92.250000,90.440002,91.760002,91.760002,10000000\n1967-11-16,91.760002,93.279999,91.500000,92.599998,92.599998,10570000\n1967-11-17,92.599998,93.620003,92.019997,92.820000,92.820000,10050000\n1967-11-20,92.379997,92.379997,90.089996,91.650002,91.650002,12750000\n1967-11-21,91.650002,93.709999,91.639999,93.099998,93.099998,12300000\n1967-11-22,93.099998,94.410004,92.699997,93.650002,93.650002,12180000\n1967-11-24,93.650002,94.459999,92.739998,93.900002,93.900002,9470000\n1967-11-27,93.900002,94.800003,93.320000,94.169998,94.169998,10040000\n1967-11-28,94.169998,95.080002,93.570000,94.489998,94.489998,11040000\n1967-11-29,94.489998,95.510002,93.849998,94.470001,94.470001,11400000\n1967-11-30,94.470001,94.940002,93.489998,94.000000,94.000000,8860000\n1967-12-01,94.000000,94.949997,93.410004,94.500000,94.500000,9740000\n1967-12-04,94.500000,95.680000,94.089996,95.099998,95.099998,11740000\n1967-12-05,95.099998,96.269997,94.519997,95.230003,95.230003,12940000\n1967-12-06,95.230003,96.160004,94.099998,95.639999,95.639999,11940000\n1967-12-07,95.639999,96.669998,95.040001,95.529999,95.529999,12490000\n1967-12-08,95.529999,96.250000,94.779999,95.419998,95.419998,10710000\n1967-12-11,95.419998,95.989998,94.500000,95.120003,95.120003,10500000\n1967-12-12,95.120003,95.779999,94.339996,95.010002,95.010002,10860000\n1967-12-13,95.010002,96.000000,94.580002,95.339996,95.339996,12480000\n1967-12-14,95.339996,96.349998,94.849998,95.470001,95.470001,12310000\n1967-12-15,95.470001,96.199997,94.510002,95.029999,95.029999,11530000\n1967-12-18,95.029999,95.879997,94.169998,94.769997,94.769997,10320000\n1967-12-19,94.769997,95.410004,94.000000,94.629997,94.629997,10610000\n1967-12-20,94.629997,95.750000,94.169998,95.150002,95.150002,11390000\n1967-12-21,95.150002,96.250000,94.690002,95.379997,95.379997,11010000\n1967-12-22,95.379997,96.110001,94.610001,95.199997,95.199997,9570000\n1967-12-26,95.199997,96.019997,94.610001,95.260002,95.260002,9150000\n1967-12-27,95.260002,96.419998,94.820000,95.910004,95.910004,12690000\n1967-12-28,95.910004,96.650002,94.910004,95.889999,95.889999,12530000\n1967-12-29,95.889999,96.900002,95.849998,96.470001,96.470001,14950000\n1968-01-02,96.470001,97.330002,95.309998,96.110001,96.110001,11080000\n1968-01-03,96.110001,96.949997,95.040001,95.669998,95.669998,12650000\n1968-01-04,95.669998,96.230003,94.309998,95.360001,95.360001,13440000\n1968-01-05,95.360001,96.660004,94.970001,95.940002,95.940002,11880000\n1968-01-08,95.940002,97.400002,95.540001,96.620003,96.620003,14260000\n1968-01-09,96.620003,97.839996,95.889999,96.500000,96.500000,13720000\n1968-01-10,96.500000,97.260002,95.660004,96.519997,96.519997,11670000\n1968-01-11,96.519997,97.820000,95.879997,96.620003,96.620003,13220000\n1968-01-12,96.620003,97.440002,95.870003,96.720001,96.720001,13080000\n1968-01-15,96.720001,97.459999,95.849998,96.419998,96.419998,12640000\n1968-01-16,96.419998,96.910004,95.320000,95.820000,95.820000,12340000\n1968-01-17,95.820000,96.410004,94.779999,95.639999,95.639999,12910000\n1968-01-18,95.639999,96.660004,95.010002,95.559998,95.559998,13840000\n1968-01-19,95.559998,96.220001,94.599998,95.239998,95.239998,11950000\n1968-01-22,95.239998,95.400002,93.550003,94.029999,94.029999,10630000\n1968-01-23,94.029999,94.660004,92.879997,93.660004,93.660004,11030000\n1968-01-24,93.660004,94.120003,92.449997,93.169998,93.169998,10570000\n1968-01-25,93.169998,94.110001,91.959999,93.300003,93.300003,12410000\n1968-01-26,93.300003,94.339996,92.769997,93.449997,93.449997,9980000\n1968-01-29,93.449997,94.379997,92.709999,93.349998,93.349998,9950000\n1968-01-30,93.349998,93.709999,92.180000,92.889999,92.889999,10110000\n1968-01-31,92.889999,93.260002,91.269997,92.239998,92.239998,9410000\n1968-02-01,92.239998,93.139999,91.570000,92.559998,92.559998,10590000\n1968-02-02,92.559998,93.440002,91.690002,92.269997,92.269997,10120000\n1968-02-05,92.269997,92.720001,91.239998,91.870003,91.870003,8980000\n1968-02-06,91.870003,92.519997,91.150002,91.900002,91.900002,8560000\n1968-02-07,91.900002,92.739998,91.480003,92.059998,92.059998,8380000\n1968-02-08,92.059998,92.400002,90.599998,90.900002,90.900002,9660000\n1968-02-09,90.900002,91.000000,89.230003,89.860001,89.860001,11850000\n1968-02-13,89.860001,90.459999,86.730003,89.070000,89.070000,10830000\n1968-02-14,89.070000,90.599998,88.660004,90.139999,90.139999,11390000\n1968-02-15,90.300003,90.300003,90.300003,90.300003,90.300003,9770000\n1968-02-16,90.300003,90.620003,89.279999,89.959999,89.959999,9070000\n1968-02-19,89.959999,90.870003,89.419998,90.309998,90.309998,7270000\n1968-02-20,90.309998,91.339996,89.949997,91.239998,91.239998,8800000\n1968-02-21,91.239998,91.870003,90.540001,91.239998,91.239998,9170000\n1968-02-23,91.239998,91.800003,90.279999,90.889999,90.889999,8810000\n1968-02-26,90.889999,91.080002,89.669998,90.180000,90.180000,7810000\n1968-02-27,90.180000,90.910004,89.559998,90.529999,90.529999,7600000\n1968-02-28,90.529999,91.190002,89.709999,90.080002,90.080002,8020000\n1968-02-29,90.080002,90.239998,88.930000,89.360001,89.360001,7700000\n1968-03-01,89.360001,89.820000,88.580002,89.110001,89.110001,8610000\n1968-03-04,89.110001,89.330002,87.519997,87.919998,87.919998,10590000\n1968-03-05,87.919998,88.720001,86.989998,87.720001,87.720001,11440000\n1968-03-06,87.720001,89.760002,87.639999,89.260002,89.260002,9900000\n1968-03-07,89.260002,89.980003,88.440002,89.099998,89.099998,8630000\n1968-03-08,89.099998,89.570000,88.230003,89.029999,89.029999,7410000\n1968-03-11,89.029999,90.559998,88.809998,90.129997,90.129997,9520000\n1968-03-12,90.129997,90.779999,89.389999,90.230003,90.230003,9250000\n1968-03-13,90.230003,90.709999,89.400002,90.029999,90.029999,8990000\n1968-03-14,89.750000,89.750000,87.809998,88.320000,88.320000,11640000\n1968-03-15,88.320000,89.750000,87.610001,89.099998,89.099998,11210000\n1968-03-18,89.110001,91.089996,89.110001,89.589996,89.589996,10800000\n1968-03-19,89.589996,90.050003,88.610001,88.989998,88.989998,7410000\n1968-03-20,88.989998,89.650002,88.480003,88.980003,88.980003,7390000\n1968-03-21,88.980003,89.480003,88.050003,88.330002,88.330002,8580000\n1968-03-22,88.330002,89.139999,87.500000,88.419998,88.419998,9900000\n1968-03-25,88.419998,88.879997,87.650002,88.330002,88.330002,6700000\n1968-03-26,88.330002,89.500000,88.099998,88.930000,88.930000,8670000\n1968-03-27,88.930000,90.199997,88.879997,89.660004,89.660004,8970000\n1968-03-28,89.660004,90.400002,89.050003,89.570000,89.570000,8000000\n1968-03-29,89.570000,90.919998,89.209999,90.199997,90.199997,9000000\n1968-04-01,91.110001,93.550003,91.110001,92.480003,92.480003,17730000\n1968-04-02,92.480003,93.440002,91.389999,92.639999,92.639999,14520000\n1968-04-03,92.639999,95.129997,92.239998,93.470001,93.470001,19290000\n1968-04-04,93.470001,94.589996,92.629997,93.839996,93.839996,14340000\n1968-04-05,93.839996,94.510002,92.669998,93.290001,93.290001,12570000\n1968-04-08,93.290001,95.449997,93.110001,94.949997,94.949997,13010000\n1968-04-10,94.949997,97.110001,94.739998,95.669998,95.669998,20410000\n1968-04-11,95.669998,96.930000,94.809998,96.529999,96.529999,14230000\n1968-04-15,96.529999,97.360001,95.330002,96.589996,96.589996,14220000\n1968-04-16,96.589996,97.540001,95.720001,96.620003,96.620003,15680000\n1968-04-17,96.620003,97.400002,95.760002,96.809998,96.809998,14090000\n1968-04-18,96.809998,97.889999,96.120003,97.080002,97.080002,15890000\n1968-04-19,97.080002,97.080002,95.150002,95.849998,95.849998,14560000\n1968-04-22,95.849998,96.070000,94.220001,95.320000,95.320000,11720000\n1968-04-23,95.680000,97.480003,95.680000,96.620003,96.620003,14010000\n1968-04-24,96.620003,97.809998,95.980003,96.919998,96.919998,14810000\n1968-04-25,96.919998,97.480003,95.680000,96.620003,96.620003,14430000\n1968-04-26,96.620003,97.830002,96.220001,97.209999,97.209999,13500000\n1968-04-29,97.209999,98.610001,96.809998,97.970001,97.970001,12030000\n1968-04-30,97.970001,98.169998,96.580002,97.459999,97.459999,14380000\n1968-05-01,97.459999,98.610001,96.839996,97.970001,97.970001,14440000\n1968-05-02,97.970001,99.180000,97.529999,98.589996,98.589996,14260000\n1968-05-03,98.589996,100.190002,97.980003,98.660004,98.660004,17990000\n1968-05-06,98.660004,99.110001,97.269997,98.349998,98.349998,12160000\n1968-05-07,98.349998,99.589996,97.860001,98.900002,98.900002,13920000\n1968-05-08,98.900002,99.739998,98.250000,98.910004,98.910004,13120000\n1968-05-09,98.910004,99.470001,97.680000,98.389999,98.389999,12890000\n1968-05-10,98.389999,99.300003,97.760002,98.500000,98.500000,11700000\n1968-05-13,98.500000,99.099998,97.519997,98.190002,98.190002,11860000\n1968-05-14,98.190002,98.849998,97.330002,98.120003,98.120003,13160000\n1968-05-15,98.120003,98.790001,97.320000,98.070000,98.070000,13180000\n1968-05-16,98.070000,98.690002,97.050003,97.599998,97.599998,13030000\n1968-05-17,97.599998,97.809998,96.110001,96.900002,96.900002,11830000\n1968-05-20,96.900002,97.410004,95.800003,96.449997,96.449997,11180000\n1968-05-21,96.449997,97.519997,95.919998,96.930000,96.930000,13160000\n1968-05-22,96.930000,98.169998,96.470001,97.180000,97.180000,14200000\n1968-05-23,97.180000,97.790001,96.379997,96.970001,96.970001,12840000\n1968-05-24,96.970001,97.730003,96.209999,97.150002,97.150002,13300000\n1968-05-27,97.150002,97.809998,96.290001,96.989998,96.989998,12720000\n1968-05-28,96.989998,98.199997,96.410004,97.620003,97.620003,13850000\n1968-05-29,97.620003,98.739998,97.010002,97.919998,97.919998,14100000\n1968-05-31,97.919998,99.400002,97.660004,98.680000,98.680000,13090000\n1968-06-03,98.720001,100.620003,98.720001,99.989998,99.989998,14970000\n1968-06-04,99.989998,101.260002,99.320000,100.379997,100.379997,18030000\n1968-06-05,100.379997,101.129997,99.260002,99.889999,99.889999,15590000\n1968-06-06,99.889999,101.589996,99.500000,100.650002,100.650002,16130000\n1968-06-07,100.650002,101.889999,100.239998,101.269997,101.269997,17320000\n1968-06-10,101.269997,102.250000,100.419998,101.410004,101.410004,14640000\n1968-06-11,101.410004,102.400002,100.739998,101.660004,101.660004,15700000\n1968-06-13,101.660004,102.839996,100.550003,101.250000,101.250000,21350000\n1968-06-14,101.250000,101.820000,99.980003,101.129997,101.129997,14690000\n1968-06-17,101.129997,101.709999,99.430000,100.129997,100.129997,12570000\n1968-06-18,100.129997,101.089996,99.430000,99.989998,99.989998,13630000\n1968-06-20,99.989998,101.599998,99.519997,101.510002,101.510002,16290000\n1968-06-21,101.510002,101.589996,99.800003,100.660004,100.660004,13450000\n1968-06-24,100.660004,101.480003,99.660004,100.389999,100.389999,12320000\n1968-06-25,100.389999,101.099998,99.279999,100.080002,100.080002,13200000\n1968-06-27,100.080002,101.010002,99.110001,99.980003,99.980003,15370000\n1968-06-28,99.980003,100.629997,98.910004,99.580002,99.580002,12040000\n1968-07-01,99.580002,100.330002,98.769997,99.400002,99.400002,11280000\n1968-07-02,99.400002,100.599998,98.599998,99.739998,99.739998,13350000\n1968-07-03,99.739998,101.360001,99.599998,100.910004,100.910004,14390000\n1968-07-08,100.910004,102.760002,100.720001,101.940002,101.940002,16860000\n1968-07-09,101.940002,102.930000,101.190002,102.230003,102.230003,16540000\n1968-07-11,102.230003,103.669998,101.410004,102.389999,102.389999,20290000\n1968-07-12,102.389999,103.239998,101.389999,102.339996,102.339996,14810000\n1968-07-15,102.339996,103.150002,101.440002,102.260002,102.260002,13390000\n1968-07-16,102.260002,102.720001,100.970001,101.699997,101.699997,13380000\n1968-07-18,101.699997,102.650002,100.489998,101.440002,101.440002,17420000\n1968-07-19,101.440002,101.820000,99.800003,100.459999,100.459999,14620000\n1968-07-22,100.459999,100.879997,98.510002,99.330002,99.330002,13530000\n1968-07-23,99.330002,99.930000,97.889999,99.209999,99.209999,13570000\n1968-07-25,99.209999,100.070000,97.430000,97.940002,97.940002,16140000\n1968-07-26,97.940002,99.139999,97.220001,98.339996,98.339996,11690000\n1968-07-29,98.339996,98.779999,96.889999,97.650002,97.650002,10940000\n1968-07-30,97.650002,98.620003,96.839996,97.739998,97.739998,10250000\n1968-08-01,97.739998,98.820000,96.779999,97.279999,97.279999,14380000\n1968-08-02,97.279999,97.470001,95.790001,96.629997,96.629997,9860000\n1968-08-05,96.629997,97.510002,95.949997,96.849998,96.849998,8850000\n1968-08-06,96.849998,97.820000,96.419998,97.250000,97.250000,9620000\n1968-08-08,97.250000,98.320000,96.580002,97.040001,97.040001,12920000\n1968-08-09,97.040001,97.559998,96.110001,97.010002,97.010002,8390000\n1968-08-12,97.010002,98.489998,96.720001,98.010002,98.010002,10420000\n1968-08-13,98.010002,99.199997,97.680000,98.529999,98.529999,12730000\n1968-08-15,98.529999,99.360001,97.480003,98.070000,98.070000,12710000\n1968-08-16,98.070000,99.209999,97.620003,98.680000,98.680000,9940000\n1968-08-19,98.680000,99.639999,98.160004,99.000000,99.000000,9900000\n1968-08-20,99.000000,99.650002,98.080002,98.959999,98.959999,10640000\n1968-08-22,98.959999,99.580002,97.709999,98.699997,98.699997,15140000\n1968-08-23,98.699997,99.570000,97.709999,98.690002,98.690002,9890000\n1968-08-26,98.690002,99.669998,98.290001,98.940002,98.940002,9740000\n1968-08-27,98.940002,99.610001,98.160004,98.809998,98.809998,9710000\n1968-08-29,98.809998,99.489998,97.900002,98.739998,98.739998,10940000\n1968-08-30,98.739998,99.519997,98.199997,98.860001,98.860001,8190000\n1968-09-03,98.860001,99.889999,98.309998,99.320000,99.320000,8620000\n1968-09-04,99.320000,100.489998,98.949997,100.019997,100.019997,10040000\n1968-09-05,100.019997,101.339996,99.629997,100.739998,100.739998,12980000\n1968-09-06,100.739998,101.879997,100.230003,101.199997,101.199997,13180000\n1968-09-09,101.199997,102.089996,100.470001,101.230003,101.230003,11890000\n1968-09-10,101.230003,101.809998,100.120003,100.730003,100.730003,11430000\n1968-09-12,100.730003,101.400002,99.699997,100.519997,100.519997,14630000\n1968-09-13,100.519997,101.529999,99.889999,100.860001,100.860001,13070000\n1968-09-16,100.860001,102.010002,100.330002,101.239998,101.239998,13260000\n1968-09-17,101.239998,102.180000,100.639999,101.500000,101.500000,13920000\n1968-09-19,101.500000,102.529999,100.839996,101.589996,101.589996,17910000\n1968-09-20,101.589996,102.370003,100.809998,101.660004,101.660004,14190000\n1968-09-23,101.660004,102.820000,101.199997,102.239998,102.239998,11550000\n1968-09-24,102.239998,103.209999,101.589996,102.589996,102.589996,15210000\n1968-09-26,102.589996,103.629997,101.589996,102.360001,102.360001,18950000\n1968-09-27,102.360001,103.070000,101.360001,102.309998,102.309998,13860000\n1968-09-30,102.309998,103.290001,101.709999,102.669998,102.669998,13610000\n1968-10-01,102.669998,103.580002,101.800003,102.860001,102.860001,15560000\n1968-10-03,102.860001,104.129997,102.339996,103.220001,103.220001,21110000\n1968-10-04,103.220001,104.349998,102.650002,103.709999,103.709999,15350000\n1968-10-07,103.709999,104.400002,102.930000,103.699997,103.699997,12420000\n1968-10-08,103.699997,104.449997,102.839996,103.739998,103.739998,14000000\n1968-10-10,103.739998,104.300003,102.610001,103.290001,103.290001,17000000\n1968-10-11,103.290001,103.900002,102.389999,103.180000,103.180000,12650000\n1968-10-14,103.180000,104.029999,102.480003,103.320000,103.320000,11980000\n1968-10-15,103.320000,104.250000,102.660004,103.529999,103.529999,13410000\n1968-10-17,103.809998,105.010002,103.809998,104.010002,104.010002,21060000\n1968-10-18,104.010002,105.339996,103.540001,104.820000,104.820000,15130000\n1968-10-21,104.820000,105.779999,104.089996,104.989998,104.989998,14380000\n1968-10-22,104.989998,105.480003,103.839996,104.570000,104.570000,13970000\n1968-10-24,104.570000,105.150002,103.150002,103.839996,103.839996,18300000\n1968-10-25,103.839996,104.809998,103.139999,104.199997,104.199997,14150000\n1968-10-28,104.199997,104.889999,103.160004,103.900002,103.900002,11740000\n1968-10-29,103.900002,104.500000,102.650002,103.300003,103.300003,12340000\n1968-10-31,103.300003,104.570000,102.430000,103.410004,103.410004,17650000\n1968-11-01,103.410004,104.300003,102.360001,103.059998,103.059998,14480000\n1968-11-04,103.059998,103.690002,101.849998,103.099998,103.099998,10930000\n1968-11-06,103.099998,104.410004,102.449997,103.269997,103.269997,12640000\n1968-11-07,103.269997,104.470001,102.309998,103.500000,103.500000,11660000\n1968-11-08,103.500000,104.589996,102.959999,103.949997,103.949997,14250000\n1968-11-12,103.949997,105.279999,103.510002,104.620003,104.620003,17250000\n1968-11-13,104.620003,105.760002,104.080002,105.129997,105.129997,15660000\n1968-11-14,105.129997,106.010002,104.339996,105.199997,105.199997,14900000\n1968-11-15,105.199997,106.440002,104.610001,105.779999,105.779999,15040000\n1968-11-18,105.779999,106.739998,105.050003,105.919998,105.919998,14390000\n1968-11-19,105.919998,106.839996,105.059998,106.139999,106.139999,15120000\n1968-11-21,106.139999,106.769997,104.849998,105.970001,105.970001,18320000\n1968-11-22,105.970001,106.889999,105.209999,106.300003,106.300003,15420000\n1968-11-25,106.300003,107.290001,105.470001,106.480003,106.480003,14490000\n1968-11-26,106.480003,107.930000,106.110001,107.260002,107.260002,16360000\n1968-11-27,107.260002,108.550003,106.589996,107.760002,107.760002,16550000\n1968-11-29,107.760002,109.089996,107.320000,108.370003,108.370003,14390000\n1968-12-02,108.370003,109.370003,107.150002,108.120003,108.120003,15390000\n1968-12-03,108.120003,108.739998,107.019997,108.019997,108.019997,15460000\n1968-12-05,108.019997,108.900002,106.709999,107.669998,107.669998,19330000\n1968-12-06,107.669998,108.910004,106.849998,107.930000,107.930000,15320000\n1968-12-09,107.930000,108.769997,106.889999,107.660004,107.660004,15800000\n1968-12-10,107.660004,108.330002,106.680000,107.389999,107.389999,14500000\n1968-12-12,107.389999,108.430000,106.330002,107.320000,107.320000,18160000\n1968-12-13,107.320000,108.500000,106.559998,107.580002,107.580002,16740000\n1968-12-16,107.580002,108.400002,106.400002,107.099998,107.099998,15950000\n1968-12-17,107.099998,107.650002,105.860001,106.660004,106.660004,14700000\n1968-12-19,106.660004,107.669998,105.099998,106.970001,106.970001,19630000\n1968-12-20,106.970001,107.980003,105.730003,106.339996,106.339996,15910000\n1968-12-23,106.339996,106.680000,104.610001,105.209999,105.209999,12970000\n1968-12-24,105.209999,105.949997,104.370003,105.040001,105.040001,11540000\n1968-12-26,105.040001,106.029999,104.290001,105.150002,105.150002,9670000\n1968-12-27,105.150002,105.870003,104.199997,104.739998,104.739998,11200000\n1968-12-30,104.739998,104.989998,103.089996,103.800003,103.800003,12080000\n1968-12-31,103.800003,104.610001,102.980003,103.860001,103.860001,13130000\n1969-01-02,103.860001,104.849998,103.209999,103.930000,103.930000,9800000\n1969-01-03,103.930000,104.870003,103.169998,103.989998,103.989998,12750000\n1969-01-06,103.989998,104.360001,101.940002,102.470001,102.470001,12720000\n1969-01-07,102.470001,102.680000,100.150002,101.220001,101.220001,15740000\n1969-01-08,101.220001,102.120003,100.139999,100.800003,100.800003,13840000\n1969-01-09,100.800003,102.089996,100.349998,101.220001,101.220001,12100000\n1969-01-10,101.220001,102.139999,100.320000,100.930000,100.930000,12680000\n1969-01-13,100.930000,101.349998,96.629997,100.440002,100.440002,11160000\n1969-01-14,100.440002,101.629997,99.040001,101.129997,101.129997,10700000\n1969-01-15,101.129997,102.480003,100.779999,101.620003,101.620003,11810000\n1969-01-16,101.620003,103.250000,101.269997,102.180000,102.180000,13120000\n1969-01-17,102.180000,103.059998,101.320000,102.029999,102.029999,11590000\n1969-01-20,102.029999,102.599998,101.000000,101.690002,101.690002,10950000\n1969-01-21,101.690002,102.400002,100.879997,101.629997,101.629997,10910000\n1969-01-22,101.629997,102.550003,101.059998,101.980003,101.980003,11480000\n1969-01-23,101.980003,103.209999,101.570000,102.430000,102.430000,13140000\n1969-01-24,102.430000,103.230003,101.709999,102.379997,102.379997,12520000\n1969-01-27,102.379997,103.150002,101.639999,102.400002,102.400002,11020000\n1969-01-28,102.400002,103.300003,101.559998,102.410004,102.410004,12070000\n1969-01-29,102.410004,103.309998,101.690002,102.510002,102.510002,11470000\n1969-01-30,102.510002,103.330002,101.730003,102.550003,102.550003,13010000\n1969-01-31,102.550003,103.639999,102.080002,103.010002,103.010002,12020000\n1969-02-03,103.010002,103.750000,102.040001,102.889999,102.889999,12510000\n1969-02-04,102.889999,103.589996,102.150002,102.919998,102.919998,12550000\n1969-02-05,102.919998,103.839996,102.260002,103.199997,103.199997,13750000\n1969-02-06,103.199997,104.300003,102.550003,103.540001,103.540001,12570000\n1969-02-07,103.540001,104.220001,102.500000,103.529999,103.529999,12780000\n1969-02-11,103.529999,104.610001,102.959999,103.650002,103.650002,12320000\n1969-02-12,103.650002,104.339996,102.980003,103.629997,103.629997,11530000\n1969-02-13,103.629997,104.360001,102.860001,103.709999,103.709999,12010000\n1969-02-14,103.709999,104.370003,102.879997,103.610001,103.610001,11460000\n1969-02-17,103.610001,104.029999,102.040001,102.440002,102.440002,11670000\n1969-02-18,102.269997,102.269997,100.580002,101.400002,101.400002,12490000\n1969-02-19,101.400002,102.070000,100.300003,100.650002,100.650002,10390000\n1969-02-20,100.650002,101.029999,99.290001,99.790001,99.790001,10990000\n1969-02-24,99.790001,100.070000,98.089996,98.599998,98.599998,12730000\n1969-02-25,98.599998,99.650002,97.500000,97.980003,97.980003,9540000\n1969-02-26,97.980003,99.099998,97.360001,98.449997,98.449997,9540000\n1969-02-27,98.449997,99.000000,97.500000,98.139999,98.139999,9670000\n1969-02-28,98.139999,99.019997,97.529999,98.129997,98.129997,8990000\n1969-03-03,98.129997,99.080002,97.610001,98.379997,98.379997,8260000\n1969-03-04,98.379997,99.760002,98.169998,99.320000,99.320000,9320000\n1969-03-05,99.320000,100.480003,98.949997,99.709999,99.709999,11370000\n1969-03-06,99.709999,99.930000,98.110001,98.699997,98.699997,9670000\n1969-03-07,98.699997,99.129997,97.320000,98.650002,98.650002,10830000\n1969-03-10,98.650002,99.470001,97.870003,98.989998,98.989998,8920000\n1969-03-11,98.989998,100.139999,98.580002,99.320000,99.320000,9870000\n1969-03-12,99.320000,99.870003,98.349998,99.050003,99.050003,8720000\n1969-03-13,99.050003,99.349998,97.820000,98.389999,98.389999,10030000\n1969-03-14,98.389999,98.699997,97.400002,98.000000,98.000000,8640000\n1969-03-17,98.000000,98.709999,97.059998,98.250000,98.250000,9150000\n1969-03-18,98.250000,99.410004,97.830002,98.489998,98.489998,11210000\n1969-03-19,98.489998,99.699997,98.029999,99.209999,99.209999,9740000\n1969-03-20,99.209999,100.389999,98.900002,99.839996,99.839996,10260000\n1969-03-21,99.839996,100.370003,98.879997,99.629997,99.629997,9830000\n1969-03-24,99.629997,100.160004,98.849998,99.500000,99.500000,8110000\n1969-03-25,99.500000,100.300003,98.879997,99.660004,99.660004,9820000\n1969-03-26,99.660004,100.860001,99.239998,100.389999,100.389999,11030000\n1969-03-27,100.389999,101.809998,100.029999,101.099998,101.099998,11900000\n1969-03-28,101.099998,102.349998,100.730003,101.510002,101.510002,12430000\n1969-04-01,101.510002,102.449997,100.839996,101.419998,101.419998,12360000\n1969-04-02,101.419998,101.650002,100.610001,100.779999,100.779999,10110000\n1969-04-03,100.779999,101.300003,99.870003,100.680000,100.680000,10300000\n1969-04-07,100.629997,100.629997,99.080002,99.889999,99.889999,9430000\n1969-04-08,99.889999,101.269997,99.349998,100.139999,100.139999,9360000\n1969-04-09,100.139999,101.440002,99.879997,101.019997,101.019997,12530000\n1969-04-10,101.019997,102.220001,100.730003,101.550003,101.550003,12200000\n1969-04-11,101.550003,102.279999,100.970001,101.650002,101.650002,10650000\n1969-04-14,101.650002,102.400002,101.019997,101.570000,101.570000,8990000\n1969-04-15,101.570000,102.150002,100.760002,101.529999,101.529999,9610000\n1969-04-16,101.529999,101.779999,100.160004,100.629997,100.629997,9680000\n1969-04-17,100.629997,101.410004,99.989998,100.779999,100.779999,9360000\n1969-04-18,100.779999,102.089996,100.300003,101.239998,101.239998,10850000\n1969-04-21,101.239998,101.680000,100.110001,100.559998,100.559998,10010000\n1969-04-22,100.559998,101.290001,99.519997,100.779999,100.779999,10250000\n1969-04-23,100.779999,101.769997,100.150002,100.800003,100.800003,12220000\n1969-04-24,100.800003,101.800003,100.209999,101.269997,101.269997,11340000\n1969-04-25,101.269997,102.290001,100.809998,101.720001,101.720001,12480000\n1969-04-28,101.720001,102.650002,100.970001,102.029999,102.029999,11120000\n1969-04-29,102.029999,103.309998,101.510002,102.790001,102.790001,14730000\n1969-04-30,102.790001,104.559998,102.500000,103.690002,103.690002,19350000\n1969-05-01,103.690002,104.589996,102.739998,103.510002,103.510002,14380000\n1969-05-02,103.510002,104.629997,102.980003,104.000000,104.000000,13070000\n1969-05-05,104.000000,105.080002,103.480003,104.370003,104.370003,13300000\n1969-05-06,104.370003,105.500000,103.839996,104.860001,104.860001,14700000\n1969-05-07,104.860001,105.589996,103.830002,104.669998,104.669998,14030000\n1969-05-08,104.669998,105.739998,104.099998,105.099998,105.099998,13050000\n1969-05-09,105.099998,106.010002,104.349998,105.050003,105.050003,12530000\n1969-05-12,105.050003,105.650002,104.120003,104.889999,104.889999,10550000\n1969-05-13,104.889999,105.910004,104.309998,105.339996,105.339996,12910000\n1969-05-14,105.339996,106.739998,105.070000,106.160004,106.160004,14360000\n1969-05-15,106.160004,106.690002,105.080002,105.849998,105.849998,11930000\n1969-05-16,105.849998,106.589996,105.180000,105.940002,105.940002,12280000\n1969-05-19,105.940002,106.150002,104.519997,104.970001,104.970001,9790000\n1969-05-20,104.970001,105.160004,103.559998,104.040001,104.040001,10280000\n1969-05-21,104.040001,105.029999,103.370003,104.470001,104.470001,12100000\n1969-05-22,104.470001,105.660004,103.919998,104.599998,104.599998,13710000\n1969-05-23,104.599998,105.320000,103.779999,104.589996,104.589996,10900000\n1969-05-26,104.589996,105.139999,103.800003,104.360001,104.360001,9030000\n1969-05-27,104.360001,104.680000,103.120003,103.570000,103.570000,10580000\n1969-05-28,103.570000,103.910004,102.290001,103.260002,103.260002,11330000\n1969-05-29,103.260002,104.269997,102.760002,103.459999,103.459999,11770000\n1969-06-02,103.459999,103.750000,102.400002,102.940002,102.940002,9180000\n1969-06-03,102.940002,103.599998,102.089996,102.629997,102.629997,11190000\n1969-06-04,102.629997,103.449997,102.070000,102.589996,102.589996,10840000\n1969-06-05,102.589996,103.449997,102.050003,102.760002,102.760002,12350000\n1969-06-06,102.760002,103.410004,101.680000,102.120003,102.120003,12520000\n1969-06-09,102.120003,102.160004,100.540001,101.199997,101.199997,10650000\n1969-06-10,101.199997,101.760002,100.019997,100.419998,100.419998,10660000\n1969-06-11,100.419998,100.709999,99.019997,99.050003,99.050003,13640000\n1969-06-12,99.050003,99.779999,97.959999,98.260002,98.260002,11790000\n1969-06-13,98.260002,99.510002,97.589996,98.650002,98.650002,13070000\n1969-06-16,98.650002,99.639999,97.910004,98.320000,98.320000,10400000\n1969-06-17,98.320000,98.709999,96.879997,97.949997,97.949997,12210000\n1969-06-18,97.949997,99.199997,97.449997,97.809998,97.809998,11290000\n1969-06-19,97.809998,98.379997,96.610001,97.239998,97.239998,11160000\n1969-06-20,97.239998,98.220001,96.290001,96.669998,96.669998,11360000\n1969-06-23,96.669998,97.169998,95.209999,96.230003,96.230003,12900000\n1969-06-24,96.290001,98.040001,96.290001,97.320000,97.320000,11460000\n1969-06-25,97.320000,98.300003,96.559998,97.010002,97.010002,10490000\n1969-06-26,97.010002,97.910004,95.970001,97.250000,97.250000,10310000\n1969-06-27,97.250000,98.150002,96.650002,97.330002,97.330002,9020000\n1969-06-30,97.330002,98.639999,96.820000,97.709999,97.709999,8640000\n1969-07-01,97.709999,98.660004,97.129997,98.080002,98.080002,9890000\n1969-07-02,98.080002,99.500000,97.809998,98.940002,98.940002,11350000\n1969-07-03,98.940002,100.250000,98.620003,99.610001,99.610001,10110000\n1969-07-07,99.610001,100.330002,98.449997,99.029999,99.029999,9970000\n1969-07-08,98.980003,98.980003,97.150002,97.629997,97.629997,9320000\n1969-07-09,97.629997,97.849998,96.330002,96.879997,96.879997,9320000\n1969-07-10,96.879997,97.040001,95.029999,95.379997,95.379997,11450000\n1969-07-11,95.379997,96.650002,94.809998,95.769997,95.769997,11730000\n1969-07-14,95.769997,96.169998,94.199997,94.550003,94.550003,8310000\n1969-07-15,94.550003,95.000000,93.110001,94.239998,94.239998,11110000\n1969-07-16,94.239998,95.830002,94.220001,95.180000,95.180000,10470000\n1969-07-17,95.180000,96.709999,95.070000,95.760002,95.760002,10450000\n1969-07-18,95.760002,95.839996,94.180000,94.949997,94.949997,8590000\n1969-07-22,94.949997,95.449997,93.150002,93.519997,93.519997,9780000\n1969-07-23,93.519997,93.989998,92.070000,93.120003,93.120003,11680000\n1969-07-24,93.120003,93.870003,92.290001,92.800003,92.800003,9750000\n1969-07-25,92.800003,93.279999,91.540001,92.059998,92.059998,9800000\n1969-07-28,91.910004,91.910004,89.830002,90.209999,90.209999,11800000\n1969-07-29,90.209999,91.559998,89.059998,89.480003,89.480003,13630000\n1969-07-30,89.480003,90.820000,88.040001,89.930000,89.930000,15580000\n1969-07-31,89.959999,92.400002,89.959999,91.830002,91.830002,14160000\n1969-08-01,91.919998,94.190002,91.919998,93.470001,93.470001,15070000\n1969-08-04,93.470001,94.419998,92.290001,92.989998,92.989998,10700000\n1969-08-05,92.989998,94.019997,92.129997,93.410004,93.410004,8940000\n1969-08-06,93.410004,94.760002,93.019997,93.919998,93.919998,11100000\n1969-08-07,93.919998,94.769997,93.169998,93.989998,93.989998,9450000\n1969-08-08,93.989998,94.629997,93.290001,93.940002,93.940002,8760000\n1969-08-11,93.940002,94.239998,92.769997,93.360001,93.360001,6680000\n1969-08-12,93.360001,93.660004,92.190002,92.629997,92.629997,7870000\n1969-08-13,92.629997,93.260002,91.480003,92.699997,92.699997,9910000\n1969-08-14,92.699997,93.870003,92.320000,93.339996,93.339996,9690000\n1969-08-15,93.339996,94.500000,92.919998,94.000000,94.000000,10210000\n1969-08-18,94.000000,95.000000,93.510002,94.570000,94.570000,9420000\n1969-08-19,94.570000,95.180000,93.949997,95.070000,95.070000,12640000\n1969-08-20,95.070000,95.639999,94.250000,95.070000,95.070000,9680000\n1969-08-21,95.070000,95.870003,94.559998,95.349998,95.349998,8420000\n1969-08-22,95.349998,96.430000,94.910004,95.919998,95.919998,10140000\n1969-08-25,95.919998,96.129997,94.519997,94.930000,94.930000,8410000\n1969-08-26,94.930000,95.040001,93.650002,94.300003,94.300003,8910000\n1969-08-27,94.300003,95.160004,93.760002,94.489998,94.489998,9100000\n1969-08-28,94.489998,95.379997,94.040001,94.889999,94.889999,7730000\n1969-08-29,94.889999,95.510002,94.459999,95.510002,95.510002,8850000\n1969-09-02,95.510002,96.309998,94.849998,95.540001,95.540001,8560000\n1969-09-03,95.540001,96.110001,94.379997,94.980003,94.980003,8760000\n1969-09-04,94.980003,95.199997,93.660004,94.199997,94.199997,9380000\n1969-09-05,94.199997,94.510002,93.089996,93.639999,93.639999,8890000\n1969-09-08,93.639999,93.760002,92.349998,92.699997,92.699997,8310000\n1969-09-09,92.699997,93.940002,91.769997,93.379997,93.379997,10980000\n1969-09-10,93.379997,95.349998,93.230003,94.949997,94.949997,11490000\n1969-09-11,94.949997,95.769997,93.720001,94.220001,94.220001,12370000\n1969-09-12,94.220001,95.040001,93.260002,94.129997,94.129997,10800000\n1969-09-15,94.129997,95.610001,93.730003,94.870003,94.870003,10680000\n1969-09-16,94.870003,95.730003,94.059998,94.949997,94.949997,11160000\n1969-09-17,94.949997,95.699997,94.040001,94.760002,94.760002,10980000\n1969-09-18,94.760002,95.529999,94.050003,94.900002,94.900002,11170000\n1969-09-19,94.900002,95.919998,94.349998,95.190002,95.190002,12270000\n1969-09-22,95.190002,96.129997,94.580002,95.629997,95.629997,9280000\n1969-09-23,95.629997,96.620003,94.860001,95.629997,95.629997,13030000\n1969-09-24,95.629997,96.199997,94.750000,95.500000,95.500000,11320000\n1969-09-25,95.500000,95.919998,94.279999,94.769997,94.769997,10690000\n1969-09-26,94.769997,95.230003,93.529999,94.160004,94.160004,9680000\n1969-09-29,94.160004,94.449997,92.620003,93.410004,93.410004,10170000\n1969-09-30,93.410004,94.050003,92.550003,93.120003,93.120003,9180000\n1969-10-01,93.120003,93.510002,92.120003,92.519997,92.519997,9090000\n1969-10-02,92.519997,93.629997,91.660004,93.239998,93.239998,11430000\n1969-10-03,93.239998,94.389999,92.650002,93.190002,93.190002,12410000\n1969-10-06,93.190002,93.989998,92.500000,93.379997,93.379997,9180000\n1969-10-07,93.379997,94.029999,92.589996,93.089996,93.089996,10050000\n1969-10-08,93.089996,93.559998,92.040001,92.669998,92.669998,10370000\n1969-10-09,92.669998,93.550003,91.750000,93.029999,93.029999,10420000\n1969-10-10,93.029999,94.190002,92.599998,93.559998,93.559998,12210000\n1969-10-13,93.559998,94.860001,93.199997,94.550003,94.550003,13620000\n1969-10-14,94.550003,96.529999,94.320000,95.699997,95.699997,19950000\n1969-10-15,95.699997,96.559998,94.650002,95.720001,95.720001,15740000\n1969-10-16,95.720001,97.540001,95.050003,96.370003,96.370003,19500000\n1969-10-17,96.370003,97.239998,95.379997,96.260002,96.260002,13740000\n1969-10-20,96.260002,97.169998,95.290001,96.459999,96.459999,13540000\n1969-10-21,96.459999,97.839996,95.860001,97.199997,97.199997,16460000\n1969-10-22,97.199997,98.610001,96.559998,97.830002,97.830002,19320000\n1969-10-23,97.830002,98.389999,96.459999,97.459999,97.459999,14780000\n1969-10-24,97.459999,98.830002,96.970001,98.120003,98.120003,15430000\n1969-10-27,98.120003,98.779999,97.489998,97.970001,97.970001,12160000\n1969-10-28,97.970001,98.550003,97.019997,97.660004,97.660004,12410000\n1969-10-29,97.660004,97.919998,96.260002,96.809998,96.809998,12380000\n1969-10-30,96.809998,97.470001,95.610001,96.930000,96.930000,12820000\n1969-10-31,96.930000,98.029999,96.330002,97.120003,97.120003,13100000\n1969-11-03,97.120003,97.820000,96.190002,97.150002,97.150002,11140000\n1969-11-04,97.150002,97.820000,95.839996,97.209999,97.209999,12340000\n1969-11-05,97.209999,98.389999,96.750000,97.639999,97.639999,12110000\n1969-11-06,97.639999,98.309998,96.800003,97.669998,97.669998,11110000\n1969-11-07,97.669998,99.010002,97.180000,98.260002,98.260002,13280000\n1969-11-10,98.260002,99.230003,97.650002,98.330002,98.330002,12490000\n1969-11-11,98.330002,98.790001,97.449997,98.070000,98.070000,10080000\n1969-11-12,98.070000,98.720001,97.279999,97.889999,97.889999,12480000\n1969-11-13,97.889999,98.339996,96.540001,97.419998,97.419998,12090000\n1969-11-14,97.419998,97.440002,96.360001,97.070000,97.070000,10580000\n1969-11-17,97.070000,97.360001,95.820000,96.410004,96.410004,10120000\n1969-11-18,96.410004,97.000000,95.570000,96.389999,96.389999,11010000\n1969-11-19,96.389999,96.949997,95.360001,95.900002,95.900002,11240000\n1969-11-20,95.900002,95.940002,94.120003,94.910004,94.910004,12010000\n1969-11-21,94.910004,95.339996,93.870003,94.320000,94.320000,9840000\n1969-11-24,94.320000,94.430000,92.629997,93.239998,93.239998,10940000\n1969-11-25,93.239998,94.169998,92.379997,92.940002,92.940002,11560000\n1969-11-26,92.940002,93.849998,92.239998,93.269997,93.269997,10630000\n1969-11-28,93.269997,94.410004,92.879997,93.809998,93.809998,8550000\n1969-12-01,93.809998,94.470001,92.779999,93.220001,93.220001,9950000\n1969-12-02,93.220001,93.540001,91.949997,92.650002,92.650002,9940000\n1969-12-03,92.650002,93.050003,91.250000,91.650002,91.650002,11300000\n1969-12-04,91.650002,92.449997,90.360001,91.949997,91.949997,13230000\n1969-12-05,91.949997,92.910004,91.139999,91.730003,91.730003,11150000\n1969-12-08,91.730003,92.050003,90.290001,90.839996,90.839996,9990000\n1969-12-09,90.839996,91.790001,89.930000,90.550003,90.550003,12290000\n1969-12-10,90.550003,91.220001,89.330002,90.480003,90.480003,12590000\n1969-12-11,90.480003,91.370003,89.739998,90.519997,90.519997,10430000\n1969-12-12,90.519997,91.669998,90.050003,90.809998,90.809998,11630000\n1969-12-15,90.809998,91.419998,89.959999,90.540001,90.540001,11100000\n1969-12-16,90.540001,91.050003,89.230003,89.720001,89.720001,11880000\n1969-12-17,89.720001,90.320000,88.940002,89.199997,89.199997,12840000\n1969-12-18,89.199997,91.150002,88.620003,90.610001,90.610001,15950000\n1969-12-19,90.610001,92.339996,90.330002,91.379997,91.379997,15420000\n1969-12-22,91.379997,92.029999,90.099998,90.580002,90.580002,12680000\n1969-12-23,90.580002,91.129997,89.400002,90.230003,90.230003,13890000\n1969-12-24,90.230003,91.889999,89.930000,91.180000,91.180000,11670000\n1969-12-26,91.180000,92.300003,90.940002,91.889999,91.889999,6750000\n1969-12-29,91.889999,92.489998,90.660004,91.250000,91.250000,12500000\n1969-12-30,91.250000,92.199997,90.470001,91.599998,91.599998,15790000\n1969-12-31,91.599998,92.940002,91.150002,92.059998,92.059998,19380000\n1970-01-02,92.059998,93.540001,91.790001,93.000000,93.000000,8050000\n1970-01-05,93.000000,94.250000,92.529999,93.459999,93.459999,11490000\n1970-01-06,93.459999,93.809998,92.129997,92.820000,92.820000,11460000\n1970-01-07,92.820000,93.379997,91.930000,92.629997,92.629997,10010000\n1970-01-08,92.629997,93.470001,91.989998,92.680000,92.680000,10670000\n1970-01-09,92.680000,93.250000,91.820000,92.400002,92.400002,9380000\n1970-01-12,92.400002,92.669998,91.199997,91.699997,91.699997,8900000\n1970-01-13,91.699997,92.610001,90.989998,91.919998,91.919998,9870000\n1970-01-14,91.919998,92.400002,90.879997,91.650002,91.650002,10380000\n1970-01-15,91.650002,92.349998,90.730003,91.680000,91.680000,11120000\n1970-01-16,91.680000,92.489998,90.360001,90.919998,90.919998,11940000\n1970-01-19,90.720001,90.720001,89.139999,89.650002,89.650002,9500000\n1970-01-20,89.650002,90.449997,88.639999,89.830002,89.830002,11050000\n1970-01-21,89.830002,90.610001,89.199997,89.949997,89.949997,9880000\n1970-01-22,89.949997,90.800003,89.199997,90.040001,90.040001,11050000\n1970-01-23,90.040001,90.449997,88.739998,89.370003,89.370003,11000000\n1970-01-26,89.230003,89.230003,87.489998,88.169998,88.169998,10670000\n1970-01-27,88.169998,88.540001,86.919998,87.620003,87.620003,9630000\n1970-01-28,87.620003,88.239998,86.440002,86.790001,86.790001,10510000\n1970-01-29,86.790001,87.089996,85.019997,85.690002,85.690002,12210000\n1970-01-30,85.690002,86.330002,84.419998,85.019997,85.019997,12320000\n1970-02-02,85.019997,86.760002,84.760002,85.750000,85.750000,13440000\n1970-02-03,85.750000,87.540001,84.639999,86.769997,86.769997,16050000\n1970-02-04,86.769997,87.660004,85.589996,86.239998,86.239998,11040000\n1970-02-05,86.239998,86.620003,84.949997,85.900002,85.900002,9430000\n1970-02-06,85.900002,86.879997,85.230003,86.330002,86.330002,10150000\n1970-02-09,86.330002,87.849998,86.160004,87.010002,87.010002,10830000\n1970-02-10,87.010002,87.400002,85.580002,86.099998,86.099998,10110000\n1970-02-11,86.099998,87.379997,85.300003,86.940002,86.940002,12260000\n1970-02-12,86.940002,87.540001,85.930000,86.730003,86.730003,10010000\n1970-02-13,86.730003,87.300003,85.709999,86.540001,86.540001,11060000\n1970-02-16,86.540001,87.300003,85.800003,86.470001,86.470001,19780000\n1970-02-17,86.470001,87.080002,85.570000,86.370003,86.370003,10140000\n1970-02-18,86.370003,88.070000,86.190002,87.440002,87.440002,11950000\n1970-02-19,87.440002,88.699997,86.940002,87.760002,87.760002,12890000\n1970-02-20,87.760002,88.739998,86.870003,88.029999,88.029999,10790000\n1970-02-24,88.029999,88.910004,87.279999,87.989998,87.989998,10810000\n1970-02-25,87.989998,89.800003,87.110001,89.349998,89.349998,13210000\n1970-02-26,89.349998,89.629997,87.629997,88.900002,88.900002,11540000\n1970-02-27,88.900002,90.330002,88.419998,89.500000,89.500000,12890000\n1970-03-02,89.500000,90.800003,88.919998,89.709999,89.709999,12270000\n1970-03-03,89.709999,90.669998,88.959999,90.230003,90.230003,11700000\n1970-03-04,90.230003,91.050003,89.320000,90.040001,90.040001,11850000\n1970-03-05,90.040001,90.989998,89.379997,90.000000,90.000000,11370000\n1970-03-06,90.000000,90.360001,88.839996,89.440002,89.440002,10980000\n1970-03-09,89.430000,89.430000,87.940002,88.510002,88.510002,9760000\n1970-03-10,88.510002,89.410004,87.889999,88.750000,88.750000,9450000\n1970-03-11,88.750000,89.580002,88.110001,88.690002,88.690002,9180000\n1970-03-12,88.690002,89.089996,87.680000,88.330002,88.330002,9140000\n1970-03-13,88.330002,89.430000,87.290001,87.860001,87.860001,9560000\n1970-03-16,87.860001,87.970001,86.389999,86.910004,86.910004,8910000\n1970-03-17,86.910004,87.860001,86.360001,87.290001,87.290001,9090000\n1970-03-18,87.290001,88.279999,86.930000,87.540001,87.540001,9790000\n1970-03-19,87.540001,88.199997,86.879997,87.419998,87.419998,8930000\n1970-03-20,87.419998,87.769997,86.430000,87.059998,87.059998,7910000\n1970-03-23,87.059998,87.639999,86.190002,86.989998,86.989998,7330000\n1970-03-24,86.989998,88.430000,86.900002,87.980003,87.980003,8840000\n1970-03-25,88.110001,91.070000,88.110001,89.769997,89.769997,17500000\n1970-03-26,89.769997,90.650002,89.180000,89.919998,89.919998,11350000\n1970-03-30,89.919998,90.410004,88.910004,89.629997,89.629997,9600000\n1970-03-31,89.629997,90.169998,88.849998,89.629997,89.629997,8370000\n1970-04-01,89.629997,90.620003,89.300003,90.070000,90.070000,9810000\n1970-04-02,90.070000,90.699997,89.279999,89.790001,89.790001,10520000\n1970-04-03,89.790001,90.160004,88.809998,89.389999,89.389999,9920000\n1970-04-06,89.389999,89.610001,88.150002,88.760002,88.760002,8380000\n1970-04-07,88.760002,89.309998,87.940002,88.519997,88.519997,8490000\n1970-04-08,88.519997,89.089996,87.830002,88.489998,88.489998,9070000\n1970-04-09,88.489998,89.320000,87.959999,88.529999,88.529999,9060000\n1970-04-10,88.529999,89.139999,87.820000,88.239998,88.239998,10020000\n1970-04-13,88.239998,88.669998,87.150002,87.639999,87.639999,8810000\n1970-04-14,87.639999,87.730003,86.010002,86.889999,86.889999,10840000\n1970-04-15,86.889999,87.709999,86.529999,86.730003,86.730003,9410000\n1970-04-16,86.730003,87.129997,85.510002,85.879997,85.879997,10250000\n1970-04-17,85.879997,86.360001,84.750000,85.669998,85.669998,10990000\n1970-04-20,85.669998,86.360001,84.989998,85.830002,85.830002,8280000\n1970-04-21,85.830002,86.540001,84.989998,85.379997,85.379997,8490000\n1970-04-22,85.379997,85.510002,83.839996,84.269997,84.269997,10780000\n1970-04-23,84.269997,84.300003,82.610001,83.040001,83.040001,11050000\n1970-04-24,83.040001,83.620003,81.959999,82.769997,82.769997,10410000\n1970-04-27,82.769997,83.080002,81.080002,81.459999,81.459999,10240000\n1970-04-28,81.459999,82.160004,79.860001,80.269997,80.269997,12620000\n1970-04-29,80.269997,83.230003,79.309998,81.809998,81.809998,15800000\n1970-04-30,81.809998,82.570000,80.760002,81.519997,81.519997,9880000\n1970-05-01,81.519997,82.320000,80.269997,81.440002,81.440002,8290000\n1970-05-04,81.279999,81.279999,78.849998,79.370003,79.370003,11450000\n1970-05-05,79.370003,79.830002,78.019997,78.599998,78.599998,10580000\n1970-05-06,78.599998,80.910004,78.230003,79.470001,79.470001,14380000\n1970-05-07,79.470001,80.599998,78.889999,79.830002,79.830002,9530000\n1970-05-08,79.830002,80.150002,78.709999,79.440002,79.440002,6930000\n1970-05-11,79.440002,79.720001,78.290001,78.599998,78.599998,6650000\n1970-05-12,78.599998,79.150002,77.059998,77.849998,77.849998,10850000\n1970-05-13,77.750000,77.750000,75.919998,76.529999,76.529999,10720000\n1970-05-14,76.529999,76.639999,74.029999,75.440002,75.440002,13920000\n1970-05-15,75.440002,77.419998,74.589996,76.900002,76.900002,14570000\n1970-05-18,76.900002,77.680000,76.070000,76.959999,76.959999,8280000\n1970-05-19,76.959999,77.199997,75.209999,75.459999,75.459999,9480000\n1970-05-20,75.349998,75.349998,73.250000,73.519997,73.519997,13020000\n1970-05-21,73.510002,73.510002,70.940002,72.160004,72.160004,16710000\n1970-05-22,72.160004,73.419998,71.419998,72.250000,72.250000,12170000\n1970-05-25,72.160004,72.160004,69.919998,70.250000,70.250000,12660000\n1970-05-26,70.250000,71.169998,68.610001,69.290001,69.290001,17030000\n1970-05-27,69.370003,73.220001,69.370003,72.769997,72.769997,17460000\n1970-05-28,72.769997,75.440002,72.589996,74.610001,74.610001,18910000\n1970-05-29,74.610001,76.919998,73.529999,76.550003,76.550003,14630000\n1970-06-01,76.550003,78.400002,75.839996,77.839996,77.839996,15020000\n1970-06-02,77.839996,78.730003,76.510002,77.839996,77.839996,13480000\n1970-06-03,77.839996,79.220001,76.970001,78.519997,78.519997,16600000\n1970-06-04,78.519997,79.419998,76.989998,77.360001,77.360001,14380000\n1970-06-05,77.360001,77.480003,75.250000,76.169998,76.169998,12450000\n1970-06-08,76.169998,77.370003,75.300003,76.290001,76.290001,8040000\n1970-06-09,76.290001,79.959999,75.580002,76.250000,76.250000,7050000\n1970-06-10,76.250000,76.620003,74.919998,75.480003,75.480003,7240000\n1970-06-11,75.480003,75.519997,73.959999,74.449997,74.449997,7770000\n1970-06-12,74.449997,74.839996,73.250000,73.879997,73.879997,8890000\n1970-06-15,73.879997,75.269997,73.669998,74.580002,74.580002,6920000\n1970-06-16,74.580002,76.760002,74.209999,76.150002,76.150002,11330000\n1970-06-17,76.150002,78.040001,75.629997,76.000000,76.000000,9870000\n1970-06-18,76.000000,77.169998,74.989998,76.510002,76.510002,8870000\n1970-06-19,76.510002,78.050003,76.309998,77.050003,77.050003,10980000\n1970-06-22,77.050003,77.430000,75.610001,76.639999,76.639999,8700000\n1970-06-23,76.639999,76.830002,74.519997,74.760002,74.760002,10790000\n1970-06-24,74.760002,75.419998,73.400002,73.970001,73.970001,12630000\n1970-06-25,73.970001,74.930000,73.300003,74.019997,74.019997,8200000\n1970-06-26,74.019997,74.680000,73.089996,73.470001,73.470001,9160000\n1970-06-29,73.470001,73.860001,72.339996,72.889999,72.889999,8770000\n1970-06-30,72.889999,73.889999,72.250000,72.720001,72.720001,9280000\n1970-07-01,72.720001,73.660004,72.110001,72.940002,72.940002,8610000\n1970-07-02,72.940002,73.919998,72.430000,72.919998,72.919998,8440000\n1970-07-06,72.919998,73.120003,71.379997,71.779999,71.779999,9340000\n1970-07-07,71.779999,72.320000,70.690002,71.230003,71.230003,10470000\n1970-07-08,71.230003,73.300003,70.989998,73.000000,73.000000,10970000\n1970-07-09,73.000000,74.769997,72.879997,74.059998,74.059998,12820000\n1970-07-10,74.059998,75.209999,73.489998,74.449997,74.449997,10160000\n1970-07-13,74.449997,75.370003,73.830002,74.550003,74.550003,7450000\n1970-07-14,74.550003,75.040001,73.779999,74.419998,74.419998,7360000\n1970-07-15,74.419998,75.680000,74.059998,75.230003,75.230003,8860000\n1970-07-16,75.230003,77.089996,75.120003,76.339996,76.339996,12200000\n1970-07-17,76.370003,78.230003,76.370003,77.690002,77.690002,13870000\n1970-07-20,77.690002,78.720001,77.040001,77.790001,77.790001,11660000\n1970-07-21,77.790001,77.940002,76.389999,76.980003,76.980003,9940000\n1970-07-22,76.980003,78.199997,76.220001,77.029999,77.029999,12460000\n1970-07-23,77.029999,78.510002,76.459999,78.000000,78.000000,12460000\n1970-07-24,78.000000,78.480003,76.959999,77.820000,77.820000,9520000\n1970-07-27,77.820000,78.269997,77.070000,77.650002,77.650002,7460000\n1970-07-28,77.650002,78.349998,76.959999,77.769997,77.769997,9040000\n1970-07-29,77.769997,78.809998,77.279999,78.040001,78.040001,12580000\n1970-07-30,78.040001,78.660004,77.360001,78.070000,78.070000,10430000\n1970-07-31,78.070000,79.029999,77.440002,78.050003,78.050003,11640000\n1970-08-03,78.050003,78.239998,76.559998,77.019997,77.019997,7650000\n1970-08-04,77.019997,77.559998,76.120003,77.190002,77.190002,8310000\n1970-08-05,77.190002,77.860001,76.589996,77.180000,77.180000,7660000\n1970-08-06,77.180000,77.680000,76.389999,77.080002,77.080002,7560000\n1970-08-07,77.080002,78.089996,76.459999,77.279999,77.279999,9370000\n1970-08-10,77.279999,77.400002,75.720001,76.199997,76.199997,7580000\n1970-08-11,76.199997,76.330002,75.160004,75.820000,75.820000,7330000\n1970-08-12,75.820000,76.239998,75.040001,75.419998,75.419998,7440000\n1970-08-13,75.419998,75.690002,74.129997,74.760002,74.760002,8640000\n1970-08-14,74.760002,75.739998,74.389999,75.180000,75.180000,7850000\n1970-08-17,75.180000,75.790001,74.519997,75.330002,75.330002,6940000\n1970-08-18,75.330002,76.790001,75.300003,76.199997,76.199997,9500000\n1970-08-19,76.199997,77.580002,76.010002,76.959999,76.959999,9870000\n1970-08-20,76.959999,77.989998,76.300003,77.839996,77.839996,10170000\n1970-08-21,77.839996,79.599998,77.459999,79.239998,79.239998,13420000\n1970-08-24,79.410004,81.620003,79.410004,80.989998,80.989998,18910000\n1970-08-25,80.989998,81.809998,79.690002,81.120003,81.120003,17520000\n1970-08-26,81.120003,82.260002,80.599998,81.209999,81.209999,15970000\n1970-08-27,81.209999,81.910004,80.129997,81.080002,81.080002,12440000\n1970-08-28,81.080002,82.470001,80.690002,81.860001,81.860001,13820000\n1970-08-31,81.860001,82.330002,80.949997,81.519997,81.519997,10740000\n1970-09-01,81.519997,81.800003,80.430000,80.949997,80.949997,10960000\n1970-09-02,80.949997,81.349998,79.949997,80.959999,80.959999,9710000\n1970-09-03,80.959999,82.629997,80.879997,82.089996,82.089996,14110000\n1970-09-04,82.089996,83.419998,81.790001,82.830002,82.830002,15360000\n1970-09-08,82.830002,83.690002,81.480003,83.040001,83.040001,17110000\n1970-09-09,83.040001,83.779999,81.900002,82.790001,82.790001,16250000\n1970-09-10,82.790001,82.980003,81.620003,82.300003,82.300003,11900000\n1970-09-11,82.300003,83.190002,81.809998,82.519997,82.519997,12140000\n1970-09-14,82.519997,83.129997,81.430000,82.070000,82.070000,11900000\n1970-09-15,82.070000,82.110001,80.750000,81.360001,81.360001,9830000\n1970-09-16,81.360001,82.570000,80.610001,81.790001,81.790001,12090000\n1970-09-17,81.790001,83.089996,81.510002,82.290001,82.290001,15530000\n1970-09-18,82.290001,83.500000,81.769997,82.620003,82.620003,15900000\n1970-09-21,82.620003,83.150002,81.519997,81.910004,81.910004,12540000\n1970-09-22,81.910004,82.239998,80.820000,81.860001,81.860001,12110000\n1970-09-23,81.860001,83.150002,81.519997,81.910004,81.910004,16940000\n1970-09-24,81.910004,82.239998,80.820000,81.660004,81.660004,21340000\n1970-09-25,81.660004,83.599998,81.410004,82.830002,82.830002,20470000\n1970-09-28,82.830002,84.559998,82.610001,83.910004,83.910004,14390000\n1970-09-29,83.910004,84.570000,83.110001,83.860001,83.860001,17880000\n1970-09-30,83.860001,84.989998,82.779999,84.300003,84.300003,14830000\n1970-10-01,84.300003,84.699997,83.459999,84.320000,84.320000,9700000\n1970-10-02,84.320000,85.559998,84.059998,85.160004,85.160004,15420000\n1970-10-05,85.160004,86.989998,85.010002,86.470001,86.470001,19760000\n1970-10-06,86.470001,87.750000,86.040001,86.849998,86.849998,20240000\n1970-10-07,86.849998,87.470001,85.550003,86.889999,86.889999,15610000\n1970-10-08,86.889999,87.370003,85.550003,85.949997,85.949997,14500000\n1970-10-09,85.949997,86.250000,84.540001,85.080002,85.080002,13980000\n1970-10-12,85.050003,85.050003,83.580002,84.169998,84.169998,8570000\n1970-10-13,84.169998,84.699997,83.239998,84.059998,84.059998,9500000\n1970-10-14,84.059998,84.830002,83.419998,84.190002,84.190002,9920000\n1970-10-15,84.190002,85.279999,83.820000,84.650002,84.650002,11250000\n1970-10-16,84.650002,85.209999,83.830002,84.279999,84.279999,11300000\n1970-10-19,84.279999,84.290001,82.809998,83.150002,83.150002,9890000\n1970-10-20,83.150002,84.190002,82.620003,83.639999,83.639999,10630000\n1970-10-21,83.639999,84.720001,83.209999,83.660004,83.660004,11330000\n1970-10-22,83.660004,84.040001,82.769997,83.379997,83.379997,9000000\n1970-10-23,83.379997,84.300003,82.910004,83.769997,83.769997,10270000\n1970-10-26,83.769997,84.260002,82.889999,83.309998,83.309998,9200000\n1970-10-27,83.309998,83.730003,82.519997,83.120003,83.120003,9680000\n1970-10-28,83.120003,83.809998,82.290001,83.430000,83.430000,10660000\n1970-10-29,83.430000,84.099998,82.820000,83.360001,83.360001,10440000\n1970-10-30,83.360001,83.800003,82.519997,83.250000,83.250000,10520000\n1970-11-02,83.250000,83.989998,82.660004,83.510002,83.510002,9470000\n1970-11-03,83.510002,84.769997,83.209999,84.220001,84.220001,11760000\n1970-11-04,84.220001,85.260002,83.820000,84.389999,84.389999,12180000\n1970-11-05,84.389999,84.790001,83.529999,84.099998,84.099998,10800000\n1970-11-06,84.099998,84.730003,83.550003,84.220001,84.220001,9970000\n1970-11-09,84.220001,85.269997,83.820000,84.669998,84.669998,10890000\n1970-11-10,84.669998,85.690002,84.180000,84.790001,84.790001,12030000\n1970-11-11,84.790001,86.239998,84.690002,85.029999,85.029999,13520000\n1970-11-12,85.029999,85.540001,83.809998,84.150002,84.150002,12520000\n1970-11-13,84.150002,84.330002,82.919998,83.370003,83.370003,11890000\n1970-11-16,83.370003,83.750000,82.339996,83.239998,83.239998,9160000\n1970-11-17,83.239998,84.169998,82.809998,83.470001,83.470001,9450000\n1970-11-18,83.470001,83.529999,82.410004,82.790001,82.790001,9850000\n1970-11-19,82.790001,83.480003,82.230003,82.910004,82.910004,9280000\n1970-11-20,82.910004,84.059998,82.489998,83.720001,83.720001,10920000\n1970-11-23,83.720001,84.919998,83.470001,84.239998,84.239998,12720000\n1970-11-24,84.239998,85.180000,83.589996,84.779999,84.779999,12560000\n1970-11-25,84.779999,85.699997,84.349998,85.089996,85.089996,13490000\n1970-11-27,85.089996,86.209999,84.669998,85.930000,85.930000,10130000\n1970-11-30,85.930000,87.599998,85.790001,87.199997,87.199997,17700000\n1970-12-01,87.199997,88.610001,86.110001,87.470001,87.470001,20170000\n1970-12-02,87.470001,88.830002,86.720001,88.480003,88.480003,17960000\n1970-12-03,88.480003,89.870003,88.110001,88.900002,88.900002,20480000\n1970-12-04,88.900002,89.889999,88.120003,89.459999,89.459999,15980000\n1970-12-07,89.459999,90.389999,88.760002,89.940002,89.940002,15530000\n1970-12-08,89.940002,90.470001,88.870003,89.470001,89.470001,14370000\n1970-12-09,89.470001,90.029999,88.480003,89.540001,89.540001,13550000\n1970-12-10,89.540001,90.870003,89.010002,89.919998,89.919998,14610000\n1970-12-11,89.919998,90.930000,89.440002,90.260002,90.260002,15790000\n1970-12-14,90.260002,90.809998,89.279999,89.800003,89.800003,13810000\n1970-12-15,89.800003,90.320000,88.930000,89.660004,89.660004,13420000\n1970-12-16,89.660004,90.220001,88.769997,89.720001,89.720001,14240000\n1970-12-17,89.720001,90.610001,89.309998,90.040001,90.040001,13660000\n1970-12-18,90.040001,90.769997,89.419998,90.220001,90.220001,14360000\n1970-12-21,90.220001,90.769997,89.360001,89.940002,89.940002,12690000\n1970-12-22,89.940002,90.839996,89.349998,90.040001,90.040001,14510000\n1970-12-23,90.040001,90.860001,89.349998,90.099998,90.099998,15400000\n1970-12-24,90.099998,91.080002,89.809998,90.610001,90.610001,12140000\n1970-12-28,90.610001,91.489998,90.279999,91.089996,91.089996,12290000\n1970-12-29,91.089996,92.379997,90.730003,92.080002,92.080002,17750000\n1970-12-30,92.080002,92.989998,91.599998,92.269997,92.269997,19140000\n1970-12-31,92.269997,92.790001,91.360001,92.150002,92.150002,13390000\n1971-01-04,92.150002,92.190002,90.639999,91.150002,91.150002,10010000\n1971-01-05,91.150002,92.279999,90.690002,91.800003,91.800003,12600000\n1971-01-06,91.800003,93.000000,91.500000,92.349998,92.349998,16960000\n1971-01-07,92.349998,93.260002,91.750000,92.379997,92.379997,16460000\n1971-01-08,92.379997,93.019997,91.599998,92.190002,92.190002,14100000\n1971-01-11,92.190002,92.669998,90.989998,91.980003,91.980003,14720000\n1971-01-12,91.980003,93.279999,91.629997,92.720001,92.720001,17820000\n1971-01-13,92.720001,93.660004,91.879997,92.559998,92.559998,19070000\n1971-01-14,92.559998,93.360001,91.669998,92.800003,92.800003,17600000\n1971-01-15,92.800003,93.940002,92.250000,93.029999,93.029999,18010000\n1971-01-18,93.029999,94.110001,92.629997,93.410004,93.410004,15400000\n1971-01-19,93.410004,94.279999,92.849998,93.760002,93.760002,15800000\n1971-01-20,93.760002,94.529999,93.070000,93.779999,93.779999,18330000\n1971-01-21,93.779999,94.690002,93.150002,94.190002,94.190002,19060000\n1971-01-22,94.190002,95.529999,93.959999,94.879997,94.879997,21680000\n1971-01-25,94.879997,95.930000,94.160004,95.279999,95.279999,19050000\n1971-01-26,95.279999,96.360001,94.690002,95.589996,95.589996,21380000\n1971-01-27,95.589996,95.779999,93.959999,94.889999,94.889999,20640000\n1971-01-28,94.889999,95.779999,94.120003,95.209999,95.209999,18840000\n1971-01-29,95.209999,96.489998,94.790001,95.879997,95.879997,20960000\n1971-02-01,95.879997,97.050003,95.379997,96.419998,96.419998,20650000\n1971-02-02,96.419998,97.190002,95.599998,96.430000,96.430000,22030000\n1971-02-03,96.430000,97.190002,95.580002,96.629997,96.629997,21680000\n1971-02-04,96.629997,97.260002,95.690002,96.620003,96.620003,20860000\n1971-02-05,96.620003,97.580002,95.839996,96.930000,96.930000,20480000\n1971-02-08,96.930000,98.040001,96.129997,97.449997,97.449997,25590000\n1971-02-09,97.449997,98.500000,96.900002,97.510002,97.510002,28250000\n1971-02-10,97.510002,97.970001,96.230003,97.389999,97.389999,19040000\n1971-02-11,97.389999,98.489998,96.989998,97.910004,97.910004,19260000\n1971-02-12,97.910004,98.959999,97.559998,98.430000,98.430000,18470000\n1971-02-16,98.430000,99.589996,97.849998,98.660004,98.660004,21350000\n1971-02-17,98.660004,99.320000,97.320000,98.199997,98.199997,18720000\n1971-02-18,98.199997,98.599998,96.959999,97.559998,97.559998,16650000\n1971-02-19,97.559998,97.790001,96.250000,96.739998,96.739998,17860000\n1971-02-22,96.650002,96.650002,94.970001,95.720001,95.720001,15840000\n1971-02-23,95.720001,96.669998,94.919998,96.089996,96.089996,15080000\n1971-02-24,96.089996,97.339996,95.860001,96.730003,96.730003,15930000\n1971-02-25,96.730003,97.709999,96.080002,96.959999,96.959999,16200000\n1971-02-26,96.959999,97.540001,95.839996,96.750000,96.750000,17250000\n1971-03-01,96.750000,97.480003,96.110001,97.000000,97.000000,13020000\n1971-03-02,97.000000,97.599998,96.320000,96.980003,96.980003,14870000\n1971-03-03,96.980003,97.540001,96.300003,96.949997,96.949997,14680000\n1971-03-04,96.949997,98.379997,96.900002,97.919998,97.919998,17350000\n1971-03-05,97.919998,99.489998,97.820000,98.959999,98.959999,22430000\n1971-03-08,98.959999,99.440002,98.419998,99.379997,99.379997,19340000\n1971-03-09,99.379997,100.309998,98.720001,99.459999,99.459999,20490000\n1971-03-10,99.459999,100.099998,98.629997,99.300003,99.300003,17220000\n1971-03-11,99.300003,100.290001,98.570000,99.389999,99.389999,19830000\n1971-03-12,99.389999,100.089996,98.639999,99.570000,99.570000,14680000\n1971-03-15,99.570000,101.150002,99.120003,100.709999,100.709999,18920000\n1971-03-16,100.709999,101.940002,100.360001,101.209999,101.209999,22270000\n1971-03-17,101.209999,101.660004,99.980003,101.120003,101.120003,17070000\n1971-03-18,101.120003,102.029999,100.430000,101.190002,101.190002,17910000\n1971-03-19,101.190002,101.739998,100.349998,101.010002,101.010002,15150000\n1971-03-22,101.010002,101.459999,100.080002,100.620003,100.620003,14290000\n1971-03-23,100.620003,101.059998,99.620003,100.279999,100.279999,16470000\n1971-03-24,100.279999,100.629997,99.150002,99.620003,99.620003,15770000\n1971-03-25,99.620003,100.029999,98.360001,99.610001,99.610001,15870000\n1971-03-26,99.610001,100.650002,99.180000,99.949997,99.949997,15560000\n1971-03-29,99.949997,100.739998,99.360001,100.029999,100.029999,13650000\n1971-03-30,100.029999,100.860001,99.410004,100.260002,100.260002,15430000\n1971-03-31,100.260002,101.050003,99.690002,100.309998,100.309998,17610000\n1971-04-01,100.309998,100.989998,99.629997,100.389999,100.389999,13470000\n1971-04-02,100.389999,101.230003,99.860001,100.559998,100.559998,14520000\n1971-04-05,100.559998,101.410004,99.879997,100.790001,100.790001,16040000\n1971-04-06,100.790001,102.110001,100.300003,101.510002,101.510002,19990000\n1971-04-07,101.510002,102.870003,101.129997,101.980003,101.980003,22270000\n1971-04-08,101.980003,102.860001,101.300003,102.099998,102.099998,17590000\n1971-04-12,102.099998,103.540001,101.750000,102.879997,102.879997,19410000\n1971-04-13,102.879997,103.959999,102.250000,102.980003,102.980003,23200000\n1971-04-14,102.980003,104.010002,102.279999,103.370003,103.370003,19440000\n1971-04-15,103.370003,104.400002,102.760002,103.519997,103.519997,22540000\n1971-04-16,103.519997,104.180000,102.680000,103.489998,103.489998,18280000\n1971-04-19,103.489998,104.629997,103.089996,104.010002,104.010002,17730000\n1971-04-20,104.010002,104.580002,103.059998,103.610001,103.610001,17880000\n1971-04-21,103.610001,104.160004,102.550003,103.360001,103.360001,17040000\n1971-04-22,103.360001,104.269997,102.580002,103.559998,103.559998,19270000\n1971-04-23,103.559998,104.629997,102.790001,104.050003,104.050003,20150000\n1971-04-26,104.050003,104.830002,103.190002,103.940002,103.940002,18860000\n1971-04-27,103.940002,105.070000,103.230003,104.589996,104.589996,21250000\n1971-04-28,104.589996,105.599998,103.849998,104.769997,104.769997,24820000\n1971-04-29,104.769997,105.580002,103.900002,104.629997,104.629997,20340000\n1971-04-30,104.629997,104.959999,103.250000,103.949997,103.949997,17490000\n1971-05-03,103.949997,104.110001,102.370003,103.290001,103.290001,16120000\n1971-05-04,103.290001,104.360001,102.709999,103.790001,103.790001,17310000\n1971-05-05,103.790001,104.279999,102.680000,103.779999,103.779999,17270000\n1971-05-06,103.779999,104.419998,102.800003,103.230003,103.230003,19300000\n1971-05-07,103.230003,103.500000,101.860001,102.870003,102.870003,16490000\n1971-05-10,102.870003,103.150002,101.709999,102.360001,102.360001,12810000\n1971-05-11,102.360001,103.370003,101.500000,102.620003,102.620003,17730000\n1971-05-12,102.620003,103.570000,102.120003,102.900002,102.900002,15140000\n1971-05-13,102.900002,103.570000,101.980003,102.690002,102.690002,17640000\n1971-05-14,102.690002,103.169998,101.650002,102.209999,102.209999,16430000\n1971-05-17,102.080002,102.080002,100.250000,100.690002,100.690002,15980000\n1971-05-18,100.690002,101.620003,99.680000,100.830002,100.830002,17640000\n1971-05-19,100.830002,101.750000,100.300003,101.070000,101.070000,17640000\n1971-05-20,101.070000,102.169998,100.610001,101.309998,101.309998,11740000\n1971-05-21,101.309998,101.839996,100.410004,100.989998,100.989998,12090000\n1971-05-24,100.989998,101.239998,99.720001,100.129997,100.129997,12060000\n1971-05-25,100.129997,100.389999,98.730003,99.470001,99.470001,16050000\n1971-05-26,99.470001,100.489998,98.930000,99.589996,99.589996,13550000\n1971-05-27,99.589996,100.139999,98.779999,99.400002,99.400002,12610000\n1971-05-28,99.400002,100.169998,98.680000,99.629997,99.629997,11760000\n1971-06-01,99.629997,100.760002,99.220001,100.199997,100.199997,11930000\n1971-06-02,100.199997,101.529999,99.889999,100.959999,100.959999,17740000\n1971-06-03,100.959999,102.070000,100.300003,101.010002,101.010002,18790000\n1971-06-04,101.010002,101.879997,100.430000,101.300003,101.300003,14400000\n1971-06-07,101.300003,102.019997,100.550003,101.089996,101.089996,13800000\n1971-06-08,101.089996,101.500000,99.910004,100.320000,100.320000,13610000\n1971-06-09,100.320000,100.970001,99.279999,100.290001,100.290001,14250000\n1971-06-10,100.290001,101.230003,99.779999,100.639999,100.639999,12450000\n1971-06-11,100.639999,101.709999,100.180000,101.070000,101.070000,12270000\n1971-06-14,101.070000,101.279999,99.779999,100.220001,100.220001,11530000\n1971-06-15,100.220001,101.099998,99.449997,100.320000,100.320000,13550000\n1971-06-16,100.320000,101.290001,99.680000,100.519997,100.519997,14300000\n1971-06-17,100.519997,101.370003,99.870003,100.500000,100.500000,13980000\n1971-06-18,100.500000,100.629997,98.650002,98.970001,98.970001,15040000\n1971-06-21,98.970001,99.180000,97.220001,97.870003,97.870003,16490000\n1971-06-22,97.870003,98.660004,96.919998,97.589996,97.589996,15200000\n1971-06-23,97.589996,98.949997,97.360001,98.410004,98.410004,12640000\n1971-06-24,98.410004,99.000000,97.589996,98.129997,98.129997,11360000\n1971-06-25,98.129997,98.660004,97.330002,97.989998,97.989998,10580000\n1971-06-28,97.989998,98.480003,97.019997,97.739998,97.739998,9810000\n1971-06-29,97.739998,99.389999,97.610001,98.820000,98.820000,14460000\n1971-06-30,98.820000,100.290001,98.680000,98.699997,98.699997,15410000\n1971-07-01,99.160004,100.650002,99.160004,99.779999,99.779999,13090000\n1971-07-02,99.779999,100.309998,99.089996,99.779999,99.779999,9960000\n1971-07-06,99.779999,100.349998,99.099998,99.760002,99.760002,10440000\n1971-07-07,99.760002,100.830002,99.250000,100.040001,100.040001,14520000\n1971-07-08,100.040001,101.029999,99.589996,100.339996,100.339996,13920000\n1971-07-09,100.339996,101.330002,99.860001,100.690002,100.690002,12640000\n1971-07-12,100.690002,101.519997,100.190002,100.820000,100.820000,12020000\n1971-07-13,100.820000,101.059998,99.070000,99.500000,99.500000,13540000\n1971-07-14,99.500000,99.830002,98.230003,99.220001,99.220001,14360000\n1971-07-15,99.220001,100.480003,98.760002,99.279999,99.279999,13080000\n1971-07-16,99.279999,100.349998,98.639999,99.110001,99.110001,13870000\n1971-07-19,99.110001,99.570000,98.110001,98.930000,98.930000,11430000\n1971-07-20,98.930000,100.010002,98.599998,99.320000,99.320000,12540000\n1971-07-21,99.320000,100.000000,98.739998,99.279999,99.279999,11920000\n1971-07-22,99.279999,99.820000,98.500000,99.110001,99.110001,12570000\n1971-07-23,99.110001,99.599998,98.260002,98.940002,98.940002,12370000\n1971-07-26,98.940002,99.470001,96.669998,98.139999,98.139999,9930000\n1971-07-27,98.139999,98.989998,97.419998,97.779999,97.779999,11560000\n1971-07-28,97.779999,98.150002,96.510002,97.070000,97.070000,13940000\n1971-07-29,97.070000,97.220001,95.370003,96.019997,96.019997,14570000\n1971-07-30,96.019997,96.779999,95.080002,95.580002,95.580002,12970000\n1971-08-02,95.580002,96.760002,95.220001,95.959999,95.959999,11870000\n1971-08-03,95.959999,96.110001,94.059998,94.510002,94.510002,13490000\n1971-08-04,94.510002,95.339996,93.349998,93.889999,93.889999,15410000\n1971-08-05,93.889999,94.889999,93.330002,94.089996,94.089996,12100000\n1971-08-06,94.089996,94.910004,93.629997,94.250000,94.250000,9490000\n1971-08-09,94.250000,94.550003,93.169998,93.529999,93.529999,8110000\n1971-08-10,93.529999,94.129997,92.809998,93.540001,93.540001,9460000\n1971-08-11,93.540001,95.059998,93.349998,94.660004,94.660004,11370000\n1971-08-12,94.809998,96.500000,94.809998,96.000000,96.000000,15910000\n1971-08-13,96.000000,96.529999,95.190002,95.690002,95.690002,9960000\n1971-08-16,97.900002,100.959999,97.900002,98.760002,98.760002,31730000\n1971-08-17,98.760002,101.000000,98.489998,99.989998,99.989998,26790000\n1971-08-18,99.989998,100.190002,98.059998,98.599998,98.599998,20680000\n1971-08-19,98.599998,99.070000,97.349998,98.160004,98.160004,14190000\n1971-08-20,98.160004,98.940002,97.519997,98.330002,98.330002,11890000\n1971-08-23,98.330002,99.959999,98.089996,99.250000,99.250000,13040000\n1971-08-24,99.250000,101.019997,99.150002,100.400002,100.400002,18700000\n1971-08-25,100.400002,101.510002,99.769997,100.410004,100.410004,18280000\n1971-08-26,100.410004,101.120003,99.400002,100.239998,100.239998,13990000\n1971-08-27,100.239998,101.220001,99.760002,100.480003,100.480003,12490000\n1971-08-30,100.480003,100.889999,99.169998,99.519997,99.519997,11140000\n1971-08-31,99.519997,99.760002,98.320000,99.029999,99.029999,10430000\n1971-09-01,99.029999,99.839996,98.500000,99.070000,99.070000,10770000\n1971-09-02,99.070000,99.800003,98.519997,99.290001,99.290001,10690000\n1971-09-03,99.290001,100.930000,99.099998,100.690002,100.690002,14040000\n1971-09-07,100.690002,102.250000,100.430000,101.150002,101.150002,17080000\n1971-09-08,101.150002,101.940002,100.519997,101.339996,101.339996,14230000\n1971-09-09,101.339996,101.879997,100.379997,100.800003,100.800003,15790000\n1971-09-10,100.800003,101.010002,99.690002,100.419998,100.419998,11380000\n1971-09-13,100.419998,100.839996,99.489998,100.070000,100.070000,10000000\n1971-09-14,100.070000,100.349998,98.989998,99.339996,99.339996,11410000\n1971-09-15,99.339996,100.239998,98.790001,99.769997,99.769997,11080000\n1971-09-16,99.769997,100.349998,99.070000,99.660004,99.660004,10550000\n1971-09-17,99.660004,100.519997,99.260002,99.959999,99.959999,11020000\n1971-09-20,99.959999,100.400002,99.139999,99.680000,99.680000,9540000\n1971-09-21,99.680000,100.080002,98.709999,99.339996,99.339996,10640000\n1971-09-22,99.339996,99.720001,98.150002,98.470001,98.470001,14250000\n1971-09-23,98.470001,99.120003,97.610001,98.379997,98.379997,13250000\n1971-09-24,98.379997,99.349998,97.779999,98.150002,98.150002,13460000\n1971-09-27,98.150002,98.410004,96.970001,97.620003,97.620003,10220000\n1971-09-28,97.620003,98.550003,97.120003,97.879997,97.879997,11250000\n1971-09-29,97.879997,98.510002,97.290001,97.900002,97.900002,8580000\n1971-09-30,97.900002,98.970001,97.480003,98.339996,98.339996,13490000\n1971-10-01,98.339996,99.489998,97.959999,98.930000,98.930000,13400000\n1971-10-04,98.930000,100.040001,98.620003,99.209999,99.209999,14570000\n1971-10-05,99.209999,99.779999,98.339996,99.110001,99.110001,12360000\n1971-10-06,99.110001,100.129997,98.489998,99.820000,99.820000,15630000\n1971-10-07,99.820000,100.959999,99.419998,100.019997,100.019997,17780000\n1971-10-08,100.019997,100.300003,98.870003,99.360001,99.360001,13870000\n1971-10-11,99.360001,99.620003,98.580002,99.209999,99.209999,7800000\n1971-10-12,99.209999,100.199997,98.620003,99.570000,99.570000,14340000\n1971-10-13,99.570000,100.080002,98.610001,99.029999,99.029999,13540000\n1971-10-14,99.029999,99.250000,97.739998,98.129997,98.129997,12870000\n1971-10-15,98.129997,98.449997,97.029999,97.790001,97.790001,13120000\n1971-10-18,97.790001,98.330002,96.980003,97.349998,97.349998,10420000\n1971-10-19,97.349998,97.660004,96.050003,97.000000,97.000000,13040000\n1971-10-20,97.000000,97.449997,95.230003,95.650002,95.650002,16340000\n1971-10-21,95.650002,96.330002,94.589996,95.599998,95.599998,14990000\n1971-10-22,95.599998,96.830002,94.970001,95.570000,95.570000,14560000\n1971-10-25,95.570000,95.760002,94.570000,95.099998,95.099998,7340000\n1971-10-26,95.019997,95.019997,94.379997,94.739998,94.739998,13390000\n1971-10-27,94.739998,94.989998,93.389999,93.790001,93.790001,13480000\n1971-10-28,93.790001,94.750000,92.959999,93.959999,93.959999,15530000\n1971-10-29,93.959999,94.709999,93.279999,94.230003,94.230003,11710000\n1971-11-01,94.230003,94.430000,92.480003,92.800003,92.800003,10960000\n1971-11-02,92.800003,93.730003,91.839996,93.180000,93.180000,13330000\n1971-11-03,93.269997,95.309998,93.269997,94.910004,94.910004,14590000\n1971-11-04,94.910004,96.080002,94.370003,94.790001,94.790001,15750000\n1971-11-05,94.790001,95.010002,93.639999,94.459999,94.459999,10780000\n1971-11-08,94.459999,94.970001,93.779999,94.389999,94.389999,8520000\n1971-11-09,94.389999,95.309998,93.940002,94.459999,94.459999,12080000\n1971-11-10,94.459999,94.839996,93.099998,93.410004,93.410004,13410000\n1971-11-11,93.410004,93.540001,91.639999,92.120003,92.120003,13310000\n1971-11-12,92.120003,92.900002,90.930000,92.120003,92.120003,14540000\n1971-11-15,92.120003,92.690002,91.379997,91.809998,91.809998,9370000\n1971-11-16,91.809998,93.150002,91.209999,92.709999,92.709999,13300000\n1971-11-17,92.709999,93.349998,91.800003,92.849998,92.849998,12840000\n1971-11-18,92.849998,93.620003,91.879997,92.129997,92.129997,13010000\n1971-11-19,92.129997,92.379997,90.949997,91.610001,91.610001,12420000\n1971-11-22,91.610001,92.120003,90.510002,90.790001,90.790001,11390000\n1971-11-23,90.790001,91.099998,89.339996,90.160004,90.160004,16840000\n1971-11-24,90.160004,91.139999,89.730003,90.330002,90.330002,11870000\n1971-11-26,90.330002,92.190002,90.269997,91.940002,91.940002,10870000\n1971-11-29,92.040001,94.900002,92.040001,93.410004,93.410004,18910000\n1971-11-30,93.410004,94.430000,92.510002,93.989998,93.989998,18320000\n1971-12-01,93.989998,96.120003,93.949997,95.440002,95.440002,21040000\n1971-12-02,95.440002,96.589996,94.730003,95.839996,95.839996,17780000\n1971-12-03,95.839996,97.570000,95.360001,97.059998,97.059998,16760000\n1971-12-06,97.059998,98.169998,96.070000,96.510002,96.510002,17480000\n1971-12-07,96.510002,97.349998,95.400002,96.870003,96.870003,15250000\n1971-12-08,96.870003,97.650002,96.080002,96.919998,96.919998,16650000\n1971-12-09,96.959999,96.959999,96.959999,96.959999,96.959999,14710000\n1971-12-10,97.690002,97.690002,97.690002,97.690002,97.690002,17510000\n1971-12-13,97.970001,97.970001,97.970001,97.970001,97.970001,17020000\n1971-12-14,97.669998,97.669998,97.669998,97.669998,97.669998,16070000\n1971-12-15,98.540001,98.540001,98.540001,98.540001,98.540001,16890000\n1971-12-16,99.739998,99.739998,99.739998,99.739998,99.739998,21070000\n1971-12-17,100.260002,100.260002,100.260002,100.260002,100.260002,18270000\n1971-12-20,101.550003,101.550003,101.550003,101.550003,101.550003,23810000\n1971-12-21,101.800003,101.800003,101.800003,101.800003,101.800003,20460000\n1971-12-22,101.180000,101.180000,101.180000,101.180000,101.180000,18930000\n1971-12-23,100.739998,100.739998,100.739998,100.739998,100.739998,16000000\n1971-12-27,100.949997,100.949997,100.949997,100.949997,100.949997,11890000\n1971-12-28,101.949997,101.949997,101.949997,101.949997,101.949997,15090000\n1971-12-29,102.209999,102.209999,102.209999,102.209999,102.209999,17150000\n1971-12-30,101.779999,101.779999,101.779999,101.779999,101.779999,13810000\n1971-12-31,102.089996,102.089996,102.089996,102.089996,102.089996,14040000\n1972-01-03,102.089996,102.849998,101.190002,101.669998,101.669998,12570000\n1972-01-04,101.669998,102.589996,100.870003,102.089996,102.089996,15190000\n1972-01-05,102.089996,103.690002,101.900002,103.059998,103.059998,21350000\n1972-01-06,103.059998,104.199997,102.660004,103.510002,103.510002,21100000\n1972-01-07,103.510002,104.290001,102.379997,103.470001,103.470001,17140000\n1972-01-10,103.470001,103.970001,102.440002,103.320000,103.320000,15320000\n1972-01-11,103.320000,104.300003,102.849998,103.650002,103.650002,17970000\n1972-01-12,103.650002,104.660004,103.050003,103.589996,103.589996,20970000\n1972-01-13,103.589996,103.800003,102.290001,102.989998,102.989998,16410000\n1972-01-14,102.989998,103.889999,102.410004,103.389999,103.389999,14960000\n1972-01-17,103.389999,104.239998,102.800003,103.699997,103.699997,15860000\n1972-01-18,103.699997,104.849998,103.349998,104.050003,104.050003,21070000\n1972-01-19,104.050003,104.610001,102.830002,103.879997,103.879997,18800000\n1972-01-20,103.879997,105.000000,103.320000,103.879997,103.879997,20210000\n1972-01-21,103.879997,104.400002,102.750000,103.650002,103.650002,18810000\n1972-01-24,103.650002,104.029999,102.199997,102.570000,102.570000,15640000\n1972-01-25,102.570000,103.589996,101.629997,102.699997,102.699997,17570000\n1972-01-26,102.699997,103.309998,101.809998,102.500000,102.500000,14940000\n1972-01-27,102.500000,103.930000,102.199997,103.500000,103.500000,20360000\n1972-01-28,103.500000,104.980003,103.220001,104.160004,104.160004,25000000\n1972-01-31,104.160004,104.879997,103.300003,103.940002,103.940002,18250000\n1972-02-01,103.940002,104.570000,103.099998,104.010002,104.010002,19600000\n1972-02-02,104.010002,105.410004,103.500000,104.680000,104.680000,24070000\n1972-02-03,104.680000,105.430000,103.849998,104.639999,104.639999,19880000\n1972-02-04,104.639999,105.480003,104.050003,104.860001,104.860001,17890000\n1972-02-07,104.860001,105.459999,103.970001,104.540001,104.540001,16930000\n1972-02-08,104.540001,105.220001,103.900002,104.739998,104.739998,17390000\n1972-02-09,104.739998,106.029999,104.360001,105.550003,105.550003,19850000\n1972-02-10,105.550003,106.690002,104.970001,105.589996,105.589996,23460000\n1972-02-11,105.589996,105.910004,104.449997,105.080002,105.080002,17850000\n1972-02-14,105.080002,105.529999,104.029999,104.589996,104.589996,15840000\n1972-02-15,104.589996,105.589996,104.099998,105.029999,105.029999,17770000\n1972-02-16,105.029999,106.250000,104.650002,105.620003,105.620003,20670000\n1972-02-17,105.620003,106.650002,104.959999,105.589996,105.589996,22330000\n1972-02-18,105.589996,106.010002,104.470001,105.279999,105.279999,16590000\n1972-02-22,105.279999,106.180000,104.650002,105.290001,105.290001,16670000\n1972-02-23,105.290001,106.180000,104.720001,105.379997,105.379997,16770000\n1972-02-24,105.379997,106.239998,104.760002,105.449997,105.449997,16000000\n1972-02-25,105.449997,106.730003,105.040001,106.180000,106.180000,18180000\n1972-02-28,106.180000,107.040001,105.370003,106.190002,106.190002,18200000\n1972-02-29,106.190002,107.160004,105.449997,106.570000,106.570000,20320000\n1972-03-01,106.570000,108.129997,106.209999,107.349998,107.349998,23670000\n1972-03-02,107.349998,108.389999,106.629997,107.320000,107.320000,22200000\n1972-03-03,107.320000,108.510002,106.779999,107.940002,107.940002,20420000\n1972-03-06,107.940002,109.400002,107.639999,108.769997,108.769997,21000000\n1972-03-07,108.769997,109.720001,108.019997,108.870003,108.870003,22640000\n1972-03-08,108.870003,109.680000,108.040001,108.959999,108.959999,21290000\n1972-03-09,108.959999,109.750000,108.190002,108.940002,108.940002,21460000\n1972-03-10,108.940002,109.370003,107.769997,108.379997,108.379997,19690000\n1972-03-13,108.379997,108.519997,106.709999,107.330002,107.330002,16730000\n1972-03-14,107.330002,108.199997,106.709999,107.610001,107.610001,22370000\n1972-03-15,107.610001,108.550003,107.089996,107.750000,107.750000,19460000\n1972-03-16,107.750000,108.220001,106.550003,107.500000,107.500000,16700000\n1972-03-17,107.500000,108.610001,106.889999,107.919998,107.919998,16040000\n1972-03-20,107.919998,108.809998,107.180000,107.589996,107.589996,16420000\n1972-03-21,107.589996,107.680000,105.860001,106.690002,106.690002,18610000\n1972-03-22,106.690002,107.519997,106.000000,106.839996,106.839996,15400000\n1972-03-23,106.839996,108.330002,106.669998,107.750000,107.750000,18380000\n1972-03-24,107.750000,108.360001,106.949997,107.519997,107.519997,15390000\n1972-03-27,107.519997,108.000000,106.529999,107.300003,107.300003,12180000\n1972-03-28,107.300003,108.080002,106.220001,107.169998,107.169998,15380000\n1972-03-29,107.169998,107.410004,105.980003,106.489998,106.489998,13860000\n1972-03-30,106.489998,107.669998,106.070000,107.199997,107.199997,14360000\n1972-04-03,107.199997,108.260002,106.750000,107.480003,107.480003,14990000\n1972-04-04,107.480003,108.620003,106.769997,108.120003,108.120003,18110000\n1972-04-05,108.120003,109.639999,107.959999,109.000000,109.000000,22960000\n1972-04-06,109.000000,110.290001,108.529999,109.529999,109.529999,22830000\n1972-04-07,109.529999,110.150002,108.529999,109.620003,109.620003,19900000\n1972-04-10,109.620003,110.540001,108.889999,109.449997,109.449997,19470000\n1972-04-11,109.449997,110.379997,108.760002,109.760002,109.760002,19930000\n1972-04-12,109.760002,111.110001,109.360001,110.180000,110.180000,24690000\n1972-04-13,110.180000,110.790001,109.370003,109.910004,109.910004,17990000\n1972-04-14,109.910004,110.559998,109.070000,109.839996,109.839996,17460000\n1972-04-17,109.839996,110.220001,108.769997,109.510002,109.510002,15390000\n1972-04-18,109.510002,110.639999,109.019997,109.769997,109.769997,19410000\n1972-04-19,109.769997,110.349998,108.709999,109.199997,109.199997,19180000\n1972-04-20,109.199997,109.690002,108.080002,109.040001,109.040001,18190000\n1972-04-21,109.040001,109.919998,108.300003,108.889999,108.889999,18200000\n1972-04-24,108.889999,109.190002,107.620003,108.190002,108.190002,14650000\n1972-04-25,108.190002,108.290001,106.699997,107.120003,107.120003,17030000\n1972-04-26,107.120003,107.889999,106.180000,106.889999,106.889999,17710000\n1972-04-27,106.889999,107.889999,106.419998,107.050003,107.050003,15740000\n1972-04-28,107.050003,108.279999,106.699997,107.669998,107.669998,14160000\n1972-05-01,107.669998,108.000000,106.300003,106.690002,106.690002,12880000\n1972-05-02,106.690002,107.370003,105.550003,106.080002,106.080002,15370000\n1972-05-03,106.080002,107.239998,105.440002,105.989998,105.989998,15900000\n1972-05-04,105.989998,106.809998,105.139999,106.250000,106.250000,14790000\n1972-05-05,106.250000,107.330002,105.699997,106.629997,106.629997,13210000\n1972-05-08,106.629997,106.809998,105.360001,106.139999,106.139999,11250000\n1972-05-09,106.059998,106.059998,103.830002,104.739998,104.739998,19910000\n1972-05-10,104.739998,106.099998,104.430000,105.419998,105.419998,13870000\n1972-05-11,105.419998,106.449997,104.900002,105.769997,105.769997,12900000\n1972-05-12,105.769997,107.019997,105.489998,106.379997,106.379997,13990000\n1972-05-15,106.379997,107.449997,106.059998,106.860001,106.860001,13600000\n1972-05-16,106.860001,107.550003,106.129997,106.660004,106.660004,14070000\n1972-05-17,106.660004,107.379997,106.019997,106.889999,106.889999,13600000\n1972-05-18,106.889999,108.389999,106.720001,107.940002,107.940002,17370000\n1972-05-19,107.940002,109.589996,107.739998,108.980003,108.980003,19580000\n1972-05-22,108.980003,110.370003,108.790001,109.690002,109.690002,16030000\n1972-05-23,109.690002,110.459999,108.910004,109.779999,109.779999,16410000\n1972-05-24,109.779999,111.070000,109.389999,110.309998,110.309998,17870000\n1972-05-25,110.309998,111.199997,109.669998,110.459999,110.459999,16480000\n1972-05-26,110.459999,111.309998,109.839996,110.660004,110.660004,15730000\n1972-05-30,110.660004,111.480003,109.779999,110.349998,110.349998,15810000\n1972-05-31,110.349998,110.519997,108.919998,109.529999,109.529999,15230000\n1972-06-01,109.529999,110.349998,108.970001,109.690002,109.690002,14910000\n1972-06-02,109.690002,110.510002,108.930000,109.730003,109.730003,15400000\n1972-06-05,109.730003,109.919998,108.279999,108.820000,108.820000,13450000\n1972-06-06,108.820000,109.320000,107.709999,108.209999,108.209999,15980000\n1972-06-07,108.209999,108.519997,106.910004,107.650002,107.650002,15220000\n1972-06-08,107.650002,108.519997,106.900002,107.279999,107.279999,13820000\n1972-06-09,107.279999,107.680000,106.300003,106.860001,106.860001,12790000\n1972-06-12,106.860001,107.919998,106.290001,107.010002,107.010002,13390000\n1972-06-13,107.010002,108.029999,106.379997,107.550003,107.550003,15710000\n1972-06-14,107.550003,109.150002,107.379997,108.389999,108.389999,18320000\n1972-06-15,108.389999,109.519997,107.779999,108.440002,108.440002,16940000\n1972-06-16,108.440002,108.940002,107.540001,108.360001,108.360001,13010000\n1972-06-19,108.360001,108.779999,107.370003,108.110001,108.110001,11660000\n1972-06-20,108.110001,109.120003,107.639999,108.559998,108.559998,14970000\n1972-06-21,108.559998,109.660004,107.980003,108.790001,108.790001,15510000\n1972-06-22,108.790001,109.260002,107.620003,108.680000,108.680000,13410000\n1972-06-23,108.680000,109.330002,107.690002,108.269997,108.269997,13940000\n1972-06-26,108.230003,108.230003,106.680000,107.480003,107.480003,12720000\n1972-06-27,107.480003,108.290001,106.699997,107.370003,107.370003,13750000\n1972-06-28,107.370003,107.870003,106.489998,107.019997,107.019997,12140000\n1972-06-29,107.019997,107.470001,105.940002,106.820000,106.820000,14610000\n1972-06-30,106.820000,107.910004,106.400002,107.139999,107.139999,12860000\n1972-07-03,107.139999,107.949997,106.720001,107.489998,107.489998,8140000\n1972-07-05,107.489998,108.800003,107.139999,108.099998,108.099998,14710000\n1972-07-06,108.279999,110.269997,108.279999,109.040001,109.040001,19520000\n1972-07-07,109.040001,109.660004,108.160004,108.690002,108.690002,12900000\n1972-07-10,108.690002,109.160004,107.620003,108.110001,108.110001,11700000\n1972-07-11,108.110001,108.349998,106.870003,107.320000,107.320000,12830000\n1972-07-12,107.320000,108.150002,106.419998,106.889999,106.889999,16150000\n1972-07-13,106.889999,107.300003,105.620003,106.279999,106.279999,14740000\n1972-07-14,106.279999,107.580002,105.769997,106.800003,106.800003,13910000\n1972-07-17,106.800003,107.370003,105.550003,105.879997,105.879997,13170000\n1972-07-18,105.879997,106.400002,104.430000,105.830002,105.830002,16820000\n1972-07-19,105.830002,107.360001,105.470001,106.139999,106.139999,17880000\n1972-07-20,106.139999,106.680000,105.120003,105.809998,105.809998,15050000\n1972-07-21,105.809998,107.050003,104.989998,106.660004,106.660004,14010000\n1972-07-24,106.660004,108.669998,106.629997,107.919998,107.919998,18020000\n1972-07-25,107.919998,108.879997,107.059998,107.599998,107.599998,17180000\n1972-07-26,107.599998,108.419998,106.790001,107.529999,107.529999,14130000\n1972-07-27,107.529999,108.309998,106.610001,107.279999,107.279999,13870000\n1972-07-28,107.279999,108.029999,106.519997,107.379997,107.379997,13050000\n1972-07-31,107.379997,108.059998,106.599998,107.389999,107.389999,11120000\n1972-08-01,107.389999,108.849998,107.059998,108.400002,108.400002,15540000\n1972-08-02,108.400002,109.849998,108.120003,109.290001,109.290001,17920000\n1972-08-03,109.290001,110.879997,108.900002,110.139999,110.139999,19970000\n1972-08-04,110.139999,111.120003,109.370003,110.430000,110.430000,15700000\n1972-08-07,110.430000,111.379997,109.690002,110.610001,110.610001,13220000\n1972-08-08,110.610001,111.320000,109.669998,110.690002,110.690002,14550000\n1972-08-09,110.690002,111.570000,109.980003,110.860001,110.860001,15730000\n1972-08-10,110.860001,111.680000,110.089996,111.050003,111.050003,15260000\n1972-08-11,111.050003,112.400002,110.519997,111.949997,111.949997,16570000\n1972-08-14,111.949997,113.449997,111.660004,112.550003,112.550003,18870000\n1972-08-15,112.550003,113.040001,111.269997,112.059998,112.059998,16670000\n1972-08-16,112.059998,112.800003,110.870003,111.660004,111.660004,14950000\n1972-08-17,111.660004,112.410004,110.720001,111.339996,111.339996,14360000\n1972-08-18,111.339996,112.529999,110.809998,111.760002,111.760002,16150000\n1972-08-21,111.760002,112.739998,110.750000,111.720001,111.720001,14290000\n1972-08-22,111.720001,113.160004,111.279999,112.410004,112.410004,18560000\n1972-08-23,112.410004,113.269997,111.300003,112.260002,112.260002,18670000\n1972-08-24,112.260002,112.809998,110.620003,111.019997,111.019997,18280000\n1972-08-25,111.019997,111.529999,109.779999,110.669998,110.669998,13840000\n1972-08-28,110.669998,111.239998,109.709999,110.230003,110.230003,10720000\n1972-08-29,110.230003,111.019997,109.260002,110.410004,110.410004,12300000\n1972-08-30,110.410004,111.330002,109.900002,110.570000,110.570000,12470000\n1972-08-31,110.570000,111.519997,110.080002,111.089996,111.089996,12340000\n1972-09-01,111.089996,112.120003,110.699997,111.510002,111.510002,11600000\n1972-09-05,111.510002,112.080002,110.750000,111.230003,111.230003,10630000\n1972-09-06,111.230003,111.379997,110.040001,110.550003,110.550003,12010000\n1972-09-07,110.550003,111.059998,109.709999,110.290001,110.290001,11090000\n1972-09-08,110.290001,110.900002,109.669998,110.150002,110.150002,10980000\n1972-09-11,110.150002,110.570000,109.010002,109.510002,109.510002,10710000\n1972-09-12,109.510002,109.839996,107.809998,108.470001,108.470001,13560000\n1972-09-13,108.470001,109.360001,107.839996,108.900002,108.900002,13070000\n1972-09-14,108.900002,109.639999,108.209999,108.930000,108.930000,12500000\n1972-09-15,108.930000,109.489998,108.099998,108.809998,108.809998,11690000\n1972-09-18,108.809998,109.220001,107.860001,108.610001,108.610001,8880000\n1972-09-19,108.610001,109.570000,108.080002,108.550003,108.550003,13330000\n1972-09-20,108.550003,109.120003,107.839996,108.599998,108.599998,11980000\n1972-09-21,108.599998,109.129997,107.750000,108.430000,108.430000,11940000\n1972-09-22,108.430000,109.199997,107.720001,108.519997,108.519997,12570000\n1972-09-25,108.519997,109.089996,107.669998,108.050003,108.050003,10920000\n1972-09-26,108.050003,108.970001,107.349998,108.120003,108.120003,13150000\n1972-09-27,108.120003,109.919998,107.790001,109.660004,109.660004,14620000\n1972-09-28,109.660004,110.750000,108.750000,110.349998,110.349998,14710000\n1972-09-29,110.349998,110.550003,108.050003,110.550003,110.550003,16250000\n1972-10-02,110.550003,110.980003,109.489998,110.160004,110.160004,12440000\n1972-10-03,110.160004,110.900002,109.470001,110.300003,110.300003,13090000\n1972-10-04,110.300003,111.349998,109.580002,110.089996,110.089996,16640000\n1972-10-05,110.089996,110.519997,108.489998,108.889999,108.889999,17730000\n1972-10-06,108.889999,110.489998,107.779999,109.620003,109.620003,16630000\n1972-10-09,109.620003,110.440002,109.279999,109.900002,109.900002,7940000\n1972-10-10,109.900002,111.110001,109.320000,109.989998,109.989998,13310000\n1972-10-11,109.989998,110.510002,108.769997,109.500000,109.500000,11900000\n1972-10-12,109.500000,109.690002,108.029999,108.599998,108.599998,13130000\n1972-10-13,108.599998,108.879997,107.169998,107.919998,107.919998,12870000\n1972-10-16,107.919998,108.400002,106.379997,106.769997,106.769997,10940000\n1972-10-17,106.769997,108.040001,106.269997,107.500000,107.500000,13410000\n1972-10-18,107.500000,109.110001,107.360001,108.190002,108.190002,17290000\n1972-10-19,108.190002,108.809998,107.400002,108.050003,108.050003,13850000\n1972-10-20,108.050003,109.790001,107.589996,109.239998,109.239998,15740000\n1972-10-23,109.510002,111.099998,109.510002,110.349998,110.349998,14190000\n1972-10-24,110.349998,111.339996,109.379997,110.809998,110.809998,15240000\n1972-10-25,110.809998,111.559998,109.959999,110.720001,110.720001,17430000\n1972-10-26,110.720001,112.260002,110.260002,110.989998,110.989998,20790000\n1972-10-27,110.989998,111.620003,109.989998,110.620003,110.620003,15470000\n1972-10-30,110.620003,111.190002,109.660004,110.589996,110.589996,11820000\n1972-10-31,110.589996,112.050003,110.400002,111.580002,111.580002,15450000\n1972-11-01,111.580002,113.309998,111.320000,112.669998,112.669998,21360000\n1972-11-02,112.669998,113.809998,111.959999,113.230003,113.230003,20690000\n1972-11-03,113.230003,114.809998,112.709999,114.220001,114.220001,22510000\n1972-11-06,114.220001,115.169998,112.910004,113.980003,113.980003,21330000\n1972-11-08,113.980003,115.230003,112.769997,113.349998,113.349998,24620000\n1972-11-09,113.349998,114.110001,112.080002,113.500000,113.500000,17040000\n1972-11-10,113.500000,115.150002,112.849998,113.730003,113.730003,24360000\n1972-11-13,113.730003,114.750000,112.910004,113.900002,113.900002,17210000\n1972-11-14,113.900002,115.410004,113.360001,114.949997,114.949997,20200000\n1972-11-15,114.949997,116.070000,113.870003,114.500000,114.500000,23270000\n1972-11-16,114.500000,115.570000,113.730003,115.129997,115.129997,19580000\n1972-11-17,115.129997,116.230003,114.440002,115.489998,115.489998,20220000\n1972-11-20,115.489998,116.250000,114.570000,115.529999,115.529999,16680000\n1972-11-21,115.529999,116.839996,115.040001,116.209999,116.209999,22110000\n1972-11-22,116.209999,117.610001,115.669998,116.900002,116.900002,24510000\n1972-11-24,116.900002,117.910004,116.190002,117.269997,117.269997,15760000\n1972-11-27,117.269997,117.550003,115.660004,116.720001,116.720001,18190000\n1972-11-28,116.720001,117.480003,115.779999,116.470001,116.470001,19210000\n1972-11-29,116.470001,117.139999,115.559998,116.519997,116.519997,17380000\n1972-11-30,116.519997,117.389999,115.739998,116.669998,116.669998,19340000\n1972-12-01,116.669998,118.180000,116.290001,117.379997,117.379997,22570000\n1972-12-04,117.379997,118.540001,116.989998,117.769997,117.769997,19730000\n1972-12-05,117.769997,118.419998,116.889999,117.580002,117.580002,17800000\n1972-12-06,117.580002,118.559998,116.900002,118.010002,118.010002,18610000\n1972-12-07,118.010002,119.169998,117.570000,118.599998,118.599998,19320000\n1972-12-08,118.599998,119.540001,117.919998,118.860001,118.860001,18030000\n1972-12-11,118.860001,119.779999,118.239998,119.120003,119.120003,17230000\n1972-12-12,119.120003,119.790001,118.089996,118.660004,118.660004,17040000\n1972-12-13,118.660004,119.230003,117.769997,118.559998,118.559998,16540000\n1972-12-14,118.559998,119.190002,117.629997,118.239998,118.239998,17930000\n1972-12-15,118.239998,119.250000,117.370003,118.260002,118.260002,18300000\n1972-12-18,117.879997,117.879997,115.889999,116.900002,116.900002,17540000\n1972-12-19,116.900002,117.370003,115.690002,116.339996,116.339996,17000000\n1972-12-20,116.339996,117.129997,115.379997,115.949997,115.949997,18490000\n1972-12-21,115.949997,116.599998,114.629997,115.110001,115.110001,18290000\n1972-12-22,115.110001,116.400002,114.779999,115.830002,115.830002,12540000\n1972-12-26,115.830002,116.870003,115.540001,116.300003,116.300003,11120000\n1972-12-27,116.300003,117.550003,115.889999,116.930000,116.930000,19100000\n1972-12-29,116.930000,118.769997,116.699997,118.050003,118.050003,27550000\n1973-01-02,118.059998,119.900002,118.059998,119.099998,119.099998,17090000\n1973-01-03,119.099998,120.449997,118.690002,119.570000,119.570000,20620000\n1973-01-04,119.570000,120.169998,118.120003,119.400002,119.400002,20230000\n1973-01-05,119.400002,120.709999,118.879997,119.870003,119.870003,19330000\n1973-01-08,119.870003,120.550003,119.040001,119.849998,119.849998,16840000\n1973-01-09,119.849998,120.400002,118.889999,119.730003,119.730003,16830000\n1973-01-10,119.730003,120.440002,118.779999,119.430000,119.430000,20880000\n1973-01-11,119.430000,121.739998,119.010002,120.239998,120.239998,25050000\n1973-01-12,120.239998,121.269997,118.690002,119.300003,119.300003,22230000\n1973-01-15,119.300003,120.820000,118.040001,118.440002,118.440002,21520000\n1973-01-16,118.440002,119.169998,117.040001,118.139999,118.139999,19170000\n1973-01-17,118.139999,119.349998,117.610001,118.680000,118.680000,17680000\n1973-01-18,118.680000,119.930000,118.150002,118.849998,118.849998,17810000\n1973-01-19,118.849998,119.449997,117.459999,118.779999,118.779999,17020000\n1973-01-22,118.779999,119.629997,117.720001,118.209999,118.209999,15570000\n1973-01-23,118.209999,119.000000,116.839996,118.220001,118.220001,19060000\n1973-01-24,118.220001,119.040001,116.089996,116.730003,116.730003,20870000\n1973-01-26,116.730003,117.290001,114.970001,116.449997,116.449997,21130000\n1973-01-29,116.449997,117.180000,115.129997,116.010002,116.010002,14680000\n1973-01-30,116.010002,117.110001,115.260002,115.830002,115.830002,15270000\n1973-01-31,115.830002,116.839996,115.050003,116.029999,116.029999,14870000\n1973-02-01,116.029999,117.010002,114.260002,114.760002,114.760002,20670000\n1973-02-02,114.760002,115.400002,113.449997,114.349998,114.349998,17470000\n1973-02-05,114.349998,115.150002,113.620003,114.230003,114.230003,14580000\n1973-02-06,114.230003,115.330002,113.449997,114.449997,114.449997,15720000\n1973-02-07,114.449997,115.480003,113.239998,113.660004,113.660004,17960000\n1973-02-08,113.660004,114.050003,111.849998,113.160004,113.160004,18440000\n1973-02-09,113.160004,115.199997,113.080002,114.680000,114.680000,19260000\n1973-02-12,114.690002,116.660004,114.690002,116.059998,116.059998,16130000\n1973-02-13,116.089996,118.980003,116.089996,116.779999,116.779999,25320000\n1973-02-14,116.779999,116.919998,114.519997,115.099998,115.099998,16520000\n1973-02-15,115.099998,115.680000,113.699997,114.449997,114.449997,13940000\n1973-02-16,114.449997,115.470001,113.730003,114.980003,114.980003,13320000\n1973-02-20,114.980003,116.260002,114.570000,115.400002,115.400002,14020000\n1973-02-21,115.400002,116.010002,114.129997,114.690002,114.690002,14880000\n1973-02-22,114.690002,115.199997,113.440002,114.440002,114.440002,14570000\n1973-02-23,114.440002,114.669998,112.769997,113.160004,113.160004,15450000\n1973-02-26,113.160004,113.260002,111.150002,112.190002,112.190002,15860000\n1973-02-27,112.190002,112.900002,110.500000,110.900002,110.900002,16130000\n1973-02-28,110.900002,112.209999,109.800003,111.680000,111.680000,17950000\n1973-03-01,111.680000,112.980003,110.680000,111.050003,111.050003,18210000\n1973-03-02,111.050003,112.620003,109.449997,112.279999,112.279999,17710000\n1973-03-05,112.279999,113.430000,111.330002,112.680000,112.680000,13720000\n1973-03-06,112.680000,114.709999,112.570000,114.099998,114.099998,17710000\n1973-03-07,114.099998,115.120003,112.830002,114.449997,114.449997,19310000\n1973-03-08,114.449997,115.230003,113.570000,114.230003,114.230003,15100000\n1973-03-09,114.230003,114.550003,112.930000,113.790001,113.790001,14070000\n1973-03-12,113.790001,114.800003,113.250000,113.860001,113.860001,13810000\n1973-03-13,113.860001,115.050003,113.320000,114.480003,114.480003,14210000\n1973-03-14,114.480003,115.610001,113.970001,114.980003,114.980003,14460000\n1973-03-15,114.980003,115.470001,113.769997,114.120003,114.120003,14450000\n1973-03-16,114.120003,114.620003,112.839996,113.540001,113.540001,15130000\n1973-03-19,113.500000,113.500000,111.650002,112.169998,112.169998,12460000\n1973-03-20,112.169998,112.680000,111.019997,111.949997,111.949997,13250000\n1973-03-21,111.949997,112.809998,110.169998,110.489998,110.489998,16080000\n1973-03-22,110.389999,110.389999,108.190002,108.839996,108.839996,17130000\n1973-03-23,108.839996,109.970001,107.410004,108.879997,108.879997,18470000\n1973-03-26,108.879997,110.400002,108.290001,109.839996,109.839996,14980000\n1973-03-27,109.949997,112.070000,109.949997,111.559998,111.559998,17500000\n1973-03-28,111.559998,112.470001,110.540001,111.620003,111.620003,15850000\n1973-03-29,111.620003,113.220001,111.070000,112.709999,112.709999,16050000\n1973-03-30,112.709999,112.870003,110.889999,111.519997,111.519997,13740000\n1973-04-02,111.519997,111.699997,109.680000,110.180000,110.180000,10640000\n1973-04-03,110.180000,110.349998,108.470001,109.239998,109.239998,12910000\n1973-04-04,109.239998,109.959999,108.099998,108.769997,108.769997,11890000\n1973-04-05,108.769997,109.150002,107.440002,108.519997,108.519997,12750000\n1973-04-06,108.519997,110.040001,108.220001,109.279999,109.279999,13890000\n1973-04-09,109.279999,111.239998,108.739998,110.860001,110.860001,13740000\n1973-04-10,110.919998,112.849998,110.919998,112.209999,112.209999,16770000\n1973-04-11,112.209999,113.269997,111.209999,112.680000,112.680000,14890000\n1973-04-12,112.680000,113.650002,111.830002,112.580002,112.580002,16360000\n1973-04-13,112.580002,112.910004,111.230003,112.080002,112.080002,14390000\n1973-04-16,112.080002,112.610001,110.910004,111.440002,111.440002,11350000\n1973-04-17,111.440002,111.809998,110.190002,110.940002,110.940002,12830000\n1973-04-18,110.940002,112.029999,109.989998,111.540001,111.540001,13890000\n1973-04-19,111.540001,112.930000,111.059998,112.169998,112.169998,14560000\n1973-04-23,112.169998,112.660004,110.910004,111.570000,111.570000,12580000\n1973-04-24,111.570000,111.889999,109.639999,109.989998,109.989998,13830000\n1973-04-25,109.820000,109.820000,107.790001,108.339996,108.339996,15960000\n1973-04-26,108.339996,109.660004,107.139999,108.889999,108.889999,16210000\n1973-04-27,108.889999,109.279999,106.760002,107.230003,107.230003,13730000\n1973-04-30,107.230003,107.900002,105.440002,106.970001,106.970001,14820000\n1973-05-01,106.970001,108.000000,105.339996,107.099998,107.099998,15380000\n1973-05-02,107.099998,109.059998,106.949997,108.430000,108.430000,14380000\n1973-05-03,108.430000,110.639999,106.809998,110.220001,110.220001,17760000\n1973-05-04,110.220001,111.989998,109.889999,111.000000,111.000000,19510000\n1973-05-07,111.000000,111.379997,109.680000,110.529999,110.529999,12500000\n1973-05-08,110.529999,111.720001,109.459999,111.250000,111.250000,13730000\n1973-05-09,111.250000,112.250000,109.970001,110.440002,110.440002,16050000\n1973-05-10,110.440002,110.860001,108.860001,109.540001,109.540001,13520000\n1973-05-11,109.489998,109.489998,107.699997,108.169998,108.169998,12980000\n1973-05-14,107.739998,107.739998,105.519997,105.900002,105.900002,13520000\n1973-05-15,105.900002,107.160004,104.120003,106.570000,106.570000,18530000\n1973-05-16,106.570000,107.610001,105.489998,106.430000,106.430000,13800000\n1973-05-17,106.430000,106.820000,105.150002,105.559998,105.559998,13060000\n1973-05-18,105.410004,105.410004,103.180000,103.860001,103.860001,17080000\n1973-05-21,103.769997,103.769997,101.360001,102.730003,102.730003,20690000\n1973-05-22,102.730003,105.040001,102.580002,103.580002,103.580002,18020000\n1973-05-23,103.580002,105.099998,102.820000,104.070000,104.070000,14950000\n1973-05-24,104.070000,107.440002,103.589996,107.139999,107.139999,17310000\n1973-05-25,107.139999,108.860001,106.080002,107.940002,107.940002,19270000\n1973-05-29,107.940002,108.580002,106.769997,107.510002,107.510002,11300000\n1973-05-30,107.510002,107.639999,105.480003,105.910004,105.910004,11730000\n1973-05-31,105.910004,106.300003,104.349998,104.949997,104.949997,12190000\n1973-06-01,104.949997,105.040001,103.309998,103.930000,103.930000,10410000\n1973-06-04,103.930000,103.980003,102.330002,102.970001,102.970001,11230000\n1973-06-05,102.970001,105.269997,102.610001,104.620003,104.620003,14080000\n1973-06-06,104.620003,105.779999,103.599998,104.309998,104.309998,13080000\n1973-06-07,104.309998,106.389999,104.190002,105.839996,105.839996,14160000\n1973-06-08,105.839996,107.750000,105.599998,107.029999,107.029999,14050000\n1973-06-11,107.029999,107.790001,106.110001,106.699997,106.699997,9940000\n1973-06-12,106.699997,108.779999,106.400002,108.290001,108.290001,13840000\n1973-06-13,108.290001,109.519997,107.080002,107.599998,107.599998,15700000\n1973-06-14,107.599998,108.269997,105.830002,106.400002,106.400002,13210000\n1973-06-15,106.209999,106.209999,104.370003,105.099998,105.099998,11970000\n1973-06-18,104.959999,104.959999,103.080002,103.599998,103.599998,11460000\n1973-06-19,103.599998,104.959999,102.459999,103.989998,103.989998,12970000\n1973-06-20,103.989998,105.129997,103.510002,104.440002,104.440002,10600000\n1973-06-21,104.440002,104.769997,102.839996,103.209999,103.209999,11630000\n1973-06-22,103.209999,105.660004,103.070000,103.699997,103.699997,18470000\n1973-06-25,103.639999,103.639999,101.709999,102.250000,102.250000,11670000\n1973-06-26,102.250000,103.779999,101.449997,103.300003,103.300003,14040000\n1973-06-27,103.300003,104.230003,102.290001,103.620003,103.620003,12660000\n1973-06-28,103.620003,105.169998,103.180000,104.690002,104.690002,12760000\n1973-06-29,104.690002,105.300003,103.680000,104.260002,104.260002,10770000\n1973-07-02,104.099998,104.099998,102.440002,102.900002,102.900002,9830000\n1973-07-03,102.900002,103.019997,101.139999,101.870003,101.870003,10560000\n1973-07-05,101.870003,102.480003,100.800003,101.779999,101.779999,10500000\n1973-07-06,101.779999,102.220001,100.669998,101.279999,101.279999,9980000\n1973-07-09,101.279999,102.449997,100.440002,102.139999,102.139999,11560000\n1973-07-10,102.260002,104.199997,102.260002,103.519997,103.519997,15090000\n1973-07-11,103.639999,106.209999,103.639999,105.800003,105.800003,18730000\n1973-07-12,105.800003,106.620003,104.379997,105.500000,105.500000,16400000\n1973-07-13,105.500000,105.800003,103.660004,104.089996,104.089996,11390000\n1973-07-16,104.089996,106.010002,103.419998,105.669998,105.669998,12920000\n1973-07-17,105.669998,107.279999,104.989998,105.720001,105.720001,18750000\n1973-07-18,105.720001,107.050003,104.730003,106.349998,106.349998,17020000\n1973-07-19,106.349998,107.580002,105.059998,106.550003,106.550003,18650000\n1973-07-20,106.550003,108.019997,105.949997,107.139999,107.139999,16300000\n1973-07-23,107.139999,108.419998,106.540001,107.519997,107.519997,15580000\n1973-07-24,107.519997,108.629997,106.309998,108.139999,108.139999,16280000\n1973-07-25,108.139999,110.760002,107.919998,109.639999,109.639999,22220000\n1973-07-26,109.639999,111.040001,108.510002,109.849998,109.849998,18410000\n1973-07-27,109.849998,110.489998,108.699997,109.589996,109.589996,12910000\n1973-07-30,109.589996,110.120003,108.239998,109.250000,109.250000,11170000\n1973-07-31,109.250000,110.089996,107.889999,108.220001,108.220001,13530000\n1973-08-01,108.169998,108.169998,106.290001,106.830002,106.830002,13530000\n1973-08-02,106.830002,107.379997,105.510002,106.669998,106.669998,16080000\n1973-08-03,106.669998,107.169998,105.680000,106.489998,106.489998,9940000\n1973-08-06,106.489998,107.540001,105.449997,106.730003,106.730003,12320000\n1973-08-07,106.730003,107.570000,105.870003,106.550003,106.550003,13510000\n1973-08-08,106.550003,106.730003,105.040001,105.550003,105.550003,12440000\n1973-08-09,105.550003,106.650002,104.889999,105.610001,105.610001,12880000\n1973-08-10,105.610001,106.029999,104.209999,104.769997,104.769997,10870000\n1973-08-13,104.769997,104.830002,103.129997,103.709999,103.709999,11330000\n1973-08-14,103.709999,104.290001,102.339996,102.709999,102.709999,11740000\n1973-08-15,102.709999,103.790001,101.919998,103.010002,103.010002,12040000\n1973-08-16,103.010002,103.970001,101.849998,102.290001,102.290001,12990000\n1973-08-17,102.290001,102.980003,101.379997,102.309998,102.309998,11110000\n1973-08-20,102.309998,102.540001,101.110001,101.610001,101.610001,8970000\n1973-08-21,101.610001,102.099998,100.510002,100.889999,100.889999,11480000\n1973-08-22,100.889999,101.389999,99.739998,100.529999,100.529999,10770000\n1973-08-23,100.620003,102.500000,100.620003,101.910004,101.910004,11390000\n1973-08-24,101.910004,102.650002,100.879997,101.620003,101.620003,11200000\n1973-08-27,101.620003,102.820000,101.089996,102.419998,102.419998,9740000\n1973-08-28,102.419998,103.660004,102.059998,103.019997,103.019997,11810000\n1973-08-29,103.019997,104.919998,102.690002,104.029999,104.029999,15690000\n1973-08-30,104.029999,104.839996,103.290001,103.879997,103.879997,12100000\n1973-08-31,103.879997,104.720001,103.150002,104.250000,104.250000,10530000\n1973-09-04,104.250000,105.349998,103.599998,104.510002,104.510002,14210000\n1973-09-05,104.510002,105.330002,103.599998,104.639999,104.639999,14580000\n1973-09-06,104.639999,105.949997,104.050003,105.150002,105.150002,15670000\n1973-09-07,105.150002,105.870003,104.040001,104.760002,104.760002,14930000\n1973-09-10,104.760002,105.120003,103.330002,103.849998,103.849998,11620000\n1973-09-11,103.849998,104.089996,102.129997,103.220001,103.220001,12690000\n1973-09-12,103.220001,103.980003,102.150002,103.059998,103.059998,12040000\n1973-09-13,103.059998,104.089996,102.370003,103.360001,103.360001,11670000\n1973-09-14,103.360001,104.750000,102.660004,104.440002,104.440002,13760000\n1973-09-17,104.440002,105.410004,103.209999,104.150002,104.150002,15100000\n1973-09-18,104.150002,104.620003,102.410004,103.769997,103.769997,16400000\n1973-09-19,103.800003,106.430000,103.800003,105.879997,105.879997,24570000\n1973-09-20,105.879997,107.550003,105.320000,106.760002,106.760002,25960000\n1973-09-21,106.760002,108.019997,105.430000,107.199997,107.199997,23760000\n1973-09-24,107.199997,108.360001,106.209999,107.360001,107.360001,19490000\n1973-09-25,107.360001,108.790001,106.500000,108.050003,108.050003,21530000\n1973-09-26,108.050003,109.610001,107.430000,108.830002,108.830002,21130000\n1973-09-27,108.830002,110.449997,108.019997,109.080002,109.080002,23660000\n1973-09-28,109.080002,109.419998,107.480003,108.430000,108.430000,16300000\n1973-10-01,108.430000,108.980003,107.080002,108.209999,108.209999,15830000\n1973-10-02,108.209999,109.459999,107.480003,108.790001,108.790001,20770000\n1973-10-03,108.790001,109.949997,107.739998,108.779999,108.779999,22040000\n1973-10-04,108.779999,109.529999,107.300003,108.410004,108.410004,19730000\n1973-10-05,108.410004,110.459999,107.760002,109.849998,109.849998,18820000\n1973-10-08,109.849998,110.930000,108.019997,110.230003,110.230003,18990000\n1973-10-09,110.230003,111.190002,109.050003,110.129997,110.129997,19440000\n1973-10-10,110.129997,111.309998,108.510002,109.220001,109.220001,19010000\n1973-10-11,109.220001,111.769997,108.959999,111.089996,111.089996,20740000\n1973-10-12,111.089996,112.820000,110.519997,111.440002,111.440002,22730000\n1973-10-15,111.320000,111.320000,109.290001,110.050003,110.050003,16160000\n1973-10-16,110.050003,110.800003,108.500000,110.190002,110.190002,18780000\n1973-10-17,110.190002,111.410004,109.190002,109.970001,109.970001,18600000\n1973-10-18,109.970001,111.430000,108.970001,110.010002,110.010002,19210000\n1973-10-19,110.010002,111.559998,109.300003,110.220001,110.220001,17880000\n1973-10-22,110.220001,110.559998,108.180000,109.160004,109.160004,14290000\n1973-10-23,109.160004,110.910004,107.400002,109.750000,109.750000,17230000\n1973-10-24,109.750000,110.980003,109.029999,110.269997,110.269997,15840000\n1973-10-25,110.269997,111.330002,108.849998,110.500000,110.500000,15580000\n1973-10-26,110.500000,112.309998,110.080002,111.379997,111.379997,17800000\n1973-10-29,111.379997,112.559998,110.519997,111.150002,111.150002,17960000\n1973-10-30,111.150002,111.300003,108.949997,109.330002,109.330002,17580000\n1973-10-31,109.330002,109.820000,107.639999,108.290001,108.290001,17890000\n1973-11-01,108.290001,109.199997,106.879997,107.690002,107.690002,16920000\n1973-11-02,107.690002,108.349998,106.330002,107.070000,107.070000,16340000\n1973-11-05,106.970001,106.970001,104.870003,105.519997,105.519997,17150000\n1973-11-06,105.519997,107.000000,104.519997,104.959999,104.959999,16430000\n1973-11-07,104.959999,106.720001,104.529999,105.800003,105.800003,16570000\n1973-11-08,106.099998,108.449997,106.099998,107.019997,107.019997,19650000\n1973-11-09,107.019997,107.269997,104.769997,105.300003,105.300003,17320000\n1973-11-12,105.300003,105.750000,103.120003,104.440002,104.440002,19250000\n1973-11-13,104.440002,105.419998,102.910004,104.360001,104.360001,20310000\n1973-11-14,104.360001,105.250000,101.870003,102.449997,102.449997,22710000\n1973-11-15,102.449997,103.849998,100.690002,102.430000,102.430000,24530000\n1973-11-16,102.430000,105.410004,101.769997,103.879997,103.879997,22510000\n1973-11-19,103.650002,103.650002,100.370003,100.709999,100.709999,16700000\n1973-11-20,100.650002,100.650002,97.639999,98.660004,98.660004,23960000\n1973-11-21,98.660004,101.330002,97.870003,99.760002,99.760002,24260000\n1973-11-23,99.760002,100.489998,98.589996,99.440002,99.440002,11470000\n1973-11-26,98.639999,98.639999,95.790001,96.580002,96.580002,19830000\n1973-11-27,96.580002,97.699997,94.879997,95.699997,95.699997,19750000\n1973-11-28,95.699997,98.400002,95.220001,97.650002,97.650002,19990000\n1973-11-29,97.650002,98.720001,96.010002,97.309998,97.309998,18870000\n1973-11-30,97.309998,97.550003,95.400002,95.959999,95.959999,15380000\n1973-12-03,95.830002,95.830002,92.919998,93.900002,93.900002,17900000\n1973-12-04,93.900002,95.230003,92.599998,93.589996,93.589996,19030000\n1973-12-05,93.589996,93.930000,91.550003,92.160004,92.160004,19180000\n1973-12-06,92.160004,94.889999,91.680000,94.419998,94.419998,23260000\n1973-12-07,94.489998,97.580002,94.489998,96.510002,96.510002,23230000\n1973-12-10,96.510002,98.580002,95.440002,97.949997,97.949997,18590000\n1973-12-11,97.949997,99.089996,95.620003,96.040001,96.040001,20100000\n1973-12-12,95.519997,95.519997,92.900002,93.570000,93.570000,18190000\n1973-12-13,93.570000,94.680000,91.639999,92.379997,92.379997,18130000\n1973-12-14,92.379997,94.529999,91.050003,93.290001,93.290001,20000000\n1973-12-17,93.290001,94.000000,91.870003,92.750000,92.750000,12930000\n1973-12-18,92.750000,95.410004,92.180000,94.739998,94.739998,19490000\n1973-12-19,94.739998,96.830002,93.809998,94.820000,94.820000,20670000\n1973-12-20,94.820000,96.260002,93.510002,94.550003,94.550003,17340000\n1973-12-21,94.550003,95.110001,92.699997,93.540001,93.540001,18680000\n1973-12-24,93.540001,93.769997,91.680000,92.900002,92.900002,11540000\n1973-12-26,93.870003,96.519997,93.870003,95.739998,95.739998,18620000\n1973-12-27,96.000000,98.529999,96.000000,97.739998,97.739998,22720000\n1973-12-28,97.739998,98.760002,96.410004,97.540001,97.540001,21310000\n1973-12-31,97.540001,98.300003,95.949997,97.550003,97.550003,23470000\n1974-01-02,97.550003,98.379997,96.250000,97.680000,97.680000,12060000\n1974-01-03,98.019997,100.940002,98.019997,99.800003,99.800003,24850000\n1974-01-04,99.800003,100.699997,97.699997,98.900002,98.900002,21700000\n1974-01-07,98.900002,99.309998,96.860001,98.070000,98.070000,19070000\n1974-01-08,98.070000,98.260002,95.580002,96.120003,96.120003,18080000\n1974-01-09,95.400002,95.400002,92.629997,93.419998,93.419998,18070000\n1974-01-10,93.419998,94.629997,91.620003,92.389999,92.389999,16120000\n1974-01-11,92.389999,94.570000,91.750000,93.660004,93.660004,15140000\n1974-01-14,93.660004,95.239998,92.349998,93.419998,93.419998,14610000\n1974-01-15,93.419998,95.260002,92.839996,94.230003,94.230003,13250000\n1974-01-16,94.230003,96.199997,93.779999,95.669998,95.669998,14930000\n1974-01-17,95.669998,98.349998,95.669998,97.300003,97.300003,21040000\n1974-01-18,97.300003,97.629997,95.000000,95.559998,95.559998,16470000\n1974-01-21,95.559998,95.959999,93.230003,95.400002,95.400002,15630000\n1974-01-22,95.400002,97.410004,94.919998,96.550003,96.550003,17330000\n1974-01-23,96.550003,98.110001,95.879997,97.070000,97.070000,16890000\n1974-01-24,97.070000,97.750000,95.489998,96.820000,96.820000,15980000\n1974-01-25,96.820000,97.639999,95.680000,96.629997,96.629997,14860000\n1974-01-28,96.629997,97.320000,95.370003,96.089996,96.089996,13410000\n1974-01-29,96.089996,96.809998,94.970001,96.010002,96.010002,12850000\n1974-01-30,96.019997,97.900002,96.019997,97.059998,97.059998,16790000\n1974-01-31,97.059998,98.059998,96.110001,96.570000,96.570000,14020000\n1974-02-01,96.570000,96.629997,94.660004,95.320000,95.320000,12480000\n1974-02-04,94.889999,94.889999,92.739998,93.290001,93.290001,14380000\n1974-02-05,93.290001,94.169998,92.260002,93.000000,93.000000,12820000\n1974-02-06,93.000000,94.089996,92.370003,93.260002,93.260002,11610000\n1974-02-07,93.260002,94.089996,92.430000,93.300003,93.300003,11750000\n1974-02-08,93.300003,93.790001,91.870003,92.330002,92.330002,12990000\n1974-02-11,92.330002,92.540001,90.260002,90.660004,90.660004,12930000\n1974-02-12,90.660004,91.599998,89.529999,90.940002,90.940002,12920000\n1974-02-13,90.940002,92.129997,90.370003,90.980003,90.980003,10990000\n1974-02-14,90.980003,91.889999,90.169998,90.949997,90.949997,12230000\n1974-02-15,90.949997,92.980003,90.620003,92.269997,92.269997,12640000\n1974-02-19,92.269997,94.440002,91.680000,92.120003,92.120003,15940000\n1974-02-20,92.120003,93.919998,91.339996,93.440002,93.440002,11670000\n1974-02-21,93.440002,95.190002,93.199997,94.709999,94.709999,13930000\n1974-02-22,94.709999,96.190002,94.080002,95.389999,95.389999,16360000\n1974-02-25,95.389999,95.959999,94.239998,95.029999,95.029999,12900000\n1974-02-26,95.029999,96.379997,94.199997,96.000000,96.000000,15860000\n1974-02-27,96.000000,97.430000,95.489998,96.400002,96.400002,18730000\n1974-02-28,96.400002,96.980003,95.199997,96.220001,96.220001,13680000\n1974-03-01,96.220001,96.400002,94.809998,95.529999,95.529999,12880000\n1974-03-04,95.529999,95.949997,94.190002,95.529999,95.529999,12270000\n1974-03-05,95.980003,98.169998,95.980003,97.320000,97.320000,21980000\n1974-03-06,97.320000,98.570000,96.540001,97.980003,97.980003,19140000\n1974-03-07,97.980003,98.199997,96.370003,96.940002,96.940002,14500000\n1974-03-08,96.940002,98.279999,95.769997,97.779999,97.779999,16210000\n1974-03-11,97.779999,99.400002,96.379997,98.879997,98.879997,18470000\n1974-03-12,98.879997,100.019997,97.970001,99.150002,99.150002,17250000\n1974-03-13,99.150002,100.730003,98.720001,99.739998,99.739998,16820000\n1974-03-14,99.739998,101.050003,98.800003,99.650002,99.650002,19770000\n1974-03-15,99.650002,99.989998,98.220001,99.279999,99.279999,14500000\n1974-03-18,99.279999,99.709999,97.620003,98.050003,98.050003,14010000\n1974-03-19,98.050003,98.199997,96.629997,97.230003,97.230003,12800000\n1974-03-20,97.230003,98.220001,96.669998,97.570000,97.570000,12960000\n1974-03-21,97.570000,98.589996,96.820000,97.339996,97.339996,12950000\n1974-03-22,97.339996,98.040001,96.349998,97.269997,97.269997,11930000\n1974-03-25,97.269997,98.019997,95.690002,97.639999,97.639999,10540000\n1974-03-26,97.639999,98.660004,97.110001,97.949997,97.949997,11840000\n1974-03-27,97.949997,98.260002,96.320000,96.589996,96.589996,11690000\n1974-03-28,96.199997,96.199997,94.360001,94.820000,94.820000,14940000\n1974-03-29,94.820000,95.120003,93.440002,93.980003,93.980003,12150000\n1974-04-01,93.980003,94.680000,92.820000,93.250000,93.250000,11470000\n1974-04-02,93.250000,94.150002,92.589996,93.349998,93.349998,12010000\n1974-04-03,93.349998,94.699997,92.940002,94.330002,94.330002,11500000\n1974-04-04,94.330002,95.139999,93.550003,94.330002,94.330002,11650000\n1974-04-05,94.239998,94.239998,92.550003,93.010002,93.010002,11670000\n1974-04-08,93.000000,93.000000,91.500000,92.029999,92.029999,10740000\n1974-04-09,92.029999,93.279999,91.610001,92.610001,92.610001,11330000\n1974-04-10,92.610001,93.519997,91.889999,92.400002,92.400002,11160000\n1974-04-11,92.400002,92.919998,91.550003,92.120003,92.120003,9970000\n1974-04-15,92.120003,92.940002,91.489998,92.050003,92.050003,10130000\n1974-04-16,92.050003,94.059998,92.050003,93.660004,93.660004,14530000\n1974-04-17,93.660004,95.040001,93.120003,94.360001,94.360001,14020000\n1974-04-18,94.360001,95.419998,93.750000,94.779999,94.779999,12470000\n1974-04-19,94.769997,94.769997,93.199997,93.750000,93.750000,10710000\n1974-04-22,93.750000,94.120003,92.709999,93.379997,93.379997,10520000\n1974-04-23,93.379997,93.510002,91.529999,91.809998,91.809998,14110000\n1974-04-24,91.809998,91.820000,89.910004,90.300003,90.300003,16010000\n1974-04-25,90.300003,90.529999,88.620003,89.570000,89.570000,15870000\n1974-04-26,89.570000,91.099998,89.059998,90.180000,90.180000,13250000\n1974-04-29,90.180000,90.779999,89.019997,90.000000,90.000000,10170000\n1974-04-30,90.000000,91.089996,89.379997,90.309998,90.309998,10980000\n1974-05-01,90.309998,93.029999,89.820000,92.220001,92.220001,15120000\n1974-05-02,92.220001,93.589996,91.459999,92.089996,92.089996,13620000\n1974-05-03,92.089996,92.269997,90.589996,91.290001,91.290001,11080000\n1974-05-06,91.290001,91.599998,90.129997,91.120003,91.120003,9450000\n1974-05-07,91.120003,92.360001,90.690002,91.459999,91.459999,10710000\n1974-05-08,91.459999,92.339996,90.709999,91.639999,91.639999,11850000\n1974-05-09,91.639999,93.489998,91.269997,92.959999,92.959999,14710000\n1974-05-10,92.959999,93.570000,91.029999,91.470001,91.470001,15270000\n1974-05-13,91.470001,91.720001,89.910004,90.660004,90.660004,11290000\n1974-05-14,90.660004,91.680000,90.050003,90.690002,90.690002,10880000\n1974-05-15,90.690002,91.220001,89.650002,90.449997,90.449997,11240000\n1974-05-16,90.449997,91.309998,89.360001,89.720001,89.720001,12090000\n1974-05-17,89.529999,89.529999,87.669998,88.209999,88.209999,13870000\n1974-05-20,88.209999,89.089996,87.190002,87.860001,87.860001,10550000\n1974-05-21,87.860001,88.980003,87.190002,87.910004,87.910004,12190000\n1974-05-22,87.910004,88.790001,86.720001,87.089996,87.089996,15450000\n1974-05-23,87.089996,87.980003,86.120003,87.290001,87.290001,14770000\n1974-05-24,87.290001,89.269997,87.199997,88.580002,88.580002,13740000\n1974-05-28,88.580002,89.370003,87.690002,88.370003,88.370003,10580000\n1974-05-29,88.370003,88.839996,86.519997,86.889999,86.889999,12300000\n1974-05-30,86.889999,88.089996,85.870003,87.430000,87.430000,13580000\n1974-05-31,87.430000,88.019997,86.190002,87.279999,87.279999,10810000\n1974-06-03,87.279999,89.400002,86.779999,89.099998,89.099998,12490000\n1974-06-04,89.099998,91.129997,89.089996,90.139999,90.139999,16040000\n1974-06-05,90.139999,91.419998,89.040001,90.309998,90.309998,13680000\n1974-06-06,90.309998,92.309998,89.709999,91.959999,91.959999,13360000\n1974-06-07,91.959999,93.760002,91.739998,92.550003,92.550003,19020000\n1974-06-10,92.550003,93.639999,91.529999,93.099998,93.099998,13540000\n1974-06-11,93.099998,93.570000,91.760002,92.279999,92.279999,12380000\n1974-06-12,92.279999,92.610001,90.889999,92.059998,92.059998,11150000\n1974-06-13,92.059998,93.330002,91.480003,92.339996,92.339996,11540000\n1974-06-14,92.230003,92.230003,90.730003,91.300003,91.300003,10030000\n1974-06-17,91.300003,91.339996,89.629997,90.040001,90.040001,9680000\n1974-06-18,90.040001,90.529999,88.919998,89.449997,89.449997,10110000\n1974-06-19,89.449997,89.800003,88.389999,88.839996,88.839996,10550000\n1974-06-20,88.839996,89.349998,87.800003,88.209999,88.209999,11990000\n1974-06-21,88.209999,88.309998,86.769997,87.459999,87.459999,11830000\n1974-06-24,87.459999,88.379997,86.699997,87.690002,87.690002,9960000\n1974-06-25,87.690002,89.480003,87.669998,88.980003,88.980003,11920000\n1974-06-26,88.980003,89.120003,87.300003,87.610001,87.610001,11410000\n1974-06-27,87.610001,87.610001,85.879997,86.309998,86.309998,12650000\n1974-06-28,86.309998,86.779999,85.129997,86.000000,86.000000,12010000\n1974-07-01,86.000000,86.889999,85.320000,86.019997,86.019997,10270000\n1974-07-02,86.019997,86.260002,83.980003,84.300003,84.300003,13460000\n1974-07-03,84.300003,85.150002,83.459999,84.250000,84.250000,13430000\n1974-07-05,84.250000,84.449997,83.169998,83.660004,83.660004,7400000\n1974-07-08,83.129997,83.129997,80.480003,81.089996,81.089996,15510000\n1974-07-09,81.089996,82.500000,80.349998,81.480003,81.480003,15580000\n1974-07-10,81.480003,82.220001,79.739998,79.989998,79.989998,13490000\n1974-07-11,79.989998,81.080002,79.080002,79.889999,79.889999,14640000\n1974-07-12,80.970001,83.650002,80.970001,83.150002,83.150002,17770000\n1974-07-15,83.150002,84.889999,82.650002,83.779999,83.779999,13560000\n1974-07-16,83.779999,83.849998,82.139999,82.809998,82.809998,9920000\n1974-07-17,82.809998,84.129997,81.699997,83.699997,83.699997,11320000\n1974-07-18,83.699997,85.389999,83.129997,83.779999,83.779999,13980000\n1974-07-19,83.779999,84.669998,82.870003,83.540001,83.540001,11080000\n1974-07-22,83.540001,84.440002,82.589996,83.809998,83.809998,9290000\n1974-07-23,83.809998,85.629997,83.669998,84.650002,84.650002,12910000\n1974-07-24,84.650002,85.639999,83.610001,84.989998,84.989998,12870000\n1974-07-25,84.989998,85.669998,83.129997,83.980003,83.980003,13310000\n1974-07-26,83.980003,84.169998,82.000000,82.400002,82.400002,10420000\n1974-07-29,82.019997,82.019997,80.220001,80.940002,80.940002,11560000\n1974-07-30,80.940002,81.519997,79.580002,80.500000,80.500000,11360000\n1974-07-31,80.500000,80.820000,78.959999,79.309998,79.309998,10960000\n1974-08-01,79.309998,80.019997,77.970001,78.750000,78.750000,11470000\n1974-08-02,78.750000,79.389999,77.839996,78.589996,78.589996,10110000\n1974-08-05,78.589996,80.309998,78.029999,79.290001,79.290001,11230000\n1974-08-06,79.779999,82.650002,79.779999,80.519997,80.519997,15770000\n1974-08-07,80.519997,82.930000,80.129997,82.650002,82.650002,13380000\n1974-08-08,82.650002,83.529999,80.860001,81.570000,81.570000,16060000\n1974-08-09,81.570000,81.879997,80.110001,80.860001,80.860001,10160000\n1974-08-12,80.860001,81.260002,79.300003,79.750000,79.750000,7780000\n1974-08-13,79.750000,79.949997,77.830002,78.489998,78.489998,10140000\n1974-08-14,76.730003,76.730003,76.730003,76.730003,76.730003,11750000\n1974-08-15,76.730003,77.519997,75.190002,76.300003,76.300003,11130000\n1974-08-16,76.300003,77.019997,75.290001,75.669998,75.669998,10510000\n1974-08-19,75.650002,75.650002,73.779999,74.570000,74.570000,11670000\n1974-08-20,74.570000,76.110001,73.820000,74.949997,74.949997,13820000\n1974-08-21,74.949997,75.500000,73.160004,73.510002,73.510002,11650000\n1974-08-22,73.510002,74.050003,71.610001,72.800003,72.800003,15690000\n1974-08-23,72.800003,73.709999,70.750000,71.550003,71.550003,13590000\n1974-08-26,71.550003,73.169998,70.419998,72.160004,72.160004,14630000\n1974-08-27,72.160004,72.500000,70.500000,70.940002,70.940002,12970000\n1974-08-28,70.940002,72.169998,70.129997,70.760002,70.760002,16670000\n1974-08-29,70.760002,71.220001,69.370003,69.989998,69.989998,13690000\n1974-08-30,70.220001,72.680000,70.220001,72.150002,72.150002,16230000\n1974-09-03,72.150002,73.010002,70.279999,70.519997,70.519997,12750000\n1974-09-04,69.849998,69.849998,67.639999,68.690002,68.690002,16930000\n1974-09-05,68.690002,71.300003,68.650002,70.870003,70.870003,14210000\n1974-09-06,70.870003,72.419998,70.080002,71.419998,71.419998,15130000\n1974-09-09,71.349998,71.349998,69.379997,69.720001,69.720001,11160000\n1974-09-10,69.720001,70.470001,68.550003,69.239998,69.239998,11980000\n1974-09-11,69.239998,70.000000,68.220001,68.550003,68.550003,11820000\n1974-09-12,68.540001,68.540001,66.220001,66.709999,66.709999,16920000\n1974-09-13,66.709999,66.910004,64.739998,65.199997,65.199997,16070000\n1974-09-16,65.199997,66.919998,64.150002,66.260002,66.260002,18370000\n1974-09-17,66.449997,68.839996,66.449997,67.379997,67.379997,13730000\n1974-09-18,67.379997,68.139999,65.919998,67.720001,67.720001,11760000\n1974-09-19,68.360001,70.760002,68.360001,70.089996,70.089996,17000000\n1974-09-20,70.089996,71.120003,68.620003,70.139999,70.139999,16250000\n1974-09-23,70.139999,71.019997,68.790001,69.419998,69.419998,12130000\n1974-09-24,69.029999,69.029999,67.419998,68.019997,68.019997,9840000\n1974-09-25,68.019997,69.769997,66.860001,67.570000,67.570000,17620000\n1974-09-26,67.400002,67.400002,65.790001,66.459999,66.459999,9060000\n1974-09-27,66.459999,67.089996,64.580002,64.940002,64.940002,12320000\n1974-09-30,64.849998,64.849998,62.520000,63.540001,63.540001,15000000\n1974-10-01,63.540001,64.370003,61.750000,63.389999,63.389999,16890000\n1974-10-02,63.389999,64.620003,62.740002,63.380001,63.380001,12230000\n1974-10-03,63.380001,63.480000,61.660000,62.279999,62.279999,13150000\n1974-10-04,62.279999,63.230000,60.959999,62.340000,62.340000,15910000\n1974-10-07,62.779999,65.400002,62.779999,64.949997,64.949997,15000000\n1974-10-08,64.949997,66.070000,63.950001,64.839996,64.839996,15460000\n1974-10-09,64.839996,68.150002,63.740002,67.820000,67.820000,18820000\n1974-10-10,68.300003,71.480003,68.300003,69.790001,69.790001,26360000\n1974-10-11,69.790001,71.989998,68.800003,71.139999,71.139999,20090000\n1974-10-14,71.169998,74.430000,71.169998,72.739998,72.739998,19770000\n1974-10-15,72.739998,73.349998,70.610001,71.440002,71.440002,17390000\n1974-10-16,71.440002,71.980003,69.540001,70.330002,70.330002,14790000\n1974-10-17,70.330002,72.000000,69.410004,71.169998,71.169998,14470000\n1974-10-18,71.199997,73.339996,71.199997,72.279999,72.279999,16460000\n1974-10-21,72.279999,73.919998,71.239998,73.500000,73.500000,14500000\n1974-10-22,73.500000,75.089996,72.550003,73.129997,73.129997,18930000\n1974-10-23,72.809998,72.809998,70.400002,71.029999,71.029999,14200000\n1974-10-24,70.980003,70.980003,68.800003,70.220001,70.220001,14910000\n1974-10-25,70.220001,71.589996,69.459999,70.120003,70.120003,12650000\n1974-10-28,70.120003,70.669998,68.889999,70.089996,70.089996,10540000\n1974-10-29,70.489998,73.190002,70.489998,72.830002,72.830002,15610000\n1974-10-30,72.830002,75.449997,72.400002,74.309998,74.309998,20130000\n1974-10-31,74.309998,75.900002,73.150002,73.900002,73.900002,18840000\n1974-11-01,73.900002,74.849998,72.680000,73.879997,73.879997,13470000\n1974-11-04,73.800003,73.800003,71.930000,73.080002,73.080002,12740000\n1974-11-05,73.080002,75.360001,72.489998,75.110001,75.110001,15960000\n1974-11-06,75.110001,77.410004,74.230003,74.750000,74.750000,23930000\n1974-11-07,74.750000,76.300003,73.849998,75.209999,75.209999,17150000\n1974-11-08,75.209999,76.000000,74.010002,74.910004,74.910004,15890000\n1974-11-11,74.910004,75.699997,74.040001,75.150002,75.150002,13220000\n1974-11-12,75.150002,75.589996,73.339996,73.669998,73.669998,15040000\n1974-11-13,73.669998,74.250000,72.320000,73.349998,73.349998,16040000\n1974-11-14,73.349998,74.540001,72.529999,73.059998,73.059998,13540000\n1974-11-15,73.059998,73.269997,71.410004,71.910004,71.910004,12480000\n1974-11-18,71.099998,71.099998,68.949997,69.269997,69.269997,15230000\n1974-11-19,69.269997,69.709999,67.660004,68.199997,68.199997,15720000\n1974-11-20,68.199997,69.250000,67.360001,67.900002,67.900002,12430000\n1974-11-21,67.900002,68.940002,66.849998,68.180000,68.180000,13820000\n1974-11-22,68.239998,70.000000,68.239998,68.900002,68.900002,13020000\n1974-11-25,68.900002,69.680000,67.790001,68.830002,68.830002,11300000\n1974-11-26,68.830002,70.360001,68.190002,69.470001,69.470001,13600000\n1974-11-27,69.470001,71.309998,69.169998,69.940002,69.940002,14810000\n1974-11-29,69.940002,70.489998,69.180000,69.970001,69.970001,7400000\n1974-12-02,69.800003,69.800003,67.809998,68.110001,68.110001,11140000\n1974-12-03,68.110001,68.129997,66.620003,67.169998,67.169998,13620000\n1974-12-04,67.169998,68.320000,66.610001,67.410004,67.410004,12580000\n1974-12-05,67.410004,68.000000,65.900002,66.129997,66.129997,12890000\n1974-12-06,66.129997,66.199997,64.400002,65.010002,65.010002,15500000\n1974-12-09,65.010002,66.290001,64.129997,65.599998,65.599998,14660000\n1974-12-10,65.879997,68.169998,65.879997,67.279999,67.279999,15690000\n1974-12-11,67.279999,69.029999,66.830002,67.669998,67.669998,15700000\n1974-12-12,67.669998,68.610001,66.559998,67.449997,67.449997,15390000\n1974-12-13,67.449997,68.150002,66.320000,67.070000,67.070000,14000000\n1974-12-16,67.070000,67.739998,66.019997,66.459999,66.459999,15370000\n1974-12-17,66.459999,67.919998,65.860001,67.580002,67.580002,16880000\n1974-12-18,67.580002,69.010002,67.300003,67.900002,67.900002,18050000\n1974-12-19,67.900002,68.620003,66.930000,67.650002,67.650002,15900000\n1974-12-20,67.650002,67.930000,66.360001,66.910004,66.910004,15840000\n1974-12-23,66.910004,67.180000,65.339996,65.959999,65.959999,18040000\n1974-12-24,65.959999,67.250000,65.860001,66.879997,66.879997,9540000\n1974-12-26,66.879997,68.190002,66.620003,67.440002,67.440002,11810000\n1974-12-27,67.440002,67.989998,66.489998,67.139999,67.139999,13060000\n1974-12-30,67.139999,67.650002,66.230003,67.160004,67.160004,18520000\n1974-12-31,67.160004,69.040001,67.150002,68.559998,68.559998,20970000\n1975-01-02,68.650002,70.919998,68.650002,70.230003,70.230003,14800000\n1975-01-03,70.230003,71.639999,69.290001,70.709999,70.709999,15270000\n1975-01-06,70.709999,72.239998,70.330002,71.070000,71.070000,17550000\n1975-01-07,71.070000,71.750000,69.919998,71.019997,71.019997,14890000\n1975-01-08,71.019997,71.529999,69.650002,70.040001,70.040001,15600000\n1975-01-09,70.040001,71.419998,69.040001,71.169998,71.169998,16340000\n1975-01-10,71.599998,73.750000,71.599998,72.610001,72.610001,25890000\n1975-01-13,72.610001,73.809998,71.830002,72.309998,72.309998,19780000\n1975-01-14,72.309998,72.699997,71.019997,71.680000,71.680000,16610000\n1975-01-15,71.680000,72.769997,70.449997,72.139999,72.139999,16580000\n1975-01-16,72.139999,72.930000,71.260002,72.050003,72.050003,17110000\n1975-01-17,72.050003,72.360001,70.559998,70.959999,70.959999,14260000\n1975-01-20,70.959999,71.459999,69.800003,71.080002,71.080002,13450000\n1975-01-21,71.080002,72.040001,70.250000,70.699997,70.699997,14780000\n1975-01-22,70.699997,71.970001,69.860001,71.739998,71.739998,15330000\n1975-01-23,71.739998,73.110001,71.089996,72.070000,72.070000,17960000\n1975-01-24,72.070000,73.570000,71.550003,72.980003,72.980003,20670000\n1975-01-27,73.760002,76.029999,73.760002,75.370003,75.370003,32130000\n1975-01-28,75.370003,77.589996,75.360001,76.029999,76.029999,31760000\n1975-01-29,76.029999,78.029999,75.230003,77.260002,77.260002,27410000\n1975-01-30,77.260002,78.690002,75.820000,76.209999,76.209999,29740000\n1975-01-31,76.209999,77.720001,75.410004,76.980003,76.980003,24640000\n1975-02-03,76.980003,78.550003,76.360001,77.820000,77.820000,25400000\n1975-02-04,77.820000,78.370003,76.000000,77.610001,77.610001,25040000\n1975-02-05,77.610001,79.400002,76.809998,78.949997,78.949997,25830000\n1975-02-06,78.949997,80.720001,78.089996,78.559998,78.559998,32020000\n1975-02-07,78.559998,79.120003,77.000000,78.629997,78.629997,19060000\n1975-02-10,78.629997,79.400002,77.769997,78.360001,78.360001,16120000\n1975-02-11,78.360001,79.070000,77.379997,78.580002,78.580002,16470000\n1975-02-12,78.580002,80.209999,77.940002,79.919998,79.919998,19790000\n1975-02-13,79.980003,82.529999,79.980003,81.010002,81.010002,35160000\n1975-02-14,81.010002,82.330002,80.129997,81.500000,81.500000,23290000\n1975-02-18,81.500000,82.449997,80.160004,80.930000,80.930000,23990000\n1975-02-19,80.930000,81.940002,79.830002,81.440002,81.440002,21930000\n1975-02-20,81.440002,82.779999,80.820000,82.209999,82.209999,22260000\n1975-02-21,82.209999,83.559998,81.720001,82.620003,82.620003,24440000\n1975-02-24,82.620003,82.709999,80.870003,81.440002,81.440002,19150000\n1975-02-25,81.089996,81.089996,79.050003,79.529999,79.529999,20910000\n1975-02-26,79.529999,80.889999,78.910004,80.370003,80.370003,18790000\n1975-02-27,80.370003,81.639999,80.059998,80.769997,80.769997,16430000\n1975-02-28,80.769997,82.019997,80.070000,81.589996,81.589996,17560000\n1975-03-03,81.589996,83.459999,81.320000,83.029999,83.029999,24100000\n1975-03-04,83.029999,85.430000,82.849998,83.559998,83.559998,34140000\n1975-03-05,83.559998,84.709999,82.160004,83.900002,83.900002,24120000\n1975-03-06,83.900002,84.169998,81.940002,83.690002,83.690002,21780000\n1975-03-07,83.690002,85.139999,83.250000,84.300003,84.300003,25930000\n1975-03-10,84.300003,85.470001,83.430000,84.949997,84.949997,25890000\n1975-03-11,84.949997,85.889999,83.800003,84.360001,84.360001,31280000\n1975-03-12,84.360001,84.730003,82.870003,83.589996,83.589996,21560000\n1975-03-13,83.589996,84.260002,82.519997,83.739998,83.739998,18620000\n1975-03-14,83.739998,85.430000,83.500000,84.760002,84.760002,24840000\n1975-03-17,84.760002,86.519997,84.389999,86.010002,86.010002,26780000\n1975-03-18,86.010002,87.080002,84.750000,85.129997,85.129997,29180000\n1975-03-19,85.129997,85.169998,83.430000,84.339996,84.339996,19030000\n1975-03-20,84.339996,85.300003,83.019997,83.610001,83.610001,20960000\n1975-03-21,83.610001,84.110001,82.519997,83.389999,83.389999,15940000\n1975-03-24,82.389999,82.389999,80.599998,81.419998,81.419998,17810000\n1975-03-25,81.419998,82.669998,80.080002,82.059998,82.059998,18500000\n1975-03-26,82.160004,84.239998,82.160004,83.589996,83.589996,18580000\n1975-03-27,83.589996,84.879997,83.040001,83.849998,83.849998,18300000\n1975-03-31,83.849998,84.620003,82.839996,83.360001,83.360001,16270000\n1975-04-01,83.360001,83.589996,81.980003,82.639999,82.639999,14480000\n1975-04-02,82.639999,83.570000,81.800003,82.430000,82.430000,15600000\n1975-04-03,82.430000,82.839996,80.879997,81.510002,81.510002,13920000\n1975-04-04,81.510002,81.900002,80.290001,80.879997,80.879997,14170000\n1975-04-07,80.879997,81.110001,79.660004,80.349998,80.349998,13860000\n1975-04-08,80.349998,81.650002,80.129997,80.989998,80.989998,14320000\n1975-04-09,80.989998,83.220001,80.910004,82.839996,82.839996,18120000\n1975-04-10,82.839996,84.699997,82.680000,83.769997,83.769997,24990000\n1975-04-11,83.769997,84.680000,82.930000,84.180000,84.180000,20160000\n1975-04-14,84.180000,86.120003,83.980003,85.599998,85.599998,26800000\n1975-04-15,85.599998,87.239998,85.029999,86.300003,86.300003,29620000\n1975-04-16,86.300003,87.099998,84.930000,86.599998,86.599998,22970000\n1975-04-17,86.599998,88.790001,86.430000,87.250000,87.250000,32650000\n1975-04-18,87.250000,87.589996,85.529999,86.300003,86.300003,26610000\n1975-04-21,86.300003,87.989998,85.919998,87.230003,87.230003,23960000\n1975-04-22,87.230003,88.639999,86.580002,87.089996,87.089996,26120000\n1975-04-23,87.089996,87.419998,85.650002,86.120003,86.120003,20040000\n1975-04-24,86.120003,86.919998,85.000000,86.040001,86.040001,19050000\n1975-04-25,86.040001,87.500000,85.620003,86.620003,86.620003,20260000\n1975-04-28,86.620003,87.330002,85.540001,86.230003,86.230003,17850000\n1975-04-29,86.230003,86.790001,85.040001,85.639999,85.639999,17740000\n1975-04-30,85.639999,87.610001,85.000000,87.300003,87.300003,18060000\n1975-05-01,87.300003,89.099998,86.940002,88.099998,88.099998,20660000\n1975-05-02,88.099998,89.980003,87.910004,89.220001,89.220001,25210000\n1975-05-05,89.220001,90.820000,88.260002,90.080002,90.080002,22370000\n1975-05-06,90.080002,90.860001,88.150002,88.639999,88.639999,25410000\n1975-05-07,88.639999,89.750000,87.599998,89.080002,89.080002,22250000\n1975-05-08,89.080002,90.129997,88.230003,89.559998,89.559998,22980000\n1975-05-09,89.559998,91.239998,89.330002,90.529999,90.529999,28440000\n1975-05-12,90.529999,91.669998,89.910004,90.610001,90.610001,22410000\n1975-05-13,90.610001,92.260002,89.989998,91.580002,91.580002,24950000\n1975-05-14,91.580002,93.230003,91.169998,92.269997,92.269997,29050000\n1975-05-15,92.269997,93.510002,90.940002,91.410004,91.410004,27690000\n1975-05-16,91.410004,91.589996,89.739998,90.430000,90.430000,16630000\n1975-05-19,90.430000,91.070000,88.980003,90.529999,90.529999,17870000\n1975-05-20,90.529999,91.449997,89.580002,90.070000,90.070000,18310000\n1975-05-21,90.070000,90.250000,88.470001,89.059998,89.059998,17640000\n1975-05-22,89.059998,90.300003,88.349998,89.389999,89.389999,17610000\n1975-05-23,89.389999,91.019997,89.300003,90.580002,90.580002,17870000\n1975-05-27,90.580002,91.290001,89.599998,90.339996,90.339996,17050000\n1975-05-28,90.339996,91.139999,89.070000,89.709999,89.709999,21850000\n1975-05-29,89.709999,90.589996,88.830002,89.680000,89.680000,18570000\n1975-05-30,89.870003,91.620003,89.870003,91.150002,91.150002,22670000\n1975-06-02,91.320000,93.410004,91.320000,92.580002,92.580002,28240000\n1975-06-03,92.580002,93.760002,91.879997,92.889999,92.889999,26560000\n1975-06-04,92.889999,93.610001,91.820000,92.599998,92.599998,24900000\n1975-06-05,92.599998,93.160004,91.410004,92.690002,92.690002,21610000\n1975-06-06,92.690002,93.599998,91.750000,92.480003,92.480003,22230000\n1975-06-09,92.480003,92.870003,90.910004,91.209999,91.209999,20670000\n1975-06-10,91.209999,91.209999,89.459999,90.440002,90.440002,21130000\n1975-06-11,90.440002,91.669998,90.000000,90.550003,90.550003,18230000\n1975-06-12,90.550003,91.360001,89.639999,90.080002,90.080002,15970000\n1975-06-13,90.080002,91.059998,89.300003,90.519997,90.519997,16300000\n1975-06-16,90.519997,91.849998,90.120003,91.459999,91.459999,16660000\n1975-06-17,91.459999,92.220001,90.169998,90.580002,90.580002,19440000\n1975-06-18,90.580002,91.070000,89.599998,90.389999,90.389999,15590000\n1975-06-19,90.389999,92.370003,90.120003,92.019997,92.019997,21450000\n1975-06-20,92.019997,93.750000,91.830002,92.610001,92.610001,26260000\n1975-06-23,92.610001,93.980003,91.809998,93.620003,93.620003,20720000\n1975-06-24,93.620003,95.230003,93.309998,94.190002,94.190002,26620000\n1975-06-25,94.190002,95.290001,93.529999,94.620003,94.620003,21610000\n1975-06-26,94.620003,95.720001,93.879997,94.809998,94.809998,24560000\n1975-06-27,94.809998,95.660004,94.099998,94.809998,94.809998,18820000\n1975-06-30,94.809998,95.849998,94.300003,95.190002,95.190002,19430000\n1975-07-01,95.190002,95.730003,94.129997,94.849998,94.849998,20390000\n1975-07-02,94.849998,94.910004,93.370003,94.180000,94.180000,18530000\n1975-07-03,94.180000,95.040001,93.489998,94.360001,94.360001,19000000\n1975-07-07,94.360001,94.820000,93.160004,93.540001,93.540001,15850000\n1975-07-08,93.540001,94.029999,92.510002,93.389999,93.389999,18990000\n1975-07-09,93.389999,95.220001,93.379997,94.800003,94.800003,26350000\n1975-07-10,94.800003,96.190002,94.250000,94.809998,94.809998,28880000\n1975-07-11,94.809998,95.690002,93.830002,94.660004,94.660004,22210000\n1975-07-14,94.660004,95.760002,94.040001,95.190002,95.190002,21900000\n1975-07-15,95.190002,96.580002,94.709999,95.610001,95.610001,28340000\n1975-07-16,95.610001,96.370003,94.199997,94.610001,94.610001,25250000\n1975-07-17,94.610001,95.029999,92.989998,93.629997,93.629997,21420000\n1975-07-18,93.629997,93.959999,92.389999,93.199997,93.199997,16870000\n1975-07-21,93.199997,93.930000,92.029999,92.440002,92.440002,16690000\n1975-07-22,92.440002,92.489998,90.629997,91.449997,91.449997,20660000\n1975-07-23,91.449997,92.150002,89.830002,90.180000,90.180000,20150000\n1975-07-24,90.180000,90.949997,88.900002,90.070000,90.070000,20550000\n1975-07-25,90.070000,90.720001,88.720001,89.290001,89.290001,15110000\n1975-07-28,89.290001,89.680000,88.019997,88.690002,88.690002,14850000\n1975-07-29,88.690002,89.910004,87.709999,88.190002,88.190002,19000000\n1975-07-30,88.190002,89.489998,87.680000,88.830002,88.830002,16150000\n1975-07-31,88.830002,90.070000,88.309998,88.750000,88.750000,14540000\n1975-08-01,88.750000,89.040001,87.459999,87.989998,87.989998,13320000\n1975-08-04,87.989998,88.169998,86.680000,87.150002,87.150002,12620000\n1975-08-05,87.150002,87.809998,85.889999,86.230003,86.230003,15470000\n1975-08-06,86.230003,87.040001,85.339996,86.250000,86.250000,16280000\n1975-08-07,86.250000,87.239998,85.690002,86.300003,86.300003,12390000\n1975-08-08,86.300003,87.000000,85.519997,86.019997,86.019997,11660000\n1975-08-11,86.019997,86.889999,85.339996,86.550003,86.550003,12350000\n1975-08-12,86.550003,88.169998,86.489998,87.120003,87.120003,14510000\n1975-08-13,87.120003,87.410004,85.610001,85.970001,85.970001,12000000\n1975-08-14,85.970001,86.339996,85.019997,85.599998,85.599998,12460000\n1975-08-15,85.599998,86.760002,85.330002,86.360001,86.360001,10610000\n1975-08-18,86.360001,87.209999,85.760002,86.199997,86.199997,10810000\n1975-08-19,86.199997,86.470001,84.660004,84.949997,84.949997,14990000\n1975-08-20,84.779999,84.779999,82.760002,83.220001,83.220001,18630000\n1975-08-21,83.220001,84.150002,82.209999,83.070000,83.070000,16610000\n1975-08-22,83.070000,84.610001,82.790001,84.279999,84.279999,13050000\n1975-08-25,84.279999,85.580002,84.059998,85.059998,85.059998,11250000\n1975-08-26,85.059998,85.400002,83.650002,83.959999,83.959999,11350000\n1975-08-27,83.959999,84.790001,83.349998,84.430000,84.430000,11100000\n1975-08-28,84.680000,86.639999,84.680000,86.400002,86.400002,14530000\n1975-08-29,86.400002,87.730003,86.099998,86.879997,86.879997,15480000\n1975-09-02,86.879997,87.419998,85.209999,85.480003,85.480003,11460000\n1975-09-03,85.480003,86.379997,84.620003,86.029999,86.029999,12260000\n1975-09-04,86.029999,86.910004,85.290001,86.199997,86.199997,12810000\n1975-09-05,86.199997,86.489998,85.190002,85.620003,85.620003,11680000\n1975-09-08,85.620003,86.309998,84.889999,85.889999,85.889999,11500000\n1975-09-09,85.889999,86.730003,84.370003,84.599998,84.599998,15790000\n1975-09-10,84.589996,84.589996,83.000000,83.790001,83.790001,14780000\n1975-09-11,83.790001,84.300003,82.879997,83.449997,83.449997,11100000\n1975-09-12,83.449997,84.470001,82.839996,83.300003,83.300003,12230000\n1975-09-15,83.300003,83.489998,82.290001,82.879997,82.879997,8670000\n1975-09-16,82.879997,83.430000,81.790001,82.089996,82.089996,13090000\n1975-09-17,82.089996,82.930000,81.570000,82.370003,82.370003,12190000\n1975-09-18,82.370003,84.339996,82.230003,84.059998,84.059998,14560000\n1975-09-19,84.260002,86.389999,84.260002,85.879997,85.879997,20830000\n1975-09-22,85.879997,86.699997,84.699997,85.070000,85.070000,14750000\n1975-09-23,85.070000,85.510002,83.800003,84.940002,84.940002,12800000\n1975-09-24,85.029999,86.699997,85.029999,85.739998,85.739998,16060000\n1975-09-25,85.739998,86.410004,84.790001,85.639999,85.639999,12890000\n1975-09-26,85.639999,86.860001,85.129997,86.190002,86.190002,12570000\n1975-09-29,86.190002,86.379997,84.739998,85.029999,85.029999,10580000\n1975-09-30,85.010002,85.010002,83.440002,83.870003,83.870003,12520000\n1975-10-01,83.870003,85.449997,82.570000,82.930000,82.930000,14070000\n1975-10-02,82.930000,84.330002,82.820000,83.820000,83.820000,14290000\n1975-10-03,83.879997,86.209999,83.879997,85.949997,85.949997,16360000\n1975-10-06,85.980003,87.639999,85.980003,86.879997,86.879997,15470000\n1975-10-07,86.879997,87.320000,85.559998,86.769997,86.769997,13530000\n1975-10-08,86.769997,88.459999,86.339996,87.940002,87.940002,17800000\n1975-10-09,87.940002,89.419998,87.599998,88.370003,88.370003,17770000\n1975-10-10,88.370003,89.169998,87.440002,88.209999,88.209999,14880000\n1975-10-13,88.209999,89.669998,87.730003,89.459999,89.459999,12020000\n1975-10-14,89.459999,90.800003,88.809998,89.279999,89.279999,19960000\n1975-10-15,89.279999,90.070000,88.500000,89.230003,89.230003,14440000\n1975-10-16,89.230003,90.730003,88.900002,89.370003,89.370003,18910000\n1975-10-17,89.370003,89.870003,88.080002,88.860001,88.860001,15650000\n1975-10-20,88.860001,90.139999,88.430000,89.820000,89.820000,13250000\n1975-10-21,89.820000,91.430000,89.790001,90.559998,90.559998,20800000\n1975-10-22,90.559998,91.379997,89.769997,90.709999,90.709999,16060000\n1975-10-23,90.709999,91.750000,90.089996,91.239998,91.239998,17900000\n1975-10-24,91.239998,91.519997,89.459999,89.830002,89.830002,18120000\n1975-10-27,89.830002,90.400002,88.849998,89.730003,89.730003,13100000\n1975-10-28,89.730003,91.010002,89.400002,90.510002,90.510002,17060000\n1975-10-29,90.510002,90.610001,88.889999,89.389999,89.389999,16110000\n1975-10-30,89.389999,90.199997,88.699997,89.309998,89.309998,15080000\n1975-10-31,89.309998,89.800003,88.349998,89.040001,89.040001,12910000\n1975-11-03,89.040001,89.209999,87.779999,88.089996,88.089996,11400000\n1975-11-04,88.089996,89.029999,87.629997,88.510002,88.510002,11570000\n1975-11-05,88.510002,90.080002,88.320000,89.150002,89.150002,17390000\n1975-11-06,89.150002,90.150002,88.160004,89.550003,89.550003,18600000\n1975-11-07,89.550003,90.180000,88.669998,89.330002,89.330002,15930000\n1975-11-10,89.330002,89.980003,88.349998,89.339996,89.339996,14910000\n1975-11-11,89.339996,90.470001,89.040001,89.870003,89.870003,14640000\n1975-11-12,89.870003,91.629997,89.800003,91.190002,91.190002,23960000\n1975-11-13,91.190002,92.330002,90.559998,91.040001,91.040001,25070000\n1975-11-14,91.040001,91.589996,90.190002,90.970001,90.970001,16460000\n1975-11-17,90.970001,91.989998,90.500000,91.459999,91.459999,17660000\n1975-11-18,91.459999,92.300003,90.599998,91.000000,91.000000,20760000\n1975-11-19,91.000000,91.279999,89.470001,89.980003,89.980003,16820000\n1975-11-20,89.980003,90.680000,89.089996,89.639999,89.639999,16460000\n1975-11-21,89.639999,90.230003,88.790001,89.529999,89.529999,14110000\n1975-11-24,89.529999,90.169998,88.650002,89.699997,89.699997,13930000\n1975-11-25,89.699997,91.099998,89.660004,90.709999,90.709999,17490000\n1975-11-26,90.709999,91.580002,90.169998,90.940002,90.940002,18780000\n1975-11-28,90.940002,91.739998,90.440002,91.239998,91.239998,12870000\n1975-12-01,91.239998,91.900002,90.330002,90.669998,90.669998,16050000\n1975-12-02,90.669998,90.809998,89.080002,89.330002,89.330002,17930000\n1975-12-03,88.830002,88.830002,87.080002,87.599998,87.599998,21320000\n1975-12-04,87.599998,88.389999,86.680000,87.839996,87.839996,16380000\n1975-12-05,87.839996,88.379997,86.540001,86.820000,86.820000,14050000\n1975-12-08,86.820000,87.750000,86.150002,87.070000,87.070000,14150000\n1975-12-09,87.070000,87.800003,86.160004,87.300003,87.300003,16040000\n1975-12-10,87.300003,88.389999,86.910004,88.080002,88.080002,15680000\n1975-12-11,88.080002,88.790001,87.410004,87.800003,87.800003,15300000\n1975-12-12,87.800003,88.220001,87.050003,87.830002,87.830002,13100000\n1975-12-15,87.830002,88.639999,87.320000,88.089996,88.089996,13960000\n1975-12-16,88.089996,89.489998,87.779999,88.930000,88.930000,18350000\n1975-12-17,88.930000,89.800003,88.459999,89.150002,89.150002,16560000\n1975-12-18,89.150002,90.089996,88.620003,89.430000,89.430000,18040000\n1975-12-19,89.430000,89.809998,88.389999,88.800003,88.800003,17720000\n1975-12-22,88.800003,89.129997,87.739998,88.139999,88.139999,15340000\n1975-12-23,88.139999,89.230003,87.639999,88.730003,88.730003,17750000\n1975-12-24,88.730003,89.839996,88.730003,89.459999,89.459999,11150000\n1975-12-26,89.459999,90.449997,89.250000,90.250000,90.250000,10020000\n1975-12-29,90.250000,91.089996,89.629997,90.129997,90.129997,17070000\n1975-12-30,90.129997,90.550003,89.199997,89.769997,89.769997,16040000\n1975-12-31,89.769997,90.750000,89.169998,90.190002,90.190002,16970000\n1976-01-02,90.190002,91.180000,89.809998,90.900002,90.900002,10300000\n1976-01-05,90.900002,92.839996,90.849998,92.580002,92.580002,21960000\n1976-01-06,92.580002,94.180000,92.370003,93.529999,93.529999,31270000\n1976-01-07,93.529999,95.150002,92.910004,93.949997,93.949997,33170000\n1976-01-08,93.949997,95.470001,93.410004,94.580002,94.580002,29030000\n1976-01-09,94.580002,95.709999,94.050003,94.949997,94.949997,26510000\n1976-01-12,94.949997,96.760002,94.379997,96.330002,96.330002,30440000\n1976-01-13,96.330002,97.389999,95.110001,95.570000,95.570000,34530000\n1976-01-14,95.570000,97.470001,94.910004,97.129997,97.129997,30340000\n1976-01-15,97.129997,98.339996,96.150002,96.610001,96.610001,38450000\n1976-01-16,96.610001,97.730003,95.839996,97.000000,97.000000,25940000\n1976-01-19,97.000000,98.839996,96.360001,98.320000,98.320000,29450000\n1976-01-20,98.320000,99.440002,97.430000,98.860001,98.860001,36690000\n1976-01-21,98.860001,99.239998,97.120003,98.239998,98.239998,34470000\n1976-01-22,98.239998,98.790001,97.070000,98.040001,98.040001,27420000\n1976-01-23,98.040001,99.879997,97.680000,99.209999,99.209999,33640000\n1976-01-26,99.209999,100.750000,98.919998,99.680000,99.680000,34470000\n1976-01-27,99.680000,100.519997,98.279999,99.070000,99.070000,32070000\n1976-01-28,99.070000,99.639999,97.660004,98.529999,98.529999,27370000\n1976-01-29,98.529999,100.540001,98.320000,100.110001,100.110001,29800000\n1976-01-30,100.110001,101.989998,99.940002,100.860001,100.860001,38510000\n1976-02-02,100.860001,101.389999,99.739998,100.870003,100.870003,24000000\n1976-02-03,100.870003,101.970001,99.580002,101.180000,101.180000,34080000\n1976-02-04,101.180000,102.570000,100.699997,101.910004,101.910004,38270000\n1976-02-05,101.910004,102.300003,100.059998,100.389999,100.389999,33780000\n1976-02-06,100.389999,100.529999,98.639999,99.459999,99.459999,27360000\n1976-02-09,99.459999,100.660004,98.769997,99.620003,99.620003,25340000\n1976-02-10,99.620003,100.959999,99.110001,100.470001,100.470001,27660000\n1976-02-11,100.470001,101.800003,100.099998,100.769997,100.769997,32300000\n1976-02-12,100.769997,101.550003,99.820000,100.250000,100.250000,28610000\n1976-02-13,100.250000,100.660004,99.010002,99.669998,99.669998,23870000\n1976-02-17,99.669998,100.250000,98.559998,99.050003,99.050003,25460000\n1976-02-18,99.050003,100.430000,98.500000,99.849998,99.849998,29900000\n1976-02-19,99.940002,101.919998,99.940002,101.410004,101.410004,39210000\n1976-02-20,101.410004,103.070000,101.180000,102.099998,102.099998,44510000\n1976-02-23,102.099998,102.540001,100.690002,101.610001,101.610001,31460000\n1976-02-24,101.610001,102.919998,101.029999,102.029999,102.029999,34380000\n1976-02-25,102.029999,102.709999,100.690002,101.690002,101.690002,34680000\n1976-02-26,101.690002,102.360001,99.739998,100.110001,100.110001,34320000\n1976-02-27,100.110001,100.529999,98.599998,99.709999,99.709999,26940000\n1976-03-01,99.709999,100.639999,98.669998,100.019997,100.019997,22070000\n1976-03-02,100.019997,101.260002,99.610001,100.580002,100.580002,25590000\n1976-03-03,100.580002,100.970001,99.230003,99.980003,99.980003,25450000\n1976-03-04,99.980003,100.400002,98.489998,98.919998,98.919998,24410000\n1976-03-05,98.919998,99.879997,98.230003,99.110001,99.110001,23030000\n1976-03-08,99.110001,100.709999,98.930000,100.190002,100.190002,25060000\n1976-03-09,100.190002,101.900002,99.949997,100.580002,100.580002,31770000\n1976-03-10,100.580002,101.800003,99.980003,100.940002,100.940002,24900000\n1976-03-11,100.940002,102.410004,100.620003,101.889999,101.889999,27300000\n1976-03-12,101.889999,102.459999,100.489998,100.860001,100.860001,26020000\n1976-03-15,100.860001,100.900002,99.239998,99.800003,99.800003,19570000\n1976-03-16,99.800003,101.250000,99.379997,100.919998,100.919998,22780000\n1976-03-17,100.919998,102.010002,100.279999,100.860001,100.860001,26190000\n1976-03-18,100.860001,101.370003,99.730003,100.449997,100.449997,20330000\n1976-03-19,100.449997,101.230003,99.699997,100.580002,100.580002,18090000\n1976-03-22,100.580002,101.529999,100.139999,100.709999,100.709999,19410000\n1976-03-23,100.709999,102.540001,100.320000,102.239998,102.239998,22450000\n1976-03-24,102.510002,104.389999,102.510002,103.419998,103.419998,32610000\n1976-03-25,103.419998,104.000000,102.190002,102.849998,102.849998,22510000\n1976-03-26,102.849998,103.650002,102.199997,102.849998,102.849998,18510000\n1976-03-29,102.849998,103.360001,101.989998,102.410004,102.410004,16100000\n1976-03-30,102.410004,103.360001,101.250000,102.010002,102.010002,17930000\n1976-03-31,102.010002,103.080002,101.599998,102.769997,102.769997,17520000\n1976-04-01,102.769997,103.239998,101.500000,102.239998,102.239998,17910000\n1976-04-02,102.239998,102.760002,101.230003,102.250000,102.250000,17420000\n1976-04-05,102.320000,104.129997,102.320000,103.510002,103.510002,21940000\n1976-04-06,103.510002,104.629997,102.930000,103.360001,103.360001,24170000\n1976-04-07,103.360001,103.849998,101.919998,102.209999,102.209999,20190000\n1976-04-08,102.209999,102.379997,100.529999,101.279999,101.279999,20860000\n1976-04-09,101.279999,101.739998,99.870003,100.349998,100.349998,19050000\n1976-04-12,100.349998,101.300003,99.570000,100.199997,100.199997,16030000\n1976-04-13,100.199997,101.389999,99.639999,101.050003,101.050003,15990000\n1976-04-14,101.050003,101.769997,99.980003,100.309998,100.309998,18440000\n1976-04-15,100.309998,101.180000,99.730003,100.669998,100.669998,15100000\n1976-04-19,100.669998,101.830002,100.320000,101.440002,101.440002,16500000\n1976-04-20,101.440002,103.320000,101.419998,102.870003,102.870003,23500000\n1976-04-21,102.870003,104.029999,102.300003,103.320000,103.320000,26600000\n1976-04-22,103.320000,104.040001,102.519997,102.980003,102.980003,20220000\n1976-04-23,102.980003,103.209999,101.699997,102.290001,102.290001,17000000\n1976-04-26,102.290001,102.800003,101.360001,102.430000,102.430000,15520000\n1976-04-27,102.430000,103.180000,101.510002,101.860001,101.860001,17760000\n1976-04-28,101.860001,102.459999,100.910004,102.129997,102.129997,15790000\n1976-04-29,102.129997,102.970001,101.449997,102.129997,102.129997,17740000\n1976-04-30,102.129997,102.650002,101.160004,101.639999,101.639999,14530000\n1976-05-03,101.639999,101.730003,100.139999,100.919998,100.919998,15180000\n1976-05-04,100.919998,101.930000,100.290001,101.459999,101.459999,17240000\n1976-05-05,101.459999,101.919998,100.449997,100.879997,100.879997,14970000\n1976-05-06,100.879997,101.699997,100.309998,101.160004,101.160004,16200000\n1976-05-07,101.160004,102.269997,100.769997,101.879997,101.879997,17810000\n1976-05-10,101.879997,103.510002,101.760002,103.099998,103.099998,22760000\n1976-05-11,103.099998,103.989998,102.389999,102.949997,102.949997,23590000\n1976-05-12,102.949997,103.550003,102.139999,102.769997,102.769997,18510000\n1976-05-13,102.769997,103.029999,101.730003,102.160004,102.160004,16730000\n1976-05-14,102.160004,102.230003,100.820000,101.339996,101.339996,16800000\n1976-05-17,101.339996,101.709999,100.410004,101.089996,101.089996,14720000\n1976-05-18,101.089996,102.000000,100.720001,101.260002,101.260002,17410000\n1976-05-19,101.260002,102.010002,100.550003,101.180000,101.180000,18450000\n1976-05-20,101.180000,102.529999,100.690002,102.000000,102.000000,22560000\n1976-05-21,102.000000,102.339996,100.809998,101.260002,101.260002,18730000\n1976-05-24,101.070000,101.070000,99.110001,99.440002,99.440002,16560000\n1976-05-25,99.440002,100.019997,98.480003,99.489998,99.489998,18770000\n1976-05-26,99.489998,100.139999,98.650002,99.339996,99.339996,16750000\n1976-05-27,99.339996,99.769997,98.260002,99.379997,99.379997,15310000\n1976-05-28,99.379997,100.639999,99.000000,100.180000,100.180000,16860000\n1976-06-01,100.180000,100.739998,99.360001,99.849998,99.849998,13880000\n1976-06-02,99.849998,100.690002,99.260002,100.220001,100.220001,16120000\n1976-06-03,100.220001,101.099998,99.680000,100.129997,100.129997,18900000\n1976-06-04,100.129997,100.269997,98.790001,99.150002,99.150002,15960000\n1976-06-07,99.150002,99.389999,97.970001,98.629997,98.629997,14510000\n1976-06-08,98.629997,99.709999,98.320000,98.800003,98.800003,16660000\n1976-06-09,98.800003,99.489998,98.230003,98.739998,98.739998,14560000\n1976-06-10,98.739998,99.980003,98.550003,99.559998,99.559998,16100000\n1976-06-11,99.559998,101.220001,99.379997,100.919998,100.919998,19470000\n1976-06-14,101.000000,102.510002,101.000000,101.949997,101.949997,21250000\n1976-06-15,101.949997,102.389999,100.839996,101.459999,101.459999,18440000\n1976-06-16,101.459999,102.650002,100.959999,102.010002,102.010002,21620000\n1976-06-17,102.010002,104.120003,101.970001,103.610001,103.610001,27810000\n1976-06-18,103.610001,104.800003,103.059998,103.760002,103.760002,25720000\n1976-06-21,103.760002,104.730003,103.180000,104.279999,104.279999,18930000\n1976-06-22,104.279999,104.820000,103.160004,103.470001,103.470001,21150000\n1976-06-23,103.470001,103.900002,102.400002,103.250000,103.250000,17530000\n1976-06-24,103.250000,104.370003,102.900002,103.790001,103.790001,19850000\n1976-06-25,103.790001,104.540001,103.169998,103.720001,103.720001,17830000\n1976-06-28,103.720001,104.349998,102.970001,103.430000,103.430000,17490000\n1976-06-29,103.430000,104.330002,102.949997,103.860001,103.860001,19620000\n1976-06-30,103.860001,105.070000,103.519997,104.279999,104.279999,23830000\n1976-07-01,104.279999,104.980003,103.139999,103.589996,103.589996,21130000\n1976-07-02,103.589996,104.529999,103.129997,104.110001,104.110001,16730000\n1976-07-06,104.110001,104.669998,103.190002,103.540001,103.540001,16130000\n1976-07-07,103.540001,104.230003,102.800003,103.830002,103.830002,18470000\n1976-07-08,103.830002,104.750000,103.440002,103.980003,103.980003,21710000\n1976-07-09,103.980003,105.410004,103.800003,104.980003,104.980003,23500000\n1976-07-12,104.980003,106.300003,104.739998,105.900002,105.900002,23750000\n1976-07-13,105.900002,106.779999,105.150002,105.669998,105.669998,27550000\n1976-07-14,105.669998,106.610001,105.050003,105.949997,105.949997,23840000\n1976-07-15,105.949997,106.250000,104.760002,105.199997,105.199997,20400000\n1976-07-16,105.199997,105.269997,103.870003,104.680000,104.680000,20450000\n1976-07-19,104.680000,105.320000,103.839996,104.290001,104.290001,18200000\n1976-07-20,104.290001,104.570000,103.050003,103.720001,103.720001,18810000\n1976-07-21,103.720001,104.559998,103.209999,103.820000,103.820000,18350000\n1976-07-22,103.820000,104.419998,103.150002,103.930000,103.930000,15600000\n1976-07-23,103.930000,104.709999,103.489998,104.059998,104.059998,15870000\n1976-07-26,104.059998,104.690002,103.459999,104.070000,104.070000,13530000\n1976-07-27,104.070000,104.510002,103.129997,103.480003,103.480003,15580000\n1976-07-28,103.480003,103.580002,102.309998,103.050003,103.050003,16000000\n1976-07-29,103.050003,103.589996,102.360001,102.930000,102.930000,13330000\n1976-07-30,102.930000,103.879997,102.470001,103.440002,103.440002,14830000\n1976-08-02,103.440002,103.980003,102.639999,103.190002,103.190002,13870000\n1976-08-03,103.190002,104.489998,102.790001,104.139999,104.139999,18500000\n1976-08-04,104.139999,105.180000,103.720001,104.430000,104.430000,20650000\n1976-08-05,104.430000,104.760002,103.480003,103.849998,103.849998,15530000\n1976-08-06,103.849998,104.250000,103.099998,103.790001,103.790001,13930000\n1976-08-09,103.790001,104.019997,103.010002,103.489998,103.489998,11700000\n1976-08-10,103.489998,104.709999,103.209999,104.410004,104.410004,16690000\n1976-08-11,104.410004,105.239998,103.730003,104.059998,104.059998,18710000\n1976-08-12,104.059998,104.639999,103.379997,104.220001,104.220001,15560000\n1976-08-13,104.220001,104.790001,103.610001,104.250000,104.250000,13930000\n1976-08-16,104.250000,104.989998,103.739998,104.430000,104.430000,16210000\n1976-08-17,104.430000,105.250000,103.980003,104.800003,104.800003,18500000\n1976-08-18,104.800003,105.410004,104.120003,104.559998,104.559998,17150000\n1976-08-19,104.559998,104.739998,103.010002,103.389999,103.389999,17230000\n1976-08-20,103.309998,103.309998,101.959999,102.370003,102.370003,14920000\n1976-08-23,102.370003,102.489998,101.040001,101.959999,101.959999,15450000\n1976-08-24,101.959999,102.650002,100.980003,101.269997,101.269997,16740000\n1976-08-25,101.269997,102.410004,100.430000,102.029999,102.029999,17400000\n1976-08-26,102.029999,102.589996,101.010002,101.320000,101.320000,15270000\n1976-08-27,101.320000,101.900002,100.550003,101.480003,101.480003,12120000\n1976-08-30,101.480003,102.510002,101.220001,102.070000,102.070000,11140000\n1976-08-31,102.070000,103.379997,101.940002,102.910004,102.910004,15480000\n1976-09-01,102.910004,104.300003,102.599998,104.059998,104.059998,18640000\n1976-09-02,104.059998,104.839996,103.470001,103.919998,103.919998,18920000\n1976-09-03,103.919998,104.629997,103.360001,104.300003,104.300003,13280000\n1976-09-07,104.300003,105.309998,103.930000,105.029999,105.029999,16310000\n1976-09-08,105.029999,105.730003,104.339996,104.940002,104.940002,19750000\n1976-09-09,104.940002,105.120003,103.910004,104.400002,104.400002,16540000\n1976-09-10,104.400002,105.029999,103.790001,104.650002,104.650002,16930000\n1976-09-13,104.650002,105.290001,103.879997,104.290001,104.290001,16100000\n1976-09-14,104.290001,104.500000,103.309998,103.940002,103.940002,15550000\n1976-09-15,103.940002,104.699997,103.279999,104.250000,104.250000,17570000\n1976-09-16,104.250000,105.589996,103.839996,105.339996,105.339996,19620000\n1976-09-17,105.339996,106.809998,105.139999,106.269997,106.269997,28270000\n1976-09-20,106.269997,107.199997,105.739998,106.320000,106.320000,21730000\n1976-09-21,106.320000,108.129997,106.089996,107.830002,107.830002,30300000\n1976-09-22,107.830002,108.720001,106.919998,107.459999,107.459999,32970000\n1976-09-23,107.459999,107.959999,106.400002,106.919998,106.919998,24210000\n1976-09-24,106.919998,107.360001,106.029999,106.800003,106.800003,17400000\n1976-09-27,106.800003,107.699997,106.349998,107.269997,107.269997,17430000\n1976-09-28,107.269997,107.540001,105.610001,105.919998,105.919998,20440000\n1976-09-29,105.919998,106.449997,104.830002,105.370003,105.370003,18090000\n1976-09-30,105.370003,105.839996,104.570000,105.239998,105.239998,14700000\n1976-10-01,105.239998,105.750000,103.599998,104.169998,104.169998,20620000\n1976-10-04,104.169998,104.620003,103.419998,104.029999,104.029999,12630000\n1976-10-05,104.029999,104.250000,102.510002,103.230003,103.230003,19200000\n1976-10-06,103.230003,103.720001,102.050003,102.970001,102.970001,20870000\n1976-10-07,102.970001,103.900002,102.160004,103.540001,103.540001,19830000\n1976-10-08,103.540001,104.000000,102.239998,102.559998,102.559998,16740000\n1976-10-11,102.480003,102.480003,100.980003,101.639999,101.639999,14620000\n1976-10-12,101.639999,102.190002,100.379997,100.809998,100.809998,18210000\n1976-10-13,100.809998,102.440002,100.540001,102.120003,102.120003,21690000\n1976-10-14,102.120003,102.139999,100.279999,100.849998,100.849998,18610000\n1976-10-15,100.849998,101.500000,100.019997,100.879997,100.879997,16210000\n1976-10-18,100.879997,101.989998,100.620003,101.470001,101.470001,15710000\n1976-10-19,101.470001,102.040001,100.419998,101.449997,101.449997,16200000\n1976-10-20,101.449997,102.230003,100.809998,101.739998,101.739998,15860000\n1976-10-21,101.739998,102.320000,100.489998,100.769997,100.769997,17980000\n1976-10-22,100.769997,100.930000,99.239998,99.959999,99.959999,17870000\n1976-10-25,99.959999,100.599998,99.209999,100.070000,100.070000,13310000\n1976-10-26,100.070000,101.500000,99.910004,101.059998,101.059998,15490000\n1976-10-27,101.059998,102.120003,100.610001,101.760002,101.760002,15790000\n1976-10-28,101.760002,102.500000,101.120003,101.610001,101.610001,16920000\n1976-10-29,101.610001,103.099998,101.150002,102.900002,102.900002,17030000\n1976-11-01,102.900002,103.779999,102.190002,103.099998,103.099998,18390000\n1976-11-03,102.489998,102.489998,100.730003,101.919998,101.919998,19350000\n1976-11-04,101.919998,103.160004,101.400002,102.410004,102.410004,21700000\n1976-11-05,102.410004,102.699997,100.480003,100.820000,100.820000,20780000\n1976-11-08,100.620003,100.620003,99.099998,99.599998,99.599998,16520000\n1976-11-09,99.599998,100.209999,98.379997,99.320000,99.320000,19210000\n1976-11-10,99.320000,99.980003,98.180000,98.809998,98.809998,18890000\n1976-11-11,98.809998,99.889999,98.349998,99.639999,99.639999,13230000\n1976-11-12,99.639999,99.949997,98.510002,99.239998,99.239998,15550000\n1976-11-15,99.239998,100.160004,98.529999,99.900002,99.900002,16710000\n1976-11-16,99.900002,101.120003,99.440002,100.040001,100.040001,21020000\n1976-11-17,100.040001,101.320000,99.639999,100.610001,100.610001,19900000\n1976-11-18,100.610001,102.220001,100.489998,101.889999,101.889999,24000000\n1976-11-19,101.889999,102.769997,101.169998,101.919998,101.919998,24550000\n1976-11-22,101.919998,103.150002,101.629997,102.589996,102.589996,20930000\n1976-11-23,102.589996,102.900002,101.500000,101.959999,101.959999,19090000\n1976-11-24,101.959999,102.849998,101.410004,102.410004,102.410004,20420000\n1976-11-26,102.410004,103.510002,102.129997,103.150002,103.150002,15000000\n1976-11-29,103.150002,103.459999,102.070000,102.440002,102.440002,18750000\n1976-11-30,102.440002,102.720001,101.459999,102.099998,102.099998,17030000\n1976-12-01,102.099998,103.029999,101.620003,102.489998,102.489998,21960000\n1976-12-02,102.489998,103.300003,101.699997,102.120003,102.120003,23300000\n1976-12-03,102.120003,103.309998,101.750000,102.760002,102.760002,22640000\n1976-12-06,102.760002,104.150002,102.529999,103.559998,103.559998,24830000\n1976-12-07,103.559998,104.400002,102.959999,103.489998,103.489998,26140000\n1976-12-08,103.489998,104.400002,102.940002,104.080002,104.080002,24560000\n1976-12-09,104.080002,105.269997,103.709999,104.510002,104.510002,31800000\n1976-12-10,104.510002,105.360001,103.900002,104.699997,104.699997,25960000\n1976-12-13,104.699997,105.330002,103.940002,104.629997,104.629997,24830000\n1976-12-14,104.629997,105.440002,103.800003,105.070000,105.070000,25130000\n1976-12-15,105.070000,105.889999,104.330002,105.139999,105.139999,28300000\n1976-12-16,105.139999,105.529999,104.070000,104.800003,104.800003,23920000\n1976-12-17,104.800003,105.599998,103.889999,104.260002,104.260002,23870000\n1976-12-20,104.260002,104.629997,103.209999,103.650002,103.650002,20690000\n1976-12-21,103.650002,104.660004,102.989998,104.220001,104.220001,24390000\n1976-12-22,104.220001,105.589996,104.029999,104.709999,104.709999,26970000\n1976-12-23,104.709999,105.489998,104.089996,104.839996,104.839996,24560000\n1976-12-27,104.839996,106.309998,104.580002,106.059998,106.059998,20130000\n1976-12-28,106.059998,107.360001,105.900002,106.769997,106.769997,25790000\n1976-12-29,106.769997,107.169998,105.830002,106.339996,106.339996,21910000\n1976-12-30,106.339996,107.410004,105.970001,106.879997,106.879997,23700000\n1976-12-31,106.879997,107.820000,106.550003,107.459999,107.459999,19170000\n1977-01-03,107.459999,107.970001,106.419998,107.000000,107.000000,21280000\n1977-01-04,107.000000,107.309998,105.400002,105.699997,105.699997,22740000\n1977-01-05,105.699997,106.070000,104.330002,104.760002,104.760002,25010000\n1977-01-06,104.760002,105.860001,104.400002,105.019997,105.019997,23920000\n1977-01-07,105.019997,105.589996,104.300003,105.010002,105.010002,21720000\n1977-01-10,105.010002,105.750000,104.459999,105.199997,105.199997,20860000\n1977-01-11,105.199997,105.599998,103.760002,104.120003,104.120003,24100000\n1977-01-12,104.120003,104.180000,102.750000,103.400002,103.400002,22670000\n1977-01-13,103.400002,104.599998,103.209999,104.199997,104.199997,24780000\n1977-01-14,104.199997,104.709999,103.370003,104.010002,104.010002,24480000\n1977-01-17,104.010002,104.370003,103.040001,103.730003,103.730003,21060000\n1977-01-18,103.730003,104.290001,102.709999,103.320000,103.320000,24380000\n1977-01-19,103.320000,104.379997,102.830002,103.849998,103.849998,27120000\n1977-01-20,103.849998,104.449997,102.500000,102.970001,102.970001,26520000\n1977-01-21,102.970001,103.910004,102.349998,103.320000,103.320000,23930000\n1977-01-24,103.320000,104.059998,102.500000,103.250000,103.250000,22890000\n1977-01-25,103.250000,104.080002,102.419998,103.129997,103.129997,26340000\n1977-01-26,103.129997,103.480003,101.839996,102.339996,102.339996,27840000\n1977-01-27,102.339996,102.809998,101.269997,101.790001,101.790001,24360000\n1977-01-28,101.790001,102.610001,101.080002,101.930000,101.930000,22700000\n1977-01-31,101.930000,102.440002,100.910004,102.029999,102.029999,22920000\n1977-02-01,102.029999,103.059998,101.570000,102.540001,102.540001,23700000\n1977-02-02,102.540001,103.320000,101.889999,102.360001,102.360001,25700000\n1977-02-03,102.360001,102.570000,101.279999,101.849998,101.849998,23790000\n1977-02-04,101.849998,102.709999,101.300003,101.879997,101.879997,23130000\n1977-02-07,101.879997,102.430000,101.250000,101.889999,101.889999,20700000\n1977-02-08,101.889999,102.650002,101.160004,101.599998,101.599998,24040000\n1977-02-09,101.599998,101.879997,100.120003,100.730003,100.730003,23640000\n1977-02-10,100.730003,101.510002,100.160004,100.820000,100.820000,22340000\n1977-02-11,100.820000,101.180000,99.739998,100.220001,100.220001,20510000\n1977-02-14,100.220001,101.059998,99.510002,100.739998,100.739998,19230000\n1977-02-15,100.739998,101.669998,100.349998,101.040001,101.040001,21620000\n1977-02-16,101.040001,102.220001,100.680000,101.500000,101.500000,23430000\n1977-02-17,101.500000,101.760002,100.430000,100.919998,100.919998,19040000\n1977-02-18,100.919998,101.129997,99.949997,100.489998,100.489998,18040000\n1977-02-22,100.489998,101.220001,99.940002,100.489998,100.489998,17730000\n1977-02-23,100.489998,100.949997,99.779999,100.190002,100.190002,18240000\n1977-02-24,100.190002,100.419998,99.180000,99.599998,99.599998,19730000\n1977-02-25,99.599998,100.019997,98.820000,99.480003,99.480003,17610000\n1977-02-28,99.480003,100.059998,98.910004,99.820000,99.820000,16220000\n1977-03-01,99.820000,101.029999,99.650002,100.660004,100.660004,19480000\n1977-03-02,100.660004,101.239998,99.970001,100.389999,100.389999,18010000\n1977-03-03,100.389999,101.279999,100.010002,100.879997,100.879997,17560000\n1977-03-04,100.879997,101.669998,100.519997,101.199997,101.199997,18950000\n1977-03-07,101.199997,101.769997,100.639999,101.250000,101.250000,17410000\n1977-03-08,101.250000,101.849998,100.480003,100.870003,100.870003,19520000\n1977-03-09,100.870003,100.889999,99.629997,100.099998,100.099998,19680000\n1977-03-10,100.099998,100.959999,99.489998,100.669998,100.669998,18620000\n1977-03-11,100.669998,101.370003,100.139999,100.650002,100.650002,18230000\n1977-03-14,100.650002,101.750000,100.239998,101.419998,101.419998,19290000\n1977-03-15,101.419998,102.610001,101.339996,101.980003,101.980003,23940000\n1977-03-16,101.980003,102.699997,101.519997,102.169998,102.169998,22140000\n1977-03-17,102.169998,102.580002,101.279999,102.080002,102.080002,20700000\n1977-03-18,102.080002,102.610001,101.389999,101.860001,101.860001,19840000\n1977-03-21,101.860001,102.129997,100.919998,101.309998,101.309998,18040000\n1977-03-22,101.309998,101.580002,100.349998,101.000000,101.000000,18660000\n1977-03-23,101.000000,101.419998,99.879997,100.199997,100.199997,19360000\n1977-03-24,100.199997,100.599998,99.260002,99.699997,99.699997,19650000\n1977-03-25,99.699997,100.050003,98.709999,99.059998,99.059998,16550000\n1977-03-28,99.059998,99.540001,98.349998,99.000000,99.000000,16710000\n1977-03-29,99.000000,100.120003,98.949997,99.690002,99.690002,17030000\n1977-03-30,99.690002,99.989998,98.180000,98.540001,98.540001,18810000\n1977-03-31,98.540001,99.139999,97.800003,98.419998,98.419998,16510000\n1977-04-01,98.419998,99.570000,98.379997,99.209999,99.209999,17050000\n1977-04-04,99.209999,99.500000,97.980003,98.230003,98.230003,16250000\n1977-04-05,98.230003,98.599998,97.430000,98.010002,98.010002,18330000\n1977-04-06,98.010002,98.610001,97.449997,97.910004,97.910004,16600000\n1977-04-07,97.910004,98.650002,97.480003,98.349998,98.349998,17260000\n1977-04-11,98.349998,99.370003,98.080002,98.879997,98.879997,17650000\n1977-04-12,98.970001,100.580002,98.970001,100.150002,100.150002,23760000\n1977-04-13,100.150002,100.720001,99.019997,100.160004,100.160004,21800000\n1977-04-14,100.419998,102.070000,100.419998,101.000000,101.000000,30490000\n1977-04-15,101.000000,101.629997,100.349998,101.040001,101.040001,20230000\n1977-04-18,101.040001,101.360001,100.089996,100.540001,100.540001,17830000\n1977-04-19,100.540001,100.809998,99.580002,100.070000,100.070000,19510000\n1977-04-20,100.070000,100.980003,99.489998,100.400002,100.400002,25090000\n1977-04-21,100.400002,101.199997,99.349998,99.750000,99.750000,22740000\n1977-04-22,99.669998,99.669998,98.080002,98.440002,98.440002,20700000\n1977-04-25,98.320000,98.320000,96.540001,97.150002,97.150002,20440000\n1977-04-26,97.150002,97.940002,96.529999,97.110001,97.110001,20040000\n1977-04-27,97.110001,98.470001,96.900002,97.959999,97.959999,20590000\n1977-04-28,97.959999,98.769997,97.470001,98.199997,98.199997,18370000\n1977-04-29,98.199997,98.870003,97.580002,98.440002,98.440002,18330000\n1977-05-02,98.440002,99.260002,97.970001,98.930000,98.930000,17970000\n1977-05-03,98.930000,99.959999,98.720001,99.430000,99.430000,21950000\n1977-05-04,99.430000,100.559998,98.900002,99.959999,99.959999,23330000\n1977-05-05,99.959999,100.790001,99.279999,100.110001,100.110001,23450000\n1977-05-06,100.110001,100.199997,98.949997,99.489998,99.489998,19370000\n1977-05-09,99.489998,99.779999,98.660004,99.180000,99.180000,15230000\n1977-05-10,99.180000,100.089996,98.820000,99.470001,99.470001,21090000\n1977-05-11,99.470001,99.769997,98.400002,98.779999,98.779999,18980000\n1977-05-12,98.779999,99.250000,97.910004,98.730003,98.730003,21980000\n1977-05-13,98.730003,99.519997,98.370003,99.029999,99.029999,19780000\n1977-05-16,99.029999,99.980003,98.790001,99.470001,99.470001,21170000\n1977-05-17,99.470001,100.110001,98.760002,99.769997,99.769997,22290000\n1977-05-18,99.769997,100.930000,99.580002,100.300003,100.300003,27800000\n1977-05-19,100.300003,100.739998,99.489998,99.879997,99.879997,21280000\n1977-05-20,99.879997,100.120003,98.910004,99.449997,99.449997,18950000\n1977-05-23,99.349998,99.349998,97.879997,98.150002,98.150002,18290000\n1977-05-24,98.150002,98.250000,97.000000,97.669998,97.669998,20050000\n1977-05-25,97.669998,98.139999,96.500000,96.769997,96.769997,20710000\n1977-05-26,96.769997,97.470001,96.199997,97.010002,97.010002,18620000\n1977-05-27,97.010002,97.260002,95.919998,96.269997,96.269997,15730000\n1977-05-31,96.269997,96.750000,95.519997,96.120003,96.120003,17800000\n1977-06-01,96.120003,97.269997,95.889999,96.930000,96.930000,18320000\n1977-06-02,96.930000,97.529999,96.230003,96.739998,96.739998,18620000\n1977-06-03,96.739998,98.120003,96.550003,97.690002,97.690002,20330000\n1977-06-06,97.690002,98.260002,96.889999,97.230003,97.230003,18930000\n1977-06-07,97.230003,98.010002,96.599998,97.730003,97.730003,21110000\n1977-06-08,97.730003,98.750000,97.489998,98.199997,98.199997,22200000\n1977-06-09,98.199997,98.620003,97.510002,98.139999,98.139999,19940000\n1977-06-10,98.139999,98.860001,97.680000,98.459999,98.459999,20630000\n1977-06-13,98.459999,99.209999,98.059998,98.739998,98.739998,20250000\n1977-06-14,98.760002,100.120003,98.760002,99.860001,99.860001,25390000\n1977-06-15,99.860001,100.309998,99.120003,99.610001,99.610001,22640000\n1977-06-16,99.610001,100.330002,98.910004,99.849998,99.849998,24310000\n1977-06-17,99.849998,100.470001,99.339996,99.970001,99.970001,21960000\n1977-06-20,99.970001,100.760002,99.559998,100.419998,100.419998,22950000\n1977-06-21,100.419998,101.410004,100.160004,100.739998,100.739998,29730000\n1977-06-22,100.739998,101.070000,99.900002,100.459999,100.459999,25070000\n1977-06-23,100.459999,101.099998,99.879997,100.620003,100.620003,24330000\n1977-06-24,100.620003,101.650002,100.410004,101.190002,101.190002,27490000\n1977-06-27,101.190002,101.629997,100.470001,100.980003,100.980003,19870000\n1977-06-28,100.980003,101.360001,99.870003,100.139999,100.139999,22670000\n1977-06-29,100.139999,100.489998,99.300003,100.110001,100.110001,19000000\n1977-06-30,100.110001,100.879997,99.680000,100.480003,100.480003,19410000\n1977-07-01,100.480003,100.760002,99.629997,100.099998,100.099998,18160000\n1977-07-05,100.099998,100.720001,99.620003,100.089996,100.089996,16850000\n1977-07-06,100.089996,100.410004,99.199997,99.580002,99.580002,21230000\n1977-07-07,99.580002,100.300003,99.120003,99.930000,99.930000,21740000\n1977-07-08,99.930000,100.620003,99.370003,99.790001,99.790001,23820000\n1977-07-11,99.790001,100.160004,98.900002,99.550003,99.550003,19790000\n1977-07-12,99.550003,100.010002,98.809998,99.449997,99.449997,22470000\n1977-07-13,99.449997,99.989998,98.830002,99.589996,99.589996,23160000\n1977-07-15,99.589996,100.680000,99.279999,100.180000,100.180000,29120000\n1977-07-18,100.180000,101.400002,99.940002,100.949997,100.949997,29890000\n1977-07-19,100.949997,102.169998,100.680000,101.790001,101.790001,31930000\n1977-07-20,101.790001,102.570000,101.139999,101.730003,101.730003,29380000\n1977-07-21,101.730003,102.190002,100.849998,101.589996,101.589996,26880000\n1977-07-22,101.589996,102.279999,101.019997,101.669998,101.669998,23110000\n1977-07-25,101.669998,101.849998,100.459999,100.849998,100.849998,20430000\n1977-07-26,100.849998,100.919998,99.720001,100.269997,100.269997,21390000\n1977-07-27,100.269997,100.290001,98.309998,98.639999,98.639999,26440000\n1977-07-28,98.639999,99.360001,97.779999,98.790001,98.790001,26340000\n1977-07-29,98.790001,99.209999,97.709999,98.849998,98.849998,20350000\n1977-08-01,98.849998,99.839996,98.459999,99.120003,99.120003,17920000\n1977-08-02,99.120003,99.269997,98.139999,98.500000,98.500000,17910000\n1977-08-03,98.500000,98.860001,97.529999,98.370003,98.370003,21710000\n1977-08-04,98.370003,99.190002,97.790001,98.739998,98.739998,18870000\n1977-08-05,98.739998,99.440002,98.309998,98.760002,98.760002,19940000\n1977-08-08,98.760002,98.860001,97.680000,97.989998,97.989998,15870000\n1977-08-09,97.989998,98.629997,97.480003,98.050003,98.050003,19900000\n1977-08-10,98.050003,99.059998,97.669998,98.919998,98.919998,18280000\n1977-08-11,98.919998,99.449997,97.900002,98.160004,98.160004,21740000\n1977-08-12,98.160004,98.510002,97.309998,97.879997,97.879997,16870000\n1977-08-15,97.879997,98.559998,97.139999,98.180000,98.180000,15750000\n1977-08-16,98.180000,98.599998,97.349998,97.730003,97.730003,19340000\n1977-08-17,97.730003,98.400002,97.120003,97.739998,97.739998,20920000\n1977-08-18,97.739998,98.690002,97.209999,97.680000,97.680000,21040000\n1977-08-19,97.680000,98.290001,96.779999,97.510002,97.510002,20800000\n1977-08-22,97.510002,98.290001,96.839996,97.790001,97.790001,17870000\n1977-08-23,97.790001,98.519997,97.180000,97.620003,97.620003,20290000\n1977-08-24,97.620003,97.989998,96.769997,97.230003,97.230003,18170000\n1977-08-25,97.180000,97.180000,95.809998,96.150002,96.150002,19400000\n1977-08-26,96.150002,96.419998,95.040001,96.059998,96.059998,18480000\n1977-08-29,96.059998,97.250000,95.930000,96.919998,96.919998,15280000\n1977-08-30,96.919998,97.550003,96.040001,96.379997,96.379997,18220000\n1977-08-31,96.379997,97.000000,95.589996,96.769997,96.769997,19080000\n1977-09-01,96.769997,97.540001,96.349998,96.830002,96.830002,18820000\n1977-09-02,96.830002,97.760002,96.510002,97.449997,97.449997,15620000\n1977-09-06,97.449997,98.129997,96.930000,97.709999,97.709999,16130000\n1977-09-07,97.709999,98.379997,97.330002,98.010002,98.010002,18070000\n1977-09-08,98.010002,98.430000,97.010002,97.279999,97.279999,18290000\n1977-09-09,97.099998,97.099998,95.970001,96.370003,96.370003,18100000\n1977-09-12,96.370003,96.639999,95.370003,96.029999,96.029999,18700000\n1977-09-13,96.029999,96.559998,95.480003,96.089996,96.089996,14900000\n1977-09-14,96.089996,96.879997,95.660004,96.550003,96.550003,17330000\n1977-09-15,96.550003,97.309998,96.150002,96.800003,96.800003,18230000\n1977-09-16,96.800003,97.300003,96.050003,96.480003,96.480003,18340000\n1977-09-19,96.480003,96.589996,95.459999,95.849998,95.849998,16890000\n1977-09-20,95.849998,96.290001,95.230003,95.889999,95.889999,19030000\n1977-09-21,95.889999,96.519997,94.830002,95.099998,95.099998,22200000\n1977-09-22,95.099998,95.610001,94.510002,95.089996,95.089996,16660000\n1977-09-23,95.089996,95.690002,94.599998,95.040001,95.040001,18760000\n1977-09-26,95.040001,95.680000,94.440002,95.379997,95.379997,18230000\n1977-09-27,95.379997,96.010002,94.760002,95.239998,95.239998,19080000\n1977-09-28,95.239998,95.910004,94.730003,95.309998,95.309998,17960000\n1977-09-29,95.309998,96.279999,95.089996,95.849998,95.849998,21160000\n1977-09-30,95.849998,96.849998,95.660004,96.529999,96.529999,21170000\n1977-10-03,96.529999,97.110001,95.860001,96.739998,96.739998,19460000\n1977-10-04,96.739998,97.269997,95.730003,96.029999,96.029999,20850000\n1977-10-05,96.029999,96.360001,95.199997,95.680000,95.680000,18300000\n1977-10-06,95.680000,96.449997,95.300003,96.050003,96.050003,18490000\n1977-10-07,96.050003,96.510002,95.480003,95.970001,95.970001,16250000\n1977-10-10,95.970001,96.150002,95.320000,95.750000,95.750000,10580000\n1977-10-11,95.750000,95.970001,94.730003,94.930000,94.930000,17870000\n1977-10-12,94.820000,94.820000,93.400002,94.040001,94.040001,22440000\n1977-10-13,94.040001,94.320000,92.889999,93.459999,93.459999,23870000\n1977-10-14,93.459999,94.190002,92.879997,93.559998,93.559998,20410000\n1977-10-17,93.559998,94.029999,92.870003,93.470001,93.470001,17340000\n1977-10-18,93.470001,94.190002,93.010002,93.459999,93.459999,20130000\n1977-10-19,93.459999,93.709999,92.070000,92.379997,92.379997,22030000\n1977-10-20,92.379997,93.120003,91.599998,92.669998,92.669998,20520000\n1977-10-21,92.669998,92.989998,91.800003,92.320000,92.320000,20230000\n1977-10-24,92.320000,92.620003,91.360001,91.629997,91.629997,19210000\n1977-10-25,91.629997,91.709999,90.199997,91.000000,91.000000,23590000\n1977-10-26,91.000000,92.459999,90.440002,92.099998,92.099998,24860000\n1977-10-27,92.099998,93.150002,91.540001,92.339996,92.339996,21920000\n1977-10-28,92.339996,93.129997,91.879997,92.610001,92.610001,18050000\n1977-10-31,92.610001,93.029999,91.849998,92.339996,92.339996,17070000\n1977-11-01,92.190002,92.190002,91.000000,91.349998,91.349998,17170000\n1977-11-02,91.349998,91.589996,90.290001,90.709999,90.709999,20760000\n1977-11-03,90.709999,91.180000,90.010002,90.760002,90.760002,18090000\n1977-11-04,90.760002,91.970001,90.720001,91.580002,91.580002,21700000\n1977-11-07,91.580002,92.699997,91.320000,92.290001,92.290001,21270000\n1977-11-08,92.290001,92.970001,91.820000,92.459999,92.459999,19210000\n1977-11-09,92.459999,93.269997,92.010002,92.980003,92.980003,21330000\n1977-11-10,92.980003,95.099998,92.690002,94.709999,94.709999,31980000\n1977-11-11,95.099998,96.489998,95.099998,95.980003,95.980003,35260000\n1977-11-14,95.980003,96.379997,94.910004,95.320000,95.320000,23220000\n1977-11-15,95.320000,96.470001,94.730003,95.930000,95.930000,27740000\n1977-11-16,95.930000,96.470001,95.059998,95.449997,95.449997,24950000\n1977-11-17,95.449997,95.879997,94.589996,95.160004,95.160004,25110000\n1977-11-18,95.160004,95.879997,94.699997,95.330002,95.330002,23930000\n1977-11-21,95.330002,95.769997,94.589996,95.250000,95.250000,20110000\n1977-11-22,95.250000,96.519997,95.050003,96.089996,96.089996,28600000\n1977-11-23,96.089996,96.940002,95.599998,96.489998,96.489998,29150000\n1977-11-25,96.489998,97.110001,95.860001,96.690002,96.690002,17910000\n1977-11-28,96.690002,96.980003,95.669998,96.040001,96.040001,21570000\n1977-11-29,96.040001,96.089996,94.279999,94.550003,94.550003,22950000\n1977-11-30,94.550003,95.169998,93.779999,94.830002,94.830002,22670000\n1977-12-01,94.830002,95.449997,94.230003,94.690002,94.690002,24220000\n1977-12-02,94.690002,95.250000,94.080002,94.669998,94.669998,21160000\n1977-12-05,94.669998,95.010002,93.910004,94.269997,94.269997,19160000\n1977-12-06,94.089996,94.089996,92.440002,92.830002,92.830002,23770000\n1977-12-07,92.830002,93.389999,92.150002,92.779999,92.779999,21050000\n1977-12-08,92.779999,93.760002,92.510002,92.959999,92.959999,20400000\n1977-12-09,92.959999,94.110001,92.769997,93.650002,93.650002,19210000\n1977-12-12,93.650002,94.290001,93.180000,93.629997,93.629997,18180000\n1977-12-13,93.629997,94.040001,92.900002,93.559998,93.559998,19190000\n1977-12-14,93.559998,94.260002,92.940002,94.029999,94.029999,22110000\n1977-12-15,94.029999,94.419998,93.230003,93.550003,93.550003,21610000\n1977-12-16,93.550003,94.040001,92.930000,93.400002,93.400002,20270000\n1977-12-19,93.400002,93.709999,92.419998,92.690002,92.690002,21150000\n1977-12-20,92.690002,93.000000,91.760002,92.500000,92.500000,23250000\n1977-12-21,92.500000,93.580002,92.199997,93.050003,93.050003,24510000\n1977-12-22,93.050003,94.370003,93.050003,93.800003,93.800003,28100000\n1977-12-23,93.800003,94.989998,93.750000,94.690002,94.690002,20080000\n1977-12-27,94.690002,95.209999,94.089996,94.690002,94.690002,16750000\n1977-12-28,94.690002,95.199997,93.989998,94.750000,94.750000,19630000\n1977-12-29,94.750000,95.430000,94.099998,94.940002,94.940002,23610000\n1977-12-30,94.940002,95.669998,94.440002,95.099998,95.099998,23560000\n1978-01-03,95.099998,95.150002,93.489998,93.820000,93.820000,17720000\n1978-01-04,93.820000,94.099998,92.570000,93.519997,93.519997,24090000\n1978-01-05,93.519997,94.529999,92.510002,92.739998,92.739998,23570000\n1978-01-06,92.660004,92.660004,91.050003,91.620003,91.620003,26150000\n1978-01-09,91.480003,91.480003,89.970001,90.639999,90.639999,27990000\n1978-01-10,90.639999,91.290001,89.720001,90.169998,90.169998,25180000\n1978-01-11,90.169998,90.699997,89.230003,89.739998,89.739998,22880000\n1978-01-12,89.739998,90.599998,89.250000,89.820000,89.820000,22730000\n1978-01-13,89.820000,90.470001,89.260002,89.690002,89.690002,18010000\n1978-01-16,89.690002,90.110001,88.879997,89.430000,89.430000,18760000\n1978-01-17,89.430000,90.309998,89.050003,89.879997,89.879997,19360000\n1978-01-18,89.879997,90.860001,89.589996,90.559998,90.559998,21390000\n1978-01-19,90.559998,91.040001,89.739998,90.089996,90.089996,21500000\n1978-01-20,90.089996,90.269997,89.410004,89.889999,89.889999,7580000\n1978-01-23,89.889999,90.080002,88.809998,89.239998,89.239998,19380000\n1978-01-24,89.239998,89.800003,88.669998,89.250000,89.250000,18690000\n1978-01-25,89.250000,89.940002,88.830002,89.389999,89.389999,18690000\n1978-01-26,89.389999,89.790001,88.309998,88.580002,88.580002,19600000\n1978-01-27,88.580002,89.099998,88.019997,88.580002,88.580002,17600000\n1978-01-30,88.580002,89.669998,88.260002,89.339996,89.339996,17400000\n1978-01-31,89.339996,89.919998,88.610001,89.250000,89.250000,19870000\n1978-02-01,89.250000,90.239998,88.820000,89.930000,89.930000,22240000\n1978-02-02,89.930000,90.910004,89.540001,90.129997,90.129997,23050000\n1978-02-03,90.129997,90.320000,89.190002,89.620003,89.620003,19400000\n1978-02-06,89.620003,89.849998,88.949997,89.500000,89.500000,11630000\n1978-02-07,89.500000,90.529999,89.379997,90.330002,90.330002,14730000\n1978-02-08,90.330002,91.320000,90.089996,90.830002,90.830002,21300000\n1978-02-09,90.830002,90.959999,89.839996,90.300003,90.300003,17940000\n1978-02-10,90.300003,90.690002,89.559998,90.080002,90.080002,19480000\n1978-02-13,90.080002,90.300003,89.379997,89.860001,89.860001,16810000\n1978-02-14,89.860001,89.889999,88.699997,89.040001,89.040001,20470000\n1978-02-15,89.040001,89.400002,88.300003,88.830002,88.830002,20170000\n1978-02-16,88.769997,88.769997,87.639999,88.080002,88.080002,21570000\n1978-02-17,88.080002,88.699997,87.550003,87.959999,87.959999,18500000\n1978-02-21,87.959999,88.190002,87.089996,87.589996,87.589996,21890000\n1978-02-22,87.589996,88.150002,87.190002,87.559998,87.559998,18450000\n1978-02-23,87.559998,87.919998,86.830002,87.639999,87.639999,18720000\n1978-02-24,87.660004,88.870003,87.660004,88.489998,88.489998,22510000\n1978-02-27,88.489998,88.970001,87.489998,87.720001,87.720001,19990000\n1978-02-28,87.720001,87.760002,86.580002,87.040001,87.040001,19750000\n1978-03-01,87.040001,87.629997,86.449997,87.190002,87.190002,21010000\n1978-03-02,87.190002,87.809998,86.690002,87.320000,87.320000,20280000\n1978-03-03,87.320000,87.980003,86.830002,87.449997,87.449997,20120000\n1978-03-06,87.449997,87.519997,86.480003,86.900002,86.900002,17230000\n1978-03-07,86.900002,87.629997,86.550003,87.360001,87.360001,19900000\n1978-03-08,87.360001,88.080002,86.970001,87.839996,87.839996,22030000\n1978-03-09,87.839996,88.489998,87.339996,87.889999,87.889999,21820000\n1978-03-10,87.889999,89.250000,87.820000,88.879997,88.879997,27090000\n1978-03-13,88.879997,89.769997,88.480003,88.949997,88.949997,24070000\n1978-03-14,88.949997,89.620003,88.209999,89.349998,89.349998,24300000\n1978-03-15,89.349998,89.730003,88.519997,89.120003,89.120003,23340000\n1978-03-16,89.120003,89.769997,88.580002,89.510002,89.510002,25400000\n1978-03-17,89.510002,90.519997,89.169998,90.199997,90.199997,28470000\n1978-03-20,90.199997,91.349998,90.099998,90.820000,90.820000,28360000\n1978-03-21,90.820000,91.059998,89.500000,89.790001,89.790001,24410000\n1978-03-22,89.790001,90.070000,88.989998,89.470001,89.470001,21950000\n1978-03-23,89.470001,89.900002,88.830002,89.360001,89.360001,21290000\n1978-03-27,89.360001,89.500000,88.510002,88.870003,88.870003,18870000\n1978-03-28,88.870003,89.760002,88.470001,89.500000,89.500000,21600000\n1978-03-29,89.500000,90.169998,89.139999,89.639999,89.639999,25450000\n1978-03-30,89.639999,89.889999,88.970001,89.410004,89.410004,20460000\n1978-03-31,89.410004,89.639999,88.680000,89.209999,89.209999,20130000\n1978-04-03,89.199997,89.199997,88.070000,88.459999,88.459999,20230000\n1978-04-04,88.459999,89.180000,88.160004,88.860001,88.860001,20130000\n1978-04-05,88.860001,89.910004,88.620003,89.639999,89.639999,27260000\n1978-04-06,89.639999,90.459999,89.309998,89.790001,89.790001,27360000\n1978-04-07,89.790001,90.589996,89.389999,90.169998,90.169998,25160000\n1978-04-10,90.169998,90.879997,89.730003,90.489998,90.489998,25740000\n1978-04-11,90.489998,90.790001,89.769997,90.250000,90.250000,24300000\n1978-04-12,90.250000,90.779999,89.650002,90.110001,90.110001,26210000\n1978-04-13,90.110001,91.269997,89.820000,90.980003,90.980003,31580000\n1978-04-14,91.400002,93.309998,91.400002,92.919998,92.919998,52280000\n1978-04-17,93.599998,95.889999,93.599998,94.449997,94.449997,63510000\n1978-04-18,94.449997,94.720001,92.870003,93.430000,93.430000,38950000\n1978-04-19,93.430000,94.480003,92.750000,93.860001,93.860001,35060000\n1978-04-20,93.970001,95.709999,93.970001,94.540001,94.540001,43230000\n1978-04-21,94.540001,95.089996,93.709999,94.339996,94.339996,31540000\n1978-04-24,94.339996,96.000000,94.080002,95.769997,95.769997,34510000\n1978-04-25,96.050003,97.910004,96.050003,96.639999,96.639999,55800000\n1978-04-26,96.639999,97.750000,95.959999,96.820000,96.820000,44430000\n1978-04-27,96.820000,96.930000,95.300003,95.860001,95.860001,35470000\n1978-04-28,95.860001,97.099998,95.239998,96.830002,96.830002,32850000\n1978-05-01,96.830002,98.300003,96.410004,97.669998,97.669998,37020000\n1978-05-02,97.669998,98.110001,96.440002,97.250000,97.250000,41400000\n1978-05-03,97.250000,97.610001,95.839996,96.260002,96.260002,37560000\n1978-05-04,96.260002,96.430000,94.570000,95.930000,95.930000,37520000\n1978-05-05,95.930000,97.440002,95.559998,96.529999,96.529999,42680000\n1978-05-08,96.529999,97.500000,95.820000,96.190002,96.190002,34680000\n1978-05-09,96.190002,96.680000,95.330002,95.900002,95.900002,30860000\n1978-05-10,95.900002,96.690002,95.349998,95.919998,95.919998,33330000\n1978-05-11,95.919998,97.470001,95.599998,97.199997,97.199997,36630000\n1978-05-12,97.199997,98.889999,97.139999,98.070000,98.070000,46600000\n1978-05-15,98.070000,99.110001,97.400002,98.760002,98.760002,33890000\n1978-05-16,98.760002,100.160004,98.610001,99.349998,99.349998,48170000\n1978-05-17,99.349998,100.320000,98.629997,99.599998,99.599998,45490000\n1978-05-18,99.599998,100.040001,98.190002,98.620003,98.620003,42270000\n1978-05-19,98.620003,99.059998,97.419998,98.120003,98.120003,34360000\n1978-05-22,98.120003,99.430000,97.650002,99.089996,99.089996,28680000\n1978-05-23,99.089996,99.169998,97.529999,98.050003,98.050003,33230000\n1978-05-24,97.739998,97.739998,96.269997,97.080002,97.080002,31450000\n1978-05-25,97.080002,97.800003,96.300003,96.800003,96.800003,28410000\n1978-05-26,96.800003,97.139999,96.010002,96.580002,96.580002,21410000\n1978-05-30,96.580002,97.230003,95.949997,96.860001,96.860001,21040000\n1978-05-31,96.860001,97.970001,96.500000,97.239998,97.239998,29070000\n1978-06-01,97.239998,97.949997,96.629997,97.349998,97.349998,28750000\n1978-06-02,97.349998,98.519997,97.010002,98.139999,98.139999,31860000\n1978-06-05,98.139999,100.269997,97.970001,99.949997,99.949997,39580000\n1978-06-06,99.949997,101.839996,99.900002,100.320000,100.320000,51970000\n1978-06-07,100.320000,100.809998,99.360001,100.120003,100.120003,33060000\n1978-06-08,100.120003,101.209999,99.550003,100.209999,100.209999,39380000\n1978-06-09,100.209999,100.709999,99.300003,99.930000,99.930000,32470000\n1978-06-12,99.930000,100.599998,99.160004,99.550003,99.550003,24440000\n1978-06-13,99.550003,99.980003,98.430000,99.570000,99.570000,30760000\n1978-06-14,99.570000,100.680000,98.889999,99.480003,99.480003,37290000\n1978-06-15,99.480003,99.540001,97.970001,98.339996,98.339996,29280000\n1978-06-16,98.339996,98.589996,97.099998,97.419998,97.419998,27690000\n1978-06-19,97.419998,97.940002,96.529999,97.489998,97.489998,25500000\n1978-06-20,97.489998,97.779999,96.150002,96.510002,96.510002,27920000\n1978-06-21,96.510002,96.739998,95.419998,96.010002,96.010002,29100000\n1978-06-22,96.010002,96.760002,95.519997,96.239998,96.239998,27160000\n1978-06-23,96.239998,96.980003,95.489998,95.849998,95.849998,28530000\n1978-06-26,95.849998,96.059998,94.309998,94.599998,94.599998,29250000\n1978-06-27,94.599998,95.480003,93.989998,94.980003,94.980003,29280000\n1978-06-28,94.980003,95.790001,94.440002,95.400002,95.400002,23260000\n1978-06-29,95.400002,96.260002,95.000000,95.570000,95.570000,21660000\n1978-06-30,95.570000,95.959999,94.870003,95.529999,95.529999,18100000\n1978-07-03,95.529999,95.650002,94.620003,95.089996,95.089996,11560000\n1978-07-05,95.089996,95.199997,93.779999,94.269997,94.269997,23730000\n1978-07-06,94.269997,94.830002,93.589996,94.320000,94.320000,24990000\n1978-07-07,94.320000,95.320000,94.019997,94.889999,94.889999,23480000\n1978-07-10,94.889999,95.669998,94.279999,95.269997,95.269997,22470000\n1978-07-11,95.269997,96.489998,94.919998,95.930000,95.930000,27470000\n1978-07-12,95.930000,96.830002,95.500000,96.239998,96.239998,26640000\n1978-07-13,96.239998,96.660004,95.419998,96.250000,96.250000,23620000\n1978-07-14,96.250000,97.879997,95.889999,97.580002,97.580002,28370000\n1978-07-17,97.580002,98.839996,97.239998,97.779999,97.779999,29180000\n1978-07-18,97.779999,97.980003,96.519997,96.870003,96.870003,22860000\n1978-07-19,96.870003,98.410004,96.709999,98.120003,98.120003,30850000\n1978-07-20,98.120003,99.180000,97.489998,98.029999,98.029999,33350000\n1978-07-21,98.029999,98.570000,97.019997,97.750000,97.750000,26060000\n1978-07-24,97.750000,98.129997,96.720001,97.720001,97.720001,23280000\n1978-07-25,97.720001,98.730003,97.199997,98.440002,98.440002,25400000\n1978-07-26,99.080002,99.080002,99.080002,99.080002,99.080002,36830000\n1978-07-27,99.080002,100.169998,98.599998,99.540001,99.540001,33970000\n1978-07-28,99.540001,100.510002,98.900002,100.000000,100.000000,33390000\n1978-07-31,100.000000,101.180000,99.370003,100.680000,100.680000,33990000\n1978-08-01,100.680000,101.459999,99.949997,100.660004,100.660004,34810000\n1978-08-02,100.660004,103.209999,100.180000,102.919998,102.919998,47470000\n1978-08-03,102.919998,105.410004,102.820000,103.510002,103.510002,66370000\n1978-08-04,103.510002,104.669998,102.750000,103.919998,103.919998,37910000\n1978-08-07,103.919998,104.839996,103.029999,103.550003,103.550003,33350000\n1978-08-08,103.550003,104.349998,102.599998,104.010002,104.010002,34290000\n1978-08-09,104.010002,105.720001,103.699997,104.500000,104.500000,48800000\n1978-08-10,104.500000,105.110001,103.099998,103.660004,103.660004,39760000\n1978-08-11,103.660004,104.669998,102.849998,103.959999,103.959999,33550000\n1978-08-14,103.959999,104.980003,103.400002,103.970001,103.970001,32320000\n1978-08-15,103.970001,104.379997,102.860001,103.849998,103.849998,29760000\n1978-08-16,103.849998,105.150002,103.410004,104.650002,104.650002,36120000\n1978-08-17,104.650002,106.269997,104.339996,105.080002,105.080002,45270000\n1978-08-18,105.080002,105.980003,104.230003,104.730003,104.730003,34650000\n1978-08-21,104.730003,105.199997,103.440002,103.889999,103.889999,29440000\n1978-08-22,103.889999,104.790001,103.139999,104.309998,104.309998,29620000\n1978-08-23,104.309998,105.680000,104.120003,104.910004,104.910004,39630000\n1978-08-24,104.910004,105.860001,104.290001,105.080002,105.080002,38500000\n1978-08-25,105.080002,105.680000,104.239998,104.900002,104.900002,36190000\n1978-08-28,104.900002,105.139999,103.610001,103.959999,103.959999,31760000\n1978-08-29,103.959999,104.339996,102.919998,103.389999,103.389999,33780000\n1978-08-30,103.389999,104.260002,102.699997,103.500000,103.500000,37750000\n1978-08-31,103.500000,104.050003,102.629997,103.290001,103.290001,33850000\n1978-09-01,103.290001,104.269997,102.730003,103.680000,103.680000,35070000\n1978-09-05,103.680000,104.830002,103.309998,104.489998,104.489998,32170000\n1978-09-06,104.510002,106.190002,104.510002,105.379997,105.379997,42600000\n1978-09-07,105.379997,106.489998,104.760002,105.419998,105.419998,40310000\n1978-09-08,105.500000,107.190002,105.500000,106.790001,106.790001,42170000\n1978-09-11,106.790001,108.050003,106.419998,106.980003,106.980003,39670000\n1978-09-12,106.980003,107.480003,106.019997,106.989998,106.989998,34400000\n1978-09-13,106.989998,107.849998,105.870003,106.339996,106.339996,43340000\n1978-09-14,106.339996,106.620003,104.769997,105.099998,105.099998,37400000\n1978-09-15,105.099998,105.120003,103.559998,104.120003,104.120003,37290000\n1978-09-18,104.120003,105.029999,102.750000,103.209999,103.209999,35860000\n1978-09-19,103.209999,103.820000,102.120003,102.529999,102.529999,31660000\n1978-09-20,102.529999,103.290001,101.279999,101.730003,101.730003,35080000\n1978-09-21,101.730003,102.540001,100.660004,101.900002,101.900002,33640000\n1978-09-22,101.900002,102.690002,101.129997,101.839996,101.839996,27960000\n1978-09-25,101.839996,102.360001,101.050003,101.860001,101.860001,20970000\n1978-09-26,101.860001,103.150002,101.580002,102.620003,102.620003,26330000\n1978-09-27,102.620003,103.440002,101.330002,101.660004,101.660004,28370000\n1978-09-28,101.660004,102.379997,100.940002,101.959999,101.959999,24390000\n1978-09-29,101.959999,103.080002,101.650002,102.540001,102.540001,23610000\n1978-10-02,102.540001,103.419998,102.129997,102.959999,102.959999,18700000\n1978-10-03,102.959999,103.559998,102.180000,102.599998,102.599998,22540000\n1978-10-04,102.599998,103.360001,101.760002,103.059998,103.059998,25090000\n1978-10-05,103.059998,104.099998,102.540001,103.269997,103.269997,27820000\n1978-10-06,103.269997,104.230003,102.820000,103.519997,103.519997,27380000\n1978-10-09,103.519997,104.889999,103.309998,104.589996,104.589996,19720000\n1978-10-10,104.589996,105.360001,103.900002,104.459999,104.459999,25470000\n1978-10-11,104.459999,105.639999,103.800003,105.389999,105.389999,21740000\n1978-10-12,105.389999,106.230003,104.419998,104.879997,104.879997,30170000\n1978-10-13,104.879997,105.339996,104.070000,104.660004,104.660004,21920000\n1978-10-16,104.629997,104.629997,102.430000,102.610001,102.610001,24600000\n1978-10-17,102.349998,102.349998,100.470001,101.260002,101.260002,37870000\n1978-10-18,101.260002,101.760002,99.889999,100.489998,100.489998,32940000\n1978-10-19,100.489998,101.029999,99.040001,99.330002,99.330002,31810000\n1978-10-20,99.260002,99.260002,97.120003,97.949997,97.949997,43670000\n1978-10-23,97.949997,98.839996,96.629997,98.180000,98.180000,36090000\n1978-10-24,98.180000,98.949997,97.129997,97.489998,97.489998,28880000\n1978-10-25,97.489998,98.559998,96.330002,97.309998,97.309998,31380000\n1978-10-26,97.309998,97.709999,95.589996,96.029999,96.029999,31990000\n1978-10-27,96.029999,96.620003,94.300003,94.589996,94.589996,40360000\n1978-10-30,94.589996,95.489998,91.650002,95.059998,95.059998,59480000\n1978-10-31,95.059998,95.800003,92.720001,93.150002,93.150002,42720000\n1978-11-01,94.129997,97.410004,94.129997,96.849998,96.849998,50450000\n1978-11-02,96.849998,97.309998,94.839996,95.610001,95.610001,41030000\n1978-11-03,95.610001,96.980003,94.779999,96.180000,96.180000,25990000\n1978-11-06,96.180000,96.489998,94.839996,95.190002,95.190002,20450000\n1978-11-07,94.750000,94.750000,93.139999,93.849998,93.849998,25320000\n1978-11-08,93.849998,94.739998,92.889999,94.449997,94.449997,23560000\n1978-11-09,94.449997,95.500000,93.809998,94.419998,94.419998,23320000\n1978-11-10,94.419998,95.389999,93.940002,94.769997,94.769997,16750000\n1978-11-13,94.769997,94.900002,92.959999,93.129997,93.129997,20960000\n1978-11-14,93.129997,93.529999,91.769997,92.489998,92.489998,30610000\n1978-11-15,92.489998,94.000000,92.290001,92.709999,92.709999,26280000\n1978-11-16,92.709999,94.080002,92.589996,93.709999,93.709999,21340000\n1978-11-17,93.709999,95.029999,93.589996,94.419998,94.419998,25170000\n1978-11-20,94.419998,95.860001,94.290001,95.250000,95.250000,24440000\n1978-11-21,95.250000,95.830002,94.489998,95.010002,95.010002,20750000\n1978-11-22,95.010002,95.910004,94.540001,95.480003,95.480003,20010000\n1978-11-24,95.480003,96.169998,94.980003,95.790001,95.790001,14590000\n1978-11-27,95.790001,96.519997,95.169998,95.989998,95.989998,19790000\n1978-11-28,95.989998,96.510002,94.879997,95.150002,95.150002,22740000\n1978-11-29,94.919998,94.919998,93.480003,93.750000,93.750000,21160000\n1978-11-30,93.750000,94.940002,93.290001,94.699997,94.699997,19900000\n1978-12-01,95.010002,96.690002,95.010002,96.279999,96.279999,26830000\n1978-12-04,96.279999,96.959999,95.370003,96.150002,96.150002,22020000\n1978-12-05,96.150002,97.699997,95.879997,97.440002,97.440002,25670000\n1978-12-06,97.440002,98.580002,96.830002,97.489998,97.489998,29680000\n1978-12-07,97.489998,98.099998,96.580002,97.080002,97.080002,21170000\n1978-12-08,97.080002,97.480003,96.139999,96.629997,96.629997,18560000\n1978-12-11,96.629997,97.559998,96.070000,97.110001,97.110001,21000000\n1978-12-12,97.110001,97.580002,96.269997,96.589996,96.589996,22210000\n1978-12-13,96.589996,97.070000,95.589996,96.059998,96.059998,22480000\n1978-12-14,96.059998,96.440002,95.199997,96.040001,96.040001,20840000\n1978-12-15,96.040001,96.279999,94.879997,95.330002,95.330002,23620000\n1978-12-18,94.330002,94.330002,92.639999,93.440002,93.440002,32900000\n1978-12-19,93.440002,94.849998,93.050003,94.239998,94.239998,25960000\n1978-12-20,94.239998,95.199997,93.699997,94.680000,94.680000,26520000\n1978-12-21,94.680000,95.660004,94.110001,94.709999,94.709999,28670000\n1978-12-22,94.769997,96.620003,94.769997,96.309998,96.309998,23790000\n1978-12-26,96.309998,97.889999,95.989998,97.519997,97.519997,21470000\n1978-12-27,97.510002,97.510002,96.150002,96.660004,96.660004,23580000\n1978-12-28,96.660004,97.190002,95.820000,96.279999,96.279999,25440000\n1978-12-29,96.279999,97.029999,95.480003,96.110001,96.110001,30030000\n1979-01-02,96.110001,96.959999,95.220001,96.730003,96.730003,18340000\n1979-01-03,96.809998,98.540001,96.809998,97.800003,97.800003,29180000\n1979-01-04,97.800003,99.419998,97.519997,98.580002,98.580002,33290000\n1979-01-05,98.580002,99.790001,98.250000,99.129997,99.129997,28890000\n1979-01-08,99.129997,99.300003,97.830002,98.800003,98.800003,21440000\n1979-01-09,98.800003,99.959999,98.620003,99.330002,99.330002,27340000\n1979-01-10,99.330002,99.750000,98.279999,98.769997,98.769997,24990000\n1979-01-11,98.769997,99.410004,97.949997,99.099998,99.099998,24580000\n1979-01-12,99.320000,100.910004,99.320000,99.930000,99.930000,37120000\n1979-01-15,99.930000,101.129997,99.580002,100.690002,100.690002,27520000\n1979-01-16,100.690002,100.879997,99.110001,99.459999,99.459999,30340000\n1979-01-17,99.459999,100.000000,98.330002,99.480003,99.480003,25310000\n1979-01-18,99.480003,100.349998,98.910004,99.720001,99.720001,27260000\n1979-01-19,99.720001,100.570000,99.220001,99.750000,99.750000,26800000\n1979-01-22,99.750000,100.349998,98.900002,99.900002,99.900002,24390000\n1979-01-23,99.900002,101.050003,99.349998,100.599998,100.599998,30130000\n1979-01-24,100.599998,101.309998,99.669998,100.160004,100.160004,31730000\n1979-01-25,100.160004,101.660004,99.989998,101.190002,101.190002,31440000\n1979-01-26,101.190002,102.589996,101.029999,101.860001,101.860001,34230000\n1979-01-29,101.860001,102.330002,100.989998,101.550003,101.550003,24170000\n1979-01-30,101.550003,102.070000,100.680000,101.050003,101.050003,26910000\n1979-01-31,101.050003,101.410004,99.470001,99.930000,99.930000,30330000\n1979-02-01,99.930000,100.379997,99.010002,99.959999,99.959999,27930000\n1979-02-02,99.959999,100.519997,99.099998,99.500000,99.500000,25350000\n1979-02-05,99.070000,99.070000,97.570000,98.089996,98.089996,26490000\n1979-02-06,98.089996,98.739998,97.480003,98.050003,98.050003,23570000\n1979-02-07,98.050003,98.070000,96.510002,97.160004,97.160004,28450000\n1979-02-08,97.160004,98.110001,96.820000,97.650002,97.650002,23360000\n1979-02-09,97.650002,98.500000,97.279999,97.870003,97.870003,24320000\n1979-02-12,97.870003,98.550003,97.050003,98.199997,98.199997,20610000\n1979-02-13,98.250000,99.580002,98.250000,98.930000,98.930000,28470000\n1979-02-14,98.930000,99.639999,98.209999,98.870003,98.870003,27220000\n1979-02-15,98.870003,99.129997,97.959999,98.730003,98.730003,22550000\n1979-02-16,98.730003,99.230003,98.110001,98.669998,98.669998,21110000\n1979-02-20,98.669998,99.669998,98.260002,99.419998,99.419998,22010000\n1979-02-21,99.419998,100.070000,98.690002,99.070000,99.070000,26050000\n1979-02-22,99.070000,99.209999,97.879997,98.330002,98.330002,26290000\n1979-02-23,98.330002,98.500000,97.290001,97.779999,97.779999,22750000\n1979-02-26,97.779999,98.279999,97.199997,97.669998,97.669998,22620000\n1979-02-27,97.650002,97.650002,95.690002,96.129997,96.129997,31470000\n1979-02-28,96.129997,96.690002,95.379997,96.279999,96.279999,25090000\n1979-03-01,96.279999,97.279999,95.980003,96.900002,96.900002,23830000\n1979-03-02,96.900002,97.550003,96.440002,96.970001,96.970001,23130000\n1979-03-05,97.029999,98.639999,97.029999,98.059998,98.059998,25690000\n1979-03-06,98.059998,98.529999,97.360001,97.870003,97.870003,24490000\n1979-03-07,97.870003,99.230003,97.669998,98.440002,98.440002,28930000\n1979-03-08,98.440002,99.820000,98.099998,99.580002,99.580002,32000000\n1979-03-09,99.580002,100.580002,99.120003,99.540001,99.540001,33410000\n1979-03-12,99.540001,100.040001,98.559998,99.669998,99.669998,25740000\n1979-03-13,99.669998,100.660004,99.129997,99.839996,99.839996,31170000\n1979-03-14,99.839996,100.430000,99.230003,99.709999,99.709999,24630000\n1979-03-15,99.709999,100.570000,99.110001,99.860001,99.860001,29370000\n1979-03-16,99.860001,101.160004,99.529999,100.690002,100.690002,31770000\n1979-03-19,100.690002,101.940002,100.349998,101.059998,101.059998,34620000\n1979-03-20,101.059998,101.339996,100.010002,100.500000,100.500000,27180000\n1979-03-21,100.500000,101.480003,99.870003,101.250000,101.250000,31120000\n1979-03-22,101.250000,102.410004,101.040001,101.669998,101.669998,34380000\n1979-03-23,101.669998,102.370003,101.019997,101.599998,101.599998,33570000\n1979-03-26,101.599998,101.769997,100.599998,101.040001,101.040001,23430000\n1979-03-27,101.040001,102.709999,100.809998,102.480003,102.480003,32940000\n1979-03-28,102.480003,103.309998,101.739998,102.120003,102.120003,39920000\n1979-03-29,102.120003,102.779999,101.430000,102.029999,102.029999,28510000\n1979-03-30,102.029999,102.510002,101.029999,101.589996,101.589996,29970000\n1979-04-02,101.559998,101.559998,100.139999,100.900002,100.900002,28990000\n1979-04-03,100.900002,102.669998,100.809998,102.400002,102.400002,33530000\n1979-04-04,102.400002,103.730003,102.160004,102.650002,102.650002,41940000\n1979-04-05,102.650002,103.599998,102.160004,103.260002,103.260002,34520000\n1979-04-06,103.260002,103.949997,102.580002,103.180000,103.180000,34710000\n1979-04-09,103.180000,103.559998,102.279999,102.870003,102.870003,27230000\n1979-04-10,102.870003,103.830002,102.419998,103.339996,103.339996,31900000\n1979-04-11,103.339996,103.769997,101.919998,102.309998,102.309998,32900000\n1979-04-12,102.309998,102.769997,101.510002,102.000000,102.000000,26780000\n1979-04-16,102.000000,102.019997,100.669998,101.120003,101.120003,28050000\n1979-04-17,101.120003,101.940002,100.650002,101.239998,101.239998,29260000\n1979-04-18,101.239998,102.230003,100.959999,101.699997,101.699997,29510000\n1979-04-19,101.699997,102.400002,100.879997,101.279999,101.279999,31150000\n1979-04-20,101.279999,101.809998,100.459999,101.230003,101.230003,28830000\n1979-04-23,101.230003,102.000000,100.680000,101.570000,101.570000,25610000\n1979-04-24,101.570000,103.019997,101.389999,102.199997,102.199997,35540000\n1979-04-25,102.199997,103.070000,101.790001,102.500000,102.500000,31750000\n1979-04-26,102.500000,102.910004,101.580002,102.010002,102.010002,32400000\n1979-04-27,102.010002,102.320000,101.040001,101.800003,101.800003,29610000\n1979-04-30,101.800003,102.239998,100.910004,101.760002,101.760002,26440000\n1979-05-01,101.760002,102.500000,101.220001,101.680000,101.680000,31040000\n1979-05-02,101.680000,102.279999,101.000000,101.720001,101.720001,30510000\n1979-05-03,101.720001,102.570000,101.250000,101.809998,101.809998,30870000\n1979-05-04,101.809998,102.080002,100.419998,100.690002,100.690002,30630000\n1979-05-07,100.370003,100.370003,98.779999,99.019997,99.019997,30480000\n1979-05-08,99.019997,99.559998,97.980003,99.169998,99.169998,32720000\n1979-05-09,99.169998,100.010002,98.500000,99.459999,99.459999,27670000\n1979-05-10,99.459999,99.629997,98.220001,98.519997,98.519997,25230000\n1979-05-11,98.519997,99.029999,97.919998,98.519997,98.519997,24010000\n1979-05-14,98.519997,98.949997,97.709999,98.059998,98.059998,22450000\n1979-05-15,98.059998,98.900002,97.599998,98.139999,98.139999,26190000\n1979-05-16,98.139999,98.800003,97.489998,98.419998,98.419998,28350000\n1979-05-17,98.419998,100.220001,98.290001,99.940002,99.940002,30550000\n1979-05-18,99.940002,100.730003,99.330002,99.930000,99.930000,26590000\n1979-05-21,99.930000,100.750000,99.370003,100.139999,100.139999,25550000\n1979-05-22,100.139999,100.930000,99.449997,100.510002,100.510002,30400000\n1979-05-23,100.510002,101.309998,99.629997,99.889999,99.889999,30390000\n1979-05-24,99.889999,100.440002,99.139999,99.930000,99.930000,25710000\n1979-05-25,99.930000,100.680000,99.519997,100.220001,100.220001,27810000\n1979-05-29,100.220001,100.760002,99.559998,100.050003,100.050003,27040000\n1979-05-30,100.050003,100.250000,98.790001,99.110001,99.110001,29250000\n1979-05-31,99.110001,99.610001,98.290001,99.080002,99.080002,30300000\n1979-06-01,99.080002,99.699997,98.570000,99.169998,99.169998,24560000\n1979-06-04,99.169998,99.760002,98.610001,99.320000,99.320000,24040000\n1979-06-05,99.320000,101.070000,99.169998,100.620003,100.620003,35050000\n1979-06-06,100.620003,101.959999,100.379997,101.300003,101.300003,39830000\n1979-06-07,101.300003,102.540001,101.150002,101.790001,101.790001,43380000\n1979-06-08,101.790001,102.230003,100.910004,101.489998,101.489998,31470000\n1979-06-11,101.489998,102.239998,100.910004,101.910004,101.910004,28270000\n1979-06-12,101.910004,103.639999,101.809998,102.849998,102.849998,45450000\n1979-06-13,102.849998,103.580002,101.830002,102.309998,102.309998,40740000\n1979-06-14,102.309998,102.629997,101.040001,102.199997,102.199997,37850000\n1979-06-15,102.199997,102.779999,101.379997,102.089996,102.089996,40740000\n1979-06-18,102.089996,102.480003,101.050003,101.559998,101.559998,30970000\n1979-06-19,101.559998,102.279999,100.910004,101.580002,101.580002,30780000\n1979-06-20,101.580002,102.190002,100.930000,101.629997,101.629997,33790000\n1979-06-21,101.629997,102.739998,101.199997,102.089996,102.089996,36490000\n1979-06-22,102.089996,103.160004,101.910004,102.639999,102.639999,36410000\n1979-06-25,102.639999,102.910004,101.449997,102.089996,102.089996,31330000\n1979-06-26,102.089996,102.089996,101.220001,101.660004,101.660004,34680000\n1979-06-27,101.660004,102.949997,101.290001,102.269997,102.269997,36720000\n1979-06-28,102.269997,103.459999,101.910004,102.800003,102.800003,38470000\n1979-06-29,102.800003,103.669998,102.040001,102.910004,102.910004,34690000\n1979-07-02,102.910004,103.000000,101.449997,101.989998,101.989998,32060000\n1979-07-03,101.989998,102.570000,101.309998,102.089996,102.089996,31670000\n1979-07-05,102.089996,102.879997,101.589996,102.430000,102.430000,30290000\n1979-07-06,102.430000,103.910004,102.120003,103.620003,103.620003,38570000\n1979-07-09,103.620003,105.070000,103.360001,104.470001,104.470001,42460000\n1979-07-10,104.470001,105.169998,103.519997,104.199997,104.199997,39730000\n1979-07-11,104.199997,104.339996,102.870003,103.639999,103.639999,36650000\n1979-07-12,103.639999,103.720001,102.220001,102.690002,102.690002,31780000\n1979-07-13,102.690002,102.989998,101.489998,102.320000,102.320000,33080000\n1979-07-16,102.320000,103.199997,101.809998,102.739998,102.739998,26620000\n1979-07-17,102.739998,103.059998,101.269997,101.830002,101.830002,34270000\n1979-07-18,101.830002,102.059998,100.349998,101.690002,101.690002,35950000\n1979-07-19,101.690002,102.419998,101.040001,101.610001,101.610001,26780000\n1979-07-20,101.610001,102.320000,101.059998,101.820000,101.820000,26360000\n1979-07-23,101.820000,102.129997,100.839996,101.589996,101.589996,26860000\n1979-07-24,101.589996,102.500000,101.139999,101.970001,101.970001,29690000\n1979-07-25,101.970001,103.440002,101.849998,103.080002,103.080002,34890000\n1979-07-26,103.080002,103.629997,102.339996,103.099998,103.099998,32270000\n1979-07-27,103.099998,103.500000,102.290001,103.099998,103.099998,27760000\n1979-07-30,103.099998,103.629997,102.419998,103.150002,103.150002,28640000\n1979-07-31,103.150002,104.260002,102.889999,103.809998,103.809998,34360000\n1979-08-01,103.809998,104.570000,103.139999,104.169998,104.169998,36570000\n1979-08-02,104.169998,105.019997,103.589996,104.099998,104.099998,37720000\n1979-08-03,104.099998,104.559998,103.360001,104.040001,104.040001,28160000\n1979-08-06,104.040001,104.660004,103.269997,104.300003,104.300003,27190000\n1979-08-07,104.300003,106.230003,104.120003,105.650002,105.650002,45410000\n1979-08-08,105.650002,106.839996,105.199997,105.980003,105.980003,44970000\n1979-08-09,105.980003,106.250000,104.889999,105.489998,105.489998,34630000\n1979-08-10,105.489998,106.790001,104.809998,106.400002,106.400002,36740000\n1979-08-13,106.400002,107.900002,106.279999,107.419998,107.419998,41980000\n1979-08-14,107.419998,108.029999,106.599998,107.519997,107.519997,40910000\n1979-08-15,107.519997,108.639999,106.750000,108.250000,108.250000,46130000\n1979-08-16,108.250000,109.180000,107.379997,108.089996,108.089996,47000000\n1979-08-17,108.089996,108.940002,107.250000,108.300003,108.300003,31630000\n1979-08-20,108.300003,109.320000,107.690002,108.830002,108.830002,32300000\n1979-08-21,108.830002,109.680000,108.169998,108.910004,108.910004,38860000\n1979-08-22,108.910004,109.559998,108.089996,108.989998,108.989998,38450000\n1979-08-23,108.989998,109.589996,108.120003,108.629997,108.629997,35710000\n1979-08-24,108.629997,109.110001,107.650002,108.599998,108.599998,32730000\n1979-08-27,108.599998,109.839996,108.120003,109.139999,109.139999,32050000\n1979-08-28,109.139999,109.650002,108.470001,109.019997,109.019997,29430000\n1979-08-29,109.019997,109.589996,108.360001,109.019997,109.019997,30810000\n1979-08-30,109.019997,109.589996,108.400002,109.019997,109.019997,29300000\n1979-08-31,109.019997,109.800003,108.580002,109.320000,109.320000,26370000\n1979-09-04,109.320000,109.410004,107.220001,107.440002,107.440002,33350000\n1979-09-05,107.190002,107.190002,105.379997,106.400002,106.400002,41650000\n1979-09-06,106.400002,107.610001,105.970001,106.849998,106.849998,30330000\n1979-09-07,106.849998,108.089996,106.300003,107.660004,107.660004,34360000\n1979-09-10,107.660004,108.709999,107.209999,108.169998,108.169998,32980000\n1979-09-11,108.169998,108.830002,106.800003,107.510002,107.510002,42530000\n1979-09-12,107.510002,108.410004,106.720001,107.820000,107.820000,39350000\n1979-09-13,107.820000,108.529999,107.059998,107.849998,107.849998,35240000\n1979-09-14,107.849998,109.480003,107.419998,108.760002,108.760002,41980000\n1979-09-17,108.760002,110.059998,108.400002,108.839996,108.839996,37610000\n1979-09-18,108.839996,109.000000,107.320000,108.000000,108.000000,38750000\n1979-09-19,108.000000,109.019997,107.519997,108.279999,108.279999,35370000\n1979-09-20,108.279999,110.690002,107.589996,110.510002,110.510002,45100000\n1979-09-21,110.510002,111.580002,109.459999,110.470001,110.470001,52380000\n1979-09-24,110.470001,110.900002,109.160004,109.610001,109.610001,33790000\n1979-09-25,109.610001,110.190002,108.269997,109.680000,109.680000,32410000\n1979-09-26,109.680000,111.250000,109.370003,109.959999,109.959999,37700000\n1979-09-27,109.959999,110.750000,109.190002,110.209999,110.209999,33110000\n1979-09-28,110.209999,110.669998,108.699997,109.320000,109.320000,35950000\n1979-10-01,109.190002,109.190002,107.699997,108.559998,108.559998,24980000\n1979-10-02,108.559998,110.080002,108.029999,109.589996,109.589996,38310000\n1979-10-03,109.589996,110.430000,108.879997,109.589996,109.589996,36470000\n1979-10-04,109.589996,110.809998,109.139999,110.169998,110.169998,38800000\n1979-10-05,110.169998,112.160004,110.160004,111.269997,111.269997,48250000\n1979-10-08,111.269997,111.830002,109.650002,109.879997,109.879997,32610000\n1979-10-09,109.430000,109.430000,106.040001,106.629997,106.629997,55560000\n1979-10-10,106.230003,106.230003,102.309998,105.300003,105.300003,81620000\n1979-10-11,105.300003,106.330002,103.699997,105.050003,105.050003,47530000\n1979-10-12,105.050003,106.199997,104.010002,104.489998,104.489998,36390000\n1979-10-15,104.489998,104.739998,102.690002,103.360001,103.360001,34850000\n1979-10-16,103.360001,104.370003,102.519997,103.190002,103.190002,33770000\n1979-10-17,103.190002,104.540001,102.739998,103.389999,103.389999,29650000\n1979-10-18,103.389999,104.620003,102.919998,103.610001,103.610001,29590000\n1979-10-19,103.580002,103.580002,101.239998,101.599998,101.599998,42430000\n1979-10-22,101.379997,101.379997,99.059998,100.709999,100.709999,45240000\n1979-10-23,100.709999,101.440002,99.610001,100.279999,100.279999,32910000\n1979-10-24,100.279999,101.449997,99.660004,100.440002,100.440002,31480000\n1979-10-25,100.440002,101.389999,99.559998,100.000000,100.000000,28440000\n1979-10-26,100.000000,101.309998,99.589996,100.570000,100.570000,29660000\n1979-10-29,100.570000,101.559998,100.129997,100.709999,100.709999,22720000\n1979-10-30,100.709999,102.830002,100.410004,102.669998,102.669998,28890000\n1979-10-31,102.669998,103.160004,101.379997,101.820000,101.820000,27780000\n1979-11-01,101.820000,103.070000,101.099998,102.570000,102.570000,25880000\n1979-11-02,102.570000,103.209999,101.919998,102.510002,102.510002,23670000\n1979-11-05,102.510002,102.660004,101.239998,101.820000,101.820000,20470000\n1979-11-06,101.820000,102.010002,100.769997,101.199997,101.199997,21960000\n1979-11-07,100.970001,100.970001,99.419998,99.870003,99.870003,30830000\n1979-11-08,99.870003,101.000000,99.489998,100.300003,100.300003,26270000\n1979-11-09,100.580002,102.180000,100.580002,101.510002,101.510002,30060000\n1979-11-12,101.510002,103.720001,101.269997,103.510002,103.510002,26640000\n1979-11-13,103.510002,104.209999,102.419998,102.940002,102.940002,29240000\n1979-11-14,102.940002,104.129997,101.910004,103.389999,103.389999,30970000\n1979-11-15,103.389999,104.940002,103.099998,104.129997,104.129997,32380000\n1979-11-16,104.129997,104.720001,103.070000,103.790001,103.790001,30060000\n1979-11-19,103.790001,105.080002,103.169998,104.230003,104.230003,33090000\n1979-11-20,104.230003,105.110001,103.139999,103.690002,103.690002,35010000\n1979-11-21,103.690002,104.230003,102.040001,103.889999,103.889999,37020000\n1979-11-23,103.889999,105.129997,103.559998,104.669998,104.669998,23300000\n1979-11-26,104.830002,107.440002,104.830002,106.800003,106.800003,47940000\n1979-11-27,106.800003,107.889999,105.639999,106.379997,106.379997,45140000\n1979-11-28,106.379997,107.550003,105.290001,106.769997,106.769997,39690000\n1979-11-29,106.769997,107.839996,106.169998,106.809998,106.809998,33550000\n1979-11-30,106.809998,107.160004,105.559998,106.160004,106.160004,30480000\n1979-12-03,106.160004,106.650002,105.070000,105.830002,105.830002,29030000\n1979-12-04,105.830002,107.250000,105.660004,106.790001,106.790001,33510000\n1979-12-05,106.790001,108.360001,106.599998,107.250000,107.250000,39300000\n1979-12-06,107.250000,108.470001,106.709999,108.000000,108.000000,37510000\n1979-12-07,108.000000,109.239998,106.550003,107.519997,107.519997,42370000\n1979-12-10,107.519997,108.269997,106.650002,107.669998,107.669998,32270000\n1979-12-11,107.669998,108.580002,106.790001,107.489998,107.489998,36160000\n1979-12-12,107.489998,108.320000,106.779999,107.519997,107.519997,34630000\n1979-12-13,107.519997,108.290001,106.680000,107.669998,107.669998,36690000\n1979-12-14,107.669998,109.489998,107.370003,108.919998,108.919998,41800000\n1979-12-17,108.919998,110.330002,108.360001,109.330002,109.330002,43830000\n1979-12-18,109.330002,109.830002,107.830002,108.300003,108.300003,43310000\n1979-12-19,108.300003,108.790001,107.019997,108.199997,108.199997,41780000\n1979-12-20,108.199997,109.239998,107.400002,108.260002,108.260002,40380000\n1979-12-21,108.260002,108.760002,106.989998,107.589996,107.589996,36160000\n1979-12-24,107.589996,108.080002,106.800003,107.660004,107.660004,19150000\n1979-12-26,107.660004,108.370003,107.059998,107.779999,107.779999,24960000\n1979-12-27,107.779999,108.500000,107.139999,107.959999,107.959999,31410000\n1979-12-28,107.959999,108.610001,107.160004,107.839996,107.839996,34430000\n1979-12-31,107.839996,108.529999,107.260002,107.940002,107.940002,31530000\n1980-01-02,107.940002,108.430000,105.290001,105.760002,105.760002,40610000\n1980-01-03,105.760002,106.080002,103.260002,105.220001,105.220001,50480000\n1980-01-04,105.220001,107.080002,105.089996,106.519997,106.519997,39130000\n1980-01-07,106.519997,107.800003,105.800003,106.809998,106.809998,44500000\n1980-01-08,106.809998,109.290001,106.290001,108.949997,108.949997,53390000\n1980-01-09,108.949997,111.089996,108.410004,109.050003,109.050003,65260000\n1980-01-10,109.050003,110.860001,108.470001,109.889999,109.889999,55980000\n1980-01-11,109.889999,111.160004,108.889999,109.919998,109.919998,52890000\n1980-01-14,109.919998,111.440002,109.339996,110.379997,110.379997,52930000\n1980-01-15,110.379997,111.930000,109.449997,111.139999,111.139999,52320000\n1980-01-16,111.139999,112.900002,110.379997,111.050003,111.050003,67700000\n1980-01-17,111.050003,112.010002,109.809998,110.699997,110.699997,54170000\n1980-01-18,110.699997,111.739998,109.879997,111.070000,111.070000,47150000\n1980-01-21,111.070000,112.900002,110.660004,112.099998,112.099998,48040000\n1980-01-22,112.099998,113.099998,110.919998,111.510002,111.510002,50620000\n1980-01-23,111.510002,113.930000,110.930000,113.440002,113.440002,50730000\n1980-01-24,113.440002,115.269997,112.949997,113.699997,113.699997,59070000\n1980-01-25,113.699997,114.449997,112.360001,113.610001,113.610001,47100000\n1980-01-28,113.610001,115.650002,112.930000,114.849998,114.849998,53620000\n1980-01-29,114.849998,115.769997,113.029999,114.070000,114.070000,55480000\n1980-01-30,114.070000,115.849998,113.370003,115.199997,115.199997,51170000\n1980-01-31,115.199997,117.169998,113.779999,114.160004,114.160004,65900000\n1980-02-01,114.160004,115.540001,113.129997,115.120003,115.120003,46610000\n1980-02-04,115.120003,116.010002,113.830002,114.370003,114.370003,43070000\n1980-02-05,114.370003,115.250000,112.150002,114.660004,114.660004,41880000\n1980-02-06,114.660004,116.570000,113.650002,115.720001,115.720001,51950000\n1980-02-07,115.720001,117.870003,115.220001,116.279999,116.279999,57690000\n1980-02-08,116.279999,118.660004,115.720001,117.949997,117.949997,57860000\n1980-02-11,117.949997,119.050003,116.309998,117.120003,117.120003,58660000\n1980-02-12,117.120003,118.410004,115.750000,117.900002,117.900002,48090000\n1980-02-13,117.900002,120.220001,117.570000,118.440002,118.440002,65230000\n1980-02-14,118.440002,119.300003,116.040001,116.720001,116.720001,50540000\n1980-02-15,116.699997,116.699997,114.120003,115.410004,115.410004,46680000\n1980-02-19,115.410004,115.669998,113.349998,114.599998,114.599998,39480000\n1980-02-20,114.599998,117.180000,114.059998,116.470001,116.470001,44340000\n1980-02-21,116.470001,117.900002,114.440002,115.279999,115.279999,51530000\n1980-02-22,115.279999,116.459999,113.430000,115.040001,115.040001,48210000\n1980-02-25,114.930000,114.930000,112.620003,113.330002,113.330002,39140000\n1980-02-26,113.330002,114.760002,112.300003,113.980003,113.980003,40000000\n1980-02-27,113.980003,115.120003,111.910004,112.379997,112.379997,46430000\n1980-02-28,112.379997,113.699997,111.330002,112.349998,112.349998,40330000\n1980-02-29,112.349998,114.120003,111.769997,113.660004,113.660004,38810000\n1980-03-03,113.660004,114.339996,112.010002,112.500000,112.500000,38690000\n1980-03-04,112.500000,113.410004,110.830002,112.779999,112.779999,44310000\n1980-03-05,112.779999,113.940002,110.580002,111.129997,111.129997,49240000\n1980-03-06,111.129997,111.290001,107.849998,108.650002,108.650002,49610000\n1980-03-07,108.650002,108.959999,105.989998,106.900002,106.900002,50950000\n1980-03-10,106.900002,107.860001,104.919998,106.510002,106.510002,43750000\n1980-03-11,106.510002,108.540001,106.180000,107.779999,107.779999,41350000\n1980-03-12,107.779999,108.400002,105.419998,106.870003,106.870003,37990000\n1980-03-13,106.870003,107.550003,105.099998,105.620003,105.620003,33070000\n1980-03-14,105.620003,106.489998,104.010002,105.430000,105.430000,35180000\n1980-03-17,105.230003,105.230003,101.820000,102.260002,102.260002,37020000\n1980-03-18,102.260002,104.709999,101.139999,104.099998,104.099998,47340000\n1980-03-19,104.099998,105.720001,103.349998,104.309998,104.309998,36520000\n1980-03-20,104.309998,105.169998,102.519997,103.120003,103.120003,32580000\n1980-03-21,103.120003,103.730003,101.550003,102.309998,102.309998,32220000\n1980-03-24,102.180000,102.180000,98.879997,99.279999,99.279999,39230000\n1980-03-25,99.279999,100.580002,97.889999,99.190002,99.190002,43790000\n1980-03-26,99.190002,101.220001,98.099998,98.680000,98.680000,37370000\n1980-03-27,98.680000,99.580002,94.230003,98.220001,98.220001,63680000\n1980-03-28,98.220001,101.430000,97.720001,100.680000,100.680000,46720000\n1980-03-31,100.680000,102.650002,100.019997,102.089996,102.089996,35840000\n1980-04-01,102.089996,103.279999,100.849998,102.180000,102.180000,32230000\n1980-04-02,102.180000,103.870003,101.449997,102.680000,102.680000,35210000\n1980-04-03,102.680000,103.339996,101.309998,102.150002,102.150002,27970000\n1980-04-07,102.150002,102.269997,99.730003,100.190002,100.190002,29130000\n1980-04-08,100.190002,101.879997,99.230003,101.199997,101.199997,31700000\n1980-04-09,101.199997,103.599998,101.010002,103.110001,103.110001,33020000\n1980-04-10,103.110001,105.000000,102.809998,104.080002,104.080002,33940000\n1980-04-11,104.080002,105.150002,103.199997,103.790001,103.790001,29960000\n1980-04-14,103.790001,103.919998,102.080002,102.839996,102.839996,23060000\n1980-04-15,102.839996,103.940002,101.849998,102.629997,102.629997,26670000\n1980-04-16,102.629997,104.419998,101.129997,101.540001,101.540001,39730000\n1980-04-17,101.540001,102.209999,100.120003,101.050003,101.050003,32770000\n1980-04-18,101.050003,102.070000,99.970001,100.550003,100.550003,26880000\n1980-04-21,100.550003,101.260002,98.949997,99.800003,99.800003,27560000\n1980-04-22,100.809998,104.019997,100.809998,103.430000,103.430000,47920000\n1980-04-23,103.430000,105.110001,102.809998,103.730003,103.730003,42620000\n1980-04-24,103.730003,105.430000,102.930000,104.400002,104.400002,35790000\n1980-04-25,104.400002,105.570000,103.019997,105.160004,105.160004,28590000\n1980-04-28,105.160004,106.790001,104.639999,105.639999,105.639999,30600000\n1980-04-29,105.639999,106.699997,104.860001,105.860001,105.860001,27940000\n1980-04-30,105.860001,106.720001,104.500000,106.290001,106.290001,30850000\n1980-05-01,106.290001,106.860001,104.720001,105.459999,105.459999,32480000\n1980-05-02,105.459999,106.250000,104.610001,105.580002,105.580002,28040000\n1980-05-05,105.580002,106.830002,104.639999,106.379997,106.379997,34090000\n1980-05-06,106.379997,107.830002,105.360001,106.250000,106.250000,40160000\n1980-05-07,106.250000,108.120003,105.830002,107.180000,107.180000,42600000\n1980-05-08,107.180000,108.019997,105.500000,106.129997,106.129997,39280000\n1980-05-09,106.129997,106.199997,104.180000,104.720001,104.720001,30280000\n1980-05-12,104.720001,105.480003,103.500000,104.779999,104.779999,28220000\n1980-05-13,104.779999,106.760002,104.440002,106.300003,106.300003,35460000\n1980-05-14,106.300003,107.889999,106.000000,106.849998,106.849998,40840000\n1980-05-15,106.849998,107.989998,106.070000,106.989998,106.989998,41120000\n1980-05-16,106.989998,107.889999,106.250000,107.349998,107.349998,31710000\n1980-05-19,107.349998,108.430000,106.510002,107.669998,107.669998,30970000\n1980-05-20,107.669998,108.389999,106.750000,107.620003,107.620003,31800000\n1980-05-21,107.620003,108.309998,106.540001,107.720001,107.720001,34830000\n1980-05-22,107.720001,109.730003,107.339996,109.010002,109.010002,41040000\n1980-05-23,109.010002,111.370003,109.010002,110.620003,110.620003,45790000\n1980-05-27,110.620003,112.300003,110.349998,111.400002,111.400002,40810000\n1980-05-28,111.400002,112.720001,110.419998,112.059998,112.059998,38580000\n1980-05-29,112.059998,112.639999,109.860001,110.269997,110.269997,42000000\n1980-05-30,110.269997,111.550003,108.870003,111.239998,111.239998,34820000\n1980-06-02,111.239998,112.150002,110.059998,110.760002,110.760002,32710000\n1980-06-03,110.760002,111.629997,109.769997,110.510002,110.510002,33150000\n1980-06-04,110.510002,113.449997,110.220001,112.610001,112.610001,44180000\n1980-06-05,112.610001,114.379997,111.889999,112.779999,112.779999,49070000\n1980-06-06,112.779999,114.010002,112.110001,113.199997,113.199997,37230000\n1980-06-09,113.199997,114.510002,112.680000,113.709999,113.709999,36820000\n1980-06-10,113.709999,115.500000,113.169998,114.660004,114.660004,42030000\n1980-06-11,114.660004,116.639999,114.220001,116.019997,116.019997,43800000\n1980-06-12,116.019997,117.010002,114.279999,115.519997,115.519997,47300000\n1980-06-13,115.519997,116.940002,114.669998,115.809998,115.809998,41880000\n1980-06-16,115.809998,116.800003,114.779999,116.089996,116.089996,36190000\n1980-06-17,116.089996,117.160004,115.129997,116.029999,116.029999,41990000\n1980-06-18,116.029999,116.839996,114.769997,116.260002,116.260002,41960000\n1980-06-19,116.260002,116.809998,114.360001,114.660004,114.660004,38280000\n1980-06-20,114.660004,114.900002,113.120003,114.059998,114.059998,36530000\n1980-06-23,114.059998,115.279999,113.349998,114.510002,114.510002,34180000\n1980-06-24,114.510002,115.750000,113.760002,115.139999,115.139999,37730000\n1980-06-25,115.139999,117.370003,115.070000,116.720001,116.720001,46500000\n1980-06-26,116.720001,117.980003,115.580002,116.190002,116.190002,45110000\n1980-06-27,116.190002,116.930000,115.059998,116.000000,116.000000,33110000\n1980-06-30,116.000000,116.040001,113.550003,114.239998,114.239998,29910000\n1980-07-01,114.239998,115.449997,113.540001,114.930000,114.930000,34340000\n1980-07-02,114.930000,116.440002,114.360001,115.680000,115.680000,42950000\n1980-07-03,115.680000,117.800003,115.489998,117.459999,117.459999,47230000\n1980-07-07,117.459999,118.849998,116.959999,118.290001,118.290001,42540000\n1980-07-08,118.290001,119.110001,117.070000,117.839996,117.839996,45830000\n1980-07-09,117.839996,119.519997,117.099998,117.980003,117.980003,52010000\n1980-07-10,117.980003,118.570000,116.379997,116.949997,116.949997,43730000\n1980-07-11,116.949997,118.379997,116.290001,117.839996,117.839996,38310000\n1980-07-14,117.839996,120.370003,117.449997,120.010002,120.010002,45500000\n1980-07-15,120.010002,121.559998,118.849998,119.300003,119.300003,60920000\n1980-07-16,119.300003,120.870003,118.540001,119.629997,119.629997,49140000\n1980-07-17,119.629997,121.839996,119.430000,121.440002,121.440002,48850000\n1980-07-18,121.440002,123.190002,120.879997,122.040001,122.040001,58040000\n1980-07-21,122.040001,123.150002,120.849998,122.510002,122.510002,42750000\n1980-07-22,122.510002,123.900002,121.379997,122.190002,122.190002,52230000\n1980-07-23,122.190002,123.260002,120.930000,121.930000,121.930000,45890000\n1980-07-24,121.930000,122.980003,120.830002,121.790001,121.790001,42420000\n1980-07-25,121.790001,121.959999,119.940002,120.779999,120.779999,36250000\n1980-07-28,120.779999,122.019997,119.779999,121.430000,121.430000,35330000\n1980-07-29,121.430000,122.989998,120.760002,122.400002,122.400002,44840000\n1980-07-30,122.400002,123.930000,121.160004,122.230003,122.230003,58060000\n1980-07-31,122.230003,122.339996,119.400002,121.669998,121.669998,54610000\n1980-08-01,121.669998,122.379997,120.080002,121.209999,121.209999,46440000\n1980-08-04,121.209999,121.629997,119.419998,120.980003,120.980003,41550000\n1980-08-05,120.980003,122.089996,119.959999,120.739998,120.739998,45510000\n1980-08-06,120.739998,122.010002,119.940002,121.550003,121.550003,45050000\n1980-08-07,121.660004,123.839996,121.660004,123.300003,123.300003,61820000\n1980-08-08,123.300003,125.230003,122.820000,123.610001,123.610001,58860000\n1980-08-11,123.610001,125.309998,122.849998,124.779999,124.779999,44690000\n1980-08-12,124.779999,125.779999,123.290001,123.790001,123.790001,52050000\n1980-08-13,123.790001,124.669998,122.489998,123.279999,123.279999,44350000\n1980-08-14,123.279999,125.620003,122.680000,125.250000,125.250000,47700000\n1980-08-15,125.250000,126.610001,124.570000,125.720001,125.720001,47780000\n1980-08-18,125.279999,125.279999,122.820000,123.389999,123.389999,41890000\n1980-08-19,123.389999,124.000000,121.970001,122.599998,122.599998,41930000\n1980-08-20,122.599998,124.269997,121.910004,123.769997,123.769997,42560000\n1980-08-21,123.769997,125.989998,123.610001,125.459999,125.459999,50770000\n1980-08-22,125.459999,127.779999,125.180000,126.019997,126.019997,58210000\n1980-08-25,126.019997,126.279999,124.650002,125.160004,125.160004,35400000\n1980-08-26,125.160004,126.290001,124.010002,124.839996,124.839996,41700000\n1980-08-27,124.839996,124.980003,122.930000,123.519997,123.519997,44000000\n1980-08-28,123.519997,123.910004,121.610001,122.080002,122.080002,39890000\n1980-08-29,122.080002,123.010002,121.059998,122.379997,122.379997,33510000\n1980-09-02,122.379997,124.360001,121.790001,123.739998,123.739998,35290000\n1980-09-03,123.870003,126.430000,123.870003,126.120003,126.120003,52370000\n1980-09-04,126.120003,127.699997,124.419998,125.419998,125.419998,59030000\n1980-09-05,125.419998,126.120003,124.080002,124.879997,124.879997,37990000\n1980-09-08,124.879997,125.669998,122.779999,123.309998,123.309998,42050000\n1980-09-09,123.309998,124.519997,121.940002,124.070000,124.070000,44460000\n1980-09-10,124.070000,125.949997,123.599998,124.809998,124.809998,51430000\n1980-09-11,124.809998,126.480003,124.190002,125.660004,125.660004,44770000\n1980-09-12,125.660004,126.750000,124.720001,125.540001,125.540001,47180000\n1980-09-15,125.540001,126.349998,124.089996,125.669998,125.669998,44630000\n1980-09-16,125.669998,127.779999,125.150002,126.739998,126.739998,57290000\n1980-09-17,126.739998,129.679993,126.370003,128.869995,128.869995,63990000\n1980-09-18,128.869995,130.380005,127.629997,128.399994,128.399994,63390000\n1980-09-19,128.399994,130.330002,127.570000,129.250000,129.250000,53780000\n1980-09-22,129.250000,130.990005,127.889999,130.399994,130.399994,53140000\n1980-09-23,130.399994,132.169998,128.550003,129.429993,129.429993,64390000\n1980-09-24,129.429993,131.339996,128.449997,130.369995,130.369995,56860000\n1980-09-25,130.369995,131.529999,128.130005,128.720001,128.720001,49510000\n1980-09-26,128.169998,128.169998,125.290001,126.349998,126.349998,49460000\n1980-09-29,125.410004,125.410004,122.870003,123.540001,123.540001,46410000\n1980-09-30,123.540001,126.089996,123.540001,125.459999,125.459999,40290000\n1980-10-01,125.459999,127.879997,124.660004,127.129997,127.129997,48720000\n1980-10-02,127.129997,128.820007,126.040001,128.089996,128.089996,46160000\n1980-10-03,128.089996,130.440002,127.650002,129.330002,129.330002,47510000\n1980-10-06,129.350006,132.380005,129.350006,131.729996,131.729996,50130000\n1980-10-07,131.729996,132.880005,130.100006,131.000000,131.000000,50310000\n1980-10-08,131.000000,132.779999,130.279999,131.649994,131.649994,46580000\n1980-10-09,131.649994,132.649994,130.250000,131.039993,131.039993,43980000\n1980-10-10,131.039993,132.149994,129.580002,130.289993,130.289993,44040000\n1980-10-13,130.289993,132.460007,129.369995,132.029999,132.029999,31360000\n1980-10-14,132.029999,133.570007,131.160004,132.020004,132.020004,48830000\n1980-10-15,132.020004,134.350006,131.589996,133.699997,133.699997,48260000\n1980-10-16,133.699997,135.880005,131.639999,132.220001,132.220001,65450000\n1980-10-17,132.220001,133.070007,130.220001,131.520004,131.520004,43920000\n1980-10-20,131.520004,133.210007,130.039993,132.610001,132.610001,40910000\n1980-10-21,132.610001,134.009995,130.779999,131.839996,131.839996,51220000\n1980-10-22,131.839996,132.970001,130.619995,131.919998,131.919998,43060000\n1980-10-23,131.919998,132.539993,128.869995,129.529999,129.529999,49200000\n1980-10-24,129.529999,130.550003,128.039993,129.850006,129.850006,41050000\n1980-10-27,129.850006,129.940002,127.339996,127.879997,127.879997,34430000\n1980-10-28,127.879997,128.860001,126.360001,128.050003,128.050003,40300000\n1980-10-29,128.050003,129.910004,127.070000,127.910004,127.910004,37200000\n1980-10-30,127.910004,128.710007,125.779999,126.290001,126.290001,39060000\n1980-10-31,126.290001,128.240005,125.290001,127.470001,127.470001,40110000\n1980-11-03,127.470001,129.850006,127.230003,129.039993,129.039993,35820000\n1980-11-05,130.770004,135.649994,130.770004,131.330002,131.330002,84080000\n1980-11-06,131.300003,131.300003,128.229996,128.910004,128.910004,48890000\n1980-11-07,128.910004,130.080002,127.739998,129.179993,129.179993,40070000\n1980-11-10,129.179993,130.509995,128.190002,129.479996,129.479996,35720000\n1980-11-11,129.479996,132.300003,129.479996,131.259995,131.259995,41520000\n1980-11-12,131.330002,135.119995,131.330002,134.589996,134.589996,58500000\n1980-11-13,134.589996,137.210007,134.119995,136.490005,136.490005,69340000\n1980-11-14,136.490005,138.960007,135.119995,137.149994,137.149994,71630000\n1980-11-17,137.149994,138.460007,134.899994,137.750000,137.750000,50260000\n1980-11-18,137.910004,140.919998,137.910004,139.699997,139.699997,70380000\n1980-11-19,139.699997,141.759995,138.059998,139.059998,139.059998,69230000\n1980-11-20,139.059998,141.240005,137.789993,140.399994,140.399994,60180000\n1980-11-21,140.399994,141.240005,138.100006,139.110001,139.110001,55950000\n1980-11-24,139.110001,139.360001,136.360001,138.309998,138.309998,51120000\n1980-11-25,138.309998,140.830002,137.419998,139.330002,139.330002,55840000\n1980-11-26,139.330002,141.960007,138.600006,140.169998,140.169998,55340000\n1980-11-28,140.169998,141.539993,139.000000,140.520004,140.520004,34240000\n1980-12-01,140.520004,140.660004,136.750000,137.210007,137.210007,48180000\n1980-12-02,137.210007,138.110001,134.369995,136.970001,136.970001,52340000\n1980-12-03,136.970001,138.089996,135.429993,136.710007,136.710007,43430000\n1980-12-04,136.710007,138.399994,135.089996,136.479996,136.479996,51170000\n1980-12-05,136.369995,136.369995,132.910004,134.029999,134.029999,51990000\n1980-12-08,133.190002,133.190002,129.710007,130.610001,130.610001,53390000\n1980-12-09,130.610001,131.919998,128.770004,130.479996,130.479996,53220000\n1980-12-10,130.479996,131.990005,127.940002,128.259995,128.259995,49860000\n1980-12-11,128.259995,128.729996,125.320000,127.360001,127.360001,60220000\n1980-12-12,127.360001,129.979996,127.150002,129.229996,129.229996,39530000\n1980-12-15,129.229996,131.330002,128.639999,129.449997,129.449997,39700000\n1980-12-16,129.449997,131.220001,128.330002,130.600006,130.600006,41630000\n1980-12-17,130.600006,133.589996,130.220001,132.889999,132.889999,50800000\n1980-12-18,132.889999,135.899994,131.889999,133.000000,133.000000,69570000\n1980-12-19,133.000000,134.000000,131.800003,133.699997,133.699997,50770000\n1980-12-22,133.699997,136.679993,132.880005,135.779999,135.779999,51950000\n1980-12-23,135.779999,137.479996,134.009995,135.300003,135.300003,55260000\n1980-12-24,135.300003,136.550003,134.149994,135.880005,135.880005,29490000\n1980-12-26,135.880005,137.020004,135.199997,136.570007,136.570007,16130000\n1980-12-29,136.570007,137.509995,134.360001,135.029999,135.029999,36060000\n1980-12-30,135.029999,136.509995,134.039993,135.330002,135.330002,39750000\n1980-12-31,135.330002,136.759995,134.289993,135.759995,135.759995,41210000\n1981-01-02,135.759995,137.100006,134.610001,136.339996,136.339996,28870000\n1981-01-05,136.339996,139.240005,135.860001,137.970001,137.970001,58710000\n1981-01-06,137.970001,140.320007,135.779999,138.119995,138.119995,67400000\n1981-01-07,136.020004,136.020004,132.300003,135.080002,135.080002,92890000\n1981-01-08,135.080002,136.100006,131.960007,133.059998,133.059998,55350000\n1981-01-09,133.059998,134.759995,131.710007,133.479996,133.479996,50190000\n1981-01-12,133.479996,135.880005,132.789993,133.520004,133.520004,48760000\n1981-01-13,133.520004,134.270004,131.690002,133.289993,133.289993,40890000\n1981-01-14,133.289993,135.250000,132.649994,133.470001,133.470001,41390000\n1981-01-15,133.470001,135.149994,132.440002,134.220001,134.220001,39640000\n1981-01-16,134.220001,135.910004,133.350006,134.770004,134.770004,43260000\n1981-01-19,134.770004,135.860001,133.509995,134.369995,134.369995,36470000\n1981-01-20,134.369995,135.300003,131.259995,131.649994,131.649994,41750000\n1981-01-21,131.649994,132.479996,129.929993,131.360001,131.360001,39190000\n1981-01-22,131.360001,132.080002,129.229996,130.259995,130.259995,39880000\n1981-01-23,130.259995,131.339996,129.000000,130.229996,130.229996,37220000\n1981-01-26,130.229996,131.179993,128.570007,129.839996,129.839996,35380000\n1981-01-27,129.839996,131.949997,129.320007,131.119995,131.119995,42260000\n1981-01-28,131.119995,132.410004,129.820007,130.339996,130.339996,36690000\n1981-01-29,130.339996,131.779999,128.970001,130.240005,130.240005,38170000\n1981-01-30,130.240005,131.649994,128.610001,129.550003,129.550003,41160000\n1981-02-02,129.479996,129.479996,125.820000,126.910004,126.910004,44070000\n1981-02-03,126.910004,128.919998,125.889999,128.460007,128.460007,45950000\n1981-02-04,128.460007,129.710007,127.290001,128.589996,128.589996,45520000\n1981-02-05,128.589996,130.490005,127.989998,129.630005,129.630005,45320000\n1981-02-06,129.630005,131.809998,129.029999,130.600006,130.600006,45820000\n1981-02-09,130.600006,131.389999,128.610001,129.270004,129.270004,38330000\n1981-02-10,129.270004,130.190002,128.050003,129.240005,129.240005,40820000\n1981-02-11,129.240005,129.919998,127.599998,128.240005,128.240005,37770000\n1981-02-12,128.240005,128.949997,126.779999,127.480003,127.480003,34700000\n1981-02-13,127.480003,128.339996,126.040001,126.980003,126.980003,33360000\n1981-02-17,126.980003,128.750000,126.430000,127.809998,127.809998,37940000\n1981-02-18,127.809998,129.250000,127.089996,128.479996,128.479996,40410000\n1981-02-19,128.479996,129.070007,125.980003,126.610001,126.610001,41630000\n1981-02-20,126.610001,127.650002,124.660004,126.580002,126.580002,41900000\n1981-02-23,126.580002,128.279999,125.690002,127.349998,127.349998,39590000\n1981-02-24,127.349998,128.759995,126.489998,127.389999,127.389999,43960000\n1981-02-25,127.389999,129.210007,125.769997,128.520004,128.520004,45710000\n1981-02-26,128.520004,130.929993,128.020004,130.100006,130.100006,60300000\n1981-02-27,130.100006,132.020004,129.350006,131.270004,131.270004,53210000\n1981-03-02,131.270004,132.960007,130.149994,132.009995,132.009995,47710000\n1981-03-03,132.009995,132.720001,129.660004,130.559998,130.559998,48730000\n1981-03-04,130.559998,132.070007,129.570007,130.860001,130.860001,47260000\n1981-03-05,130.860001,131.820007,129.250000,129.929993,129.929993,45380000\n1981-03-06,129.929993,131.179993,128.559998,129.850006,129.850006,43940000\n1981-03-09,129.850006,131.940002,129.389999,131.119995,131.119995,46180000\n1981-03-10,131.119995,132.639999,129.720001,130.460007,130.460007,56610000\n1981-03-11,130.460007,131.199997,128.720001,129.949997,129.949997,47390000\n1981-03-12,129.949997,133.559998,129.759995,133.190002,133.190002,54640000\n1981-03-13,133.190002,135.529999,132.389999,133.110001,133.110001,68290000\n1981-03-16,133.110001,135.350006,132.100006,134.679993,134.679993,49940000\n1981-03-17,134.679993,136.089996,132.800003,133.919998,133.919998,65920000\n1981-03-18,133.919998,135.660004,132.800003,134.220001,134.220001,55740000\n1981-03-19,134.220001,135.369995,132.369995,133.460007,133.460007,62440000\n1981-03-20,133.460007,135.289993,132.500000,134.080002,134.080002,61980000\n1981-03-23,134.080002,136.500000,133.410004,135.690002,135.690002,57880000\n1981-03-24,135.690002,137.399994,134.100006,134.669998,134.669998,66400000\n1981-03-25,134.669998,137.320007,133.919998,137.110001,137.110001,56320000\n1981-03-26,137.110001,138.380005,135.289993,136.270004,136.270004,60370000\n1981-03-27,136.270004,136.889999,133.910004,134.649994,134.649994,46930000\n1981-03-30,134.649994,135.869995,133.509995,134.279999,134.279999,33500000\n1981-03-31,134.679993,137.149994,134.679993,136.000000,136.000000,50980000\n1981-04-01,136.000000,137.559998,135.039993,136.570007,136.570007,54880000\n1981-04-02,136.570007,137.720001,135.160004,136.320007,136.320007,52570000\n1981-04-03,136.320007,137.039993,134.669998,135.490005,135.490005,48680000\n1981-04-06,135.490005,135.610001,132.910004,133.929993,133.929993,43190000\n1981-04-07,133.929993,135.270004,132.960007,133.910004,133.910004,44540000\n1981-04-08,133.910004,135.339996,133.259995,134.309998,134.309998,48000000\n1981-04-09,134.309998,135.800003,132.589996,134.669998,134.669998,59520000\n1981-04-10,134.669998,136.229996,133.179993,134.509995,134.509995,58130000\n1981-04-13,134.509995,134.910004,132.240005,133.149994,133.149994,49860000\n1981-04-14,133.149994,134.029999,131.580002,132.679993,132.679993,48350000\n1981-04-15,132.679993,134.789993,132.199997,134.169998,134.169998,56040000\n1981-04-16,134.169998,135.820007,133.429993,134.699997,134.699997,52950000\n1981-04-20,134.699997,136.250000,133.190002,135.449997,135.449997,51020000\n1981-04-21,135.449997,136.380005,133.490005,134.229996,134.229996,60280000\n1981-04-22,134.229996,135.539993,132.720001,134.139999,134.139999,60660000\n1981-04-23,134.139999,135.899994,132.899994,133.940002,133.940002,64200000\n1981-04-24,133.940002,136.000000,132.880005,135.139999,135.139999,60000000\n1981-04-27,135.139999,136.559998,134.130005,135.479996,135.479996,51080000\n1981-04-28,135.479996,136.089996,133.100006,134.330002,134.330002,58210000\n1981-04-29,134.330002,134.690002,131.820007,133.050003,133.050003,53340000\n1981-04-30,133.050003,134.440002,131.850006,132.809998,132.809998,47970000\n1981-05-01,132.809998,134.169998,131.429993,132.720001,132.720001,48360000\n1981-05-04,131.779999,131.779999,129.610001,130.669998,130.669998,40430000\n1981-05-05,130.669998,131.330002,128.929993,130.320007,130.320007,49000000\n1981-05-06,130.320007,132.380005,130.089996,130.779999,130.779999,47100000\n1981-05-07,130.779999,132.410004,130.210007,131.669998,131.669998,42590000\n1981-05-08,131.669998,132.690002,130.839996,131.660004,131.660004,41860000\n1981-05-11,131.660004,132.229996,129.110001,129.710007,129.710007,37640000\n1981-05-12,129.710007,131.169998,128.779999,130.720001,130.720001,40440000\n1981-05-13,130.720001,131.960007,129.529999,130.550003,130.550003,42600000\n1981-05-14,130.550003,132.149994,129.910004,131.279999,131.279999,42750000\n1981-05-15,131.279999,133.210007,130.750000,132.169998,132.169998,45460000\n1981-05-18,132.169998,133.649994,131.490005,132.539993,132.539993,42510000\n1981-05-19,132.539993,133.220001,130.779999,132.089996,132.089996,42220000\n1981-05-20,132.089996,133.029999,130.589996,132.000000,132.000000,42370000\n1981-05-21,132.000000,133.029999,130.699997,131.750000,131.750000,46820000\n1981-05-22,131.750000,132.649994,130.419998,131.330002,131.330002,40710000\n1981-05-26,131.330002,133.300003,130.639999,132.770004,132.770004,42760000\n1981-05-27,132.770004,134.649994,131.850006,133.770004,133.770004,58730000\n1981-05-28,133.770004,134.919998,132.000000,133.449997,133.449997,59500000\n1981-05-29,133.449997,134.360001,131.520004,132.589996,132.589996,51580000\n1981-06-01,132.589996,134.619995,131.490005,132.410004,132.410004,62170000\n1981-06-02,132.410004,132.960007,129.839996,130.619995,130.619995,53930000\n1981-06-03,130.619995,131.369995,128.770004,130.710007,130.710007,54700000\n1981-06-04,130.710007,132.210007,129.720001,130.960007,130.960007,48940000\n1981-06-05,130.960007,132.979996,130.169998,132.220001,132.220001,47180000\n1981-06-08,132.220001,133.679993,131.289993,132.240005,132.240005,41580000\n1981-06-09,132.240005,133.300003,130.940002,131.970001,131.970001,44600000\n1981-06-10,131.970001,133.490005,131.039993,132.320007,132.320007,53200000\n1981-06-11,132.320007,134.309998,131.580002,133.750000,133.750000,59530000\n1981-06-12,133.750000,135.089996,132.399994,133.490005,133.490005,60790000\n1981-06-15,133.490005,135.669998,132.779999,133.610001,133.610001,63350000\n1981-06-16,133.610001,134.000000,131.289993,132.149994,132.149994,57780000\n1981-06-17,132.149994,133.979996,130.809998,133.320007,133.320007,55470000\n1981-06-18,133.320007,133.979996,130.940002,131.639999,131.639999,48400000\n1981-06-19,131.639999,133.270004,130.490005,132.270004,132.270004,46430000\n1981-06-22,132.270004,133.539993,131.100006,131.949997,131.949997,41790000\n1981-06-23,131.949997,133.979996,131.160004,133.350006,133.350006,51840000\n1981-06-24,133.350006,133.899994,131.649994,132.660004,132.660004,46650000\n1981-06-25,132.660004,134.300003,131.779999,132.809998,132.809998,43920000\n1981-06-26,132.809998,133.750000,131.710007,132.559998,132.559998,39240000\n1981-06-29,132.559998,133.500000,131.199997,131.889999,131.889999,37930000\n1981-06-30,131.889999,132.669998,130.309998,131.210007,131.210007,41550000\n1981-07-01,131.210007,131.690002,129.039993,129.770004,129.770004,49080000\n1981-07-02,129.770004,130.479996,127.839996,128.639999,128.639999,45100000\n1981-07-06,128.639999,128.990005,126.440002,127.370003,127.370003,44590000\n1981-07-07,127.370003,129.600006,126.389999,128.240005,128.240005,53560000\n1981-07-08,128.240005,129.570007,126.949997,128.320007,128.320007,46000000\n1981-07-09,128.320007,130.080002,127.570000,129.300003,129.300003,45510000\n1981-07-10,129.300003,130.429993,128.380005,129.369995,129.369995,39950000\n1981-07-13,129.369995,130.820007,128.789993,129.639999,129.639999,38100000\n1981-07-14,129.639999,130.779999,128.139999,129.649994,129.649994,45230000\n1981-07-15,129.649994,131.589996,128.889999,130.229996,130.229996,48950000\n1981-07-16,130.229996,131.410004,129.300003,130.339996,130.339996,39010000\n1981-07-17,130.339996,131.600006,129.490005,130.759995,130.759995,42780000\n1981-07-20,130.600006,130.600006,127.980003,128.720001,128.720001,40240000\n1981-07-21,128.720001,129.600006,127.080002,128.339996,128.339996,47280000\n1981-07-22,128.339996,129.720001,126.699997,127.129997,127.129997,47500000\n1981-07-23,127.129997,128.259995,125.959999,127.400002,127.400002,41790000\n1981-07-24,127.400002,129.309998,127.110001,128.460007,128.460007,38880000\n1981-07-27,128.460007,130.610001,128.429993,129.899994,129.899994,39610000\n1981-07-28,129.899994,130.440002,128.279999,129.139999,129.139999,38160000\n1981-07-29,129.139999,130.089996,128.369995,129.160004,129.160004,37610000\n1981-07-30,129.160004,130.679993,128.559998,130.009995,130.009995,41560000\n1981-07-31,130.009995,131.779999,129.600006,130.919998,130.919998,43480000\n1981-08-03,130.919998,131.740005,129.419998,130.479996,130.479996,39650000\n1981-08-04,130.479996,131.660004,129.429993,131.179993,131.179993,39460000\n1981-08-05,131.179993,133.389999,130.759995,132.669998,132.669998,54290000\n1981-08-06,132.669998,134.039993,131.740005,132.639999,132.639999,52070000\n1981-08-07,132.639999,133.039993,130.960007,131.750000,131.750000,38370000\n1981-08-10,131.750000,133.320007,130.830002,132.539993,132.539993,38370000\n1981-08-11,132.539993,134.630005,132.089996,133.850006,133.850006,52600000\n1981-08-12,133.850006,135.179993,132.729996,133.399994,133.399994,53650000\n1981-08-13,133.399994,134.580002,132.529999,133.509995,133.509995,42460000\n1981-08-14,133.509995,134.330002,131.910004,132.490005,132.490005,42580000\n1981-08-17,132.490005,133.020004,130.750000,131.220001,131.220001,40840000\n1981-08-18,131.220001,131.729996,129.100006,130.110001,130.110001,47270000\n1981-08-19,130.110001,131.199997,128.990005,130.490005,130.490005,39390000\n1981-08-20,130.490005,131.740005,129.839996,130.690002,130.690002,38270000\n1981-08-21,130.690002,131.059998,128.699997,129.229996,129.229996,37670000\n1981-08-24,128.589996,128.589996,125.019997,125.500000,125.500000,46750000\n1981-08-25,125.500000,125.769997,123.000000,125.129997,125.129997,54600000\n1981-08-26,125.129997,126.169998,123.989998,124.959999,124.959999,39980000\n1981-08-27,124.959999,125.309998,122.900002,123.510002,123.510002,43900000\n1981-08-28,123.510002,125.089996,122.849998,124.080002,124.080002,38020000\n1981-08-31,124.080002,125.580002,122.290001,122.790001,122.790001,40360000\n1981-09-01,122.790001,123.919998,121.589996,123.019997,123.019997,45110000\n1981-09-02,123.019997,124.580002,122.540001,123.489998,123.489998,37570000\n1981-09-03,123.489998,124.160004,120.820000,121.239998,121.239998,41730000\n1981-09-04,121.239998,121.540001,119.239998,120.070000,120.070000,42760000\n1981-09-08,120.070000,120.120003,116.849998,117.980003,117.980003,47340000\n1981-09-09,117.980003,119.489998,116.870003,118.400002,118.400002,43910000\n1981-09-10,118.400002,122.180000,118.330002,120.139999,120.139999,47430000\n1981-09-11,120.139999,122.129997,119.290001,121.610001,121.610001,42170000\n1981-09-14,121.610001,122.000000,119.669998,120.660004,120.660004,34040000\n1981-09-15,120.660004,121.769997,119.269997,119.769997,119.769997,38580000\n1981-09-16,119.769997,120.000000,117.889999,118.870003,118.870003,43660000\n1981-09-17,118.870003,119.870003,116.629997,117.150002,117.150002,48300000\n1981-09-18,117.150002,117.690002,115.180000,116.260002,116.260002,47350000\n1981-09-21,116.260002,118.070000,115.040001,117.239998,117.239998,44570000\n1981-09-22,117.239998,118.190002,115.930000,116.680000,116.680000,46830000\n1981-09-23,116.680000,116.680000,113.599998,115.650002,115.650002,52700000\n1981-09-24,115.650002,117.470001,114.320000,115.010002,115.010002,48880000\n1981-09-25,114.690002,114.690002,111.639999,112.769997,112.769997,54390000\n1981-09-28,112.769997,115.830002,110.190002,115.529999,115.529999,61320000\n1981-09-29,115.529999,117.750000,114.750000,115.940002,115.940002,49800000\n1981-09-30,115.940002,117.050003,114.599998,116.180000,116.180000,40700000\n1981-10-01,116.180000,117.660004,115.000000,117.080002,117.080002,41600000\n1981-10-02,117.080002,120.160004,117.070000,119.360001,119.360001,54540000\n1981-10-05,119.360001,121.540001,118.610001,119.510002,119.510002,51290000\n1981-10-06,119.510002,121.389999,118.080002,119.389999,119.389999,45460000\n1981-10-07,119.389999,121.870003,119.089996,121.309998,121.309998,50030000\n1981-10-08,121.309998,123.080002,120.230003,122.309998,122.309998,47090000\n1981-10-09,122.309998,123.279999,120.629997,121.449997,121.449997,50060000\n1981-10-12,121.449997,122.370003,120.169998,121.209999,121.209999,30030000\n1981-10-13,121.209999,122.370003,119.959999,120.779999,120.779999,43360000\n1981-10-14,120.779999,120.970001,118.379997,118.800003,118.800003,40260000\n1981-10-15,118.800003,120.580002,118.010002,119.709999,119.709999,42830000\n1981-10-16,119.709999,120.459999,118.379997,119.190002,119.190002,37800000\n1981-10-19,119.190002,119.849998,117.580002,118.980003,118.980003,41590000\n1981-10-20,118.980003,121.290001,118.779999,120.279999,120.279999,51530000\n1981-10-21,120.279999,121.940002,119.349998,120.099998,120.099998,48490000\n1981-10-22,120.099998,120.779999,118.480003,119.639999,119.639999,40630000\n1981-10-23,119.639999,119.919998,117.779999,118.599998,118.599998,41990000\n1981-10-26,118.599998,119.000000,116.809998,118.160004,118.160004,38210000\n1981-10-27,118.160004,120.430000,117.800003,119.290001,119.290001,53030000\n1981-10-28,119.290001,120.959999,118.389999,119.449997,119.449997,48100000\n1981-10-29,119.449997,120.370003,118.139999,119.059998,119.059998,40070000\n1981-10-30,119.059998,122.529999,118.430000,121.889999,121.889999,59570000\n1981-11-02,122.349998,125.139999,122.349998,124.199997,124.199997,65100000\n1981-11-03,124.199997,125.519997,123.139999,124.800003,124.800003,54620000\n1981-11-04,124.800003,126.000000,123.639999,124.739998,124.739998,53450000\n1981-11-05,124.739998,125.800003,122.980003,123.540001,123.540001,50860000\n1981-11-06,123.540001,124.029999,121.849998,122.669998,122.669998,43270000\n1981-11-09,122.669998,124.129997,121.589996,123.290001,123.290001,48310000\n1981-11-10,123.290001,124.690002,122.010002,122.699997,122.699997,53940000\n1981-11-11,122.699997,123.820000,121.510002,122.919998,122.919998,41920000\n1981-11-12,122.919998,124.709999,122.190002,123.190002,123.190002,55720000\n1981-11-13,123.190002,123.610001,121.059998,121.669998,121.669998,45550000\n1981-11-16,121.639999,121.639999,119.129997,120.239998,120.239998,43740000\n1981-11-17,120.239998,121.779999,119.500000,121.150002,121.150002,43190000\n1981-11-18,121.150002,121.660004,119.610001,120.260002,120.260002,49980000\n1981-11-19,120.260002,121.669998,119.419998,120.709999,120.709999,48890000\n1981-11-20,120.709999,122.589996,120.129997,121.709999,121.709999,52010000\n1981-11-23,121.709999,123.089996,120.760002,121.599998,121.599998,45250000\n1981-11-24,121.599998,124.040001,121.220001,123.510002,123.510002,53200000\n1981-11-25,123.510002,125.290001,123.070000,124.050003,124.050003,58570000\n1981-11-27,124.050003,125.709999,123.629997,125.089996,125.089996,32770000\n1981-11-30,125.089996,126.970001,124.180000,126.349998,126.349998,47580000\n1981-12-01,126.349998,127.300003,124.839996,126.099998,126.099998,53980000\n1981-12-02,126.099998,126.449997,124.180000,124.690002,124.690002,44510000\n1981-12-03,124.690002,125.839996,123.629997,125.120003,125.120003,43770000\n1981-12-04,125.120003,127.320000,125.120003,126.260002,126.260002,55040000\n1981-12-07,126.260002,126.910004,124.669998,125.190002,125.190002,45720000\n1981-12-08,125.190002,125.750000,123.519997,124.820000,124.820000,45140000\n1981-12-09,124.820000,126.080002,124.089996,125.480003,125.480003,44810000\n1981-12-10,125.480003,126.540001,124.599998,125.709999,125.709999,47020000\n1981-12-11,125.709999,126.260002,124.320000,124.930000,124.930000,45850000\n1981-12-14,124.370003,124.370003,122.169998,122.779999,122.779999,44740000\n1981-12-15,122.779999,123.779999,121.830002,122.989998,122.989998,44130000\n1981-12-16,122.989998,123.660004,121.730003,122.419998,122.419998,42770000\n1981-12-17,122.419998,123.790001,121.820000,123.120003,123.120003,47230000\n1981-12-18,123.120003,124.870003,122.559998,124.000000,124.000000,50940000\n1981-12-21,124.000000,124.709999,122.669998,123.339996,123.339996,41290000\n1981-12-22,123.339996,124.169998,122.190002,122.879997,122.879997,48320000\n1981-12-23,122.879997,123.589996,121.580002,122.309998,122.309998,42910000\n1981-12-24,122.309998,123.059998,121.570000,122.540001,122.540001,23940000\n1981-12-28,122.540001,123.360001,121.730003,122.269997,122.269997,28320000\n1981-12-29,122.269997,122.900002,121.120003,121.669998,121.669998,35300000\n1981-12-30,121.669998,123.110001,121.040001,122.300003,122.300003,42960000\n1981-12-31,122.300003,123.419998,121.570000,122.550003,122.550003,40780000\n1982-01-04,122.550003,123.720001,121.480003,122.739998,122.739998,36760000\n1982-01-05,122.610001,122.610001,119.570000,120.050003,120.050003,47510000\n1982-01-06,120.050003,120.449997,117.989998,119.180000,119.180000,51510000\n1982-01-07,119.180000,119.879997,117.699997,118.930000,118.930000,43410000\n1982-01-08,118.930000,120.589996,118.550003,119.550003,119.550003,42050000\n1982-01-11,119.550003,120.339996,116.470001,116.779999,116.779999,51900000\n1982-01-12,116.779999,117.489998,115.180000,116.300003,116.300003,49800000\n1982-01-13,116.300003,117.459999,114.239998,114.879997,114.879997,49130000\n1982-01-14,114.879997,116.300003,114.070000,115.540001,115.540001,42940000\n1982-01-15,115.540001,117.139999,115.099998,116.330002,116.330002,43310000\n1982-01-18,116.330002,117.690002,114.849998,117.220001,117.220001,44920000\n1982-01-19,117.220001,118.150002,115.519997,115.970001,115.970001,45070000\n1982-01-20,115.970001,116.639999,114.290001,115.269997,115.269997,48860000\n1982-01-21,115.269997,116.919998,114.599998,115.750000,115.750000,48610000\n1982-01-22,115.750000,116.529999,114.580002,115.379997,115.379997,44370000\n1982-01-25,115.379997,115.930000,113.629997,115.410004,115.410004,43170000\n1982-01-26,115.410004,116.599998,114.489998,115.190002,115.190002,44870000\n1982-01-27,115.190002,116.599998,114.379997,115.739998,115.739998,50060000\n1982-01-28,116.099998,119.349998,116.099998,118.919998,118.919998,66690000\n1982-01-29,118.919998,121.379997,118.639999,120.400002,120.400002,73400000\n1982-02-01,119.809998,119.809998,117.139999,117.779999,117.779999,47720000\n1982-02-02,117.779999,119.150002,116.910004,118.010002,118.010002,45020000\n1982-02-03,118.010002,118.669998,116.040001,116.480003,116.480003,49560000\n1982-02-04,116.480003,117.489998,114.879997,116.419998,116.419998,53300000\n1982-02-05,116.419998,118.260002,115.739998,117.260002,117.260002,53350000\n1982-02-08,117.040001,117.040001,114.199997,114.629997,114.629997,48500000\n1982-02-09,114.629997,115.150002,112.820000,113.680000,113.680000,54420000\n1982-02-10,113.680000,115.620003,113.449997,114.660004,114.660004,46620000\n1982-02-11,114.660004,115.589996,113.410004,114.430000,114.430000,46730000\n1982-02-12,114.430000,115.389999,113.699997,114.379997,114.379997,37070000\n1982-02-16,114.379997,114.629997,112.059998,114.059998,114.059998,48880000\n1982-02-17,114.059998,115.089996,112.970001,113.690002,113.690002,47660000\n1982-02-18,113.690002,115.040001,112.970001,113.820000,113.820000,60810000\n1982-02-19,113.820000,114.580002,112.330002,113.220001,113.220001,51340000\n1982-02-22,113.220001,114.900002,111.199997,111.589996,111.589996,58310000\n1982-02-23,111.589996,112.459999,110.029999,111.510002,111.510002,60100000\n1982-02-24,111.510002,113.879997,110.709999,113.470001,113.470001,64800000\n1982-02-25,113.470001,114.860001,112.440002,113.209999,113.209999,54160000\n1982-02-26,113.209999,114.010002,112.040001,113.110001,113.110001,43840000\n1982-03-01,113.110001,114.320000,111.860001,113.309998,113.309998,53010000\n1982-03-02,113.309998,114.800003,112.029999,112.680000,112.680000,63800000\n1982-03-03,112.510002,112.510002,109.980003,110.919998,110.919998,70230000\n1982-03-04,110.919998,111.779999,108.769997,109.879997,109.879997,74340000\n1982-03-05,109.879997,110.900002,108.309998,109.339996,109.339996,67440000\n1982-03-08,109.339996,111.059998,107.029999,107.339996,107.339996,67330000\n1982-03-09,107.339996,109.879997,106.169998,108.830002,108.830002,76060000\n1982-03-10,108.830002,110.980003,108.089996,109.410004,109.410004,59440000\n1982-03-11,109.410004,110.870003,108.379997,109.360001,109.360001,52960000\n1982-03-12,109.360001,109.720001,104.459999,108.610001,108.610001,49600000\n1982-03-15,108.610001,109.989998,107.470001,109.449997,109.449997,43370000\n1982-03-16,109.449997,110.919998,108.570000,109.279999,109.279999,48900000\n1982-03-17,109.279999,110.099998,108.110001,109.080002,109.080002,48900000\n1982-03-18,109.080002,111.019997,108.849998,110.300003,110.300003,54270000\n1982-03-19,110.300003,111.589996,109.639999,110.610001,110.610001,46250000\n1982-03-22,110.709999,113.349998,110.709999,112.769997,112.769997,57610000\n1982-03-23,112.769997,114.510002,112.290001,113.550003,113.550003,67130000\n1982-03-24,113.550003,114.309998,112.230003,112.970001,112.970001,49380000\n1982-03-25,112.970001,114.260002,112.019997,113.209999,113.209999,51970000\n1982-03-26,113.209999,113.430000,111.260002,111.940002,111.940002,42400000\n1982-03-29,111.940002,112.820000,110.900002,112.300003,112.300003,37100000\n1982-03-30,112.300003,113.089996,111.300003,112.269997,112.269997,43900000\n1982-03-31,112.269997,113.169998,111.320000,111.959999,111.959999,43300000\n1982-04-01,111.959999,114.220001,111.480003,113.790001,113.790001,57100000\n1982-04-02,113.790001,115.790001,113.650002,115.120003,115.120003,59800000\n1982-04-05,115.120003,115.900002,113.940002,114.730003,114.730003,46900000\n1982-04-06,114.730003,115.919998,113.699997,115.360001,115.360001,43200000\n1982-04-07,115.360001,116.449997,114.580002,115.459999,115.459999,53130000\n1982-04-08,115.459999,116.940002,114.940002,116.220001,116.220001,60190000\n1982-04-12,116.220001,117.019997,115.160004,116.000000,116.000000,46520000\n1982-04-13,116.000000,117.120003,115.160004,115.989998,115.989998,48660000\n1982-04-14,115.989998,116.690002,114.800003,115.830002,115.830002,45150000\n1982-04-15,115.830002,116.860001,115.019997,116.349998,116.349998,45700000\n1982-04-16,116.349998,117.699997,115.680000,116.809998,116.809998,55890000\n1982-04-19,116.809998,118.160004,115.830002,116.699997,116.699997,58470000\n1982-04-20,115.800003,117.139999,114.830002,115.440002,115.440002,54610000\n1982-04-21,115.480003,115.870003,115.300003,115.720001,115.720001,57820000\n1982-04-22,115.720001,117.250000,115.720001,117.190002,117.190002,64470000\n1982-04-23,118.019997,118.639999,117.190002,118.639999,118.639999,71840000\n1982-04-26,118.940002,119.330002,118.250000,119.260002,119.260002,60500000\n1982-04-27,119.070000,119.260002,117.730003,118.000000,118.000000,56480000\n1982-04-28,117.830002,118.050003,116.940002,117.260002,117.260002,50530000\n1982-04-29,116.400002,117.239998,116.110001,116.139999,116.139999,51330000\n1982-04-30,116.209999,116.779999,116.070000,116.440002,116.440002,48200000\n1982-05-03,115.959999,116.820000,115.910004,116.820000,116.820000,46490000\n1982-05-04,117.410004,117.639999,116.849998,117.459999,117.459999,58720000\n1982-05-05,117.849998,118.050003,117.309998,117.669998,117.669998,58860000\n1982-05-06,118.820000,118.830002,117.680000,118.680000,118.680000,67540000\n1982-05-07,119.080002,119.889999,118.709999,119.470001,119.470001,67130000\n1982-05-10,119.080002,119.489998,118.370003,118.379997,118.379997,46300000\n1982-05-11,118.540001,119.589996,118.320000,119.419998,119.419998,54680000\n1982-05-12,119.889999,119.919998,118.760002,119.169998,119.169998,59210000\n1982-05-13,119.080002,119.199997,118.129997,118.220001,118.220001,58230000\n1982-05-14,118.199997,118.400002,118.010002,118.010002,118.010002,49900000\n1982-05-17,117.620003,118.019997,116.660004,116.709999,116.709999,45600000\n1982-05-18,116.349998,116.699997,115.709999,115.839996,115.839996,48970000\n1982-05-19,115.610001,115.959999,114.820000,114.889999,114.889999,48840000\n1982-05-20,114.849998,115.070000,114.370003,114.589996,114.589996,48330000\n1982-05-21,115.029999,115.129997,114.599998,114.889999,114.889999,45260000\n1982-05-24,114.459999,114.860001,114.239998,114.790001,114.790001,38510000\n1982-05-25,115.500000,115.510002,114.400002,114.400002,114.400002,44010000\n1982-05-26,113.680000,114.400002,112.879997,113.110001,113.110001,51250000\n1982-05-27,113.110001,113.120003,112.580002,112.660004,112.660004,44730000\n1982-05-28,112.790001,112.800003,111.660004,111.879997,111.879997,43900000\n1982-06-01,111.970001,112.070000,111.660004,111.680000,111.680000,41650000\n1982-06-02,111.739998,112.190002,111.550003,112.040001,112.040001,49220000\n1982-06-03,112.040001,112.480003,111.449997,111.860001,111.860001,48450000\n1982-06-04,111.660004,111.849998,110.019997,110.089996,110.089996,44110000\n1982-06-07,109.589996,110.589996,109.419998,110.120003,110.120003,44630000\n1982-06-08,110.330002,110.330002,109.599998,109.629997,109.629997,46820000\n1982-06-09,109.459999,109.629997,108.529999,108.989998,108.989998,55770000\n1982-06-10,109.349998,109.699997,108.959999,109.610001,109.610001,50950000\n1982-06-11,111.110001,111.480003,109.650002,111.239998,111.239998,68610000\n1982-06-14,110.500000,111.220001,109.900002,109.959999,109.959999,40100000\n1982-06-15,109.629997,109.959999,108.980003,109.690002,109.690002,44970000\n1982-06-16,110.099998,110.129997,108.820000,108.870003,108.870003,56280000\n1982-06-17,108.010002,108.849998,107.480003,107.599998,107.599998,49230000\n1982-06-18,107.599998,107.599998,107.070000,107.279999,107.279999,53800000\n1982-06-21,107.279999,107.879997,107.010002,107.199997,107.199997,50370000\n1982-06-22,107.250000,108.300003,107.169998,108.300003,108.300003,55290000\n1982-06-23,108.589996,110.139999,108.089996,110.139999,110.139999,62710000\n1982-06-24,110.250000,110.919998,109.790001,109.830002,109.830002,55860000\n1982-06-25,109.559998,109.830002,109.089996,109.139999,109.139999,38740000\n1982-06-28,109.300003,110.449997,109.169998,110.260002,110.260002,40700000\n1982-06-29,110.260002,110.570000,109.680000,110.209999,110.209999,46990000\n1982-06-30,110.949997,111.000000,109.500000,109.610001,109.610001,65280000\n1982-07-01,109.519997,109.629997,108.620003,108.709999,108.709999,47900000\n1982-07-02,108.099998,108.709999,107.599998,107.650002,107.650002,43760000\n1982-07-06,107.269997,107.669998,106.739998,107.290001,107.290001,44350000\n1982-07-07,107.080002,107.610001,106.989998,107.220001,107.220001,46920000\n1982-07-08,106.849998,107.529999,105.570000,107.529999,107.529999,63270000\n1982-07-09,108.230003,108.970001,107.559998,108.830002,108.830002,65870000\n1982-07-12,109.480003,109.620003,108.889999,109.570000,109.570000,74690000\n1982-07-13,109.190002,110.070000,109.190002,109.449997,109.449997,66170000\n1982-07-14,109.680000,110.440002,109.080002,110.440002,110.440002,58160000\n1982-07-15,110.830002,110.949997,110.269997,110.470001,110.470001,61090000\n1982-07-16,110.160004,111.480003,110.160004,111.070000,111.070000,58740000\n1982-07-19,111.750000,111.779999,110.660004,110.730003,110.730003,53030000\n1982-07-20,111.110001,111.559998,110.349998,111.540001,111.540001,61060000\n1982-07-21,112.150002,112.389999,111.379997,111.419998,111.419998,66770000\n1982-07-22,110.949997,112.019997,110.940002,111.480003,111.480003,53870000\n1982-07-23,111.459999,111.580002,111.050003,111.169998,111.169998,47280000\n1982-07-26,110.660004,111.160004,110.290001,110.360001,110.360001,37740000\n1982-07-27,110.260002,110.349998,109.360001,109.430000,109.430000,45740000\n1982-07-28,109.419998,109.419998,107.529999,107.739998,107.739998,53830000\n1982-07-29,107.419998,107.919998,106.620003,107.720001,107.720001,55680000\n1982-07-30,107.349998,107.949997,107.010002,107.089996,107.089996,39270000\n1982-08-02,107.709999,109.089996,107.110001,108.980003,108.980003,53460000\n1982-08-03,108.980003,109.430000,107.809998,107.830002,107.830002,60480000\n1982-08-04,107.830002,107.830002,106.110001,106.139999,106.139999,53440000\n1982-08-05,106.099998,106.099998,104.760002,105.160004,105.160004,54700000\n1982-08-06,105.160004,105.160004,103.669998,103.709999,103.709999,48660000\n1982-08-09,103.690002,103.690002,102.199997,103.080002,103.080002,54560000\n1982-08-10,103.110001,103.839996,102.820000,102.839996,102.839996,52680000\n1982-08-11,102.830002,103.010002,102.480003,102.599998,102.599998,49040000\n1982-08-12,102.599998,103.220001,102.389999,102.419998,102.419998,50080000\n1982-08-13,102.419998,103.849998,102.400002,103.849998,103.849998,44720000\n1982-08-16,103.860001,105.519997,103.860001,104.089996,104.089996,55420000\n1982-08-17,105.400002,109.040001,104.089996,109.040001,109.040001,92860000\n1982-08-18,109.040001,111.580002,108.459999,108.540001,108.540001,132690000\n1982-08-19,108.529999,109.860001,108.339996,109.160004,109.160004,78270000\n1982-08-20,109.190002,113.019997,109.190002,113.019997,113.019997,95890000\n1982-08-23,113.019997,116.110001,112.650002,116.110001,116.110001,110310000\n1982-08-24,116.110001,116.389999,115.080002,115.349998,115.349998,121650000\n1982-08-25,115.349998,118.120003,115.110001,117.580002,117.580002,106200000\n1982-08-26,117.570000,120.260002,117.570000,118.550003,118.550003,137330000\n1982-08-27,117.379997,118.559998,116.629997,117.110001,117.110001,74410000\n1982-08-30,117.050003,117.660004,115.790001,117.660004,117.660004,59560000\n1982-08-31,117.650002,119.599998,117.650002,119.510002,119.510002,86360000\n1982-09-01,119.519997,120.050003,117.980003,118.250000,118.250000,82830000\n1982-09-02,118.239998,120.320000,117.839996,120.290001,120.290001,74740000\n1982-09-03,120.309998,123.639999,120.309998,122.680000,122.680000,130910000\n1982-09-07,122.680000,122.680000,121.190002,121.370003,121.370003,68960000\n1982-09-08,121.330002,123.110001,121.190002,122.199997,122.199997,77960000\n1982-09-09,122.190002,123.220001,121.900002,121.970001,121.970001,73090000\n1982-09-10,121.970001,121.980003,120.269997,120.970001,120.970001,71080000\n1982-09-13,120.940002,122.239998,120.250000,122.239998,122.239998,59520000\n1982-09-14,122.269997,123.690002,122.269997,123.099998,123.099998,83070000\n1982-09-15,123.089996,124.809998,122.720001,124.290001,124.290001,69680000\n1982-09-16,124.279999,124.879997,123.650002,123.769997,123.769997,78900000\n1982-09-17,123.760002,123.760002,122.339996,122.550003,122.550003,63950000\n1982-09-20,122.540001,122.540001,121.480003,122.510002,122.510002,58520000\n1982-09-21,122.510002,124.910004,122.510002,124.879997,124.879997,82920000\n1982-09-22,124.900002,126.430000,123.989998,123.989998,123.989998,113150000\n1982-09-23,123.989998,124.190002,122.959999,123.809998,123.809998,68260000\n1982-09-24,123.790001,123.800003,123.110001,123.320000,123.320000,54600000\n1982-09-27,123.320000,123.620003,122.750000,123.620003,123.620003,44840000\n1982-09-28,123.620003,124.160004,123.209999,123.239998,123.239998,65900000\n1982-09-29,123.239998,123.239998,121.279999,121.629997,121.629997,62550000\n1982-09-30,121.620003,121.620003,120.139999,120.419998,120.419998,62610000\n1982-10-01,120.400002,121.970001,120.150002,121.970001,121.970001,65000000\n1982-10-04,121.970001,121.970001,120.559998,121.510002,121.510002,55650000\n1982-10-05,121.599998,122.730003,121.599998,121.980003,121.980003,69770000\n1982-10-06,122.000000,125.970001,122.000000,125.970001,125.970001,93570000\n1982-10-07,125.989998,128.960007,125.989998,128.800003,128.800003,147070000\n1982-10-08,128.789993,131.110001,128.789993,131.050003,131.050003,122250000\n1982-10-11,131.059998,135.529999,131.059998,134.470001,134.470001,138530000\n1982-10-12,134.479996,135.850006,133.589996,134.440002,134.440002,126310000\n1982-10-13,134.419998,137.970001,134.139999,136.710007,136.710007,139800000\n1982-10-14,136.710007,136.889999,134.550003,134.570007,134.570007,107530000\n1982-10-15,134.550003,134.610001,133.279999,133.570007,133.570007,80290000\n1982-10-18,133.589996,136.729996,133.589996,136.729996,136.729996,83790000\n1982-10-19,136.729996,137.960007,135.720001,136.580002,136.580002,100850000\n1982-10-20,136.580002,139.229996,136.369995,139.229996,139.229996,98680000\n1982-10-21,139.229996,140.270004,137.630005,139.059998,139.059998,122460000\n1982-10-22,139.059998,140.399994,138.750000,138.830002,138.830002,101120000\n1982-10-25,138.809998,138.809998,133.320007,133.320007,133.320007,83720000\n1982-10-26,133.289993,134.479996,131.500000,134.479996,134.479996,102080000\n1982-10-27,134.479996,135.919998,134.479996,135.289993,135.289993,81670000\n1982-10-28,135.279999,135.419998,133.589996,133.589996,133.589996,73590000\n1982-10-29,133.539993,134.020004,132.639999,133.720001,133.720001,74830000\n1982-11-01,133.720001,136.029999,133.220001,135.470001,135.470001,73530000\n1982-11-02,135.479996,138.509995,135.479996,137.490005,137.490005,104770000\n1982-11-03,137.529999,142.880005,137.529999,142.869995,142.869995,137010000\n1982-11-04,142.850006,143.990005,141.649994,141.850006,141.850006,149350000\n1982-11-05,141.850006,142.429993,141.320007,142.160004,142.160004,96550000\n1982-11-08,142.119995,142.119995,139.979996,140.440002,140.440002,75240000\n1982-11-09,140.479996,143.160004,140.460007,143.020004,143.020004,111220000\n1982-11-10,143.039993,144.360001,140.800003,141.160004,141.160004,113240000\n1982-11-11,141.149994,141.750000,139.880005,141.750000,141.750000,78410000\n1982-11-12,141.750000,141.850006,139.529999,139.529999,139.529999,95080000\n1982-11-15,139.539993,139.539993,137.000000,137.029999,137.029999,78900000\n1982-11-16,136.970001,136.970001,134.050003,135.419998,135.419998,102910000\n1982-11-17,135.470001,137.929993,135.470001,137.929993,137.929993,84440000\n1982-11-18,137.929993,138.779999,137.470001,138.339996,138.339996,77620000\n1982-11-19,138.350006,138.929993,137.000000,137.020004,137.020004,70310000\n1982-11-22,137.029999,137.100006,134.210007,134.220001,134.220001,74960000\n1982-11-23,134.210007,134.279999,132.889999,132.929993,132.929993,72920000\n1982-11-24,132.919998,133.880005,132.919998,133.880005,133.880005,67220000\n1982-11-26,133.889999,134.880005,133.889999,134.880005,134.880005,38810000\n1982-11-29,134.889999,135.289993,133.690002,134.199997,134.199997,61080000\n1982-11-30,134.199997,138.529999,134.190002,138.529999,138.529999,93470000\n1982-12-01,138.559998,140.369995,138.350006,138.720001,138.720001,107850000\n1982-12-02,138.720001,139.630005,138.660004,138.820007,138.820007,77600000\n1982-12-03,138.869995,139.589996,138.589996,138.690002,138.690002,71540000\n1982-12-06,138.699997,141.770004,138.009995,141.770004,141.770004,83880000\n1982-12-07,141.789993,143.679993,141.789993,142.720001,142.720001,111620000\n1982-12-08,142.710007,143.580002,141.820007,141.820007,141.820007,97430000\n1982-12-09,141.800003,141.800003,139.919998,140.000000,140.000000,90320000\n1982-12-10,139.990005,141.149994,139.350006,139.570007,139.570007,86430000\n1982-12-13,139.570007,140.119995,139.500000,139.949997,139.949997,63140000\n1982-12-14,139.990005,142.500000,137.339996,137.399994,137.399994,98380000\n1982-12-15,137.399994,137.399994,135.119995,135.240005,135.240005,81030000\n1982-12-16,135.220001,135.779999,134.789993,135.300003,135.300003,73680000\n1982-12-17,135.350006,137.710007,135.350006,137.490005,137.490005,76010000\n1982-12-20,137.490005,137.839996,136.190002,136.250000,136.250000,62210000\n1982-12-21,136.240005,139.270004,136.070007,138.610001,138.610001,78010000\n1982-12-22,138.630005,139.690002,138.600006,138.830002,138.830002,83470000\n1982-12-23,138.839996,139.940002,138.839996,139.720001,139.720001,62880000\n1982-12-27,139.729996,142.320007,139.720001,142.169998,142.169998,64690000\n1982-12-28,142.179993,142.339996,140.750000,140.770004,140.770004,58610000\n1982-12-29,140.770004,141.729996,140.679993,141.240005,141.240005,54810000\n1982-12-30,141.240005,141.679993,140.220001,140.330002,140.330002,56380000\n1982-12-31,140.339996,140.779999,140.270004,140.639999,140.639999,42110000\n1983-01-03,140.649994,141.330002,138.199997,138.339996,138.339996,59080000\n1983-01-04,138.330002,141.360001,138.080002,141.360001,141.360001,75530000\n1983-01-05,141.350006,142.600006,141.149994,141.960007,141.960007,95390000\n1983-01-06,142.009995,145.770004,142.009995,145.270004,145.270004,129410000\n1983-01-07,145.270004,146.460007,145.149994,145.179993,145.179993,127290000\n1983-01-10,145.190002,147.250000,144.580002,146.779999,146.779999,101890000\n1983-01-11,146.789993,146.830002,145.380005,145.779999,145.779999,98250000\n1983-01-12,145.759995,148.360001,145.759995,146.690002,146.690002,109850000\n1983-01-13,146.669998,146.940002,145.669998,145.729996,145.729996,77030000\n1983-01-14,145.720001,147.119995,145.720001,146.649994,146.649994,86480000\n1983-01-17,146.649994,147.899994,146.639999,146.720001,146.720001,89210000\n1983-01-18,146.710007,146.740005,145.520004,146.399994,146.399994,78380000\n1983-01-19,146.399994,146.449997,144.509995,145.270004,145.270004,80900000\n1983-01-20,145.289993,146.619995,145.289993,146.289993,146.289993,82790000\n1983-01-21,146.300003,146.300003,143.250000,143.850006,143.850006,77110000\n1983-01-24,143.839996,143.839996,139.100006,139.970001,139.970001,90800000\n1983-01-25,139.979996,141.750000,139.979996,141.750000,141.750000,79740000\n1983-01-26,141.770004,142.160004,141.160004,141.539993,141.539993,73720000\n1983-01-27,141.539993,144.300003,141.539993,144.270004,144.270004,88120000\n1983-01-28,144.309998,145.470001,144.250000,144.509995,144.509995,89490000\n1983-01-31,144.509995,145.300003,143.929993,145.300003,145.300003,67140000\n1983-02-01,145.289993,145.289993,142.960007,142.960007,142.960007,82750000\n1983-02-02,142.949997,143.520004,141.899994,143.229996,143.229996,77220000\n1983-02-03,143.250000,144.429993,143.250000,144.259995,144.259995,78890000\n1983-02-04,144.259995,146.139999,144.139999,146.139999,146.139999,87000000\n1983-02-07,146.139999,147.419998,146.139999,146.929993,146.929993,86030000\n1983-02-08,146.929993,147.210007,145.520004,145.699997,145.699997,76580000\n1983-02-09,145.699997,145.830002,144.089996,145.000000,145.000000,84520000\n1983-02-10,145.039993,147.750000,145.039993,147.500000,147.500000,93510000\n1983-02-11,147.509995,148.809998,147.179993,147.649994,147.649994,86700000\n1983-02-14,147.710007,149.139999,147.399994,148.929993,148.929993,72640000\n1983-02-15,148.940002,149.410004,148.130005,148.300003,148.300003,89040000\n1983-02-16,148.309998,148.660004,147.410004,147.429993,147.429993,82100000\n1983-02-17,147.429993,147.570007,143.839996,147.440002,147.440002,74930000\n1983-02-18,147.440002,148.289993,147.210007,148.000000,148.000000,77420000\n1983-02-22,148.009995,148.110001,145.419998,145.479996,145.479996,84080000\n1983-02-23,145.470001,146.789993,145.399994,146.789993,146.789993,84100000\n1983-02-24,146.800003,149.669998,146.800003,149.600006,149.600006,113220000\n1983-02-25,149.600006,150.880005,149.600006,149.740005,149.740005,100970000\n1983-02-28,149.740005,149.740005,147.809998,148.059998,148.059998,83750000\n1983-03-01,148.070007,150.880005,148.070007,150.880005,150.880005,103750000\n1983-03-02,150.910004,152.630005,150.910004,152.300003,152.300003,112600000\n1983-03-03,152.309998,154.160004,152.309998,153.479996,153.479996,114440000\n1983-03-04,153.470001,153.669998,152.529999,153.669998,153.669998,90930000\n1983-03-07,153.669998,154.000000,152.649994,153.669998,153.669998,84020000\n1983-03-08,153.630005,153.630005,151.259995,151.259995,151.259995,79410000\n1983-03-09,151.250000,152.869995,150.839996,152.869995,152.869995,84250000\n1983-03-10,152.869995,154.009995,151.750000,151.800003,151.800003,95410000\n1983-03-11,151.750000,151.750000,150.649994,151.240005,151.240005,67240000\n1983-03-14,151.279999,151.300003,150.240005,150.830002,150.830002,61890000\n1983-03-15,150.830002,151.369995,150.399994,151.369995,151.369995,62410000\n1983-03-16,151.360001,151.619995,149.779999,149.809998,149.809998,83570000\n1983-03-17,149.800003,149.800003,149.119995,149.589996,149.589996,70290000\n1983-03-18,149.589996,150.289993,149.559998,149.899994,149.899994,75110000\n1983-03-21,149.820007,151.199997,149.320007,151.190002,151.190002,72160000\n1983-03-22,151.210007,151.589996,150.600006,150.660004,150.660004,79610000\n1983-03-23,150.649994,152.979996,150.649994,152.809998,152.809998,94980000\n1983-03-24,152.820007,153.779999,152.820007,153.369995,153.369995,92340000\n1983-03-25,153.369995,153.710007,152.300003,152.669998,152.669998,77330000\n1983-03-28,152.669998,152.669998,151.559998,151.850006,151.850006,58510000\n1983-03-29,151.850006,152.460007,151.419998,151.589996,151.589996,65300000\n1983-03-30,151.600006,153.389999,151.600006,153.389999,153.389999,75800000\n1983-03-31,153.410004,155.020004,152.860001,152.960007,152.960007,100570000\n1983-04-04,152.919998,153.020004,152.229996,153.020004,153.020004,66010000\n1983-04-05,153.039993,153.919998,151.809998,151.899994,151.899994,76810000\n1983-04-06,151.899994,151.899994,150.169998,151.039993,151.039993,77140000\n1983-04-07,151.039993,151.759995,150.809998,151.759995,151.759995,69480000\n1983-04-08,151.770004,152.850006,151.389999,152.850006,152.850006,67710000\n1983-04-11,152.869995,155.139999,152.869995,155.139999,155.139999,81440000\n1983-04-12,155.149994,155.820007,154.779999,155.820007,155.820007,79900000\n1983-04-13,155.820007,157.220001,155.820007,156.770004,156.770004,100520000\n1983-04-14,156.800003,158.119995,156.550003,158.119995,158.119995,90160000\n1983-04-15,158.110001,158.750000,158.110001,158.750000,158.750000,89590000\n1983-04-18,158.750000,159.750000,158.410004,159.740005,159.740005,88560000\n1983-04-19,159.740005,159.740005,158.539993,158.710007,158.710007,91210000\n1983-04-20,158.710007,160.830002,158.710007,160.710007,160.710007,110240000\n1983-04-21,160.729996,161.080002,159.960007,160.050003,160.050003,106170000\n1983-04-22,160.039993,160.759995,160.020004,160.419998,160.419998,92270000\n1983-04-25,160.429993,160.830002,158.720001,158.809998,158.809998,90150000\n1983-04-26,158.809998,161.809998,158.070007,161.809998,161.809998,91210000\n1983-04-27,161.850006,162.770004,160.759995,161.440002,161.440002,118140000\n1983-04-28,161.440002,162.960007,161.440002,162.949997,162.949997,94410000\n1983-04-29,162.970001,164.429993,162.720001,164.429993,164.429993,105750000\n1983-05-02,164.410004,164.419998,161.990005,162.110001,162.110001,88170000\n1983-05-03,162.100006,162.350006,160.800003,162.339996,162.339996,89550000\n1983-05-04,162.380005,163.639999,162.380005,163.309998,163.309998,101690000\n1983-05-05,163.350006,164.300003,163.350006,164.279999,164.279999,107860000\n1983-05-06,164.300003,166.990005,164.300003,166.100006,166.100006,128200000\n1983-05-09,166.100006,166.460007,164.899994,165.809998,165.809998,93670000\n1983-05-10,165.820007,166.399994,165.740005,165.949997,165.949997,104010000\n1983-05-11,165.949997,166.300003,164.529999,164.960007,164.960007,99820000\n1983-05-12,164.979996,165.350006,163.820007,164.250000,164.250000,84060000\n1983-05-13,164.259995,165.229996,164.259995,164.910004,164.910004,83110000\n1983-05-16,164.899994,164.899994,162.330002,163.399994,163.399994,76250000\n1983-05-17,163.399994,163.710007,162.550003,163.710007,163.710007,79510000\n1983-05-18,163.729996,165.179993,163.160004,163.270004,163.270004,99780000\n1983-05-19,163.270004,163.610001,161.979996,161.990005,161.990005,83260000\n1983-05-20,161.970001,162.139999,161.250000,162.139999,162.139999,73150000\n1983-05-23,162.059998,163.500000,160.289993,163.429993,163.429993,84960000\n1983-05-24,163.449997,165.589996,163.449997,165.539993,165.539993,109850000\n1983-05-25,165.539993,166.210007,164.789993,166.210007,166.210007,121050000\n1983-05-26,166.220001,166.389999,165.270004,165.479996,165.479996,94980000\n1983-05-27,165.490005,165.490005,164.330002,164.460007,164.460007,76290000\n1983-05-31,164.440002,164.440002,162.119995,162.389999,162.389999,73910000\n1983-06-01,162.380005,162.639999,161.330002,162.550003,162.550003,84460000\n1983-06-02,162.559998,164.000000,162.559998,163.979996,163.979996,89750000\n1983-06-03,163.960007,164.789993,163.960007,164.419998,164.419998,83110000\n1983-06-06,164.429993,165.089996,163.750000,164.830002,164.830002,87670000\n1983-06-07,164.839996,164.929993,162.770004,162.770004,162.770004,88550000\n1983-06-08,162.779999,162.779999,161.350006,161.360001,161.360001,96600000\n1983-06-09,161.369995,161.919998,160.800003,161.830002,161.830002,87440000\n1983-06-10,161.860001,162.759995,161.860001,162.679993,162.679993,78470000\n1983-06-13,162.699997,164.839996,162.699997,164.839996,164.839996,90700000\n1983-06-14,164.869995,165.929993,164.869995,165.529999,165.529999,97710000\n1983-06-15,165.520004,167.119995,165.070007,167.119995,167.119995,93410000\n1983-06-16,167.110001,169.380005,167.110001,169.139999,169.139999,124560000\n1983-06-17,169.110001,169.639999,168.600006,169.130005,169.130005,93630000\n1983-06-20,169.130005,170.100006,168.589996,169.020004,169.020004,84270000\n1983-06-21,169.029999,170.600006,168.250000,170.529999,170.529999,102880000\n1983-06-22,170.529999,171.600006,170.419998,170.990005,170.990005,110270000\n1983-06-23,170.990005,171.000000,170.130005,170.570007,170.570007,89590000\n1983-06-24,170.570007,170.690002,170.029999,170.410004,170.410004,80810000\n1983-06-27,170.399994,170.460007,168.320007,168.460007,168.460007,69360000\n1983-06-28,168.449997,168.809998,165.669998,165.679993,165.679993,82730000\n1983-06-29,165.779999,166.639999,165.429993,166.639999,166.639999,81580000\n1983-06-30,167.639999,167.639999,167.639999,167.639999,167.639999,76310000\n1983-07-01,168.110001,168.639999,167.770004,168.639999,168.639999,65110000\n1983-07-05,166.550003,168.800003,165.800003,166.600006,166.600006,67320000\n1983-07-06,166.710007,168.880005,166.490005,168.479996,168.479996,85670000\n1983-07-07,168.479996,169.149994,167.080002,167.559998,167.559998,97130000\n1983-07-08,167.559998,167.979996,166.949997,167.080002,167.080002,66520000\n1983-07-11,167.089996,168.110001,167.089996,168.110001,168.110001,61610000\n1983-07-12,168.050003,168.050003,165.509995,165.529999,165.529999,70220000\n1983-07-13,165.000000,165.679993,164.770004,165.460007,165.460007,68900000\n1983-07-14,165.610001,166.960007,165.610001,166.009995,166.009995,83500000\n1983-07-15,166.009995,166.039993,164.029999,164.289993,164.289993,63160000\n1983-07-18,164.279999,164.289993,163.300003,163.949997,163.949997,69110000\n1983-07-19,163.949997,165.179993,163.949997,164.820007,164.820007,74030000\n1983-07-20,164.889999,169.289993,164.889999,169.289993,169.289993,109310000\n1983-07-21,169.289993,169.800003,168.330002,169.059998,169.059998,101830000\n1983-07-22,168.509995,169.080002,168.399994,168.889999,168.889999,68850000\n1983-07-25,167.669998,169.740005,167.630005,169.529999,169.529999,73680000\n1983-07-26,169.619995,170.630005,169.259995,170.529999,170.529999,91280000\n1983-07-27,170.679993,170.720001,167.490005,167.589996,167.589996,99290000\n1983-07-28,167.320007,167.789993,164.990005,165.039993,165.039993,78410000\n1983-07-29,165.029999,165.029999,161.500000,162.559998,162.559998,95240000\n1983-08-01,162.339996,162.779999,161.550003,162.039993,162.039993,77210000\n1983-08-02,162.059998,163.039993,161.970001,162.009995,162.009995,74460000\n1983-08-03,162.009995,163.440002,161.520004,163.440002,163.440002,80370000\n1983-08-04,163.279999,163.419998,159.630005,161.330002,161.330002,100870000\n1983-08-05,161.330002,161.880005,160.889999,161.740005,161.740005,67850000\n1983-08-08,161.729996,161.729996,159.179993,159.179993,159.179993,71460000\n1983-08-09,159.199997,160.139999,158.500000,160.130005,160.130005,81420000\n1983-08-10,160.110001,161.770004,159.470001,161.539993,161.539993,82900000\n1983-08-11,161.550003,162.139999,161.410004,161.539993,161.539993,70630000\n1983-08-12,161.550003,162.600006,161.550003,162.160004,162.160004,71840000\n1983-08-15,162.220001,164.759995,162.220001,163.699997,163.699997,83200000\n1983-08-16,163.740005,163.839996,162.720001,163.410004,163.410004,71780000\n1983-08-17,163.580002,165.399994,163.429993,165.289993,165.289993,87800000\n1983-08-18,165.289993,165.910004,163.550003,163.550003,163.550003,82280000\n1983-08-19,163.580002,164.270004,163.220001,163.979996,163.979996,58950000\n1983-08-22,164.179993,165.639999,163.770004,164.339996,164.339996,76420000\n1983-08-23,164.330002,164.330002,162.539993,162.770004,162.770004,66800000\n1983-08-24,162.770004,162.770004,161.199997,161.250000,161.250000,72200000\n1983-08-25,161.270004,161.279999,159.960007,160.839996,160.839996,70140000\n1983-08-26,160.850006,162.160004,160.250000,162.139999,162.139999,61650000\n1983-08-29,162.139999,162.320007,160.970001,162.250000,162.250000,53030000\n1983-08-30,162.250000,163.130005,162.110001,162.580002,162.580002,62370000\n1983-08-31,162.550003,164.399994,162.320007,164.399994,164.399994,80800000\n1983-09-01,164.399994,164.660004,163.949997,164.229996,164.229996,76120000\n1983-09-02,164.250000,165.070007,164.210007,165.000000,165.000000,59300000\n1983-09-06,165.199997,167.899994,165.029999,167.889999,167.889999,87500000\n1983-09-07,167.899994,168.479996,167.460007,167.960007,167.960007,94240000\n1983-09-08,167.960007,168.139999,167.119995,167.770004,167.770004,79250000\n1983-09-09,167.770004,167.770004,166.910004,166.919998,166.919998,77990000\n1983-09-12,166.949997,169.199997,165.270004,165.479996,165.479996,114020000\n1983-09-13,165.479996,165.479996,164.169998,164.800003,164.800003,73970000\n1983-09-14,164.800003,165.419998,164.630005,165.350006,165.350006,73370000\n1983-09-15,165.389999,165.580002,164.380005,164.380005,164.380005,70420000\n1983-09-16,164.419998,166.570007,164.389999,166.250000,166.250000,75530000\n1983-09-19,166.270004,168.089996,166.259995,167.619995,167.619995,85630000\n1983-09-20,167.639999,169.380005,167.639999,169.240005,169.240005,103050000\n1983-09-21,169.270004,169.300003,168.210007,168.410004,168.410004,91280000\n1983-09-22,168.399994,169.779999,168.220001,169.759995,169.759995,97050000\n1983-09-23,169.759995,170.169998,168.880005,169.509995,169.509995,93180000\n1983-09-26,169.529999,170.410004,169.160004,170.070007,170.070007,86400000\n1983-09-27,170.020004,170.020004,167.949997,168.429993,168.429993,81100000\n1983-09-28,168.419998,168.529999,167.520004,168.000000,168.000000,75820000\n1983-09-29,168.020004,168.350006,167.229996,167.229996,167.229996,73730000\n1983-09-30,167.229996,167.229996,165.630005,166.070007,166.070007,70860000\n1983-10-03,165.990005,166.070007,164.929993,165.809998,165.809998,77230000\n1983-10-04,165.809998,166.800003,165.809998,166.270004,166.270004,90270000\n1983-10-05,166.289993,167.740005,165.919998,167.740005,167.740005,101710000\n1983-10-06,167.759995,170.279999,167.759995,170.279999,170.279999,118270000\n1983-10-07,170.320007,171.100006,170.309998,170.800003,170.800003,103630000\n1983-10-10,170.770004,172.649994,170.050003,172.649994,172.649994,67050000\n1983-10-11,172.589996,172.589996,170.339996,170.339996,170.339996,79510000\n1983-10-12,170.339996,170.839996,169.339996,169.619995,169.619995,75630000\n1983-10-13,169.630005,170.119995,169.130005,169.869995,169.869995,67750000\n1983-10-14,169.880005,169.990005,169.179993,169.860001,169.860001,71600000\n1983-10-17,169.850006,171.179993,169.630005,170.429993,170.429993,77730000\n1983-10-18,170.410004,170.410004,167.669998,167.809998,167.809998,91080000\n1983-10-19,167.809998,167.809998,165.669998,166.729996,166.729996,107790000\n1983-10-20,166.770004,167.350006,166.440002,166.979996,166.979996,86000000\n1983-10-21,166.970001,167.229996,164.979996,165.949997,165.949997,91640000\n1983-10-24,165.850006,165.990005,163.850006,165.990005,165.990005,85420000\n1983-10-25,166.000000,167.149994,166.000000,166.470001,166.470001,82530000\n1983-10-26,166.490005,166.649994,165.360001,165.380005,165.380005,79570000\n1983-10-27,165.309998,165.380005,164.410004,164.839996,164.839996,79570000\n1983-10-28,164.889999,165.190002,163.229996,163.369995,163.369995,81180000\n1983-10-31,163.369995,164.580002,162.860001,163.550003,163.550003,79460000\n1983-11-01,163.550003,163.660004,162.369995,163.660004,163.660004,84460000\n1983-11-02,165.210007,165.210007,163.550003,164.839996,164.839996,95210000\n1983-11-03,164.839996,164.850006,163.419998,163.449997,163.449997,85350000\n1983-11-04,162.679993,163.449997,162.220001,162.440002,162.440002,72080000\n1983-11-07,162.419998,162.559998,161.839996,161.910004,161.910004,69400000\n1983-11-08,161.910004,162.149994,161.630005,161.759995,161.759995,64900000\n1983-11-09,161.740005,163.970001,161.740005,163.970001,163.970001,83100000\n1983-11-10,163.990005,164.710007,163.970001,164.410004,164.410004,88730000\n1983-11-11,164.410004,166.300003,164.339996,166.289993,166.289993,74270000\n1983-11-14,166.289993,167.580002,166.270004,166.580002,166.580002,86880000\n1983-11-15,166.580002,166.589996,165.279999,165.360001,165.360001,77840000\n1983-11-16,165.360001,166.410004,165.339996,166.080002,166.080002,83380000\n1983-11-17,166.080002,166.490005,165.509995,166.130005,166.130005,80740000\n1983-11-18,166.080002,166.130005,164.500000,165.089996,165.089996,88280000\n1983-11-21,165.039993,166.050003,165.000000,166.050003,166.050003,97740000\n1983-11-22,166.050003,167.259995,166.050003,166.839996,166.839996,117550000\n1983-11-23,166.880005,167.210007,166.259995,166.960007,166.960007,108080000\n1983-11-25,167.020004,167.199997,166.729996,167.179993,167.179993,57820000\n1983-11-28,167.199997,167.220001,166.210007,166.539993,166.539993,78210000\n1983-11-29,166.539993,167.919998,166.169998,167.910004,167.910004,100460000\n1983-11-30,167.910004,168.070007,166.330002,166.399994,166.399994,120130000\n1983-12-01,166.369995,166.770004,166.080002,166.490005,166.490005,106970000\n1983-12-02,166.490005,166.699997,165.250000,165.440002,165.440002,93960000\n1983-12-05,165.440002,165.789993,164.710007,165.759995,165.759995,88330000\n1983-12-06,165.770004,165.929993,165.339996,165.470001,165.470001,89690000\n1983-12-07,165.470001,166.339996,165.350006,165.910004,165.910004,105670000\n1983-12-08,165.910004,166.009995,164.860001,165.199997,165.199997,96530000\n1983-12-09,165.199997,165.289993,164.500000,165.080002,165.080002,98280000\n1983-12-12,165.130005,165.619995,164.990005,165.619995,165.619995,77340000\n1983-12-13,165.619995,165.630005,164.850006,164.929993,164.929993,93500000\n1983-12-14,164.929993,164.929993,163.250000,163.330002,163.330002,85430000\n1983-12-15,163.330002,163.330002,161.660004,161.660004,161.660004,88300000\n1983-12-16,161.690002,162.389999,161.580002,162.389999,162.389999,81030000\n1983-12-19,162.339996,162.880005,162.270004,162.320007,162.320007,75180000\n1983-12-20,162.330002,162.800003,161.639999,162.000000,162.000000,83740000\n1983-12-21,162.000000,163.570007,161.990005,163.559998,163.559998,108080000\n1983-12-22,163.559998,164.179993,163.169998,163.529999,163.529999,106260000\n1983-12-23,163.270004,163.309998,162.899994,163.220001,163.220001,62710000\n1983-12-27,163.220001,164.759995,163.220001,164.759995,164.759995,63800000\n1983-12-28,164.690002,165.339996,164.300003,165.339996,165.339996,85660000\n1983-12-29,165.330002,165.839996,164.830002,164.860001,164.860001,86560000\n1983-12-30,164.860001,165.050003,164.580002,164.929993,164.929993,71840000\n1984-01-03,164.929993,164.929993,163.979996,164.039993,164.039993,71340000\n1984-01-04,164.089996,166.779999,164.039993,166.779999,166.779999,112980000\n1984-01-05,166.779999,169.100006,166.779999,168.809998,168.809998,159990000\n1984-01-06,168.809998,169.309998,168.490005,169.279999,169.279999,137590000\n1984-01-09,169.179993,169.460007,168.479996,168.899994,168.899994,107100000\n1984-01-10,168.899994,169.539993,167.869995,167.949997,167.949997,109570000\n1984-01-11,167.949997,168.070007,167.270004,167.800003,167.800003,98660000\n1984-01-12,167.789993,168.399994,167.679993,167.750000,167.750000,99410000\n1984-01-13,167.750000,168.589996,166.639999,167.020004,167.020004,101790000\n1984-01-16,167.020004,167.550003,166.770004,167.179993,167.179993,93790000\n1984-01-17,167.179993,167.839996,167.009995,167.830002,167.830002,92750000\n1984-01-18,167.830002,168.339996,167.020004,167.550003,167.550003,109010000\n1984-01-19,167.550003,167.649994,166.669998,167.039993,167.039993,98340000\n1984-01-20,167.039993,167.059998,165.869995,166.210007,166.210007,93360000\n1984-01-23,166.210007,166.210007,164.830002,164.869995,164.869995,82010000\n1984-01-24,164.869995,166.350006,164.839996,165.940002,165.940002,103050000\n1984-01-25,165.940002,167.119995,164.740005,164.839996,164.839996,113470000\n1984-01-26,164.839996,165.550003,164.119995,164.240005,164.240005,111100000\n1984-01-27,164.240005,164.330002,163.070007,163.940002,163.940002,103720000\n1984-01-30,164.399994,164.669998,162.399994,162.869995,162.869995,103120000\n1984-01-31,162.869995,163.600006,162.029999,163.410004,163.410004,113510000\n1984-02-01,163.410004,164.000000,162.270004,162.740005,162.740005,107100000\n1984-02-02,162.740005,163.360001,162.240005,163.360001,163.360001,111330000\n1984-02-03,163.440002,163.979996,160.820007,160.910004,160.910004,109100000\n1984-02-06,160.910004,160.910004,158.020004,158.080002,158.080002,109090000\n1984-02-07,157.910004,158.809998,157.009995,158.740005,158.740005,107640000\n1984-02-08,158.740005,159.070007,155.669998,155.850006,155.850006,96890000\n1984-02-09,155.850006,156.169998,154.300003,155.419998,155.419998,128190000\n1984-02-10,155.419998,156.520004,155.419998,156.300003,156.300003,92220000\n1984-02-13,156.300003,156.320007,154.130005,154.949997,154.949997,78460000\n1984-02-14,154.949997,156.610001,154.949997,156.610001,156.610001,91800000\n1984-02-15,156.610001,157.479996,156.100006,156.250000,156.250000,94870000\n1984-02-16,155.940002,156.440002,155.440002,156.130005,156.130005,81750000\n1984-02-17,156.130005,156.800003,155.509995,155.740005,155.740005,76600000\n1984-02-21,155.710007,155.740005,154.470001,154.639999,154.639999,71890000\n1984-02-22,154.520004,155.100006,153.940002,154.309998,154.309998,90080000\n1984-02-23,154.020004,154.449997,152.130005,154.289993,154.289993,100220000\n1984-02-24,154.309998,157.509995,154.289993,157.509995,157.509995,102620000\n1984-02-27,157.509995,159.580002,157.080002,159.300003,159.300003,99140000\n1984-02-28,159.300003,159.300003,156.589996,156.820007,156.820007,91010000\n1984-02-29,156.820007,158.270004,156.410004,157.059998,157.059998,92810000\n1984-03-01,157.059998,158.190002,156.770004,158.190002,158.190002,82010000\n1984-03-02,158.190002,159.899994,158.190002,159.240005,159.240005,108270000\n1984-03-05,159.240005,159.240005,157.589996,157.889999,157.889999,69870000\n1984-03-06,157.889999,158.369995,156.210007,156.250000,156.250000,83590000\n1984-03-07,156.250000,156.250000,153.809998,154.570007,154.570007,90080000\n1984-03-08,154.570007,155.800003,154.350006,155.190002,155.190002,80630000\n1984-03-09,155.119995,155.190002,153.770004,154.350006,154.350006,73170000\n1984-03-12,154.350006,156.350006,154.350006,156.339996,156.339996,84470000\n1984-03-13,156.339996,157.929993,156.339996,156.779999,156.779999,102600000\n1984-03-14,156.779999,157.169998,156.220001,156.770004,156.770004,77250000\n1984-03-15,156.779999,158.050003,156.729996,157.410004,157.410004,79520000\n1984-03-16,157.410004,160.449997,157.410004,159.270004,159.270004,118000000\n1984-03-19,159.270004,159.270004,157.279999,157.779999,157.779999,64060000\n1984-03-20,157.779999,159.169998,157.779999,158.860001,158.860001,86460000\n1984-03-21,158.860001,159.259995,158.589996,158.660004,158.660004,87170000\n1984-03-22,158.660004,158.669998,156.610001,156.690002,156.690002,87340000\n1984-03-23,156.690002,156.919998,156.020004,156.860001,156.860001,79760000\n1984-03-26,156.860001,157.179993,156.309998,156.669998,156.669998,69070000\n1984-03-27,156.669998,157.300003,156.610001,157.300003,157.300003,73670000\n1984-03-28,157.300003,159.899994,157.300003,159.880005,159.880005,104870000\n1984-03-29,159.880005,160.460007,159.520004,159.520004,159.520004,81470000\n1984-03-30,159.520004,159.520004,158.919998,159.179993,159.179993,71590000\n1984-04-02,159.179993,159.869995,157.630005,157.979996,157.979996,85680000\n1984-04-03,157.990005,158.270004,157.169998,157.660004,157.660004,87980000\n1984-04-04,157.660004,158.110001,157.289993,157.539993,157.539993,92860000\n1984-04-05,157.539993,158.100006,154.960007,155.039993,155.039993,101750000\n1984-04-06,155.039993,155.479996,154.119995,155.479996,155.479996,86620000\n1984-04-09,155.479996,155.860001,154.710007,155.449997,155.449997,71570000\n1984-04-10,155.449997,156.570007,155.449997,155.869995,155.869995,78990000\n1984-04-11,155.929993,156.309998,154.899994,155.000000,155.000000,80280000\n1984-04-12,155.000000,157.740005,154.169998,157.729996,157.729996,96330000\n1984-04-13,157.729996,158.869995,157.130005,157.309998,157.309998,99620000\n1984-04-16,157.309998,158.350006,156.490005,158.320007,158.320007,73870000\n1984-04-17,158.320007,159.589996,158.320007,158.970001,158.970001,98150000\n1984-04-18,158.970001,158.970001,157.639999,157.899994,157.899994,85040000\n1984-04-19,157.899994,158.020004,157.100006,158.020004,158.020004,75860000\n1984-04-23,158.020004,158.050003,156.789993,156.800003,156.800003,73080000\n1984-04-24,156.800003,158.380005,156.610001,158.070007,158.070007,87060000\n1984-04-25,158.070007,158.770004,157.800003,158.649994,158.649994,83520000\n1984-04-26,158.649994,160.500000,158.649994,160.300003,160.300003,98000000\n1984-04-27,160.300003,160.690002,159.770004,159.889999,159.889999,88530000\n1984-04-30,159.889999,160.429993,159.300003,160.050003,160.050003,72740000\n1984-05-01,160.050003,161.690002,160.050003,161.679993,161.679993,110550000\n1984-05-02,161.679993,162.110001,161.410004,161.899994,161.899994,107080000\n1984-05-03,161.899994,161.899994,160.949997,161.199997,161.199997,91910000\n1984-05-04,161.199997,161.199997,158.929993,159.110001,159.110001,98580000\n1984-05-07,159.110001,159.479996,158.630005,159.470001,159.470001,72760000\n1984-05-08,159.470001,160.520004,159.139999,160.520004,160.520004,81610000\n1984-05-09,160.520004,161.309998,159.389999,160.110001,160.110001,100590000\n1984-05-10,160.110001,160.449997,159.610001,160.000000,160.000000,101810000\n1984-05-11,160.000000,160.000000,157.419998,158.490005,158.490005,82780000\n1984-05-14,158.490005,158.490005,157.199997,157.500000,157.500000,64900000\n1984-05-15,157.500000,158.270004,157.289993,158.000000,158.000000,88250000\n1984-05-16,158.000000,158.410004,157.830002,157.990005,157.990005,89210000\n1984-05-17,157.990005,157.990005,156.149994,156.570007,156.570007,90310000\n1984-05-18,156.570007,156.770004,155.240005,155.779999,155.779999,81270000\n1984-05-21,155.779999,156.110001,154.630005,154.729996,154.729996,73380000\n1984-05-22,154.729996,154.729996,152.990005,153.880005,153.880005,88030000\n1984-05-23,153.880005,154.020004,153.100006,153.149994,153.149994,82690000\n1984-05-24,153.149994,153.149994,150.800003,151.229996,151.229996,99040000\n1984-05-25,151.229996,152.020004,150.850006,151.619995,151.619995,78190000\n1984-05-29,151.619995,151.860001,149.949997,150.289993,150.289993,69060000\n1984-05-30,150.289993,151.429993,148.679993,150.350006,150.350006,105660000\n1984-05-31,150.350006,150.690002,149.759995,150.550003,150.550003,81890000\n1984-06-01,150.550003,153.240005,150.550003,153.240005,153.240005,96040000\n1984-06-04,153.240005,155.100006,153.240005,154.339996,154.339996,96740000\n1984-06-05,154.339996,154.339996,153.279999,153.649994,153.649994,84840000\n1984-06-06,153.649994,155.029999,153.380005,155.009995,155.009995,83440000\n1984-06-07,155.009995,155.110001,154.360001,154.919998,154.919998,82120000\n1984-06-08,154.919998,155.399994,154.570007,155.169998,155.169998,67840000\n1984-06-11,155.169998,155.169998,153.000000,153.059998,153.059998,69050000\n1984-06-12,153.059998,153.070007,151.610001,152.190002,152.190002,84660000\n1984-06-13,152.190002,152.850006,151.860001,152.130005,152.130005,67510000\n1984-06-14,152.119995,152.139999,150.309998,150.389999,150.389999,79120000\n1984-06-15,150.490005,150.710007,149.020004,149.029999,149.029999,85460000\n1984-06-18,149.029999,151.919998,148.529999,151.729996,151.729996,94900000\n1984-06-19,151.729996,153.000000,151.729996,152.610001,152.610001,98000000\n1984-06-20,151.889999,154.839996,150.960007,154.839996,154.839996,99090000\n1984-06-21,154.839996,155.639999,154.050003,154.509995,154.509995,123380000\n1984-06-22,154.509995,154.919998,153.889999,154.460007,154.460007,98400000\n1984-06-25,154.460007,154.669998,153.860001,153.970001,153.970001,72850000\n1984-06-26,153.970001,153.970001,152.470001,152.710007,152.710007,82600000\n1984-06-27,152.710007,152.880005,151.300003,151.639999,151.639999,78400000\n1984-06-28,151.639999,153.070007,151.619995,152.839996,152.839996,77660000\n1984-06-29,152.839996,154.080002,152.820007,153.179993,153.179993,90770000\n1984-07-02,153.160004,153.220001,152.440002,153.199997,153.199997,69230000\n1984-07-03,153.199997,153.860001,153.100006,153.699997,153.699997,69960000\n1984-07-05,153.699997,153.869995,152.710007,152.759995,152.759995,66100000\n1984-07-06,152.759995,152.759995,151.630005,152.240005,152.240005,65850000\n1984-07-09,152.240005,153.529999,151.440002,153.360001,153.360001,74830000\n1984-07-10,153.360001,153.529999,152.570007,152.889999,152.889999,74010000\n1984-07-11,152.889999,152.889999,150.550003,150.559998,150.559998,89540000\n1984-07-12,150.559998,151.059998,149.630005,150.029999,150.029999,86050000\n1984-07-13,150.029999,151.160004,150.029999,150.880005,150.880005,75480000\n1984-07-16,150.880005,151.600006,150.009995,151.600006,151.600006,73420000\n1984-07-17,151.600006,152.600006,151.259995,152.380005,152.380005,82890000\n1984-07-18,152.380005,152.380005,151.110001,151.399994,151.399994,76640000\n1984-07-19,151.399994,151.399994,150.270004,150.369995,150.369995,85230000\n1984-07-20,150.369995,150.580002,149.070007,149.550003,149.550003,79090000\n1984-07-23,149.550003,149.550003,147.850006,148.949997,148.949997,77990000\n1984-07-24,148.949997,149.279999,147.779999,147.820007,147.820007,74370000\n1984-07-25,147.820007,149.300003,147.259995,148.830002,148.830002,90520000\n1984-07-26,148.830002,150.160004,148.830002,150.080002,150.080002,90410000\n1984-07-27,150.080002,151.380005,149.990005,151.190002,151.190002,101350000\n1984-07-30,151.190002,151.190002,150.139999,150.190002,150.190002,72330000\n1984-07-31,150.190002,150.770004,149.649994,150.660004,150.660004,86910000\n1984-08-01,150.660004,154.080002,150.660004,154.080002,154.080002,127500000\n1984-08-02,154.080002,157.990005,154.080002,157.990005,157.990005,172800000\n1984-08-03,160.279999,162.559998,158.000000,162.350006,162.350006,236500000\n1984-08-06,162.350006,165.270004,162.089996,162.600006,162.600006,203000000\n1984-08-07,162.600006,163.580002,160.809998,162.720001,162.720001,127900000\n1984-08-08,162.710007,163.869995,161.750000,161.750000,161.750000,121200000\n1984-08-09,161.750000,165.880005,161.470001,165.539993,165.539993,131100000\n1984-08-10,165.539993,168.589996,165.240005,165.419998,165.419998,171000000\n1984-08-13,164.839996,165.490005,163.979996,165.429993,165.429993,77960000\n1984-08-14,165.429993,166.089996,164.279999,164.419998,164.419998,81470000\n1984-08-15,164.419998,164.419998,162.750000,162.800003,162.800003,91880000\n1984-08-16,162.800003,164.419998,162.750000,163.770004,163.770004,93610000\n1984-08-17,164.300003,164.610001,163.779999,164.139999,164.139999,71500000\n1984-08-20,164.139999,164.940002,163.759995,164.940002,164.940002,75450000\n1984-08-21,164.940002,168.220001,164.929993,167.830002,167.830002,128100000\n1984-08-22,167.830002,168.800003,166.919998,167.059998,167.059998,116000000\n1984-08-23,167.059998,167.779999,166.610001,167.119995,167.119995,83130000\n1984-08-24,167.119995,167.520004,167.119995,167.509995,167.509995,69640000\n1984-08-27,167.509995,167.509995,165.809998,166.440002,166.440002,57660000\n1984-08-28,166.440002,167.429993,166.210007,167.399994,167.399994,70560000\n1984-08-29,167.399994,168.210007,167.029999,167.089996,167.089996,90660000\n1984-08-30,167.100006,167.190002,166.550003,166.600006,166.600006,70840000\n1984-08-31,166.600006,166.679993,165.779999,166.679993,166.679993,57460000\n1984-09-04,166.679993,166.679993,164.729996,164.880005,164.880005,62110000\n1984-09-05,164.880005,164.880005,163.839996,164.289993,164.289993,69250000\n1984-09-06,164.289993,165.949997,164.289993,165.649994,165.649994,91920000\n1984-09-07,165.649994,166.309998,164.220001,164.369995,164.369995,84110000\n1984-09-10,164.369995,165.050003,163.059998,164.259995,164.259995,74410000\n1984-09-11,165.220001,166.169998,164.279999,164.449997,164.449997,101300000\n1984-09-12,164.449997,164.809998,164.139999,164.679993,164.679993,77980000\n1984-09-13,164.679993,167.940002,164.679993,167.940002,167.940002,110500000\n1984-09-14,167.940002,169.649994,167.940002,168.779999,168.779999,137400000\n1984-09-17,168.779999,169.369995,167.990005,168.869995,168.869995,88790000\n1984-09-18,168.869995,168.869995,167.639999,167.649994,167.649994,107700000\n1984-09-19,167.649994,168.759995,166.889999,166.940002,166.940002,119900000\n1984-09-20,166.940002,167.470001,166.699997,167.470001,167.470001,92030000\n1984-09-21,167.470001,168.669998,165.660004,165.669998,165.669998,120600000\n1984-09-24,165.669998,166.119995,164.979996,165.279999,165.279999,76380000\n1984-09-25,165.279999,165.970001,164.449997,165.619995,165.619995,86250000\n1984-09-26,165.619995,167.199997,165.610001,166.279999,166.279999,100200000\n1984-09-27,166.750000,167.179993,166.330002,166.960007,166.960007,88880000\n1984-09-28,166.960007,166.960007,165.770004,166.100006,166.100006,78950000\n1984-10-01,166.100006,166.100006,164.479996,164.619995,164.619995,73630000\n1984-10-02,164.619995,165.240005,163.550003,163.589996,163.589996,89360000\n1984-10-03,163.589996,163.589996,162.199997,162.440002,162.440002,92400000\n1984-10-04,162.440002,163.220001,162.440002,162.919998,162.919998,76700000\n1984-10-05,162.919998,163.320007,162.509995,162.679993,162.679993,82950000\n1984-10-08,162.679993,162.679993,161.800003,162.130005,162.130005,46360000\n1984-10-09,162.130005,162.839996,161.619995,161.669998,161.669998,76840000\n1984-10-10,161.669998,162.119995,160.020004,162.110001,162.110001,94270000\n1984-10-11,162.110001,162.869995,162.000000,162.779999,162.779999,87020000\n1984-10-12,162.779999,164.470001,162.779999,164.179993,164.179993,92190000\n1984-10-15,164.179993,166.149994,164.089996,165.770004,165.770004,87590000\n1984-10-16,165.779999,165.779999,164.660004,164.779999,164.779999,82930000\n1984-10-17,164.779999,165.039993,163.710007,164.139999,164.139999,99740000\n1984-10-18,164.139999,168.100006,163.800003,168.100006,168.100006,149500000\n1984-10-19,168.080002,169.619995,167.309998,167.960007,167.960007,186900000\n1984-10-22,167.960007,168.360001,167.259995,167.360001,167.360001,81020000\n1984-10-23,167.360001,168.270004,166.830002,167.089996,167.089996,92260000\n1984-10-24,167.089996,167.539993,166.820007,167.199997,167.199997,91620000\n1984-10-25,167.199997,167.619995,166.169998,166.309998,166.309998,92760000\n1984-10-26,166.309998,166.309998,164.929993,165.289993,165.289993,83900000\n1984-10-29,165.289993,165.289993,164.669998,164.779999,164.779999,63200000\n1984-10-30,164.779999,167.330002,164.779999,166.839996,166.839996,95200000\n1984-10-31,166.740005,166.949997,165.990005,166.089996,166.089996,91890000\n1984-11-01,166.089996,167.830002,166.089996,167.490005,167.490005,107300000\n1984-11-02,167.490005,167.949997,167.240005,167.419998,167.419998,96810000\n1984-11-05,167.419998,168.649994,167.330002,168.580002,168.580002,84730000\n1984-11-06,168.580002,170.410004,168.580002,170.410004,170.410004,101200000\n1984-11-07,170.410004,170.410004,168.440002,169.169998,169.169998,110800000\n1984-11-08,169.190002,169.270004,168.270004,168.679993,168.679993,88580000\n1984-11-09,168.679993,169.460007,167.440002,167.600006,167.600006,83620000\n1984-11-12,167.649994,167.649994,166.669998,167.360001,167.360001,55610000\n1984-11-13,167.360001,167.380005,165.789993,165.970001,165.970001,69790000\n1984-11-14,165.970001,166.429993,165.389999,165.990005,165.990005,73940000\n1984-11-15,165.990005,166.490005,165.610001,165.889999,165.889999,81530000\n1984-11-16,165.889999,166.240005,164.089996,164.100006,164.100006,83140000\n1984-11-19,164.100006,164.339996,163.029999,163.089996,163.089996,69730000\n1984-11-20,163.100006,164.470001,163.100006,164.179993,164.179993,83240000\n1984-11-21,164.179993,164.679993,163.289993,164.509995,164.509995,81620000\n1984-11-23,164.520004,166.919998,164.520004,166.919998,166.919998,73910000\n1984-11-26,166.919998,166.919998,165.369995,165.550003,165.550003,76520000\n1984-11-27,165.550003,166.850006,165.070007,166.289993,166.289993,95470000\n1984-11-28,166.289993,166.899994,164.970001,165.020004,165.020004,86300000\n1984-11-29,165.020004,165.020004,163.779999,163.910004,163.910004,75860000\n1984-11-30,163.910004,163.910004,162.990005,163.580002,163.580002,77580000\n1984-12-03,163.580002,163.580002,162.289993,162.820007,162.820007,95300000\n1984-12-04,162.820007,163.910004,162.820007,163.380005,163.380005,81250000\n1984-12-05,163.380005,163.399994,161.929993,162.100006,162.100006,88700000\n1984-12-06,162.100006,163.110001,161.759995,162.759995,162.759995,96560000\n1984-12-07,162.759995,163.309998,162.259995,162.259995,162.259995,81000000\n1984-12-10,162.259995,163.320007,161.539993,162.830002,162.830002,81140000\n1984-12-11,162.830002,163.179993,162.559998,163.070007,163.070007,80240000\n1984-12-12,163.070007,163.179993,162.550003,162.630005,162.630005,78710000\n1984-12-13,162.630005,162.919998,161.539993,161.809998,161.809998,80850000\n1984-12-14,161.809998,163.529999,161.630005,162.690002,162.690002,95060000\n1984-12-17,162.690002,163.630005,162.440002,163.610001,163.610001,89490000\n1984-12-18,163.610001,168.110001,163.610001,168.110001,168.110001,169000000\n1984-12-19,168.110001,169.029999,166.839996,167.160004,167.160004,139600000\n1984-12-20,167.160004,167.580002,166.289993,166.380005,166.380005,93220000\n1984-12-21,166.339996,166.380005,164.619995,165.509995,165.509995,101200000\n1984-12-24,165.509995,166.929993,165.500000,166.759995,166.759995,55550000\n1984-12-26,166.759995,166.759995,166.289993,166.470001,166.470001,46700000\n1984-12-27,166.470001,166.500000,165.619995,165.750000,165.750000,70100000\n1984-12-28,165.750000,166.320007,165.669998,166.259995,166.259995,77070000\n1984-12-31,166.259995,167.339996,166.059998,167.240005,167.240005,80260000\n1985-01-02,167.199997,167.199997,165.190002,165.369995,165.369995,67820000\n1985-01-03,165.369995,166.110001,164.380005,164.570007,164.570007,88880000\n1985-01-04,164.550003,164.550003,163.360001,163.679993,163.679993,77480000\n1985-01-07,163.679993,164.710007,163.679993,164.240005,164.240005,86190000\n1985-01-08,164.240005,164.589996,163.910004,163.990005,163.990005,92110000\n1985-01-09,163.990005,165.570007,163.990005,165.179993,165.179993,99230000\n1985-01-10,165.179993,168.309998,164.990005,168.309998,168.309998,124700000\n1985-01-11,168.309998,168.720001,167.580002,167.910004,167.910004,107600000\n1985-01-14,167.910004,170.550003,167.580002,170.509995,170.509995,124900000\n1985-01-15,170.509995,171.820007,170.399994,170.809998,170.809998,155300000\n1985-01-16,170.809998,171.940002,170.410004,171.190002,171.190002,135500000\n1985-01-17,171.190002,171.339996,170.220001,170.729996,170.729996,113600000\n1985-01-18,170.729996,171.419998,170.660004,171.320007,171.320007,104700000\n1985-01-21,171.320007,175.449997,171.309998,175.229996,175.229996,146800000\n1985-01-22,175.229996,176.630005,175.139999,175.479996,175.479996,174800000\n1985-01-23,175.479996,177.300003,175.149994,177.300003,177.300003,144400000\n1985-01-24,177.300003,178.160004,176.559998,176.710007,176.710007,160700000\n1985-01-25,176.710007,177.750000,176.539993,177.350006,177.350006,122400000\n1985-01-28,177.350006,178.190002,176.559998,177.399994,177.399994,128400000\n1985-01-29,177.399994,179.190002,176.580002,179.179993,179.179993,115700000\n1985-01-30,179.179993,180.270004,179.050003,179.389999,179.389999,170000000\n1985-01-31,179.389999,179.830002,178.559998,179.630005,179.630005,132500000\n1985-02-01,179.630005,179.630005,178.440002,178.630005,178.630005,105400000\n1985-02-04,178.630005,180.350006,177.750000,180.350006,180.350006,113700000\n1985-02-05,180.350006,181.529999,180.070007,180.610001,180.610001,143900000\n1985-02-06,180.610001,181.500000,180.320007,180.429993,180.429993,141000000\n1985-02-07,180.429993,181.960007,180.429993,181.820007,181.820007,151700000\n1985-02-08,181.820007,182.389999,181.669998,182.190002,182.190002,116500000\n1985-02-11,182.190002,182.190002,180.110001,180.509995,180.509995,104000000\n1985-02-12,180.509995,180.750000,179.449997,180.559998,180.559998,111100000\n1985-02-13,180.559998,183.860001,180.500000,183.350006,183.350006,142500000\n1985-02-14,183.350006,183.949997,182.389999,182.410004,182.410004,139700000\n1985-02-15,182.410004,182.649994,181.229996,181.600006,181.600006,106500000\n1985-02-19,181.600006,181.610001,180.949997,181.330002,181.330002,90400000\n1985-02-20,181.330002,182.100006,180.639999,181.179993,181.179993,118200000\n1985-02-21,181.179993,181.179993,180.020004,180.190002,180.190002,104000000\n1985-02-22,180.190002,180.410004,179.229996,179.360001,179.360001,93680000\n1985-02-25,179.360001,179.360001,178.130005,179.229996,179.229996,89740000\n1985-02-26,179.229996,181.580002,179.160004,181.169998,181.169998,114200000\n1985-02-27,181.169998,181.869995,180.500000,180.710007,180.710007,107700000\n1985-02-28,180.710007,181.210007,180.330002,181.179993,181.179993,100700000\n1985-03-01,181.179993,183.889999,181.160004,183.229996,183.229996,139900000\n1985-03-04,183.229996,183.410004,181.399994,182.059998,182.059998,102100000\n1985-03-05,182.059998,182.649994,181.419998,182.229996,182.229996,116400000\n1985-03-06,182.229996,182.250000,180.589996,180.649994,180.649994,116900000\n1985-03-07,180.649994,180.649994,179.440002,179.509995,179.509995,112100000\n1985-03-08,179.509995,179.970001,179.070007,179.100006,179.100006,96390000\n1985-03-11,179.100006,179.460007,178.149994,178.789993,178.789993,84110000\n1985-03-12,178.789993,180.139999,178.699997,179.660004,179.660004,92840000\n1985-03-13,179.660004,179.960007,178.020004,178.190002,178.190002,101700000\n1985-03-14,178.190002,178.529999,177.610001,177.839996,177.839996,103400000\n1985-03-15,177.839996,178.410004,176.529999,176.529999,176.529999,105200000\n1985-03-18,176.529999,177.660004,176.529999,176.880005,176.880005,94020000\n1985-03-19,176.880005,179.559998,176.869995,179.539993,179.539993,119200000\n1985-03-20,179.539993,179.779999,178.789993,179.080002,179.080002,107500000\n1985-03-21,179.080002,180.220001,178.889999,179.350006,179.350006,95930000\n1985-03-22,179.350006,179.919998,178.860001,179.039993,179.039993,99250000\n1985-03-25,179.039993,179.039993,177.850006,177.970001,177.970001,74040000\n1985-03-26,177.970001,178.860001,177.880005,178.429993,178.429993,89930000\n1985-03-27,178.429993,179.800003,178.429993,179.539993,179.539993,101000000\n1985-03-28,179.539993,180.600006,179.429993,179.539993,179.539993,99780000\n1985-03-29,179.539993,180.660004,179.539993,180.660004,180.660004,101400000\n1985-04-01,180.660004,181.270004,180.429993,181.270004,181.270004,89900000\n1985-04-02,181.270004,181.860001,180.279999,180.529999,180.529999,101700000\n1985-04-03,180.529999,180.529999,178.639999,179.110001,179.110001,95480000\n1985-04-04,179.110001,179.130005,178.289993,179.029999,179.029999,86910000\n1985-04-08,179.029999,179.460007,177.860001,178.029999,178.029999,79960000\n1985-04-09,178.029999,178.669998,177.970001,178.210007,178.210007,83980000\n1985-04-10,178.210007,179.899994,178.210007,179.419998,179.419998,108200000\n1985-04-11,179.419998,180.910004,179.419998,180.190002,180.190002,108400000\n1985-04-12,180.190002,180.550003,180.059998,180.539993,180.539993,86220000\n1985-04-15,180.539993,181.149994,180.449997,180.919998,180.919998,80660000\n1985-04-16,180.919998,181.779999,180.190002,181.199997,181.199997,98480000\n1985-04-17,181.199997,181.910004,181.139999,181.679993,181.679993,96020000\n1985-04-18,181.679993,182.559998,180.750000,180.839996,180.839996,100600000\n1985-04-19,180.839996,181.250000,180.419998,181.110001,181.110001,81110000\n1985-04-22,181.110001,181.229996,180.250000,180.699997,180.699997,79930000\n1985-04-23,180.699997,181.970001,180.339996,181.880005,181.880005,108900000\n1985-04-24,181.880005,182.270004,181.740005,182.259995,182.259995,99600000\n1985-04-25,182.259995,183.429993,182.119995,183.429993,183.429993,108600000\n1985-04-26,183.429993,183.610001,182.110001,182.179993,182.179993,86570000\n1985-04-29,182.179993,182.339996,180.619995,180.630005,180.630005,88860000\n1985-04-30,180.630005,180.630005,178.860001,179.830002,179.830002,111800000\n1985-05-01,179.830002,180.039993,178.350006,178.369995,178.369995,101600000\n1985-05-02,178.369995,179.009995,178.369995,179.009995,179.009995,107700000\n1985-05-03,179.009995,180.300003,179.009995,180.080002,180.080002,94870000\n1985-05-06,180.080002,180.559998,179.820007,179.990005,179.990005,85650000\n1985-05-07,179.990005,181.089996,179.869995,180.759995,180.759995,100200000\n1985-05-08,180.759995,180.759995,179.960007,180.619995,180.619995,101300000\n1985-05-09,180.619995,181.970001,180.619995,181.919998,181.919998,111000000\n1985-05-10,181.919998,184.740005,181.919998,184.279999,184.279999,140300000\n1985-05-13,184.279999,184.610001,184.190002,184.610001,184.610001,85830000\n1985-05-14,184.610001,185.169998,183.649994,183.869995,183.869995,97360000\n1985-05-15,183.869995,185.429993,183.860001,184.539993,184.539993,106100000\n1985-05-16,184.539993,185.740005,184.539993,185.660004,185.660004,99420000\n1985-05-17,185.660004,187.940002,185.470001,187.419998,187.419998,124600000\n1985-05-20,187.419998,189.979996,187.419998,189.720001,189.720001,146300000\n1985-05-21,189.720001,189.809998,188.779999,189.639999,189.639999,130200000\n1985-05-22,189.639999,189.639999,187.710007,188.559998,188.559998,101400000\n1985-05-23,188.559998,188.559998,187.449997,187.600006,187.600006,101000000\n1985-05-24,187.600006,188.289993,187.289993,188.289993,188.289993,85970000\n1985-05-28,188.289993,188.940002,187.380005,187.860001,187.860001,90600000\n1985-05-29,187.860001,187.860001,187.110001,187.679993,187.679993,96540000\n1985-05-30,187.679993,188.039993,187.089996,187.750000,187.750000,108300000\n1985-05-31,187.750000,189.589996,187.449997,189.550003,189.550003,134100000\n1985-06-03,189.550003,190.360001,188.929993,189.320007,189.320007,125000000\n1985-06-04,189.320007,190.270004,188.880005,190.039993,190.039993,115400000\n1985-06-05,190.039993,191.020004,190.039993,190.160004,190.160004,143900000\n1985-06-06,189.750000,191.059998,189.130005,191.059998,191.059998,117200000\n1985-06-07,191.059998,191.289993,189.550003,189.679993,189.679993,99630000\n1985-06-10,189.679993,189.679993,188.820007,189.509995,189.509995,87940000\n1985-06-11,189.509995,189.610001,188.779999,189.039993,189.039993,102100000\n1985-06-12,189.039993,189.039993,187.589996,187.610001,187.610001,97700000\n1985-06-13,187.610001,187.610001,185.029999,185.330002,185.330002,107000000\n1985-06-14,185.330002,187.100006,185.330002,187.100006,187.100006,93090000\n1985-06-17,187.100006,187.100006,185.979996,186.529999,186.529999,82170000\n1985-06-18,186.529999,187.649994,186.509995,187.339996,187.339996,106900000\n1985-06-19,187.339996,187.979996,186.630005,186.630005,186.630005,108300000\n1985-06-20,186.630005,186.740005,185.970001,186.729996,186.729996,87500000\n1985-06-21,186.729996,189.660004,186.429993,189.610001,189.610001,125400000\n1985-06-24,188.770004,189.610001,187.839996,189.149994,189.149994,96040000\n1985-06-25,189.149994,190.960007,189.149994,189.740005,189.740005,115700000\n1985-06-26,189.740005,190.259995,189.440002,190.059998,190.059998,94130000\n1985-06-27,190.059998,191.360001,190.059998,191.229996,191.229996,106700000\n1985-06-28,191.229996,191.850006,191.039993,191.850006,191.850006,105200000\n1985-07-01,191.850006,192.429993,191.169998,192.429993,192.429993,96080000\n1985-07-02,192.429993,192.630005,191.839996,192.009995,192.009995,111100000\n1985-07-03,192.009995,192.080002,191.369995,191.449997,191.449997,98410000\n1985-07-05,191.449997,192.669998,191.449997,192.520004,192.520004,62450000\n1985-07-08,192.470001,192.520004,191.259995,191.929993,191.929993,83670000\n1985-07-09,191.929993,191.929993,190.809998,191.050003,191.050003,99060000\n1985-07-10,191.050003,192.369995,190.990005,192.369995,192.369995,108200000\n1985-07-11,192.369995,192.949997,192.279999,192.940002,192.940002,122800000\n1985-07-12,192.940002,193.320007,192.639999,193.289993,193.289993,120300000\n1985-07-15,193.289993,193.839996,192.550003,192.720001,192.720001,103900000\n1985-07-16,192.720001,194.720001,192.720001,194.720001,194.720001,132500000\n1985-07-17,194.860001,196.070007,194.720001,195.649994,195.649994,159900000\n1985-07-18,195.649994,195.649994,194.339996,194.380005,194.380005,131400000\n1985-07-19,194.380005,195.130005,194.279999,195.130005,195.130005,114800000\n1985-07-22,195.130005,195.130005,193.580002,194.350006,194.350006,93540000\n1985-07-23,194.350006,194.979996,192.279999,192.550003,192.550003,143600000\n1985-07-24,192.550003,192.550003,190.660004,191.580002,191.580002,128600000\n1985-07-25,191.580002,192.229996,191.169998,192.059998,192.059998,123300000\n1985-07-26,192.059998,192.779999,191.580002,192.399994,192.399994,107000000\n1985-07-29,192.399994,192.419998,189.529999,189.600006,189.600006,95960000\n1985-07-30,189.619995,190.050003,189.300003,189.929993,189.929993,102300000\n1985-07-31,189.929993,191.330002,189.929993,190.919998,190.919998,124200000\n1985-08-01,190.919998,192.169998,190.910004,192.110001,192.110001,121500000\n1985-08-02,192.110001,192.110001,191.270004,191.479996,191.479996,87860000\n1985-08-05,191.479996,191.479996,189.949997,190.619995,190.619995,79610000\n1985-08-06,190.619995,190.720001,187.869995,187.929993,187.929993,104000000\n1985-08-07,187.929993,187.929993,187.389999,187.679993,187.679993,100000000\n1985-08-08,187.679993,188.960007,187.679993,188.949997,188.949997,102900000\n1985-08-09,188.949997,189.050003,188.110001,188.320007,188.320007,81750000\n1985-08-12,188.320007,188.320007,187.429993,187.630005,187.630005,77340000\n1985-08-13,187.630005,188.149994,186.509995,187.300003,187.300003,80300000\n1985-08-14,187.300003,187.869995,187.300003,187.410004,187.410004,85780000\n1985-08-15,187.410004,187.740005,186.619995,187.259995,187.259995,86100000\n1985-08-16,187.259995,187.259995,186.100006,186.100006,186.100006,87910000\n1985-08-19,186.100006,186.820007,186.100006,186.380005,186.380005,67930000\n1985-08-20,186.380005,188.270004,186.380005,188.080002,188.080002,91230000\n1985-08-21,188.080002,189.160004,188.080002,189.160004,189.160004,94880000\n1985-08-22,189.110001,189.229996,187.199997,187.360001,187.360001,90600000\n1985-08-23,187.220001,187.350006,186.589996,187.169998,187.169998,75270000\n1985-08-26,187.169998,187.440002,186.460007,187.309998,187.309998,70290000\n1985-08-27,187.309998,188.100006,187.309998,188.100006,188.100006,82140000\n1985-08-28,188.100006,188.830002,187.899994,188.830002,188.830002,88530000\n1985-08-29,188.729996,188.940002,188.380005,188.929993,188.929993,85660000\n1985-08-30,188.929993,189.130005,188.000000,188.630005,188.630005,81620000\n1985-09-03,188.630005,188.630005,187.380005,187.910004,187.910004,81190000\n1985-09-04,187.910004,187.919998,186.970001,187.369995,187.369995,85510000\n1985-09-05,187.369995,187.520004,186.889999,187.270004,187.270004,94480000\n1985-09-06,187.270004,188.429993,187.270004,188.240005,188.240005,95040000\n1985-09-09,188.240005,188.800003,187.899994,188.250000,188.250000,89850000\n1985-09-10,188.250000,188.259995,186.500000,186.899994,186.899994,104700000\n1985-09-11,186.899994,186.899994,184.789993,185.029999,185.029999,100400000\n1985-09-12,185.029999,185.210007,183.490005,183.690002,183.690002,107100000\n1985-09-13,183.690002,184.190002,182.050003,182.910004,182.910004,111400000\n1985-09-16,182.910004,182.910004,182.449997,182.880005,182.880005,66700000\n1985-09-17,182.880005,182.880005,180.779999,181.360001,181.360001,111900000\n1985-09-18,181.360001,181.830002,180.809998,181.710007,181.710007,105700000\n1985-09-19,181.710007,183.399994,181.710007,183.389999,183.389999,100300000\n1985-09-20,183.389999,183.990005,182.039993,182.050003,182.050003,101400000\n1985-09-23,182.050003,184.649994,182.050003,184.300003,184.300003,104800000\n1985-09-24,184.300003,184.300003,182.419998,182.619995,182.619995,97870000\n1985-09-25,182.619995,182.619995,180.619995,180.660004,180.660004,92120000\n1985-09-26,180.660004,181.289993,179.449997,181.289993,181.289993,106100000\n1985-09-30,181.300003,182.080002,181.220001,182.080002,182.080002,103600000\n1985-10-01,182.059998,185.080002,182.020004,185.070007,185.070007,130200000\n1985-10-02,185.070007,185.940002,184.059998,184.059998,184.059998,147300000\n1985-10-03,184.059998,185.169998,183.589996,184.360001,184.360001,127500000\n1985-10-04,184.360001,184.360001,182.649994,183.220001,183.220001,101200000\n1985-10-07,183.220001,183.220001,181.300003,181.869995,181.869995,95550000\n1985-10-08,181.869995,182.300003,181.160004,181.869995,181.869995,97170000\n1985-10-09,181.869995,183.270004,181.869995,182.520004,182.520004,99140000\n1985-10-10,182.520004,182.789993,182.050003,182.779999,182.779999,90910000\n1985-10-11,182.779999,184.279999,182.610001,184.279999,184.279999,96370000\n1985-10-14,184.309998,186.369995,184.279999,186.369995,186.369995,78540000\n1985-10-15,186.369995,187.160004,185.660004,186.080002,186.080002,110400000\n1985-10-16,186.080002,187.979996,186.080002,187.979996,187.979996,117400000\n1985-10-17,187.979996,188.520004,187.419998,187.660004,187.660004,140500000\n1985-10-18,187.660004,188.110001,186.889999,187.039993,187.039993,107100000\n1985-10-21,187.039993,187.300003,186.789993,186.960007,186.960007,95680000\n1985-10-22,186.960007,188.559998,186.960007,188.039993,188.039993,111300000\n1985-10-23,188.039993,189.089996,188.039993,189.089996,189.089996,121700000\n1985-10-24,189.089996,189.449997,188.410004,188.500000,188.500000,123100000\n1985-10-25,188.500000,188.509995,187.320007,187.520004,187.520004,101800000\n1985-10-28,187.520004,187.759995,186.929993,187.759995,187.759995,97880000\n1985-10-29,187.759995,189.779999,187.759995,189.229996,189.229996,110600000\n1985-10-30,189.229996,190.089996,189.139999,190.070007,190.070007,120400000\n1985-10-31,190.070007,190.149994,189.350006,189.820007,189.820007,121500000\n1985-11-01,189.820007,191.529999,189.369995,191.529999,191.529999,129400000\n1985-11-04,191.449997,191.960007,190.660004,191.250000,191.250000,104900000\n1985-11-05,191.250000,192.429993,190.990005,192.369995,192.369995,119200000\n1985-11-06,192.369995,193.009995,191.830002,192.759995,192.759995,129500000\n1985-11-07,192.779999,192.960007,192.160004,192.619995,192.619995,119000000\n1985-11-08,192.619995,193.970001,192.529999,193.720001,193.720001,115000000\n1985-11-11,193.720001,197.289993,193.699997,197.279999,197.279999,126500000\n1985-11-12,197.279999,198.660004,196.970001,198.080002,198.080002,170800000\n1985-11-13,198.080002,198.110001,196.910004,197.100006,197.100006,109700000\n1985-11-14,197.100006,199.190002,196.880005,199.059998,199.059998,124900000\n1985-11-15,199.059998,199.580002,197.899994,198.110001,198.110001,130200000\n1985-11-18,198.110001,198.710007,197.509995,198.710007,198.710007,108400000\n1985-11-19,198.710007,199.520004,198.009995,198.669998,198.669998,126100000\n1985-11-20,198.669998,199.199997,198.520004,198.990005,198.990005,105100000\n1985-11-21,198.990005,201.429993,198.990005,201.410004,201.410004,150300000\n1985-11-22,201.410004,202.009995,201.050003,201.520004,201.520004,133800000\n1985-11-25,201.520004,201.520004,200.080002,200.350006,200.350006,91710000\n1985-11-26,200.350006,201.160004,200.110001,200.669998,200.669998,123100000\n1985-11-27,200.669998,202.649994,200.669998,202.539993,202.539993,143700000\n1985-11-29,202.539993,203.399994,201.919998,202.169998,202.169998,84060000\n1985-12-02,202.169998,202.190002,200.199997,200.460007,200.460007,103500000\n1985-12-03,200.460007,200.979996,200.100006,200.860001,200.860001,109700000\n1985-12-04,200.860001,204.229996,200.860001,204.229996,204.229996,153200000\n1985-12-05,204.229996,205.860001,203.789993,203.880005,203.880005,181000000\n1985-12-06,203.880005,203.880005,202.449997,202.990005,202.990005,125500000\n1985-12-09,202.990005,204.649994,202.979996,204.250000,204.250000,144000000\n1985-12-10,204.250000,205.160004,203.679993,204.389999,204.389999,156500000\n1985-12-11,204.389999,206.679993,204.169998,206.309998,206.309998,178500000\n1985-12-12,206.309998,207.649994,205.830002,206.729996,206.729996,170500000\n1985-12-13,206.729996,210.309998,206.729996,209.940002,209.940002,177900000\n1985-12-16,209.940002,213.080002,209.910004,212.020004,212.020004,176000000\n1985-12-17,212.020004,212.449997,210.580002,210.649994,210.649994,155200000\n1985-12-18,210.649994,211.229996,209.240005,209.809998,209.809998,137900000\n1985-12-19,209.809998,210.130005,209.250000,210.020004,210.020004,130200000\n1985-12-20,210.020004,211.770004,210.020004,210.940002,210.940002,170300000\n1985-12-23,210.570007,210.940002,208.440002,208.570007,208.570007,107900000\n1985-12-24,208.570007,208.570007,206.440002,207.139999,207.139999,78300000\n1985-12-26,207.139999,207.759995,207.050003,207.649994,207.649994,62050000\n1985-12-27,207.649994,209.619995,207.649994,209.610001,209.610001,81560000\n1985-12-30,209.610001,210.699997,209.169998,210.679993,210.679993,91970000\n1985-12-31,210.679993,211.610001,210.679993,211.279999,211.279999,112700000\n1986-01-02,211.279999,211.279999,208.929993,209.589996,209.589996,98960000\n1986-01-03,209.589996,210.880005,209.509995,210.880005,210.880005,105000000\n1986-01-06,210.880005,210.979996,209.929993,210.649994,210.649994,99610000\n1986-01-07,210.649994,213.800003,210.649994,213.800003,213.800003,153000000\n1986-01-08,213.800003,214.570007,207.490005,207.970001,207.970001,180300000\n1986-01-09,207.970001,207.970001,204.509995,206.110001,206.110001,176500000\n1986-01-10,206.110001,207.330002,205.520004,205.960007,205.960007,122800000\n1986-01-13,205.960007,206.830002,205.520004,206.720001,206.720001,108700000\n1986-01-14,206.720001,207.369995,206.059998,206.639999,206.639999,113900000\n1986-01-15,206.639999,208.270004,206.639999,208.259995,208.259995,122400000\n1986-01-16,208.259995,209.179993,207.610001,209.169998,209.169998,130500000\n1986-01-17,209.169998,209.399994,207.589996,208.429993,208.429993,132100000\n1986-01-20,208.429993,208.429993,206.619995,207.529999,207.529999,85340000\n1986-01-21,207.529999,207.779999,205.050003,205.789993,205.789993,128300000\n1986-01-22,205.789993,206.029999,203.410004,203.490005,203.490005,131200000\n1986-01-23,203.490005,204.429993,202.600006,204.250000,204.250000,130300000\n1986-01-24,204.250000,206.429993,204.250000,206.429993,206.429993,128900000\n1986-01-27,206.429993,207.690002,206.429993,207.389999,207.389999,122900000\n1986-01-28,207.419998,209.820007,207.399994,209.809998,209.809998,145700000\n1986-01-29,209.809998,212.360001,209.809998,210.289993,210.289993,193800000\n1986-01-30,210.289993,211.539993,209.149994,209.330002,209.330002,125300000\n1986-01-31,209.330002,212.419998,209.190002,211.779999,211.779999,143500000\n1986-02-03,211.779999,214.179993,211.600006,213.960007,213.960007,145300000\n1986-02-04,213.960007,214.570007,210.820007,212.789993,212.789993,175700000\n1986-02-05,212.839996,213.029999,211.210007,212.960007,212.960007,134300000\n1986-02-06,212.960007,214.509995,212.600006,213.470001,213.470001,146100000\n1986-02-07,213.470001,215.270004,211.130005,214.559998,214.559998,144400000\n1986-02-10,214.559998,216.240005,214.470001,216.240005,216.240005,129900000\n1986-02-11,216.240005,216.669998,215.539993,215.919998,215.919998,141300000\n1986-02-12,215.919998,216.279999,215.130005,215.970001,215.970001,136400000\n1986-02-13,215.970001,217.410004,215.380005,217.399994,217.399994,136500000\n1986-02-14,217.399994,219.759995,217.220001,219.759995,219.759995,155600000\n1986-02-18,219.759995,222.449997,219.259995,222.449997,222.449997,160200000\n1986-02-19,222.449997,222.960007,219.729996,219.759995,219.759995,152000000\n1986-02-20,219.759995,222.220001,219.220001,222.220001,222.220001,139700000\n1986-02-21,222.220001,224.619995,222.220001,224.619995,224.619995,177600000\n1986-02-24,224.580002,225.289993,223.309998,224.339996,224.339996,144700000\n1986-02-25,224.339996,224.399994,222.630005,223.789993,223.789993,148000000\n1986-02-26,223.720001,224.589996,223.149994,224.039993,224.039993,158000000\n1986-02-27,224.039993,226.880005,223.410004,226.770004,226.770004,181700000\n1986-02-28,226.770004,227.919998,225.419998,226.919998,226.919998,191700000\n1986-03-03,226.919998,226.919998,224.410004,225.419998,225.419998,142700000\n1986-03-04,225.419998,227.330002,223.940002,224.380005,224.380005,174500000\n1986-03-05,224.139999,224.369995,222.179993,224.339996,224.339996,154600000\n1986-03-06,224.389999,225.500000,224.130005,225.130005,225.130005,159000000\n1986-03-07,225.130005,226.330002,224.440002,225.570007,225.570007,163200000\n1986-03-10,225.570007,226.979996,225.360001,226.580002,226.580002,129900000\n1986-03-11,226.580002,231.809998,226.580002,231.690002,231.690002,187300000\n1986-03-12,231.690002,234.699997,231.679993,232.539993,232.539993,210300000\n1986-03-13,232.539993,233.889999,231.270004,233.190002,233.190002,171500000\n1986-03-14,233.190002,236.550003,232.580002,236.550003,236.550003,181900000\n1986-03-17,236.550003,236.550003,233.690002,234.669998,234.669998,137500000\n1986-03-18,234.669998,236.520004,234.139999,235.779999,235.779999,148000000\n1986-03-19,235.779999,236.520004,235.130005,235.600006,235.600006,150000000\n1986-03-20,235.600006,237.089996,235.600006,236.539993,236.539993,148000000\n1986-03-21,236.539993,237.350006,233.289993,233.339996,233.339996,199100000\n1986-03-24,233.339996,235.330002,232.919998,235.330002,235.330002,143800000\n1986-03-25,235.330002,235.330002,233.619995,234.720001,234.720001,139300000\n1986-03-26,234.720001,237.789993,234.710007,237.300003,237.300003,161500000\n1986-03-27,237.300003,240.110001,237.300003,238.970001,238.970001,178100000\n1986-03-31,238.970001,239.860001,238.080002,238.899994,238.899994,134400000\n1986-04-01,238.899994,239.100006,234.570007,235.139999,235.139999,167400000\n1986-04-02,235.139999,235.710007,233.399994,235.710007,235.710007,145300000\n1986-04-03,235.710007,236.419998,232.070007,232.470001,232.470001,148200000\n1986-04-04,232.470001,232.559998,228.320007,228.690002,228.690002,147300000\n1986-04-07,228.690002,228.830002,226.300003,228.630005,228.630005,129800000\n1986-04-08,228.630005,233.699997,228.630005,233.520004,233.520004,146300000\n1986-04-09,233.520004,235.570007,232.130005,233.750000,233.750000,156300000\n1986-04-10,233.750000,236.539993,233.750000,236.440002,236.440002,184800000\n1986-04-11,236.440002,237.850006,235.130005,235.970001,235.970001,139400000\n1986-04-14,235.970001,237.479996,235.429993,237.279999,237.279999,106700000\n1986-04-15,237.279999,238.089996,236.639999,237.729996,237.729996,123700000\n1986-04-16,237.729996,242.570007,237.729996,242.220001,242.220001,173800000\n1986-04-17,242.220001,243.360001,241.889999,243.029999,243.029999,161400000\n1986-04-18,243.029999,243.470001,241.740005,242.380005,242.380005,153600000\n1986-04-21,242.380005,244.779999,241.880005,244.740005,244.740005,136100000\n1986-04-22,244.740005,245.470001,241.300003,242.419998,242.419998,161500000\n1986-04-23,242.419998,242.419998,240.080002,241.750000,241.750000,149700000\n1986-04-24,241.750000,243.130005,241.649994,242.020004,242.020004,146600000\n1986-04-25,242.020004,242.800003,240.910004,242.289993,242.289993,142300000\n1986-04-28,242.289993,243.080002,241.229996,243.080002,243.080002,123900000\n1986-04-29,243.080002,243.570007,239.229996,240.509995,240.509995,148800000\n1986-04-30,240.520004,240.520004,235.259995,235.520004,235.520004,147500000\n1986-05-01,235.520004,236.009995,234.210007,235.160004,235.160004,146500000\n1986-05-02,235.160004,236.520004,234.149994,234.789993,234.789993,126300000\n1986-05-05,234.789993,237.729996,234.789993,237.729996,237.729996,102400000\n1986-05-06,237.729996,238.279999,236.259995,237.240005,237.240005,121200000\n1986-05-07,236.559998,237.240005,233.979996,236.080002,236.080002,129900000\n1986-05-08,236.080002,237.960007,236.080002,237.130005,237.130005,136000000\n1986-05-09,237.130005,238.009995,235.850006,237.850006,237.850006,137400000\n1986-05-12,237.850006,238.529999,237.020004,237.580002,237.580002,125400000\n1986-05-13,237.580002,237.869995,236.020004,236.410004,236.410004,119200000\n1986-05-14,236.410004,237.539993,235.850006,237.539993,237.539993,132100000\n1986-05-15,237.539993,237.539993,233.929993,234.429993,234.429993,131600000\n1986-05-16,234.429993,234.429993,232.259995,232.759995,232.759995,113500000\n1986-05-19,232.759995,233.539993,232.410004,233.199997,233.199997,85840000\n1986-05-20,233.199997,236.119995,232.580002,236.110001,236.110001,113000000\n1986-05-21,236.110001,236.830002,235.449997,235.449997,235.449997,117100000\n1986-05-22,235.449997,240.250000,235.449997,240.119995,240.119995,144900000\n1986-05-23,240.119995,242.160004,240.119995,241.350006,241.350006,130200000\n1986-05-27,241.350006,244.759995,241.350006,244.750000,244.750000,121200000\n1986-05-28,244.750000,247.399994,244.750000,246.630005,246.630005,159600000\n1986-05-29,246.630005,248.320007,245.289993,247.979996,247.979996,135700000\n1986-05-30,247.979996,249.190002,246.429993,247.350006,247.350006,151200000\n1986-06-02,246.039993,247.740005,243.830002,245.039993,245.039993,120600000\n1986-06-03,245.039993,245.509995,243.669998,245.509995,245.509995,114700000\n1986-06-04,245.509995,246.300003,242.589996,243.940002,243.940002,117000000\n1986-06-05,243.940002,245.660004,243.410004,245.649994,245.649994,110900000\n1986-06-06,245.649994,246.070007,244.429993,245.669998,245.669998,110900000\n1986-06-09,245.669998,245.669998,239.679993,239.960007,239.960007,123300000\n1986-06-10,239.960007,240.080002,238.229996,239.580002,239.580002,125000000\n1986-06-11,239.580002,241.130005,239.210007,241.130005,241.130005,127400000\n1986-06-12,241.240005,241.639999,240.699997,241.490005,241.490005,109100000\n1986-06-13,241.710007,245.910004,241.710007,245.729996,245.729996,141200000\n1986-06-16,245.729996,246.500000,245.169998,246.130005,246.130005,112100000\n1986-06-17,246.130005,246.259995,243.600006,244.350006,244.350006,123100000\n1986-06-18,244.350006,245.250000,242.570007,244.990005,244.990005,117000000\n1986-06-19,244.990005,245.800003,244.050003,244.059998,244.059998,129000000\n1986-06-20,244.059998,247.600006,243.979996,247.580002,247.580002,149100000\n1986-06-23,247.580002,247.580002,244.449997,245.259995,245.259995,123800000\n1986-06-24,245.259995,248.259995,244.529999,247.029999,247.029999,140600000\n1986-06-25,247.029999,250.130005,247.029999,248.929993,248.929993,161800000\n1986-06-26,248.929993,249.429993,247.720001,248.740005,248.740005,134100000\n1986-06-27,248.740005,249.740005,248.740005,249.600006,249.600006,123800000\n1986-06-30,249.600006,251.809998,249.600006,250.839996,250.839996,135100000\n1986-07-01,250.669998,252.039993,250.529999,252.039993,252.039993,147700000\n1986-07-02,252.039993,253.199997,251.789993,252.699997,252.699997,150000000\n1986-07-03,252.699997,252.940002,251.229996,251.789993,251.789993,108300000\n1986-07-07,251.789993,251.809998,243.630005,244.050003,244.050003,138200000\n1986-07-08,244.050003,244.059998,239.070007,241.589996,241.589996,174100000\n1986-07-09,241.589996,243.070007,241.460007,242.820007,242.820007,142900000\n1986-07-10,242.820007,243.440002,239.660004,243.009995,243.009995,146200000\n1986-07-11,243.009995,243.479996,241.679993,242.220001,242.220001,124500000\n1986-07-14,242.220001,242.220001,238.039993,238.110001,238.110001,123200000\n1986-07-15,238.089996,238.119995,233.600006,233.660004,233.660004,184000000\n1986-07-16,233.660004,236.190002,233.660004,235.009995,235.009995,160800000\n1986-07-17,235.009995,236.649994,235.009995,236.070007,236.070007,132400000\n1986-07-18,236.070007,238.220001,233.940002,236.360001,236.360001,149700000\n1986-07-21,236.360001,236.449997,235.529999,236.240005,236.240005,106300000\n1986-07-22,236.240005,238.419998,235.919998,238.179993,238.179993,138500000\n1986-07-23,238.190002,239.250000,238.169998,238.669998,238.669998,133300000\n1986-07-24,238.690002,239.050003,237.320007,237.949997,237.949997,134700000\n1986-07-25,237.990005,240.360001,237.949997,240.220001,240.220001,132000000\n1986-07-28,240.199997,240.250000,235.229996,236.009995,236.009995,128000000\n1986-07-29,235.720001,236.009995,234.399994,234.550003,234.550003,115700000\n1986-07-30,234.570007,237.380005,233.070007,236.589996,236.589996,146700000\n1986-07-31,236.589996,236.919998,235.889999,236.119995,236.119995,112700000\n1986-08-01,236.119995,236.889999,234.589996,234.910004,234.910004,114900000\n1986-08-04,234.910004,236.860001,231.919998,235.990005,235.990005,130000000\n1986-08-05,235.990005,238.309998,235.970001,237.029999,237.029999,153100000\n1986-08-06,237.029999,237.350006,235.479996,236.839996,236.839996,127500000\n1986-08-07,236.839996,238.020004,236.309998,237.039993,237.039993,122400000\n1986-08-08,237.039993,238.059998,236.369995,236.880005,236.880005,106300000\n1986-08-11,236.880005,241.199997,236.869995,240.679993,240.679993,125600000\n1986-08-12,240.679993,243.369995,240.350006,243.339996,243.339996,131700000\n1986-08-13,243.339996,246.509995,243.059998,245.669998,245.669998,156400000\n1986-08-14,245.669998,246.789993,245.529999,246.250000,246.250000,123800000\n1986-08-15,246.250000,247.149994,245.699997,247.149994,247.149994,123500000\n1986-08-18,247.149994,247.830002,245.479996,247.380005,247.380005,112800000\n1986-08-19,247.380005,247.419998,245.820007,246.509995,246.509995,109300000\n1986-08-20,246.529999,249.770004,246.509995,249.770004,249.770004,156600000\n1986-08-21,249.770004,250.449997,249.110001,249.669998,249.669998,135200000\n1986-08-22,249.669998,250.610001,249.270004,250.190002,250.190002,118100000\n1986-08-25,250.190002,250.259995,247.759995,247.809998,247.809998,104400000\n1986-08-26,247.809998,252.910004,247.809998,252.839996,252.839996,156600000\n1986-08-27,252.839996,254.240005,252.660004,253.300003,253.300003,143300000\n1986-08-28,253.300003,253.669998,251.910004,252.839996,252.839996,125100000\n1986-08-29,252.839996,254.070007,251.729996,252.929993,252.929993,125300000\n1986-09-02,252.929993,253.300003,248.139999,248.520004,248.520004,135500000\n1986-09-03,248.520004,250.080002,247.589996,250.080002,250.080002,154300000\n1986-09-04,250.080002,254.009995,250.029999,253.830002,253.830002,189400000\n1986-09-05,253.830002,254.130005,250.330002,250.470001,250.470001,180600000\n1986-09-08,250.470001,250.470001,247.020004,248.139999,248.139999,153300000\n1986-09-09,248.139999,250.210007,246.940002,247.669998,247.669998,137500000\n1986-09-10,247.669998,247.759995,246.110001,247.059998,247.059998,140300000\n1986-09-11,247.059998,247.059998,234.669998,235.179993,235.179993,237600000\n1986-09-12,235.179993,235.449997,228.740005,230.669998,230.669998,240500000\n1986-09-15,230.669998,232.820007,229.440002,231.940002,231.940002,155600000\n1986-09-16,231.929993,231.940002,228.320007,231.720001,231.720001,131200000\n1986-09-17,231.729996,233.809998,231.380005,231.679993,231.679993,141000000\n1986-09-18,231.669998,232.869995,230.570007,232.309998,232.309998,132200000\n1986-09-19,232.300003,232.309998,230.690002,232.210007,232.210007,153900000\n1986-09-22,232.199997,234.929993,232.199997,234.929993,234.929993,126100000\n1986-09-23,234.960007,235.880005,234.500000,235.669998,235.669998,132600000\n1986-09-24,235.660004,237.059998,235.529999,236.279999,236.279999,134600000\n1986-09-25,231.830002,236.279999,230.669998,231.830002,231.830002,134300000\n1986-09-26,231.830002,233.679993,230.639999,232.229996,232.229996,115300000\n1986-09-29,232.229996,232.229996,228.080002,229.910004,229.910004,115600000\n1986-09-30,229.910004,233.009995,229.910004,231.320007,231.320007,124900000\n1986-10-01,231.320007,234.619995,231.320007,233.600006,233.600006,143600000\n1986-10-02,233.600006,234.330002,232.770004,233.919998,233.919998,128100000\n1986-10-03,233.919998,236.160004,232.789993,233.710007,233.710007,128100000\n1986-10-06,233.710007,235.339996,233.169998,234.779999,234.779999,88250000\n1986-10-07,234.740005,235.179993,233.460007,234.410004,234.410004,125100000\n1986-10-08,234.410004,236.839996,233.679993,236.679993,236.679993,141700000\n1986-10-09,236.669998,238.199997,235.720001,235.850006,235.850006,153400000\n1986-10-10,235.839996,236.270004,235.309998,235.479996,235.479996,105100000\n1986-10-13,235.520004,235.910004,235.020004,235.910004,235.910004,54990000\n1986-10-14,235.899994,236.369995,234.369995,235.369995,235.369995,116800000\n1986-10-15,235.360001,239.029999,235.270004,238.800003,238.800003,144300000\n1986-10-16,238.830002,240.179993,238.800003,239.529999,239.529999,156900000\n1986-10-17,239.500000,239.529999,237.710007,238.839996,238.839996,124100000\n1986-10-20,238.839996,238.839996,234.779999,235.970001,235.970001,109000000\n1986-10-21,236.029999,236.490005,234.949997,235.880005,235.880005,110000000\n1986-10-22,235.889999,236.639999,235.820007,236.259995,236.259995,114000000\n1986-10-23,236.279999,239.759995,236.259995,239.279999,239.279999,150900000\n1986-10-24,239.300003,239.649994,238.250000,238.259995,238.259995,137500000\n1986-10-27,238.220001,238.770004,236.720001,238.770004,238.770004,133200000\n1986-10-28,238.809998,240.580002,238.770004,239.259995,239.259995,145900000\n1986-10-29,239.229996,241.000000,238.979996,240.940002,240.940002,164400000\n1986-10-30,240.970001,244.080002,240.940002,243.710007,243.710007,194200000\n1986-10-31,243.699997,244.509995,242.949997,243.979996,243.979996,147200000\n1986-11-03,243.970001,245.800003,243.929993,245.800003,245.800003,138200000\n1986-11-04,245.800003,246.429993,244.419998,246.199997,246.199997,163200000\n1986-11-05,246.089996,247.050003,245.210007,246.580002,246.580002,183200000\n1986-11-06,246.539993,246.899994,244.300003,245.869995,245.869995,165300000\n1986-11-07,245.850006,246.130005,244.919998,245.770004,245.770004,142300000\n1986-11-10,245.750000,246.220001,244.679993,246.130005,246.130005,120200000\n1986-11-11,246.149994,247.100006,246.119995,247.080002,247.080002,118500000\n1986-11-12,247.059998,247.669998,245.679993,246.639999,246.639999,162200000\n1986-11-13,246.630005,246.660004,242.979996,243.020004,243.020004,164000000\n1986-11-14,243.009995,244.509995,241.960007,244.500000,244.500000,172100000\n1986-11-17,244.500000,244.800003,242.289993,243.210007,243.210007,133300000\n1986-11-18,243.199997,243.229996,236.649994,236.779999,236.779999,185300000\n1986-11-19,236.770004,237.940002,235.509995,237.660004,237.660004,183300000\n1986-11-20,237.660004,242.050003,237.660004,242.050003,242.050003,158100000\n1986-11-21,242.029999,246.380005,241.970001,245.860001,245.860001,200700000\n1986-11-24,245.860001,248.000000,245.210007,247.449997,247.449997,150800000\n1986-11-25,247.440002,248.179993,246.300003,248.169998,248.169998,154600000\n1986-11-26,248.139999,248.899994,247.729996,248.770004,248.770004,152000000\n1986-11-28,248.820007,249.220001,248.070007,249.220001,249.220001,93530000\n1986-12-01,249.220001,249.220001,245.720001,249.050003,249.050003,133800000\n1986-12-02,249.059998,254.000000,249.050003,254.000000,254.000000,230400000\n1986-12-03,254.000000,254.869995,253.240005,253.850006,253.850006,200100000\n1986-12-04,253.850006,254.419998,252.880005,253.039993,253.039993,156900000\n1986-12-05,253.050003,253.889999,250.710007,251.169998,251.169998,139800000\n1986-12-08,251.160004,252.360001,248.820007,251.160004,251.160004,159000000\n1986-12-09,251.160004,251.270004,249.250000,249.279999,249.279999,128700000\n1986-12-10,249.279999,251.529999,248.940002,250.960007,250.960007,139700000\n1986-12-11,250.970001,250.979996,247.149994,248.169998,248.169998,136000000\n1986-12-12,248.169998,248.309998,247.020004,247.350006,247.350006,126600000\n1986-12-15,247.309998,248.229996,244.919998,248.210007,248.210007,148200000\n1986-12-16,248.210007,250.039993,247.399994,250.039993,250.039993,157000000\n1986-12-17,250.009995,250.039993,247.190002,247.559998,247.559998,148800000\n1986-12-18,247.559998,247.809998,246.449997,246.779999,246.779999,155400000\n1986-12-19,246.789993,249.960007,245.889999,249.729996,249.729996,244700000\n1986-12-22,249.729996,249.729996,247.449997,248.750000,248.750000,157600000\n1986-12-23,248.750000,248.750000,245.850006,246.339996,246.339996,188700000\n1986-12-24,246.339996,247.220001,246.020004,246.750000,246.750000,95410000\n1986-12-26,246.750000,247.089996,246.729996,246.919998,246.919998,48860000\n1986-12-29,246.899994,246.919998,244.309998,244.669998,244.669998,99800000\n1986-12-30,244.660004,244.669998,243.039993,243.369995,243.369995,126200000\n1986-12-31,243.369995,244.029999,241.279999,242.169998,242.169998,139200000\n1987-01-02,242.169998,246.449997,242.169998,246.449997,246.449997,91880000\n1987-01-05,246.449997,252.570007,246.449997,252.190002,252.190002,181900000\n1987-01-06,252.199997,253.990005,252.139999,252.779999,252.779999,189300000\n1987-01-07,252.779999,255.720001,252.649994,255.330002,255.330002,190900000\n1987-01-08,255.360001,257.279999,254.970001,257.279999,257.279999,194500000\n1987-01-09,257.260010,259.200012,256.109985,258.730011,258.730011,193000000\n1987-01-12,258.720001,261.359985,257.920013,260.299988,260.299988,184200000\n1987-01-13,260.299988,260.450012,259.209991,259.950012,259.950012,170900000\n1987-01-14,259.950012,262.720001,259.619995,262.640015,262.640015,214200000\n1987-01-15,262.649994,266.679993,262.640015,265.489990,265.489990,253100000\n1987-01-16,265.459991,267.239990,264.309998,266.279999,266.279999,218400000\n1987-01-19,266.260010,269.339996,264.000000,269.339996,269.339996,162800000\n1987-01-20,269.339996,271.029999,267.649994,269.040009,269.040009,224800000\n1987-01-21,269.040009,270.869995,267.350006,267.839996,267.839996,184200000\n1987-01-22,267.839996,274.049988,267.320007,273.910004,273.910004,188700000\n1987-01-23,273.910004,280.959991,268.410004,270.100006,270.100006,302400000\n1987-01-26,270.100006,270.399994,267.730011,269.609985,269.609985,138900000\n1987-01-27,269.609985,274.309998,269.609985,273.750000,273.750000,192300000\n1987-01-28,273.750000,275.709991,273.029999,275.399994,275.399994,195800000\n1987-01-29,275.399994,276.850006,272.540009,274.239990,274.239990,205300000\n1987-01-30,274.239990,274.239990,271.380005,274.079987,274.079987,163400000\n1987-02-02,274.079987,277.350006,273.160004,276.450012,276.450012,177400000\n1987-02-03,276.450012,277.829987,275.839996,275.989990,275.989990,198100000\n1987-02-04,275.989990,279.649994,275.350006,279.640015,279.640015,222400000\n1987-02-05,279.640015,282.260010,278.660004,281.160004,281.160004,256700000\n1987-02-06,281.160004,281.790009,279.869995,280.040009,280.040009,184100000\n1987-02-09,280.040009,280.040009,277.239990,278.160004,278.160004,143300000\n1987-02-10,278.160004,278.160004,273.489990,275.070007,275.070007,168300000\n1987-02-11,275.070007,277.709991,274.709991,277.540009,277.540009,172400000\n1987-02-12,277.540009,278.040009,273.890015,275.619995,275.619995,200400000\n1987-02-13,275.619995,280.910004,275.010010,279.700012,279.700012,184400000\n1987-02-17,279.700012,285.489990,279.700012,285.489990,285.489990,187800000\n1987-02-18,285.489990,287.549988,282.970001,285.420013,285.420013,218200000\n1987-02-19,285.420013,286.239990,283.839996,285.570007,285.570007,181500000\n1987-02-20,285.570007,285.980011,284.309998,285.480011,285.480011,175800000\n1987-02-23,285.480011,285.500000,279.369995,282.380005,282.380005,170500000\n1987-02-24,282.380005,283.329987,281.450012,282.880005,282.880005,151300000\n1987-02-25,282.880005,285.350006,282.140015,284.000000,284.000000,184100000\n1987-02-26,284.000000,284.399994,280.730011,282.959991,282.959991,165800000\n1987-02-27,282.959991,284.549988,282.769989,284.200012,284.200012,142800000\n1987-03-02,284.170013,284.829987,282.299988,283.000000,283.000000,156700000\n1987-03-03,283.000000,284.190002,282.920013,284.119995,284.119995,149200000\n1987-03-04,284.119995,288.619995,284.119995,288.619995,288.619995,198400000\n1987-03-05,288.619995,291.239990,288.600006,290.519989,290.519989,205400000\n1987-03-06,290.519989,290.670013,288.769989,290.660004,290.660004,181600000\n1987-03-09,290.660004,290.660004,287.119995,288.299988,288.299988,165400000\n1987-03-10,288.299988,290.869995,287.890015,290.859985,290.859985,174800000\n1987-03-11,290.869995,292.510010,289.329987,290.309998,290.309998,186900000\n1987-03-12,290.329987,291.910004,289.660004,291.220001,291.220001,174500000\n1987-03-13,291.220001,291.790009,289.880005,289.890015,289.890015,150900000\n1987-03-16,289.880005,289.890015,286.640015,288.230011,288.230011,134900000\n1987-03-17,288.089996,292.470001,287.959991,292.470001,292.470001,177300000\n1987-03-18,292.489990,294.579987,290.869995,292.779999,292.779999,198100000\n1987-03-19,292.730011,294.459991,292.260010,294.079987,294.079987,166100000\n1987-03-20,294.079987,298.170013,294.079987,298.170013,298.170013,234000000\n1987-03-23,298.160004,301.170013,297.500000,301.160004,301.160004,189100000\n1987-03-24,301.170013,301.920013,300.140015,301.640015,301.640015,189900000\n1987-03-25,301.519989,301.850006,299.359985,300.380005,300.380005,171300000\n1987-03-26,300.390015,302.720001,300.380005,300.929993,300.929993,196000000\n1987-03-27,300.959991,301.410004,296.059998,296.130005,296.130005,184400000\n1987-03-30,296.100006,296.130005,286.690002,289.200012,289.200012,208400000\n1987-03-31,289.209991,291.869995,289.070007,291.700012,291.700012,171800000\n1987-04-01,291.589996,292.380005,288.339996,292.380005,292.380005,182600000\n1987-04-02,292.410004,294.470001,292.019989,293.630005,293.630005,183000000\n1987-04-03,293.640015,301.299988,292.299988,300.410004,300.410004,213400000\n1987-04-06,300.459991,302.209991,300.410004,301.950012,301.950012,173700000\n1987-04-07,301.940002,303.649994,296.670013,296.690002,296.690002,186400000\n1987-04-08,296.720001,299.200012,295.179993,297.260010,297.260010,179800000\n1987-04-09,297.250000,297.709991,291.500000,292.859985,292.859985,180300000\n1987-04-10,292.820007,293.739990,290.940002,292.489990,292.489990,169500000\n1987-04-13,292.480011,293.359985,285.619995,285.619995,285.619995,181000000\n1987-04-14,285.609985,285.619995,275.670013,279.160004,279.160004,266500000\n1987-04-15,279.170013,285.140015,279.160004,284.440002,284.440002,198200000\n1987-04-16,284.450012,289.570007,284.440002,286.910004,286.910004,189600000\n1987-04-20,286.910004,288.359985,284.549988,286.089996,286.089996,139100000\n1987-04-21,285.880005,293.070007,282.890015,293.070007,293.070007,191300000\n1987-04-22,293.049988,293.459991,286.980011,287.190002,287.190002,185900000\n1987-04-23,287.190002,289.119995,284.279999,286.820007,286.820007,173900000\n1987-04-24,286.809998,286.820007,281.179993,281.519989,281.519989,178000000\n1987-04-27,281.519989,284.450012,276.220001,281.829987,281.829987,222700000\n1987-04-28,281.829987,285.950012,281.829987,282.510010,282.510010,180100000\n1987-04-29,282.579987,286.420013,282.579987,284.570007,284.570007,173600000\n1987-04-30,284.579987,290.079987,284.570007,288.359985,288.359985,183100000\n1987-05-01,286.989990,289.709991,286.519989,288.029999,288.029999,160100000\n1987-05-04,288.019989,289.989990,286.390015,289.359985,289.359985,140600000\n1987-05-05,289.359985,295.399994,289.339996,295.339996,295.339996,192300000\n1987-05-06,295.350006,296.190002,293.600006,295.470001,295.470001,196600000\n1987-05-07,295.450012,296.799988,294.070007,294.709991,294.709991,215200000\n1987-05-08,294.730011,296.179993,291.730011,293.369995,293.369995,161900000\n1987-05-11,293.369995,298.690002,291.549988,291.570007,291.570007,203700000\n1987-05-12,291.570007,293.299988,290.179993,293.299988,293.299988,155300000\n1987-05-13,293.309998,294.540009,290.739990,293.980011,293.980011,171000000\n1987-05-14,293.980011,295.100006,292.950012,294.239990,294.239990,152000000\n1987-05-15,294.230011,294.239990,287.109985,287.429993,287.429993,180800000\n1987-05-18,287.429993,287.429993,282.570007,286.649994,286.649994,174200000\n1987-05-19,286.660004,287.390015,278.829987,279.619995,279.619995,175400000\n1987-05-20,279.619995,280.890015,277.010010,278.209991,278.209991,206800000\n1987-05-21,278.230011,282.309998,278.209991,280.170013,280.170013,164800000\n1987-05-22,280.170013,283.329987,280.170013,282.160004,282.160004,135800000\n1987-05-26,282.160004,289.109985,282.160004,289.109985,289.109985,152500000\n1987-05-27,289.070007,290.779999,288.190002,288.730011,288.730011,171400000\n1987-05-28,288.730011,291.500000,286.329987,290.760010,290.760010,153800000\n1987-05-29,290.769989,292.869995,289.700012,290.100006,290.100006,153500000\n1987-06-01,290.119995,291.959991,289.230011,289.829987,289.829987,149300000\n1987-06-02,289.820007,290.940002,286.929993,288.459991,288.459991,153400000\n1987-06-03,288.559998,293.470001,288.559998,293.470001,293.470001,164200000\n1987-06-04,293.459991,295.089996,292.760010,295.089996,295.089996,140300000\n1987-06-05,295.109985,295.109985,292.799988,293.450012,293.450012,129100000\n1987-06-08,293.459991,297.029999,291.549988,296.720001,296.720001,136400000\n1987-06-09,296.720001,297.589996,295.899994,297.279999,297.279999,164200000\n1987-06-10,297.279999,300.809998,295.660004,297.470001,297.470001,197400000\n1987-06-11,297.500000,298.940002,297.470001,298.730011,298.730011,138900000\n1987-06-12,298.769989,302.260010,298.730011,301.619995,301.619995,175100000\n1987-06-15,301.619995,304.109985,301.619995,303.140015,303.140015,156900000\n1987-06-16,303.119995,304.859985,302.600006,304.760010,304.760010,157800000\n1987-06-17,304.769989,305.739990,304.029999,304.809998,304.809998,184700000\n1987-06-18,304.779999,306.130005,303.380005,305.690002,305.690002,168600000\n1987-06-19,305.709991,306.970001,305.549988,306.970001,306.970001,220500000\n1987-06-22,306.980011,310.200012,306.970001,309.649994,309.649994,178200000\n1987-06-23,309.660004,310.269989,307.480011,308.429993,308.429993,194200000\n1987-06-24,308.440002,308.910004,306.320007,306.859985,306.859985,153800000\n1987-06-25,306.869995,309.440002,306.859985,308.959991,308.959991,173500000\n1987-06-26,308.940002,308.959991,306.359985,307.160004,307.160004,150500000\n1987-06-29,307.149994,308.149994,306.750000,307.899994,307.899994,142500000\n1987-06-30,307.890015,308.000000,303.010010,304.000000,304.000000,165500000\n1987-07-01,303.989990,304.000000,302.529999,302.940002,302.940002,157000000\n1987-07-02,302.959991,306.339996,302.940002,305.630005,305.630005,154900000\n1987-07-06,305.640015,306.750000,304.230011,304.920013,304.920013,155000000\n1987-07-07,304.910004,308.630005,304.730011,307.399994,307.399994,200700000\n1987-07-08,307.410004,308.480011,306.010010,308.290009,308.290009,207500000\n1987-07-09,308.299988,309.559998,307.420013,307.519989,307.519989,195400000\n1987-07-10,307.549988,308.399994,306.959991,308.369995,308.369995,172100000\n1987-07-13,308.410004,308.410004,305.489990,307.630005,307.630005,152500000\n1987-07-14,307.670013,310.690002,307.459991,310.679993,310.679993,185900000\n1987-07-15,310.670013,312.079987,309.070007,310.420013,310.420013,202300000\n1987-07-16,311.000000,312.829987,310.420013,312.700012,312.700012,210900000\n1987-07-17,312.709991,314.589996,312.380005,314.589996,314.589996,210000000\n1987-07-20,314.559998,314.589996,311.239990,311.390015,311.390015,168100000\n1987-07-21,311.359985,312.410004,307.510010,308.549988,308.549988,186600000\n1987-07-22,308.559998,309.119995,307.220001,308.470001,308.470001,174700000\n1987-07-23,308.500000,309.630005,306.100006,307.809998,307.809998,163700000\n1987-07-24,307.820007,309.279999,307.779999,309.269989,309.269989,158400000\n1987-07-27,309.299988,310.700012,308.609985,310.649994,310.649994,152000000\n1987-07-28,310.649994,312.329987,310.279999,312.329987,312.329987,172600000\n1987-07-29,312.339996,315.649994,311.730011,315.649994,315.649994,196200000\n1987-07-30,315.690002,318.529999,315.649994,318.049988,318.049988,208000000\n1987-07-31,318.049988,318.850006,317.559998,318.660004,318.660004,181900000\n1987-08-03,318.619995,320.260010,316.519989,317.570007,317.570007,207800000\n1987-08-04,317.589996,318.250000,314.510010,316.230011,316.230011,166500000\n1987-08-05,316.250000,319.739990,316.230011,318.450012,318.450012,192700000\n1987-08-06,318.489990,322.089996,317.500000,322.089996,322.089996,192000000\n1987-08-07,322.100006,324.149994,321.820007,323.000000,323.000000,212700000\n1987-08-10,322.980011,328.000000,322.950012,328.000000,328.000000,187200000\n1987-08-11,328.019989,333.399994,328.000000,333.329987,333.329987,278100000\n1987-08-12,333.320007,334.570007,331.059998,332.390015,332.390015,235800000\n1987-08-13,332.380005,335.519989,332.380005,334.649994,334.649994,217100000\n1987-08-14,334.630005,336.079987,332.630005,333.989990,333.989990,196100000\n1987-08-17,333.980011,335.429993,332.880005,334.109985,334.109985,166100000\n1987-08-18,334.100006,334.109985,326.429993,329.250000,329.250000,198400000\n1987-08-19,329.260010,329.890015,326.540009,329.829987,329.829987,180900000\n1987-08-20,331.489990,335.190002,329.829987,334.839996,334.839996,196600000\n1987-08-21,334.850006,336.369995,334.299988,335.899994,335.899994,189600000\n1987-08-24,335.890015,335.899994,331.920013,333.329987,333.329987,149400000\n1987-08-25,333.369995,337.890015,333.329987,336.769989,336.769989,213500000\n1987-08-26,336.769989,337.390015,334.459991,334.570007,334.570007,196200000\n1987-08-27,334.559998,334.570007,331.100006,331.380005,331.380005,163600000\n1987-08-28,331.369995,331.380005,327.029999,327.040009,327.040009,156300000\n1987-08-31,327.029999,330.089996,326.989990,329.799988,329.799988,165800000\n1987-09-01,329.809998,332.179993,322.829987,323.399994,323.399994,193500000\n1987-09-02,323.399994,324.529999,318.760010,321.679993,321.679993,199900000\n1987-09-03,321.470001,324.290009,317.390015,320.209991,320.209991,165200000\n1987-09-04,320.209991,322.029999,316.529999,316.700012,316.700012,129100000\n1987-09-08,316.679993,316.700012,308.559998,313.559998,313.559998,242900000\n1987-09-09,313.600006,315.410004,312.290009,313.920013,313.920013,164900000\n1987-09-10,313.920013,317.589996,313.920013,317.130005,317.130005,179800000\n1987-09-11,317.140015,322.450012,317.130005,321.980011,321.980011,178000000\n1987-09-14,322.019989,323.809998,320.399994,323.079987,323.079987,154400000\n1987-09-15,323.070007,323.079987,317.630005,317.739990,317.739990,136200000\n1987-09-16,317.750000,319.500000,314.609985,314.859985,314.859985,195700000\n1987-09-17,314.940002,316.079987,313.450012,314.929993,314.929993,150700000\n1987-09-18,314.980011,316.989990,314.859985,314.859985,314.859985,188100000\n1987-09-21,314.920013,317.660004,310.119995,310.540009,310.540009,170100000\n1987-09-22,310.540009,319.510010,308.690002,319.500000,319.500000,209500000\n1987-09-23,319.489990,321.829987,319.119995,321.190002,321.190002,220300000\n1987-09-24,321.089996,322.010010,319.119995,319.720001,319.720001,162200000\n1987-09-25,319.720001,320.549988,318.100006,320.160004,320.160004,138000000\n1987-09-28,320.160004,325.329987,320.160004,323.200012,323.200012,188100000\n1987-09-29,323.200012,324.630005,320.269989,321.690002,321.690002,173500000\n1987-09-30,321.690002,322.529999,320.160004,321.829987,321.829987,183100000\n1987-10-01,321.829987,327.339996,321.829987,327.329987,327.329987,193200000\n1987-10-02,327.329987,328.940002,327.220001,328.070007,328.070007,189100000\n1987-10-05,328.070007,328.570007,326.089996,328.079987,328.079987,159700000\n1987-10-06,328.079987,328.079987,319.170013,319.220001,319.220001,175600000\n1987-10-07,319.220001,319.390015,315.779999,318.540009,318.540009,186300000\n1987-10-08,318.540009,319.339996,312.019989,314.160004,314.160004,198700000\n1987-10-09,314.160004,315.040009,310.970001,311.070007,311.070007,158300000\n1987-10-12,311.070007,311.070007,306.760010,309.390015,309.390015,141900000\n1987-10-13,309.390015,314.529999,309.390015,314.519989,314.519989,172900000\n1987-10-14,314.519989,314.519989,304.779999,305.230011,305.230011,207400000\n1987-10-15,305.209991,305.230011,298.070007,298.079987,298.079987,263200000\n1987-10-16,298.079987,298.920013,281.519989,282.700012,282.700012,338500000\n1987-10-19,282.700012,282.700012,224.830002,224.839996,224.839996,604300000\n1987-10-20,225.059998,245.619995,216.460007,236.830002,236.830002,608100000\n1987-10-21,236.830002,259.269989,236.830002,258.380005,258.380005,449600000\n1987-10-22,258.239990,258.380005,242.990005,248.250000,248.250000,392200000\n1987-10-23,248.289993,250.699997,242.759995,248.220001,248.220001,245600000\n1987-10-26,248.199997,248.220001,227.259995,227.669998,227.669998,308800000\n1987-10-27,227.669998,237.809998,227.669998,233.190002,233.190002,260200000\n1987-10-28,233.190002,238.580002,226.259995,233.279999,233.279999,279400000\n1987-10-29,233.309998,246.690002,233.279999,244.770004,244.770004,258100000\n1987-10-30,244.770004,254.039993,244.770004,251.789993,251.789993,303400000\n1987-11-02,251.729996,255.750000,249.149994,255.750000,255.750000,176000000\n1987-11-03,255.750000,255.750000,242.779999,250.820007,250.820007,227800000\n1987-11-04,250.809998,251.000000,246.339996,248.960007,248.960007,202500000\n1987-11-05,248.929993,256.089996,247.720001,254.479996,254.479996,226000000\n1987-11-06,254.490005,257.209991,249.679993,250.410004,250.410004,228290000\n1987-11-09,250.410004,250.410004,243.009995,243.169998,243.169998,160690000\n1987-11-10,243.139999,243.169998,237.639999,239.000000,239.000000,184310000\n1987-11-11,239.009995,243.860001,239.000000,241.899994,241.899994,147850000\n1987-11-12,241.929993,249.899994,241.899994,248.520004,248.520004,206280000\n1987-11-13,248.539993,249.419998,245.639999,245.639999,245.639999,174920000\n1987-11-16,245.690002,249.539993,244.979996,246.759995,246.759995,164340000\n1987-11-17,246.729996,246.759995,240.809998,243.039993,243.039993,148240000\n1987-11-18,243.089996,245.550003,240.669998,245.550003,245.550003,158270000\n1987-11-19,245.539993,245.550003,239.699997,240.050003,240.050003,157140000\n1987-11-20,240.039993,242.009995,235.889999,242.000000,242.000000,189170000\n1987-11-23,242.000000,242.990005,240.500000,242.990005,242.990005,143160000\n1987-11-24,242.979996,247.899994,242.979996,246.389999,246.389999,199520000\n1987-11-25,246.419998,246.539993,244.080002,244.100006,244.100006,139780000\n1987-11-27,244.110001,244.119995,240.339996,240.339996,240.339996,86360000\n1987-11-30,240.270004,240.339996,225.750000,230.300003,230.300003,268910000\n1987-12-01,230.320007,234.020004,230.300003,232.000000,232.000000,149870000\n1987-12-02,232.009995,234.559998,230.309998,233.449997,233.449997,148890000\n1987-12-03,233.460007,233.899994,225.210007,225.210007,225.210007,204160000\n1987-12-04,225.199997,225.770004,221.240005,223.919998,223.919998,184800000\n1987-12-07,223.979996,228.770004,223.919998,228.759995,228.759995,146660000\n1987-12-08,228.770004,234.919998,228.690002,234.910004,234.910004,227310000\n1987-12-09,234.910004,240.089996,233.830002,238.889999,238.889999,231430000\n1987-12-10,238.889999,240.050003,233.399994,233.570007,233.570007,188960000\n1987-12-11,233.600006,235.479996,233.350006,235.320007,235.320007,151680000\n1987-12-14,235.300003,242.339996,235.039993,242.190002,242.190002,187680000\n1987-12-15,242.190002,245.589996,241.309998,242.809998,242.809998,214970000\n1987-12-16,242.809998,248.110001,242.800003,248.080002,248.080002,193820000\n1987-12-17,248.080002,248.600006,242.960007,242.979996,242.979996,191780000\n1987-12-18,243.009995,249.179993,243.009995,249.160004,249.160004,276220000\n1987-12-21,249.139999,250.250000,248.300003,249.539993,249.539993,161790000\n1987-12-22,249.559998,249.970001,247.009995,249.949997,249.949997,192650000\n1987-12-23,249.960007,253.350006,249.949997,253.160004,253.160004,203110000\n1987-12-24,253.130005,253.160004,251.679993,252.029999,252.029999,108800000\n1987-12-28,252.009995,252.020004,244.190002,245.570007,245.570007,131220000\n1987-12-29,245.580002,245.880005,244.279999,244.589996,244.589996,111580000\n1987-12-30,244.630005,248.059998,244.589996,247.860001,247.860001,149230000\n1987-12-31,247.839996,247.860001,245.220001,247.080002,247.080002,170140000\n1988-01-04,247.100006,256.440002,247.080002,255.940002,255.940002,181810000\n1988-01-05,255.949997,261.779999,255.949997,258.630005,258.630005,209520000\n1988-01-06,258.640015,259.790009,257.179993,258.890015,258.890015,169730000\n1988-01-07,258.869995,261.320007,256.179993,261.070007,261.070007,175360000\n1988-01-08,261.049988,261.070007,242.949997,243.399994,243.399994,197300000\n1988-01-11,243.380005,247.509995,241.070007,247.490005,247.490005,158980000\n1988-01-12,247.440002,247.490005,240.460007,245.419998,245.419998,165730000\n1988-01-13,245.410004,249.250000,241.410004,245.809998,245.809998,154020000\n1988-01-14,245.830002,247.000000,243.970001,245.880005,245.880005,140570000\n1988-01-15,246.020004,253.649994,245.880005,252.050003,252.050003,197940000\n1988-01-18,252.050003,252.860001,249.979996,251.880005,251.880005,135100000\n1988-01-19,251.839996,253.330002,248.750000,249.320007,249.320007,153550000\n1988-01-20,249.309998,249.320007,241.139999,242.630005,242.630005,181660000\n1988-01-21,242.649994,244.250000,240.169998,243.139999,243.139999,158080000\n1988-01-22,243.139999,246.500000,243.139999,246.500000,246.500000,147050000\n1988-01-25,246.529999,252.869995,246.500000,252.169998,252.169998,275250000\n1988-01-26,252.130005,252.169998,249.100006,249.570007,249.570007,138380000\n1988-01-27,249.580002,253.020004,248.500000,249.380005,249.380005,176360000\n1988-01-28,249.389999,253.660004,249.380005,253.289993,253.289993,166430000\n1988-01-29,253.309998,257.070007,252.699997,257.070007,257.070007,211880000\n1988-02-01,257.049988,258.269989,254.929993,255.039993,255.039993,210660000\n1988-02-02,255.050003,256.079987,252.800003,255.570007,255.570007,164920000\n1988-02-03,255.559998,256.980011,250.559998,252.210007,252.210007,237270000\n1988-02-04,252.199997,253.029999,250.339996,252.210007,252.210007,186490000\n1988-02-05,252.220001,253.850006,250.899994,250.960007,250.960007,161310000\n1988-02-08,250.949997,250.960007,247.820007,249.100006,249.100006,168850000\n1988-02-09,249.110001,251.720001,248.660004,251.720001,251.720001,162350000\n1988-02-10,251.740005,256.920013,251.720001,256.660004,256.660004,187980000\n1988-02-11,256.630005,257.769989,255.119995,255.949997,255.949997,200760000\n1988-02-12,255.949997,258.859985,255.850006,257.630005,257.630005,177190000\n1988-02-16,257.609985,259.839996,256.570007,259.829987,259.829987,135380000\n1988-02-17,259.940002,261.470001,257.829987,259.209991,259.209991,176830000\n1988-02-18,258.820007,259.600006,256.899994,257.910004,257.910004,151430000\n1988-02-19,257.899994,261.609985,257.619995,261.609985,261.609985,180300000\n1988-02-22,261.600006,266.059998,260.880005,265.640015,265.640015,178930000\n1988-02-23,265.619995,266.119995,263.109985,265.019989,265.019989,192260000\n1988-02-24,265.010010,266.250000,263.869995,264.429993,264.429993,212730000\n1988-02-25,264.390015,267.750000,261.049988,261.579987,261.579987,213490000\n1988-02-26,261.559998,263.000000,261.380005,262.459991,262.459991,158060000\n1988-02-29,262.459991,267.820007,262.459991,267.820007,267.820007,236050000\n1988-03-01,267.820007,267.950012,265.390015,267.220001,267.220001,199990000\n1988-03-02,267.230011,268.750000,267.000000,267.980011,267.980011,199630000\n1988-03-03,267.980011,268.399994,266.820007,267.880005,267.880005,203310000\n1988-03-04,267.869995,268.399994,264.720001,267.299988,267.299988,201410000\n1988-03-07,267.279999,267.690002,265.940002,267.380005,267.380005,152980000\n1988-03-08,267.380005,270.059998,267.380005,269.429993,269.429993,237680000\n1988-03-09,269.459991,270.760010,268.649994,269.059998,269.059998,210900000\n1988-03-10,269.070007,269.350006,263.799988,263.839996,263.839996,197260000\n1988-03-11,263.850006,264.940002,261.269989,264.940002,264.940002,200020000\n1988-03-14,264.929993,266.549988,264.519989,266.369995,266.369995,131890000\n1988-03-15,266.339996,266.410004,264.920013,266.130005,266.130005,133170000\n1988-03-16,266.109985,268.679993,264.809998,268.649994,268.649994,153590000\n1988-03-17,268.660004,271.220001,268.649994,271.220001,271.220001,211920000\n1988-03-18,271.220001,272.640015,269.760010,271.119995,271.119995,245750000\n1988-03-21,271.100006,271.119995,267.420013,268.739990,268.739990,128830000\n1988-03-22,268.730011,269.609985,267.899994,268.839996,268.839996,142000000\n1988-03-23,268.809998,269.790009,268.010010,268.910004,268.910004,167370000\n1988-03-24,268.910004,268.910004,262.480011,263.350006,263.350006,184910000\n1988-03-25,263.339996,263.440002,258.119995,258.510010,258.510010,163170000\n1988-03-28,258.500000,258.510010,256.070007,258.059998,258.059998,142820000\n1988-03-29,258.109985,260.859985,258.059998,260.070007,260.070007,152690000\n1988-03-30,260.059998,261.589996,257.920013,258.070007,258.070007,151810000\n1988-03-31,258.029999,259.029999,256.160004,258.890015,258.890015,139870000\n1988-04-04,258.890015,259.059998,255.679993,256.089996,256.089996,182240000\n1988-04-05,256.100006,258.519989,256.029999,258.510010,258.510010,135290000\n1988-04-06,258.519989,265.500000,258.220001,265.489990,265.489990,189760000\n1988-04-07,265.510010,267.320007,265.220001,266.160004,266.160004,177840000\n1988-04-08,266.149994,270.220001,266.109985,269.429993,269.429993,169300000\n1988-04-11,269.429993,270.410004,268.609985,270.160004,270.160004,146370000\n1988-04-12,269.880005,272.049988,269.660004,271.369995,271.369995,146400000\n1988-04-13,271.329987,271.700012,269.230011,271.579987,271.579987,185120000\n1988-04-14,271.549988,271.570007,259.369995,259.750000,259.750000,211810000\n1988-04-15,259.739990,260.390015,255.970001,259.769989,259.769989,234160000\n1988-04-18,259.750000,259.809998,258.029999,259.209991,259.209991,144650000\n1988-04-19,259.239990,262.380005,257.910004,257.920013,257.920013,161910000\n1988-04-20,257.910004,258.540009,256.119995,256.130005,256.130005,147590000\n1988-04-21,256.149994,260.440002,254.710007,256.420013,256.420013,168440000\n1988-04-22,256.450012,261.160004,256.420013,260.140015,260.140015,152520000\n1988-04-25,260.149994,263.290009,260.140015,262.510010,262.510010,156950000\n1988-04-26,262.450012,265.059998,262.179993,263.929993,263.929993,152300000\n1988-04-27,263.940002,265.089996,263.450012,263.799988,263.799988,133810000\n1988-04-28,263.790009,263.799988,262.220001,262.609985,262.609985,128680000\n1988-04-29,262.589996,262.609985,259.970001,261.329987,261.329987,135620000\n1988-05-02,261.359985,261.559998,259.989990,261.559998,261.559998,136470000\n1988-05-03,261.549988,263.700012,261.549988,263.000000,263.000000,176920000\n1988-05-04,263.049988,263.230011,260.309998,260.320007,260.320007,141320000\n1988-05-05,260.299988,260.320007,258.130005,258.790009,258.790009,171840000\n1988-05-06,258.799988,260.309998,257.029999,257.480011,257.480011,129080000\n1988-05-09,257.470001,258.220001,255.449997,256.540009,256.540009,166320000\n1988-05-10,256.529999,258.299988,255.929993,257.619995,257.619995,131200000\n1988-05-11,257.600006,257.619995,252.320007,253.309998,253.309998,176720000\n1988-05-12,253.320007,254.869995,253.309998,253.850006,253.850006,143880000\n1988-05-13,253.880005,256.829987,253.850006,256.779999,256.779999,147240000\n1988-05-16,256.750000,258.709991,256.279999,258.709991,258.709991,155010000\n1988-05-17,258.720001,260.200012,255.350006,255.389999,255.389999,133850000\n1988-05-18,255.399994,255.669998,250.729996,251.350006,251.350006,209420000\n1988-05-19,251.360001,252.570007,248.850006,252.570007,252.570007,165160000\n1988-05-20,252.610001,253.699997,251.789993,253.020004,253.020004,120600000\n1988-05-23,253.000000,253.020004,249.820007,250.830002,250.830002,102640000\n1988-05-24,250.839996,253.509995,250.830002,253.509995,253.509995,139930000\n1988-05-25,253.520004,255.339996,253.509995,253.759995,253.759995,138310000\n1988-05-26,253.750000,254.979996,253.520004,254.630005,254.630005,164260000\n1988-05-27,254.619995,254.630005,252.740005,253.419998,253.419998,133590000\n1988-05-31,253.440002,262.160004,253.419998,262.160004,262.160004,247610000\n1988-06-01,262.160004,267.429993,262.100006,266.690002,266.690002,234560000\n1988-06-02,266.649994,266.709991,264.119995,265.329987,265.329987,193540000\n1988-06-03,265.339996,267.109985,264.420013,266.450012,266.450012,189600000\n1988-06-06,266.459991,267.049988,264.970001,267.049988,267.049988,152460000\n1988-06-07,267.019989,267.279999,264.500000,265.170013,265.170013,168710000\n1988-06-08,265.320007,272.010010,265.170013,271.519989,271.519989,310030000\n1988-06-09,271.500000,272.290009,270.190002,270.200012,270.200012,235160000\n1988-06-10,270.220001,273.209991,270.200012,271.260010,271.260010,155710000\n1988-06-13,271.279999,271.940002,270.529999,271.429993,271.429993,125310000\n1988-06-14,271.579987,276.140015,271.440002,274.299988,274.299988,227150000\n1988-06-15,274.290009,274.450012,272.750000,274.450012,274.450012,150260000\n1988-06-16,274.440002,274.450012,268.760010,269.769989,269.769989,161550000\n1988-06-17,269.790009,270.769989,268.089996,270.679993,270.679993,343920000\n1988-06-20,270.670013,270.679993,268.589996,268.940002,268.940002,116750000\n1988-06-21,268.950012,271.670013,267.519989,271.670013,271.670013,155060000\n1988-06-22,271.690002,276.880005,271.670013,275.660004,275.660004,217510000\n1988-06-23,275.619995,275.890015,274.260010,274.820007,274.820007,185770000\n1988-06-24,274.809998,275.190002,273.529999,273.779999,273.779999,179880000\n1988-06-27,273.779999,273.790009,268.850006,269.059998,269.059998,264410000\n1988-06-28,269.070007,272.799988,269.059998,272.309998,272.309998,152370000\n1988-06-29,272.320007,273.010010,269.489990,270.980011,270.980011,159590000\n1988-06-30,271.000000,273.510010,270.970001,273.500000,273.500000,227410000\n1988-07-01,273.500000,273.799988,270.779999,271.779999,271.779999,238330000\n1988-07-05,271.779999,275.809998,270.510010,275.809998,275.809998,171790000\n1988-07-06,275.799988,276.359985,269.920013,272.019989,272.019989,189630000\n1988-07-07,272.000000,272.049988,269.309998,271.779999,271.779999,156100000\n1988-07-08,271.760010,272.309998,269.859985,270.019989,270.019989,136070000\n1988-07-11,270.029999,271.640015,270.019989,270.549988,270.549988,123300000\n1988-07-12,270.540009,270.700012,266.959991,267.850006,267.850006,161650000\n1988-07-13,267.869995,269.459991,266.119995,269.320007,269.320007,218930000\n1988-07-14,269.329987,270.690002,268.579987,270.260010,270.260010,172410000\n1988-07-15,270.230011,272.059998,269.529999,272.049988,272.049988,199710000\n1988-07-18,271.989990,272.049988,268.660004,270.510010,270.510010,156210000\n1988-07-19,270.489990,271.209991,267.010010,268.470001,268.470001,144110000\n1988-07-20,268.519989,270.239990,268.470001,270.000000,270.000000,151990000\n1988-07-21,269.989990,270.000000,266.660004,266.660004,266.660004,149460000\n1988-07-22,266.649994,266.660004,263.290009,263.500000,263.500000,148880000\n1988-07-25,263.489990,265.170013,263.029999,264.679993,264.679993,215140000\n1988-07-26,264.700012,266.089996,264.320007,265.190002,265.190002,121960000\n1988-07-27,265.179993,265.829987,262.480011,262.500000,262.500000,135890000\n1988-07-28,262.519989,266.549988,262.500000,266.019989,266.019989,154570000\n1988-07-29,266.040009,272.019989,266.019989,272.019989,272.019989,192340000\n1988-08-01,272.029999,272.799988,271.209991,272.209991,272.209991,138170000\n1988-08-02,272.190002,273.679993,270.369995,272.059998,272.059998,166660000\n1988-08-03,272.029999,273.420013,271.149994,272.980011,272.980011,203590000\n1988-08-04,273.000000,274.200012,271.769989,271.929993,271.929993,157240000\n1988-08-05,271.700012,271.929993,270.079987,271.149994,271.149994,113400000\n1988-08-08,271.130005,272.470001,269.929993,269.980011,269.980011,148800000\n1988-08-09,270.000000,270.200012,265.059998,266.489990,266.489990,200710000\n1988-08-10,266.429993,266.489990,261.029999,261.899994,261.899994,200950000\n1988-08-11,261.920013,262.769989,260.339996,262.750000,262.750000,173000000\n1988-08-12,262.700012,262.940002,261.369995,262.549988,262.549988,176960000\n1988-08-15,262.489990,262.549988,258.679993,258.690002,258.690002,128560000\n1988-08-16,258.679993,262.609985,257.500000,260.559998,260.559998,162790000\n1988-08-17,260.570007,261.839996,259.329987,260.769989,260.769989,169500000\n1988-08-18,260.760010,262.760010,260.750000,261.029999,261.029999,139820000\n1988-08-19,261.049988,262.269989,260.230011,260.239990,260.239990,122370000\n1988-08-22,260.239990,260.709991,256.940002,256.980011,256.980011,122250000\n1988-08-23,256.989990,257.859985,256.529999,257.089996,257.089996,119540000\n1988-08-24,257.160004,261.130005,257.089996,261.130005,261.130005,127800000\n1988-08-25,261.100006,261.130005,257.559998,259.179993,259.179993,127640000\n1988-08-26,259.179993,260.149994,258.869995,259.679993,259.679993,89240000\n1988-08-29,259.679993,262.559998,259.679993,262.329987,262.329987,99280000\n1988-08-30,262.329987,263.179993,261.529999,262.510010,262.510010,108720000\n1988-08-31,262.510010,263.799988,261.209991,261.519989,261.519989,130480000\n1988-09-01,261.519989,261.519989,256.980011,258.350006,258.350006,144090000\n1988-09-02,258.350006,264.899994,258.350006,264.480011,264.480011,159840000\n1988-09-06,264.420013,265.940002,264.399994,265.589996,265.589996,122250000\n1988-09-07,265.619995,266.980011,264.929993,265.869995,265.869995,139590000\n1988-09-08,265.869995,266.540009,264.880005,265.880005,265.880005,149380000\n1988-09-09,265.880005,268.260010,263.660004,266.839996,266.839996,141540000\n1988-09-12,266.850006,267.640015,266.220001,266.470001,266.470001,114880000\n1988-09-13,266.450012,267.429993,265.220001,267.429993,267.429993,162490000\n1988-09-14,267.500000,269.470001,267.410004,269.309998,269.309998,177220000\n1988-09-15,269.299988,269.779999,268.029999,268.130005,268.130005,161210000\n1988-09-16,268.130005,270.809998,267.329987,270.649994,270.649994,211110000\n1988-09-19,270.640015,270.649994,267.410004,268.820007,268.820007,135770000\n1988-09-20,268.829987,270.070007,268.500000,269.730011,269.730011,142220000\n1988-09-21,269.760010,270.640015,269.480011,270.160004,270.160004,127400000\n1988-09-22,270.190002,270.579987,268.260010,269.179993,269.179993,150670000\n1988-09-23,269.160004,270.309998,268.279999,269.760010,269.760010,145100000\n1988-09-26,269.769989,269.799988,268.609985,268.880005,268.880005,116420000\n1988-09-27,268.890015,269.359985,268.010010,268.260010,268.260010,113010000\n1988-09-28,268.220001,269.079987,267.769989,269.079987,269.079987,113720000\n1988-09-29,269.089996,273.019989,269.079987,272.589996,272.589996,155790000\n1988-09-30,272.549988,274.869995,271.660004,271.910004,271.910004,175750000\n1988-10-03,271.890015,271.910004,268.839996,271.380005,271.380005,130380000\n1988-10-04,271.369995,271.790009,270.339996,270.619995,270.619995,157760000\n1988-10-05,270.630005,272.450012,270.079987,271.859985,271.859985,175130000\n1988-10-06,271.869995,272.390015,271.299988,272.390015,272.390015,153570000\n1988-10-07,272.380005,278.070007,272.369995,278.070007,278.070007,216390000\n1988-10-10,278.059998,278.690002,277.100006,278.239990,278.239990,124660000\n1988-10-11,278.149994,278.239990,276.329987,277.929993,277.929993,140900000\n1988-10-12,277.910004,277.929993,273.049988,273.980011,273.980011,154840000\n1988-10-13,273.950012,275.829987,273.390015,275.220001,275.220001,154530000\n1988-10-14,275.269989,277.010010,274.079987,275.500000,275.500000,160240000\n1988-10-17,275.480011,276.649994,275.010010,276.410004,276.410004,119290000\n1988-10-18,276.429993,279.390015,276.410004,279.380005,279.380005,162500000\n1988-10-19,279.399994,280.529999,274.410004,276.970001,276.970001,186350000\n1988-10-20,276.970001,282.880005,276.929993,282.880005,282.880005,189580000\n1988-10-21,282.880005,283.660004,281.160004,283.660004,283.660004,195410000\n1988-10-24,283.630005,283.950012,282.279999,282.279999,282.279999,170590000\n1988-10-25,282.279999,282.839996,281.869995,282.380005,282.380005,155190000\n1988-10-26,282.369995,282.519989,280.540009,281.380005,281.380005,181550000\n1988-10-27,281.350006,281.380005,276.000000,277.279999,277.279999,196540000\n1988-10-28,277.290009,279.480011,277.279999,278.529999,278.529999,146300000\n1988-10-31,278.540009,279.390015,277.140015,278.970001,278.970001,143460000\n1988-11-01,278.970001,279.570007,278.010010,279.059998,279.059998,151250000\n1988-11-02,279.070007,279.450012,277.079987,279.059998,279.059998,161300000\n1988-11-03,279.040009,280.369995,279.040009,279.200012,279.200012,152980000\n1988-11-04,279.109985,279.200012,276.309998,276.309998,276.309998,143580000\n1988-11-07,276.299988,276.309998,273.619995,273.929993,273.929993,133870000\n1988-11-08,273.950012,275.799988,273.929993,275.149994,275.149994,141660000\n1988-11-09,275.140015,275.149994,272.149994,273.329987,273.329987,153140000\n1988-11-10,273.320007,274.369995,272.980011,273.690002,273.690002,128920000\n1988-11-11,273.649994,273.690002,267.920013,267.920013,267.920013,135500000\n1988-11-14,267.929993,269.250000,266.790009,267.720001,267.720001,142900000\n1988-11-15,267.730011,268.750000,267.720001,268.339996,268.339996,115170000\n1988-11-16,268.410004,268.410004,262.850006,263.820007,263.820007,161710000\n1988-11-17,264.609985,265.630005,263.450012,264.600006,264.600006,141280000\n1988-11-18,264.600006,266.619995,264.600006,266.470001,266.470001,119320000\n1988-11-21,266.350006,266.470001,263.410004,266.220001,266.220001,120430000\n1988-11-22,266.190002,267.850006,265.420013,267.209991,267.209991,127000000\n1988-11-23,267.220001,269.559998,267.209991,269.000000,269.000000,112010000\n1988-11-25,268.989990,269.000000,266.470001,267.230011,267.230011,72090000\n1988-11-28,267.220001,268.980011,266.970001,268.640015,268.640015,123480000\n1988-11-29,268.600006,271.309998,268.130005,270.910004,270.910004,127420000\n1988-11-30,270.910004,274.359985,270.899994,273.700012,273.700012,157810000\n1988-12-01,273.679993,273.700012,272.269989,272.489990,272.489990,129380000\n1988-12-02,272.489990,272.489990,270.470001,271.809998,271.809998,124610000\n1988-12-05,274.929993,275.619995,271.809998,274.929993,274.929993,144660000\n1988-12-06,274.929993,277.890015,274.619995,277.589996,277.589996,158340000\n1988-12-07,277.589996,279.010010,277.339996,278.130005,278.130005,148360000\n1988-12-08,278.130005,278.130005,276.549988,276.589996,276.589996,124150000\n1988-12-09,276.570007,277.820007,276.339996,277.029999,277.029999,133770000\n1988-12-12,277.029999,278.820007,276.519989,276.519989,276.519989,124160000\n1988-12-13,276.519989,276.519989,274.579987,276.309998,276.309998,132340000\n1988-12-14,276.309998,276.309998,274.579987,275.309998,275.309998,132350000\n1988-12-15,275.320007,275.619995,274.010010,274.279999,274.279999,136820000\n1988-12-16,274.279999,276.290009,274.279999,276.290009,276.290009,196480000\n1988-12-19,276.290009,279.309998,275.609985,278.910004,278.910004,162250000\n1988-12-20,278.910004,280.450012,277.470001,277.470001,277.470001,161090000\n1988-12-21,277.470001,277.829987,276.299988,277.380005,277.380005,147250000\n1988-12-22,277.380005,277.890015,276.859985,276.869995,276.869995,150510000\n1988-12-23,276.869995,277.989990,276.869995,277.869995,277.869995,81760000\n1988-12-27,277.869995,278.089996,276.739990,276.829987,276.829987,87490000\n1988-12-28,276.829987,277.549988,276.170013,277.079987,277.079987,110630000\n1988-12-29,277.079987,279.420013,277.079987,279.399994,279.399994,131290000\n1988-12-30,279.390015,279.779999,277.720001,277.720001,277.720001,127210000\n1989-01-03,277.720001,277.720001,273.809998,275.309998,275.309998,128500000\n1989-01-04,275.309998,279.750000,275.309998,279.429993,279.429993,149700000\n1989-01-05,279.429993,281.510010,279.429993,280.010010,280.010010,174040000\n1989-01-06,280.010010,282.059998,280.010010,280.670013,280.670013,161330000\n1989-01-09,280.670013,281.890015,280.320007,280.980011,280.980011,163180000\n1989-01-10,280.980011,281.579987,279.440002,280.380005,280.380005,140420000\n1989-01-11,280.380005,282.010010,280.209991,282.010010,282.010010,148950000\n1989-01-12,282.010010,284.630005,282.010010,283.170013,283.170013,183000000\n1989-01-13,283.170013,284.119995,282.709991,283.869995,283.869995,132320000\n1989-01-16,283.869995,284.880005,283.630005,284.140015,284.140015,117380000\n1989-01-17,284.140015,284.140015,283.059998,283.549988,283.549988,143930000\n1989-01-18,283.549988,286.869995,282.649994,286.529999,286.529999,187450000\n1989-01-19,286.529999,287.899994,286.140015,286.910004,286.910004,192030000\n1989-01-20,286.899994,287.040009,285.750000,286.630005,286.630005,166120000\n1989-01-23,287.850006,287.980011,284.500000,284.500000,284.500000,141640000\n1989-01-24,284.500000,288.489990,284.500000,288.489990,288.489990,189620000\n1989-01-25,288.489990,289.149994,287.970001,289.140015,289.140015,183610000\n1989-01-26,289.140015,292.619995,288.130005,291.690002,291.690002,212250000\n1989-01-27,291.690002,296.079987,291.690002,293.820007,293.820007,254870000\n1989-01-30,293.820007,295.130005,293.540009,294.989990,294.989990,167830000\n1989-01-31,294.989990,297.510010,293.570007,297.470001,297.470001,194050000\n1989-02-01,297.470001,298.329987,296.220001,297.089996,297.089996,215640000\n1989-02-02,297.089996,297.920013,295.809998,296.839996,296.839996,183430000\n1989-02-03,296.839996,297.660004,296.149994,296.970001,296.970001,172980000\n1989-02-06,296.970001,296.989990,294.959991,296.040009,296.040009,150980000\n1989-02-07,296.040009,300.339996,295.779999,299.630005,299.630005,217260000\n1989-02-08,299.619995,300.570007,298.410004,298.649994,298.649994,189420000\n1989-02-09,298.649994,298.790009,295.160004,296.059998,296.059998,224220000\n1989-02-10,296.059998,296.059998,291.959991,292.019989,292.019989,173560000\n1989-02-13,292.019989,293.070007,290.880005,292.540009,292.540009,143520000\n1989-02-14,292.540009,294.369995,291.410004,291.809998,291.809998,150610000\n1989-02-15,291.809998,294.420013,291.489990,294.239990,294.239990,154220000\n1989-02-16,294.239990,295.149994,294.220001,294.809998,294.809998,177450000\n1989-02-17,294.809998,297.119995,294.690002,296.760010,296.760010,159520000\n1989-02-21,296.760010,297.040009,295.160004,295.980011,295.980011,141950000\n1989-02-22,295.980011,295.980011,290.760010,290.910004,290.910004,163140000\n1989-02-23,290.910004,292.049988,289.829987,292.049988,292.049988,150370000\n1989-02-24,292.049988,292.049988,287.130005,287.130005,287.130005,160680000\n1989-02-27,287.130005,288.119995,286.260010,287.820007,287.820007,139900000\n1989-02-28,287.820007,289.420013,287.630005,288.859985,288.859985,147430000\n1989-03-01,288.859985,290.279999,286.459991,287.109985,287.109985,177210000\n1989-03-02,287.109985,290.320007,287.109985,289.950012,289.950012,161980000\n1989-03-03,289.940002,291.179993,289.440002,291.179993,291.179993,151790000\n1989-03-06,291.200012,294.809998,291.179993,294.809998,294.809998,168880000\n1989-03-07,294.809998,295.160004,293.500000,293.869995,293.869995,172500000\n1989-03-08,293.869995,295.619995,293.510010,294.079987,294.079987,167620000\n1989-03-09,294.079987,294.690002,293.850006,293.929993,293.929993,143160000\n1989-03-10,293.929993,293.929993,291.600006,292.880005,292.880005,146830000\n1989-03-13,292.880005,296.179993,292.880005,295.320007,295.320007,140460000\n1989-03-14,295.320007,296.290009,294.630005,295.140015,295.140015,139970000\n1989-03-15,295.140015,296.779999,295.140015,296.670013,296.670013,167070000\n1989-03-16,296.670013,299.989990,296.660004,299.440002,299.440002,196040000\n1989-03-17,299.440002,299.440002,291.079987,292.690002,292.690002,242900000\n1989-03-20,292.690002,292.690002,288.559998,289.920013,289.920013,151260000\n1989-03-21,289.920013,292.380005,289.920013,291.329987,291.329987,142010000\n1989-03-22,291.329987,291.459991,289.899994,290.489990,290.489990,146570000\n1989-03-23,290.489990,291.510010,288.559998,288.980011,288.980011,153750000\n1989-03-27,288.980011,290.570007,288.070007,290.570007,290.570007,112960000\n1989-03-28,290.570007,292.320007,290.570007,291.589996,291.589996,146420000\n1989-03-29,291.589996,292.750000,291.420013,292.350006,292.350006,144240000\n1989-03-30,292.350006,293.799988,291.500000,292.519989,292.519989,159950000\n1989-03-31,292.519989,294.959991,292.519989,294.869995,294.869995,170960000\n1989-04-03,294.869995,297.040009,294.619995,296.390015,296.390015,164660000\n1989-04-04,296.399994,296.399994,294.720001,295.309998,295.309998,160680000\n1989-04-05,295.309998,296.429993,295.279999,296.239990,296.239990,165880000\n1989-04-06,296.220001,296.239990,294.519989,295.290009,295.290009,146530000\n1989-04-07,295.290009,297.619995,294.350006,297.160004,297.160004,156950000\n1989-04-10,297.160004,297.940002,296.850006,297.109985,297.109985,123990000\n1989-04-11,297.109985,298.869995,297.109985,298.489990,298.489990,146830000\n1989-04-12,298.489990,299.809998,298.489990,298.989990,298.989990,165200000\n1989-04-13,298.989990,299.000000,296.269989,296.399994,296.399994,141590000\n1989-04-14,296.399994,301.380005,296.399994,301.359985,301.359985,169780000\n1989-04-17,301.359985,302.010010,300.709991,301.720001,301.720001,128540000\n1989-04-18,301.720001,306.250000,301.720001,306.019989,306.019989,208650000\n1989-04-19,306.019989,307.679993,305.359985,307.149994,307.149994,191510000\n1989-04-20,307.149994,307.959991,304.529999,306.190002,306.190002,175970000\n1989-04-21,306.190002,309.609985,306.190002,309.609985,309.609985,187310000\n1989-04-24,309.609985,309.609985,307.829987,308.690002,308.690002,142100000\n1989-04-25,308.690002,309.649994,306.739990,306.750000,306.750000,165430000\n1989-04-26,306.779999,307.299988,306.070007,306.929993,306.929993,146090000\n1989-04-27,306.929993,310.450012,306.929993,309.579987,309.579987,191170000\n1989-04-28,309.579987,309.649994,308.480011,309.640015,309.640015,158390000\n1989-05-01,309.640015,309.640015,307.399994,309.119995,309.119995,138050000\n1989-05-02,309.130005,310.450012,308.119995,308.119995,308.119995,172560000\n1989-05-03,308.119995,308.519989,307.109985,308.160004,308.160004,171690000\n1989-05-04,308.160004,308.399994,307.320007,307.769989,307.769989,153130000\n1989-05-05,307.769989,310.690002,306.980011,307.609985,307.609985,180810000\n1989-05-08,307.609985,307.609985,304.739990,306.000000,306.000000,135130000\n1989-05-09,306.000000,306.989990,304.059998,305.190002,305.190002,150090000\n1989-05-10,305.190002,306.250000,304.850006,305.799988,305.799988,146000000\n1989-05-11,305.799988,307.339996,305.799988,306.950012,306.950012,151620000\n1989-05-12,306.950012,313.839996,306.950012,313.839996,313.839996,221490000\n1989-05-15,313.839996,316.160004,313.839996,316.160004,316.160004,179350000\n1989-05-16,316.160004,316.160004,314.989990,315.279999,315.279999,173100000\n1989-05-17,315.279999,317.940002,315.109985,317.480011,317.480011,191210000\n1989-05-18,317.480011,318.519989,316.540009,317.970001,317.970001,177480000\n1989-05-19,317.970001,321.380005,317.970001,321.239990,321.239990,242410000\n1989-05-22,321.239990,323.059998,320.450012,321.980011,321.980011,185010000\n1989-05-23,321.980011,321.980011,318.200012,318.320007,318.320007,187690000\n1989-05-24,318.320007,319.140015,317.579987,319.140015,319.140015,178600000\n1989-05-25,319.140015,319.600006,318.420013,319.170013,319.170013,154470000\n1989-05-26,319.170013,321.589996,319.140015,321.589996,321.589996,143120000\n1989-05-30,321.589996,322.529999,317.829987,319.049988,319.049988,151780000\n1989-05-31,319.049988,321.299988,318.679993,320.519989,320.519989,162530000\n1989-06-01,320.510010,322.570007,320.010010,321.970001,321.970001,223160000\n1989-06-02,321.970001,325.630005,321.970001,325.519989,325.519989,229140000\n1989-06-05,325.519989,325.929993,322.019989,322.029999,322.029999,163420000\n1989-06-06,322.029999,324.480011,321.269989,324.239990,324.239990,187570000\n1989-06-07,324.239990,327.390015,324.239990,326.950012,326.950012,213710000\n1989-06-08,326.950012,327.369995,325.920013,326.750000,326.750000,212310000\n1989-06-09,326.750000,327.320007,325.160004,326.690002,326.690002,173240000\n1989-06-12,326.690002,326.690002,323.730011,326.239990,326.239990,151460000\n1989-06-13,326.239990,326.239990,322.959991,323.910004,323.910004,164870000\n1989-06-14,323.910004,324.890015,322.799988,323.829987,323.829987,170540000\n1989-06-15,323.829987,323.829987,319.209991,320.079987,320.079987,179480000\n1989-06-16,319.959991,321.359985,318.690002,321.350006,321.350006,244510000\n1989-06-19,321.350006,321.890015,320.399994,321.890015,321.890015,130720000\n1989-06-20,321.890015,322.779999,321.029999,321.250000,321.250000,167650000\n1989-06-21,321.250000,321.869995,319.250000,320.480011,320.480011,168830000\n1989-06-22,320.480011,322.339996,320.200012,322.320007,322.320007,176510000\n1989-06-23,322.320007,328.000000,322.320007,328.000000,328.000000,198720000\n1989-06-26,328.000000,328.149994,326.309998,326.600006,326.600006,143600000\n1989-06-27,326.600006,329.190002,326.589996,328.440002,328.440002,171090000\n1989-06-28,328.440002,328.440002,324.299988,325.809998,325.809998,158470000\n1989-06-29,325.809998,325.809998,319.540009,319.679993,319.679993,167100000\n1989-06-30,319.670013,319.970001,314.380005,317.980011,317.980011,170490000\n1989-07-03,317.980011,319.269989,317.269989,319.230011,319.230011,68870000\n1989-07-05,319.230011,321.220001,317.260010,320.640015,320.640015,127710000\n1989-07-06,320.640015,321.549988,320.450012,321.549988,321.549988,140450000\n1989-07-07,321.549988,325.869995,321.079987,324.910004,324.910004,166430000\n1989-07-10,324.929993,327.070007,324.910004,327.070007,327.070007,131870000\n1989-07-11,327.070007,330.420013,327.070007,328.779999,328.779999,171590000\n1989-07-12,328.779999,330.390015,327.920013,329.809998,329.809998,160550000\n1989-07-13,329.809998,330.369995,329.079987,329.950012,329.950012,153820000\n1989-07-14,329.959991,331.890015,327.130005,331.839996,331.839996,183480000\n1989-07-17,331.779999,333.019989,331.019989,332.440002,332.440002,131960000\n1989-07-18,332.420013,332.440002,330.750000,331.350006,331.350006,152350000\n1989-07-19,331.369995,335.730011,331.350006,335.730011,335.730011,215740000\n1989-07-20,335.739990,337.399994,333.220001,333.510010,333.510010,204590000\n1989-07-21,333.500000,335.910004,332.459991,335.899994,335.899994,174880000\n1989-07-24,335.899994,335.899994,333.440002,333.670013,333.670013,136260000\n1989-07-25,333.670013,336.290009,332.600006,333.880005,333.880005,179270000\n1989-07-26,333.880005,338.049988,333.190002,338.049988,338.049988,188270000\n1989-07-27,338.049988,342.000000,338.049988,341.989990,341.989990,213680000\n1989-07-28,341.940002,342.959991,341.299988,342.149994,342.149994,180610000\n1989-07-31,342.130005,346.079987,342.019989,346.079987,346.079987,166650000\n1989-08-01,346.079987,347.989990,342.929993,343.750000,343.750000,225280000\n1989-08-02,343.750000,344.339996,342.470001,344.339996,344.339996,181760000\n1989-08-03,344.339996,345.220001,343.809998,344.739990,344.739990,168690000\n1989-08-04,344.739990,345.420013,342.600006,343.920013,343.920013,169750000\n1989-08-07,343.920013,349.420013,343.910004,349.410004,349.410004,197580000\n1989-08-08,349.410004,349.839996,348.279999,349.350006,349.350006,200340000\n1989-08-09,349.299988,351.000000,346.859985,346.940002,346.940002,209900000\n1989-08-10,346.940002,349.779999,345.309998,348.250000,348.250000,198660000\n1989-08-11,348.279999,351.179993,344.010010,344.739990,344.739990,197550000\n1989-08-14,344.709991,345.440002,341.959991,343.059998,343.059998,142010000\n1989-08-15,343.059998,345.029999,343.049988,344.709991,344.709991,148770000\n1989-08-16,344.709991,346.369995,344.709991,345.660004,345.660004,150060000\n1989-08-17,345.660004,346.390015,342.970001,344.450012,344.450012,157560000\n1989-08-18,344.450012,346.029999,343.890015,346.029999,346.029999,145810000\n1989-08-21,346.029999,346.250000,340.549988,340.670013,340.670013,136800000\n1989-08-22,340.670013,341.250000,339.000000,341.190002,341.190002,141930000\n1989-08-23,341.190002,344.799988,341.190002,344.700012,344.700012,159640000\n1989-08-24,344.700012,351.519989,344.700012,351.519989,351.519989,225520000\n1989-08-25,351.519989,352.730011,350.089996,350.519989,350.519989,165930000\n1989-08-28,350.519989,352.089996,349.079987,352.089996,352.089996,131180000\n1989-08-29,352.089996,352.119995,348.859985,349.839996,349.839996,175210000\n1989-08-30,349.839996,352.269989,348.660004,350.649994,350.649994,174350000\n1989-08-31,350.649994,351.450012,350.209991,351.450012,351.450012,144820000\n1989-09-01,351.450012,353.899994,350.880005,353.730011,353.730011,133300000\n1989-09-05,353.730011,354.130005,351.820007,352.559998,352.559998,145180000\n1989-09-06,352.559998,352.559998,347.980011,349.239990,349.239990,161800000\n1989-09-07,349.239990,350.309998,348.149994,348.350006,348.350006,160160000\n1989-09-08,348.350006,349.179993,345.739990,348.760010,348.760010,154090000\n1989-09-11,348.760010,348.760010,345.910004,347.660004,347.660004,126020000\n1989-09-12,347.660004,349.459991,347.500000,348.700012,348.700012,142140000\n1989-09-13,348.700012,350.100006,345.459991,345.459991,345.459991,175330000\n1989-09-14,345.459991,345.609985,342.549988,343.160004,343.160004,149250000\n1989-09-15,343.160004,345.059998,341.369995,345.059998,345.059998,234860000\n1989-09-18,345.059998,346.839996,344.600006,346.730011,346.730011,136940000\n1989-09-19,346.730011,348.170013,346.440002,346.549988,346.549988,141610000\n1989-09-20,346.549988,347.269989,346.179993,346.470001,346.470001,136640000\n1989-09-21,346.470001,348.459991,344.959991,345.700012,345.700012,146930000\n1989-09-22,345.700012,347.570007,345.690002,347.049988,347.049988,133350000\n1989-09-25,347.049988,347.049988,343.700012,344.230011,344.230011,121130000\n1989-09-26,344.230011,347.019989,344.130005,344.329987,344.329987,158350000\n1989-09-27,344.329987,345.470001,342.850006,345.100006,345.100006,158400000\n1989-09-28,345.100006,348.609985,345.100006,348.600006,348.600006,164240000\n1989-09-29,348.600006,350.309998,348.119995,349.149994,349.149994,155300000\n1989-10-02,349.149994,350.989990,348.350006,350.869995,350.869995,127410000\n1989-10-03,350.869995,354.730011,350.850006,354.709991,354.709991,182550000\n1989-10-04,354.709991,357.489990,354.709991,356.940002,356.940002,194590000\n1989-10-05,356.940002,357.630005,356.279999,356.970001,356.970001,177890000\n1989-10-06,356.970001,359.049988,356.970001,358.779999,358.779999,172520000\n1989-10-09,358.760010,359.859985,358.059998,359.799988,359.799988,86810000\n1989-10-10,359.799988,360.440002,358.109985,359.130005,359.130005,147560000\n1989-10-11,359.130005,359.130005,356.079987,356.989990,356.989990,164070000\n1989-10-12,356.989990,356.989990,354.910004,355.390015,355.390015,160120000\n1989-10-13,355.390015,355.529999,332.809998,333.649994,333.649994,251170000\n1989-10-16,333.649994,342.869995,327.119995,342.850006,342.850006,416290000\n1989-10-17,342.839996,342.850006,335.690002,341.160004,341.160004,224070000\n1989-10-18,341.160004,343.390015,339.029999,341.760010,341.760010,166900000\n1989-10-19,341.760010,348.820007,341.760010,347.130005,347.130005,198120000\n1989-10-20,347.040009,347.570007,344.470001,347.160004,347.160004,164830000\n1989-10-23,347.109985,348.190002,344.220001,344.829987,344.829987,135860000\n1989-10-24,344.829987,344.829987,335.130005,343.700012,343.700012,237960000\n1989-10-25,343.700012,344.510010,341.959991,342.500000,342.500000,155650000\n1989-10-26,342.500000,342.500000,337.200012,337.929993,337.929993,175240000\n1989-10-27,337.929993,337.970001,333.260010,335.059998,335.059998,170330000\n1989-10-30,335.059998,337.040009,334.480011,335.070007,335.070007,126630000\n1989-10-31,335.079987,340.859985,335.070007,340.359985,340.359985,176100000\n1989-11-01,340.359985,341.739990,339.790009,341.200012,341.200012,154240000\n1989-11-02,341.200012,341.200012,336.609985,338.480011,338.480011,152440000\n1989-11-03,338.480011,339.670013,337.369995,337.619995,337.619995,131500000\n1989-11-06,337.609985,337.619995,332.329987,332.609985,332.609985,135480000\n1989-11-07,332.609985,334.820007,330.910004,334.809998,334.809998,163000000\n1989-11-08,334.809998,339.410004,334.809998,338.149994,338.149994,170150000\n1989-11-09,338.149994,338.730011,336.209991,336.570007,336.570007,143390000\n1989-11-10,336.570007,339.100006,336.570007,339.100006,339.100006,131800000\n1989-11-13,339.079987,340.510010,337.929993,339.549988,339.549988,140750000\n1989-11-14,339.549988,340.410004,337.059998,337.989990,337.989990,143170000\n1989-11-15,338.000000,340.540009,337.140015,340.540009,340.540009,155130000\n1989-11-16,340.540009,341.019989,338.929993,340.579987,340.579987,148370000\n1989-11-17,340.579987,342.239990,339.850006,341.609985,341.609985,151020000\n1989-11-20,341.609985,341.899994,338.290009,339.350006,339.350006,128170000\n1989-11-21,339.350006,340.209991,337.529999,339.589996,339.589996,147900000\n1989-11-22,339.589996,341.920013,339.589996,341.910004,341.910004,145730000\n1989-11-24,341.920013,344.239990,341.910004,343.970001,343.970001,86290000\n1989-11-27,343.980011,346.239990,343.970001,345.609985,345.609985,149390000\n1989-11-28,345.609985,346.329987,344.410004,345.769989,345.769989,153770000\n1989-11-29,345.769989,345.769989,343.359985,343.600006,343.600006,147270000\n1989-11-30,343.600006,346.500000,343.570007,345.989990,345.989990,153200000\n1989-12-01,346.010010,351.880005,345.989990,350.630005,350.630005,199200000\n1989-12-04,350.630005,351.510010,350.320007,351.410004,351.410004,150360000\n1989-12-05,351.410004,352.239990,349.579987,349.579987,349.579987,154640000\n1989-12-06,349.579987,349.940002,347.910004,348.549988,348.549988,145850000\n1989-12-07,348.549988,349.839996,346.000000,347.589996,347.589996,161980000\n1989-12-08,347.600006,349.600006,347.589996,348.690002,348.690002,144910000\n1989-12-11,348.679993,348.739990,346.390015,348.559998,348.559998,147130000\n1989-12-12,348.559998,352.209991,348.410004,351.730011,351.730011,176820000\n1989-12-13,351.700012,354.100006,351.649994,352.750000,352.750000,184660000\n1989-12-14,352.739990,352.750000,350.079987,350.929993,350.929993,178700000\n1989-12-15,350.970001,351.859985,346.079987,350.140015,350.140015,240390000\n1989-12-18,350.140015,350.880005,342.190002,343.690002,343.690002,184750000\n1989-12-19,343.690002,343.739990,339.630005,342.459991,342.459991,186060000\n1989-12-20,342.500000,343.700012,341.790009,342.839996,342.839996,176520000\n1989-12-21,342.839996,345.029999,342.839996,344.779999,344.779999,175150000\n1989-12-22,344.779999,347.529999,344.760010,347.420013,347.420013,120980000\n1989-12-26,347.420013,347.869995,346.529999,346.809998,346.809998,77610000\n1989-12-27,346.839996,349.119995,346.809998,348.809998,348.809998,133740000\n1989-12-28,348.799988,350.679993,348.760010,350.670013,350.670013,128030000\n1989-12-29,350.679993,353.410004,350.670013,353.399994,353.399994,145940000\n1990-01-02,353.399994,359.690002,351.980011,359.690002,359.690002,162070000\n1990-01-03,359.690002,360.589996,357.890015,358.760010,358.760010,192330000\n1990-01-04,358.760010,358.760010,352.890015,355.670013,355.670013,177000000\n1990-01-05,355.670013,355.670013,351.350006,352.200012,352.200012,158530000\n1990-01-08,352.200012,354.239990,350.540009,353.790009,353.790009,140110000\n1990-01-09,353.829987,354.170013,349.609985,349.619995,349.619995,155210000\n1990-01-10,349.619995,349.619995,344.320007,347.309998,347.309998,175990000\n1990-01-11,347.309998,350.140015,347.309998,348.529999,348.529999,154390000\n1990-01-12,348.529999,348.529999,339.489990,339.929993,339.929993,183880000\n1990-01-15,339.929993,339.940002,336.570007,337.000000,337.000000,140590000\n1990-01-16,337.000000,340.750000,333.369995,340.750000,340.750000,186070000\n1990-01-17,340.769989,342.010010,336.260010,337.399994,337.399994,170470000\n1990-01-18,337.399994,338.380005,333.980011,338.190002,338.190002,178590000\n1990-01-19,338.190002,340.480011,338.190002,339.149994,339.149994,185590000\n1990-01-22,339.140015,339.959991,330.279999,330.380005,330.380005,148380000\n1990-01-23,330.380005,332.760010,328.670013,331.609985,331.609985,179300000\n1990-01-24,331.609985,331.709991,324.170013,330.260010,330.260010,207830000\n1990-01-25,330.260010,332.329987,325.329987,326.079987,326.079987,172270000\n1990-01-26,326.089996,328.579987,321.440002,325.799988,325.799988,198190000\n1990-01-29,325.799988,327.309998,321.790009,325.200012,325.200012,150770000\n1990-01-30,325.200012,325.730011,319.829987,322.980011,322.980011,186030000\n1990-01-31,322.980011,329.079987,322.980011,329.079987,329.079987,189660000\n1990-02-01,329.079987,329.859985,327.760010,328.790009,328.790009,154580000\n1990-02-02,328.790009,332.100006,328.089996,330.920013,330.920013,164400000\n1990-02-05,330.920013,332.160004,330.450012,331.850006,331.850006,130950000\n1990-02-06,331.850006,331.859985,328.200012,329.660004,329.660004,134070000\n1990-02-07,329.660004,333.760010,326.549988,333.750000,333.750000,186710000\n1990-02-08,333.750000,336.089996,332.000000,332.959991,332.959991,176240000\n1990-02-09,333.019989,334.600006,332.410004,333.619995,333.619995,146910000\n1990-02-12,333.619995,333.619995,329.970001,330.079987,330.079987,118390000\n1990-02-13,330.079987,331.609985,327.920013,331.019989,331.019989,144490000\n1990-02-14,331.019989,333.200012,330.640015,332.010010,332.010010,138530000\n1990-02-15,332.010010,335.209991,331.609985,334.890015,334.890015,174620000\n1990-02-16,334.890015,335.640015,332.420013,332.720001,332.720001,166840000\n1990-02-20,332.720001,332.720001,326.260010,327.989990,327.989990,147300000\n1990-02-21,327.910004,328.170013,324.470001,327.670013,327.670013,159240000\n1990-02-22,327.670013,330.980011,325.700012,325.700012,325.700012,184320000\n1990-02-23,325.700012,326.149994,322.100006,324.149994,324.149994,148490000\n1990-02-26,324.160004,328.670013,323.980011,328.670013,328.670013,148900000\n1990-02-27,328.679993,331.940002,328.470001,330.260010,330.260010,152590000\n1990-02-28,330.260010,333.480011,330.160004,331.890015,331.890015,184400000\n1990-03-01,331.890015,334.399994,331.079987,332.739990,332.739990,157930000\n1990-03-02,332.739990,335.540009,332.720001,335.540009,335.540009,164330000\n1990-03-05,335.540009,336.380005,333.489990,333.739990,333.739990,140110000\n1990-03-06,333.739990,337.929993,333.570007,337.929993,337.929993,143640000\n1990-03-07,337.929993,338.839996,336.329987,336.950012,336.950012,163580000\n1990-03-08,336.950012,340.660004,336.950012,340.269989,340.269989,170900000\n1990-03-09,340.119995,340.269989,336.839996,337.929993,337.929993,150410000\n1990-03-12,337.929993,339.079987,336.140015,338.670013,338.670013,114790000\n1990-03-13,338.670013,338.670013,335.359985,336.000000,336.000000,145440000\n1990-03-14,336.000000,337.630005,334.929993,336.869995,336.869995,145060000\n1990-03-15,336.869995,338.910004,336.869995,338.070007,338.070007,144410000\n1990-03-16,338.070007,341.910004,338.070007,341.910004,341.910004,222520000\n1990-03-19,341.910004,343.760010,339.119995,343.529999,343.529999,142300000\n1990-03-20,343.529999,344.489990,340.869995,341.570007,341.570007,177320000\n1990-03-21,341.570007,342.339996,339.559998,339.739990,339.739990,130990000\n1990-03-22,339.739990,339.769989,333.619995,335.690002,335.690002,175930000\n1990-03-23,335.690002,337.579987,335.690002,337.220001,337.220001,132070000\n1990-03-26,337.220001,339.739990,337.220001,337.630005,337.630005,116110000\n1990-03-27,337.630005,341.500000,337.029999,341.500000,341.500000,131610000\n1990-03-28,341.500000,342.579987,340.600006,342.000000,342.000000,142300000\n1990-03-29,342.000000,342.070007,339.769989,340.790009,340.790009,132190000\n1990-03-30,340.790009,341.410004,338.209991,339.940002,339.940002,139340000\n1990-04-02,339.940002,339.940002,336.329987,338.700012,338.700012,124360000\n1990-04-03,338.700012,343.760010,338.700012,343.640015,343.640015,154310000\n1990-04-04,343.640015,344.119995,340.399994,341.089996,341.089996,159530000\n1990-04-05,341.089996,342.850006,340.630005,340.730011,340.730011,144170000\n1990-04-06,340.730011,341.730011,338.940002,340.079987,340.079987,137490000\n1990-04-09,340.079987,341.829987,339.880005,341.369995,341.369995,114970000\n1990-04-10,341.369995,342.410004,340.619995,342.070007,342.070007,136020000\n1990-04-11,342.070007,343.000000,341.260010,341.920013,341.920013,141080000\n1990-04-12,341.920013,344.790009,341.910004,344.339996,344.339996,142470000\n1990-04-16,344.339996,347.299988,344.100006,344.739990,344.739990,142810000\n1990-04-17,344.739990,345.190002,342.059998,344.679993,344.679993,127990000\n1990-04-18,344.679993,345.329987,340.109985,340.720001,340.720001,147130000\n1990-04-19,340.720001,340.720001,337.589996,338.089996,338.089996,152930000\n1990-04-20,338.089996,338.519989,333.410004,335.119995,335.119995,174260000\n1990-04-23,335.119995,335.119995,330.089996,331.049988,331.049988,136150000\n1990-04-24,331.049988,332.970001,329.709991,330.359985,330.359985,137360000\n1990-04-25,330.359985,332.739990,330.359985,332.029999,332.029999,133480000\n1990-04-26,332.029999,333.760010,330.670013,332.920013,332.920013,141330000\n1990-04-27,332.920013,333.570007,328.709991,329.109985,329.109985,130630000\n1990-04-30,329.109985,331.309998,327.760010,330.799988,330.799988,122750000\n1990-05-01,330.799988,332.829987,330.799988,332.250000,332.250000,149020000\n1990-05-02,332.250000,334.480011,332.149994,334.480011,334.480011,141610000\n1990-05-03,334.480011,337.019989,334.470001,335.570007,335.570007,145560000\n1990-05-04,335.579987,338.459991,335.170013,338.390015,338.390015,140550000\n1990-05-07,338.390015,341.070007,338.109985,340.529999,340.529999,132760000\n1990-05-08,340.529999,342.029999,340.170013,342.010010,342.010010,144230000\n1990-05-09,342.010010,343.079987,340.899994,342.859985,342.859985,152220000\n1990-05-10,342.869995,344.980011,342.769989,343.820007,343.820007,158460000\n1990-05-11,343.820007,352.309998,343.820007,352.000000,352.000000,234040000\n1990-05-14,352.000000,358.410004,351.950012,354.750000,354.750000,225410000\n1990-05-15,354.750000,355.089996,352.839996,354.279999,354.279999,165730000\n1990-05-16,354.269989,354.679993,351.950012,354.000000,354.000000,159810000\n1990-05-17,354.000000,356.920013,354.000000,354.470001,354.470001,164770000\n1990-05-18,354.470001,354.640015,352.519989,354.640015,354.640015,162520000\n1990-05-21,354.640015,359.070007,353.779999,358.000000,358.000000,166280000\n1990-05-22,358.000000,360.500000,356.089996,358.429993,358.429993,203350000\n1990-05-23,358.429993,359.290009,356.989990,359.290009,359.290009,172330000\n1990-05-24,359.290009,359.559998,357.869995,358.410004,358.410004,155140000\n1990-05-25,358.410004,358.410004,354.320007,354.579987,354.579987,120250000\n1990-05-29,354.579987,360.649994,354.549988,360.649994,360.649994,137410000\n1990-05-30,360.649994,362.260010,360.000000,360.859985,360.859985,199540000\n1990-05-31,360.859985,361.839996,360.230011,361.230011,361.230011,165690000\n1990-06-01,361.260010,363.519989,361.209991,363.160004,363.160004,187860000\n1990-06-04,363.160004,367.850006,362.429993,367.399994,367.399994,175520000\n1990-06-05,367.399994,368.779999,365.489990,366.640015,366.640015,199720000\n1990-06-06,366.640015,366.640015,364.420013,364.959991,364.959991,164030000\n1990-06-07,365.920013,365.920013,361.600006,363.149994,363.149994,160360000\n1990-06-08,363.149994,363.489990,357.679993,358.709991,358.709991,142600000\n1990-06-11,358.709991,361.630005,357.700012,361.630005,361.630005,119550000\n1990-06-12,361.630005,367.269989,361.149994,366.250000,366.250000,157100000\n1990-06-13,366.250000,367.089996,364.510010,364.899994,364.899994,158910000\n1990-06-14,364.899994,364.899994,361.640015,362.899994,362.899994,135770000\n1990-06-15,362.890015,363.140015,360.709991,362.910004,362.910004,205130000\n1990-06-18,362.910004,362.910004,356.880005,356.880005,356.880005,133470000\n1990-06-19,356.880005,358.899994,356.179993,358.470001,358.470001,134930000\n1990-06-20,358.470001,359.910004,357.000000,359.100006,359.100006,137420000\n1990-06-21,359.100006,360.880005,357.630005,360.470001,360.470001,138570000\n1990-06-22,360.519989,363.200012,355.309998,355.429993,355.429993,172570000\n1990-06-25,355.420013,356.410004,351.910004,352.309998,352.309998,133100000\n1990-06-26,352.320007,356.089996,351.850006,352.059998,352.059998,141420000\n1990-06-27,352.059998,355.890015,351.230011,355.140015,355.140015,146620000\n1990-06-28,355.160004,357.630005,355.160004,357.630005,357.630005,136120000\n1990-06-29,357.640015,359.089996,357.299988,358.019989,358.019989,145510000\n1990-07-02,358.019989,359.579987,357.540009,359.540009,359.540009,130200000\n1990-07-03,359.540009,360.730011,359.440002,360.160004,360.160004,130050000\n1990-07-05,360.160004,360.160004,354.859985,355.679993,355.679993,128320000\n1990-07-06,355.690002,359.019989,354.640015,358.420013,358.420013,111730000\n1990-07-09,358.420013,360.049988,358.109985,359.519989,359.519989,119390000\n1990-07-10,359.519989,359.739990,356.410004,356.489990,356.489990,147630000\n1990-07-11,356.489990,361.230011,356.489990,361.230011,361.230011,162220000\n1990-07-12,361.230011,365.459991,360.570007,365.440002,365.440002,213180000\n1990-07-13,365.450012,369.679993,365.450012,367.309998,367.309998,215600000\n1990-07-16,367.309998,369.779999,367.309998,368.950012,368.950012,149430000\n1990-07-17,368.950012,369.399994,364.989990,367.519989,367.519989,176790000\n1990-07-18,367.519989,367.519989,362.950012,364.220001,364.220001,168760000\n1990-07-19,364.220001,365.320007,361.290009,365.320007,365.320007,161990000\n1990-07-20,365.320007,366.640015,361.579987,361.609985,361.609985,177810000\n1990-07-23,361.609985,361.609985,350.089996,355.309998,355.309998,209030000\n1990-07-24,355.309998,356.089996,351.459991,355.790009,355.790009,181920000\n1990-07-25,355.790009,357.519989,354.799988,357.089996,357.089996,163530000\n1990-07-26,357.089996,357.470001,353.950012,355.910004,355.910004,155040000\n1990-07-27,355.899994,355.940002,352.140015,353.440002,353.440002,149070000\n1990-07-30,353.440002,355.549988,351.149994,355.549988,355.549988,146470000\n1990-07-31,355.549988,357.540009,353.910004,356.149994,356.149994,175380000\n1990-08-01,356.149994,357.350006,353.820007,355.519989,355.519989,178260000\n1990-08-02,355.519989,355.519989,349.730011,351.480011,351.480011,253090000\n1990-08-03,351.480011,351.480011,338.200012,344.859985,344.859985,295880000\n1990-08-06,344.859985,344.859985,333.269989,334.429993,334.429993,240400000\n1990-08-07,334.429993,338.630005,332.220001,334.829987,334.829987,231580000\n1990-08-08,334.829987,339.209991,334.829987,338.350006,338.350006,190400000\n1990-08-09,338.350006,340.559998,337.559998,339.940002,339.940002,155810000\n1990-08-10,339.899994,339.899994,334.220001,335.519989,335.519989,145340000\n1990-08-13,335.390015,338.880005,332.019989,338.839996,338.839996,122820000\n1990-08-14,338.839996,340.959991,337.190002,339.390015,339.390015,130320000\n1990-08-15,339.390015,341.920013,339.380005,340.059998,340.059998,136710000\n1990-08-16,340.059998,340.059998,332.390015,332.390015,332.390015,138850000\n1990-08-17,332.359985,332.359985,324.630005,327.829987,327.829987,212560000\n1990-08-20,327.829987,329.899994,327.070007,328.510010,328.510010,129630000\n1990-08-21,328.510010,328.510010,318.779999,321.859985,321.859985,194630000\n1990-08-22,321.859985,324.149994,316.549988,316.549988,316.549988,175550000\n1990-08-23,316.549988,316.549988,306.559998,307.059998,307.059998,250440000\n1990-08-24,307.059998,311.649994,306.179993,311.510010,311.510010,199040000\n1990-08-27,311.549988,323.109985,311.549988,321.440002,321.440002,160150000\n1990-08-28,321.440002,322.200012,320.250000,321.339996,321.339996,127660000\n1990-08-29,321.339996,325.829987,320.869995,324.190002,324.190002,134240000\n1990-08-30,324.190002,324.570007,317.820007,318.709991,318.709991,120890000\n1990-08-31,318.709991,322.570007,316.589996,322.559998,322.559998,96480000\n1990-09-04,322.559998,323.089996,319.109985,323.089996,323.089996,92940000\n1990-09-05,323.089996,324.519989,320.989990,324.390015,324.390015,120610000\n1990-09-06,324.390015,324.390015,319.369995,320.459991,320.459991,125620000\n1990-09-07,320.459991,324.179993,319.709991,323.399994,323.399994,123800000\n1990-09-10,323.420013,326.529999,320.309998,321.630005,321.630005,119730000\n1990-09-11,321.630005,322.179993,319.600006,321.040009,321.040009,113220000\n1990-09-12,321.040009,322.549988,319.600006,322.540009,322.540009,129890000\n1990-09-13,322.510010,322.510010,318.019989,318.649994,318.649994,123390000\n1990-09-14,318.649994,318.649994,314.760010,316.829987,316.829987,133390000\n1990-09-17,316.829987,318.049988,315.209991,317.769989,317.769989,110600000\n1990-09-18,317.769989,318.850006,314.269989,318.600006,318.600006,141130000\n1990-09-19,318.600006,319.350006,316.250000,316.600006,316.600006,147530000\n1990-09-20,316.600006,316.600006,310.549988,311.480011,311.480011,145100000\n1990-09-21,311.529999,312.170013,307.980011,311.320007,311.320007,201050000\n1990-09-24,311.299988,311.299988,303.579987,304.589996,304.589996,164070000\n1990-09-25,305.459991,308.269989,304.230011,308.260010,308.260010,155940000\n1990-09-26,308.260010,308.279999,303.049988,305.059998,305.059998,155570000\n1990-09-27,305.059998,307.470001,299.100006,300.970001,300.970001,182690000\n1990-09-28,300.970001,306.049988,295.980011,306.049988,306.049988,201010000\n1990-10-01,306.100006,314.940002,306.100006,314.940002,314.940002,202210000\n1990-10-02,314.940002,319.690002,314.940002,315.209991,315.209991,188360000\n1990-10-03,315.209991,316.260010,310.700012,311.399994,311.399994,135490000\n1990-10-04,311.399994,313.399994,308.589996,312.690002,312.690002,145410000\n1990-10-05,312.690002,314.790009,305.760010,311.500000,311.500000,153380000\n1990-10-08,311.500000,315.029999,311.500000,313.480011,313.480011,99470000\n1990-10-09,313.459991,313.459991,305.089996,305.100006,305.100006,145610000\n1990-10-10,305.089996,306.429993,299.209991,300.390015,300.390015,169190000\n1990-10-11,300.390015,301.450012,294.510010,295.459991,295.459991,180060000\n1990-10-12,295.450012,301.679993,295.220001,300.029999,300.029999,187940000\n1990-10-15,300.029999,304.790009,296.410004,303.230011,303.230011,164980000\n1990-10-16,303.230011,304.339996,298.119995,298.920013,298.920013,149570000\n1990-10-17,298.920013,301.500000,297.790009,298.760010,298.760010,161260000\n1990-10-18,298.750000,305.739990,298.750000,305.739990,305.739990,204110000\n1990-10-19,305.739990,312.480011,305.739990,312.480011,312.480011,221480000\n1990-10-22,312.480011,315.829987,310.470001,314.760010,314.760010,152650000\n1990-10-23,314.760010,315.059998,312.059998,312.359985,312.359985,146300000\n1990-10-24,312.359985,313.510010,310.739990,312.600006,312.600006,149290000\n1990-10-25,312.600006,313.709991,309.700012,310.170013,310.170013,141460000\n1990-10-26,310.170013,310.170013,304.709991,304.709991,304.709991,130190000\n1990-10-29,304.739990,307.410004,300.690002,301.880005,301.880005,133980000\n1990-10-30,301.880005,304.359985,299.440002,304.059998,304.059998,153450000\n1990-10-31,304.059998,305.700012,302.329987,304.000000,304.000000,156060000\n1990-11-01,303.989990,307.269989,301.609985,307.019989,307.019989,159270000\n1990-11-02,307.019989,311.940002,306.880005,311.850006,311.850006,168700000\n1990-11-05,311.850006,314.609985,311.410004,314.589996,314.589996,147510000\n1990-11-06,314.589996,314.760010,311.429993,311.619995,311.619995,142660000\n1990-11-07,311.619995,311.619995,305.790009,306.010010,306.010010,149130000\n1990-11-08,306.010010,309.769989,305.029999,307.609985,307.609985,155570000\n1990-11-09,307.609985,313.779999,307.609985,313.739990,313.739990,145160000\n1990-11-12,313.739990,319.769989,313.730011,319.480011,319.480011,161390000\n1990-11-13,319.480011,319.480011,317.260010,317.670013,317.670013,160240000\n1990-11-14,317.660004,321.700012,317.230011,320.399994,320.399994,179310000\n1990-11-15,320.399994,320.399994,316.130005,317.019989,317.019989,151370000\n1990-11-16,317.019989,318.799988,314.989990,317.119995,317.119995,165440000\n1990-11-19,317.149994,319.390015,317.149994,319.339996,319.339996,140950000\n1990-11-20,319.339996,319.339996,315.309998,315.309998,315.309998,161170000\n1990-11-21,315.309998,316.149994,312.420013,316.029999,316.029999,140660000\n1990-11-23,316.029999,317.299988,315.059998,315.100006,315.100006,63350000\n1990-11-26,315.079987,316.510010,311.480011,316.510010,316.510010,131540000\n1990-11-27,316.510010,318.690002,315.799988,318.100006,318.100006,147590000\n1990-11-28,318.109985,319.959991,317.619995,317.950012,317.950012,145490000\n1990-11-29,317.950012,317.950012,315.029999,316.420013,316.420013,140920000\n1990-11-30,316.420013,323.019989,315.420013,322.220001,322.220001,192350000\n1990-12-03,322.230011,324.899994,322.230011,324.100006,324.100006,177000000\n1990-12-04,324.109985,326.769989,321.970001,326.350006,326.350006,185820000\n1990-12-05,326.359985,329.920013,325.660004,329.920013,329.920013,205820000\n1990-12-06,329.940002,333.980011,328.369995,329.070007,329.070007,256380000\n1990-12-07,329.089996,329.390015,326.390015,327.750000,327.750000,164950000\n1990-12-10,327.750000,328.970001,326.149994,328.890015,328.890015,138650000\n1990-12-11,328.880005,328.880005,325.649994,326.440002,326.440002,145330000\n1990-12-12,326.440002,330.359985,326.440002,330.190002,330.190002,182270000\n1990-12-13,330.140015,330.579987,328.769989,329.339996,329.339996,162110000\n1990-12-14,329.339996,329.339996,325.160004,326.820007,326.820007,151010000\n1990-12-17,326.820007,326.820007,324.459991,326.019989,326.019989,118560000\n1990-12-18,326.019989,330.429993,325.750000,330.049988,330.049988,176460000\n1990-12-19,330.040009,330.799988,329.390015,330.200012,330.200012,180380000\n1990-12-20,330.200012,330.739990,326.940002,330.119995,330.119995,174700000\n1990-12-21,330.119995,332.470001,330.119995,331.750000,331.750000,233400000\n1990-12-24,331.739990,331.739990,329.160004,329.899994,329.899994,57200000\n1990-12-26,329.890015,331.690002,329.890015,330.850006,330.850006,78730000\n1990-12-27,330.850006,331.040009,328.230011,328.290009,328.290009,102900000\n1990-12-28,328.290009,328.720001,327.239990,328.720001,328.720001,111030000\n1990-12-31,328.709991,330.230011,327.500000,330.220001,330.220001,114130000\n1991-01-02,330.200012,330.750000,326.450012,326.450012,326.450012,126280000\n1991-01-03,326.459991,326.529999,321.899994,321.910004,321.910004,141450000\n1991-01-04,321.910004,322.350006,318.869995,321.000000,321.000000,140820000\n1991-01-07,320.970001,320.970001,315.440002,315.440002,315.440002,130610000\n1991-01-08,315.440002,316.970001,313.790009,314.899994,314.899994,143390000\n1991-01-09,314.899994,320.730011,310.929993,311.489990,311.489990,191100000\n1991-01-10,311.510010,314.769989,311.510010,314.529999,314.529999,124510000\n1991-01-11,314.529999,315.239990,313.589996,315.230011,315.230011,123050000\n1991-01-14,315.230011,315.230011,309.350006,312.489990,312.489990,120830000\n1991-01-15,312.489990,313.730011,311.839996,313.730011,313.730011,110000000\n1991-01-16,313.730011,316.940002,312.940002,316.170013,316.170013,134560000\n1991-01-17,316.250000,327.970001,316.250000,327.970001,327.970001,319080000\n1991-01-18,327.929993,332.230011,327.079987,332.230011,332.230011,226770000\n1991-01-21,332.230011,332.230011,328.869995,331.059998,331.059998,136290000\n1991-01-22,331.059998,331.260010,327.829987,328.309998,328.309998,177060000\n1991-01-23,328.299988,331.040009,327.929993,330.209991,330.209991,168620000\n1991-01-24,330.209991,335.829987,330.190002,334.779999,334.779999,223150000\n1991-01-25,334.779999,336.920013,334.200012,336.070007,336.070007,194350000\n1991-01-28,336.059998,337.410004,335.809998,336.029999,336.029999,141270000\n1991-01-29,336.029999,336.029999,334.260010,335.839996,335.839996,155740000\n1991-01-30,335.799988,340.910004,335.709991,340.910004,340.910004,226790000\n1991-01-31,340.920013,343.929993,340.470001,343.929993,343.929993,204520000\n1991-02-01,343.910004,344.899994,340.369995,343.049988,343.049988,246670000\n1991-02-04,343.049988,348.709991,342.959991,348.339996,348.339996,250750000\n1991-02-05,348.339996,351.839996,347.209991,351.260010,351.260010,290570000\n1991-02-06,351.260010,358.070007,349.579987,358.070007,358.070007,276940000\n1991-02-07,358.070007,363.429993,355.529999,356.519989,356.519989,292190000\n1991-02-08,356.519989,359.350006,356.019989,359.350006,359.350006,187830000\n1991-02-11,359.359985,368.579987,359.320007,368.579987,368.579987,265350000\n1991-02-12,368.579987,370.540009,365.500000,365.500000,365.500000,256160000\n1991-02-13,365.500000,369.489990,364.640015,369.019989,369.019989,209960000\n1991-02-14,369.019989,370.260010,362.769989,364.220001,364.220001,230750000\n1991-02-15,364.230011,369.489990,364.230011,369.059998,369.059998,228480000\n1991-02-19,369.059998,370.109985,367.049988,369.390015,369.390015,189900000\n1991-02-20,369.369995,369.369995,364.380005,365.140015,365.140015,185680000\n1991-02-21,365.140015,366.790009,364.500000,364.970001,364.970001,180770000\n1991-02-22,364.970001,370.959991,364.230011,365.649994,365.649994,218760000\n1991-02-25,365.649994,370.190002,365.160004,367.260010,367.260010,193820000\n1991-02-26,367.260010,367.260010,362.190002,362.809998,362.809998,164170000\n1991-02-27,362.809998,368.380005,362.809998,367.739990,367.739990,211410000\n1991-02-28,367.730011,369.910004,365.950012,367.070007,367.070007,223010000\n1991-03-01,367.070007,370.470001,363.730011,370.470001,370.470001,221510000\n1991-03-04,370.470001,371.989990,369.070007,369.329987,369.329987,199830000\n1991-03-05,369.329987,377.890015,369.329987,376.720001,376.720001,253700000\n1991-03-06,376.720001,379.660004,375.019989,376.170013,376.170013,262290000\n1991-03-07,376.160004,377.489990,375.579987,375.910004,375.910004,197060000\n1991-03-08,375.910004,378.690002,374.429993,374.950012,374.950012,206850000\n1991-03-11,374.940002,375.100006,372.519989,372.959991,372.959991,161600000\n1991-03-12,372.959991,374.350006,369.549988,370.029999,370.029999,176440000\n1991-03-13,370.029999,374.649994,370.029999,374.570007,374.570007,176000000\n1991-03-14,374.589996,378.279999,371.760010,373.500000,373.500000,232070000\n1991-03-15,373.500000,374.579987,370.209991,373.589996,373.589996,237650000\n1991-03-18,373.589996,374.089996,369.459991,372.109985,372.109985,163100000\n1991-03-19,372.109985,372.109985,366.540009,366.589996,366.589996,177070000\n1991-03-20,366.589996,368.850006,365.799988,367.920013,367.920013,196810000\n1991-03-21,367.940002,371.010010,366.510010,366.579987,366.579987,199830000\n1991-03-22,366.579987,368.220001,365.579987,367.480011,367.480011,160890000\n1991-03-25,367.480011,371.309998,367.459991,369.829987,369.829987,153920000\n1991-03-26,369.829987,376.299988,369.369995,376.299988,376.299988,198720000\n1991-03-27,376.279999,378.480011,374.730011,375.350006,375.350006,201830000\n1991-03-28,375.350006,376.600006,374.399994,375.220001,375.220001,150750000\n1991-04-01,375.220001,375.220001,370.269989,371.299988,371.299988,144010000\n1991-04-02,371.299988,379.500000,371.299988,379.500000,379.500000,189530000\n1991-04-03,379.500000,381.559998,378.489990,378.940002,378.940002,213720000\n1991-04-04,378.940002,381.880005,377.049988,379.769989,379.769989,198120000\n1991-04-05,379.779999,381.119995,374.149994,375.359985,375.359985,187410000\n1991-04-08,375.350006,378.760010,374.690002,378.660004,378.660004,138580000\n1991-04-09,378.649994,379.019989,373.109985,373.559998,373.559998,169940000\n1991-04-10,373.570007,374.829987,371.209991,373.149994,373.149994,167940000\n1991-04-11,373.149994,379.529999,373.149994,377.630005,377.630005,196570000\n1991-04-12,377.649994,381.070007,376.890015,380.399994,380.399994,198610000\n1991-04-15,380.399994,382.320007,378.779999,381.190002,381.190002,161800000\n1991-04-16,381.190002,387.619995,379.640015,387.619995,387.619995,214480000\n1991-04-17,387.619995,391.260010,387.299988,390.450012,390.450012,246930000\n1991-04-18,390.450012,390.970001,388.130005,388.459991,388.459991,217410000\n1991-04-19,388.459991,388.459991,383.899994,384.200012,384.200012,195520000\n1991-04-22,384.190002,384.190002,380.160004,380.950012,380.950012,164410000\n1991-04-23,380.950012,383.549988,379.670013,381.760010,381.760010,167840000\n1991-04-24,381.760010,383.019989,379.989990,382.760010,382.760010,166800000\n1991-04-25,382.890015,382.890015,378.429993,379.250000,379.250000,166940000\n1991-04-26,379.250000,380.109985,376.769989,379.019989,379.019989,154550000\n1991-04-29,379.010010,380.959991,373.660004,373.660004,373.660004,149860000\n1991-04-30,373.660004,377.859985,373.010010,375.339996,375.339996,206230000\n1991-05-01,375.350006,380.459991,375.269989,380.290009,380.290009,181900000\n1991-05-02,380.290009,382.140015,379.820007,380.519989,380.519989,187090000\n1991-05-03,380.519989,381.000000,378.820007,380.799988,380.799988,158150000\n1991-05-06,380.779999,380.779999,377.859985,380.079987,380.079987,129110000\n1991-05-07,380.079987,380.910004,377.309998,377.320007,377.320007,153290000\n1991-05-08,377.329987,379.260010,376.209991,378.510010,378.510010,157240000\n1991-05-09,378.510010,383.559998,378.510010,383.250000,383.250000,180460000\n1991-05-10,383.260010,383.910004,375.609985,375.739990,375.739990,172730000\n1991-05-13,375.739990,377.019989,374.619995,376.760010,376.760010,129620000\n1991-05-14,375.510010,375.529999,370.820007,371.619995,371.619995,207890000\n1991-05-15,371.549988,372.470001,365.829987,368.570007,368.570007,193110000\n1991-05-16,368.570007,372.510010,368.570007,372.190002,372.190002,154460000\n1991-05-17,372.190002,373.010010,369.440002,372.390015,372.390015,174210000\n1991-05-20,372.390015,373.649994,371.260010,372.279999,372.279999,109510000\n1991-05-21,372.279999,376.660004,372.279999,375.350006,375.350006,176620000\n1991-05-22,375.350006,376.500000,374.399994,376.190002,376.190002,159310000\n1991-05-23,376.190002,378.070007,373.549988,374.959991,374.959991,173080000\n1991-05-24,374.970001,378.079987,374.970001,377.489990,377.489990,124640000\n1991-05-28,377.489990,382.100006,377.119995,381.940002,381.940002,162350000\n1991-05-29,381.940002,383.660004,381.369995,382.790009,382.790009,188450000\n1991-05-30,382.790009,388.170013,382.500000,386.959991,386.959991,234440000\n1991-05-31,386.959991,389.850006,385.010010,389.829987,389.829987,232040000\n1991-06-03,389.809998,389.809998,386.970001,388.059998,388.059998,173990000\n1991-06-04,388.059998,388.059998,385.140015,387.739990,387.739990,180450000\n1991-06-05,387.739990,388.230011,384.450012,385.089996,385.089996,186560000\n1991-06-06,385.100006,385.850006,383.130005,383.630005,383.630005,168260000\n1991-06-07,383.630005,383.630005,378.760010,379.429993,379.429993,169570000\n1991-06-10,379.429993,379.750000,377.950012,378.570007,378.570007,127720000\n1991-06-11,378.570007,381.630005,378.570007,381.049988,381.049988,161610000\n1991-06-12,381.049988,381.049988,374.459991,376.649994,376.649994,166140000\n1991-06-13,376.649994,377.899994,376.079987,377.630005,377.630005,145650000\n1991-06-14,377.630005,382.299988,377.630005,382.290009,382.290009,167950000\n1991-06-17,382.299988,382.309998,380.130005,380.130005,380.130005,134230000\n1991-06-18,380.130005,381.829987,377.989990,378.589996,378.589996,155200000\n1991-06-19,378.570007,378.570007,374.359985,375.089996,375.089996,156440000\n1991-06-20,375.089996,376.290009,373.869995,375.420013,375.420013,163980000\n1991-06-21,375.420013,377.750000,375.329987,377.750000,377.750000,193310000\n1991-06-24,377.739990,377.739990,370.730011,370.940002,370.940002,137940000\n1991-06-25,370.940002,372.619995,369.559998,370.649994,370.649994,155710000\n1991-06-26,370.649994,372.730011,368.339996,371.589996,371.589996,187170000\n1991-06-27,371.589996,374.399994,371.589996,374.399994,374.399994,163080000\n1991-06-28,374.399994,374.399994,367.980011,371.160004,371.160004,163770000\n1991-07-01,371.179993,377.920013,371.179993,377.920013,377.920013,167480000\n1991-07-02,377.920013,377.929993,376.619995,377.470001,377.470001,157290000\n1991-07-03,377.470001,377.470001,372.079987,373.329987,373.329987,140580000\n1991-07-05,373.339996,375.510010,372.170013,374.079987,374.079987,69910000\n1991-07-08,374.089996,377.940002,370.920013,377.940002,377.940002,138330000\n1991-07-09,377.940002,378.579987,375.369995,376.109985,376.109985,151820000\n1991-07-10,376.109985,380.350006,375.200012,375.739990,375.739990,178290000\n1991-07-11,375.730011,377.679993,375.510010,376.970001,376.970001,157930000\n1991-07-12,376.970001,381.410004,375.790009,380.250000,380.250000,174770000\n1991-07-15,380.279999,383.000000,380.239990,382.390015,382.390015,161750000\n1991-07-16,382.390015,382.940002,380.799988,381.540009,381.540009,182990000\n1991-07-17,381.500000,382.859985,381.130005,381.179993,381.179993,195460000\n1991-07-18,381.179993,385.369995,381.179993,385.369995,385.369995,200930000\n1991-07-19,385.380005,385.829987,383.649994,384.220001,384.220001,190700000\n1991-07-22,384.209991,384.549988,381.839996,382.880005,382.880005,149050000\n1991-07-23,382.880005,384.859985,379.390015,379.420013,379.420013,160190000\n1991-07-24,379.420013,380.459991,378.290009,378.640015,378.640015,158700000\n1991-07-25,378.640015,381.130005,378.149994,380.959991,380.959991,145800000\n1991-07-26,380.959991,381.760010,379.809998,380.929993,380.929993,127760000\n1991-07-29,380.929993,383.149994,380.450012,383.149994,383.149994,136000000\n1991-07-30,383.149994,386.920013,383.149994,386.690002,386.690002,169010000\n1991-07-31,386.690002,387.809998,386.190002,387.809998,387.809998,166830000\n1991-08-01,387.809998,387.950012,386.480011,387.119995,387.119995,170610000\n1991-08-02,387.119995,389.559998,386.049988,387.179993,387.179993,162270000\n1991-08-05,387.170013,387.170013,384.480011,385.059998,385.059998,128050000\n1991-08-06,385.059998,390.799988,384.290009,390.619995,390.619995,174460000\n1991-08-07,390.619995,391.589996,389.859985,390.559998,390.559998,172220000\n1991-08-08,390.559998,391.799988,388.149994,389.320007,389.320007,163890000\n1991-08-09,389.320007,389.890015,387.040009,387.119995,387.119995,143740000\n1991-08-12,387.109985,388.170013,385.899994,388.019989,388.019989,145440000\n1991-08-13,388.019989,392.119995,388.019989,389.619995,389.619995,212760000\n1991-08-14,389.619995,391.850006,389.130005,389.899994,389.899994,124230000\n1991-08-15,389.910004,391.920013,389.290009,389.329987,389.329987,174690000\n1991-08-16,389.329987,390.410004,383.160004,385.579987,385.579987,189480000\n1991-08-19,385.579987,385.579987,374.089996,376.470001,376.470001,230350000\n1991-08-20,376.470001,380.350006,376.470001,379.429993,379.429993,184260000\n1991-08-21,379.549988,390.589996,379.549988,390.589996,390.589996,232690000\n1991-08-22,390.589996,391.980011,390.209991,391.329987,391.329987,173090000\n1991-08-23,391.329987,395.339996,390.690002,394.170013,394.170013,188870000\n1991-08-26,394.170013,394.390015,392.750000,393.850006,393.850006,130570000\n1991-08-27,393.850006,393.869995,391.769989,393.059998,393.059998,144670000\n1991-08-28,393.059998,396.640015,393.049988,396.640015,396.640015,169890000\n1991-08-29,396.649994,396.820007,395.140015,396.470001,396.470001,154150000\n1991-08-30,396.470001,396.470001,393.600006,395.429993,395.429993,143440000\n1991-09-03,395.429993,397.619995,392.100006,392.149994,392.149994,153600000\n1991-09-04,392.149994,392.619995,388.679993,389.970001,389.970001,157520000\n1991-09-05,389.970001,390.970001,388.489990,389.140015,389.140015,162380000\n1991-09-06,389.140015,390.709991,387.359985,389.100006,389.100006,166560000\n1991-09-09,389.109985,389.339996,387.880005,388.570007,388.570007,115100000\n1991-09-10,388.570007,388.630005,383.779999,384.559998,384.559998,143390000\n1991-09-11,384.559998,385.600006,383.589996,385.089996,385.089996,148000000\n1991-09-12,385.089996,387.339996,385.089996,387.339996,387.339996,160420000\n1991-09-13,387.160004,387.950012,382.850006,383.589996,383.589996,169630000\n1991-09-16,383.589996,385.790009,382.769989,385.779999,385.779999,172560000\n1991-09-17,385.779999,387.130005,384.970001,385.500000,385.500000,168340000\n1991-09-18,385.489990,386.940002,384.279999,386.940002,386.940002,141340000\n1991-09-19,386.940002,389.420013,386.269989,387.559998,387.559998,211010000\n1991-09-20,387.559998,388.820007,386.489990,387.920013,387.920013,254520000\n1991-09-23,387.899994,388.549988,385.760010,385.920013,385.920013,145940000\n1991-09-24,385.920013,388.130005,384.459991,387.709991,387.709991,170350000\n1991-09-25,387.720001,388.250000,385.989990,386.880005,386.880005,153910000\n1991-09-26,386.869995,388.390015,385.299988,386.489990,386.489990,158980000\n1991-09-27,386.489990,389.089996,384.869995,385.899994,385.899994,160660000\n1991-09-30,385.910004,388.290009,384.320007,387.859985,387.859985,146780000\n1991-10-01,387.859985,389.559998,387.859985,389.200012,389.200012,163570000\n1991-10-02,389.200012,390.029999,387.619995,388.260010,388.260010,166380000\n1991-10-03,388.230011,388.230011,384.470001,384.470001,384.470001,174360000\n1991-10-04,384.470001,385.190002,381.239990,381.250000,381.250000,164000000\n1991-10-07,381.220001,381.269989,379.070007,379.500000,379.500000,148430000\n1991-10-08,379.500000,381.230011,379.179993,380.670013,380.670013,177120000\n1991-10-09,380.570007,380.570007,376.350006,376.799988,376.799988,186710000\n1991-10-10,376.799988,380.549988,376.109985,380.549988,380.549988,164240000\n1991-10-11,380.549988,381.459991,379.899994,381.450012,381.450012,148850000\n1991-10-14,381.450012,386.470001,381.450012,386.470001,386.470001,130120000\n1991-10-15,386.470001,391.500000,385.950012,391.010010,391.010010,213540000\n1991-10-16,391.010010,393.290009,390.140015,392.799988,392.799988,225380000\n1991-10-17,392.790009,393.809998,390.320007,391.920013,391.920013,206030000\n1991-10-18,391.920013,392.799988,391.769989,392.500000,392.500000,204090000\n1991-10-21,392.489990,392.489990,388.959991,390.019989,390.019989,154140000\n1991-10-22,390.019989,391.200012,387.399994,387.829987,387.829987,194160000\n1991-10-23,387.829987,389.079987,386.519989,387.940002,387.940002,185390000\n1991-10-24,387.940002,388.320007,383.450012,385.070007,385.070007,179040000\n1991-10-25,385.070007,386.130005,382.970001,384.200012,384.200012,167310000\n1991-10-28,384.200012,389.519989,384.200012,389.519989,389.519989,161630000\n1991-10-29,389.519989,391.700012,386.880005,391.480011,391.480011,192810000\n1991-10-30,391.480011,393.109985,390.779999,392.959991,392.959991,195400000\n1991-10-31,392.959991,392.959991,391.579987,392.450012,392.450012,179680000\n1991-11-01,392.459991,395.100006,389.670013,391.320007,391.320007,205780000\n1991-11-04,391.290009,391.290009,388.089996,390.279999,390.279999,155660000\n1991-11-05,390.279999,392.170013,388.190002,388.709991,388.709991,172090000\n1991-11-06,388.709991,389.970001,387.579987,389.970001,389.970001,167440000\n1991-11-07,389.970001,393.720001,389.970001,393.720001,393.720001,205480000\n1991-11-08,393.720001,396.429993,392.420013,392.890015,392.890015,183260000\n1991-11-11,392.899994,393.570007,392.320007,393.119995,393.119995,128920000\n1991-11-12,393.119995,397.130005,393.119995,396.739990,396.739990,198610000\n1991-11-13,396.739990,397.420013,394.010010,397.410004,397.410004,184480000\n1991-11-14,397.410004,398.220001,395.850006,397.149994,397.149994,200030000\n1991-11-15,397.149994,397.160004,382.619995,382.619995,382.619995,239690000\n1991-11-18,382.619995,385.399994,379.700012,385.239990,385.239990,241940000\n1991-11-19,385.239990,385.239990,374.899994,379.420013,379.420013,243880000\n1991-11-20,379.420013,381.510010,377.839996,378.529999,378.529999,192760000\n1991-11-21,378.529999,381.119995,377.410004,380.059998,380.059998,195130000\n1991-11-22,380.049988,380.049988,374.519989,376.140015,376.140015,188240000\n1991-11-25,376.140015,377.070007,374.000000,375.339996,375.339996,175870000\n1991-11-26,375.339996,378.290009,371.630005,377.959991,377.959991,213810000\n1991-11-27,377.959991,378.109985,375.980011,376.549988,376.549988,167720000\n1991-11-29,376.549988,376.549988,374.649994,375.220001,375.220001,76830000\n1991-12-02,375.109985,381.399994,371.359985,381.399994,381.399994,188410000\n1991-12-03,381.399994,381.480011,379.920013,380.959991,380.959991,187230000\n1991-12-04,380.959991,381.510010,378.070007,380.070007,380.070007,187960000\n1991-12-05,380.070007,380.070007,376.579987,377.390015,377.390015,166350000\n1991-12-06,377.390015,382.390015,375.410004,379.100006,379.100006,199160000\n1991-12-09,379.089996,381.420013,377.670013,378.260010,378.260010,174760000\n1991-12-10,378.260010,379.570007,376.640015,377.899994,377.899994,192920000\n1991-12-11,377.899994,379.420013,374.779999,377.700012,377.700012,207430000\n1991-12-12,377.700012,381.619995,377.700012,381.549988,381.549988,192950000\n1991-12-13,381.549988,385.040009,381.549988,384.470001,384.470001,194470000\n1991-12-16,384.480011,385.839996,384.369995,384.459991,384.459991,173080000\n1991-12-17,384.459991,385.049988,382.600006,382.739990,382.739990,191310000\n1991-12-18,382.739990,383.510010,380.880005,383.480011,383.480011,192410000\n1991-12-19,383.459991,383.459991,380.640015,382.519989,382.519989,199330000\n1991-12-20,382.519989,388.239990,382.519989,387.040009,387.040009,316140000\n1991-12-23,387.049988,397.440002,386.959991,396.820007,396.820007,228900000\n1991-12-24,396.820007,401.790009,396.820007,399.329987,399.329987,162640000\n1991-12-26,399.329987,404.920013,399.309998,404.839996,404.839996,149230000\n1991-12-27,404.839996,406.579987,404.589996,406.459991,406.459991,157950000\n1991-12-30,406.489990,415.140015,406.489990,415.140015,415.140015,245600000\n1991-12-31,415.140015,418.320007,412.730011,417.089996,417.089996,247080000\n1992-01-02,417.029999,417.269989,411.040009,417.260010,417.260010,207570000\n1992-01-03,417.269989,419.790009,416.160004,419.339996,419.339996,224270000\n1992-01-06,419.309998,419.440002,416.920013,417.959991,417.959991,251210000\n1992-01-07,417.959991,417.959991,415.200012,417.399994,417.399994,252780000\n1992-01-08,417.359985,420.230011,415.019989,418.100006,418.100006,290750000\n1992-01-09,418.089996,420.500000,415.850006,417.609985,417.609985,292350000\n1992-01-10,417.619995,417.619995,413.309998,415.100006,415.100006,236130000\n1992-01-13,415.049988,415.359985,413.540009,414.339996,414.339996,200270000\n1992-01-14,414.339996,420.440002,414.320007,420.440002,420.440002,265900000\n1992-01-15,420.450012,421.179993,418.790009,420.769989,420.769989,314830000\n1992-01-16,420.769989,420.850006,415.369995,418.209991,418.209991,336240000\n1992-01-17,418.200012,419.450012,416.000000,418.859985,418.859985,287370000\n1992-01-20,418.859985,418.859985,415.799988,416.359985,416.359985,180900000\n1992-01-21,416.359985,416.390015,411.320007,412.640015,412.640015,218750000\n1992-01-22,412.649994,418.130005,412.489990,418.130005,418.130005,228140000\n1992-01-23,418.130005,419.779999,414.359985,414.959991,414.959991,234580000\n1992-01-24,414.959991,417.269989,414.290009,415.480011,415.480011,213630000\n1992-01-27,415.440002,416.839996,414.480011,414.989990,414.989990,190970000\n1992-01-28,414.980011,416.410004,414.540009,414.959991,414.959991,218400000\n1992-01-29,414.959991,417.829987,409.170013,410.339996,410.339996,248940000\n1992-01-30,410.339996,412.170013,409.260010,411.619995,411.619995,194680000\n1992-01-31,411.649994,412.630005,408.640015,408.779999,408.779999,197620000\n1992-02-03,408.790009,409.950012,407.450012,409.529999,409.529999,185290000\n1992-02-04,409.600006,413.850006,409.279999,413.850006,413.850006,233680000\n1992-02-05,413.880005,416.170013,413.179993,413.839996,413.839996,262440000\n1992-02-06,413.869995,414.549988,411.929993,413.820007,413.820007,242050000\n1992-02-07,413.820007,415.290009,408.040009,411.089996,411.089996,231120000\n1992-02-10,411.070007,413.769989,411.070007,413.769989,413.769989,184410000\n1992-02-11,413.769989,414.380005,412.239990,413.760010,413.760010,200130000\n1992-02-12,413.769989,418.079987,413.359985,417.130005,417.130005,237630000\n1992-02-13,417.130005,417.769989,412.070007,413.690002,413.690002,229360000\n1992-02-14,413.690002,413.839996,411.200012,412.480011,412.480011,215110000\n1992-02-18,412.480011,413.269989,406.339996,407.380005,407.380005,234300000\n1992-02-19,407.380005,408.700012,406.540009,408.260010,408.260010,232970000\n1992-02-20,408.260010,413.899994,408.260010,413.899994,413.899994,270650000\n1992-02-21,413.899994,414.260010,409.720001,411.429993,411.429993,261650000\n1992-02-24,411.459991,412.940002,410.339996,412.269989,412.269989,177540000\n1992-02-25,412.269989,412.269989,408.019989,410.450012,410.450012,210350000\n1992-02-26,410.480011,415.350006,410.480011,415.350006,415.350006,241500000\n1992-02-27,415.350006,415.989990,413.470001,413.859985,413.859985,215110000\n1992-02-28,413.859985,416.070007,411.799988,412.700012,412.700012,202320000\n1992-03-02,412.679993,413.739990,411.519989,412.450012,412.450012,180380000\n1992-03-03,412.450012,413.779999,411.880005,412.850006,412.850006,200890000\n1992-03-04,412.859985,413.269989,409.329987,409.329987,409.329987,206860000\n1992-03-05,409.329987,409.329987,405.420013,406.510010,406.510010,205770000\n1992-03-06,406.510010,407.510010,403.649994,404.440002,404.440002,185190000\n1992-03-09,404.450012,405.640015,404.250000,405.209991,405.209991,160650000\n1992-03-10,405.209991,409.160004,405.209991,406.890015,406.890015,203000000\n1992-03-11,406.880005,407.019989,402.640015,404.029999,404.029999,186330000\n1992-03-12,404.029999,404.720001,401.940002,403.890015,403.890015,180310000\n1992-03-13,403.920013,406.690002,403.920013,405.839996,405.839996,177900000\n1992-03-16,405.850006,406.399994,403.549988,406.390015,406.390015,155950000\n1992-03-17,406.390015,409.720001,406.390015,409.579987,409.579987,187250000\n1992-03-18,409.579987,410.839996,408.230011,409.149994,409.149994,191720000\n1992-03-19,409.149994,410.570007,409.119995,409.799988,409.799988,197310000\n1992-03-20,409.799988,411.299988,408.529999,411.299988,411.299988,246210000\n1992-03-23,411.290009,411.290009,408.869995,409.910004,409.910004,157050000\n1992-03-24,409.910004,411.429993,407.989990,408.880005,408.880005,191610000\n1992-03-25,408.880005,409.869995,407.519989,407.519989,407.519989,192650000\n1992-03-26,407.519989,409.440002,406.750000,407.859985,407.859985,176720000\n1992-03-27,407.859985,407.859985,402.869995,403.500000,403.500000,166140000\n1992-03-30,403.500000,404.299988,402.970001,403.000000,403.000000,133990000\n1992-03-31,403.000000,405.209991,402.220001,403.690002,403.690002,182360000\n1992-04-01,403.670013,404.500000,400.750000,404.230011,404.230011,186530000\n1992-04-02,404.170013,404.630005,399.279999,400.500000,400.500000,185210000\n1992-04-03,400.500000,401.589996,398.209991,401.549988,401.549988,188580000\n1992-04-06,401.540009,405.929993,401.519989,405.589996,405.589996,179910000\n1992-04-07,405.589996,405.750000,397.970001,398.059998,398.059998,205210000\n1992-04-08,398.049988,398.049988,392.410004,394.500000,394.500000,249280000\n1992-04-09,394.500000,401.040009,394.500000,400.640015,400.640015,231430000\n1992-04-10,400.589996,405.119995,400.589996,404.290009,404.290009,199530000\n1992-04-13,404.279999,406.079987,403.899994,406.079987,406.079987,143140000\n1992-04-14,406.079987,413.859985,406.079987,412.390015,412.390015,231130000\n1992-04-15,412.390015,416.279999,412.390015,416.279999,416.279999,229710000\n1992-04-16,416.279999,416.279999,413.399994,416.040009,416.040009,233230000\n1992-04-20,416.049988,416.049988,407.929993,410.179993,410.179993,191980000\n1992-04-21,410.160004,411.089996,408.200012,410.260010,410.260010,214460000\n1992-04-22,410.260010,411.299988,409.230011,409.809998,409.809998,218850000\n1992-04-23,409.809998,411.600006,406.859985,411.600006,411.600006,235860000\n1992-04-24,411.600006,412.480011,408.739990,409.019989,409.019989,199310000\n1992-04-27,409.029999,409.600006,407.640015,408.450012,408.450012,172900000\n1992-04-28,408.450012,409.690002,406.329987,409.109985,409.109985,189220000\n1992-04-29,409.109985,412.309998,409.109985,412.019989,412.019989,206780000\n1992-04-30,412.019989,414.950012,412.019989,414.950012,414.950012,223590000\n1992-05-01,414.950012,415.209991,409.869995,412.529999,412.529999,177390000\n1992-05-04,412.540009,417.839996,412.540009,416.910004,416.910004,174540000\n1992-05-05,416.910004,418.529999,415.769989,416.839996,416.839996,200550000\n1992-05-06,416.839996,418.480011,416.399994,416.790009,416.790009,199950000\n1992-05-07,416.790009,416.839996,415.380005,415.850006,415.850006,168980000\n1992-05-08,415.869995,416.850006,414.410004,416.049988,416.049988,168720000\n1992-05-11,416.049988,418.750000,416.049988,418.489990,418.489990,155730000\n1992-05-12,418.489990,418.679993,414.690002,416.290009,416.290009,192870000\n1992-05-13,416.290009,417.040009,415.859985,416.450012,416.450012,175850000\n1992-05-14,416.450012,416.519989,411.820007,413.140015,413.140015,189150000\n1992-05-15,413.140015,413.140015,409.850006,410.089996,410.089996,192740000\n1992-05-18,410.130005,413.339996,410.130005,412.809998,412.809998,151380000\n1992-05-19,412.820007,416.510010,412.260010,416.369995,416.369995,187130000\n1992-05-20,416.369995,416.829987,415.369995,415.390015,415.390015,198180000\n1992-05-21,415.399994,415.410004,411.570007,412.600006,412.600006,184860000\n1992-05-22,412.609985,414.820007,412.600006,414.019989,414.019989,146710000\n1992-05-26,414.019989,414.019989,410.230011,411.410004,411.410004,197700000\n1992-05-27,411.410004,412.679993,411.059998,412.170013,412.170013,182240000\n1992-05-28,412.170013,416.769989,411.809998,416.739990,416.739990,195300000\n1992-05-29,416.739990,418.359985,415.350006,415.350006,415.350006,204010000\n1992-06-01,415.350006,417.299988,412.440002,417.299988,417.299988,180800000\n1992-06-02,417.299988,417.299988,413.500000,413.500000,413.500000,202560000\n1992-06-03,413.500000,416.540009,413.040009,414.589996,414.589996,215770000\n1992-06-04,414.600006,414.980011,412.970001,413.260010,413.260010,204450000\n1992-06-05,413.260010,413.850006,410.970001,413.480011,413.480011,199050000\n1992-06-08,413.480011,413.950012,412.029999,413.359985,413.359985,161240000\n1992-06-09,413.399994,413.559998,409.299988,410.059998,410.059998,191170000\n1992-06-10,410.059998,410.100006,406.809998,407.250000,407.250000,210750000\n1992-06-11,407.250000,409.049988,406.109985,409.049988,409.049988,204780000\n1992-06-12,409.079987,411.859985,409.079987,409.760010,409.760010,181860000\n1992-06-15,409.760010,411.679993,408.130005,410.290009,410.290009,164080000\n1992-06-16,410.290009,411.399994,408.320007,408.320007,408.320007,194400000\n1992-06-17,408.329987,408.329987,401.980011,402.260010,402.260010,227760000\n1992-06-18,402.260010,402.679993,400.510010,400.959991,400.959991,225600000\n1992-06-19,400.959991,404.230011,400.959991,403.670013,403.670013,233460000\n1992-06-22,403.640015,403.640015,399.920013,403.399994,403.399994,169370000\n1992-06-23,403.399994,405.410004,403.399994,404.040009,404.040009,189190000\n1992-06-24,404.049988,404.760010,403.260010,403.839996,403.839996,193870000\n1992-06-25,403.829987,405.529999,402.010010,403.119995,403.119995,182960000\n1992-06-26,403.119995,403.510010,401.940002,403.450012,403.450012,154430000\n1992-06-29,403.470001,408.959991,403.470001,408.940002,408.940002,176750000\n1992-06-30,408.940002,409.630005,407.850006,408.140015,408.140015,195530000\n1992-07-01,408.200012,412.880005,408.200012,412.880005,412.880005,214250000\n1992-07-02,412.880005,415.709991,410.070007,411.769989,411.769989,220200000\n1992-07-06,411.769989,413.839996,410.459991,413.839996,413.839996,186920000\n1992-07-07,413.829987,415.329987,408.579987,409.160004,409.160004,226050000\n1992-07-08,409.149994,410.279999,407.200012,410.279999,410.279999,201030000\n1992-07-09,410.279999,414.690002,410.260010,414.230011,414.230011,207980000\n1992-07-10,414.230011,415.880005,413.339996,414.619995,414.619995,164770000\n1992-07-13,414.619995,415.859985,413.929993,414.869995,414.869995,148870000\n1992-07-14,414.859985,417.690002,414.329987,417.679993,417.679993,195570000\n1992-07-15,417.679993,417.809998,416.290009,417.100006,417.100006,206560000\n1992-07-16,417.040009,417.929993,414.790009,417.540009,417.540009,206900000\n1992-07-17,417.540009,417.540009,412.959991,415.619995,415.619995,192120000\n1992-07-20,415.619995,415.619995,410.720001,413.750000,413.750000,165760000\n1992-07-21,413.750000,414.920013,413.100006,413.760010,413.760010,173760000\n1992-07-22,413.739990,413.739990,409.950012,410.929993,410.929993,190160000\n1992-07-23,410.929993,412.079987,409.809998,412.079987,412.079987,175490000\n1992-07-24,412.070007,412.070007,409.929993,411.600006,411.600006,163890000\n1992-07-27,411.600006,412.670013,411.269989,411.540009,411.540009,164700000\n1992-07-28,411.549988,417.549988,411.549988,417.519989,417.519989,218060000\n1992-07-29,417.519989,423.019989,417.519989,422.230011,422.230011,275850000\n1992-07-30,422.200012,423.940002,421.570007,423.920013,423.920013,193410000\n1992-07-31,423.920013,424.799988,422.459991,424.209991,424.209991,172920000\n1992-08-03,424.190002,425.089996,422.839996,425.089996,425.089996,164460000\n1992-08-04,425.089996,425.140015,423.100006,424.359985,424.359985,166760000\n1992-08-05,424.350006,424.350006,421.920013,422.190002,422.190002,172450000\n1992-08-06,422.190002,422.359985,420.260010,420.589996,420.589996,181440000\n1992-08-07,420.589996,423.450012,418.510010,418.880005,418.880005,190640000\n1992-08-10,418.869995,419.420013,417.040009,419.420013,419.420013,142480000\n1992-08-11,419.450012,419.720001,416.529999,418.899994,418.899994,173940000\n1992-08-12,418.890015,419.750000,416.429993,417.779999,417.779999,176560000\n1992-08-13,417.779999,419.880005,416.399994,417.730011,417.730011,185750000\n1992-08-14,417.739990,420.399994,417.739990,419.910004,419.910004,166820000\n1992-08-17,419.890015,421.890015,419.440002,420.739990,420.739990,152830000\n1992-08-18,420.739990,421.399994,419.779999,421.339996,421.339996,171750000\n1992-08-19,421.339996,421.619995,418.190002,418.190002,418.190002,187070000\n1992-08-20,418.190002,418.850006,416.929993,418.260010,418.260010,183420000\n1992-08-21,418.269989,420.350006,413.579987,414.850006,414.850006,204800000\n1992-08-24,414.799988,414.799988,410.070007,410.720001,410.720001,165690000\n1992-08-25,410.730011,411.640015,408.299988,411.609985,411.609985,202760000\n1992-08-26,411.649994,413.609985,410.529999,413.510010,413.510010,171860000\n1992-08-27,413.510010,415.829987,413.510010,413.529999,413.529999,178600000\n1992-08-28,413.540009,414.950012,413.380005,414.839996,414.839996,152260000\n1992-08-31,414.869995,415.290009,413.760010,414.029999,414.029999,161480000\n1992-09-01,414.029999,416.070007,413.350006,416.070007,416.070007,172680000\n1992-09-02,416.070007,418.279999,415.309998,417.980011,417.980011,187480000\n1992-09-03,417.980011,420.309998,417.489990,417.980011,417.980011,212500000\n1992-09-04,417.980011,418.619995,416.760010,417.079987,417.079987,124380000\n1992-09-08,417.079987,417.179993,414.299988,414.440002,414.440002,161440000\n1992-09-09,414.440002,416.440002,414.440002,416.359985,416.359985,178800000\n1992-09-10,416.339996,420.519989,416.339996,419.950012,419.950012,221990000\n1992-09-11,419.950012,420.579987,419.130005,419.579987,419.579987,180560000\n1992-09-14,419.649994,425.269989,419.649994,425.269989,425.269989,250940000\n1992-09-15,425.220001,425.220001,419.540009,419.769989,419.769989,211860000\n1992-09-16,419.709991,422.440002,417.769989,419.920013,419.920013,231450000\n1992-09-17,419.920013,421.429993,419.619995,419.929993,419.929993,188270000\n1992-09-18,419.920013,422.929993,419.920013,422.929993,422.929993,237440000\n1992-09-21,422.899994,422.899994,421.179993,422.140015,422.140015,153940000\n1992-09-22,422.140015,422.140015,417.130005,417.140015,417.140015,188810000\n1992-09-23,417.140015,417.880005,416.000000,417.440002,417.440002,205700000\n1992-09-24,417.459991,419.010010,417.459991,418.470001,418.470001,187960000\n1992-09-25,418.470001,418.630005,412.709991,414.350006,414.350006,213670000\n1992-09-28,414.350006,416.619995,413.000000,416.619995,416.619995,158760000\n1992-09-29,416.619995,417.380005,415.339996,416.799988,416.799988,170750000\n1992-09-30,416.790009,418.579987,416.670013,417.799988,417.799988,184470000\n1992-10-01,417.799988,418.670013,415.459991,416.290009,416.290009,204780000\n1992-10-02,416.290009,416.350006,410.450012,410.470001,410.470001,188030000\n1992-10-05,410.470001,410.470001,396.799988,407.570007,407.570007,286550000\n1992-10-06,407.570007,408.559998,404.839996,407.179993,407.179993,203500000\n1992-10-07,407.170013,408.600006,403.910004,404.250000,404.250000,184380000\n1992-10-08,404.290009,408.040009,404.290009,407.750000,407.750000,205000000\n1992-10-09,407.750000,407.750000,402.420013,402.660004,402.660004,178940000\n1992-10-12,402.660004,407.440002,402.660004,407.440002,407.440002,126670000\n1992-10-13,407.440002,410.640015,406.829987,409.299988,409.299988,186650000\n1992-10-14,409.299988,411.519989,407.859985,409.369995,409.369995,175900000\n1992-10-15,409.339996,411.029999,407.920013,409.600006,409.600006,213590000\n1992-10-16,409.600006,411.730011,407.429993,411.730011,411.730011,235920000\n1992-10-19,411.730011,414.980011,410.660004,414.980011,414.980011,222150000\n1992-10-20,414.980011,417.980011,414.489990,415.480011,415.480011,258210000\n1992-10-21,415.529999,416.149994,414.540009,415.670013,415.670013,219100000\n1992-10-22,415.670013,416.809998,413.100006,414.899994,414.899994,216400000\n1992-10-23,414.899994,416.230011,413.679993,414.100006,414.100006,199060000\n1992-10-26,414.089996,418.170013,413.709991,418.160004,418.160004,188060000\n1992-10-27,418.179993,419.200012,416.970001,418.489990,418.489990,201730000\n1992-10-28,418.489990,420.130005,417.559998,420.130005,420.130005,203910000\n1992-10-29,420.149994,421.160004,419.829987,420.859985,420.859985,206550000\n1992-10-30,420.859985,421.130005,418.540009,418.679993,418.679993,201930000\n1992-11-02,418.660004,422.750000,418.119995,422.750000,422.750000,203280000\n1992-11-03,422.750000,422.809998,418.589996,419.920013,419.920013,208140000\n1992-11-04,419.910004,421.070007,416.609985,417.109985,417.109985,194400000\n1992-11-05,417.079987,418.399994,415.579987,418.339996,418.339996,219730000\n1992-11-06,418.350006,418.350006,417.010010,417.579987,417.579987,205310000\n1992-11-09,417.579987,420.130005,416.790009,418.589996,418.589996,197560000\n1992-11-10,418.589996,419.709991,417.980011,418.619995,418.619995,223180000\n1992-11-11,418.619995,422.329987,418.399994,422.200012,422.200012,243750000\n1992-11-12,422.200012,423.100006,421.700012,422.869995,422.869995,226010000\n1992-11-13,422.890015,422.910004,421.040009,422.429993,422.429993,192950000\n1992-11-16,422.440002,422.440002,420.350006,420.679993,420.679993,173600000\n1992-11-17,420.630005,420.970001,418.309998,419.269989,419.269989,187660000\n1992-11-18,419.269989,423.489990,419.239990,422.850006,422.850006,219080000\n1992-11-19,422.859985,423.609985,422.500000,423.609985,423.609985,218720000\n1992-11-20,423.609985,426.980011,423.609985,426.649994,426.649994,257460000\n1992-11-23,426.649994,426.649994,424.950012,425.119995,425.119995,192530000\n1992-11-24,425.140015,429.309998,424.829987,427.589996,427.589996,241540000\n1992-11-25,427.589996,429.410004,427.579987,429.190002,429.190002,207700000\n1992-11-27,429.190002,431.929993,429.170013,430.160004,430.160004,106020000\n1992-11-30,430.190002,431.529999,429.359985,431.350006,431.350006,230150000\n1992-12-01,431.350006,431.470001,429.200012,430.779999,430.779999,259050000\n1992-12-02,430.779999,430.869995,428.609985,429.890015,429.890015,247010000\n1992-12-03,429.980011,430.989990,428.799988,429.910004,429.910004,238050000\n1992-12-04,429.929993,432.890015,429.739990,432.059998,432.059998,234960000\n1992-12-07,432.059998,435.309998,432.059998,435.309998,435.309998,217700000\n1992-12-08,435.309998,436.989990,434.679993,436.989990,436.989990,234330000\n1992-12-09,436.989990,436.989990,433.980011,435.649994,435.649994,230060000\n1992-12-10,435.660004,435.660004,432.649994,434.640015,434.640015,240640000\n1992-12-11,434.640015,434.640015,433.339996,433.730011,433.730011,164510000\n1992-12-14,433.730011,435.260010,432.829987,432.839996,432.839996,187040000\n1992-12-15,432.820007,433.660004,431.920013,432.570007,432.570007,227770000\n1992-12-16,432.579987,434.220001,430.880005,431.519989,431.519989,242130000\n1992-12-17,431.519989,435.440002,431.459991,435.429993,435.429993,251640000\n1992-12-18,435.459991,441.290009,435.459991,441.279999,441.279999,389300000\n1992-12-21,441.260010,441.260010,439.649994,440.700012,440.700012,224680000\n1992-12-22,440.700012,441.640015,438.250000,440.309998,440.309998,250430000\n1992-12-23,440.290009,441.109985,439.029999,439.029999,439.029999,234140000\n1992-12-24,439.029999,439.809998,439.029999,439.769989,439.769989,95240000\n1992-12-28,439.769989,439.769989,437.260010,439.149994,439.149994,143970000\n1992-12-29,439.149994,442.649994,437.600006,437.980011,437.980011,213660000\n1992-12-30,437.980011,439.369995,437.119995,438.820007,438.820007,183930000\n1992-12-31,438.820007,439.589996,435.709991,435.709991,435.709991,165910000\n1993-01-04,435.700012,437.320007,434.480011,435.380005,435.380005,201210000\n1993-01-05,435.380005,435.399994,433.549988,434.339996,434.339996,240350000\n1993-01-06,434.339996,435.170013,432.519989,434.519989,434.519989,295240000\n1993-01-07,434.519989,435.459991,429.760010,430.730011,430.730011,304850000\n1993-01-08,430.730011,430.730011,426.880005,429.049988,429.049988,263470000\n1993-01-11,429.040009,431.040009,429.010010,430.950012,430.950012,217150000\n1993-01-12,430.950012,431.390015,428.190002,431.040009,431.040009,239410000\n1993-01-13,431.029999,433.440002,429.989990,433.029999,433.029999,245360000\n1993-01-14,433.079987,435.959991,433.079987,435.940002,435.940002,281040000\n1993-01-15,435.869995,439.489990,435.839996,437.149994,437.149994,309720000\n1993-01-18,437.130005,437.130005,435.920013,436.839996,436.839996,196030000\n1993-01-19,436.839996,437.700012,434.589996,435.130005,435.130005,283240000\n1993-01-20,435.140015,436.230011,433.369995,433.369995,433.369995,268790000\n1993-01-21,433.369995,435.750000,432.480011,435.489990,435.489990,257620000\n1993-01-22,435.489990,437.809998,435.489990,436.109985,436.109985,293320000\n1993-01-25,436.109985,440.529999,436.109985,440.010010,440.010010,288740000\n1993-01-26,440.049988,442.660004,439.540009,439.950012,439.950012,314110000\n1993-01-27,439.950012,440.040009,436.820007,438.109985,438.109985,277020000\n1993-01-28,438.130005,439.140015,437.299988,438.660004,438.660004,256980000\n1993-01-29,438.670013,438.929993,436.910004,438.779999,438.779999,247200000\n1993-02-01,438.779999,442.519989,438.779999,442.519989,442.519989,238570000\n1993-02-02,442.519989,442.869995,440.760010,442.549988,442.549988,271560000\n1993-02-03,442.559998,447.350006,442.559998,447.200012,447.200012,345410000\n1993-02-04,447.200012,449.859985,447.200012,449.559998,449.559998,351140000\n1993-02-05,449.559998,449.559998,446.950012,448.929993,448.929993,324710000\n1993-02-08,448.940002,450.040009,447.700012,447.850006,447.850006,243400000\n1993-02-09,448.040009,448.040009,444.519989,445.329987,445.329987,240410000\n1993-02-10,445.329987,446.369995,444.239990,446.230011,446.230011,251910000\n1993-02-11,446.209991,449.359985,446.209991,447.660004,447.660004,257190000\n1993-02-12,447.660004,447.700012,444.579987,444.579987,444.579987,216810000\n1993-02-16,444.529999,444.529999,433.470001,433.910004,433.910004,332850000\n1993-02-17,433.929993,433.970001,430.920013,433.299988,433.299988,302210000\n1993-02-18,433.299988,437.790009,428.250000,431.899994,431.899994,311180000\n1993-02-19,431.929993,434.260010,431.679993,434.220001,434.220001,310700000\n1993-02-22,434.209991,436.489990,433.529999,435.239990,435.239990,311570000\n1993-02-23,435.339996,436.839996,432.410004,434.799988,434.799988,329060000\n1993-02-24,434.760010,440.869995,434.679993,440.869995,440.869995,316750000\n1993-02-25,440.700012,442.339996,439.670013,442.339996,442.339996,252860000\n1993-02-26,442.339996,443.769989,440.980011,443.380005,443.380005,234160000\n1993-03-01,443.380005,444.179993,441.339996,442.010010,442.010010,232460000\n1993-03-02,442.000000,447.910004,441.070007,447.899994,447.899994,269750000\n1993-03-03,447.899994,450.000000,447.730011,449.260010,449.260010,277380000\n1993-03-04,449.260010,449.519989,446.720001,447.339996,447.339996,234220000\n1993-03-05,447.339996,449.589996,445.559998,446.109985,446.109985,253480000\n1993-03-08,446.119995,454.709991,446.119995,454.709991,454.709991,275290000\n1993-03-09,454.670013,455.519989,453.679993,454.399994,454.399994,290670000\n1993-03-10,454.399994,456.339996,452.700012,456.329987,456.329987,255610000\n1993-03-11,456.350006,456.760010,453.480011,453.720001,453.720001,257060000\n1993-03-12,453.700012,453.700012,447.040009,449.829987,449.829987,255420000\n1993-03-15,449.829987,451.429993,449.399994,451.429993,451.429993,195930000\n1993-03-16,451.429993,452.359985,451.010010,451.369995,451.369995,218820000\n1993-03-17,451.359985,451.359985,447.989990,448.309998,448.309998,241270000\n1993-03-18,448.359985,452.390015,448.359985,451.890015,451.890015,241180000\n1993-03-19,451.899994,453.320007,449.910004,450.179993,450.179993,339660000\n1993-03-22,450.170013,450.170013,446.079987,448.880005,448.880005,233190000\n1993-03-23,448.880005,449.799988,448.299988,448.760010,448.760010,232730000\n1993-03-24,448.709991,450.899994,446.100006,448.070007,448.070007,274300000\n1993-03-25,448.089996,451.750000,447.929993,450.880005,450.880005,251530000\n1993-03-26,450.910004,452.089996,447.690002,447.779999,447.779999,226650000\n1993-03-29,447.760010,452.809998,447.750000,450.769989,450.769989,199970000\n1993-03-30,450.790009,452.059998,449.630005,451.970001,451.970001,231190000\n1993-03-31,451.970001,454.880005,451.670013,451.670013,451.670013,279190000\n1993-04-01,451.670013,452.630005,449.600006,450.299988,450.299988,234530000\n1993-04-02,450.279999,450.279999,440.709991,441.390015,441.390015,323330000\n1993-04-05,441.420013,442.429993,440.529999,442.290009,442.290009,296080000\n1993-04-06,442.290009,443.380005,439.480011,441.160004,441.160004,293680000\n1993-04-07,441.160004,442.730011,440.500000,442.730011,442.730011,300000000\n1993-04-08,442.709991,443.769989,440.019989,441.839996,441.839996,284370000\n1993-04-12,441.839996,448.369995,441.839996,448.369995,448.369995,259690000\n1993-04-13,448.410004,450.399994,447.660004,449.220001,449.220001,286690000\n1993-04-14,449.220001,450.000000,448.019989,448.660004,448.660004,257340000\n1993-04-15,448.600006,449.109985,446.390015,448.399994,448.399994,259500000\n1993-04-16,448.410004,449.390015,447.670013,448.940002,448.940002,305160000\n1993-04-19,448.940002,449.140015,445.850006,447.459991,447.459991,244710000\n1993-04-20,447.459991,447.459991,441.809998,445.100006,445.100006,317990000\n1993-04-21,445.089996,445.769989,443.079987,443.630005,443.630005,287300000\n1993-04-22,443.549988,445.730011,439.459991,439.459991,439.459991,310390000\n1993-04-23,439.489990,439.489990,436.820007,437.029999,437.029999,259810000\n1993-04-26,437.029999,438.350006,432.299988,433.540009,433.540009,283260000\n1993-04-27,433.519989,438.019989,433.140015,438.010010,438.010010,284140000\n1993-04-28,438.010010,438.799988,436.679993,438.019989,438.019989,267980000\n1993-04-29,438.019989,438.959991,435.589996,438.890015,438.890015,249760000\n1993-04-30,438.890015,442.290009,438.890015,440.190002,440.190002,247460000\n1993-05-03,440.190002,442.589996,438.250000,442.459991,442.459991,224970000\n1993-05-04,442.579987,445.190002,442.450012,444.049988,444.049988,268310000\n1993-05-05,443.980011,446.089996,443.760010,444.519989,444.519989,274240000\n1993-05-06,444.600006,444.809998,442.899994,443.260010,443.260010,255460000\n1993-05-07,443.279999,443.700012,441.690002,442.309998,442.309998,223570000\n1993-05-10,442.339996,445.420013,442.049988,442.799988,442.799988,235580000\n1993-05-11,442.799988,444.570007,441.519989,444.359985,444.359985,218480000\n1993-05-12,444.320007,445.160004,442.869995,444.799988,444.799988,255680000\n1993-05-13,444.750000,444.750000,439.230011,439.230011,439.230011,293920000\n1993-05-14,439.220001,439.820007,438.100006,439.559998,439.559998,252910000\n1993-05-17,439.559998,440.380005,437.829987,440.369995,440.369995,227580000\n1993-05-18,440.390015,441.260010,437.950012,440.320007,440.320007,264300000\n1993-05-19,440.320007,447.859985,436.859985,447.570007,447.570007,342420000\n1993-05-20,447.570007,450.589996,447.359985,450.589996,450.589996,289160000\n1993-05-21,450.589996,450.589996,444.890015,445.839996,445.839996,279120000\n1993-05-24,445.839996,448.440002,445.260010,448.000000,448.000000,197990000\n1993-05-25,448.000000,449.040009,447.700012,448.850006,448.850006,222090000\n1993-05-26,448.850006,453.510010,448.820007,453.440002,453.440002,274230000\n1993-05-27,453.440002,454.549988,451.140015,452.410004,452.410004,300810000\n1993-05-28,452.410004,452.410004,447.670013,450.190002,450.190002,207820000\n1993-06-01,450.230011,455.630005,450.230011,453.829987,453.829987,229690000\n1993-06-02,453.829987,454.529999,452.679993,453.850006,453.850006,295560000\n1993-06-03,453.839996,453.850006,451.119995,452.489990,452.489990,285570000\n1993-06-04,452.429993,452.429993,448.920013,450.059998,450.059998,226440000\n1993-06-07,450.070007,450.750000,447.320007,447.690002,447.690002,236920000\n1993-06-08,447.649994,447.649994,444.309998,444.709991,444.709991,240640000\n1993-06-09,444.709991,447.390015,444.660004,445.779999,445.779999,249030000\n1993-06-10,445.779999,446.220001,444.089996,445.380005,445.380005,232600000\n1993-06-11,445.380005,448.190002,445.380005,447.260010,447.260010,256750000\n1993-06-14,447.260010,448.640015,447.230011,447.709991,447.709991,210440000\n1993-06-15,447.730011,448.279999,446.179993,446.269989,446.269989,234110000\n1993-06-16,446.269989,447.429993,443.609985,447.429993,447.429993,267500000\n1993-06-17,447.429993,448.980011,446.910004,448.540009,448.540009,239810000\n1993-06-18,448.540009,448.589996,443.679993,443.679993,443.679993,300500000\n1993-06-21,443.679993,446.220001,443.679993,446.220001,446.220001,223650000\n1993-06-22,446.250000,446.290009,444.940002,445.929993,445.929993,259530000\n1993-06-23,445.959991,445.959991,443.190002,443.190002,443.190002,278260000\n1993-06-24,443.040009,447.209991,442.500000,446.619995,446.619995,267450000\n1993-06-25,446.619995,448.640015,446.619995,447.600006,447.600006,210430000\n1993-06-28,447.600006,451.899994,447.600006,451.850006,451.850006,242090000\n1993-06-29,451.890015,451.899994,449.670013,450.690002,450.690002,276310000\n1993-06-30,450.690002,451.470001,450.149994,450.529999,450.529999,281120000\n1993-07-01,450.540009,451.149994,448.709991,449.019989,449.019989,292040000\n1993-07-02,449.019989,449.019989,445.200012,445.839996,445.839996,220750000\n1993-07-06,445.859985,446.869995,441.420013,441.429993,441.429993,234810000\n1993-07-07,441.399994,443.630005,441.399994,442.829987,442.829987,253170000\n1993-07-08,442.839996,448.640015,442.839996,448.640015,448.640015,282910000\n1993-07-09,448.640015,448.940002,446.739990,448.109985,448.109985,235210000\n1993-07-12,448.130005,449.109985,447.709991,448.980011,448.980011,202310000\n1993-07-13,449.000000,450.700012,448.070007,448.089996,448.089996,236720000\n1993-07-14,448.079987,451.119995,448.079987,450.079987,450.079987,297430000\n1993-07-15,450.089996,450.119995,447.260010,449.220001,449.220001,277810000\n1993-07-16,449.070007,449.079987,445.660004,445.750000,445.750000,263100000\n1993-07-19,445.750000,446.779999,444.829987,446.029999,446.029999,216370000\n1993-07-20,446.029999,447.630005,443.709991,447.309998,447.309998,277420000\n1993-07-21,447.279999,447.500000,445.839996,447.179993,447.179993,278590000\n1993-07-22,447.179993,447.230011,443.720001,444.510010,444.510010,249630000\n1993-07-23,444.540009,447.100006,444.540009,447.100006,447.100006,222170000\n1993-07-26,447.059998,449.500000,447.040009,449.089996,449.089996,222580000\n1993-07-27,449.000000,449.440002,446.760010,448.239990,448.239990,256750000\n1993-07-28,448.250000,448.609985,446.589996,447.190002,447.190002,273100000\n1993-07-29,447.190002,450.769989,447.190002,450.239990,450.239990,261240000\n1993-07-30,450.190002,450.220001,446.980011,448.130005,448.130005,254420000\n1993-08-02,448.130005,450.149994,448.029999,450.149994,450.149994,230380000\n1993-08-03,450.149994,450.429993,447.589996,449.269989,449.269989,253110000\n1993-08-04,449.269989,449.720001,447.929993,448.540009,448.540009,230040000\n1993-08-05,448.549988,449.609985,446.940002,448.130005,448.130005,261900000\n1993-08-06,448.130005,449.260010,447.869995,448.679993,448.679993,221170000\n1993-08-09,448.679993,451.510010,448.309998,450.720001,450.720001,232750000\n1993-08-10,450.709991,450.709991,449.100006,449.450012,449.450012,255520000\n1993-08-11,449.600006,451.000000,449.600006,450.459991,450.459991,268330000\n1993-08-12,450.470001,451.630005,447.529999,448.959991,448.959991,278530000\n1993-08-13,448.970001,450.250000,448.970001,450.140015,450.140015,214370000\n1993-08-16,450.250000,453.410004,450.250000,452.380005,452.380005,233640000\n1993-08-17,452.380005,453.700012,451.959991,453.130005,453.130005,261320000\n1993-08-18,453.209991,456.989990,453.209991,456.040009,456.040009,312940000\n1993-08-19,456.010010,456.760010,455.200012,456.429993,456.429993,293330000\n1993-08-20,456.510010,456.679993,454.600006,456.160004,456.160004,276800000\n1993-08-23,456.119995,456.119995,454.290009,455.230011,455.230011,212500000\n1993-08-24,455.230011,459.769989,455.040009,459.769989,459.769989,270700000\n1993-08-25,459.750000,462.040009,459.299988,460.130005,460.130005,301650000\n1993-08-26,460.040009,462.869995,458.820007,461.040009,461.040009,254070000\n1993-08-27,461.049988,461.049988,459.190002,460.540009,460.540009,196140000\n1993-08-30,460.540009,462.579987,460.279999,461.899994,461.899994,194180000\n1993-08-31,461.899994,463.559998,461.290009,463.559998,463.559998,252830000\n1993-09-01,463.549988,463.799988,461.769989,463.149994,463.149994,245040000\n1993-09-02,463.130005,463.540009,461.070007,461.299988,461.299988,259870000\n1993-09-03,461.299988,462.049988,459.910004,461.339996,461.339996,197160000\n1993-09-07,461.339996,462.070007,457.950012,458.519989,458.519989,229500000\n1993-09-08,458.519989,458.529999,453.750000,456.649994,456.649994,283100000\n1993-09-09,456.649994,458.109985,455.170013,457.500000,457.500000,258070000\n1993-09-10,457.489990,461.859985,457.489990,461.720001,461.720001,269950000\n1993-09-13,461.700012,463.380005,461.410004,462.059998,462.059998,244970000\n1993-09-14,461.929993,461.929993,458.149994,459.899994,459.899994,258650000\n1993-09-15,459.899994,461.959991,456.309998,461.600006,461.600006,294410000\n1993-09-16,461.540009,461.540009,459.000000,459.429993,459.429993,229700000\n1993-09-17,459.429993,459.429993,457.089996,458.829987,458.829987,381370000\n1993-09-20,458.839996,459.910004,455.000000,455.049988,455.049988,231130000\n1993-09-21,455.049988,455.799988,449.640015,452.950012,452.950012,300310000\n1993-09-22,452.940002,456.920013,452.940002,456.200012,456.200012,298960000\n1993-09-23,456.250000,458.690002,456.250000,457.739990,457.739990,275350000\n1993-09-24,457.739990,458.559998,456.920013,457.630005,457.630005,248270000\n1993-09-27,457.630005,461.809998,457.630005,461.799988,461.799988,244920000\n1993-09-28,461.839996,462.079987,460.910004,461.529999,461.529999,243320000\n1993-09-29,461.600006,462.170013,459.510010,460.109985,460.109985,277690000\n1993-09-30,460.109985,460.559998,458.279999,458.929993,458.929993,280980000\n1993-10-01,458.929993,461.480011,458.350006,461.279999,461.279999,256880000\n1993-10-04,461.279999,461.799988,460.019989,461.339996,461.339996,229380000\n1993-10-05,461.339996,463.149994,459.450012,461.200012,461.200012,294570000\n1993-10-06,461.239990,462.600006,460.260010,460.739990,460.739990,277070000\n1993-10-07,460.709991,461.130005,459.079987,459.179993,459.179993,255210000\n1993-10-08,459.179993,460.989990,456.399994,460.309998,460.309998,243600000\n1993-10-11,460.309998,461.869995,460.309998,460.880005,460.880005,183060000\n1993-10-12,461.040009,462.470001,460.730011,461.119995,461.119995,263970000\n1993-10-13,461.119995,461.980011,460.760010,461.489990,461.489990,290930000\n1993-10-14,461.549988,466.829987,461.549988,466.829987,466.829987,352530000\n1993-10-15,466.829987,471.100006,466.829987,469.500000,469.500000,366110000\n1993-10-18,469.500000,470.040009,468.019989,468.450012,468.450012,329580000\n1993-10-19,468.410004,468.640015,464.799988,466.209991,466.209991,304400000\n1993-10-20,466.209991,466.869995,464.540009,466.070007,466.070007,305670000\n1993-10-21,466.059998,466.640015,464.380005,465.359985,465.359985,289600000\n1993-10-22,465.359985,467.820007,463.269989,463.269989,463.269989,301440000\n1993-10-25,463.269989,464.489990,462.049988,464.200012,464.200012,260310000\n1993-10-26,464.200012,464.320007,462.649994,464.299988,464.299988,284530000\n1993-10-27,464.299988,464.609985,463.359985,464.609985,464.609985,279830000\n1993-10-28,464.519989,468.760010,464.519989,467.730011,467.730011,301220000\n1993-10-29,467.720001,468.200012,467.369995,467.829987,467.829987,270570000\n1993-11-01,467.829987,469.109985,467.329987,469.100006,469.100006,256030000\n1993-11-02,469.100006,469.100006,466.200012,468.440002,468.440002,304780000\n1993-11-03,468.440002,468.609985,460.950012,463.019989,463.019989,342110000\n1993-11-04,463.019989,463.160004,457.260010,457.489990,457.489990,323430000\n1993-11-05,457.489990,459.630005,454.359985,459.570007,459.570007,336890000\n1993-11-08,459.570007,461.540009,458.779999,460.209991,460.209991,234340000\n1993-11-09,460.209991,463.420013,460.209991,460.329987,460.329987,276360000\n1993-11-10,460.399994,463.720001,459.570007,463.720001,463.720001,283450000\n1993-11-11,463.720001,464.959991,462.489990,462.640015,462.640015,283820000\n1993-11-12,462.640015,465.839996,462.640015,465.390015,465.390015,326240000\n1993-11-15,465.390015,466.130005,463.010010,463.750000,463.750000,251030000\n1993-11-16,463.750000,466.739990,462.970001,466.739990,466.739990,303980000\n1993-11-17,466.739990,467.239990,462.730011,464.809998,464.809998,316940000\n1993-11-18,464.829987,464.880005,461.730011,463.619995,463.619995,313490000\n1993-11-19,463.589996,463.600006,460.029999,462.600006,462.600006,302970000\n1993-11-22,462.600006,462.600006,457.079987,459.130005,459.130005,280130000\n1993-11-23,459.130005,461.769989,458.470001,461.029999,461.029999,260400000\n1993-11-24,461.029999,462.899994,461.029999,462.359985,462.359985,230630000\n1993-11-26,462.359985,463.630005,462.359985,463.059998,463.059998,90220000\n1993-11-29,463.059998,464.829987,461.829987,461.899994,461.899994,272710000\n1993-11-30,461.899994,463.619995,460.450012,461.790009,461.790009,286660000\n1993-12-01,461.929993,464.470001,461.630005,461.890015,461.890015,293870000\n1993-12-02,461.890015,463.220001,461.450012,463.109985,463.109985,256370000\n1993-12-03,463.130005,464.890015,462.670013,464.890015,464.890015,268360000\n1993-12-06,464.890015,466.890015,464.399994,466.429993,466.429993,292370000\n1993-12-07,466.429993,466.769989,465.440002,466.760010,466.760010,285690000\n1993-12-08,465.880005,466.730011,465.420013,466.290009,466.290009,314460000\n1993-12-09,466.290009,466.540009,463.869995,464.179993,464.179993,287570000\n1993-12-10,464.179993,464.869995,462.660004,463.929993,463.929993,245620000\n1993-12-13,463.929993,465.709991,462.709991,465.700012,465.700012,256580000\n1993-12-14,465.730011,466.119995,462.459991,463.059998,463.059998,275050000\n1993-12-15,463.059998,463.690002,461.839996,461.839996,461.839996,331770000\n1993-12-16,461.859985,463.980011,461.859985,463.339996,463.339996,284620000\n1993-12-17,463.339996,466.380005,463.339996,466.380005,466.380005,363750000\n1993-12-20,466.380005,466.899994,465.529999,465.850006,465.850006,255900000\n1993-12-21,465.839996,465.920013,464.029999,465.299988,465.299988,273370000\n1993-12-22,465.079987,467.380005,465.079987,467.320007,467.320007,272440000\n1993-12-23,467.299988,468.970001,467.299988,467.380005,467.380005,227240000\n1993-12-27,467.399994,470.549988,467.350006,470.540009,470.540009,171200000\n1993-12-28,470.609985,471.049988,469.429993,470.940002,470.940002,200960000\n1993-12-29,470.880005,471.290009,469.869995,470.579987,470.579987,269570000\n1993-12-30,470.579987,470.579987,468.089996,468.640015,468.640015,195860000\n1993-12-31,468.660004,470.750000,466.450012,466.450012,466.450012,168590000\n1994-01-03,466.510010,466.940002,464.359985,465.440002,465.440002,270140000\n1994-01-04,465.440002,466.890015,464.440002,466.890015,466.890015,326600000\n1994-01-05,466.890015,467.820007,465.920013,467.549988,467.549988,400030000\n1994-01-06,467.549988,469.000000,467.019989,467.119995,467.119995,365960000\n1994-01-07,467.089996,470.260010,467.029999,469.899994,469.899994,324920000\n1994-01-10,469.899994,475.269989,469.549988,475.269989,475.269989,319490000\n1994-01-11,475.269989,475.279999,473.269989,474.130005,474.130005,305490000\n1994-01-12,474.130005,475.059998,472.140015,474.170013,474.170013,310690000\n1994-01-13,474.170013,474.170013,471.799988,472.470001,472.470001,277970000\n1994-01-14,472.500000,475.320007,472.500000,474.910004,474.910004,304920000\n1994-01-17,474.910004,474.910004,472.839996,473.299988,473.299988,233980000\n1994-01-18,473.299988,475.190002,473.290009,474.250000,474.250000,308840000\n1994-01-19,474.250000,474.700012,472.209991,474.299988,474.299988,311370000\n1994-01-20,474.299988,475.000000,473.420013,474.980011,474.980011,310450000\n1994-01-21,474.980011,475.559998,473.720001,474.720001,474.720001,346350000\n1994-01-24,474.720001,475.200012,471.489990,471.970001,471.970001,296900000\n1994-01-25,471.970001,472.559998,470.269989,470.920013,470.920013,326120000\n1994-01-26,470.920013,473.440002,470.720001,473.200012,473.200012,304660000\n1994-01-27,473.200012,477.519989,473.200012,477.049988,477.049988,346500000\n1994-01-28,477.049988,479.750000,477.049988,478.700012,478.700012,313140000\n1994-01-31,478.700012,482.850006,478.700012,481.609985,481.609985,322870000\n1994-02-01,481.600006,481.640015,479.179993,479.619995,479.619995,322510000\n1994-02-02,479.619995,482.230011,479.570007,482.000000,482.000000,328960000\n1994-02-03,481.959991,481.959991,478.709991,480.709991,480.709991,318350000\n1994-02-04,480.679993,481.019989,469.279999,469.809998,469.809998,378380000\n1994-02-07,469.809998,472.089996,467.570007,471.760010,471.760010,348270000\n1994-02-08,471.760010,472.329987,469.500000,471.049988,471.049988,318180000\n1994-02-09,471.049988,473.410004,471.049988,472.769989,472.769989,332670000\n1994-02-10,472.809998,473.130005,468.910004,468.929993,468.929993,327250000\n1994-02-11,468.929993,471.130005,466.890015,470.179993,470.179993,213740000\n1994-02-14,470.179993,471.989990,469.049988,470.230011,470.230011,263190000\n1994-02-15,470.230011,473.410004,470.230011,472.519989,472.519989,306790000\n1994-02-16,472.529999,474.160004,471.940002,472.790009,472.790009,295450000\n1994-02-17,472.790009,475.119995,468.440002,470.339996,470.339996,340030000\n1994-02-18,470.290009,471.089996,466.070007,467.690002,467.690002,293210000\n1994-02-22,467.690002,471.649994,467.579987,471.459991,471.459991,270900000\n1994-02-23,471.480011,472.410004,469.470001,470.690002,470.690002,309910000\n1994-02-24,470.649994,470.649994,464.260010,464.260010,464.260010,342940000\n1994-02-25,464.329987,466.480011,464.329987,466.070007,466.070007,273680000\n1994-02-28,466.070007,469.160004,466.070007,467.140015,467.140015,268690000\n1994-03-01,467.190002,467.429993,462.019989,464.440002,464.440002,304450000\n1994-03-02,464.399994,464.869995,457.489990,464.809998,464.809998,361130000\n1994-03-03,464.809998,464.829987,462.500000,463.010010,463.010010,291790000\n1994-03-04,463.029999,466.160004,462.410004,464.739990,464.739990,311850000\n1994-03-07,464.739990,468.070007,464.739990,466.910004,466.910004,285590000\n1994-03-08,466.920013,467.790009,465.019989,465.880005,465.880005,298110000\n1994-03-09,465.940002,467.420013,463.399994,467.059998,467.059998,309810000\n1994-03-10,467.079987,467.290009,462.459991,463.899994,463.899994,369370000\n1994-03-11,463.859985,466.609985,462.540009,466.440002,466.440002,303890000\n1994-03-14,466.440002,467.600006,466.079987,467.390015,467.390015,260150000\n1994-03-15,467.390015,468.989990,466.040009,467.010010,467.010010,303750000\n1994-03-16,467.040009,469.850006,465.480011,469.420013,469.420013,307640000\n1994-03-17,469.420013,471.049988,468.619995,470.899994,470.899994,303930000\n1994-03-18,470.890015,471.089996,467.829987,471.059998,471.059998,462240000\n1994-03-21,471.059998,471.059998,467.230011,468.540009,468.540009,247380000\n1994-03-22,468.399994,470.470001,467.880005,468.799988,468.799988,282240000\n1994-03-23,468.890015,470.380005,468.519989,468.540009,468.540009,281500000\n1994-03-24,468.570007,468.570007,462.410004,464.350006,464.350006,303740000\n1994-03-25,464.350006,465.290009,460.579987,460.579987,460.579987,249640000\n1994-03-28,460.579987,461.119995,456.100006,460.000000,460.000000,287350000\n1994-03-29,460.000000,460.320007,452.429993,452.480011,452.480011,305360000\n1994-03-30,452.480011,452.489990,445.549988,445.549988,445.549988,390520000\n1994-03-31,445.549988,447.160004,436.160004,445.769989,445.769989,403580000\n1994-04-04,445.660004,445.660004,435.859985,438.920013,438.920013,344390000\n1994-04-05,439.140015,448.290009,439.140015,448.290009,448.290009,365990000\n1994-04-06,448.290009,449.630005,444.980011,448.049988,448.049988,302000000\n1994-04-07,448.109985,451.100006,446.380005,450.880005,450.880005,289280000\n1994-04-08,450.890015,450.890015,445.510010,447.100006,447.100006,264090000\n1994-04-11,447.119995,450.339996,447.100006,449.869995,449.869995,243180000\n1994-04-12,449.829987,450.799988,447.329987,447.570007,447.570007,257990000\n1994-04-13,447.630005,448.570007,442.619995,446.260010,446.260010,278030000\n1994-04-14,446.260010,447.549988,443.570007,446.380005,446.380005,275130000\n1994-04-15,446.380005,447.850006,445.809998,446.179993,446.179993,309550000\n1994-04-18,446.269989,447.869995,441.480011,442.459991,442.459991,271470000\n1994-04-19,442.540009,444.820007,438.829987,442.540009,442.540009,323280000\n1994-04-20,442.540009,445.010010,439.399994,441.959991,441.959991,366540000\n1994-04-21,441.959991,449.140015,441.959991,448.730011,448.730011,378770000\n1994-04-22,448.730011,449.959991,447.160004,447.630005,447.630005,295710000\n1994-04-25,447.640015,452.709991,447.579987,452.709991,452.709991,262320000\n1994-04-26,452.709991,452.790009,450.660004,451.869995,451.869995,288120000\n1994-04-28,451.839996,452.230011,447.970001,449.100006,449.100006,325200000\n1994-04-29,449.070007,451.350006,447.910004,450.910004,450.910004,293970000\n1994-05-02,450.910004,453.570007,449.049988,453.019989,453.019989,296130000\n1994-05-03,453.059998,453.980011,450.510010,453.029999,453.029999,288270000\n1994-05-04,453.040009,453.109985,449.869995,451.720001,451.720001,267940000\n1994-05-05,451.720001,452.820007,450.720001,451.380005,451.380005,255690000\n1994-05-06,451.369995,451.369995,445.640015,447.820007,447.820007,291910000\n1994-05-09,447.820007,447.820007,441.839996,442.320007,442.320007,250870000\n1994-05-10,442.369995,446.839996,442.369995,446.010010,446.010010,297660000\n1994-05-11,446.029999,446.029999,440.779999,441.489990,441.489990,277400000\n1994-05-12,441.500000,444.799988,441.500000,443.750000,443.750000,272770000\n1994-05-13,443.619995,444.720001,441.209991,444.140015,444.140015,252070000\n1994-05-16,444.149994,445.820007,443.619995,444.489990,444.489990,234700000\n1994-05-17,444.489990,449.369995,443.700012,449.369995,449.369995,311280000\n1994-05-18,449.390015,454.450012,448.869995,453.690002,453.690002,337670000\n1994-05-19,453.690002,456.880005,453.000000,456.480011,456.480011,303680000\n1994-05-20,456.480011,456.480011,454.220001,454.920013,454.920013,295180000\n1994-05-23,454.920013,454.920013,451.790009,453.200012,453.200012,249420000\n1994-05-24,453.209991,456.769989,453.209991,454.809998,454.809998,280040000\n1994-05-25,454.839996,456.339996,452.200012,456.339996,456.339996,254420000\n1994-05-26,456.329987,457.769989,455.790009,457.059998,457.059998,255740000\n1994-05-27,457.029999,457.329987,454.670013,457.329987,457.329987,186430000\n1994-05-31,457.320007,457.609985,455.160004,456.500000,456.500000,216700000\n1994-06-01,456.500000,458.290009,453.989990,457.630005,457.630005,279910000\n1994-06-02,457.619995,458.500000,457.260010,457.649994,457.649994,271630000\n1994-06-03,457.649994,460.859985,456.269989,460.130005,460.130005,271490000\n1994-06-06,460.130005,461.869995,458.850006,458.880005,458.880005,259080000\n1994-06-07,458.880005,459.459991,457.649994,458.209991,458.209991,234680000\n1994-06-08,458.209991,459.739990,455.429993,457.059998,457.059998,256000000\n1994-06-09,457.059998,457.869995,455.859985,457.859985,457.859985,252870000\n1994-06-10,457.859985,459.480011,457.359985,458.670013,458.670013,222480000\n1994-06-13,458.670013,459.359985,457.179993,459.100006,459.100006,243640000\n1994-06-14,459.100006,462.519989,459.100006,462.369995,462.369995,288550000\n1994-06-15,462.380005,463.230011,459.950012,460.609985,460.609985,269740000\n1994-06-16,460.609985,461.929993,459.799988,461.929993,461.929993,256390000\n1994-06-17,461.929993,462.160004,458.440002,458.450012,458.450012,373450000\n1994-06-20,458.450012,458.450012,454.459991,455.480011,455.480011,229520000\n1994-06-21,455.480011,455.480011,449.450012,451.339996,451.339996,298730000\n1994-06-22,451.399994,453.910004,451.399994,453.089996,453.089996,251110000\n1994-06-23,453.089996,454.160004,449.429993,449.630005,449.630005,256480000\n1994-06-24,449.630005,449.630005,442.510010,442.799988,442.799988,261260000\n1994-06-27,442.779999,447.760010,439.829987,447.309998,447.309998,250080000\n1994-06-28,447.359985,448.470001,443.079987,446.070007,446.070007,267740000\n1994-06-29,446.049988,449.829987,446.040009,447.630005,447.630005,264430000\n1994-06-30,447.630005,448.609985,443.660004,444.269989,444.269989,293410000\n1994-07-01,444.269989,446.450012,443.579987,446.200012,446.200012,199030000\n1994-07-05,446.200012,447.619995,445.140015,446.369995,446.369995,195410000\n1994-07-06,446.290009,447.279999,444.179993,446.130005,446.130005,236230000\n1994-07-07,446.149994,448.640015,446.149994,448.380005,448.380005,259740000\n1994-07-08,448.380005,449.750000,446.529999,449.549988,449.549988,236520000\n1994-07-11,449.559998,450.239990,445.269989,448.059998,448.059998,222970000\n1994-07-12,448.019989,448.160004,444.649994,447.950012,447.950012,252250000\n1994-07-13,448.029999,450.059998,447.970001,448.730011,448.730011,265840000\n1994-07-14,448.730011,454.329987,448.730011,453.410004,453.410004,322330000\n1994-07-15,453.279999,454.329987,452.799988,454.160004,454.160004,275860000\n1994-07-18,454.410004,455.709991,453.260010,455.220001,455.220001,227460000\n1994-07-19,455.220001,455.299988,453.859985,453.859985,453.859985,251530000\n1994-07-20,453.890015,454.160004,450.690002,451.600006,451.600006,267840000\n1994-07-21,451.600006,453.220001,451.000000,452.609985,452.609985,292120000\n1994-07-22,452.609985,454.029999,452.329987,453.109985,453.109985,261600000\n1994-07-25,453.100006,454.320007,452.760010,454.250000,454.250000,213470000\n1994-07-26,454.250000,454.250000,452.779999,453.359985,453.359985,232670000\n1994-07-27,453.359985,453.380005,451.359985,452.570007,452.570007,251680000\n1994-07-28,452.570007,454.929993,452.299988,454.239990,454.239990,245990000\n1994-07-29,454.250000,459.329987,454.250000,458.260010,458.260010,269560000\n1994-08-01,458.279999,461.010010,458.079987,461.010010,461.010010,258180000\n1994-08-02,461.010010,462.769989,459.700012,460.559998,460.559998,294740000\n1994-08-03,460.649994,461.459991,459.510010,461.450012,461.450012,283840000\n1994-08-04,461.450012,461.489990,458.399994,458.399994,458.399994,289150000\n1994-08-05,458.339996,458.339996,456.079987,457.089996,457.089996,230270000\n1994-08-08,457.079987,458.299988,457.010010,457.890015,457.890015,217680000\n1994-08-09,457.890015,458.160004,456.660004,457.920013,457.920013,259140000\n1994-08-10,457.980011,460.480011,457.980011,460.299988,460.299988,279500000\n1994-08-11,460.309998,461.410004,456.880005,458.880005,458.880005,275690000\n1994-08-12,458.880005,462.269989,458.880005,461.940002,461.940002,249280000\n1994-08-15,461.970001,463.339996,461.209991,461.230011,461.230011,223210000\n1994-08-16,461.220001,465.200012,459.890015,465.010010,465.010010,306640000\n1994-08-17,465.109985,465.910004,464.570007,465.170013,465.170013,309250000\n1994-08-18,465.100006,465.100006,462.299988,463.170013,463.170013,287330000\n1994-08-19,463.250000,464.369995,461.809998,463.679993,463.679993,276630000\n1994-08-22,463.609985,463.609985,461.459991,462.320007,462.320007,235870000\n1994-08-23,462.390015,466.579987,462.390015,464.510010,464.510010,307240000\n1994-08-24,464.510010,469.049988,464.510010,469.029999,469.029999,310510000\n1994-08-25,469.070007,470.119995,467.640015,468.079987,468.079987,284230000\n1994-08-26,468.079987,474.649994,468.079987,473.799988,473.799988,305120000\n1994-08-29,473.890015,477.140015,473.890015,474.589996,474.589996,266080000\n1994-08-30,474.589996,476.609985,473.559998,476.070007,476.070007,294520000\n1994-08-31,476.070007,477.589996,474.429993,475.489990,475.489990,354650000\n1994-09-01,475.489990,475.489990,471.739990,473.170013,473.170013,282830000\n1994-09-02,473.200012,474.890015,470.670013,470.989990,470.989990,216150000\n1994-09-06,471.000000,471.920013,469.640015,471.859985,471.859985,199670000\n1994-09-07,471.859985,472.410004,470.200012,470.989990,470.989990,290330000\n1994-09-08,470.959991,473.399994,470.859985,473.140015,473.140015,295010000\n1994-09-09,473.130005,473.130005,466.549988,468.179993,468.179993,293360000\n1994-09-12,468.179993,468.420013,466.149994,466.209991,466.209991,244680000\n1994-09-13,466.269989,468.760010,466.269989,467.510010,467.510010,293370000\n1994-09-14,467.549988,468.859985,466.820007,468.799988,468.799988,297480000\n1994-09-15,468.799988,474.809998,468.790009,474.809998,474.809998,281920000\n1994-09-16,474.809998,474.809998,470.059998,471.190002,471.190002,410750000\n1994-09-19,471.209991,473.149994,470.679993,470.850006,470.850006,277110000\n1994-09-20,470.829987,470.829987,463.359985,463.359985,463.359985,326050000\n1994-09-21,463.420013,464.010010,458.470001,461.459991,461.459991,351830000\n1994-09-22,461.450012,463.220001,460.959991,461.269989,461.269989,305210000\n1994-09-23,461.269989,462.140015,459.010010,459.670013,459.670013,300060000\n1994-09-26,459.649994,460.869995,459.309998,460.820007,460.820007,272530000\n1994-09-27,460.820007,462.750000,459.829987,462.049988,462.049988,290330000\n1994-09-28,462.100006,465.549988,462.100006,464.839996,464.839996,330020000\n1994-09-29,464.839996,464.839996,461.510010,462.239990,462.239990,302280000\n1994-09-30,462.269989,465.299988,461.910004,462.709991,462.709991,291900000\n1994-10-03,462.690002,463.309998,460.329987,461.739990,461.739990,269130000\n1994-10-04,461.769989,462.459991,454.029999,454.589996,454.589996,325620000\n1994-10-05,454.589996,454.589996,449.269989,453.519989,453.519989,359670000\n1994-10-06,453.519989,454.489990,452.130005,452.359985,452.359985,272620000\n1994-10-07,452.369995,455.670013,452.130005,455.100006,455.100006,284230000\n1994-10-10,455.119995,459.290009,455.119995,459.040009,459.040009,213110000\n1994-10-11,459.040009,466.339996,459.040009,465.790009,465.790009,355540000\n1994-10-12,465.779999,466.700012,464.790009,465.470001,465.470001,269550000\n1994-10-13,465.559998,471.299988,465.559998,467.790009,467.790009,337900000\n1994-10-14,467.779999,469.529999,466.109985,469.100006,469.100006,251770000\n1994-10-17,469.109985,469.880005,468.160004,468.959991,468.959991,238490000\n1994-10-18,469.019989,469.190002,466.540009,467.660004,467.660004,259730000\n1994-10-19,467.690002,471.429993,465.959991,470.279999,470.279999,317030000\n1994-10-20,470.369995,470.369995,465.390015,466.850006,466.850006,331460000\n1994-10-21,466.690002,466.690002,463.829987,464.890015,464.890015,315310000\n1994-10-24,464.890015,466.369995,460.799988,460.829987,460.829987,282800000\n1994-10-25,460.829987,461.950012,458.260010,461.529999,461.529999,326110000\n1994-10-26,461.549988,463.769989,461.220001,462.619995,462.619995,322570000\n1994-10-27,462.679993,465.850006,462.619995,465.850006,465.850006,327790000\n1994-10-28,465.839996,473.779999,465.799988,473.769989,473.769989,381450000\n1994-10-31,473.760010,474.739990,472.329987,472.350006,472.350006,302820000\n1994-11-01,472.260010,472.260010,467.640015,468.420013,468.420013,314940000\n1994-11-02,468.410004,470.920013,466.359985,466.510010,466.510010,331360000\n1994-11-03,466.500000,468.640015,466.399994,467.910004,467.910004,285170000\n1994-11-04,467.959991,469.279999,462.279999,462.279999,462.279999,280560000\n1994-11-07,462.309998,463.559998,461.250000,463.070007,463.070007,255030000\n1994-11-08,463.079987,467.540009,463.070007,465.649994,465.649994,290860000\n1994-11-09,465.649994,469.950012,463.459991,465.399994,465.399994,337780000\n1994-11-10,465.399994,467.790009,463.730011,464.369995,464.369995,280910000\n1994-11-11,464.170013,464.170013,461.450012,462.350006,462.350006,220800000\n1994-11-14,462.440002,466.290009,462.350006,466.040009,466.040009,260380000\n1994-11-15,466.040009,468.510010,462.950012,465.029999,465.029999,336450000\n1994-11-16,465.059998,466.250000,464.279999,465.619995,465.619995,296980000\n1994-11-17,465.709991,465.829987,461.470001,463.570007,463.570007,323190000\n1994-11-18,463.600006,463.839996,460.250000,461.470001,461.470001,356730000\n1994-11-21,461.690002,463.410004,457.549988,458.299988,458.299988,293030000\n1994-11-22,457.950012,458.029999,450.079987,450.089996,450.089996,387270000\n1994-11-23,450.010010,450.609985,444.179993,449.929993,449.929993,430760000\n1994-11-25,449.940002,452.869995,449.940002,452.290009,452.290009,118290000\n1994-11-28,452.260010,454.190002,451.040009,454.160004,454.160004,265480000\n1994-11-29,454.230011,455.170013,452.140015,455.170013,455.170013,286620000\n1994-11-30,455.170013,457.130005,453.269989,453.690002,453.690002,298650000\n1994-12-01,453.549988,453.910004,447.970001,448.920013,448.920013,285920000\n1994-12-02,448.920013,453.309998,448.000000,453.299988,453.299988,284750000\n1994-12-05,453.299988,455.040009,452.059998,453.320007,453.320007,258490000\n1994-12-06,453.290009,453.929993,450.350006,453.109985,453.109985,298930000\n1994-12-07,453.109985,453.109985,450.010010,451.230011,451.230011,283490000\n1994-12-08,451.230011,452.059998,444.589996,445.450012,445.450012,362290000\n1994-12-09,445.450012,446.980011,442.880005,446.959991,446.959991,336440000\n1994-12-12,446.950012,449.480011,445.619995,449.470001,449.470001,285730000\n1994-12-13,449.519989,451.690002,449.429993,450.149994,450.149994,307110000\n1994-12-14,450.049988,456.160004,450.049988,454.970001,454.970001,355000000\n1994-12-15,454.970001,456.839996,454.500000,455.339996,455.339996,332790000\n1994-12-16,455.350006,458.799988,455.350006,458.799988,458.799988,481860000\n1994-12-19,458.779999,458.779999,456.640015,457.910004,457.910004,271850000\n1994-12-20,458.079987,458.450012,456.369995,457.100006,457.100006,326530000\n1994-12-21,457.239990,461.700012,457.170013,459.609985,459.609985,379130000\n1994-12-22,459.619995,461.209991,459.329987,459.679993,459.679993,340330000\n1994-12-23,459.700012,461.320007,459.390015,459.829987,459.829987,196540000\n1994-12-27,459.850006,462.730011,459.850006,462.470001,462.470001,211180000\n1994-12-28,462.470001,462.489990,459.000000,460.859985,460.859985,246260000\n1994-12-29,460.920013,461.809998,460.359985,461.170013,461.170013,250650000\n1994-12-30,461.170013,462.119995,459.239990,459.269989,459.269989,256260000\n1995-01-03,459.209991,459.269989,457.200012,459.109985,459.109985,262450000\n1995-01-04,459.130005,460.720001,457.559998,460.709991,460.709991,319510000\n1995-01-05,460.730011,461.299988,459.750000,460.339996,460.339996,309050000\n1995-01-06,460.380005,462.489990,459.470001,460.679993,460.679993,308070000\n1995-01-09,460.670013,461.769989,459.739990,460.829987,460.829987,278790000\n1995-01-10,460.899994,464.589996,460.899994,461.679993,461.679993,352450000\n1995-01-11,461.679993,463.609985,458.649994,461.660004,461.660004,346310000\n1995-01-12,461.640015,461.929993,460.630005,461.640015,461.640015,313040000\n1995-01-13,461.640015,466.429993,461.640015,465.970001,465.970001,336740000\n1995-01-16,465.970001,470.390015,465.970001,469.380005,469.380005,315810000\n1995-01-17,469.380005,470.149994,468.190002,470.049988,470.049988,331520000\n1995-01-18,470.049988,470.429993,468.029999,469.709991,469.709991,344660000\n1995-01-19,469.720001,469.720001,466.399994,466.950012,466.950012,297220000\n1995-01-20,466.950012,466.989990,463.989990,464.779999,464.779999,378190000\n1995-01-23,464.779999,466.230011,461.140015,465.820007,465.820007,325830000\n1995-01-24,465.809998,466.880005,465.470001,465.859985,465.859985,315430000\n1995-01-25,465.859985,469.510010,464.399994,467.440002,467.440002,342610000\n1995-01-26,467.440002,468.619995,466.899994,468.320007,468.320007,304730000\n1995-01-27,468.320007,471.359985,468.320007,470.390015,470.390015,339510000\n1995-01-30,470.390015,470.519989,467.489990,468.510010,468.510010,318550000\n1995-01-31,468.510010,471.029999,468.179993,470.420013,470.420013,411590000\n1995-02-01,470.420013,472.750000,469.290009,470.399994,470.399994,395310000\n1995-02-02,470.399994,472.790009,469.950012,472.790009,472.790009,322110000\n1995-02-03,472.779999,479.910004,472.779999,478.649994,478.649994,441000000\n1995-02-06,478.640015,481.950012,478.359985,481.140015,481.140015,325660000\n1995-02-07,481.140015,481.320007,479.690002,480.809998,480.809998,314660000\n1995-02-08,480.809998,482.600006,480.399994,481.190002,481.190002,318430000\n1995-02-09,481.190002,482.000000,479.910004,480.190002,480.190002,325570000\n1995-02-10,480.190002,481.959991,479.529999,481.459991,481.459991,295600000\n1995-02-13,481.459991,482.859985,481.070007,481.649994,481.649994,256270000\n1995-02-14,481.649994,482.940002,480.890015,482.549988,482.549988,300720000\n1995-02-15,482.549988,485.540009,481.769989,484.540009,484.540009,378040000\n1995-02-16,484.559998,485.220001,483.049988,485.220001,485.220001,360990000\n1995-02-17,485.149994,485.220001,481.970001,481.970001,481.970001,347970000\n1995-02-21,481.950012,483.260010,481.940002,482.720001,482.720001,308090000\n1995-02-22,482.739990,486.149994,482.450012,485.070007,485.070007,339460000\n1995-02-23,485.070007,489.190002,485.070007,486.910004,486.910004,394280000\n1995-02-24,486.820007,488.279999,485.700012,488.109985,488.109985,302930000\n1995-02-27,488.260010,488.260010,483.179993,483.809998,483.809998,285790000\n1995-02-28,483.809998,487.440002,483.769989,487.390015,487.390015,317220000\n1995-03-01,487.390015,487.829987,484.920013,485.649994,485.649994,362600000\n1995-03-02,485.649994,485.709991,483.190002,485.130005,485.130005,330030000\n1995-03-03,485.130005,485.420013,483.070007,485.420013,485.420013,330840000\n1995-03-06,485.420013,485.700012,481.519989,485.630005,485.630005,298870000\n1995-03-07,485.630005,485.630005,479.700012,482.119995,482.119995,355550000\n1995-03-08,482.119995,484.079987,481.570007,483.140015,483.140015,349780000\n1995-03-09,483.140015,483.739990,482.049988,483.160004,483.160004,319320000\n1995-03-10,483.160004,490.369995,483.160004,489.570007,489.570007,382940000\n1995-03-13,489.570007,491.279999,489.350006,490.049988,490.049988,275280000\n1995-03-14,490.049988,493.690002,490.049988,492.890015,492.890015,346160000\n1995-03-15,492.890015,492.890015,490.829987,491.880005,491.880005,309540000\n1995-03-16,491.869995,495.739990,491.779999,495.410004,495.410004,336670000\n1995-03-17,495.429993,496.670013,494.950012,495.519989,495.519989,417380000\n1995-03-20,495.519989,496.609985,495.269989,496.140015,496.140015,301740000\n1995-03-21,496.149994,499.190002,494.040009,495.070007,495.070007,367110000\n1995-03-22,495.070007,495.670013,493.670013,495.670013,495.670013,313120000\n1995-03-23,495.670013,496.769989,494.190002,495.950012,495.950012,318530000\n1995-03-24,496.070007,500.970001,496.070007,500.970001,500.970001,358370000\n1995-03-27,500.970001,503.200012,500.929993,503.200012,503.200012,296270000\n1995-03-28,503.190002,503.910004,501.829987,503.899994,503.899994,320360000\n1995-03-29,503.920013,508.149994,500.959991,503.119995,503.119995,385940000\n1995-03-30,503.170013,504.660004,501.000000,502.220001,502.220001,362940000\n1995-03-31,501.940002,502.220001,495.700012,500.709991,500.709991,353060000\n1995-04-03,500.700012,501.910004,500.200012,501.850006,501.850006,296430000\n1995-04-04,501.850006,505.260010,501.850006,505.239990,505.239990,330580000\n1995-04-05,505.269989,505.570007,503.170013,505.570007,505.570007,315170000\n1995-04-06,505.630005,507.100006,505.000000,506.079987,506.079987,320460000\n1995-04-07,506.130005,507.190002,503.589996,506.420013,506.420013,314760000\n1995-04-10,506.299988,507.010010,504.609985,507.010010,507.010010,260980000\n1995-04-11,507.239990,508.850006,505.290009,505.529999,505.529999,310660000\n1995-04-12,505.589996,507.170013,505.070007,507.170013,507.170013,327880000\n1995-04-13,507.190002,509.829987,507.170013,509.230011,509.230011,301580000\n1995-04-17,509.230011,512.030029,505.429993,506.130005,506.130005,333930000\n1995-04-18,506.429993,507.649994,504.119995,505.369995,505.369995,344680000\n1995-04-19,505.369995,505.890015,501.190002,504.920013,504.920013,378050000\n1995-04-20,504.920013,506.500000,503.440002,505.290009,505.290009,368450000\n1995-04-21,505.630005,508.489990,505.630005,508.489990,508.489990,403250000\n1995-04-24,508.489990,513.020020,507.440002,512.890015,512.890015,326280000\n1995-04-25,512.799988,513.539978,511.320007,512.099976,512.099976,351790000\n1995-04-26,511.989990,513.039978,510.470001,512.659973,512.659973,350810000\n1995-04-27,512.700012,513.619995,511.630005,513.549988,513.549988,350850000\n1995-04-28,513.640015,515.289978,510.899994,514.710022,514.710022,320440000\n1995-05-01,514.760010,515.599976,513.419983,514.260010,514.260010,296830000\n1995-05-02,514.229980,515.179993,513.030029,514.859985,514.859985,302560000\n1995-05-03,514.929993,520.539978,514.859985,520.479980,520.479980,392370000\n1995-05-04,520.479980,525.400024,519.440002,520.539978,520.539978,434990000\n1995-05-05,520.750000,522.349976,518.280029,520.119995,520.119995,342380000\n1995-05-08,520.090027,525.150024,519.140015,523.960022,523.960022,291810000\n1995-05-09,523.960022,525.989990,521.789978,523.559998,523.559998,361300000\n1995-05-10,523.739990,524.400024,521.530029,524.359985,524.359985,381990000\n1995-05-11,524.330017,524.890015,522.700012,524.369995,524.369995,339900000\n1995-05-12,524.369995,527.049988,523.299988,525.549988,525.549988,361000000\n1995-05-15,525.549988,527.739990,525.000000,527.739990,527.739990,316240000\n1995-05-16,527.739990,529.080017,526.450012,528.190002,528.190002,366180000\n1995-05-17,528.190002,528.419983,525.380005,527.070007,527.070007,347930000\n1995-05-18,526.880005,526.880005,519.580017,519.580017,519.580017,351900000\n1995-05-19,519.580017,519.580017,517.070007,519.190002,519.190002,354010000\n1995-05-22,519.190002,524.340027,519.190002,523.650024,523.650024,285600000\n1995-05-23,523.650024,528.590027,523.650024,528.590027,528.590027,362690000\n1995-05-24,528.590027,531.909973,525.570007,528.609985,528.609985,391770000\n1995-05-25,528.369995,529.039978,524.890015,528.590027,528.590027,341820000\n1995-05-26,528.590027,528.590027,522.510010,523.650024,523.650024,291220000\n1995-05-30,523.650024,525.580017,521.380005,523.580017,523.580017,283020000\n1995-05-31,523.700012,533.409973,522.169983,533.400024,533.400024,358180000\n1995-06-01,533.400024,534.210022,530.049988,533.489990,533.489990,345920000\n1995-06-02,533.489990,536.909973,529.549988,532.510010,532.510010,366000000\n1995-06-05,532.510010,537.729980,532.469971,535.599976,535.599976,337520000\n1995-06-06,535.599976,537.090027,535.140015,535.549988,535.549988,340490000\n1995-06-07,535.549988,535.549988,531.659973,533.130005,533.130005,327790000\n1995-06-08,533.130005,533.559998,531.650024,532.349976,532.349976,289880000\n1995-06-09,532.349976,532.349976,526.000000,527.940002,527.940002,327570000\n1995-06-12,527.940002,532.539978,527.940002,530.880005,530.880005,289920000\n1995-06-13,530.880005,536.229980,530.880005,536.049988,536.049988,339660000\n1995-06-14,536.049988,536.479980,533.830017,536.469971,536.469971,330770000\n1995-06-15,536.479980,539.070007,535.559998,537.119995,537.119995,334700000\n1995-06-16,537.510010,539.979980,537.119995,539.830017,539.830017,442740000\n1995-06-19,539.830017,545.219971,539.830017,545.219971,545.219971,322990000\n1995-06-20,545.219971,545.440002,543.429993,544.979980,544.979980,382370000\n1995-06-21,544.979980,545.929993,543.900024,543.979980,543.979980,398210000\n1995-06-22,543.979980,551.070007,543.979980,551.070007,551.070007,421000000\n1995-06-23,551.070007,551.070007,548.229980,549.710022,549.710022,321660000\n1995-06-26,549.710022,549.789978,544.059998,544.130005,544.130005,296720000\n1995-06-27,544.109985,547.070007,542.190002,542.429993,542.429993,346950000\n1995-06-28,542.429993,546.330017,540.719971,544.729980,544.729980,368060000\n1995-06-29,544.729980,546.250000,540.789978,543.869995,543.869995,313080000\n1995-06-30,543.869995,546.820007,543.510010,544.750000,544.750000,311650000\n1995-07-03,544.750000,547.099976,544.429993,547.090027,547.090027,117900000\n1995-07-05,547.090027,549.979980,546.280029,547.260010,547.260010,357850000\n1995-07-06,547.260010,553.989990,546.590027,553.989990,553.989990,420500000\n1995-07-07,553.900024,556.570007,553.049988,556.369995,556.369995,466540000\n1995-07-10,556.369995,558.479980,555.770020,557.190002,557.190002,409700000\n1995-07-11,556.780029,557.190002,553.799988,554.780029,554.780029,376770000\n1995-07-12,555.270020,561.559998,554.270020,560.890015,560.890015,416360000\n1995-07-13,560.890015,562.000000,559.070007,561.000000,561.000000,387500000\n1995-07-14,561.000000,561.000000,556.409973,559.890015,559.890015,312930000\n1995-07-17,560.340027,562.940002,559.450012,562.719971,562.719971,322540000\n1995-07-18,562.549988,562.719971,556.859985,558.460022,558.460022,372230000\n1995-07-19,556.580017,558.460022,542.510010,550.979980,550.979980,489850000\n1995-07-20,550.979980,554.429993,549.099976,553.539978,553.539978,383380000\n1995-07-21,553.340027,554.729980,550.909973,553.619995,553.619995,431830000\n1995-07-24,553.619995,557.210022,553.619995,556.630005,556.630005,315300000\n1995-07-25,556.630005,561.750000,556.340027,561.099976,561.099976,373200000\n1995-07-26,561.099976,563.780029,560.849976,561.609985,561.609985,393470000\n1995-07-27,561.609985,565.330017,561.609985,565.219971,565.219971,356570000\n1995-07-28,565.219971,565.400024,562.039978,562.929993,562.929993,311590000\n1995-07-31,562.929993,563.489990,560.059998,562.059998,562.059998,291950000\n1995-08-01,562.059998,562.109985,556.669983,559.640015,559.640015,332210000\n1995-08-02,559.640015,565.619995,557.869995,558.799988,558.799988,374330000\n1995-08-03,558.799988,558.799988,554.099976,558.750000,558.750000,353110000\n1995-08-04,558.750000,559.570007,557.909973,558.940002,558.940002,314740000\n1995-08-07,558.940002,561.239990,558.940002,560.030029,560.030029,277050000\n1995-08-08,560.030029,561.530029,558.320007,560.390015,560.390015,306090000\n1995-08-09,560.390015,561.590027,559.289978,559.710022,559.710022,303390000\n1995-08-10,559.710022,560.630005,556.049988,557.450012,557.450012,306660000\n1995-08-11,557.450012,558.500000,553.039978,555.109985,555.109985,267850000\n1995-08-14,555.109985,559.739990,554.760010,559.739990,559.739990,264920000\n1995-08-15,559.739990,559.979980,555.219971,558.570007,558.570007,330070000\n1995-08-16,558.570007,559.979980,557.369995,559.969971,559.969971,390170000\n1995-08-17,559.969971,559.969971,557.419983,559.039978,559.039978,354460000\n1995-08-18,559.039978,561.239990,558.340027,559.210022,559.210022,320490000\n1995-08-21,559.210022,563.340027,557.890015,558.109985,558.109985,303200000\n1995-08-22,558.109985,559.520020,555.869995,559.520020,559.520020,290890000\n1995-08-23,559.520020,560.000000,557.080017,557.140015,557.140015,291890000\n1995-08-24,557.140015,558.630005,555.200012,557.460022,557.460022,299200000\n1995-08-25,557.460022,561.309998,557.460022,560.099976,560.099976,255990000\n1995-08-28,560.099976,562.219971,557.989990,559.049988,559.049988,267860000\n1995-08-29,559.049988,560.010010,555.710022,560.000000,560.000000,311290000\n1995-08-30,560.000000,561.520020,559.489990,560.919983,560.919983,329840000\n1995-08-31,561.090027,562.359985,560.489990,561.880005,561.880005,300920000\n1995-09-01,561.880005,564.619995,561.010010,563.840027,563.840027,256730000\n1995-09-05,563.859985,569.200012,563.840027,569.169983,569.169983,332670000\n1995-09-06,569.169983,570.530029,569.000000,570.169983,570.169983,369540000\n1995-09-07,570.169983,571.109985,569.229980,570.289978,570.289978,321720000\n1995-09-08,570.289978,572.679993,569.270020,572.679993,572.679993,317940000\n1995-09-11,572.679993,575.150024,572.679993,573.909973,573.909973,296840000\n1995-09-12,573.909973,576.510010,573.109985,576.510010,576.510010,344540000\n1995-09-13,576.510010,579.719971,575.469971,578.770020,578.770020,384380000\n1995-09-14,578.770020,583.989990,578.770020,583.609985,583.609985,382880000\n1995-09-15,583.609985,585.070007,581.789978,583.349976,583.349976,459370000\n1995-09-18,583.349976,583.369995,579.359985,582.770020,582.770020,326090000\n1995-09-19,582.780029,584.239990,580.750000,584.200012,584.200012,371170000\n1995-09-20,584.200012,586.770020,584.179993,586.770020,586.770020,400050000\n1995-09-21,586.770020,586.789978,580.909973,583.000000,583.000000,367100000\n1995-09-22,583.000000,583.000000,578.250000,581.729980,581.729980,370790000\n1995-09-25,581.729980,582.140015,579.500000,581.809998,581.809998,273120000\n1995-09-26,581.809998,584.659973,580.650024,581.409973,581.409973,363630000\n1995-09-27,581.409973,581.419983,574.679993,581.039978,581.039978,411300000\n1995-09-28,581.039978,585.880005,580.690002,585.869995,585.869995,367720000\n1995-09-29,585.869995,587.609985,584.000000,584.409973,584.409973,335250000\n1995-10-02,584.409973,585.049988,580.539978,581.719971,581.719971,304990000\n1995-10-03,581.719971,582.340027,578.479980,582.340027,582.340027,385940000\n1995-10-04,582.340027,582.340027,579.909973,581.469971,581.469971,339380000\n1995-10-05,581.469971,582.630005,579.580017,582.630005,582.630005,367480000\n1995-10-06,582.630005,584.539978,582.099976,582.489990,582.489990,313680000\n1995-10-09,582.489990,582.489990,576.349976,578.369995,578.369995,275320000\n1995-10-10,578.369995,578.369995,571.549988,577.520020,577.520020,412710000\n1995-10-11,577.520020,579.520020,577.080017,579.460022,579.460022,340740000\n1995-10-12,579.460022,583.119995,579.460022,583.099976,583.099976,344060000\n1995-10-13,583.099976,587.390015,583.099976,584.500000,584.500000,374680000\n1995-10-16,584.500000,584.859985,582.630005,583.030029,583.030029,300750000\n1995-10-17,583.030029,586.780029,581.900024,586.780029,586.780029,356380000\n1995-10-18,586.780029,589.770020,586.270020,587.440002,587.440002,411270000\n1995-10-19,587.440002,590.659973,586.340027,590.650024,590.650024,406620000\n1995-10-20,590.650024,590.659973,586.780029,587.460022,587.460022,389360000\n1995-10-23,587.460022,587.460022,583.729980,585.059998,585.059998,330750000\n1995-10-24,585.059998,587.309998,584.750000,586.539978,586.539978,415540000\n1995-10-25,586.539978,587.190002,581.409973,582.469971,582.469971,433620000\n1995-10-26,582.469971,582.630005,572.530029,576.719971,576.719971,464270000\n1995-10-27,576.719971,579.710022,573.210022,579.700012,579.700012,379230000\n1995-10-30,579.700012,583.789978,579.700012,583.250000,583.250000,319160000\n1995-10-31,583.250000,586.710022,581.500000,581.500000,581.500000,377390000\n1995-11-01,581.500000,584.239990,581.039978,584.219971,584.219971,378090000\n1995-11-02,584.219971,589.719971,584.219971,589.719971,589.719971,397070000\n1995-11-03,589.719971,590.570007,588.650024,590.570007,590.570007,348500000\n1995-11-06,590.570007,590.640015,588.309998,588.460022,588.460022,309100000\n1995-11-07,588.460022,588.460022,584.239990,586.320007,586.320007,364680000\n1995-11-08,586.320007,591.710022,586.320007,591.710022,591.710022,359780000\n1995-11-09,591.710022,593.900024,590.890015,593.260010,593.260010,380760000\n1995-11-10,593.260010,593.260010,590.390015,592.719971,592.719971,298690000\n1995-11-13,592.719971,593.719971,590.580017,592.299988,592.299988,295840000\n1995-11-14,592.299988,592.299988,588.979980,589.289978,589.289978,354420000\n1995-11-15,589.289978,593.969971,588.359985,593.960022,593.960022,376100000\n1995-11-16,593.960022,597.909973,593.520020,597.340027,597.340027,423280000\n1995-11-17,597.340027,600.140015,597.299988,600.070007,600.070007,437200000\n1995-11-20,600.070007,600.400024,596.169983,596.849976,596.849976,333150000\n1995-11-21,596.849976,600.280029,595.419983,600.239990,600.239990,408320000\n1995-11-22,600.239990,600.710022,598.400024,598.400024,598.400024,404980000\n1995-11-24,598.400024,600.239990,598.400024,599.969971,599.969971,125870000\n1995-11-27,599.969971,603.349976,599.969971,601.320007,601.320007,359130000\n1995-11-28,601.320007,606.450012,599.020020,606.450012,606.450012,408860000\n1995-11-29,606.450012,607.659973,605.469971,607.640015,607.640015,398280000\n1995-11-30,607.640015,608.690002,605.369995,605.369995,605.369995,440050000\n1995-12-01,605.369995,608.109985,605.369995,606.979980,606.979980,393310000\n1995-12-04,606.979980,613.830017,606.840027,613.679993,613.679993,405480000\n1995-12-05,613.679993,618.479980,613.140015,617.679993,617.679993,437360000\n1995-12-06,617.679993,621.109985,616.690002,620.179993,620.179993,417780000\n1995-12-07,620.179993,620.190002,615.210022,616.169983,616.169983,379260000\n1995-12-08,616.169983,617.820007,614.320007,617.479980,617.479980,327900000\n1995-12-11,617.479980,620.900024,617.140015,619.520020,619.520020,342070000\n1995-12-12,619.520020,619.549988,617.679993,618.780029,618.780029,349860000\n1995-12-13,618.780029,622.020020,618.270020,621.690002,621.690002,415290000\n1995-12-14,621.690002,622.880005,616.130005,616.919983,616.919983,465300000\n1995-12-15,616.919983,617.719971,614.460022,616.340027,616.340027,636800000\n1995-12-18,616.340027,616.340027,606.130005,606.809998,606.809998,426270000\n1995-12-19,606.809998,611.940002,605.049988,611.929993,611.929993,478280000\n1995-12-20,611.929993,614.270020,605.929993,605.940002,605.940002,437680000\n1995-12-21,605.940002,610.520020,605.940002,610.489990,610.489990,415810000\n1995-12-22,610.489990,613.500000,610.450012,611.950012,611.950012,289600000\n1995-12-26,611.960022,614.500000,611.960022,614.299988,614.299988,217280000\n1995-12-27,614.299988,615.729980,613.750000,614.530029,614.530029,252300000\n1995-12-28,614.530029,615.500000,612.400024,614.119995,614.119995,288660000\n1995-12-29,614.119995,615.929993,612.359985,615.929993,615.929993,321250000\n1996-01-02,615.929993,620.739990,613.169983,620.729980,620.729980,364180000\n1996-01-03,620.729980,623.250000,619.559998,621.320007,621.320007,468950000\n1996-01-04,621.320007,624.489990,613.960022,617.700012,617.700012,512580000\n1996-01-05,617.700012,617.700012,612.020020,616.710022,616.710022,437110000\n1996-01-08,616.710022,618.460022,616.489990,618.460022,618.460022,130360000\n1996-01-09,618.460022,619.150024,608.210022,609.450012,609.450012,417400000\n1996-01-10,609.450012,609.450012,597.289978,598.479980,598.479980,496830000\n1996-01-11,598.479980,602.710022,597.539978,602.690002,602.690002,408800000\n1996-01-12,602.690002,604.799988,597.460022,601.809998,601.809998,383400000\n1996-01-15,601.809998,603.429993,598.469971,599.820007,599.820007,306180000\n1996-01-16,599.820007,608.440002,599.049988,608.440002,608.440002,425220000\n1996-01-17,608.440002,609.929993,604.700012,606.369995,606.369995,458720000\n1996-01-18,606.369995,608.270020,604.119995,608.239990,608.239990,450410000\n1996-01-19,608.239990,612.919983,606.760010,611.830017,611.830017,497720000\n1996-01-22,611.830017,613.450012,610.950012,613.400024,613.400024,398040000\n1996-01-23,613.400024,613.400024,610.650024,612.789978,612.789978,416910000\n1996-01-24,612.789978,619.960022,612.789978,619.960022,619.960022,476380000\n1996-01-25,619.960022,620.150024,616.619995,617.030029,617.030029,453270000\n1996-01-26,617.030029,621.700012,615.260010,621.619995,621.619995,385700000\n1996-01-29,621.619995,624.219971,621.419983,624.219971,624.219971,363330000\n1996-01-30,624.219971,630.289978,624.219971,630.150024,630.150024,464350000\n1996-01-31,630.150024,636.179993,629.479980,636.020020,636.020020,472210000\n1996-02-01,636.020020,638.460022,634.539978,638.460022,638.460022,461430000\n1996-02-02,638.460022,639.260010,634.289978,635.840027,635.840027,420020000\n1996-02-05,635.840027,641.429993,633.710022,641.429993,641.429993,377760000\n1996-02-06,641.429993,646.669983,639.679993,646.330017,646.330017,465940000\n1996-02-07,646.330017,649.929993,645.590027,649.929993,649.929993,462730000\n1996-02-08,649.929993,656.539978,647.929993,656.070007,656.070007,474970000\n1996-02-09,656.070007,661.080017,653.640015,656.369995,656.369995,477640000\n1996-02-12,656.369995,662.950012,656.340027,661.450012,661.450012,397890000\n1996-02-13,661.450012,664.229980,657.919983,660.510010,660.510010,441540000\n1996-02-14,660.510010,661.530029,654.359985,655.580017,655.580017,421790000\n1996-02-15,655.580017,656.840027,651.150024,651.320007,651.320007,415320000\n1996-02-16,651.320007,651.419983,646.989990,647.979980,647.979980,445570000\n1996-02-20,647.979980,647.979980,638.789978,640.650024,640.650024,395910000\n1996-02-21,640.650024,648.109985,640.650024,648.099976,648.099976,431220000\n1996-02-22,648.099976,659.750000,648.099976,658.859985,658.859985,485470000\n1996-02-23,658.859985,663.000000,652.250000,659.080017,659.080017,443130000\n1996-02-26,659.080017,659.080017,650.159973,650.460022,650.460022,399330000\n1996-02-27,650.460022,650.619995,643.869995,647.239990,647.239990,431340000\n1996-02-28,647.239990,654.390015,643.989990,644.750000,644.750000,447790000\n1996-02-29,644.750000,646.950012,639.010010,640.429993,640.429993,453170000\n1996-03-01,640.429993,644.380005,635.000000,644.369995,644.369995,471480000\n1996-03-04,644.369995,653.539978,644.369995,650.809998,650.809998,417270000\n1996-03-05,650.809998,655.799988,648.770020,655.789978,655.789978,445700000\n1996-03-06,655.789978,656.969971,651.609985,652.000000,652.000000,428220000\n1996-03-07,652.000000,653.650024,649.539978,653.650024,653.650024,425790000\n1996-03-08,653.650024,653.650024,627.630005,633.500000,633.500000,546550000\n1996-03-11,633.500000,640.409973,629.950012,640.020020,640.020020,449500000\n1996-03-12,640.020020,640.020020,628.820007,637.090027,637.090027,454980000\n1996-03-13,637.090027,640.520020,635.190002,638.549988,638.549988,413030000\n1996-03-14,638.549988,644.169983,638.549988,640.869995,640.869995,492630000\n1996-03-15,640.869995,642.869995,638.349976,641.429993,641.429993,529970000\n1996-03-18,641.429993,652.650024,641.429993,652.650024,652.650024,437100000\n1996-03-19,652.650024,656.179993,649.799988,651.690002,651.690002,438300000\n1996-03-20,651.690002,653.130005,645.570007,649.979980,649.979980,409780000\n1996-03-21,649.979980,651.539978,648.099976,649.190002,649.190002,367180000\n1996-03-22,649.190002,652.080017,649.190002,650.619995,650.619995,329390000\n1996-03-25,650.619995,655.500000,648.820007,650.039978,650.039978,336700000\n1996-03-26,650.039978,654.309998,648.150024,652.969971,652.969971,400090000\n1996-03-27,652.969971,653.940002,647.599976,648.909973,648.909973,406280000\n1996-03-28,648.909973,649.580017,646.359985,648.940002,648.940002,370750000\n1996-03-29,648.940002,650.960022,644.890015,645.500000,645.500000,413510000\n1996-04-01,645.500000,653.869995,645.500000,653.729980,653.729980,392120000\n1996-04-02,653.729980,655.270020,652.809998,655.260010,655.260010,406640000\n1996-04-03,655.260010,655.890015,651.809998,655.880005,655.880005,386620000\n1996-04-04,655.880005,656.679993,654.890015,655.859985,655.859985,383400000\n1996-04-08,655.859985,655.859985,638.039978,644.239990,644.239990,411810000\n1996-04-09,644.239990,646.330017,640.840027,642.190002,642.190002,426790000\n1996-04-10,642.190002,642.780029,631.760010,633.500000,633.500000,475150000\n1996-04-11,633.500000,635.260010,624.140015,631.179993,631.179993,519710000\n1996-04-12,631.179993,637.140015,631.179993,636.710022,636.710022,413270000\n1996-04-15,636.710022,642.489990,636.710022,642.489990,642.489990,346370000\n1996-04-16,642.489990,645.570007,642.150024,645.000000,645.000000,453310000\n1996-04-17,645.000000,645.000000,638.710022,641.609985,641.609985,465200000\n1996-04-18,641.609985,644.659973,640.760010,643.609985,643.609985,415150000\n1996-04-19,643.609985,647.320007,643.609985,645.070007,645.070007,435690000\n1996-04-22,645.070007,650.909973,645.070007,647.890015,647.890015,395370000\n1996-04-23,647.890015,651.590027,647.700012,651.580017,651.580017,452690000\n1996-04-24,651.580017,653.369995,648.250000,650.169983,650.169983,494220000\n1996-04-25,650.169983,654.179993,647.059998,652.869995,652.869995,462120000\n1996-04-26,652.869995,656.429993,651.960022,653.460022,653.460022,402530000\n1996-04-29,653.460022,654.710022,651.599976,654.159973,654.159973,344030000\n1996-04-30,654.159973,654.590027,651.049988,654.169983,654.169983,393390000\n1996-05-01,654.169983,656.440002,652.260010,654.580017,654.580017,404620000\n1996-05-02,654.580017,654.580017,642.130005,643.380005,643.380005,442960000\n1996-05-03,643.380005,648.450012,640.229980,641.630005,641.630005,434010000\n1996-05-06,641.630005,644.640015,636.190002,640.809998,640.809998,375820000\n1996-05-07,640.809998,641.400024,636.960022,638.260010,638.260010,410770000\n1996-05-08,638.260010,644.789978,630.070007,644.770020,644.770020,495460000\n1996-05-09,644.770020,647.950012,643.179993,645.440002,645.440002,404310000\n1996-05-10,645.440002,653.000000,645.440002,652.090027,652.090027,428370000\n1996-05-13,652.090027,662.159973,652.090027,661.510010,661.510010,394180000\n1996-05-14,661.510010,666.960022,661.510010,665.599976,665.599976,460440000\n1996-05-15,665.599976,669.820007,664.460022,665.419983,665.419983,447790000\n1996-05-16,665.419983,667.109985,662.789978,664.849976,664.849976,392070000\n1996-05-17,664.849976,669.840027,664.849976,668.909973,668.909973,429140000\n1996-05-20,668.909973,673.659973,667.640015,673.150024,673.150024,385000000\n1996-05-21,673.150024,675.559998,672.260010,672.760010,672.760010,409610000\n1996-05-22,672.760010,678.419983,671.229980,678.419983,678.419983,423670000\n1996-05-23,678.419983,681.099976,673.450012,676.000000,676.000000,431850000\n1996-05-24,676.000000,679.719971,676.000000,678.510010,678.510010,329150000\n1996-05-28,678.510010,679.979980,671.520020,672.229980,672.229980,341480000\n1996-05-29,672.229980,673.729980,666.090027,667.929993,667.929993,346730000\n1996-05-30,667.929993,673.510010,664.559998,671.700012,671.700012,381960000\n1996-05-31,671.700012,673.460022,667.000000,669.119995,669.119995,351750000\n1996-06-03,669.119995,669.119995,665.190002,667.679993,667.679993,318470000\n1996-06-04,667.679993,672.599976,667.679993,672.559998,672.559998,386040000\n1996-06-05,672.559998,678.450012,672.090027,678.440002,678.440002,380360000\n1996-06-06,678.440002,680.320007,673.020020,673.030029,673.030029,466940000\n1996-06-07,673.030029,673.309998,662.479980,673.309998,673.309998,445710000\n1996-06-10,673.309998,673.609985,670.150024,672.159973,672.159973,337480000\n1996-06-11,672.159973,676.719971,669.940002,670.969971,670.969971,405390000\n1996-06-12,670.969971,673.669983,668.770020,669.039978,669.039978,397190000\n1996-06-13,669.039978,670.539978,665.489990,667.919983,667.919983,397620000\n1996-06-14,667.919983,668.400024,664.349976,665.849976,665.849976,390630000\n1996-06-17,665.849976,668.270020,664.090027,665.159973,665.159973,298410000\n1996-06-18,665.159973,666.359985,661.340027,662.059998,662.059998,373290000\n1996-06-19,662.059998,665.619995,661.210022,661.960022,661.960022,383610000\n1996-06-20,661.960022,664.960022,658.750000,662.099976,662.099976,441060000\n1996-06-21,662.099976,666.840027,662.099976,666.840027,666.840027,520340000\n1996-06-24,666.840027,671.070007,666.840027,668.849976,668.849976,333840000\n1996-06-25,668.849976,670.650024,667.289978,668.479980,668.479980,391900000\n1996-06-26,668.479980,668.489990,663.669983,664.390015,664.390015,386520000\n1996-06-27,664.390015,668.900024,661.559998,668.549988,668.549988,405580000\n1996-06-28,668.549988,672.679993,668.549988,670.630005,670.630005,470460000\n1996-07-01,670.630005,675.880005,670.630005,675.880005,675.880005,345750000\n1996-07-02,675.880005,675.880005,672.549988,673.609985,673.609985,388000000\n1996-07-03,673.609985,673.640015,670.210022,672.400024,672.400024,336260000\n1996-07-05,672.400024,672.400024,657.409973,657.440002,657.440002,181470000\n1996-07-08,657.440002,657.650024,651.130005,652.539978,652.539978,367560000\n1996-07-09,652.539978,656.599976,652.539978,654.750000,654.750000,400170000\n1996-07-10,654.750000,656.270020,648.390015,656.059998,656.059998,421350000\n1996-07-11,656.059998,656.059998,639.520020,645.669983,645.669983,520470000\n1996-07-12,645.669983,647.640015,640.210022,646.190002,646.190002,396740000\n1996-07-15,646.190002,646.190002,629.690002,629.799988,629.799988,419020000\n1996-07-16,629.799988,631.989990,605.880005,628.369995,628.369995,682980000\n1996-07-17,628.369995,636.609985,628.369995,634.070007,634.070007,513830000\n1996-07-18,634.070007,644.440002,633.289978,643.559998,643.559998,474460000\n1996-07-19,643.510010,643.510010,635.500000,638.729980,638.729980,408070000\n1996-07-22,638.729980,638.729980,630.380005,633.770020,633.770020,327300000\n1996-07-23,633.789978,637.700012,625.650024,626.869995,626.869995,421900000\n1996-07-24,626.190002,629.099976,616.429993,626.650024,626.650024,463030000\n1996-07-25,626.650024,633.570007,626.650024,631.169983,631.169983,405390000\n1996-07-26,631.169983,636.229980,631.169983,635.900024,635.900024,349900000\n1996-07-29,635.900024,635.900024,630.900024,630.909973,630.909973,281560000\n1996-07-30,630.909973,635.260010,629.219971,635.260010,635.260010,341090000\n1996-07-31,635.260010,640.539978,633.739990,639.950012,639.950012,403560000\n1996-08-01,639.950012,650.659973,639.489990,650.020020,650.020020,439110000\n1996-08-02,650.020020,662.489990,650.020020,662.489990,662.489990,442080000\n1996-08-05,662.489990,663.640015,659.030029,660.229980,660.229980,307240000\n1996-08-06,660.229980,662.750000,656.830017,662.380005,662.380005,347290000\n1996-08-07,662.380005,664.609985,660.000000,664.159973,664.159973,394340000\n1996-08-08,664.159973,664.169983,661.280029,662.590027,662.590027,334570000\n1996-08-09,662.590027,665.369995,660.309998,662.099976,662.099976,327280000\n1996-08-12,662.099976,665.770020,658.950012,665.770020,665.770020,312170000\n1996-08-13,665.770020,665.770020,659.130005,660.200012,660.200012,362470000\n1996-08-14,660.200012,662.419983,658.469971,662.049988,662.049988,343460000\n1996-08-15,662.049988,664.179993,660.640015,662.280029,662.280029,323950000\n1996-08-16,662.280029,666.340027,662.260010,665.210022,665.210022,337650000\n1996-08-19,665.210022,667.119995,665.000000,666.580017,666.580017,294080000\n1996-08-20,666.580017,666.989990,665.150024,665.690002,665.690002,334960000\n1996-08-21,665.690002,665.690002,662.159973,665.070007,665.070007,348820000\n1996-08-22,665.070007,670.679993,664.880005,670.679993,670.679993,354950000\n1996-08-23,670.679993,670.679993,664.929993,667.030029,667.030029,308010000\n1996-08-26,667.030029,667.030029,662.359985,663.880005,663.880005,281430000\n1996-08-27,663.880005,666.400024,663.500000,666.400024,666.400024,310520000\n1996-08-28,666.400024,667.409973,664.390015,664.809998,664.809998,296440000\n1996-08-29,664.809998,664.809998,655.349976,657.400024,657.400024,321120000\n1996-08-30,657.400024,657.710022,650.520020,651.989990,651.989990,258380000\n1996-09-03,651.989990,655.130005,643.969971,654.719971,654.719971,345740000\n1996-09-04,654.719971,655.820007,652.929993,655.609985,655.609985,351290000\n1996-09-05,655.609985,655.609985,648.890015,649.440002,649.440002,361430000\n1996-09-06,649.440002,658.210022,649.440002,655.679993,655.679993,348710000\n1996-09-09,655.679993,663.770020,655.679993,663.760010,663.760010,311530000\n1996-09-10,663.760010,665.570007,661.549988,663.809998,663.809998,372960000\n1996-09-11,663.809998,667.729980,661.789978,667.280029,667.280029,376880000\n1996-09-12,667.280029,673.070007,667.280029,671.150024,671.150024,398820000\n1996-09-13,671.150024,681.390015,671.150024,680.539978,680.539978,488360000\n1996-09-16,680.539978,686.479980,680.530029,683.979980,683.979980,430080000\n1996-09-17,683.979980,685.799988,679.960022,682.940002,682.940002,449850000\n1996-09-18,682.940002,683.770020,679.750000,681.469971,681.469971,396600000\n1996-09-19,681.469971,684.070007,679.059998,683.000000,683.000000,398580000\n1996-09-20,683.000000,687.070007,683.000000,687.030029,687.030029,519420000\n1996-09-23,687.030029,687.030029,681.010010,686.479980,686.479980,297760000\n1996-09-24,686.479980,690.880005,683.539978,685.609985,685.609985,460150000\n1996-09-25,685.609985,688.260010,684.919983,685.830017,685.830017,451710000\n1996-09-26,685.830017,690.150024,683.770020,685.859985,685.859985,500870000\n1996-09-27,685.859985,687.109985,683.729980,686.190002,686.190002,414760000\n1996-09-30,686.190002,690.109985,686.030029,687.330017,687.330017,388570000\n1996-10-01,687.309998,689.539978,684.440002,689.080017,689.080017,421550000\n1996-10-02,689.080017,694.820007,689.080017,694.010010,694.010010,440130000\n1996-10-03,694.010010,694.809998,691.780029,692.780029,692.780029,386500000\n1996-10-04,692.780029,701.739990,692.780029,701.460022,701.460022,463940000\n1996-10-07,701.460022,704.169983,701.390015,703.340027,703.340027,380750000\n1996-10-08,703.340027,705.760010,699.880005,700.640015,700.640015,435070000\n1996-10-09,700.640015,702.359985,694.419983,696.739990,696.739990,408450000\n1996-10-10,696.739990,696.820007,693.340027,694.609985,694.609985,394950000\n1996-10-11,694.609985,700.669983,694.609985,700.659973,700.659973,396050000\n1996-10-14,700.659973,705.159973,700.659973,703.539978,703.539978,322000000\n1996-10-15,703.539978,708.070007,699.070007,702.570007,702.570007,458980000\n1996-10-16,702.570007,704.419983,699.150024,704.409973,704.409973,441410000\n1996-10-17,705.000000,708.520020,704.760010,706.989990,706.989990,478550000\n1996-10-18,706.989990,711.039978,706.109985,710.820007,710.820007,473020000\n1996-10-21,710.820007,714.099976,707.710022,709.849976,709.849976,414630000\n1996-10-22,709.849976,709.849976,704.549988,706.570007,706.570007,410790000\n1996-10-23,706.570007,707.309998,700.979980,707.270020,707.270020,442170000\n1996-10-24,707.270020,708.250000,702.109985,702.289978,702.289978,418970000\n1996-10-25,702.289978,704.109985,700.530029,700.919983,700.919983,367640000\n1996-10-28,700.919983,705.400024,697.250000,697.260010,697.260010,383620000\n1996-10-29,697.260010,703.250000,696.219971,701.500000,701.500000,443890000\n1996-10-30,701.500000,703.440002,700.049988,700.900024,700.900024,437770000\n1996-10-31,700.900024,706.609985,700.349976,705.270020,705.270020,482840000\n1996-11-01,705.270020,708.599976,701.299988,703.770020,703.770020,465510000\n1996-11-04,703.770020,707.020020,702.840027,706.729980,706.729980,398790000\n1996-11-05,706.729980,714.559998,706.729980,714.140015,714.140015,486660000\n1996-11-06,714.140015,724.599976,712.830017,724.590027,724.590027,509600000\n1996-11-07,724.590027,729.489990,722.229980,727.650024,727.650024,502530000\n1996-11-08,727.650024,730.820007,725.219971,730.820007,730.820007,402320000\n1996-11-11,730.820007,732.599976,729.940002,731.869995,731.869995,353960000\n1996-11-12,731.869995,733.039978,728.200012,729.559998,729.559998,471740000\n1996-11-13,729.559998,732.109985,728.030029,731.130005,731.130005,429840000\n1996-11-14,731.130005,735.989990,729.200012,735.880005,735.880005,480350000\n1996-11-15,735.880005,741.919983,735.150024,737.619995,737.619995,529100000\n1996-11-18,737.619995,739.239990,734.390015,737.020020,737.020020,388520000\n1996-11-19,737.020020,742.179993,736.869995,742.159973,742.159973,461980000\n1996-11-20,742.159973,746.989990,740.400024,743.950012,743.950012,497900000\n1996-11-21,743.950012,745.200012,741.080017,742.750000,742.750000,464430000\n1996-11-22,742.750000,748.729980,742.750000,748.729980,748.729980,525210000\n1996-11-25,748.729980,757.049988,747.989990,757.030029,757.030029,475260000\n1996-11-26,757.030029,762.119995,752.830017,755.960022,755.960022,527380000\n1996-11-27,755.960022,757.299988,753.179993,755.000000,755.000000,377780000\n1996-11-29,755.000000,758.270020,755.000000,757.020020,757.020020,14990000\n1996-12-02,757.020020,757.030029,751.489990,756.559998,756.559998,412520000\n1996-12-03,756.559998,761.750000,747.580017,748.280029,748.280029,516160000\n1996-12-04,748.280029,748.400024,738.460022,745.099976,745.099976,498240000\n1996-12-05,745.099976,747.650024,742.609985,744.380005,744.380005,483710000\n1996-12-06,744.380005,744.380005,726.890015,739.599976,739.599976,500860000\n1996-12-09,739.599976,749.760010,739.599976,749.760010,749.760010,381570000\n1996-12-10,749.760010,753.429993,747.020020,747.539978,747.539978,446120000\n1996-12-11,747.539978,747.539978,732.750000,740.729980,740.729980,494210000\n1996-12-12,740.729980,744.859985,729.299988,729.299988,729.299988,492920000\n1996-12-13,729.330017,731.400024,721.969971,728.640015,728.640015,458540000\n1996-12-16,728.640015,732.679993,719.400024,720.979980,720.979980,447560000\n1996-12-17,720.979980,727.669983,716.690002,726.039978,726.039978,519840000\n1996-12-18,726.039978,732.760010,726.039978,731.539978,731.539978,500490000\n1996-12-19,731.539978,746.059998,731.539978,745.760010,745.760010,526410000\n1996-12-20,745.760010,755.409973,745.760010,748.869995,748.869995,654340000\n1996-12-23,748.869995,750.400024,743.280029,746.919983,746.919983,343280000\n1996-12-24,746.919983,751.030029,746.919983,751.030029,751.030029,165140000\n1996-12-26,751.030029,757.070007,751.020020,755.820007,755.820007,254630000\n1996-12-27,755.820007,758.750000,754.820007,756.789978,756.789978,253810000\n1996-12-30,756.789978,759.200012,752.729980,753.849976,753.849976,339060000\n1996-12-31,753.849976,753.950012,740.739990,740.739990,740.739990,399760000\n1997-01-02,740.739990,742.809998,729.549988,737.010010,737.010010,463230000\n1997-01-03,737.010010,748.239990,737.010010,748.030029,748.030029,452970000\n1997-01-06,748.030029,753.309998,743.820007,747.650024,747.650024,531350000\n1997-01-07,747.650024,753.260010,742.179993,753.229980,753.229980,538220000\n1997-01-08,753.229980,755.719971,747.710022,748.409973,748.409973,557510000\n1997-01-09,748.409973,757.679993,748.409973,754.849976,754.849976,555370000\n1997-01-10,754.849976,759.650024,746.919983,759.500000,759.500000,545850000\n1997-01-13,759.500000,762.849976,756.690002,759.510010,759.510010,445400000\n1997-01-14,759.510010,772.039978,759.510010,768.859985,768.859985,531600000\n1997-01-15,768.859985,770.950012,763.719971,767.200012,767.200012,524990000\n1997-01-16,767.200012,772.049988,765.250000,769.750000,769.750000,537290000\n1997-01-17,769.750000,776.369995,769.719971,776.169983,776.169983,534640000\n1997-01-20,776.169983,780.080017,774.190002,776.700012,776.700012,440470000\n1997-01-21,776.700012,783.719971,772.000000,782.719971,782.719971,571280000\n1997-01-22,782.719971,786.229980,779.559998,786.229980,786.229980,589230000\n1997-01-23,786.229980,794.669983,776.640015,777.559998,777.559998,685070000\n1997-01-24,777.559998,778.210022,768.169983,770.520020,770.520020,542920000\n1997-01-27,770.520020,771.429993,764.179993,765.020020,765.020020,445760000\n1997-01-28,765.020020,776.320007,761.750000,765.020020,765.020020,541580000\n1997-01-29,765.020020,772.700012,765.020020,772.500000,772.500000,498390000\n1997-01-30,772.500000,784.169983,772.500000,784.169983,784.169983,524160000\n1997-01-31,784.169983,791.859985,784.169983,786.159973,786.159973,578550000\n1997-02-03,786.159973,787.140015,783.119995,786.729980,786.729980,463600000\n1997-02-04,786.729980,789.280029,783.679993,789.260010,789.260010,506530000\n1997-02-05,789.260010,792.710022,773.429993,778.280029,778.280029,580520000\n1997-02-06,778.280029,780.349976,774.450012,780.150024,780.150024,519660000\n1997-02-07,780.150024,789.719971,778.190002,789.559998,789.559998,540910000\n1997-02-10,789.559998,793.460022,784.690002,785.429993,785.429993,471590000\n1997-02-11,785.429993,789.599976,780.950012,789.590027,789.590027,483090000\n1997-02-12,789.590027,802.770020,789.590027,802.770020,802.770020,563890000\n1997-02-13,802.770020,812.929993,802.770020,811.820007,811.820007,593710000\n1997-02-14,811.820007,812.200012,808.150024,808.479980,808.479980,491540000\n1997-02-18,808.479980,816.289978,806.340027,816.289978,816.289978,474110000\n1997-02-19,816.289978,817.679993,811.200012,812.489990,812.489990,519350000\n1997-02-20,812.489990,812.489990,800.349976,802.799988,802.799988,492220000\n1997-02-21,802.799988,804.940002,799.989990,801.770020,801.770020,478450000\n1997-02-24,801.770020,810.640015,798.419983,810.280029,810.280029,462450000\n1997-02-25,810.280029,812.849976,807.650024,812.030029,812.030029,527450000\n1997-02-26,812.099976,812.700012,798.130005,805.679993,805.679993,573920000\n1997-02-27,805.679993,805.679993,795.059998,795.070007,795.070007,464660000\n1997-02-28,795.070007,795.700012,788.500000,790.820007,790.820007,508280000\n1997-03-03,790.820007,795.309998,785.659973,795.309998,795.309998,437220000\n1997-03-04,795.309998,798.929993,789.979980,790.950012,790.950012,537890000\n1997-03-05,790.950012,801.989990,790.950012,801.989990,801.989990,532500000\n1997-03-06,801.989990,804.109985,797.500000,798.559998,798.559998,540310000\n1997-03-07,798.559998,808.190002,798.559998,804.969971,804.969971,508270000\n1997-03-10,804.969971,813.659973,803.659973,813.650024,813.650024,468780000\n1997-03-11,813.650024,814.900024,810.770020,811.340027,811.340027,493250000\n1997-03-12,811.340027,811.340027,801.070007,804.260010,804.260010,490200000\n1997-03-13,804.260010,804.260010,789.440002,789.559998,789.559998,507560000\n1997-03-14,789.559998,796.880005,789.559998,793.169983,793.169983,491540000\n1997-03-17,793.169983,796.280029,782.979980,795.710022,795.710022,495260000\n1997-03-18,795.710022,797.179993,785.469971,789.659973,789.659973,467330000\n1997-03-19,789.659973,791.590027,780.030029,785.770020,785.770020,535580000\n1997-03-20,785.770020,786.289978,778.039978,782.650024,782.650024,497480000\n1997-03-21,782.650024,786.440002,782.650024,784.099976,784.099976,638760000\n1997-03-24,784.099976,791.010010,780.789978,790.890015,790.890015,451970000\n1997-03-25,790.890015,798.109985,788.390015,789.070007,789.070007,487520000\n1997-03-26,789.070007,794.890015,786.770020,790.500000,790.500000,506670000\n1997-03-27,790.500000,792.580017,767.320007,773.880005,773.880005,476790000\n1997-03-31,773.880005,773.880005,756.130005,757.119995,757.119995,555880000\n1997-04-01,757.119995,761.489990,751.260010,759.640015,759.640015,515770000\n1997-04-02,759.640015,759.650024,747.590027,750.109985,750.109985,478210000\n1997-04-03,750.109985,751.039978,744.400024,750.320007,750.320007,498010000\n1997-04-04,750.320007,757.900024,744.039978,757.900024,757.900024,544580000\n1997-04-07,757.900024,764.820007,757.900024,762.130005,762.130005,453790000\n1997-04-08,762.130005,766.250000,758.359985,766.119995,766.119995,450790000\n1997-04-09,766.119995,769.530029,759.150024,760.599976,760.599976,451500000\n1997-04-10,760.599976,763.729980,757.650024,758.340027,758.340027,421790000\n1997-04-11,758.340027,758.340027,737.640015,737.650024,737.650024,444380000\n1997-04-14,737.650024,743.729980,733.539978,743.729980,743.729980,406800000\n1997-04-15,743.729980,754.719971,743.729980,754.719971,754.719971,507370000\n1997-04-16,754.719971,763.530029,751.989990,763.530029,763.530029,498820000\n1997-04-17,763.530029,768.549988,760.489990,761.770020,761.770020,503760000\n1997-04-18,761.770020,767.929993,761.770020,766.340027,766.340027,468940000\n1997-04-21,766.340027,767.390015,756.380005,760.369995,760.369995,397300000\n1997-04-22,760.369995,774.640015,759.900024,774.609985,774.609985,507500000\n1997-04-23,774.609985,778.190002,771.900024,773.640015,773.640015,489350000\n1997-04-24,773.640015,779.890015,769.719971,771.179993,771.179993,493640000\n1997-04-25,771.179993,771.179993,764.630005,765.369995,765.369995,414350000\n1997-04-28,765.369995,773.890015,763.299988,772.960022,772.960022,404470000\n1997-04-29,772.960022,794.440002,772.960022,794.049988,794.049988,547690000\n1997-04-30,794.049988,804.130005,791.210022,801.340027,801.340027,556070000\n1997-05-01,801.340027,802.950012,793.210022,798.530029,798.530029,460380000\n1997-05-02,798.530029,812.989990,798.530029,812.969971,812.969971,499770000\n1997-05-05,812.969971,830.289978,811.799988,830.289978,830.289978,549410000\n1997-05-06,830.239990,832.289978,824.700012,827.760010,827.760010,603680000\n1997-05-07,827.760010,827.760010,814.700012,815.619995,815.619995,500580000\n1997-05-08,815.619995,829.090027,811.840027,820.260010,820.260010,534120000\n1997-05-09,820.260010,827.690002,815.780029,824.780029,824.780029,455690000\n1997-05-12,824.780029,838.559998,824.780029,837.659973,837.659973,459370000\n1997-05-13,837.659973,838.489990,829.119995,833.130005,833.130005,489760000\n1997-05-14,833.130005,841.289978,833.130005,836.039978,836.039978,504960000\n1997-05-15,836.039978,842.450012,833.340027,841.880005,841.880005,458170000\n1997-05-16,841.880005,841.880005,829.150024,829.750000,829.750000,486780000\n1997-05-19,829.750000,835.919983,828.869995,833.270020,833.270020,345140000\n1997-05-20,833.270020,841.960022,826.409973,841.659973,841.659973,450850000\n1997-05-21,841.659973,846.869995,835.219971,839.349976,839.349976,540730000\n1997-05-22,839.349976,841.909973,833.859985,835.659973,835.659973,426940000\n1997-05-23,835.659973,848.489990,835.659973,847.030029,847.030029,417030000\n1997-05-27,847.030029,851.530029,840.960022,849.710022,849.710022,436150000\n1997-05-28,849.710022,850.950012,843.210022,847.210022,847.210022,487340000\n1997-05-29,847.210022,848.960022,842.609985,844.080017,844.080017,462600000\n1997-05-30,844.080017,851.869995,831.869995,848.280029,848.280029,537200000\n1997-06-02,848.280029,851.340027,844.609985,846.359985,846.359985,435950000\n1997-06-03,846.359985,850.559998,841.510010,845.479980,845.479980,527120000\n1997-06-04,845.479980,845.549988,838.820007,840.109985,840.109985,466690000\n1997-06-05,840.109985,848.890015,840.109985,843.429993,843.429993,452610000\n1997-06-06,843.429993,859.239990,843.359985,858.010010,858.010010,488940000\n1997-06-09,858.010010,865.140015,858.010010,862.909973,862.909973,465810000\n1997-06-10,862.909973,870.049988,862.179993,865.270020,865.270020,526980000\n1997-06-11,865.270020,870.659973,865.150024,869.570007,869.570007,513740000\n1997-06-12,869.570007,884.340027,869.010010,883.460022,883.460022,592730000\n1997-06-13,883.479980,894.690002,883.479980,893.270020,893.270020,575810000\n1997-06-16,893.270020,895.169983,891.210022,893.900024,893.900024,414280000\n1997-06-17,893.900024,897.599976,886.190002,894.419983,894.419983,543010000\n1997-06-18,894.419983,894.419983,887.030029,889.059998,889.059998,491740000\n1997-06-19,889.059998,900.090027,888.989990,897.989990,897.989990,536940000\n1997-06-20,897.989990,901.770020,897.770020,898.700012,898.700012,653110000\n1997-06-23,898.700012,898.700012,878.429993,878.619995,878.619995,492940000\n1997-06-24,878.619995,896.750000,878.619995,896.340027,896.340027,542650000\n1997-06-25,896.340027,902.090027,882.239990,888.989990,888.989990,603040000\n1997-06-26,888.989990,893.210022,879.320007,883.679993,883.679993,499780000\n1997-06-27,883.679993,894.700012,883.679993,887.299988,887.299988,472540000\n1997-06-30,887.299988,892.619995,879.820007,885.140015,885.140015,561540000\n1997-07-01,885.140015,893.880005,884.539978,891.030029,891.030029,544190000\n1997-07-02,891.030029,904.049988,891.030029,904.030029,904.030029,526970000\n1997-07-03,904.030029,917.820007,904.030029,916.919983,916.919983,374680000\n1997-07-07,916.919983,923.260010,909.690002,912.200012,912.200012,518780000\n1997-07-08,912.200012,918.760010,911.559998,918.750000,918.750000,526010000\n1997-07-09,918.750000,922.030029,902.479980,907.539978,907.539978,589110000\n1997-07-10,907.539978,916.539978,904.309998,913.780029,913.780029,551340000\n1997-07-11,913.780029,919.739990,913.109985,916.679993,916.679993,500050000\n1997-07-14,916.679993,921.780029,912.020020,918.380005,918.380005,485960000\n1997-07-15,918.380005,926.150024,914.520020,925.760010,925.760010,598370000\n1997-07-16,925.760010,939.320007,925.760010,936.590027,936.590027,647390000\n1997-07-17,936.590027,936.960022,927.900024,931.609985,931.609985,629250000\n1997-07-18,931.609985,931.609985,912.900024,915.299988,915.299988,589710000\n1997-07-21,915.299988,915.380005,907.119995,912.940002,912.940002,459500000\n1997-07-22,912.940002,934.380005,912.940002,933.979980,933.979980,579590000\n1997-07-23,933.979980,941.799988,933.979980,936.559998,936.559998,616930000\n1997-07-24,936.559998,941.510010,926.909973,940.299988,940.299988,571020000\n1997-07-25,940.299988,945.650024,936.090027,938.789978,938.789978,521510000\n1997-07-28,938.789978,942.969971,935.190002,936.450012,936.450012,466920000\n1997-07-29,936.450012,942.960022,932.559998,942.289978,942.289978,544540000\n1997-07-30,942.289978,953.979980,941.979980,952.289978,952.289978,568470000\n1997-07-31,952.289978,957.729980,948.890015,954.309998,954.309998,547830000\n1997-08-01,954.289978,955.349976,939.039978,947.140015,947.140015,513750000\n1997-08-04,947.140015,953.179993,943.599976,950.299988,950.299988,456000000\n1997-08-05,950.299988,954.210022,948.919983,952.369995,952.369995,525710000\n1997-08-06,952.369995,962.429993,949.450012,960.320007,960.320007,565200000\n1997-08-07,960.320007,964.169983,950.869995,951.190002,951.190002,576030000\n1997-08-08,951.190002,951.190002,925.739990,933.539978,933.539978,563420000\n1997-08-11,933.539978,938.500000,925.390015,937.000000,937.000000,480340000\n1997-08-12,937.000000,942.989990,925.659973,926.530029,926.530029,499310000\n1997-08-13,926.530029,935.770020,916.539978,922.020020,922.020020,587210000\n1997-08-14,922.020020,930.070007,916.919983,924.770020,924.770020,530460000\n1997-08-15,924.770020,924.770020,900.809998,900.809998,900.809998,537820000\n1997-08-18,900.809998,912.570007,893.340027,912.489990,912.489990,514330000\n1997-08-19,912.489990,926.010010,912.489990,926.010010,926.010010,545630000\n1997-08-20,926.010010,939.349976,924.580017,939.349976,939.349976,521270000\n1997-08-21,939.349976,939.469971,921.349976,925.049988,925.049988,499000000\n1997-08-22,925.049988,925.049988,905.419983,923.539978,923.539978,460160000\n1997-08-25,923.549988,930.929993,917.289978,920.159973,920.159973,388990000\n1997-08-26,920.159973,922.469971,911.719971,913.020020,913.020020,449110000\n1997-08-27,913.020020,916.229980,903.830017,913.700012,913.700012,492150000\n1997-08-28,913.700012,915.900024,898.650024,903.669983,903.669983,486300000\n1997-08-29,903.669983,907.280029,896.820007,899.469971,899.469971,413910000\n1997-09-02,899.469971,927.580017,899.469971,927.580017,927.580017,491870000\n1997-09-03,927.580017,935.900024,926.869995,927.859985,927.859985,549060000\n1997-09-04,927.859985,933.359985,925.590027,930.869995,930.869995,559310000\n1997-09-05,930.869995,940.369995,924.049988,929.049988,929.049988,536400000\n1997-09-08,929.049988,936.500000,929.049988,931.200012,931.200012,466430000\n1997-09-09,931.200012,938.900024,927.280029,933.619995,933.619995,502200000\n1997-09-10,933.619995,933.619995,918.760010,919.030029,919.030029,517620000\n1997-09-11,919.030029,919.030029,902.559998,912.590027,912.590027,575020000\n1997-09-12,912.590027,925.049988,906.700012,923.909973,923.909973,544150000\n1997-09-15,923.909973,928.900024,919.409973,919.770020,919.770020,468030000\n1997-09-16,919.770020,947.659973,919.770020,945.640015,945.640015,636380000\n1997-09-17,945.640015,950.289978,941.989990,943.000000,943.000000,590550000\n1997-09-18,943.000000,958.190002,943.000000,947.289978,947.289978,566830000\n1997-09-19,947.289978,952.349976,943.900024,950.510010,950.510010,631040000\n1997-09-22,950.510010,960.590027,950.510010,955.429993,955.429993,490900000\n1997-09-23,955.429993,955.780029,948.070007,951.929993,951.929993,522930000\n1997-09-24,951.929993,959.780029,944.070007,944.479980,944.479980,639460000\n1997-09-25,944.479980,947.000000,937.380005,937.909973,937.909973,524880000\n1997-09-26,937.909973,946.440002,937.909973,945.219971,945.219971,505340000\n1997-09-29,945.219971,953.960022,941.940002,953.340027,953.340027,477100000\n1997-09-30,953.340027,955.169983,947.280029,947.280029,947.280029,587500000\n1997-10-01,947.280029,956.710022,947.280029,955.409973,955.409973,598660000\n1997-10-02,955.409973,960.460022,952.940002,960.460022,960.460022,474760000\n1997-10-03,960.460022,975.469971,955.130005,965.030029,965.030029,623370000\n1997-10-06,965.030029,974.159973,965.030029,972.690002,972.690002,495620000\n1997-10-07,972.690002,983.119995,971.950012,983.119995,983.119995,551970000\n1997-10-08,983.119995,983.119995,968.650024,973.840027,973.840027,573110000\n1997-10-09,973.840027,974.719971,963.340027,970.619995,970.619995,551840000\n1997-10-10,970.619995,970.619995,963.419983,966.979980,966.979980,500680000\n1997-10-13,966.979980,973.460022,966.950012,968.099976,968.099976,354800000\n1997-10-14,968.099976,972.859985,961.869995,970.280029,970.280029,510330000\n1997-10-15,970.280029,970.280029,962.750000,965.719971,965.719971,505310000\n1997-10-16,965.719971,973.380005,950.770020,955.250000,955.250000,597010000\n1997-10-17,955.229980,955.229980,931.580017,944.159973,944.159973,624980000\n1997-10-20,944.159973,955.719971,941.429993,955.609985,955.609985,483880000\n1997-10-21,955.609985,972.559998,955.609985,972.280029,972.280029,582310000\n1997-10-22,972.280029,972.609985,965.659973,968.489990,968.489990,613490000\n1997-10-23,968.489990,968.489990,944.159973,950.690002,950.690002,673270000\n1997-10-24,950.690002,960.039978,937.549988,941.640015,941.640015,677630000\n1997-10-27,941.640015,941.640015,876.729980,876.989990,876.989990,693730000\n1997-10-28,876.989990,923.090027,855.270020,921.849976,921.849976,1202550000\n1997-10-29,921.849976,935.239990,913.880005,919.159973,919.159973,777660000\n1997-10-30,919.159973,923.280029,903.679993,903.679993,903.679993,712230000\n1997-10-31,903.679993,919.929993,903.679993,914.619995,914.619995,638070000\n1997-11-03,914.619995,939.020020,914.619995,938.989990,938.989990,564740000\n1997-11-04,938.989990,941.400024,932.659973,940.760010,940.760010,541590000\n1997-11-05,940.760010,949.619995,938.159973,942.760010,942.760010,565680000\n1997-11-06,942.760010,942.849976,934.159973,938.030029,938.030029,522890000\n1997-11-07,938.030029,938.030029,915.390015,927.510010,927.510010,569980000\n1997-11-10,927.510010,935.900024,920.260010,921.130005,921.130005,464140000\n1997-11-11,921.130005,928.289978,919.630005,923.780029,923.780029,435660000\n1997-11-12,923.780029,923.880005,905.340027,905.960022,905.960022,585340000\n1997-11-13,905.960022,917.789978,900.609985,916.659973,916.659973,653960000\n1997-11-14,916.659973,930.440002,915.340027,928.349976,928.349976,635760000\n1997-11-17,928.349976,949.659973,928.349976,946.200012,946.200012,576540000\n1997-11-18,946.200012,947.650024,937.429993,938.229980,938.229980,521380000\n1997-11-19,938.229980,947.280029,934.830017,944.590027,944.590027,542720000\n1997-11-20,944.590027,961.830017,944.590027,958.979980,958.979980,602610000\n1997-11-21,958.979980,964.549988,954.599976,963.090027,963.090027,611000000\n1997-11-24,963.090027,963.090027,945.219971,946.669983,946.669983,514920000\n1997-11-25,946.669983,954.469971,944.710022,950.820007,950.820007,587890000\n1997-11-26,950.820007,956.469971,950.820007,951.640015,951.640015,487750000\n1997-11-28,951.640015,959.130005,951.640015,955.400024,955.400024,189070000\n1997-12-01,955.400024,974.770020,955.400024,974.770020,974.770020,590300000\n1997-12-02,974.780029,976.200012,969.830017,971.679993,971.679993,576120000\n1997-12-03,971.679993,980.809998,966.159973,976.770020,976.770020,624610000\n1997-12-04,976.770020,983.359985,971.369995,973.099976,973.099976,633470000\n1997-12-05,973.099976,986.250000,969.099976,983.789978,983.789978,563590000\n1997-12-08,983.789978,985.669983,979.570007,982.369995,982.369995,490320000\n1997-12-09,982.369995,982.369995,973.809998,975.780029,975.780029,539130000\n1997-12-10,975.780029,975.780029,962.679993,969.789978,969.789978,602290000\n1997-12-11,969.789978,969.789978,951.890015,954.940002,954.940002,631770000\n1997-12-12,954.940002,961.320007,947.000000,953.390015,953.390015,579280000\n1997-12-15,953.390015,965.960022,953.390015,963.390015,963.390015,597150000\n1997-12-16,963.390015,973.000000,963.390015,968.039978,968.039978,623320000\n1997-12-17,968.039978,974.299988,964.250000,965.539978,965.539978,618900000\n1997-12-18,965.539978,965.539978,950.549988,955.299988,955.299988,618870000\n1997-12-19,955.299988,955.299988,924.919983,946.780029,946.780029,793200000\n1997-12-22,946.780029,956.729980,946.250000,953.700012,953.700012,530670000\n1997-12-23,953.700012,954.510010,938.909973,939.130005,939.130005,515070000\n1997-12-24,939.130005,942.880005,932.700012,932.700012,932.700012,265980000\n1997-12-26,932.700012,939.989990,932.700012,936.460022,936.460022,154900000\n1997-12-29,936.460022,953.950012,936.460022,953.349976,953.349976,443160000\n1997-12-30,953.349976,970.840027,953.349976,970.840027,970.840027,499500000\n1997-12-31,970.840027,975.020020,967.409973,970.429993,970.429993,467280000\n1998-01-02,970.429993,975.039978,965.729980,975.039978,975.039978,366730000\n1998-01-05,975.039978,982.630005,969.000000,977.070007,977.070007,628070000\n1998-01-06,977.070007,977.070007,962.679993,966.580017,966.580017,618360000\n1998-01-07,966.580017,966.580017,952.669983,964.000000,964.000000,667390000\n1998-01-08,964.000000,964.000000,955.039978,956.049988,956.049988,652140000\n1998-01-09,956.049988,956.049988,921.719971,927.690002,927.690002,746420000\n1998-01-12,927.690002,939.250000,912.830017,939.210022,939.210022,705450000\n1998-01-13,939.210022,952.140015,939.210022,952.119995,952.119995,646740000\n1998-01-14,952.119995,958.119995,948.000000,957.940002,957.940002,603280000\n1998-01-15,957.940002,957.940002,950.270020,950.729980,950.729980,569050000\n1998-01-16,950.729980,965.119995,950.729980,961.510010,961.510010,670080000\n1998-01-20,961.510010,978.599976,961.479980,978.599976,978.599976,644790000\n1998-01-21,978.599976,978.599976,963.289978,970.809998,970.809998,626160000\n1998-01-22,970.809998,970.809998,959.489990,963.039978,963.039978,646570000\n1998-01-23,963.039978,966.440002,950.859985,957.590027,957.590027,635770000\n1998-01-26,957.590027,963.039978,954.239990,956.950012,956.950012,555080000\n1998-01-27,956.950012,973.229980,956.260010,969.020020,969.020020,679140000\n1998-01-28,969.020020,978.630005,969.020020,977.460022,977.460022,708470000\n1998-01-29,977.460022,992.650024,975.210022,985.489990,985.489990,750760000\n1998-01-30,985.489990,987.409973,979.630005,980.280029,980.280029,613380000\n1998-02-02,980.280029,1002.479980,980.280029,1001.270020,1001.270020,724320000\n1998-02-03,1001.270020,1006.130005,996.900024,1006.000000,1006.000000,692120000\n1998-02-04,1006.000000,1009.520020,999.429993,1006.900024,1006.900024,695420000\n1998-02-05,1006.900024,1013.510010,1000.270020,1003.539978,1003.539978,703980000\n1998-02-06,1003.539978,1013.070007,1003.359985,1012.460022,1012.460022,569650000\n1998-02-09,1012.460022,1015.330017,1006.280029,1010.739990,1010.739990,524810000\n1998-02-10,1010.739990,1022.150024,1010.710022,1019.010010,1019.010010,642800000\n1998-02-11,1019.010010,1020.710022,1016.380005,1020.010010,1020.010010,599300000\n1998-02-12,1020.010010,1026.300049,1008.549988,1024.140015,1024.140015,611480000\n1998-02-13,1024.140015,1024.140015,1017.710022,1020.090027,1020.090027,531940000\n1998-02-17,1020.090027,1028.020020,1020.090027,1022.760010,1022.760010,605890000\n1998-02-18,1022.760010,1032.079956,1021.700012,1032.079956,1032.079956,606000000\n1998-02-19,1032.079956,1032.930054,1026.619995,1028.280029,1028.280029,581820000\n1998-02-20,1028.280029,1034.209961,1022.690002,1034.209961,1034.209961,594300000\n1998-02-23,1034.209961,1038.680054,1031.760010,1038.140015,1038.140015,550730000\n1998-02-24,1038.140015,1038.729980,1028.890015,1030.560059,1030.560059,589880000\n1998-02-25,1030.560059,1045.790039,1030.560059,1042.900024,1042.900024,611350000\n1998-02-26,1042.900024,1048.680054,1039.849976,1048.670044,1048.670044,646280000\n1998-02-27,1048.670044,1051.660034,1044.400024,1049.339966,1049.339966,574480000\n1998-03-02,1049.339966,1053.979980,1044.699951,1047.699951,1047.699951,591470000\n1998-03-03,1047.699951,1052.020020,1043.410034,1052.020020,1052.020020,612360000\n1998-03-04,1052.020020,1052.020020,1042.739990,1047.329956,1047.329956,644280000\n1998-03-05,1047.329956,1047.329956,1030.869995,1035.050049,1035.050049,648270000\n1998-03-06,1035.050049,1055.689941,1035.050049,1055.689941,1055.689941,665500000\n1998-03-09,1055.689941,1058.550049,1050.020020,1052.310059,1052.310059,624700000\n1998-03-10,1052.310059,1064.589966,1052.310059,1064.250000,1064.250000,631920000\n1998-03-11,1064.250000,1069.180054,1064.219971,1068.469971,1068.469971,655260000\n1998-03-12,1068.469971,1071.869995,1063.540039,1069.920044,1069.920044,594940000\n1998-03-13,1069.920044,1075.859985,1066.569946,1068.609985,1068.609985,597800000\n1998-03-16,1068.609985,1079.459961,1068.609985,1079.270020,1079.270020,548980000\n1998-03-17,1079.270020,1080.520020,1073.290039,1080.449951,1080.449951,680960000\n1998-03-18,1080.449951,1085.520020,1077.770020,1085.520020,1085.520020,632690000\n1998-03-19,1085.520020,1089.739990,1084.300049,1089.739990,1089.739990,598240000\n1998-03-20,1089.739990,1101.040039,1089.390015,1099.160034,1099.160034,717310000\n1998-03-23,1099.160034,1101.160034,1094.250000,1095.550049,1095.550049,631350000\n1998-03-24,1095.550049,1106.750000,1095.550049,1105.650024,1105.650024,605720000\n1998-03-25,1105.650024,1113.069946,1092.839966,1101.930054,1101.930054,676550000\n1998-03-26,1101.930054,1106.280029,1097.000000,1100.800049,1100.800049,606770000\n1998-03-27,1100.800049,1107.180054,1091.140015,1095.439941,1095.439941,582190000\n1998-03-30,1095.439941,1099.099976,1090.020020,1093.599976,1093.599976,497400000\n1998-03-31,1093.550049,1110.130005,1093.550049,1101.750000,1101.750000,674930000\n1998-04-01,1101.750000,1109.189941,1095.290039,1108.150024,1108.150024,677310000\n1998-04-02,1108.150024,1121.010010,1107.890015,1120.010010,1120.010010,674340000\n1998-04-03,1120.010010,1126.359985,1118.119995,1122.699951,1122.699951,653880000\n1998-04-06,1122.699951,1131.989990,1121.369995,1121.380005,1121.380005,625810000\n1998-04-07,1121.380005,1121.380005,1102.439941,1109.550049,1109.550049,670760000\n1998-04-08,1109.550049,1111.599976,1098.209961,1101.650024,1101.650024,616330000\n1998-04-09,1101.650024,1111.449951,1101.650024,1110.670044,1110.670044,548940000\n1998-04-13,1110.670044,1110.750000,1100.599976,1109.689941,1109.689941,564480000\n1998-04-14,1109.689941,1115.949951,1109.479980,1115.750000,1115.750000,613730000\n1998-04-15,1115.750000,1119.900024,1112.239990,1119.319946,1119.319946,685020000\n1998-04-16,1119.319946,1119.319946,1105.270020,1108.170044,1108.170044,699570000\n1998-04-17,1108.170044,1122.719971,1104.949951,1122.719971,1122.719971,672290000\n1998-04-20,1122.719971,1124.880005,1118.430054,1123.650024,1123.650024,595190000\n1998-04-21,1123.650024,1129.650024,1119.540039,1126.670044,1126.670044,675640000\n1998-04-22,1126.670044,1132.979980,1126.290039,1130.540039,1130.540039,696740000\n1998-04-23,1130.540039,1130.540039,1117.489990,1119.579956,1119.579956,653190000\n1998-04-24,1119.579956,1122.810059,1104.770020,1107.900024,1107.900024,633890000\n1998-04-27,1107.900024,1107.900024,1076.699951,1086.540039,1086.540039,685960000\n1998-04-28,1086.540039,1095.939941,1081.489990,1085.109985,1085.109985,678600000\n1998-04-29,1085.109985,1098.239990,1084.650024,1094.619995,1094.619995,638790000\n1998-04-30,1094.630005,1116.969971,1094.630005,1111.750000,1111.750000,695600000\n1998-05-01,1111.750000,1121.020020,1111.750000,1121.000000,1121.000000,581970000\n1998-05-04,1121.000000,1130.520020,1121.000000,1122.069946,1122.069946,551700000\n1998-05-05,1122.069946,1122.069946,1111.160034,1115.500000,1115.500000,583630000\n1998-05-06,1115.500000,1118.390015,1104.640015,1104.920044,1104.920044,606540000\n1998-05-07,1104.920044,1105.579956,1094.589966,1095.140015,1095.140015,582240000\n1998-05-08,1095.140015,1111.420044,1094.530029,1108.140015,1108.140015,567890000\n1998-05-11,1108.140015,1119.130005,1103.719971,1106.640015,1106.640015,560840000\n1998-05-12,1106.640015,1115.959961,1102.780029,1115.790039,1115.790039,604420000\n1998-05-13,1115.790039,1122.219971,1114.930054,1118.859985,1118.859985,600010000\n1998-05-14,1118.859985,1124.030029,1112.430054,1117.369995,1117.369995,578380000\n1998-05-15,1117.369995,1118.660034,1107.109985,1108.729980,1108.729980,621990000\n1998-05-18,1108.729980,1112.439941,1097.989990,1105.819946,1105.819946,519100000\n1998-05-19,1105.819946,1113.500000,1105.819946,1109.520020,1109.520020,566020000\n1998-05-20,1109.520020,1119.079956,1107.510010,1119.060059,1119.060059,587240000\n1998-05-21,1119.060059,1124.449951,1111.939941,1114.640015,1114.640015,551970000\n1998-05-22,1114.640015,1116.890015,1107.989990,1110.469971,1110.469971,444070000\n1998-05-26,1110.469971,1116.790039,1094.010010,1094.020020,1094.020020,541410000\n1998-05-27,1094.020020,1094.439941,1074.390015,1092.229980,1092.229980,682040000\n1998-05-28,1092.229980,1099.729980,1089.060059,1097.599976,1097.599976,588900000\n1998-05-29,1097.599976,1104.160034,1090.819946,1090.819946,1090.819946,556780000\n1998-06-01,1090.819946,1097.849976,1084.219971,1090.979980,1090.979980,537660000\n1998-06-02,1090.979980,1098.709961,1089.670044,1093.219971,1093.219971,590930000\n1998-06-03,1093.219971,1097.430054,1081.089966,1082.729980,1082.729980,584480000\n1998-06-04,1082.729980,1095.930054,1078.099976,1094.829956,1094.829956,577470000\n1998-06-05,1095.099976,1113.880005,1094.829956,1113.859985,1113.859985,558440000\n1998-06-08,1113.859985,1119.699951,1113.310059,1115.719971,1115.719971,543390000\n1998-06-09,1115.719971,1119.920044,1111.310059,1118.410034,1118.410034,563610000\n1998-06-10,1118.410034,1126.000000,1110.270020,1112.280029,1112.280029,609410000\n1998-06-11,1112.280029,1114.199951,1094.280029,1094.579956,1094.579956,627470000\n1998-06-12,1094.579956,1098.839966,1080.829956,1098.839966,1098.839966,633300000\n1998-06-15,1098.839966,1098.839966,1077.010010,1077.010010,1077.010010,595820000\n1998-06-16,1077.010010,1087.589966,1074.670044,1087.589966,1087.589966,664600000\n1998-06-17,1087.589966,1112.869995,1087.579956,1107.109985,1107.109985,744400000\n1998-06-18,1107.109985,1109.359985,1103.709961,1106.369995,1106.369995,590440000\n1998-06-19,1106.369995,1111.250000,1097.099976,1100.650024,1100.650024,715500000\n1998-06-22,1100.650024,1109.010010,1099.420044,1103.209961,1103.209961,531550000\n1998-06-23,1103.209961,1119.489990,1103.209961,1119.489990,1119.489990,657100000\n1998-06-24,1119.489990,1134.400024,1115.099976,1132.880005,1132.880005,714900000\n1998-06-25,1132.880005,1142.040039,1127.599976,1129.280029,1129.280029,669900000\n1998-06-26,1129.280029,1136.829956,1129.280029,1133.199951,1133.199951,520050000\n1998-06-29,1133.199951,1145.150024,1133.199951,1138.489990,1138.489990,564350000\n1998-06-30,1138.489990,1140.800049,1131.979980,1133.839966,1133.839966,757200000\n1998-07-01,1133.839966,1148.560059,1133.839966,1148.560059,1148.560059,701600000\n1998-07-02,1148.560059,1148.560059,1142.989990,1146.420044,1146.420044,510210000\n1998-07-06,1146.420044,1157.329956,1145.030029,1157.329956,1157.329956,514750000\n1998-07-07,1157.329956,1159.810059,1152.849976,1154.660034,1154.660034,624890000\n1998-07-08,1154.660034,1166.890015,1154.660034,1166.380005,1166.380005,607230000\n1998-07-09,1166.380005,1166.380005,1156.030029,1158.560059,1158.560059,663600000\n1998-07-10,1158.569946,1166.930054,1150.880005,1164.329956,1164.329956,576080000\n1998-07-13,1164.329956,1166.979980,1160.209961,1165.189941,1165.189941,574880000\n1998-07-14,1165.189941,1179.760010,1165.189941,1177.579956,1177.579956,700300000\n1998-07-15,1177.579956,1181.479980,1174.729980,1174.810059,1174.810059,723900000\n1998-07-16,1174.810059,1184.020020,1170.400024,1183.989990,1183.989990,677800000\n1998-07-17,1183.989990,1188.099976,1182.420044,1186.750000,1186.750000,618030000\n1998-07-20,1186.750000,1190.579956,1179.189941,1184.099976,1184.099976,560580000\n1998-07-21,1184.099976,1187.369995,1163.050049,1165.069946,1165.069946,659700000\n1998-07-22,1165.069946,1167.670044,1155.199951,1164.079956,1164.079956,739800000\n1998-07-23,1164.079956,1164.349976,1139.750000,1139.750000,1139.750000,741600000\n1998-07-24,1139.750000,1150.140015,1129.109985,1140.800049,1140.800049,698600000\n1998-07-27,1140.800049,1147.270020,1128.189941,1147.270020,1147.270020,619990000\n1998-07-28,1147.270020,1147.270020,1119.439941,1130.239990,1130.239990,703600000\n1998-07-29,1130.239990,1138.560059,1121.979980,1125.209961,1125.209961,644350000\n1998-07-30,1125.209961,1143.069946,1125.209961,1142.949951,1142.949951,687400000\n1998-07-31,1142.949951,1142.969971,1114.300049,1120.670044,1120.670044,645910000\n1998-08-03,1120.670044,1121.790039,1110.390015,1112.439941,1112.439941,620400000\n1998-08-04,1112.439941,1119.729980,1071.819946,1072.119995,1072.119995,852600000\n1998-08-05,1072.119995,1084.800049,1057.349976,1081.430054,1081.430054,851600000\n1998-08-06,1081.430054,1090.949951,1074.939941,1089.630005,1089.630005,768400000\n1998-08-07,1089.630005,1102.540039,1084.719971,1089.449951,1089.449951,759100000\n1998-08-10,1089.449951,1092.819946,1081.760010,1083.140015,1083.140015,579180000\n1998-08-11,1083.140015,1083.140015,1054.000000,1068.979980,1068.979980,774400000\n1998-08-12,1068.979980,1084.699951,1068.979980,1084.219971,1084.219971,711700000\n1998-08-13,1084.219971,1091.500000,1074.910034,1074.910034,1074.910034,660700000\n1998-08-14,1074.910034,1083.920044,1057.219971,1062.750000,1062.750000,644030000\n1998-08-17,1062.750000,1083.670044,1055.079956,1083.670044,1083.670044,584380000\n1998-08-18,1083.670044,1101.719971,1083.670044,1101.199951,1101.199951,690600000\n1998-08-19,1101.199951,1106.319946,1094.930054,1098.060059,1098.060059,633630000\n1998-08-20,1098.060059,1098.790039,1089.550049,1091.599976,1091.599976,621630000\n1998-08-21,1091.599976,1091.599976,1054.920044,1081.239990,1081.239990,725700000\n1998-08-24,1081.239990,1093.819946,1081.239990,1088.140015,1088.140015,558100000\n1998-08-25,1088.140015,1106.640015,1085.530029,1092.849976,1092.849976,664900000\n1998-08-26,1092.849976,1092.849976,1075.910034,1084.189941,1084.189941,674100000\n1998-08-27,1084.189941,1084.189941,1037.609985,1042.589966,1042.589966,938600000\n1998-08-28,1042.589966,1051.800049,1021.039978,1027.140015,1027.140015,840300000\n1998-08-31,1027.140015,1033.469971,957.280029,957.280029,957.280029,917500000\n1998-09-01,957.280029,1000.710022,939.979980,994.260010,994.260010,1216600000\n1998-09-02,994.260010,1013.190002,988.400024,990.479980,990.479980,894600000\n1998-09-03,990.469971,990.469971,969.320007,982.260010,982.260010,880500000\n1998-09-04,982.260010,991.409973,956.510010,973.890015,973.890015,780300000\n1998-09-08,973.890015,1023.460022,973.890015,1023.460022,1023.460022,814800000\n1998-09-09,1023.460022,1027.719971,1004.559998,1006.200012,1006.200012,704300000\n1998-09-10,1006.200012,1006.200012,968.640015,980.190002,980.190002,880300000\n1998-09-11,980.190002,1009.059998,969.710022,1009.059998,1009.059998,819100000\n1998-09-14,1009.059998,1038.380005,1009.059998,1029.719971,1029.719971,714400000\n1998-09-15,1029.719971,1037.900024,1021.419983,1037.680054,1037.680054,724600000\n1998-09-16,1037.680054,1046.069946,1029.310059,1045.479980,1045.479980,797500000\n1998-09-17,1045.479980,1045.479980,1016.049988,1018.869995,1018.869995,694500000\n1998-09-18,1018.869995,1022.010010,1011.859985,1020.090027,1020.090027,794700000\n1998-09-21,1020.090027,1026.020020,993.820007,1023.890015,1023.890015,609880000\n1998-09-22,1023.890015,1033.890015,1021.960022,1029.630005,1029.630005,694900000\n1998-09-23,1029.630005,1066.089966,1029.630005,1066.089966,1066.089966,899700000\n1998-09-24,1066.089966,1066.109985,1033.040039,1042.719971,1042.719971,805900000\n1998-09-25,1042.719971,1051.890015,1028.489990,1044.750000,1044.750000,736800000\n1998-09-28,1044.750000,1061.459961,1042.229980,1048.689941,1048.689941,690500000\n1998-09-29,1048.689941,1056.310059,1039.880005,1049.020020,1049.020020,760100000\n1998-09-30,1049.020020,1049.020020,1015.729980,1017.010010,1017.010010,800100000\n1998-10-01,1017.010010,1017.010010,981.289978,986.390015,986.390015,899700000\n1998-10-02,986.390015,1005.450012,971.690002,1002.599976,1002.599976,902900000\n1998-10-05,1002.599976,1002.599976,964.719971,988.559998,988.559998,817500000\n1998-10-06,988.559998,1008.770020,974.809998,984.590027,984.590027,845700000\n1998-10-07,984.590027,995.659973,957.150024,970.679993,970.679993,977000000\n1998-10-08,970.679993,970.679993,923.320007,959.440002,959.440002,1114600000\n1998-10-09,959.440002,984.419983,953.039978,984.390015,984.390015,878100000\n1998-10-12,984.390015,1010.710022,984.390015,997.710022,997.710022,691100000\n1998-10-13,997.710022,1000.780029,987.549988,994.799988,994.799988,733300000\n1998-10-14,994.799988,1014.419983,987.799988,1005.530029,1005.530029,791200000\n1998-10-15,1005.530029,1053.089966,1000.119995,1047.489990,1047.489990,937600000\n1998-10-16,1047.489990,1062.650024,1047.489990,1056.420044,1056.420044,1042200000\n1998-10-19,1056.420044,1065.209961,1054.229980,1062.390015,1062.390015,738600000\n1998-10-20,1062.390015,1084.060059,1060.609985,1063.930054,1063.930054,958200000\n1998-10-21,1063.930054,1073.609985,1058.079956,1069.920044,1069.920044,745100000\n1998-10-22,1069.920044,1080.430054,1061.469971,1078.479980,1078.479980,754900000\n1998-10-23,1078.479980,1078.479980,1067.430054,1070.670044,1070.670044,637640000\n1998-10-26,1070.670044,1081.229980,1068.170044,1072.319946,1072.319946,609910000\n1998-10-27,1072.319946,1087.079956,1063.060059,1065.339966,1065.339966,764500000\n1998-10-28,1065.339966,1072.790039,1059.650024,1068.089966,1068.089966,677500000\n1998-10-29,1068.089966,1086.109985,1065.949951,1085.930054,1085.930054,699400000\n1998-10-30,1085.930054,1103.780029,1085.930054,1098.670044,1098.670044,785000000\n1998-11-02,1098.670044,1114.439941,1098.670044,1111.599976,1111.599976,753800000\n1998-11-03,1111.599976,1115.020020,1106.420044,1110.839966,1110.839966,704300000\n1998-11-04,1110.839966,1127.180054,1110.589966,1118.670044,1118.670044,861100000\n1998-11-05,1118.670044,1133.880005,1109.550049,1133.849976,1133.849976,770200000\n1998-11-06,1133.849976,1141.300049,1131.180054,1141.010010,1141.010010,683100000\n1998-11-09,1141.010010,1141.010010,1123.170044,1130.199951,1130.199951,592990000\n1998-11-10,1130.199951,1135.369995,1122.800049,1128.260010,1128.260010,671300000\n1998-11-11,1128.260010,1136.250000,1117.400024,1120.969971,1120.969971,715700000\n1998-11-12,1120.969971,1126.569946,1115.550049,1117.689941,1117.689941,662300000\n1998-11-13,1117.689941,1126.339966,1116.760010,1125.719971,1125.719971,602270000\n1998-11-16,1125.719971,1138.719971,1125.719971,1135.869995,1135.869995,615580000\n1998-11-17,1135.869995,1151.709961,1129.670044,1139.319946,1139.319946,705200000\n1998-11-18,1139.319946,1144.520020,1133.069946,1144.479980,1144.479980,652510000\n1998-11-19,1144.479980,1155.099976,1144.420044,1152.609985,1152.609985,671000000\n1998-11-20,1152.609985,1163.550049,1152.609985,1163.550049,1163.550049,721200000\n1998-11-23,1163.550049,1188.209961,1163.550049,1188.209961,1188.209961,774100000\n1998-11-24,1188.209961,1191.300049,1181.810059,1182.989990,1182.989990,766200000\n1998-11-25,1182.989990,1187.160034,1179.369995,1186.869995,1186.869995,583580000\n1998-11-27,1186.869995,1192.969971,1186.829956,1192.329956,1192.329956,256950000\n1998-11-30,1192.329956,1192.719971,1163.630005,1163.630005,1163.630005,687900000\n1998-12-01,1163.630005,1175.890015,1150.310059,1175.280029,1175.280029,789200000\n1998-12-02,1175.280029,1175.280029,1157.760010,1171.250000,1171.250000,727400000\n1998-12-03,1171.250000,1176.989990,1149.609985,1150.140015,1150.140015,799100000\n1998-12-04,1150.140015,1176.739990,1150.140015,1176.739990,1176.739990,709700000\n1998-12-07,1176.739990,1188.959961,1176.709961,1187.699951,1187.699951,671200000\n1998-12-08,1187.699951,1193.530029,1172.780029,1181.380005,1181.380005,727700000\n1998-12-09,1181.380005,1185.219971,1175.890015,1183.489990,1183.489990,694200000\n1998-12-10,1183.489990,1183.770020,1163.750000,1165.020020,1165.020020,748600000\n1998-12-11,1165.020020,1167.890015,1153.189941,1166.459961,1166.459961,688900000\n1998-12-14,1166.459961,1166.459961,1136.890015,1141.199951,1141.199951,741800000\n1998-12-15,1141.199951,1162.829956,1141.199951,1162.829956,1162.829956,777900000\n1998-12-16,1162.829956,1166.290039,1154.689941,1161.939941,1161.939941,725500000\n1998-12-17,1161.939941,1180.030029,1161.939941,1179.979980,1179.979980,739400000\n1998-12-18,1179.979980,1188.890015,1178.270020,1188.030029,1188.030029,839600000\n1998-12-21,1188.030029,1210.880005,1188.030029,1202.839966,1202.839966,744800000\n1998-12-22,1202.839966,1209.219971,1192.719971,1203.569946,1203.569946,680500000\n1998-12-23,1203.569946,1229.890015,1203.569946,1228.540039,1228.540039,697500000\n1998-12-24,1228.540039,1229.719971,1224.849976,1226.270020,1226.270020,246980000\n1998-12-28,1226.270020,1231.520020,1221.170044,1225.489990,1225.489990,531560000\n1998-12-29,1225.489990,1241.859985,1220.780029,1241.810059,1241.810059,586490000\n1998-12-30,1241.810059,1244.930054,1231.199951,1231.930054,1231.930054,594220000\n1998-12-31,1231.930054,1237.180054,1224.959961,1229.229980,1229.229980,719200000\n1999-01-04,1229.229980,1248.810059,1219.099976,1228.099976,1228.099976,877000000\n1999-01-05,1228.099976,1246.109985,1228.099976,1244.780029,1244.780029,775000000\n1999-01-06,1244.780029,1272.500000,1244.780029,1272.339966,1272.339966,986900000\n1999-01-07,1272.339966,1272.339966,1257.680054,1269.729980,1269.729980,863000000\n1999-01-08,1269.729980,1278.239990,1261.819946,1275.089966,1275.089966,937800000\n1999-01-11,1275.089966,1276.219971,1253.339966,1263.880005,1263.880005,818000000\n1999-01-12,1263.880005,1264.449951,1238.290039,1239.510010,1239.510010,800200000\n1999-01-13,1239.510010,1247.750000,1205.459961,1234.400024,1234.400024,931500000\n1999-01-14,1234.400024,1236.810059,1209.540039,1212.189941,1212.189941,797200000\n1999-01-15,1212.189941,1243.260010,1212.189941,1243.260010,1243.260010,798100000\n1999-01-19,1243.260010,1253.270020,1234.910034,1252.000000,1252.000000,785500000\n1999-01-20,1252.000000,1274.069946,1251.540039,1256.619995,1256.619995,905700000\n1999-01-21,1256.619995,1256.939941,1232.189941,1235.160034,1235.160034,871800000\n1999-01-22,1235.160034,1236.410034,1217.969971,1225.189941,1225.189941,785900000\n1999-01-25,1225.189941,1233.979980,1219.459961,1233.979980,1233.979980,723900000\n1999-01-26,1233.979980,1253.250000,1233.979980,1252.310059,1252.310059,896400000\n1999-01-27,1252.310059,1262.609985,1242.819946,1243.170044,1243.170044,893800000\n1999-01-28,1243.170044,1266.400024,1243.170044,1265.369995,1265.369995,848800000\n1999-01-29,1265.369995,1280.369995,1255.180054,1279.640015,1279.640015,917000000\n1999-02-01,1279.640015,1283.750000,1271.310059,1273.000000,1273.000000,799400000\n1999-02-02,1273.000000,1273.489990,1247.560059,1261.989990,1261.989990,845500000\n1999-02-03,1261.989990,1276.040039,1255.270020,1272.069946,1272.069946,876500000\n1999-02-04,1272.069946,1272.229980,1248.359985,1248.489990,1248.489990,854400000\n1999-02-05,1248.489990,1251.859985,1232.280029,1239.400024,1239.400024,872000000\n1999-02-08,1239.400024,1246.930054,1231.979980,1243.770020,1243.770020,705400000\n1999-02-09,1243.770020,1243.969971,1215.630005,1216.140015,1216.140015,736000000\n1999-02-10,1216.140015,1226.780029,1211.890015,1223.550049,1223.550049,721400000\n1999-02-11,1223.550049,1254.050049,1223.189941,1254.040039,1254.040039,815800000\n1999-02-12,1254.040039,1254.040039,1225.530029,1230.130005,1230.130005,691500000\n1999-02-16,1230.130005,1252.170044,1230.130005,1241.869995,1241.869995,653760000\n1999-02-17,1241.869995,1249.310059,1220.920044,1224.030029,1224.030029,735100000\n1999-02-18,1224.030029,1239.130005,1220.699951,1237.280029,1237.280029,742400000\n1999-02-19,1237.280029,1247.910034,1232.030029,1239.219971,1239.219971,700000000\n1999-02-22,1239.219971,1272.219971,1239.219971,1272.140015,1272.140015,718500000\n1999-02-23,1272.140015,1280.380005,1263.359985,1271.180054,1271.180054,781100000\n1999-02-24,1271.180054,1283.839966,1251.939941,1253.410034,1253.410034,782000000\n1999-02-25,1253.410034,1253.410034,1225.010010,1245.020020,1245.020020,740500000\n1999-02-26,1245.020020,1246.729980,1226.239990,1238.329956,1238.329956,784600000\n1999-03-01,1238.329956,1238.699951,1221.880005,1236.160034,1236.160034,699500000\n1999-03-02,1236.160034,1248.310059,1221.869995,1225.500000,1225.500000,753600000\n1999-03-03,1225.500000,1231.630005,1216.030029,1227.699951,1227.699951,751700000\n1999-03-04,1227.699951,1247.739990,1227.699951,1246.640015,1246.640015,770900000\n1999-03-05,1246.640015,1275.729980,1246.640015,1275.469971,1275.469971,834900000\n1999-03-08,1275.469971,1282.739990,1271.579956,1282.729980,1282.729980,714600000\n1999-03-09,1282.729980,1293.739990,1275.109985,1279.839966,1279.839966,803700000\n1999-03-10,1279.839966,1287.020020,1275.160034,1286.839966,1286.839966,841900000\n1999-03-11,1286.839966,1306.430054,1286.839966,1297.680054,1297.680054,904800000\n1999-03-12,1297.680054,1304.420044,1289.170044,1294.589966,1294.589966,825800000\n1999-03-15,1294.589966,1307.469971,1291.030029,1307.260010,1307.260010,727200000\n1999-03-16,1307.260010,1311.109985,1302.290039,1306.380005,1306.380005,751900000\n1999-03-17,1306.380005,1306.550049,1292.630005,1297.819946,1297.819946,752300000\n1999-03-18,1297.819946,1317.619995,1294.750000,1316.550049,1316.550049,831000000\n1999-03-19,1316.550049,1323.819946,1298.920044,1299.290039,1299.290039,914700000\n1999-03-22,1299.290039,1303.839966,1294.260010,1297.010010,1297.010010,658200000\n1999-03-23,1297.010010,1297.010010,1257.459961,1262.140015,1262.140015,811300000\n1999-03-24,1262.140015,1269.020020,1256.430054,1268.589966,1268.589966,761900000\n1999-03-25,1268.589966,1289.989990,1268.589966,1289.989990,1289.989990,784200000\n1999-03-26,1289.989990,1289.989990,1277.250000,1282.800049,1282.800049,707200000\n1999-03-29,1282.800049,1311.760010,1282.800049,1310.170044,1310.170044,747900000\n1999-03-30,1310.170044,1310.170044,1295.469971,1300.750000,1300.750000,729000000\n1999-03-31,1300.750000,1313.599976,1285.869995,1286.369995,1286.369995,924300000\n1999-04-01,1286.369995,1294.540039,1282.560059,1293.719971,1293.719971,703000000\n1999-04-05,1293.719971,1321.119995,1293.719971,1321.119995,1321.119995,695800000\n1999-04-06,1321.119995,1326.760010,1311.069946,1317.890015,1317.890015,787500000\n1999-04-07,1317.890015,1329.579956,1312.589966,1326.890015,1326.890015,816400000\n1999-04-08,1326.890015,1344.079956,1321.599976,1343.979980,1343.979980,850500000\n1999-04-09,1343.979980,1351.219971,1335.239990,1348.349976,1348.349976,716100000\n1999-04-12,1348.349976,1358.689941,1333.479980,1358.630005,1358.630005,810800000\n1999-04-13,1358.640015,1362.380005,1344.030029,1349.819946,1349.819946,810900000\n1999-04-14,1349.819946,1357.239990,1326.410034,1328.439941,1328.439941,952000000\n1999-04-15,1328.439941,1332.410034,1308.380005,1322.849976,1322.849976,1089800000\n1999-04-16,1322.859985,1325.030029,1311.400024,1319.000000,1319.000000,1002300000\n1999-04-19,1319.000000,1340.099976,1284.479980,1289.479980,1289.479980,1214400000\n1999-04-20,1289.479980,1306.300049,1284.209961,1306.170044,1306.170044,985400000\n1999-04-21,1306.170044,1336.119995,1301.839966,1336.119995,1336.119995,920000000\n1999-04-22,1336.119995,1358.839966,1336.119995,1358.819946,1358.819946,927900000\n1999-04-23,1358.829956,1363.650024,1348.449951,1356.849976,1356.849976,744900000\n1999-04-26,1356.849976,1363.560059,1353.719971,1360.040039,1360.040039,712000000\n1999-04-27,1360.040039,1371.560059,1356.550049,1362.800049,1362.800049,891700000\n1999-04-28,1362.800049,1368.619995,1348.290039,1350.910034,1350.910034,951700000\n1999-04-29,1350.910034,1356.750000,1336.810059,1342.829956,1342.829956,1003600000\n1999-04-30,1342.829956,1351.829956,1314.579956,1335.180054,1335.180054,936500000\n1999-05-03,1335.180054,1354.630005,1329.010010,1354.630005,1354.630005,811400000\n1999-05-04,1354.630005,1354.640015,1330.640015,1332.000000,1332.000000,933100000\n1999-05-05,1332.000000,1347.319946,1317.439941,1347.310059,1347.310059,913500000\n1999-05-06,1347.310059,1348.359985,1322.560059,1332.050049,1332.050049,875400000\n1999-05-07,1332.050049,1345.989990,1332.050049,1345.000000,1345.000000,814900000\n1999-05-10,1345.000000,1352.010010,1334.000000,1340.300049,1340.300049,773300000\n1999-05-11,1340.300049,1360.000000,1340.300049,1355.609985,1355.609985,836100000\n1999-05-12,1355.609985,1367.359985,1333.099976,1364.000000,1364.000000,825500000\n1999-05-13,1364.000000,1375.979980,1364.000000,1367.560059,1367.560059,796900000\n1999-05-14,1367.560059,1367.560059,1332.630005,1337.800049,1337.800049,727800000\n1999-05-17,1337.800049,1339.949951,1321.189941,1339.489990,1339.489990,665500000\n1999-05-18,1339.489990,1345.439941,1323.459961,1333.319946,1333.319946,753400000\n1999-05-19,1333.319946,1344.229980,1327.050049,1344.229980,1344.229980,801100000\n1999-05-20,1344.229980,1350.489990,1338.829956,1338.829956,1338.829956,752200000\n1999-05-21,1338.829956,1340.880005,1326.189941,1330.290039,1330.290039,686600000\n1999-05-24,1330.290039,1333.020020,1303.530029,1306.650024,1306.650024,754700000\n1999-05-25,1306.650024,1317.520020,1284.380005,1284.400024,1284.400024,826700000\n1999-05-26,1284.400024,1304.849976,1278.430054,1304.760010,1304.760010,870800000\n1999-05-27,1304.760010,1304.760010,1277.310059,1281.410034,1281.410034,811400000\n1999-05-28,1281.410034,1304.000000,1281.410034,1301.839966,1301.839966,649960000\n1999-06-01,1301.839966,1301.839966,1281.439941,1294.260010,1294.260010,683800000\n1999-06-02,1294.260010,1297.099976,1277.469971,1294.810059,1294.810059,728000000\n1999-06-03,1294.810059,1304.150024,1294.199951,1299.540039,1299.540039,719600000\n1999-06-04,1299.540039,1327.750000,1299.540039,1327.750000,1327.750000,694500000\n1999-06-07,1327.750000,1336.420044,1325.890015,1334.520020,1334.520020,664300000\n1999-06-08,1334.520020,1334.520020,1312.829956,1317.329956,1317.329956,685900000\n1999-06-09,1317.329956,1326.010010,1314.729980,1318.640015,1318.640015,662000000\n1999-06-10,1318.640015,1318.640015,1293.280029,1302.819946,1302.819946,716500000\n1999-06-11,1302.819946,1311.969971,1287.880005,1293.640015,1293.640015,698200000\n1999-06-14,1293.640015,1301.989990,1292.199951,1294.000000,1294.000000,669400000\n1999-06-15,1294.000000,1310.760010,1294.000000,1301.160034,1301.160034,696600000\n1999-06-16,1301.160034,1332.829956,1301.160034,1330.410034,1330.410034,806800000\n1999-06-17,1330.410034,1343.540039,1322.750000,1339.900024,1339.900024,700300000\n1999-06-18,1339.900024,1344.479980,1333.520020,1342.839966,1342.839966,914500000\n1999-06-21,1342.839966,1349.060059,1337.630005,1349.000000,1349.000000,686600000\n1999-06-22,1349.000000,1351.119995,1335.520020,1335.880005,1335.880005,716500000\n1999-06-23,1335.869995,1335.880005,1322.550049,1333.060059,1333.060059,731800000\n1999-06-24,1333.060059,1333.060059,1308.469971,1315.780029,1315.780029,690400000\n1999-06-25,1315.780029,1329.130005,1312.640015,1315.310059,1315.310059,623460000\n1999-06-28,1315.310059,1333.680054,1315.310059,1331.349976,1331.349976,652910000\n1999-06-29,1331.349976,1351.510010,1328.400024,1351.449951,1351.449951,820100000\n1999-06-30,1351.449951,1372.930054,1338.780029,1372.709961,1372.709961,1117000000\n1999-07-01,1372.709961,1382.800049,1360.800049,1380.959961,1380.959961,843400000\n1999-07-02,1380.959961,1391.219971,1379.569946,1391.219971,1391.219971,613570000\n1999-07-06,1391.219971,1405.290039,1387.079956,1388.119995,1388.119995,722900000\n1999-07-07,1388.119995,1395.880005,1384.949951,1395.859985,1395.859985,791200000\n1999-07-08,1395.859985,1403.250000,1386.689941,1394.420044,1394.420044,830600000\n1999-07-09,1394.420044,1403.280029,1394.420044,1403.280029,1403.280029,701000000\n1999-07-12,1403.280029,1406.819946,1394.699951,1399.099976,1399.099976,685300000\n1999-07-13,1399.099976,1399.099976,1386.839966,1393.560059,1393.560059,736000000\n1999-07-14,1393.560059,1400.050049,1386.510010,1398.170044,1398.170044,756100000\n1999-07-15,1398.170044,1409.839966,1398.170044,1409.619995,1409.619995,818800000\n1999-07-16,1409.619995,1418.780029,1407.069946,1418.780029,1418.780029,714100000\n1999-07-19,1418.780029,1420.329956,1404.560059,1407.650024,1407.650024,642330000\n1999-07-20,1407.650024,1407.650024,1375.150024,1377.099976,1377.099976,754800000\n1999-07-21,1377.099976,1386.660034,1372.630005,1379.290039,1379.290039,785500000\n1999-07-22,1379.290039,1379.290039,1353.979980,1360.969971,1360.969971,771700000\n1999-07-23,1360.969971,1367.410034,1349.910034,1356.939941,1356.939941,630580000\n1999-07-26,1356.939941,1358.609985,1346.199951,1347.760010,1347.760010,613450000\n1999-07-27,1347.750000,1368.699951,1347.750000,1362.839966,1362.839966,723800000\n1999-07-28,1362.839966,1370.530029,1355.540039,1365.400024,1365.400024,690900000\n1999-07-29,1365.400024,1365.400024,1332.819946,1341.030029,1341.030029,770100000\n1999-07-30,1341.030029,1350.920044,1328.489990,1328.719971,1328.719971,736800000\n1999-08-02,1328.719971,1344.689941,1325.209961,1328.050049,1328.050049,649550000\n1999-08-03,1328.050049,1336.130005,1314.910034,1322.180054,1322.180054,739600000\n1999-08-04,1322.180054,1330.160034,1304.500000,1305.329956,1305.329956,789300000\n1999-08-05,1305.329956,1313.709961,1287.229980,1313.709961,1313.709961,859300000\n1999-08-06,1313.709961,1316.739990,1293.189941,1300.290039,1300.290039,698900000\n1999-08-09,1300.290039,1306.680054,1295.989990,1297.800049,1297.800049,684300000\n1999-08-10,1297.800049,1298.619995,1267.729980,1281.430054,1281.430054,836200000\n1999-08-11,1281.430054,1301.930054,1281.430054,1301.930054,1301.930054,792300000\n1999-08-12,1301.930054,1313.609985,1298.060059,1298.160034,1298.160034,745600000\n1999-08-13,1298.160034,1327.719971,1298.160034,1327.680054,1327.680054,691700000\n1999-08-16,1327.680054,1331.050049,1320.510010,1330.770020,1330.770020,583550000\n1999-08-17,1330.770020,1344.160034,1328.760010,1344.160034,1344.160034,691500000\n1999-08-18,1344.160034,1344.160034,1332.130005,1332.839966,1332.839966,682800000\n1999-08-19,1332.839966,1332.839966,1315.349976,1323.589966,1323.589966,684200000\n1999-08-20,1323.589966,1336.609985,1323.589966,1336.609985,1336.609985,661200000\n1999-08-23,1336.609985,1360.239990,1336.609985,1360.219971,1360.219971,682600000\n1999-08-24,1360.219971,1373.319946,1353.630005,1363.500000,1363.500000,732700000\n1999-08-25,1363.500000,1382.839966,1359.199951,1381.790039,1381.790039,864600000\n1999-08-26,1381.790039,1381.790039,1361.530029,1362.010010,1362.010010,719000000\n1999-08-27,1362.010010,1365.630005,1347.349976,1348.270020,1348.270020,570050000\n1999-08-30,1348.270020,1350.699951,1322.800049,1324.020020,1324.020020,597900000\n1999-08-31,1324.020020,1333.270020,1306.959961,1320.410034,1320.410034,861700000\n1999-09-01,1320.410034,1331.180054,1320.390015,1331.069946,1331.069946,708200000\n1999-09-02,1331.069946,1331.069946,1304.880005,1319.109985,1319.109985,687100000\n1999-09-03,1319.109985,1357.739990,1319.109985,1357.239990,1357.239990,663200000\n1999-09-07,1357.239990,1361.390015,1349.589966,1350.449951,1350.449951,715300000\n1999-09-08,1350.449951,1355.180054,1337.359985,1344.150024,1344.150024,791200000\n1999-09-09,1344.150024,1347.660034,1333.910034,1347.660034,1347.660034,773900000\n1999-09-10,1347.660034,1357.619995,1346.199951,1351.660034,1351.660034,808500000\n1999-09-13,1351.660034,1351.660034,1341.699951,1344.130005,1344.130005,657900000\n1999-09-14,1344.130005,1344.180054,1330.609985,1336.290039,1336.290039,734500000\n1999-09-15,1336.290039,1347.209961,1317.969971,1317.969971,1317.969971,787300000\n1999-09-16,1317.969971,1322.510010,1299.969971,1318.479980,1318.479980,739000000\n1999-09-17,1318.479980,1337.589966,1318.479980,1335.420044,1335.420044,861900000\n1999-09-20,1335.420044,1338.380005,1330.609985,1335.530029,1335.530029,568000000\n1999-09-21,1335.520020,1335.530029,1301.969971,1307.579956,1307.579956,817300000\n1999-09-22,1307.579956,1316.180054,1297.810059,1310.510010,1310.510010,822200000\n1999-09-23,1310.510010,1315.250000,1277.300049,1280.410034,1280.410034,890800000\n1999-09-24,1280.410034,1281.170044,1263.839966,1277.359985,1277.359985,872800000\n1999-09-27,1277.359985,1295.030029,1277.359985,1283.310059,1283.310059,780600000\n1999-09-28,1283.310059,1285.550049,1256.260010,1282.199951,1282.199951,885400000\n1999-09-29,1282.199951,1288.829956,1268.160034,1268.369995,1268.369995,856000000\n1999-09-30,1268.369995,1291.310059,1268.369995,1282.709961,1282.709961,1017600000\n1999-10-01,1282.709961,1283.170044,1265.780029,1282.810059,1282.810059,896200000\n1999-10-04,1282.810059,1304.599976,1282.810059,1304.599976,1304.599976,803300000\n1999-10-05,1304.599976,1316.410034,1286.439941,1301.349976,1301.349976,965700000\n1999-10-06,1301.349976,1325.459961,1301.349976,1325.400024,1325.400024,895200000\n1999-10-07,1325.400024,1328.050049,1314.130005,1317.640015,1317.640015,827800000\n1999-10-08,1317.640015,1336.609985,1311.880005,1336.020020,1336.020020,897300000\n1999-10-11,1336.020020,1339.229980,1332.959961,1335.209961,1335.209961,655900000\n1999-10-12,1335.209961,1335.209961,1311.800049,1313.040039,1313.040039,778300000\n1999-10-13,1313.040039,1313.040039,1282.800049,1285.550049,1285.550049,821500000\n1999-10-14,1285.550049,1289.630005,1267.619995,1283.420044,1283.420044,892300000\n1999-10-15,1283.420044,1283.420044,1245.390015,1247.410034,1247.410034,912600000\n1999-10-18,1247.410034,1254.130005,1233.699951,1254.130005,1254.130005,818700000\n1999-10-19,1254.130005,1279.319946,1254.130005,1261.319946,1261.319946,905700000\n1999-10-20,1261.319946,1289.439941,1261.319946,1289.430054,1289.430054,928800000\n1999-10-21,1289.430054,1289.430054,1265.609985,1283.609985,1283.609985,1012500000\n1999-10-22,1283.609985,1308.810059,1283.609985,1301.650024,1301.650024,959200000\n1999-10-25,1301.650024,1301.680054,1286.069946,1293.630005,1293.630005,777000000\n1999-10-26,1293.630005,1303.459961,1281.859985,1281.910034,1281.910034,878300000\n1999-10-27,1281.910034,1299.390015,1280.479980,1296.709961,1296.709961,950100000\n1999-10-28,1296.709961,1342.469971,1296.709961,1342.439941,1342.439941,1135100000\n1999-10-29,1342.439941,1373.170044,1342.439941,1362.930054,1362.930054,1120500000\n1999-11-01,1362.930054,1367.300049,1354.050049,1354.119995,1354.119995,861000000\n1999-11-02,1354.119995,1369.319946,1346.410034,1347.739990,1347.739990,904500000\n1999-11-03,1347.739990,1360.329956,1347.739990,1354.930054,1354.930054,914400000\n1999-11-04,1354.930054,1369.410034,1354.930054,1362.640015,1362.640015,981700000\n1999-11-05,1362.640015,1387.479980,1362.640015,1370.229980,1370.229980,1007300000\n1999-11-08,1370.229980,1380.780029,1365.869995,1377.010010,1377.010010,806800000\n1999-11-09,1377.010010,1383.810059,1361.449951,1365.280029,1365.280029,854300000\n1999-11-10,1365.280029,1379.180054,1359.979980,1373.459961,1373.459961,984700000\n1999-11-11,1373.459961,1382.119995,1372.189941,1381.459961,1381.459961,891300000\n1999-11-12,1381.459961,1396.119995,1368.540039,1396.060059,1396.060059,900200000\n1999-11-15,1396.060059,1398.579956,1392.280029,1394.390015,1394.390015,795700000\n1999-11-16,1394.390015,1420.359985,1394.390015,1420.069946,1420.069946,942200000\n1999-11-17,1420.069946,1423.439941,1410.689941,1410.709961,1410.709961,960000000\n1999-11-18,1410.709961,1425.310059,1410.709961,1424.939941,1424.939941,1022800000\n1999-11-19,1424.939941,1424.939941,1417.540039,1422.000000,1422.000000,893800000\n1999-11-22,1422.000000,1425.000000,1412.400024,1420.939941,1420.939941,873500000\n1999-11-23,1420.939941,1423.910034,1402.199951,1404.640015,1404.640015,926100000\n1999-11-24,1404.640015,1419.709961,1399.170044,1417.079956,1417.079956,734800000\n1999-11-26,1417.079956,1425.239990,1416.140015,1416.619995,1416.619995,312120000\n1999-11-29,1416.619995,1416.619995,1404.150024,1407.829956,1407.829956,866100000\n1999-11-30,1407.829956,1410.589966,1386.949951,1388.910034,1388.910034,951500000\n1999-12-01,1388.910034,1400.119995,1387.380005,1397.719971,1397.719971,884000000\n1999-12-02,1397.719971,1409.040039,1397.719971,1409.040039,1409.040039,900700000\n1999-12-03,1409.040039,1447.420044,1409.040039,1433.300049,1433.300049,1006400000\n1999-12-06,1433.300049,1434.150024,1418.250000,1423.329956,1423.329956,916800000\n1999-12-07,1423.329956,1426.810059,1409.170044,1409.170044,1409.170044,1085800000\n1999-12-08,1409.170044,1415.660034,1403.880005,1403.880005,1403.880005,957000000\n1999-12-09,1403.880005,1418.430054,1391.469971,1408.109985,1408.109985,1122100000\n1999-12-10,1408.109985,1421.579956,1405.650024,1417.040039,1417.040039,987200000\n1999-12-13,1417.040039,1421.579956,1410.099976,1415.219971,1415.219971,977600000\n1999-12-14,1415.219971,1418.300049,1401.589966,1403.170044,1403.170044,1027800000\n1999-12-15,1403.170044,1417.400024,1396.199951,1413.329956,1413.329956,1033900000\n1999-12-16,1413.319946,1423.109985,1408.349976,1418.780029,1418.780029,1070300000\n1999-12-17,1418.780029,1431.770020,1418.780029,1421.030029,1421.030029,1349800000\n1999-12-20,1421.030029,1429.160034,1411.099976,1418.089966,1418.089966,904600000\n1999-12-21,1418.089966,1436.469971,1414.800049,1433.430054,1433.430054,963500000\n1999-12-22,1433.430054,1440.020020,1429.130005,1436.130005,1436.130005,850000000\n1999-12-23,1436.130005,1461.439941,1436.130005,1458.339966,1458.339966,728600000\n1999-12-27,1458.339966,1463.189941,1450.829956,1457.099976,1457.099976,722600000\n1999-12-28,1457.089966,1462.680054,1452.780029,1457.660034,1457.660034,655400000\n1999-12-29,1457.660034,1467.469971,1457.660034,1463.459961,1463.459961,567860000\n1999-12-30,1463.459961,1473.099976,1462.599976,1464.469971,1464.469971,554680000\n1999-12-31,1464.469971,1472.420044,1458.189941,1469.250000,1469.250000,374050000\n2000-01-03,1469.250000,1478.000000,1438.359985,1455.219971,1455.219971,931800000\n2000-01-04,1455.219971,1455.219971,1397.430054,1399.420044,1399.420044,1009000000\n2000-01-05,1399.420044,1413.270020,1377.680054,1402.109985,1402.109985,1085500000\n2000-01-06,1402.109985,1411.900024,1392.099976,1403.449951,1403.449951,1092300000\n2000-01-07,1403.449951,1441.469971,1400.729980,1441.469971,1441.469971,1225200000\n2000-01-10,1441.469971,1464.359985,1441.469971,1457.599976,1457.599976,1064800000\n2000-01-11,1457.599976,1458.660034,1434.420044,1438.560059,1438.560059,1014000000\n2000-01-12,1438.560059,1442.599976,1427.079956,1432.250000,1432.250000,974600000\n2000-01-13,1432.250000,1454.199951,1432.250000,1449.680054,1449.680054,1030400000\n2000-01-14,1449.680054,1473.000000,1449.680054,1465.150024,1465.150024,1085900000\n2000-01-18,1465.150024,1465.150024,1451.300049,1455.140015,1455.140015,1056700000\n2000-01-19,1455.140015,1461.390015,1448.680054,1455.900024,1455.900024,1087800000\n2000-01-20,1455.900024,1465.709961,1438.540039,1445.569946,1445.569946,1100700000\n2000-01-21,1445.569946,1453.180054,1439.599976,1441.359985,1441.359985,1209800000\n2000-01-24,1441.359985,1454.089966,1395.420044,1401.530029,1401.530029,1115800000\n2000-01-25,1401.530029,1414.260010,1388.489990,1410.030029,1410.030029,1073700000\n2000-01-26,1410.030029,1412.729980,1400.160034,1404.089966,1404.089966,1117300000\n2000-01-27,1404.089966,1418.859985,1370.989990,1398.560059,1398.560059,1129500000\n2000-01-28,1398.560059,1398.560059,1356.199951,1360.160034,1360.160034,1095800000\n2000-01-31,1360.160034,1394.479980,1350.140015,1394.459961,1394.459961,993800000\n2000-02-01,1394.459961,1412.489990,1384.790039,1409.280029,1409.280029,981000000\n2000-02-02,1409.280029,1420.609985,1403.489990,1409.119995,1409.119995,1038600000\n2000-02-03,1409.119995,1425.780029,1398.520020,1424.969971,1424.969971,1146500000\n2000-02-04,1424.969971,1435.910034,1420.630005,1424.369995,1424.369995,1045100000\n2000-02-07,1424.369995,1427.150024,1413.329956,1424.239990,1424.239990,918100000\n2000-02-08,1424.239990,1441.829956,1424.239990,1441.719971,1441.719971,1047700000\n2000-02-09,1441.719971,1444.550049,1411.650024,1411.709961,1411.709961,1050500000\n2000-02-10,1411.699951,1422.099976,1406.430054,1416.829956,1416.829956,1058800000\n2000-02-11,1416.829956,1416.829956,1378.890015,1387.119995,1387.119995,1025700000\n2000-02-14,1387.119995,1394.930054,1380.530029,1389.939941,1389.939941,927300000\n2000-02-15,1389.939941,1407.719971,1376.250000,1402.050049,1402.050049,1092100000\n2000-02-16,1402.050049,1404.550049,1385.579956,1387.670044,1387.670044,1018800000\n2000-02-17,1387.670044,1399.880005,1380.069946,1388.260010,1388.260010,1034800000\n2000-02-18,1388.260010,1388.589966,1345.319946,1346.089966,1346.089966,1042300000\n2000-02-22,1346.089966,1358.109985,1331.880005,1352.170044,1352.170044,980000000\n2000-02-23,1352.170044,1370.109985,1342.439941,1360.689941,1360.689941,993700000\n2000-02-24,1360.689941,1364.800049,1329.880005,1353.430054,1353.430054,1215000000\n2000-02-25,1353.430054,1362.140015,1329.150024,1333.359985,1333.359985,1065200000\n2000-02-28,1333.359985,1360.819946,1325.069946,1348.050049,1348.050049,1026500000\n2000-02-29,1348.050049,1369.630005,1348.050049,1366.420044,1366.420044,1204300000\n2000-03-01,1366.420044,1383.459961,1366.420044,1379.189941,1379.189941,1274100000\n2000-03-02,1379.189941,1386.560059,1370.349976,1381.760010,1381.760010,1198600000\n2000-03-03,1381.760010,1410.880005,1381.760010,1409.170044,1409.170044,1150300000\n2000-03-06,1409.170044,1409.739990,1384.750000,1391.280029,1391.280029,1029000000\n2000-03-07,1391.280029,1399.209961,1349.989990,1355.619995,1355.619995,1314100000\n2000-03-08,1355.619995,1373.790039,1346.619995,1366.699951,1366.699951,1203000000\n2000-03-09,1366.699951,1401.819946,1357.880005,1401.689941,1401.689941,1123000000\n2000-03-10,1401.689941,1413.459961,1392.069946,1395.069946,1395.069946,1138800000\n2000-03-13,1395.069946,1398.390015,1364.839966,1383.619995,1383.619995,1016100000\n2000-03-14,1383.619995,1395.150024,1359.150024,1359.150024,1359.150024,1094000000\n2000-03-15,1359.150024,1397.989990,1356.989990,1392.140015,1392.140015,1302800000\n2000-03-16,1392.150024,1458.469971,1392.150024,1458.469971,1458.469971,1482300000\n2000-03-17,1458.469971,1477.329956,1453.319946,1464.469971,1464.469971,1295100000\n2000-03-20,1464.469971,1470.300049,1448.489990,1456.630005,1456.630005,920800000\n2000-03-21,1456.630005,1493.920044,1446.060059,1493.869995,1493.869995,1065900000\n2000-03-22,1493.869995,1505.079956,1487.329956,1500.640015,1500.640015,1075000000\n2000-03-23,1500.640015,1532.500000,1492.390015,1527.349976,1527.349976,1078300000\n2000-03-24,1527.349976,1552.869995,1516.829956,1527.459961,1527.459961,1052200000\n2000-03-27,1527.459961,1534.630005,1518.459961,1523.859985,1523.859985,901000000\n2000-03-28,1523.859985,1527.359985,1507.089966,1507.729980,1507.729980,959100000\n2000-03-29,1507.729980,1521.449951,1497.449951,1508.520020,1508.520020,1061900000\n2000-03-30,1508.520020,1517.380005,1474.630005,1487.920044,1487.920044,1193400000\n2000-03-31,1487.920044,1519.810059,1484.380005,1498.579956,1498.579956,1227400000\n2000-04-03,1498.579956,1507.189941,1486.959961,1505.969971,1505.969971,1021700000\n2000-04-04,1505.979980,1526.449951,1416.410034,1494.729980,1494.729980,1515460000\n2000-04-05,1494.729980,1506.550049,1478.050049,1487.369995,1487.369995,1110300000\n2000-04-06,1487.369995,1511.760010,1487.369995,1501.339966,1501.339966,1008000000\n2000-04-07,1501.339966,1518.680054,1501.339966,1516.349976,1516.349976,891600000\n2000-04-10,1516.349976,1527.189941,1503.349976,1504.459961,1504.459961,853700000\n2000-04-11,1504.459961,1512.800049,1486.780029,1500.589966,1500.589966,971400000\n2000-04-12,1500.589966,1509.079956,1466.150024,1467.170044,1467.170044,1175900000\n2000-04-13,1467.170044,1477.520020,1439.339966,1440.510010,1440.510010,1032000000\n2000-04-14,1440.510010,1440.510010,1339.400024,1356.560059,1356.560059,1279700000\n2000-04-17,1356.560059,1401.530029,1346.500000,1401.439941,1401.439941,1204700000\n2000-04-18,1401.439941,1441.609985,1397.810059,1441.609985,1441.609985,1109400000\n2000-04-19,1441.609985,1447.689941,1424.260010,1427.469971,1427.469971,1001400000\n2000-04-20,1427.469971,1435.489990,1422.079956,1434.540039,1434.540039,896200000\n2000-04-24,1434.540039,1434.540039,1407.130005,1429.859985,1429.859985,868700000\n2000-04-25,1429.859985,1477.670044,1429.859985,1477.439941,1477.439941,1071100000\n2000-04-26,1477.439941,1482.939941,1456.979980,1460.989990,1460.989990,999600000\n2000-04-27,1460.989990,1469.209961,1434.810059,1464.920044,1464.920044,1111000000\n2000-04-28,1464.920044,1473.619995,1448.150024,1452.430054,1452.430054,984600000\n2000-05-01,1452.430054,1481.510010,1452.430054,1468.250000,1468.250000,966300000\n2000-05-02,1468.250000,1468.250000,1445.219971,1446.290039,1446.290039,1011500000\n2000-05-03,1446.290039,1446.290039,1398.359985,1415.099976,1415.099976,991600000\n2000-05-04,1415.099976,1420.989990,1404.939941,1409.569946,1409.569946,925800000\n2000-05-05,1409.569946,1436.030029,1405.079956,1432.630005,1432.630005,805500000\n2000-05-08,1432.630005,1432.630005,1417.050049,1424.170044,1424.170044,787600000\n2000-05-09,1424.170044,1430.280029,1401.849976,1412.140015,1412.140015,896600000\n2000-05-10,1412.140015,1412.140015,1375.140015,1383.050049,1383.050049,1006400000\n2000-05-11,1383.050049,1410.260010,1383.050049,1407.810059,1407.810059,953600000\n2000-05-12,1407.810059,1430.130005,1407.810059,1420.959961,1420.959961,858200000\n2000-05-15,1420.959961,1452.390015,1416.540039,1452.359985,1452.359985,854600000\n2000-05-16,1452.359985,1470.400024,1450.760010,1466.040039,1466.040039,955500000\n2000-05-17,1466.040039,1466.040039,1441.670044,1447.800049,1447.800049,820500000\n2000-05-18,1447.800049,1458.040039,1436.589966,1437.209961,1437.209961,807900000\n2000-05-19,1437.209961,1437.209961,1401.739990,1406.949951,1406.949951,853700000\n2000-05-22,1406.949951,1410.550049,1368.729980,1400.719971,1400.719971,869000000\n2000-05-23,1400.719971,1403.770020,1373.430054,1373.859985,1373.859985,869900000\n2000-05-24,1373.859985,1401.750000,1361.089966,1399.050049,1399.050049,1152300000\n2000-05-25,1399.050049,1411.650024,1373.930054,1381.520020,1381.520020,984500000\n2000-05-26,1381.520020,1391.420044,1369.750000,1378.020020,1378.020020,722600000\n2000-05-30,1378.020020,1422.449951,1378.020020,1422.449951,1422.449951,844200000\n2000-05-31,1422.439941,1434.489990,1415.500000,1420.599976,1420.599976,960500000\n2000-06-01,1420.599976,1448.810059,1420.599976,1448.810059,1448.810059,960100000\n2000-06-02,1448.810059,1483.229980,1448.810059,1477.260010,1477.260010,1162400000\n2000-06-05,1477.260010,1477.280029,1464.680054,1467.630005,1467.630005,838600000\n2000-06-06,1467.630005,1471.359985,1454.739990,1457.839966,1457.839966,950100000\n2000-06-07,1457.839966,1474.640015,1455.060059,1471.359985,1471.359985,854600000\n2000-06-08,1471.359985,1475.650024,1456.489990,1461.670044,1461.670044,854300000\n2000-06-09,1461.670044,1472.670044,1454.959961,1456.949951,1456.949951,786000000\n2000-06-12,1456.949951,1462.930054,1445.989990,1446.000000,1446.000000,774100000\n2000-06-13,1446.000000,1470.420044,1442.380005,1469.439941,1469.439941,935900000\n2000-06-14,1469.439941,1483.619995,1467.709961,1470.540039,1470.540039,929700000\n2000-06-15,1470.540039,1482.040039,1464.619995,1478.729980,1478.729980,1011400000\n2000-06-16,1478.729980,1480.770020,1460.420044,1464.459961,1464.459961,1250800000\n2000-06-19,1464.459961,1488.930054,1459.050049,1486.000000,1486.000000,921700000\n2000-06-20,1486.000000,1487.319946,1470.180054,1475.949951,1475.949951,1031500000\n2000-06-21,1475.949951,1482.189941,1468.000000,1479.130005,1479.130005,1009600000\n2000-06-22,1479.130005,1479.130005,1448.030029,1452.180054,1452.180054,1022700000\n2000-06-23,1452.180054,1459.939941,1438.310059,1441.479980,1441.479980,847600000\n2000-06-26,1441.479980,1459.660034,1441.479980,1455.310059,1455.310059,889000000\n2000-06-27,1455.310059,1463.349976,1450.550049,1450.550049,1450.550049,1042500000\n2000-06-28,1450.550049,1467.630005,1450.550049,1454.819946,1454.819946,1095100000\n2000-06-29,1454.819946,1455.140015,1434.630005,1442.390015,1442.390015,1110900000\n2000-06-30,1442.390015,1454.680054,1438.709961,1454.599976,1454.599976,1459700000\n2000-07-03,1454.599976,1469.579956,1450.849976,1469.540039,1469.540039,451900000\n2000-07-05,1469.540039,1469.540039,1442.449951,1446.229980,1446.229980,1019300000\n2000-07-06,1446.229980,1461.650024,1439.560059,1456.670044,1456.670044,947300000\n2000-07-07,1456.670044,1484.119995,1456.670044,1478.900024,1478.900024,931700000\n2000-07-10,1478.900024,1486.560059,1474.760010,1475.619995,1475.619995,838700000\n2000-07-11,1475.619995,1488.770020,1470.479980,1480.880005,1480.880005,980500000\n2000-07-12,1480.880005,1497.689941,1480.880005,1492.920044,1492.920044,1001200000\n2000-07-13,1492.920044,1501.390015,1489.650024,1495.839966,1495.839966,1026800000\n2000-07-14,1495.839966,1509.989990,1494.560059,1509.979980,1509.979980,960600000\n2000-07-17,1509.979980,1517.319946,1505.260010,1510.489990,1510.489990,906000000\n2000-07-18,1510.489990,1510.489990,1491.349976,1493.739990,1493.739990,908300000\n2000-07-19,1493.739990,1495.630005,1479.920044,1481.959961,1481.959961,909400000\n2000-07-20,1481.959961,1501.920044,1481.959961,1495.569946,1495.569946,1064600000\n2000-07-21,1495.569946,1495.569946,1477.910034,1480.189941,1480.189941,968300000\n2000-07-24,1480.189941,1485.880005,1463.800049,1464.290039,1464.290039,880300000\n2000-07-25,1464.290039,1476.229980,1464.290039,1474.469971,1474.469971,969400000\n2000-07-26,1474.469971,1474.469971,1452.420044,1452.420044,1452.420044,1235800000\n2000-07-27,1452.420044,1464.910034,1445.329956,1449.619995,1449.619995,1156400000\n2000-07-28,1449.619995,1456.680054,1413.890015,1419.890015,1419.890015,980000000\n2000-07-31,1419.890015,1437.650024,1418.709961,1430.829956,1430.829956,952600000\n2000-08-01,1430.829956,1443.540039,1428.959961,1438.099976,1438.099976,938700000\n2000-08-02,1438.099976,1451.589966,1433.489990,1438.699951,1438.699951,994500000\n2000-08-03,1438.699951,1454.189941,1425.430054,1452.560059,1452.560059,1095600000\n2000-08-04,1452.560059,1462.930054,1451.310059,1462.930054,1462.930054,956000000\n2000-08-07,1462.930054,1480.800049,1460.719971,1479.319946,1479.319946,854800000\n2000-08-08,1479.319946,1484.520020,1472.609985,1482.800049,1482.800049,992200000\n2000-08-09,1482.800049,1490.329956,1471.160034,1472.869995,1472.869995,1054000000\n2000-08-10,1472.869995,1475.150024,1459.890015,1460.250000,1460.250000,940800000\n2000-08-11,1460.250000,1475.719971,1453.060059,1471.839966,1471.839966,835500000\n2000-08-14,1471.839966,1491.640015,1468.560059,1491.560059,1491.560059,783800000\n2000-08-15,1491.560059,1493.119995,1482.739990,1484.430054,1484.430054,895900000\n2000-08-16,1484.430054,1496.089966,1475.739990,1479.849976,1479.849976,929800000\n2000-08-17,1479.849976,1499.319946,1479.849976,1496.069946,1496.069946,922400000\n2000-08-18,1496.069946,1499.469971,1488.989990,1491.719971,1491.719971,821400000\n2000-08-21,1491.719971,1502.839966,1491.130005,1499.479980,1499.479980,731600000\n2000-08-22,1499.479980,1508.449951,1497.420044,1498.130005,1498.130005,818800000\n2000-08-23,1498.130005,1507.199951,1489.520020,1505.969971,1505.969971,871000000\n2000-08-24,1505.969971,1511.160034,1501.250000,1508.310059,1508.310059,837100000\n2000-08-25,1508.310059,1513.469971,1505.089966,1506.449951,1506.449951,685600000\n2000-08-28,1506.449951,1523.949951,1506.449951,1514.089966,1514.089966,733600000\n2000-08-29,1514.089966,1514.810059,1505.459961,1509.839966,1509.839966,795600000\n2000-08-30,1509.839966,1510.489990,1500.089966,1502.589966,1502.589966,818400000\n2000-08-31,1502.589966,1525.209961,1502.589966,1517.680054,1517.680054,1056600000\n2000-09-01,1517.680054,1530.089966,1515.530029,1520.770020,1520.770020,767700000\n2000-09-05,1520.770020,1520.770020,1504.209961,1507.079956,1507.079956,838500000\n2000-09-06,1507.079956,1512.609985,1492.119995,1492.250000,1492.250000,995100000\n2000-09-07,1492.250000,1505.339966,1492.250000,1502.510010,1502.510010,985500000\n2000-09-08,1502.510010,1502.510010,1489.880005,1494.500000,1494.500000,961000000\n2000-09-11,1494.500000,1506.760010,1483.010010,1489.260010,1489.260010,899300000\n2000-09-12,1489.260010,1496.930054,1479.670044,1481.989990,1481.989990,991200000\n2000-09-13,1481.989990,1487.449951,1473.609985,1484.910034,1484.910034,1068300000\n2000-09-14,1484.910034,1494.160034,1476.729980,1480.869995,1480.869995,1014000000\n2000-09-15,1480.869995,1480.959961,1460.219971,1465.810059,1465.810059,1268400000\n2000-09-18,1465.810059,1467.770020,1441.920044,1444.510010,1444.510010,962500000\n2000-09-19,1444.510010,1461.160034,1444.510010,1459.900024,1459.900024,1024900000\n2000-09-20,1459.900024,1460.489990,1430.949951,1451.339966,1451.339966,1104000000\n2000-09-21,1451.339966,1452.770020,1436.300049,1449.050049,1449.050049,1105400000\n2000-09-22,1449.050049,1449.050049,1421.880005,1448.719971,1448.719971,1185500000\n2000-09-25,1448.719971,1457.420044,1435.930054,1439.030029,1439.030029,982400000\n2000-09-26,1439.030029,1448.040039,1425.250000,1427.209961,1427.209961,1106600000\n2000-09-27,1427.209961,1437.219971,1419.439941,1426.569946,1426.569946,1174700000\n2000-09-28,1426.569946,1461.689941,1425.780029,1458.290039,1458.290039,1206200000\n2000-09-29,1458.290039,1458.290039,1436.290039,1436.510010,1436.510010,1197100000\n2000-10-02,1436.520020,1445.599976,1429.829956,1436.229980,1436.229980,1051200000\n2000-10-03,1436.229980,1454.819946,1425.280029,1426.459961,1426.459961,1098100000\n2000-10-04,1426.459961,1439.989990,1416.310059,1434.319946,1434.319946,1167400000\n2000-10-05,1434.319946,1444.170044,1431.800049,1436.280029,1436.280029,1176100000\n2000-10-06,1436.280029,1443.300049,1397.060059,1408.989990,1408.989990,1150100000\n2000-10-09,1408.989990,1409.689941,1392.479980,1402.030029,1402.030029,716600000\n2000-10-10,1402.030029,1408.829956,1383.849976,1387.020020,1387.020020,1044000000\n2000-10-11,1387.020020,1387.020020,1349.670044,1364.589966,1364.589966,1387500000\n2000-10-12,1364.589966,1374.930054,1328.060059,1329.780029,1329.780029,1388600000\n2000-10-13,1329.780029,1374.170044,1327.079956,1374.170044,1374.170044,1223900000\n2000-10-16,1374.170044,1379.479980,1365.060059,1374.619995,1374.619995,1005400000\n2000-10-17,1374.619995,1380.989990,1342.339966,1349.969971,1349.969971,1161500000\n2000-10-18,1349.969971,1356.650024,1305.790039,1342.130005,1342.130005,1441700000\n2000-10-19,1342.130005,1389.930054,1342.130005,1388.760010,1388.760010,1297900000\n2000-10-20,1388.760010,1408.469971,1382.189941,1396.930054,1396.930054,1177400000\n2000-10-23,1396.930054,1406.959961,1387.750000,1395.780029,1395.780029,1046800000\n2000-10-24,1395.780029,1415.640015,1388.130005,1398.130005,1398.130005,1158600000\n2000-10-25,1398.130005,1398.130005,1362.209961,1364.900024,1364.900024,1315600000\n2000-10-26,1364.900024,1372.719971,1337.810059,1364.439941,1364.439941,1303800000\n2000-10-27,1364.439941,1384.569946,1364.130005,1379.579956,1379.579956,1086300000\n2000-10-30,1379.579956,1406.359985,1376.859985,1398.660034,1398.660034,1186500000\n2000-10-31,1398.660034,1432.219971,1398.660034,1429.400024,1429.400024,1366400000\n2000-11-01,1429.400024,1429.599976,1410.449951,1421.219971,1421.219971,1206800000\n2000-11-02,1421.219971,1433.400024,1421.219971,1428.319946,1428.319946,1167700000\n2000-11-03,1428.319946,1433.209961,1420.920044,1426.689941,1426.689941,997700000\n2000-11-06,1428.760010,1438.459961,1427.719971,1432.189941,1432.189941,930900000\n2000-11-07,1432.189941,1436.219971,1423.260010,1431.869995,1431.869995,880900000\n2000-11-08,1431.869995,1437.280029,1408.780029,1409.280029,1409.280029,909300000\n2000-11-09,1409.280029,1409.280029,1369.680054,1400.140015,1400.140015,1111000000\n2000-11-10,1400.140015,1400.140015,1365.969971,1365.979980,1365.979980,962500000\n2000-11-13,1365.979980,1365.979980,1328.619995,1351.260010,1351.260010,1129300000\n2000-11-14,1351.260010,1390.060059,1351.260010,1382.949951,1382.949951,1118800000\n2000-11-15,1382.949951,1395.959961,1374.750000,1389.810059,1389.810059,1066800000\n2000-11-16,1389.810059,1394.760010,1370.390015,1372.319946,1372.319946,956300000\n2000-11-17,1372.319946,1384.849976,1355.550049,1367.719971,1367.719971,1070400000\n2000-11-20,1367.719971,1367.719971,1341.670044,1342.619995,1342.619995,955800000\n2000-11-21,1342.619995,1355.869995,1333.619995,1347.349976,1347.349976,1137100000\n2000-11-22,1347.349976,1347.349976,1321.890015,1322.359985,1322.359985,963200000\n2000-11-24,1322.359985,1343.829956,1322.359985,1341.770020,1341.770020,404870000\n2000-11-27,1341.770020,1362.500000,1341.770020,1348.969971,1348.969971,946100000\n2000-11-28,1348.969971,1358.810059,1334.969971,1336.089966,1336.089966,1028200000\n2000-11-29,1336.089966,1352.380005,1329.280029,1341.930054,1341.930054,402100000\n2000-11-30,1341.910034,1341.910034,1294.900024,1314.949951,1314.949951,1186530000\n2000-12-01,1314.949951,1334.670044,1307.020020,1315.229980,1315.229980,1195200000\n2000-12-04,1315.180054,1332.060059,1310.229980,1324.969971,1324.969971,1103000000\n2000-12-05,1324.969971,1376.560059,1324.969971,1376.540039,1376.540039,900300000\n2000-12-06,1376.540039,1376.540039,1346.150024,1351.459961,1351.459961,1399300000\n2000-12-07,1351.459961,1353.500000,1339.260010,1343.550049,1343.550049,1128000000\n2000-12-08,1343.550049,1380.329956,1343.550049,1369.890015,1369.890015,1358300000\n2000-12-11,1369.890015,1389.050049,1364.140015,1380.199951,1380.199951,1202400000\n2000-12-12,1380.199951,1380.270020,1370.270020,1371.180054,1371.180054,1083400000\n2000-12-13,1371.180054,1385.819946,1358.479980,1359.989990,1359.989990,1195100000\n2000-12-14,1359.989990,1359.989990,1340.479980,1340.930054,1340.930054,1061300000\n2000-12-15,1340.930054,1340.930054,1305.380005,1312.150024,1312.150024,1561100000\n2000-12-18,1312.150024,1332.319946,1312.150024,1322.739990,1322.739990,1189900000\n2000-12-19,1322.959961,1346.439941,1305.199951,1305.599976,1305.599976,1324900000\n2000-12-20,1305.599976,1305.599976,1261.160034,1264.739990,1264.739990,1421600000\n2000-12-21,1264.739990,1285.310059,1254.069946,1274.859985,1274.859985,1449900000\n2000-12-22,1274.859985,1305.969971,1274.859985,1305.949951,1305.949951,1087100000\n2000-12-26,1305.969971,1315.939941,1301.640015,1315.189941,1315.189941,806500000\n2000-12-27,1315.189941,1332.030029,1310.959961,1328.920044,1328.920044,1092700000\n2000-12-28,1328.920044,1335.930054,1325.780029,1334.219971,1334.219971,1015300000\n2000-12-29,1334.219971,1340.099976,1317.510010,1320.280029,1320.280029,1035500000\n2001-01-02,1320.280029,1320.280029,1276.050049,1283.270020,1283.270020,1129400000\n2001-01-03,1283.270020,1347.760010,1274.619995,1347.560059,1347.560059,1880700000\n2001-01-04,1347.560059,1350.239990,1329.140015,1333.339966,1333.339966,2131000000\n2001-01-05,1333.339966,1334.770020,1294.949951,1298.349976,1298.349976,1430800000\n2001-01-08,1298.349976,1298.349976,1276.290039,1295.859985,1295.859985,1115500000\n2001-01-09,1295.859985,1311.719971,1295.140015,1300.800049,1300.800049,1191300000\n2001-01-10,1300.800049,1313.760010,1287.280029,1313.270020,1313.270020,1296500000\n2001-01-11,1313.270020,1332.189941,1309.719971,1326.819946,1326.819946,1411200000\n2001-01-12,1326.819946,1333.209961,1311.589966,1318.550049,1318.550049,1276000000\n2001-01-16,1318.319946,1327.810059,1313.329956,1326.650024,1326.650024,1205700000\n2001-01-17,1326.650024,1346.920044,1325.410034,1329.469971,1329.469971,1349100000\n2001-01-18,1329.890015,1352.709961,1327.410034,1347.969971,1347.969971,1445000000\n2001-01-19,1347.969971,1354.550049,1336.739990,1342.540039,1342.540039,1407800000\n2001-01-22,1342.540039,1353.619995,1333.839966,1342.900024,1342.900024,1164000000\n2001-01-23,1342.900024,1362.900024,1339.630005,1360.400024,1360.400024,1232600000\n2001-01-24,1360.400024,1369.750000,1357.280029,1364.300049,1364.300049,1309000000\n2001-01-25,1364.300049,1367.349976,1354.630005,1357.510010,1357.510010,1258000000\n2001-01-26,1357.510010,1357.510010,1342.750000,1354.949951,1354.949951,1098000000\n2001-01-29,1354.920044,1365.540039,1350.359985,1364.170044,1364.170044,1053100000\n2001-01-30,1364.170044,1375.680054,1356.199951,1373.729980,1373.729980,1149800000\n2001-01-31,1373.729980,1383.369995,1364.660034,1366.010010,1366.010010,1295300000\n2001-02-01,1366.010010,1373.500000,1359.339966,1373.469971,1373.469971,1118800000\n2001-02-02,1373.469971,1376.380005,1348.719971,1349.469971,1349.469971,1048400000\n2001-02-05,1349.469971,1354.560059,1344.479980,1354.310059,1354.310059,1013000000\n2001-02-06,1354.310059,1363.550049,1350.040039,1352.260010,1352.260010,1059600000\n2001-02-07,1352.260010,1352.260010,1334.260010,1340.890015,1340.890015,1158300000\n2001-02-08,1341.099976,1350.319946,1332.420044,1332.530029,1332.530029,1107200000\n2001-02-09,1332.530029,1332.530029,1309.979980,1314.760010,1314.760010,1075500000\n2001-02-12,1314.760010,1330.959961,1313.640015,1330.310059,1330.310059,1039100000\n2001-02-13,1330.310059,1336.619995,1317.510010,1318.800049,1318.800049,1075200000\n2001-02-14,1318.800049,1320.729980,1304.719971,1315.920044,1315.920044,1150300000\n2001-02-15,1315.920044,1331.290039,1315.920044,1326.609985,1326.609985,1153700000\n2001-02-16,1326.609985,1326.609985,1293.180054,1301.530029,1301.530029,1257200000\n2001-02-20,1301.530029,1307.160034,1278.439941,1278.939941,1278.939941,1112200000\n2001-02-21,1278.939941,1282.969971,1253.160034,1255.270020,1255.270020,1208500000\n2001-02-22,1255.270020,1259.939941,1228.329956,1252.819946,1252.819946,1365900000\n2001-02-23,1252.819946,1252.819946,1215.439941,1245.859985,1245.859985,1231300000\n2001-02-26,1245.859985,1267.689941,1241.709961,1267.650024,1267.650024,1130800000\n2001-02-27,1267.650024,1272.760010,1252.260010,1257.939941,1257.939941,1114100000\n2001-02-28,1257.939941,1263.469971,1229.650024,1239.939941,1239.939941,1225300000\n2001-03-01,1239.939941,1241.359985,1214.500000,1241.229980,1241.229980,1294900000\n2001-03-02,1241.229980,1251.010010,1219.739990,1234.180054,1234.180054,1294000000\n2001-03-05,1234.180054,1242.550049,1234.040039,1241.410034,1241.410034,929200000\n2001-03-06,1241.410034,1267.420044,1241.410034,1253.800049,1253.800049,1091800000\n2001-03-07,1253.800049,1263.859985,1253.800049,1261.890015,1261.890015,1132200000\n2001-03-08,1261.890015,1266.500000,1257.599976,1264.739990,1264.739990,1114100000\n2001-03-09,1264.739990,1264.739990,1228.420044,1233.420044,1233.420044,1085900000\n2001-03-12,1233.420044,1233.420044,1176.780029,1180.160034,1180.160034,1229000000\n2001-03-13,1180.160034,1197.829956,1171.500000,1197.660034,1197.660034,1360900000\n2001-03-14,1197.660034,1197.660034,1155.349976,1166.709961,1166.709961,1397400000\n2001-03-15,1166.709961,1182.040039,1166.709961,1173.560059,1173.560059,1259500000\n2001-03-16,1173.560059,1173.560059,1148.640015,1150.530029,1150.530029,1543560000\n2001-03-19,1150.530029,1173.500000,1147.180054,1170.810059,1170.810059,1126200000\n2001-03-20,1170.810059,1180.560059,1142.189941,1142.619995,1142.619995,1235900000\n2001-03-21,1142.619995,1149.390015,1118.739990,1122.140015,1122.140015,1346300000\n2001-03-22,1122.140015,1124.270020,1081.189941,1117.579956,1117.579956,1723950000\n2001-03-23,1117.579956,1141.829956,1117.579956,1139.829956,1139.829956,1364900000\n2001-03-26,1139.829956,1160.020020,1139.829956,1152.689941,1152.689941,1114000000\n2001-03-27,1152.689941,1183.349976,1150.959961,1182.170044,1182.170044,1314200000\n2001-03-28,1182.170044,1182.170044,1147.829956,1153.290039,1153.290039,1333400000\n2001-03-29,1153.290039,1161.689941,1136.260010,1147.949951,1147.949951,1234500000\n2001-03-30,1147.949951,1162.800049,1143.829956,1160.329956,1160.329956,1280800000\n2001-04-02,1160.329956,1169.510010,1137.510010,1145.869995,1145.869995,1254900000\n2001-04-03,1145.869995,1145.869995,1100.189941,1106.459961,1106.459961,1386100000\n2001-04-04,1106.459961,1117.500000,1091.989990,1103.250000,1103.250000,1425590000\n2001-04-05,1103.250000,1151.469971,1103.250000,1151.439941,1151.439941,1368000000\n2001-04-06,1151.439941,1151.439941,1119.290039,1128.430054,1128.430054,1266800000\n2001-04-09,1128.430054,1146.130005,1126.380005,1137.589966,1137.589966,1062800000\n2001-04-10,1137.589966,1173.920044,1137.589966,1168.380005,1168.380005,1349600000\n2001-04-11,1168.380005,1182.239990,1160.260010,1165.890015,1165.890015,1290300000\n2001-04-12,1165.890015,1183.510010,1157.729980,1183.500000,1183.500000,1102000000\n2001-04-16,1183.500000,1184.640015,1167.380005,1179.680054,1179.680054,913900000\n2001-04-17,1179.680054,1192.250000,1168.900024,1191.810059,1191.810059,1109600000\n2001-04-18,1191.810059,1248.420044,1191.810059,1238.160034,1238.160034,1918900000\n2001-04-19,1238.160034,1253.709961,1233.390015,1253.689941,1253.689941,1486800000\n2001-04-20,1253.699951,1253.699951,1234.410034,1242.979980,1242.979980,1338700000\n2001-04-23,1242.979980,1242.979980,1217.469971,1224.359985,1224.359985,1012600000\n2001-04-24,1224.359985,1233.540039,1208.890015,1209.469971,1209.469971,1216500000\n2001-04-25,1209.469971,1232.359985,1207.380005,1228.750000,1228.750000,1203600000\n2001-04-26,1228.750000,1248.300049,1228.750000,1234.520020,1234.520020,1345200000\n2001-04-27,1234.520020,1253.069946,1234.520020,1253.050049,1253.050049,1091300000\n2001-04-30,1253.050049,1269.300049,1243.989990,1249.459961,1249.459961,1266800000\n2001-05-01,1249.459961,1266.469971,1243.550049,1266.439941,1266.439941,1181300000\n2001-05-02,1266.439941,1272.930054,1257.699951,1267.430054,1267.430054,1342200000\n2001-05-03,1267.430054,1267.430054,1239.880005,1248.579956,1248.579956,1137900000\n2001-05-04,1248.579956,1267.510010,1232.000000,1266.609985,1266.609985,1082100000\n2001-05-07,1266.609985,1270.000000,1259.189941,1263.510010,1263.510010,949000000\n2001-05-08,1266.709961,1267.010010,1253.000000,1261.199951,1261.199951,1006300000\n2001-05-09,1261.199951,1261.650024,1247.829956,1255.540039,1255.540039,1132400000\n2001-05-10,1255.540039,1268.140015,1254.560059,1255.180054,1255.180054,1056700000\n2001-05-11,1255.180054,1259.839966,1240.790039,1245.670044,1245.670044,906200000\n2001-05-14,1245.670044,1249.680054,1241.020020,1248.920044,1248.920044,858200000\n2001-05-15,1248.920044,1257.449951,1245.359985,1249.439941,1249.439941,1071800000\n2001-05-16,1249.439941,1286.390015,1243.020020,1284.989990,1284.989990,1405300000\n2001-05-17,1284.989990,1296.479980,1282.650024,1288.489990,1288.489990,1355600000\n2001-05-18,1288.489990,1292.060059,1281.150024,1291.959961,1291.959961,1130800000\n2001-05-21,1291.959961,1312.949951,1287.869995,1312.829956,1312.829956,1174900000\n2001-05-22,1312.829956,1315.930054,1306.890015,1309.380005,1309.380005,1260400000\n2001-05-23,1309.380005,1309.380005,1288.699951,1289.050049,1289.050049,1134800000\n2001-05-24,1289.050049,1295.040039,1281.219971,1293.170044,1293.170044,1100700000\n2001-05-25,1293.170044,1293.170044,1276.420044,1277.890015,1277.890015,828100000\n2001-05-29,1277.890015,1278.420044,1265.410034,1267.930054,1267.930054,1026000000\n2001-05-30,1267.930054,1267.930054,1245.959961,1248.079956,1248.079956,1158600000\n2001-05-31,1248.079956,1261.910034,1248.069946,1255.819946,1255.819946,1226600000\n2001-06-01,1255.819946,1265.339966,1246.880005,1260.670044,1260.670044,1015000000\n2001-06-04,1260.670044,1267.170044,1256.359985,1267.109985,1267.109985,836500000\n2001-06-05,1267.109985,1286.619995,1267.109985,1283.569946,1283.569946,1116800000\n2001-06-06,1283.569946,1283.849976,1269.010010,1270.030029,1270.030029,1061900000\n2001-06-07,1270.030029,1277.079956,1265.079956,1276.959961,1276.959961,1089600000\n2001-06-08,1276.959961,1277.109985,1259.989990,1264.959961,1264.959961,726200000\n2001-06-11,1264.959961,1264.959961,1249.229980,1254.390015,1254.390015,870100000\n2001-06-12,1254.390015,1261.000000,1235.750000,1255.849976,1255.849976,1136500000\n2001-06-13,1255.849976,1259.750000,1241.589966,1241.599976,1241.599976,1063600000\n2001-06-14,1241.599976,1241.599976,1218.900024,1219.869995,1219.869995,1242900000\n2001-06-15,1219.869995,1221.500000,1203.030029,1214.359985,1214.359985,1635550000\n2001-06-18,1214.359985,1221.229980,1208.329956,1208.430054,1208.430054,1111600000\n2001-06-19,1208.430054,1226.109985,1207.709961,1212.579956,1212.579956,1184900000\n2001-06-20,1212.579956,1225.609985,1210.069946,1223.140015,1223.140015,1350100000\n2001-06-21,1223.140015,1240.239990,1220.250000,1237.040039,1237.040039,1546820000\n2001-06-22,1237.040039,1237.729980,1221.410034,1225.349976,1225.349976,1189200000\n2001-06-25,1225.349976,1231.500000,1213.599976,1218.599976,1218.599976,1050100000\n2001-06-26,1218.599976,1220.699951,1204.640015,1216.760010,1216.760010,1198900000\n2001-06-27,1216.760010,1219.920044,1207.290039,1211.069946,1211.069946,1162100000\n2001-06-28,1211.069946,1234.439941,1211.069946,1226.199951,1226.199951,1327300000\n2001-06-29,1226.199951,1237.290039,1221.140015,1224.380005,1224.380005,1832360000\n2001-07-02,1224.420044,1239.780029,1224.030029,1236.719971,1236.719971,1128300000\n2001-07-03,1236.709961,1236.709961,1229.430054,1234.449951,1234.449951,622110000\n2001-07-05,1234.449951,1234.449951,1219.150024,1219.239990,1219.239990,934900000\n2001-07-06,1219.239990,1219.239990,1188.739990,1190.589966,1190.589966,1056700000\n2001-07-09,1190.589966,1201.760010,1189.750000,1198.780029,1198.780029,1045700000\n2001-07-10,1198.780029,1203.430054,1179.930054,1181.520020,1181.520020,1263800000\n2001-07-11,1181.520020,1184.930054,1168.459961,1180.180054,1180.180054,1384100000\n2001-07-12,1180.180054,1210.250000,1180.180054,1208.140015,1208.140015,1394000000\n2001-07-13,1208.140015,1218.540039,1203.609985,1215.680054,1215.680054,1121700000\n2001-07-16,1215.680054,1219.630005,1200.050049,1202.449951,1202.449951,1039800000\n2001-07-17,1202.449951,1215.359985,1196.140015,1214.439941,1214.439941,1238100000\n2001-07-18,1214.439941,1214.439941,1198.329956,1207.709961,1207.709961,1316300000\n2001-07-19,1207.709961,1225.040039,1205.800049,1215.020020,1215.020020,1343500000\n2001-07-20,1215.020020,1215.689941,1207.040039,1210.849976,1210.849976,1170900000\n2001-07-23,1210.849976,1215.219971,1190.500000,1191.030029,1191.030029,986900000\n2001-07-24,1191.030029,1191.030029,1165.540039,1171.650024,1171.650024,1198700000\n2001-07-25,1171.650024,1190.520020,1171.280029,1190.489990,1190.489990,1280700000\n2001-07-26,1190.489990,1204.180054,1182.650024,1202.930054,1202.930054,1213900000\n2001-07-27,1202.930054,1209.260010,1195.989990,1205.819946,1205.819946,1015300000\n2001-07-30,1205.819946,1209.050049,1200.410034,1204.520020,1204.520020,909100000\n2001-07-31,1204.520020,1222.739990,1204.520020,1211.229980,1211.229980,1129200000\n2001-08-01,1211.229980,1223.040039,1211.229980,1215.930054,1215.930054,1340300000\n2001-08-02,1215.930054,1226.270020,1215.310059,1220.750000,1220.750000,1218300000\n2001-08-03,1220.750000,1220.750000,1205.310059,1214.349976,1214.349976,939900000\n2001-08-06,1214.349976,1214.349976,1197.349976,1200.479980,1200.479980,811700000\n2001-08-07,1200.469971,1207.560059,1195.640015,1204.400024,1204.400024,1012000000\n2001-08-08,1204.400024,1206.790039,1181.270020,1183.530029,1183.530029,1124600000\n2001-08-09,1183.530029,1184.709961,1174.680054,1183.430054,1183.430054,1104200000\n2001-08-10,1183.430054,1193.329956,1169.550049,1190.160034,1190.160034,960900000\n2001-08-13,1190.160034,1193.819946,1185.119995,1191.290039,1191.290039,837600000\n2001-08-14,1191.290039,1198.790039,1184.260010,1186.729980,1186.729980,964600000\n2001-08-15,1186.729980,1191.209961,1177.609985,1178.020020,1178.020020,1065600000\n2001-08-16,1178.020020,1181.800049,1166.079956,1181.660034,1181.660034,1055400000\n2001-08-17,1181.660034,1181.660034,1156.069946,1161.969971,1161.969971,974300000\n2001-08-20,1161.969971,1171.410034,1160.939941,1171.410034,1171.410034,897100000\n2001-08-21,1171.410034,1179.849976,1156.560059,1157.260010,1157.260010,1041600000\n2001-08-22,1157.260010,1168.560059,1153.339966,1165.310059,1165.310059,1110800000\n2001-08-23,1165.310059,1169.859985,1160.959961,1162.089966,1162.089966,986200000\n2001-08-24,1162.089966,1185.150024,1162.089966,1184.930054,1184.930054,1043600000\n2001-08-27,1184.930054,1186.849976,1178.069946,1179.209961,1179.209961,842600000\n2001-08-28,1179.209961,1179.660034,1161.170044,1161.510010,1161.510010,987100000\n2001-08-29,1161.510010,1166.969971,1147.380005,1148.560059,1148.560059,963700000\n2001-08-30,1148.599976,1151.750000,1124.869995,1129.030029,1129.030029,1157000000\n2001-08-31,1129.030029,1141.829956,1126.380005,1133.579956,1133.579956,920100000\n2001-09-04,1133.579956,1155.400024,1129.060059,1132.939941,1132.939941,1178300000\n2001-09-05,1132.939941,1135.520020,1114.859985,1131.739990,1131.739990,1384500000\n2001-09-06,1131.739990,1131.739990,1105.829956,1106.400024,1106.400024,1359700000\n2001-09-07,1106.400024,1106.400024,1082.119995,1085.780029,1085.780029,1424300000\n2001-09-10,1085.780029,1096.939941,1073.150024,1092.540039,1092.540039,1276600000\n2001-09-17,1092.540039,1092.540039,1037.459961,1038.770020,1038.770020,2330830000\n2001-09-18,1038.770020,1046.420044,1029.250000,1032.739990,1032.739990,1650410000\n2001-09-19,1032.739990,1038.910034,984.619995,1016.099976,1016.099976,2120550000\n2001-09-20,1016.099976,1016.099976,984.489990,984.539978,984.539978,2004800000\n2001-09-21,984.539978,984.539978,944.750000,965.799988,965.799988,2317300000\n2001-09-24,965.799988,1008.440002,965.799988,1003.450012,1003.450012,1746600000\n2001-09-25,1003.450012,1017.140015,998.330017,1012.270020,1012.270020,1613800000\n2001-09-26,1012.270020,1020.289978,1002.619995,1007.039978,1007.039978,1519100000\n2001-09-27,1007.039978,1018.919983,998.239990,1018.609985,1018.609985,1467000000\n2001-09-28,1018.609985,1040.939941,1018.609985,1040.939941,1040.939941,1631500000\n2001-10-01,1040.939941,1040.939941,1026.760010,1038.550049,1038.550049,1175600000\n2001-10-02,1038.550049,1051.329956,1034.469971,1051.329956,1051.329956,1289800000\n2001-10-03,1051.329956,1075.380005,1041.479980,1072.280029,1072.280029,1650600000\n2001-10-04,1072.280029,1084.119995,1067.819946,1069.630005,1069.630005,1609100000\n2001-10-05,1069.619995,1072.349976,1053.500000,1071.380005,1071.380005,1301700000\n2001-10-08,1071.369995,1071.369995,1056.880005,1062.439941,1062.439941,979000000\n2001-10-09,1062.439941,1063.369995,1053.829956,1056.750000,1056.750000,1227800000\n2001-10-10,1056.750000,1081.619995,1052.760010,1080.989990,1080.989990,1312400000\n2001-10-11,1080.989990,1099.160034,1080.989990,1097.430054,1097.430054,1704580000\n2001-10-12,1097.430054,1097.430054,1072.150024,1091.650024,1091.650024,1331400000\n2001-10-15,1091.650024,1091.650024,1078.189941,1089.979980,1089.979980,1024700000\n2001-10-16,1089.979980,1101.660034,1087.130005,1097.540039,1097.540039,1210500000\n2001-10-17,1097.540039,1107.119995,1076.569946,1077.089966,1077.089966,1452200000\n2001-10-18,1077.089966,1077.939941,1064.540039,1068.609985,1068.609985,1262900000\n2001-10-19,1068.609985,1075.520020,1057.239990,1073.479980,1073.479980,1294900000\n2001-10-22,1073.479980,1090.569946,1070.790039,1089.900024,1089.900024,1105700000\n2001-10-23,1089.900024,1098.989990,1081.530029,1084.780029,1084.780029,1317300000\n2001-10-24,1084.780029,1090.260010,1079.979980,1085.199951,1085.199951,1336200000\n2001-10-25,1085.199951,1100.089966,1065.640015,1100.089966,1100.089966,1364400000\n2001-10-26,1100.089966,1110.609985,1094.239990,1104.609985,1104.609985,1244500000\n2001-10-29,1104.609985,1104.609985,1078.300049,1078.300049,1078.300049,1106100000\n2001-10-30,1078.300049,1078.300049,1053.609985,1059.790039,1059.790039,1297400000\n2001-10-31,1059.790039,1074.790039,1057.550049,1059.780029,1059.780029,1352500000\n2001-11-01,1059.780029,1085.609985,1054.310059,1084.099976,1084.099976,1317400000\n2001-11-02,1084.099976,1089.630005,1075.579956,1087.199951,1087.199951,1121900000\n2001-11-05,1087.199951,1106.719971,1087.199951,1102.839966,1102.839966,1267700000\n2001-11-06,1102.839966,1119.729980,1095.359985,1118.859985,1118.859985,1356000000\n2001-11-07,1118.859985,1126.619995,1112.979980,1115.800049,1115.800049,1411300000\n2001-11-08,1115.800049,1135.750000,1115.420044,1118.540039,1118.540039,1517500000\n2001-11-09,1118.540039,1123.020020,1111.130005,1120.310059,1120.310059,1093800000\n2001-11-12,1120.310059,1121.709961,1098.319946,1118.329956,1118.329956,991600000\n2001-11-13,1118.329956,1139.140015,1118.329956,1139.089966,1139.089966,1370100000\n2001-11-14,1139.089966,1148.280029,1132.869995,1141.209961,1141.209961,1443400000\n2001-11-15,1141.209961,1146.459961,1135.060059,1142.239990,1142.239990,1454500000\n2001-11-16,1142.239990,1143.520020,1129.920044,1138.650024,1138.650024,1337400000\n2001-11-19,1138.650024,1151.060059,1138.650024,1151.060059,1151.060059,1316800000\n2001-11-20,1151.060059,1152.449951,1142.170044,1142.660034,1142.660034,1330200000\n2001-11-21,1142.660034,1142.660034,1129.780029,1137.030029,1137.030029,1029300000\n2001-11-23,1137.030029,1151.050049,1135.900024,1150.339966,1150.339966,410300000\n2001-11-26,1150.339966,1157.880005,1146.170044,1157.420044,1157.420044,1129800000\n2001-11-27,1157.420044,1163.380005,1140.810059,1149.500000,1149.500000,1288000000\n2001-11-28,1149.500000,1149.500000,1128.290039,1128.520020,1128.520020,1423700000\n2001-11-29,1128.520020,1140.400024,1125.510010,1140.199951,1140.199951,1375700000\n2001-11-30,1140.199951,1143.569946,1135.890015,1139.449951,1139.449951,1343600000\n2001-12-03,1139.449951,1139.449951,1125.780029,1129.900024,1129.900024,1202900000\n2001-12-04,1129.900024,1144.800049,1128.859985,1144.800049,1144.800049,1318500000\n2001-12-05,1143.770020,1173.619995,1143.770020,1170.349976,1170.349976,1765300000\n2001-12-06,1170.349976,1173.349976,1164.430054,1167.099976,1167.099976,1487900000\n2001-12-07,1167.099976,1167.099976,1152.660034,1158.310059,1158.310059,1248200000\n2001-12-10,1158.310059,1158.310059,1139.660034,1139.930054,1139.930054,1218700000\n2001-12-11,1139.930054,1150.890015,1134.319946,1136.760010,1136.760010,1367200000\n2001-12-12,1136.760010,1141.579956,1126.010010,1137.069946,1137.069946,1449700000\n2001-12-13,1137.069946,1137.069946,1117.849976,1119.380005,1119.380005,1511500000\n2001-12-14,1119.380005,1128.280029,1114.530029,1123.089966,1123.089966,1306800000\n2001-12-17,1123.089966,1137.300049,1122.660034,1134.359985,1134.359985,1260400000\n2001-12-18,1134.359985,1145.099976,1134.359985,1142.920044,1142.920044,1354000000\n2001-12-19,1142.920044,1152.439941,1134.750000,1149.560059,1149.560059,1484900000\n2001-12-20,1149.560059,1151.420044,1139.930054,1139.930054,1139.930054,1490500000\n2001-12-21,1139.930054,1147.459961,1139.930054,1144.890015,1144.890015,1694000000\n2001-12-24,1144.890015,1147.829956,1144.619995,1144.650024,1144.650024,439670000\n2001-12-26,1144.650024,1159.180054,1144.650024,1149.369995,1149.369995,791100000\n2001-12-27,1149.369995,1157.130005,1149.369995,1157.130005,1157.130005,876300000\n2001-12-28,1157.130005,1164.640015,1157.130005,1161.020020,1161.020020,917400000\n2001-12-31,1161.020020,1161.160034,1148.040039,1148.079956,1148.079956,943600000\n2002-01-02,1148.079956,1154.670044,1136.229980,1154.670044,1154.670044,1171000000\n2002-01-03,1154.670044,1165.270020,1154.010010,1165.270020,1165.270020,1398900000\n2002-01-04,1165.270020,1176.550049,1163.420044,1172.510010,1172.510010,1513000000\n2002-01-07,1172.510010,1176.969971,1163.550049,1164.890015,1164.890015,1308300000\n2002-01-08,1164.890015,1167.599976,1157.459961,1160.709961,1160.709961,1258800000\n2002-01-09,1160.709961,1174.260010,1151.890015,1155.140015,1155.140015,1452000000\n2002-01-10,1155.140015,1159.930054,1150.849976,1156.550049,1156.550049,1299000000\n2002-01-11,1156.550049,1159.410034,1145.449951,1145.599976,1145.599976,1211900000\n2002-01-14,1145.599976,1145.599976,1138.150024,1138.410034,1138.410034,1286400000\n2002-01-15,1138.410034,1148.810059,1136.880005,1146.189941,1146.189941,1386900000\n2002-01-16,1146.189941,1146.189941,1127.489990,1127.569946,1127.569946,1482500000\n2002-01-17,1127.569946,1139.270020,1127.569946,1138.880005,1138.880005,1380100000\n2002-01-18,1138.880005,1138.880005,1124.449951,1127.579956,1127.579956,1333300000\n2002-01-22,1127.579956,1135.260010,1117.910034,1119.310059,1119.310059,1311600000\n2002-01-23,1119.310059,1131.939941,1117.430054,1128.180054,1128.180054,1479200000\n2002-01-24,1128.180054,1139.500000,1128.180054,1132.150024,1132.150024,1552800000\n2002-01-25,1132.150024,1138.310059,1127.819946,1133.280029,1133.280029,1345100000\n2002-01-28,1133.280029,1138.630005,1126.660034,1133.060059,1133.060059,1186800000\n2002-01-29,1133.060059,1137.469971,1098.739990,1100.640015,1100.640015,1812000000\n2002-01-30,1100.640015,1113.790039,1081.660034,1113.569946,1113.569946,2019600000\n2002-01-31,1113.569946,1130.209961,1113.300049,1130.199951,1130.199951,1557000000\n2002-02-01,1130.199951,1130.199951,1118.510010,1122.199951,1122.199951,1367200000\n2002-02-04,1122.199951,1122.199951,1092.250000,1094.439941,1094.439941,1437600000\n2002-02-05,1094.439941,1100.959961,1082.579956,1090.020020,1090.020020,1778300000\n2002-02-06,1090.020020,1093.579956,1077.780029,1083.510010,1083.510010,1665800000\n2002-02-07,1083.510010,1094.030029,1078.439941,1080.170044,1080.170044,1441600000\n2002-02-08,1080.170044,1096.300049,1079.910034,1096.219971,1096.219971,1371900000\n2002-02-11,1096.219971,1112.010010,1094.680054,1111.939941,1111.939941,1159400000\n2002-02-12,1111.939941,1112.680054,1102.979980,1107.500000,1107.500000,1094200000\n2002-02-13,1107.500000,1120.560059,1107.500000,1118.510010,1118.510010,1215900000\n2002-02-14,1118.510010,1124.719971,1112.300049,1116.479980,1116.479980,1272500000\n2002-02-15,1116.479980,1117.089966,1103.229980,1104.180054,1104.180054,1359200000\n2002-02-19,1104.180054,1104.180054,1082.239990,1083.339966,1083.339966,1189900000\n2002-02-20,1083.339966,1098.319946,1074.359985,1097.979980,1097.979980,1438900000\n2002-02-21,1097.979980,1101.500000,1080.239990,1080.949951,1080.949951,1381600000\n2002-02-22,1080.949951,1093.930054,1074.390015,1089.839966,1089.839966,1411000000\n2002-02-25,1089.839966,1112.709961,1089.839966,1109.430054,1109.430054,1367400000\n2002-02-26,1109.430054,1115.050049,1101.719971,1109.380005,1109.380005,1309200000\n2002-02-27,1109.380005,1123.060059,1102.260010,1109.890015,1109.890015,1393800000\n2002-02-28,1109.890015,1121.569946,1106.729980,1106.729980,1106.729980,1392200000\n2002-03-01,1106.729980,1131.790039,1106.729980,1131.780029,1131.780029,1456500000\n2002-03-04,1131.780029,1153.839966,1130.930054,1153.839966,1153.839966,1594300000\n2002-03-05,1153.839966,1157.739990,1144.780029,1146.140015,1146.140015,1549300000\n2002-03-06,1146.140015,1165.290039,1145.109985,1162.770020,1162.770020,1541300000\n2002-03-07,1162.770020,1167.939941,1150.689941,1157.540039,1157.540039,1517400000\n2002-03-08,1157.540039,1172.760010,1157.540039,1164.310059,1164.310059,1412000000\n2002-03-11,1164.310059,1173.030029,1159.579956,1168.260010,1168.260010,1210200000\n2002-03-12,1168.260010,1168.260010,1154.339966,1165.579956,1165.579956,1304400000\n2002-03-13,1165.579956,1165.579956,1151.010010,1154.089966,1154.089966,1354000000\n2002-03-14,1154.089966,1157.829956,1151.079956,1153.040039,1153.040039,1208800000\n2002-03-15,1153.040039,1166.479980,1153.040039,1166.160034,1166.160034,1493900000\n2002-03-18,1166.160034,1172.729980,1159.140015,1165.550049,1165.550049,1169500000\n2002-03-19,1165.550049,1173.939941,1165.550049,1170.290039,1170.290039,1255000000\n2002-03-20,1170.290039,1170.290039,1151.609985,1151.849976,1151.849976,1304900000\n2002-03-21,1151.849976,1155.099976,1139.479980,1153.589966,1153.589966,1339200000\n2002-03-22,1153.589966,1156.489990,1144.599976,1148.699951,1148.699951,1243300000\n2002-03-25,1148.699951,1151.040039,1131.869995,1131.869995,1131.869995,1057900000\n2002-03-26,1131.869995,1147.000000,1131.609985,1138.489990,1138.489990,1223600000\n2002-03-27,1138.489990,1146.949951,1135.329956,1144.579956,1144.579956,1180100000\n2002-03-28,1144.579956,1154.449951,1144.579956,1147.390015,1147.390015,1147600000\n2002-04-01,1147.390015,1147.839966,1132.869995,1146.540039,1146.540039,1050900000\n2002-04-02,1146.540039,1146.540039,1135.709961,1136.760010,1136.760010,1176700000\n2002-04-03,1136.760010,1138.849976,1119.680054,1125.400024,1125.400024,1219700000\n2002-04-04,1125.400024,1130.449951,1120.060059,1126.339966,1126.339966,1283800000\n2002-04-05,1126.339966,1133.310059,1119.489990,1122.729980,1122.729980,1110200000\n2002-04-08,1122.729980,1125.410034,1111.790039,1125.290039,1125.290039,1095300000\n2002-04-09,1125.290039,1128.290039,1116.729980,1117.800049,1117.800049,1235400000\n2002-04-10,1117.800049,1131.760010,1117.800049,1130.469971,1130.469971,1447900000\n2002-04-11,1130.469971,1130.469971,1102.420044,1103.689941,1103.689941,1505600000\n2002-04-12,1103.689941,1112.770020,1102.739990,1111.010010,1111.010010,1282100000\n2002-04-15,1111.010010,1114.859985,1099.410034,1102.550049,1102.550049,1120400000\n2002-04-16,1102.550049,1129.400024,1102.550049,1128.369995,1128.369995,1341300000\n2002-04-17,1128.369995,1133.000000,1123.369995,1126.069946,1126.069946,1376900000\n2002-04-18,1126.069946,1130.489990,1109.290039,1124.469971,1124.469971,1359300000\n2002-04-19,1124.469971,1128.819946,1122.589966,1125.170044,1125.170044,1185000000\n2002-04-22,1125.170044,1125.170044,1105.619995,1107.829956,1107.829956,1181800000\n2002-04-23,1107.829956,1111.170044,1098.939941,1100.959961,1100.959961,1388500000\n2002-04-24,1100.959961,1108.459961,1092.510010,1093.140015,1093.140015,1373200000\n2002-04-25,1093.140015,1094.359985,1084.810059,1091.479980,1091.479980,1517400000\n2002-04-26,1091.479980,1096.770020,1076.310059,1076.319946,1076.319946,1374200000\n2002-04-29,1076.319946,1078.949951,1063.619995,1065.449951,1065.449951,1314700000\n2002-04-30,1065.449951,1082.619995,1063.459961,1076.920044,1076.920044,1628600000\n2002-05-01,1076.920044,1088.319946,1065.290039,1086.459961,1086.459961,1451400000\n2002-05-02,1086.459961,1091.420044,1079.459961,1084.560059,1084.560059,1364000000\n2002-05-03,1084.560059,1084.560059,1068.890015,1073.430054,1073.430054,1284500000\n2002-05-06,1073.430054,1075.959961,1052.650024,1052.670044,1052.670044,1122600000\n2002-05-07,1052.670044,1058.670044,1048.959961,1049.489990,1049.489990,1354700000\n2002-05-08,1049.489990,1088.920044,1049.489990,1088.849976,1088.849976,1502000000\n2002-05-09,1088.849976,1088.849976,1072.229980,1073.010010,1073.010010,1153000000\n2002-05-10,1073.010010,1075.430054,1053.930054,1054.989990,1054.989990,1171900000\n2002-05-13,1054.989990,1074.839966,1053.900024,1074.560059,1074.560059,1088600000\n2002-05-14,1074.560059,1097.709961,1074.560059,1097.280029,1097.280029,1414500000\n2002-05-15,1097.280029,1104.229980,1088.939941,1091.069946,1091.069946,1420200000\n2002-05-16,1091.069946,1099.290039,1089.170044,1098.229980,1098.229980,1256600000\n2002-05-17,1098.229980,1106.589966,1096.770020,1106.589966,1106.589966,1274400000\n2002-05-20,1106.589966,1106.589966,1090.609985,1091.880005,1091.880005,989800000\n2002-05-21,1091.880005,1099.550049,1079.079956,1079.880005,1079.880005,1200500000\n2002-05-22,1079.880005,1086.020020,1075.640015,1086.020020,1086.020020,1136300000\n2002-05-23,1086.020020,1097.099976,1080.550049,1097.079956,1097.079956,1192900000\n2002-05-24,1097.079956,1097.079956,1082.189941,1083.819946,1083.819946,885400000\n2002-05-28,1083.819946,1085.979980,1070.310059,1074.550049,1074.550049,996500000\n2002-05-29,1074.550049,1074.829956,1067.660034,1067.660034,1067.660034,1081800000\n2002-05-30,1067.660034,1069.500000,1054.260010,1064.660034,1064.660034,1286600000\n2002-05-31,1064.660034,1079.930054,1064.660034,1067.140015,1067.140015,1277300000\n2002-06-03,1067.140015,1070.739990,1039.900024,1040.680054,1040.680054,1324300000\n2002-06-04,1040.680054,1046.060059,1030.520020,1040.689941,1040.689941,1466600000\n2002-06-05,1040.689941,1050.109985,1038.839966,1049.900024,1049.900024,1300100000\n2002-06-06,1049.900024,1049.900024,1026.910034,1029.150024,1029.150024,1601500000\n2002-06-07,1029.150024,1033.020020,1012.489990,1027.530029,1027.530029,1341300000\n2002-06-10,1027.530029,1038.180054,1025.449951,1030.739990,1030.739990,1226200000\n2002-06-11,1030.739990,1039.040039,1012.940002,1013.599976,1013.599976,1212400000\n2002-06-12,1013.260010,1021.849976,1002.580017,1020.260010,1020.260010,1795720000\n2002-06-13,1020.260010,1023.469971,1008.119995,1009.559998,1009.559998,1405500000\n2002-06-14,1009.559998,1009.559998,981.630005,1007.270020,1007.270020,1549000000\n2002-06-17,1007.270020,1036.170044,1007.270020,1036.170044,1036.170044,1236600000\n2002-06-18,1036.170044,1040.829956,1030.920044,1037.140015,1037.140015,1193100000\n2002-06-19,1037.140015,1037.609985,1017.880005,1019.989990,1019.989990,1336100000\n2002-06-20,1019.989990,1023.330017,1004.590027,1006.289978,1006.289978,1389700000\n2002-06-21,1006.289978,1006.289978,985.650024,989.140015,989.140015,1497200000\n2002-06-24,989.140015,1002.109985,970.849976,992.719971,992.719971,1552600000\n2002-06-25,992.719971,1005.880005,974.210022,976.140015,976.140015,1513700000\n2002-06-26,976.140015,977.429993,952.919983,973.530029,973.530029,2014290000\n2002-06-27,973.530029,990.669983,963.739990,990.640015,990.640015,1908600000\n2002-06-28,990.640015,1001.789978,988.309998,989.820007,989.820007,2117000000\n2002-07-01,989.820007,994.460022,967.429993,968.650024,968.650024,1425500000\n2002-07-02,968.650024,968.650024,945.539978,948.090027,948.090027,1823000000\n2002-07-03,948.090027,954.299988,934.869995,953.989990,953.989990,1527800000\n2002-07-05,953.989990,989.070007,953.989990,989.030029,989.030029,699400000\n2002-07-08,989.030029,993.559998,972.909973,976.979980,976.979980,1184400000\n2002-07-09,976.979980,979.630005,951.710022,952.830017,952.830017,1348900000\n2002-07-10,952.830017,956.340027,920.289978,920.469971,920.469971,1816900000\n2002-07-11,920.469971,929.159973,900.940002,927.369995,927.369995,2080480000\n2002-07-12,927.369995,934.309998,913.710022,921.390015,921.390015,1607400000\n2002-07-15,921.390015,921.390015,876.460022,917.929993,917.929993,2574800000\n2002-07-16,917.929993,918.650024,897.130005,900.940002,900.940002,1843700000\n2002-07-17,901.049988,926.520020,895.030029,906.039978,906.039978,2566500000\n2002-07-18,905.450012,907.799988,880.599976,881.559998,881.559998,1736300000\n2002-07-19,881.559998,881.559998,842.070007,847.750000,847.750000,2654100000\n2002-07-22,847.760010,854.130005,813.260010,819.849976,819.849976,2248060000\n2002-07-23,819.849976,827.690002,796.130005,797.700012,797.700012,2441020000\n2002-07-24,797.710022,844.320007,775.679993,843.429993,843.429993,2775560000\n2002-07-25,843.419983,853.830017,816.109985,838.679993,838.679993,2424700000\n2002-07-26,838.679993,852.849976,835.919983,852.840027,852.840027,1796100000\n2002-07-29,852.840027,898.960022,852.840027,898.960022,898.960022,1778650000\n2002-07-30,898.960022,909.809998,884.700012,902.780029,902.780029,1826090000\n2002-07-31,902.780029,911.640015,889.880005,911.619995,911.619995,2049360000\n2002-08-01,911.619995,911.619995,882.479980,884.659973,884.659973,1672200000\n2002-08-02,884.400024,884.719971,853.950012,864.239990,864.239990,1538100000\n2002-08-05,864.239990,864.239990,833.440002,834.599976,834.599976,1425500000\n2002-08-06,834.599976,874.440002,834.599976,859.570007,859.570007,1514100000\n2002-08-07,859.570007,878.739990,854.150024,876.770020,876.770020,1490400000\n2002-08-08,876.770020,905.840027,875.169983,905.460022,905.460022,1646700000\n2002-08-09,898.729980,913.950012,890.770020,908.640015,908.640015,1294900000\n2002-08-12,908.640015,908.640015,892.380005,903.799988,903.799988,1036500000\n2002-08-13,903.799988,911.710022,883.619995,884.210022,884.210022,1297700000\n2002-08-14,884.210022,920.210022,876.200012,919.619995,919.619995,1533800000\n2002-08-15,919.619995,933.289978,918.169983,930.250000,930.250000,1505100000\n2002-08-16,930.250000,935.380005,916.210022,928.770020,928.770020,1265300000\n2002-08-19,928.770020,951.169983,927.210022,950.700012,950.700012,1299800000\n2002-08-20,950.700012,950.700012,931.859985,937.429993,937.429993,1308500000\n2002-08-21,937.429993,951.590027,931.320007,949.359985,949.359985,1353100000\n2002-08-22,949.359985,965.000000,946.429993,962.700012,962.700012,1373000000\n2002-08-23,962.700012,962.700012,937.169983,940.859985,940.859985,1071500000\n2002-08-26,940.859985,950.799988,930.419983,947.950012,947.950012,1016900000\n2002-08-27,947.950012,955.820007,930.359985,934.820007,934.820007,1307700000\n2002-08-28,934.820007,934.820007,913.210022,917.869995,917.869995,1146600000\n2002-08-29,917.869995,924.590027,903.330017,917.799988,917.799988,1271100000\n2002-08-30,917.799988,928.150024,910.169983,916.070007,916.070007,929900000\n2002-09-03,916.070007,916.070007,877.510010,878.020020,878.020020,1289800000\n2002-09-04,878.020020,896.099976,875.729980,893.400024,893.400024,1372100000\n2002-09-05,893.400024,893.400024,870.500000,879.150024,879.150024,1401300000\n2002-09-06,879.150024,899.070007,879.150024,893.919983,893.919983,1184500000\n2002-09-09,893.919983,907.340027,882.919983,902.960022,902.960022,1130600000\n2002-09-10,902.960022,909.890015,900.500000,909.580017,909.580017,1186400000\n2002-09-11,910.630005,924.020020,908.469971,909.450012,909.450012,846600000\n2002-09-12,909.450012,909.450012,884.840027,886.909973,886.909973,1191600000\n2002-09-13,886.909973,892.750000,877.049988,889.809998,889.809998,1271000000\n2002-09-16,889.809998,891.840027,878.909973,891.099976,891.099976,1001400000\n2002-09-17,891.099976,902.679993,872.380005,873.520020,873.520020,1448600000\n2002-09-18,873.520020,878.450012,857.390015,869.460022,869.460022,1501000000\n2002-09-19,869.460022,869.460022,843.090027,843.320007,843.320007,1524000000\n2002-09-20,843.320007,849.320007,839.090027,845.390015,845.390015,1792800000\n2002-09-23,845.390015,845.390015,825.760010,833.700012,833.700012,1381100000\n2002-09-24,833.700012,833.700012,817.380005,819.289978,819.289978,1670240000\n2002-09-25,819.270020,844.219971,818.460022,839.659973,839.659973,1651500000\n2002-09-26,839.659973,856.599976,839.659973,854.950012,854.950012,1650000000\n2002-09-27,854.950012,854.950012,826.840027,827.369995,827.369995,1507300000\n2002-09-30,827.369995,827.369995,800.200012,815.280029,815.280029,1721870000\n2002-10-01,815.280029,847.929993,812.820007,847.909973,847.909973,1780900000\n2002-10-02,843.770020,851.929993,826.500000,827.909973,827.909973,1668900000\n2002-10-03,827.909973,840.020020,817.250000,818.950012,818.950012,1674500000\n2002-10-04,818.950012,825.900024,794.099976,800.580017,800.580017,1835930000\n2002-10-07,800.580017,808.210022,782.960022,785.280029,785.280029,1576500000\n2002-10-08,785.280029,808.859985,779.500000,798.549988,798.549988,1938430000\n2002-10-09,798.549988,798.549988,775.799988,776.760010,776.760010,1885030000\n2002-10-10,776.760010,806.510010,768.630005,803.919983,803.919983,2090230000\n2002-10-11,803.919983,843.270020,803.919983,835.320007,835.320007,1854130000\n2002-10-14,835.320007,844.390015,828.369995,841.440002,841.440002,1200300000\n2002-10-15,841.440002,881.270020,841.440002,881.270020,881.270020,1956000000\n2002-10-16,881.270020,881.270020,856.280029,860.020020,860.020020,1585000000\n2002-10-17,860.020020,885.349976,860.020020,879.200012,879.200012,1780390000\n2002-10-18,879.200012,886.679993,866.580017,884.390015,884.390015,1423100000\n2002-10-21,884.390015,900.690002,873.059998,899.719971,899.719971,1447000000\n2002-10-22,899.719971,899.719971,882.400024,890.159973,890.159973,1549200000\n2002-10-23,890.159973,896.140015,873.820007,896.140015,896.140015,1593900000\n2002-10-24,896.140015,902.940002,879.000000,882.500000,882.500000,1700570000\n2002-10-25,882.500000,897.710022,877.030029,897.650024,897.650024,1340400000\n2002-10-28,897.650024,907.440002,886.150024,890.229980,890.229980,1382600000\n2002-10-29,890.229980,890.640015,867.909973,882.150024,882.150024,1529700000\n2002-10-30,882.150024,895.280029,879.190002,890.710022,890.710022,1422300000\n2002-10-31,890.710022,898.830017,879.750000,885.760010,885.760010,1641300000\n2002-11-01,885.760010,903.419983,877.710022,900.960022,900.960022,1450400000\n2002-11-04,900.960022,924.580017,900.960022,908.349976,908.349976,1645900000\n2002-11-05,908.349976,915.830017,904.909973,915.390015,915.390015,1354100000\n2002-11-06,915.390015,925.659973,905.000000,923.760010,923.760010,1674000000\n2002-11-07,923.760010,923.760010,898.679993,902.650024,902.650024,1466900000\n2002-11-08,902.650024,910.109985,891.619995,894.739990,894.739990,1446500000\n2002-11-11,894.739990,894.739990,874.630005,876.190002,876.190002,1113000000\n2002-11-12,876.190002,894.299988,876.190002,882.950012,882.950012,1377100000\n2002-11-13,882.950012,892.510010,872.049988,882.530029,882.530029,1463400000\n2002-11-14,882.530029,904.270020,882.530029,904.270020,904.270020,1519000000\n2002-11-15,904.270020,910.210022,895.349976,909.830017,909.830017,1400100000\n2002-11-18,909.830017,915.909973,899.479980,900.359985,900.359985,1282600000\n2002-11-19,900.359985,905.450012,893.090027,896.739990,896.739990,1337400000\n2002-11-20,896.739990,915.010010,894.929993,914.150024,914.150024,1517300000\n2002-11-21,914.150024,935.130005,914.150024,933.760010,933.760010,2415100000\n2002-11-22,933.760010,937.280029,928.409973,930.549988,930.549988,1626800000\n2002-11-25,930.549988,937.150024,923.309998,932.869995,932.869995,1574000000\n2002-11-26,932.869995,932.869995,912.099976,913.309998,913.309998,1543600000\n2002-11-27,913.309998,940.409973,913.309998,938.869995,938.869995,1350300000\n2002-11-29,938.869995,941.820007,935.580017,936.309998,936.309998,643460000\n2002-12-02,936.309998,954.280029,927.719971,934.530029,934.530029,1612000000\n2002-12-03,934.530029,934.530029,918.729980,920.750000,920.750000,1488400000\n2002-12-04,920.750000,925.250000,909.510010,917.580017,917.580017,1588900000\n2002-12-05,917.580017,921.489990,905.900024,906.549988,906.549988,1250200000\n2002-12-06,906.549988,915.479980,895.960022,912.229980,912.229980,1241100000\n2002-12-09,912.229980,912.229980,891.969971,892.000000,892.000000,1320800000\n2002-12-10,892.000000,904.950012,892.000000,904.450012,904.450012,1286600000\n2002-12-11,904.450012,909.940002,896.479980,904.960022,904.960022,1285100000\n2002-12-12,904.960022,908.369995,897.000000,901.580017,901.580017,1255300000\n2002-12-13,901.580017,901.580017,888.479980,889.479980,889.479980,1330800000\n2002-12-16,889.479980,910.419983,889.479980,910.400024,910.400024,1271600000\n2002-12-17,910.400024,911.219971,901.739990,902.989990,902.989990,1251800000\n2002-12-18,902.989990,902.989990,887.820007,891.119995,891.119995,1446200000\n2002-12-19,890.020020,899.190002,880.320007,884.250000,884.250000,1385900000\n2002-12-20,884.250000,897.789978,884.250000,895.760010,895.760010,1782730000\n2002-12-23,895.739990,902.429993,892.260010,897.380005,897.380005,1112100000\n2002-12-24,897.380005,897.380005,892.289978,892.469971,892.469971,458310000\n2002-12-26,892.469971,903.890015,887.479980,889.659973,889.659973,721100000\n2002-12-27,889.659973,890.460022,873.619995,875.400024,875.400024,758400000\n2002-12-30,875.400024,882.099976,870.229980,879.390015,879.390015,1057800000\n2002-12-31,879.390015,881.929993,869.450012,879.820007,879.820007,1088500000\n2003-01-02,879.820007,909.030029,879.820007,909.030029,909.030029,1229200000\n2003-01-03,909.030029,911.250000,903.070007,908.590027,908.590027,1130800000\n2003-01-06,908.590027,931.770020,908.590027,929.010010,929.010010,1435900000\n2003-01-07,929.010010,930.809998,919.929993,922.929993,922.929993,1545200000\n2003-01-08,922.929993,922.929993,908.320007,909.929993,909.929993,1467600000\n2003-01-09,909.929993,928.309998,909.929993,927.570007,927.570007,1560300000\n2003-01-10,927.580017,932.890015,917.659973,927.570007,927.570007,1485400000\n2003-01-13,927.570007,935.049988,922.049988,926.260010,926.260010,1396300000\n2003-01-14,926.260010,931.659973,921.719971,931.659973,931.659973,1379400000\n2003-01-15,931.659973,932.590027,916.700012,918.219971,918.219971,1432100000\n2003-01-16,918.219971,926.030029,911.979980,914.599976,914.599976,1534600000\n2003-01-17,914.599976,914.599976,899.020020,901.780029,901.780029,1358200000\n2003-01-21,901.780029,906.000000,887.619995,887.619995,887.619995,1335200000\n2003-01-22,887.619995,889.739990,877.640015,878.359985,878.359985,1560800000\n2003-01-23,878.359985,890.250000,876.890015,887.340027,887.340027,1744550000\n2003-01-24,887.340027,887.340027,859.710022,861.400024,861.400024,1574800000\n2003-01-27,861.400024,863.950012,844.250000,847.479980,847.479980,1435900000\n2003-01-28,847.479980,860.760010,847.479980,858.539978,858.539978,1459100000\n2003-01-29,858.539978,868.719971,845.859985,864.359985,864.359985,1595400000\n2003-01-30,864.359985,865.479980,843.739990,844.609985,844.609985,1510300000\n2003-01-31,844.609985,858.330017,840.340027,855.700012,855.700012,1578530000\n2003-02-03,855.700012,864.640015,855.700012,860.320007,860.320007,1258500000\n2003-02-04,860.320007,860.320007,840.190002,848.200012,848.200012,1451600000\n2003-02-05,848.200012,861.630005,842.109985,843.590027,843.590027,1450800000\n2003-02-06,843.590027,844.229980,833.250000,838.150024,838.150024,1430900000\n2003-02-07,838.150024,845.729980,826.700012,829.690002,829.690002,1276800000\n2003-02-10,829.690002,837.159973,823.530029,835.969971,835.969971,1238200000\n2003-02-11,835.969971,843.020020,825.090027,829.200012,829.200012,1307000000\n2003-02-12,829.200012,832.119995,818.489990,818.679993,818.679993,1260500000\n2003-02-13,818.679993,821.250000,806.289978,817.369995,817.369995,1489300000\n2003-02-14,817.369995,834.890015,815.030029,834.890015,834.890015,1404600000\n2003-02-18,834.890015,852.869995,834.890015,851.169983,851.169983,1250800000\n2003-02-19,851.169983,851.169983,838.789978,845.130005,845.130005,1075600000\n2003-02-20,845.130005,849.369995,836.559998,837.099976,837.099976,1194100000\n2003-02-21,837.099976,852.280029,831.479980,848.169983,848.169983,1398200000\n2003-02-24,848.169983,848.169983,832.159973,832.580017,832.580017,1229200000\n2003-02-25,832.580017,839.549988,818.539978,838.570007,838.570007,1483700000\n2003-02-26,838.570007,840.099976,826.679993,827.549988,827.549988,1374400000\n2003-02-27,827.549988,842.190002,827.549988,837.280029,837.280029,1287800000\n2003-02-28,837.280029,847.000000,837.280029,841.150024,841.150024,1373300000\n2003-03-03,841.150024,852.340027,832.739990,834.809998,834.809998,1208900000\n2003-03-04,834.809998,835.429993,821.960022,821.989990,821.989990,1256600000\n2003-03-05,821.989990,829.869995,819.000000,829.849976,829.849976,1332700000\n2003-03-06,829.849976,829.849976,819.849976,822.099976,822.099976,1299200000\n2003-03-07,822.099976,829.549988,811.229980,828.890015,828.890015,1368500000\n2003-03-10,828.890015,828.890015,806.570007,807.479980,807.479980,1255000000\n2003-03-11,807.479980,814.250000,800.299988,800.729980,800.729980,1427700000\n2003-03-12,800.729980,804.190002,788.900024,804.190002,804.190002,1620000000\n2003-03-13,804.190002,832.020020,804.190002,831.900024,831.900024,1816300000\n2003-03-14,831.890015,841.390015,828.260010,833.270020,833.270020,1541900000\n2003-03-17,833.270020,862.789978,827.169983,862.789978,862.789978,1700420000\n2003-03-18,862.789978,866.940002,857.359985,866.450012,866.450012,1555100000\n2003-03-19,866.450012,874.989990,861.210022,874.020020,874.020020,1473400000\n2003-03-20,874.020020,879.599976,859.010010,875.669983,875.669983,1439100000\n2003-03-21,875.840027,895.900024,875.840027,895.789978,895.789978,1883710000\n2003-03-24,895.789978,895.789978,862.020020,864.229980,864.229980,1293000000\n2003-03-25,864.229980,879.869995,862.590027,874.739990,874.739990,1333400000\n2003-03-26,874.739990,875.799988,866.469971,869.950012,869.950012,1319700000\n2003-03-27,869.950012,874.150024,858.090027,868.520020,868.520020,1232900000\n2003-03-28,868.520020,869.880005,860.830017,863.500000,863.500000,1227000000\n2003-03-31,863.500000,863.500000,843.679993,848.179993,848.179993,1495500000\n2003-04-01,848.179993,861.280029,847.849976,858.479980,858.479980,1461600000\n2003-04-02,858.479980,884.570007,858.479980,880.900024,880.900024,1589800000\n2003-04-03,880.900024,885.890015,876.119995,876.450012,876.450012,1339500000\n2003-04-04,876.450012,882.729980,874.229980,878.849976,878.849976,1241200000\n2003-04-07,878.849976,904.890015,878.849976,879.929993,879.929993,1494000000\n2003-04-08,879.929993,883.109985,874.679993,878.289978,878.289978,1235400000\n2003-04-09,878.289978,887.349976,865.719971,865.989990,865.989990,1293700000\n2003-04-10,865.989990,871.780029,862.760010,871.580017,871.580017,1275300000\n2003-04-11,871.580017,883.340027,865.919983,868.299988,868.299988,1141600000\n2003-04-14,868.299988,885.260010,868.299988,885.229980,885.229980,1131000000\n2003-04-15,885.229980,891.270020,881.849976,890.809998,890.809998,1460200000\n2003-04-16,890.809998,896.770020,877.929993,879.909973,879.909973,1587600000\n2003-04-17,879.909973,893.830017,879.200012,893.580017,893.580017,1430600000\n2003-04-21,893.580017,898.010010,888.169983,892.010010,892.010010,1118700000\n2003-04-22,892.010010,911.739990,886.700012,911.369995,911.369995,1631200000\n2003-04-23,911.369995,919.739990,909.890015,919.020020,919.020020,1667200000\n2003-04-24,919.020020,919.020020,906.690002,911.429993,911.429993,1648100000\n2003-04-25,911.429993,911.429993,897.520020,898.809998,898.809998,1335800000\n2003-04-28,898.809998,918.150024,898.809998,914.840027,914.840027,1273000000\n2003-04-29,914.840027,924.239990,911.099976,917.840027,917.840027,1525600000\n2003-04-30,917.840027,922.010010,911.700012,916.919983,916.919983,1788510000\n2003-05-01,916.919983,919.679993,902.830017,916.299988,916.299988,1397500000\n2003-05-02,916.299988,930.559998,912.349976,930.080017,930.080017,1554300000\n2003-05-05,930.080017,933.880005,924.549988,926.549988,926.549988,1446300000\n2003-05-06,926.549988,939.609985,926.380005,934.390015,934.390015,1649600000\n2003-05-07,934.390015,937.219971,926.409973,929.619995,929.619995,1531900000\n2003-05-08,929.619995,929.619995,919.719971,920.270020,920.270020,1379600000\n2003-05-09,920.270020,933.770020,920.270020,933.409973,933.409973,1326100000\n2003-05-12,933.409973,946.840027,929.299988,945.109985,945.109985,1378800000\n2003-05-13,945.109985,947.510010,938.909973,942.299988,942.299988,1418100000\n2003-05-14,942.299988,947.289978,935.239990,939.280029,939.280029,1401800000\n2003-05-15,939.280029,948.229980,938.789978,946.669983,946.669983,1508700000\n2003-05-16,946.669983,948.650024,938.599976,944.299988,944.299988,1505500000\n2003-05-19,944.299988,944.299988,920.229980,920.770020,920.770020,1375700000\n2003-05-20,920.770020,925.340027,912.049988,919.729980,919.729980,1505300000\n2003-05-21,919.729980,923.849976,914.909973,923.419983,923.419983,1457800000\n2003-05-22,923.419983,935.299988,922.539978,931.869995,931.869995,1448500000\n2003-05-23,931.869995,935.200012,927.419983,933.219971,933.219971,1201000000\n2003-05-27,933.219971,952.760010,927.330017,951.479980,951.479980,1532000000\n2003-05-28,951.479980,959.390015,950.119995,953.219971,953.219971,1559000000\n2003-05-29,953.219971,962.080017,946.229980,949.640015,949.640015,1685800000\n2003-05-30,949.640015,965.380005,949.640015,963.590027,963.590027,1688800000\n2003-06-02,963.590027,979.109985,963.590027,967.000000,967.000000,1662500000\n2003-06-03,967.000000,973.020020,964.469971,971.559998,971.559998,1450200000\n2003-06-04,971.559998,987.849976,970.719971,986.239990,986.239990,1618700000\n2003-06-05,986.239990,990.140015,978.130005,990.140015,990.140015,1693100000\n2003-06-06,990.140015,1007.690002,986.010010,987.760010,987.760010,1837200000\n2003-06-09,987.760010,987.760010,972.590027,975.929993,975.929993,1307000000\n2003-06-10,975.929993,984.840027,975.929993,984.840027,984.840027,1275400000\n2003-06-11,984.840027,997.479980,981.609985,997.479980,997.479980,1520000000\n2003-06-12,997.479980,1002.739990,991.270020,998.510010,998.510010,1553100000\n2003-06-13,998.510010,1000.919983,984.270020,988.609985,988.609985,1271600000\n2003-06-16,988.609985,1010.859985,988.609985,1010.739990,1010.739990,1345900000\n2003-06-17,1010.739990,1015.330017,1007.039978,1011.659973,1011.659973,1479700000\n2003-06-18,1011.659973,1015.119995,1004.609985,1010.090027,1010.090027,1488900000\n2003-06-19,1010.090027,1011.219971,993.080017,994.700012,994.700012,1530100000\n2003-06-20,994.700012,1002.090027,993.359985,995.690002,995.690002,1698000000\n2003-06-23,995.690002,995.690002,977.400024,981.640015,981.640015,1398100000\n2003-06-24,981.640015,987.840027,979.080017,983.450012,983.450012,1388300000\n2003-06-25,983.450012,991.640015,974.859985,975.320007,975.320007,1459200000\n2003-06-26,975.320007,986.530029,973.799988,985.820007,985.820007,1387400000\n2003-06-27,985.820007,988.880005,974.289978,976.219971,976.219971,1267800000\n2003-06-30,976.219971,983.609985,973.599976,974.500000,974.500000,1587200000\n2003-07-01,974.500000,983.260010,962.099976,982.320007,982.320007,1460200000\n2003-07-02,982.320007,993.780029,982.320007,993.750000,993.750000,1519300000\n2003-07-03,993.750000,995.000000,983.340027,985.700012,985.700012,775900000\n2003-07-07,985.700012,1005.559998,985.700012,1004.419983,1004.419983,1429100000\n2003-07-08,1004.419983,1008.919983,998.729980,1007.840027,1007.840027,1565700000\n2003-07-09,1007.840027,1010.429993,998.169983,1002.210022,1002.210022,1618000000\n2003-07-10,1002.210022,1002.210022,983.630005,988.700012,988.700012,1465700000\n2003-07-11,988.700012,1000.859985,988.700012,998.140015,998.140015,1212700000\n2003-07-14,998.140015,1015.409973,998.140015,1003.859985,1003.859985,1448900000\n2003-07-15,1003.859985,1009.609985,996.669983,1000.419983,1000.419983,1518600000\n2003-07-16,1000.419983,1003.469971,989.299988,994.090027,994.090027,1662000000\n2003-07-17,994.000000,994.000000,978.599976,981.729980,981.729980,1661400000\n2003-07-18,981.729980,994.250000,981.710022,993.320007,993.320007,1365200000\n2003-07-21,993.320007,993.320007,975.630005,978.799988,978.799988,1254200000\n2003-07-22,978.799988,990.289978,976.080017,988.109985,988.109985,1439700000\n2003-07-23,988.109985,989.859985,979.789978,988.609985,988.609985,1362700000\n2003-07-24,988.609985,998.890015,981.070007,981.599976,981.599976,1559000000\n2003-07-25,981.599976,998.710022,977.489990,998.679993,998.679993,1397500000\n2003-07-28,998.679993,1000.679993,993.590027,996.520020,996.520020,1328600000\n2003-07-29,996.520020,998.640015,984.150024,989.280029,989.280029,1508900000\n2003-07-30,989.280029,992.619995,985.960022,987.489990,987.489990,1391900000\n2003-07-31,987.489990,1004.590027,987.489990,990.309998,990.309998,1608000000\n2003-08-01,990.309998,990.309998,978.859985,980.150024,980.150024,1390600000\n2003-08-04,980.150024,985.750000,966.789978,982.820007,982.820007,1318700000\n2003-08-05,982.820007,982.820007,964.969971,965.460022,965.460022,1351700000\n2003-08-06,965.460022,975.739990,960.840027,967.080017,967.080017,1491000000\n2003-08-07,967.080017,974.890015,963.820007,974.119995,974.119995,1389300000\n2003-08-08,974.119995,980.570007,973.830017,977.590027,977.590027,1086600000\n2003-08-11,977.590027,985.460022,974.210022,980.590027,980.590027,1022200000\n2003-08-12,980.590027,990.409973,979.900024,990.349976,990.349976,1132300000\n2003-08-13,990.349976,992.500000,980.849976,984.030029,984.030029,1208800000\n2003-08-14,984.030029,991.909973,980.359985,990.510010,990.510010,1186800000\n2003-08-15,990.510010,992.390015,987.099976,990.669983,990.669983,636370000\n2003-08-18,990.669983,1000.349976,990.669983,999.739990,999.739990,1127600000\n2003-08-19,999.739990,1003.299988,995.299988,1002.349976,1002.349976,1300600000\n2003-08-20,1002.349976,1003.539978,996.619995,1000.299988,1000.299988,1210800000\n2003-08-21,1000.299988,1009.530029,999.330017,1003.270020,1003.270020,1407100000\n2003-08-22,1003.270020,1011.010010,992.619995,993.059998,993.059998,1308900000\n2003-08-25,993.059998,993.710022,987.909973,993.710022,993.710022,971700000\n2003-08-26,993.710022,997.929993,983.570007,996.729980,996.729980,1178700000\n2003-08-27,996.729980,998.049988,993.330017,996.789978,996.789978,1051400000\n2003-08-28,996.789978,1004.119995,991.419983,1002.840027,1002.840027,1165200000\n2003-08-29,1002.840027,1008.849976,999.520020,1008.010010,1008.010010,945100000\n2003-09-02,1008.010010,1022.590027,1005.729980,1021.989990,1021.989990,1470500000\n2003-09-03,1021.989990,1029.339966,1021.989990,1026.270020,1026.270020,1675600000\n2003-09-04,1026.270020,1029.170044,1022.190002,1027.969971,1027.969971,1453900000\n2003-09-05,1027.969971,1029.209961,1018.190002,1021.390015,1021.390015,1465200000\n2003-09-08,1021.390015,1032.410034,1021.390015,1031.640015,1031.640015,1299300000\n2003-09-09,1031.640015,1031.640015,1021.140015,1023.169983,1023.169983,1414800000\n2003-09-10,1023.169983,1023.169983,1009.739990,1010.919983,1010.919983,1582100000\n2003-09-11,1010.919983,1020.880005,1010.919983,1016.419983,1016.419983,1335900000\n2003-09-12,1016.419983,1019.650024,1007.710022,1018.630005,1018.630005,1236700000\n2003-09-15,1018.630005,1019.789978,1013.590027,1014.809998,1014.809998,1151300000\n2003-09-16,1014.809998,1029.660034,1014.809998,1029.319946,1029.319946,1403200000\n2003-09-17,1029.319946,1031.339966,1024.530029,1025.969971,1025.969971,1338210000\n2003-09-18,1025.969971,1040.160034,1025.750000,1039.579956,1039.579956,1498800000\n2003-09-19,1039.579956,1040.290039,1031.890015,1036.300049,1036.300049,1518600000\n2003-09-22,1036.300049,1036.300049,1018.299988,1022.820007,1022.820007,1278800000\n2003-09-23,1022.820007,1030.119995,1021.539978,1029.030029,1029.030029,1301700000\n2003-09-24,1029.030029,1029.829956,1008.929993,1009.380005,1009.380005,1556000000\n2003-09-25,1009.380005,1015.969971,1003.260010,1003.270020,1003.270020,1530000000\n2003-09-26,1003.270020,1003.450012,996.080017,996.849976,996.849976,1472500000\n2003-09-29,996.849976,1006.890015,995.309998,1006.580017,1006.580017,1366500000\n2003-09-30,1006.580017,1006.580017,990.359985,995.969971,995.969971,1590500000\n2003-10-01,995.969971,1018.219971,995.969971,1018.219971,1018.219971,1566300000\n2003-10-02,1018.219971,1021.869995,1013.380005,1020.239990,1020.239990,1269300000\n2003-10-03,1020.239990,1039.310059,1020.239990,1029.849976,1029.849976,1570500000\n2003-10-06,1029.849976,1036.479980,1029.150024,1034.349976,1034.349976,1025800000\n2003-10-07,1034.349976,1039.250000,1026.270020,1039.250000,1039.250000,1279500000\n2003-10-08,1039.250000,1040.060059,1030.959961,1033.780029,1033.780029,1262500000\n2003-10-09,1033.780029,1048.280029,1033.780029,1038.729980,1038.729980,1578700000\n2003-10-10,1038.729980,1040.839966,1035.739990,1038.060059,1038.060059,1108100000\n2003-10-13,1038.060059,1048.900024,1038.060059,1045.349976,1045.349976,1040500000\n2003-10-14,1045.349976,1049.489990,1040.839966,1049.479980,1049.479980,1271900000\n2003-10-15,1049.479980,1053.790039,1043.150024,1046.760010,1046.760010,1521100000\n2003-10-16,1046.760010,1052.939941,1044.040039,1050.069946,1050.069946,1417700000\n2003-10-17,1050.069946,1051.890015,1036.569946,1039.319946,1039.319946,1352000000\n2003-10-20,1039.319946,1044.689941,1036.130005,1044.680054,1044.680054,1172600000\n2003-10-21,1044.680054,1048.569946,1042.589966,1046.030029,1046.030029,1498000000\n2003-10-22,1046.030029,1046.030029,1028.390015,1030.359985,1030.359985,1647200000\n2003-10-23,1030.359985,1035.439941,1025.890015,1033.770020,1033.770020,1604300000\n2003-10-24,1033.770020,1033.770020,1018.320007,1028.910034,1028.910034,1420300000\n2003-10-27,1028.910034,1037.750000,1028.910034,1031.130005,1031.130005,1371800000\n2003-10-28,1031.130005,1046.790039,1031.130005,1046.790039,1046.790039,1629200000\n2003-10-29,1046.790039,1049.829956,1043.349976,1048.109985,1048.109985,1562600000\n2003-10-30,1048.109985,1052.810059,1043.819946,1046.939941,1046.939941,1629700000\n2003-10-31,1046.939941,1053.089966,1046.939941,1050.709961,1050.709961,1498900000\n2003-11-03,1050.709961,1061.439941,1050.709961,1059.020020,1059.020020,1378200000\n2003-11-04,1059.020020,1059.020020,1051.699951,1053.250000,1053.250000,1417600000\n2003-11-05,1053.250000,1054.540039,1044.880005,1051.810059,1051.810059,1401800000\n2003-11-06,1051.810059,1058.939941,1046.930054,1058.050049,1058.050049,1453900000\n2003-11-07,1058.050049,1062.390015,1052.170044,1053.209961,1053.209961,1440500000\n2003-11-10,1053.209961,1053.650024,1045.579956,1047.109985,1047.109985,1243600000\n2003-11-11,1047.109985,1048.229980,1043.459961,1046.569946,1046.569946,1162500000\n2003-11-12,1046.569946,1059.099976,1046.569946,1058.530029,1058.530029,1349300000\n2003-11-13,1058.560059,1059.619995,1052.959961,1058.410034,1058.410034,1383000000\n2003-11-14,1058.410034,1063.650024,1048.109985,1050.349976,1050.349976,1356100000\n2003-11-17,1050.349976,1050.349976,1035.280029,1043.630005,1043.630005,1374300000\n2003-11-18,1043.630005,1048.770020,1034.000000,1034.150024,1034.150024,1354300000\n2003-11-19,1034.150024,1043.949951,1034.150024,1042.439941,1042.439941,1326200000\n2003-11-20,1042.439941,1046.479980,1033.420044,1033.650024,1033.650024,1326700000\n2003-11-21,1033.650024,1037.569946,1031.199951,1035.280029,1035.280029,1273800000\n2003-11-24,1035.280029,1052.079956,1035.280029,1052.079956,1052.079956,1302800000\n2003-11-25,1052.079956,1058.050049,1049.310059,1053.890015,1053.890015,1333700000\n2003-11-26,1053.890015,1058.449951,1048.280029,1058.449951,1058.449951,1097700000\n2003-11-28,1058.449951,1060.630005,1056.770020,1058.199951,1058.199951,487220000\n2003-12-01,1058.199951,1070.469971,1058.199951,1070.119995,1070.119995,1375000000\n2003-12-02,1070.119995,1071.219971,1065.219971,1066.619995,1066.619995,1383200000\n2003-12-03,1066.619995,1074.300049,1064.630005,1064.729980,1064.729980,1441700000\n2003-12-04,1064.729980,1070.369995,1063.150024,1069.719971,1069.719971,1463100000\n2003-12-05,1069.719971,1069.719971,1060.089966,1061.500000,1061.500000,1265900000\n2003-12-08,1061.500000,1069.589966,1060.930054,1069.300049,1069.300049,1218900000\n2003-12-09,1069.300049,1071.939941,1059.160034,1060.180054,1060.180054,1465500000\n2003-12-10,1060.180054,1063.020020,1053.410034,1059.050049,1059.050049,1444000000\n2003-12-11,1059.050049,1073.630005,1059.050049,1071.209961,1071.209961,1441100000\n2003-12-12,1071.209961,1074.760010,1067.640015,1074.140015,1074.140015,1223100000\n2003-12-15,1074.140015,1082.790039,1068.000000,1068.040039,1068.040039,1520800000\n2003-12-16,1068.040039,1075.939941,1068.040039,1075.130005,1075.130005,1547900000\n2003-12-17,1075.130005,1076.540039,1071.140015,1076.479980,1076.479980,1441700000\n2003-12-18,1076.479980,1089.500000,1076.479980,1089.180054,1089.180054,1579900000\n2003-12-19,1089.180054,1091.060059,1084.189941,1088.660034,1088.660034,1657300000\n2003-12-22,1088.660034,1092.939941,1086.140015,1092.939941,1092.939941,1251700000\n2003-12-23,1092.939941,1096.949951,1091.729980,1096.020020,1096.020020,1145300000\n2003-12-24,1096.020020,1096.400024,1092.729980,1094.040039,1094.040039,518060000\n2003-12-26,1094.040039,1098.469971,1094.040039,1095.890015,1095.890015,356070000\n2003-12-29,1095.890015,1109.479980,1095.890015,1109.479980,1109.479980,1058800000\n2003-12-30,1109.479980,1109.750000,1106.410034,1109.640015,1109.640015,1012600000\n2003-12-31,1109.640015,1112.560059,1106.209961,1111.920044,1111.920044,1027500000\n2004-01-02,1111.920044,1118.849976,1105.079956,1108.479980,1108.479980,1153200000\n2004-01-05,1108.479980,1122.219971,1108.479980,1122.219971,1122.219971,1578200000\n2004-01-06,1122.219971,1124.459961,1118.439941,1123.670044,1123.670044,1494500000\n2004-01-07,1123.670044,1126.329956,1116.449951,1126.329956,1126.329956,1704900000\n2004-01-08,1126.329956,1131.920044,1124.910034,1131.920044,1131.920044,1868400000\n2004-01-09,1131.920044,1131.920044,1120.900024,1121.859985,1121.859985,1720700000\n2004-01-12,1121.859985,1127.849976,1120.900024,1127.229980,1127.229980,1510200000\n2004-01-13,1127.229980,1129.069946,1115.189941,1121.219971,1121.219971,1595900000\n2004-01-14,1121.219971,1130.750000,1121.219971,1130.520020,1130.520020,1514600000\n2004-01-15,1130.520020,1137.109985,1124.540039,1132.050049,1132.050049,1695000000\n2004-01-16,1132.050049,1139.829956,1132.050049,1139.829956,1139.829956,1721100000\n2004-01-20,1139.829956,1142.930054,1135.400024,1138.770020,1138.770020,1698200000\n2004-01-21,1138.770020,1149.209961,1134.619995,1147.619995,1147.619995,1757600000\n2004-01-22,1147.619995,1150.510010,1143.010010,1143.939941,1143.939941,1693700000\n2004-01-23,1143.939941,1150.310059,1136.849976,1141.550049,1141.550049,1561200000\n2004-01-26,1141.550049,1155.380005,1141.000000,1155.369995,1155.369995,1480600000\n2004-01-27,1155.369995,1155.369995,1144.050049,1144.050049,1144.050049,1673100000\n2004-01-28,1144.050049,1149.140015,1126.500000,1128.479980,1128.479980,1842000000\n2004-01-29,1128.479980,1134.390015,1122.380005,1134.109985,1134.109985,1921900000\n2004-01-30,1134.109985,1134.170044,1127.729980,1131.130005,1131.130005,1635000000\n2004-02-02,1131.130005,1142.449951,1127.869995,1135.260010,1135.260010,1599200000\n2004-02-03,1135.260010,1137.439941,1131.329956,1136.030029,1136.030029,1476900000\n2004-02-04,1136.030029,1136.030029,1124.739990,1126.520020,1126.520020,1634800000\n2004-02-05,1126.520020,1131.170044,1124.439941,1128.589966,1128.589966,1566600000\n2004-02-06,1128.589966,1142.790039,1128.390015,1142.760010,1142.760010,1477600000\n2004-02-09,1142.760010,1144.459961,1139.209961,1139.810059,1139.810059,1303500000\n2004-02-10,1139.810059,1147.020020,1138.699951,1145.540039,1145.540039,1403900000\n2004-02-11,1145.540039,1158.890015,1142.329956,1157.760010,1157.760010,1699300000\n2004-02-12,1157.760010,1157.760010,1151.439941,1152.109985,1152.109985,1464300000\n2004-02-13,1152.109985,1156.880005,1143.239990,1145.810059,1145.810059,1329200000\n2004-02-17,1145.810059,1158.979980,1145.810059,1156.989990,1156.989990,1396500000\n2004-02-18,1156.989990,1157.400024,1149.540039,1151.819946,1151.819946,1382400000\n2004-02-19,1151.819946,1158.569946,1146.849976,1147.060059,1147.060059,1562800000\n2004-02-20,1147.060059,1149.810059,1139.000000,1144.109985,1144.109985,1479600000\n2004-02-23,1144.109985,1146.689941,1136.979980,1140.989990,1140.989990,1380400000\n2004-02-24,1140.989990,1144.540039,1134.430054,1139.089966,1139.089966,1543600000\n2004-02-25,1139.089966,1145.239990,1138.959961,1143.670044,1143.670044,1360700000\n2004-02-26,1143.670044,1147.229980,1138.619995,1144.910034,1144.910034,1383900000\n2004-02-27,1145.800049,1151.680054,1141.800049,1144.939941,1144.939941,1540400000\n2004-03-01,1144.939941,1157.449951,1144.939941,1155.969971,1155.969971,1497100000\n2004-03-02,1155.969971,1156.540039,1147.310059,1149.099976,1149.099976,1476000000\n2004-03-03,1149.099976,1152.439941,1143.780029,1151.030029,1151.030029,1334500000\n2004-03-04,1151.030029,1154.969971,1149.810059,1154.869995,1154.869995,1265800000\n2004-03-05,1154.869995,1163.229980,1148.770020,1156.859985,1156.859985,1398200000\n2004-03-08,1156.859985,1159.939941,1146.969971,1147.199951,1147.199951,1254400000\n2004-03-09,1147.199951,1147.319946,1136.839966,1140.579956,1140.579956,1499400000\n2004-03-10,1140.579956,1141.449951,1122.530029,1123.890015,1123.890015,1648400000\n2004-03-11,1123.890015,1125.959961,1105.869995,1106.780029,1106.780029,1889900000\n2004-03-12,1106.780029,1120.630005,1106.780029,1120.569946,1120.569946,1388500000\n2004-03-15,1120.569946,1120.569946,1103.359985,1104.489990,1104.489990,1600600000\n2004-03-16,1104.489990,1113.760010,1102.609985,1110.699951,1110.699951,1500700000\n2004-03-17,1110.699951,1125.760010,1110.699951,1123.750000,1123.750000,1490100000\n2004-03-18,1123.750000,1125.500000,1113.250000,1122.319946,1122.319946,1369200000\n2004-03-19,1122.319946,1122.719971,1109.689941,1109.780029,1109.780029,1457400000\n2004-03-22,1109.780029,1109.780029,1089.540039,1095.400024,1095.400024,1452300000\n2004-03-23,1095.400024,1101.520020,1091.569946,1093.949951,1093.949951,1458200000\n2004-03-24,1093.949951,1098.319946,1087.160034,1091.329956,1091.329956,1527800000\n2004-03-25,1091.329956,1110.380005,1091.329956,1109.189941,1109.189941,1471700000\n2004-03-26,1109.189941,1115.270020,1106.130005,1108.060059,1108.060059,1319100000\n2004-03-29,1108.060059,1124.369995,1108.060059,1122.469971,1122.469971,1405500000\n2004-03-30,1122.469971,1127.599976,1119.660034,1127.000000,1127.000000,1332400000\n2004-03-31,1127.000000,1130.829956,1121.459961,1126.209961,1126.209961,1560700000\n2004-04-01,1126.209961,1135.670044,1126.199951,1132.170044,1132.170044,1560700000\n2004-04-02,1132.170044,1144.810059,1132.170044,1141.810059,1141.810059,1629200000\n2004-04-05,1141.810059,1150.569946,1141.640015,1150.569946,1150.569946,1413700000\n2004-04-06,1150.569946,1150.569946,1143.300049,1148.160034,1148.160034,1397700000\n2004-04-07,1148.160034,1148.160034,1138.410034,1140.530029,1140.530029,1458800000\n2004-04-08,1140.530029,1148.969971,1134.520020,1139.319946,1139.319946,1199800000\n2004-04-12,1139.319946,1147.290039,1139.319946,1145.199951,1145.199951,1102400000\n2004-04-13,1145.199951,1147.780029,1127.699951,1129.439941,1129.439941,1423200000\n2004-04-14,1129.439941,1132.520020,1122.150024,1128.170044,1128.170044,1547700000\n2004-04-15,1128.170044,1134.079956,1120.750000,1128.839966,1128.839966,1568700000\n2004-04-16,1128.839966,1136.800049,1126.900024,1134.609985,1134.609985,1487800000\n2004-04-19,1134.560059,1136.180054,1129.839966,1135.819946,1135.819946,1194900000\n2004-04-20,1135.819946,1139.260010,1118.089966,1118.150024,1118.150024,1508500000\n2004-04-21,1118.150024,1125.719971,1116.030029,1124.089966,1124.089966,1738100000\n2004-04-22,1124.089966,1142.770020,1121.949951,1139.930054,1139.930054,1826700000\n2004-04-23,1139.930054,1141.920044,1134.810059,1140.599976,1140.599976,1396100000\n2004-04-26,1140.599976,1145.079956,1132.910034,1135.530029,1135.530029,1290600000\n2004-04-27,1135.530029,1146.560059,1135.530029,1138.109985,1138.109985,1518000000\n2004-04-28,1138.109985,1138.109985,1121.699951,1122.410034,1122.410034,1855600000\n2004-04-29,1122.410034,1128.800049,1108.040039,1113.890015,1113.890015,1859000000\n2004-04-30,1113.890015,1119.260010,1107.229980,1107.300049,1107.300049,1634700000\n2004-05-03,1107.300049,1118.719971,1107.300049,1117.489990,1117.489990,1571600000\n2004-05-04,1117.489990,1127.739990,1112.890015,1119.550049,1119.550049,1662100000\n2004-05-05,1119.550049,1125.069946,1117.900024,1121.530029,1121.530029,1469000000\n2004-05-06,1121.530029,1121.530029,1106.300049,1113.989990,1113.989990,1509300000\n2004-05-07,1113.989990,1117.300049,1098.630005,1098.699951,1098.699951,1653600000\n2004-05-10,1098.699951,1098.699951,1079.630005,1087.119995,1087.119995,1918400000\n2004-05-11,1087.119995,1095.689941,1087.119995,1095.449951,1095.449951,1533800000\n2004-05-12,1095.449951,1097.550049,1076.319946,1097.280029,1097.280029,1697600000\n2004-05-13,1097.280029,1102.770020,1091.760010,1096.439941,1096.439941,1411100000\n2004-05-14,1096.439941,1102.099976,1088.239990,1095.699951,1095.699951,1335900000\n2004-05-17,1095.699951,1095.699951,1079.359985,1084.099976,1084.099976,1430100000\n2004-05-18,1084.099976,1094.099976,1084.099976,1091.489990,1091.489990,1353000000\n2004-05-19,1091.489990,1105.930054,1088.489990,1088.680054,1088.680054,1548600000\n2004-05-20,1088.680054,1092.619995,1085.430054,1089.189941,1089.189941,1211000000\n2004-05-21,1089.189941,1099.640015,1089.189941,1093.560059,1093.560059,1258600000\n2004-05-24,1093.560059,1101.280029,1091.770020,1095.410034,1095.410034,1227500000\n2004-05-25,1095.410034,1113.800049,1090.739990,1113.050049,1113.050049,1545700000\n2004-05-26,1113.050049,1116.709961,1109.910034,1114.939941,1114.939941,1369400000\n2004-05-27,1114.939941,1123.949951,1114.859985,1121.280029,1121.280029,1447500000\n2004-05-28,1121.280029,1122.689941,1118.099976,1120.680054,1120.680054,1172600000\n2004-06-01,1120.680054,1122.699951,1113.319946,1121.199951,1121.199951,1238000000\n2004-06-02,1121.199951,1128.099976,1118.640015,1124.989990,1124.989990,1251700000\n2004-06-03,1124.989990,1125.310059,1116.569946,1116.640015,1116.640015,1232400000\n2004-06-04,1116.640015,1129.170044,1116.640015,1122.500000,1122.500000,1115300000\n2004-06-07,1122.500000,1140.540039,1122.500000,1140.420044,1140.420044,1211800000\n2004-06-08,1140.420044,1142.180054,1135.449951,1142.180054,1142.180054,1190300000\n2004-06-09,1142.180054,1142.180054,1131.170044,1131.329956,1131.329956,1276800000\n2004-06-10,1131.329956,1136.469971,1131.329956,1136.469971,1136.469971,1160600000\n2004-06-14,1136.469971,1136.469971,1122.160034,1125.290039,1125.290039,1179400000\n2004-06-15,1125.290039,1137.359985,1125.290039,1132.010010,1132.010010,1345900000\n2004-06-16,1132.010010,1135.280029,1130.550049,1133.560059,1133.560059,1168400000\n2004-06-17,1133.560059,1133.560059,1126.890015,1132.050049,1132.050049,1296700000\n2004-06-18,1132.050049,1138.959961,1129.829956,1135.020020,1135.020020,1500600000\n2004-06-21,1135.020020,1138.050049,1129.640015,1130.300049,1130.300049,1123900000\n2004-06-22,1130.300049,1135.050049,1124.369995,1134.410034,1134.410034,1382300000\n2004-06-23,1134.410034,1145.150024,1131.729980,1144.060059,1144.060059,1444200000\n2004-06-24,1144.060059,1146.339966,1139.939941,1140.650024,1140.650024,1394900000\n2004-06-25,1140.650024,1145.969971,1134.239990,1134.430054,1134.430054,1812900000\n2004-06-28,1134.430054,1142.599976,1131.719971,1133.349976,1133.349976,1354600000\n2004-06-29,1133.349976,1138.260010,1131.810059,1136.199951,1136.199951,1375000000\n2004-06-30,1136.199951,1144.199951,1133.619995,1140.839966,1140.839966,1473800000\n2004-07-01,1140.839966,1140.839966,1123.060059,1128.939941,1128.939941,1495700000\n2004-07-02,1128.939941,1129.150024,1123.260010,1125.380005,1125.380005,1085000000\n2004-07-06,1125.380005,1125.380005,1113.209961,1116.209961,1116.209961,1283300000\n2004-07-07,1116.209961,1122.369995,1114.920044,1118.329956,1118.329956,1328600000\n2004-07-08,1118.329956,1119.119995,1108.719971,1109.109985,1109.109985,1401100000\n2004-07-09,1109.109985,1115.569946,1109.109985,1112.810059,1112.810059,1186300000\n2004-07-12,1112.810059,1116.109985,1106.709961,1114.349976,1114.349976,1114600000\n2004-07-13,1114.349976,1116.300049,1112.989990,1115.140015,1115.140015,1199700000\n2004-07-14,1115.140015,1119.599976,1107.829956,1111.469971,1111.469971,1462000000\n2004-07-15,1111.469971,1114.630005,1106.670044,1106.689941,1106.689941,1408700000\n2004-07-16,1106.689941,1112.170044,1101.069946,1101.390015,1101.390015,1450300000\n2004-07-19,1101.390015,1105.520020,1096.550049,1100.900024,1100.900024,1319900000\n2004-07-20,1100.900024,1108.880005,1099.099976,1108.670044,1108.670044,1445800000\n2004-07-21,1108.670044,1116.270020,1093.880005,1093.880005,1093.880005,1679500000\n2004-07-22,1093.880005,1099.660034,1084.160034,1096.839966,1096.839966,1680800000\n2004-07-23,1096.839966,1096.839966,1083.560059,1086.199951,1086.199951,1337500000\n2004-07-26,1086.199951,1089.819946,1078.780029,1084.069946,1084.069946,1413400000\n2004-07-27,1084.069946,1096.650024,1084.069946,1094.829956,1094.829956,1610800000\n2004-07-28,1094.829956,1098.839966,1082.170044,1095.420044,1095.420044,1554300000\n2004-07-29,1095.420044,1103.510010,1095.420044,1100.430054,1100.430054,1530100000\n2004-07-30,1100.430054,1103.729980,1096.959961,1101.719971,1101.719971,1298200000\n2004-08-02,1101.719971,1108.599976,1097.339966,1106.619995,1106.619995,1276000000\n2004-08-03,1106.619995,1106.619995,1099.260010,1099.689941,1099.689941,1338300000\n2004-08-04,1099.689941,1102.449951,1092.400024,1098.630005,1098.630005,1369200000\n2004-08-05,1098.630005,1098.790039,1079.979980,1080.699951,1080.699951,1397400000\n2004-08-06,1080.699951,1080.699951,1062.229980,1063.969971,1063.969971,1521000000\n2004-08-09,1063.969971,1069.459961,1063.969971,1065.219971,1065.219971,1086000000\n2004-08-10,1065.219971,1079.040039,1065.219971,1079.040039,1079.040039,1245600000\n2004-08-11,1079.040039,1079.040039,1065.920044,1075.790039,1075.790039,1410400000\n2004-08-12,1075.790039,1075.790039,1062.819946,1063.229980,1063.229980,1405100000\n2004-08-13,1063.229980,1067.579956,1060.719971,1064.800049,1064.800049,1175100000\n2004-08-16,1064.800049,1080.660034,1064.800049,1079.339966,1079.339966,1206200000\n2004-08-17,1079.339966,1086.780029,1079.339966,1081.709961,1081.709961,1267800000\n2004-08-18,1081.709961,1095.170044,1078.930054,1095.170044,1095.170044,1282500000\n2004-08-19,1095.170044,1095.170044,1086.280029,1091.229980,1091.229980,1249400000\n2004-08-20,1091.229980,1100.260010,1089.569946,1098.349976,1098.349976,1199900000\n2004-08-23,1098.349976,1101.400024,1094.729980,1095.680054,1095.680054,1021900000\n2004-08-24,1095.680054,1100.939941,1092.819946,1096.189941,1096.189941,1092500000\n2004-08-25,1096.189941,1106.290039,1093.239990,1104.959961,1104.959961,1192200000\n2004-08-26,1104.959961,1106.780029,1102.459961,1105.089966,1105.089966,1023600000\n2004-08-27,1105.089966,1109.680054,1104.619995,1107.770020,1107.770020,845400000\n2004-08-30,1107.770020,1107.770020,1099.150024,1099.150024,1099.150024,843100000\n2004-08-31,1099.150024,1104.239990,1094.719971,1104.239990,1104.239990,1138200000\n2004-09-01,1104.239990,1109.239990,1099.180054,1105.910034,1105.910034,1142100000\n2004-09-02,1105.910034,1119.109985,1105.599976,1118.310059,1118.310059,1118400000\n2004-09-03,1118.310059,1120.800049,1113.569946,1113.630005,1113.630005,924170000\n2004-09-07,1113.630005,1124.079956,1113.630005,1121.300049,1121.300049,1214400000\n2004-09-08,1121.300049,1123.050049,1116.270020,1116.270020,1116.270020,1246300000\n2004-09-09,1116.270020,1121.300049,1113.619995,1118.380005,1118.380005,1371300000\n2004-09-10,1118.380005,1125.260010,1114.390015,1123.920044,1123.920044,1261200000\n2004-09-13,1123.920044,1129.780029,1123.349976,1125.819946,1125.819946,1299800000\n2004-09-14,1125.819946,1129.459961,1124.719971,1128.329956,1128.329956,1204500000\n2004-09-15,1128.329956,1128.329956,1119.819946,1120.369995,1120.369995,1256000000\n2004-09-16,1120.369995,1126.060059,1120.369995,1123.500000,1123.500000,1113900000\n2004-09-17,1123.500000,1130.140015,1123.500000,1128.550049,1128.550049,1422600000\n2004-09-20,1128.550049,1128.550049,1120.339966,1122.199951,1122.199951,1197600000\n2004-09-21,1122.199951,1131.540039,1122.199951,1129.300049,1129.300049,1325000000\n2004-09-22,1129.300049,1129.300049,1112.670044,1113.560059,1113.560059,1379900000\n2004-09-23,1113.560059,1113.609985,1108.050049,1108.359985,1108.359985,1286300000\n2004-09-24,1108.359985,1113.810059,1108.359985,1110.109985,1110.109985,1255400000\n2004-09-27,1110.109985,1110.109985,1103.239990,1103.520020,1103.520020,1263500000\n2004-09-28,1103.520020,1111.770020,1101.290039,1110.060059,1110.060059,1396600000\n2004-09-29,1110.060059,1114.800049,1107.420044,1114.800049,1114.800049,1402900000\n2004-09-30,1114.800049,1116.310059,1109.680054,1114.579956,1114.579956,1748000000\n2004-10-01,1114.579956,1131.640015,1114.579956,1131.500000,1131.500000,1582200000\n2004-10-04,1131.500000,1140.130005,1131.500000,1135.170044,1135.170044,1534000000\n2004-10-05,1135.170044,1137.869995,1132.030029,1134.479980,1134.479980,1418400000\n2004-10-06,1134.479980,1142.050049,1132.939941,1142.050049,1142.050049,1416700000\n2004-10-07,1142.050049,1142.050049,1130.500000,1130.650024,1130.650024,1447500000\n2004-10-08,1130.650024,1132.920044,1120.189941,1122.140015,1122.140015,1291600000\n2004-10-11,1122.140015,1126.199951,1122.140015,1124.390015,1124.390015,943800000\n2004-10-12,1124.390015,1124.390015,1115.770020,1121.839966,1121.839966,1320100000\n2004-10-13,1121.839966,1127.010010,1109.630005,1113.650024,1113.650024,1546200000\n2004-10-14,1113.650024,1114.959961,1102.060059,1103.290039,1103.290039,1489500000\n2004-10-15,1103.290039,1113.170044,1102.140015,1108.199951,1108.199951,1645100000\n2004-10-18,1108.199951,1114.459961,1103.329956,1114.020020,1114.020020,1373300000\n2004-10-19,1114.020020,1117.959961,1103.150024,1103.229980,1103.229980,1737500000\n2004-10-20,1103.229980,1104.089966,1094.250000,1103.660034,1103.660034,1685700000\n2004-10-21,1103.660034,1108.869995,1098.469971,1106.489990,1106.489990,1673000000\n2004-10-22,1106.489990,1108.140015,1095.469971,1095.739990,1095.739990,1469600000\n2004-10-25,1095.739990,1096.810059,1090.290039,1094.800049,1094.800049,1380500000\n2004-10-26,1094.810059,1111.099976,1094.810059,1111.089966,1111.089966,1685400000\n2004-10-27,1111.089966,1126.290039,1107.430054,1125.400024,1125.400024,1741900000\n2004-10-28,1125.339966,1130.670044,1120.599976,1127.439941,1127.439941,1628200000\n2004-10-29,1127.439941,1131.400024,1124.619995,1130.199951,1130.199951,1500800000\n2004-11-01,1130.199951,1133.410034,1127.599976,1130.510010,1130.510010,1395900000\n2004-11-02,1130.510010,1140.479980,1128.119995,1130.560059,1130.560059,1659000000\n2004-11-03,1130.540039,1147.569946,1130.540039,1143.199951,1143.199951,1767500000\n2004-11-04,1143.199951,1161.670044,1142.339966,1161.670044,1161.670044,1782700000\n2004-11-05,1161.670044,1170.869995,1160.660034,1166.170044,1166.170044,1724400000\n2004-11-08,1166.170044,1166.770020,1162.319946,1164.890015,1164.890015,1358700000\n2004-11-09,1164.890015,1168.959961,1162.479980,1164.079956,1164.079956,1450800000\n2004-11-10,1164.079956,1169.250000,1162.510010,1162.910034,1162.910034,1504300000\n2004-11-11,1162.910034,1174.800049,1162.910034,1173.479980,1173.479980,1393000000\n2004-11-12,1173.479980,1184.170044,1171.430054,1184.170044,1184.170044,1531600000\n2004-11-15,1184.170044,1184.479980,1179.849976,1183.810059,1183.810059,1453300000\n2004-11-16,1183.810059,1183.810059,1175.319946,1175.430054,1175.430054,1364400000\n2004-11-17,1175.430054,1188.459961,1175.430054,1181.939941,1181.939941,1684200000\n2004-11-18,1181.939941,1184.900024,1180.150024,1183.550049,1183.550049,1456700000\n2004-11-19,1183.550049,1184.000000,1169.189941,1170.339966,1170.339966,1526600000\n2004-11-22,1170.339966,1178.180054,1167.890015,1177.239990,1177.239990,1392700000\n2004-11-23,1177.239990,1179.520020,1171.410034,1176.939941,1176.939941,1428300000\n2004-11-24,1176.939941,1182.459961,1176.939941,1181.760010,1181.760010,1149600000\n2004-11-26,1181.760010,1186.619995,1181.079956,1182.650024,1182.650024,504580000\n2004-11-29,1182.650024,1186.939941,1172.369995,1178.569946,1178.569946,1378500000\n2004-11-30,1178.569946,1178.660034,1173.810059,1173.819946,1173.819946,1553500000\n2004-12-01,1173.780029,1191.369995,1173.780029,1191.369995,1191.369995,1772800000\n2004-12-02,1191.369995,1194.800049,1186.719971,1190.329956,1190.329956,1774900000\n2004-12-03,1190.329956,1197.459961,1187.709961,1191.170044,1191.170044,1566700000\n2004-12-06,1191.170044,1192.410034,1185.180054,1190.250000,1190.250000,1354400000\n2004-12-07,1190.250000,1192.170044,1177.069946,1177.069946,1177.069946,1533900000\n2004-12-08,1177.069946,1184.050049,1177.069946,1182.810059,1182.810059,1525200000\n2004-12-09,1182.810059,1190.510010,1173.790039,1189.239990,1189.239990,1624700000\n2004-12-10,1189.239990,1191.449951,1185.239990,1188.000000,1188.000000,1443700000\n2004-12-13,1188.000000,1198.739990,1188.000000,1198.680054,1198.680054,1436100000\n2004-12-14,1198.680054,1205.290039,1197.839966,1203.380005,1203.380005,1544400000\n2004-12-15,1203.380005,1206.609985,1199.439941,1205.719971,1205.719971,1695800000\n2004-12-16,1205.719971,1207.969971,1198.410034,1203.209961,1203.209961,1793900000\n2004-12-17,1203.209961,1203.209961,1193.489990,1194.199951,1194.199951,2335000000\n2004-12-20,1194.199951,1203.430054,1193.359985,1194.650024,1194.650024,1422800000\n2004-12-21,1194.650024,1205.930054,1194.650024,1205.449951,1205.449951,1483700000\n2004-12-22,1205.449951,1211.420044,1203.849976,1209.569946,1209.569946,1390800000\n2004-12-23,1209.569946,1213.660034,1208.709961,1210.130005,1210.130005,956100000\n2004-12-27,1210.130005,1214.130005,1204.920044,1204.920044,1204.920044,922000000\n2004-12-28,1204.920044,1213.540039,1204.920044,1213.540039,1213.540039,983000000\n2004-12-29,1213.540039,1213.849976,1210.949951,1213.449951,1213.449951,925900000\n2004-12-30,1213.449951,1216.469971,1213.410034,1213.550049,1213.550049,829800000\n2004-12-31,1213.550049,1217.329956,1211.650024,1211.920044,1211.920044,786900000\n2005-01-03,1211.920044,1217.800049,1200.319946,1202.079956,1202.079956,1510800000\n2005-01-04,1202.079956,1205.839966,1185.390015,1188.050049,1188.050049,1721000000\n2005-01-05,1188.050049,1192.729980,1183.719971,1183.739990,1183.739990,1738900000\n2005-01-06,1183.739990,1191.630005,1183.270020,1187.890015,1187.890015,1569100000\n2005-01-07,1187.890015,1192.199951,1182.160034,1186.189941,1186.189941,1477900000\n2005-01-10,1186.189941,1194.780029,1184.800049,1190.250000,1190.250000,1490400000\n2005-01-11,1190.250000,1190.250000,1180.430054,1182.989990,1182.989990,1488800000\n2005-01-12,1182.989990,1187.920044,1175.640015,1187.699951,1187.699951,1562100000\n2005-01-13,1187.699951,1187.699951,1175.810059,1177.449951,1177.449951,1510300000\n2005-01-14,1177.449951,1185.209961,1177.449951,1184.520020,1184.520020,1335400000\n2005-01-18,1184.520020,1195.979980,1180.099976,1195.979980,1195.979980,1596800000\n2005-01-19,1195.979980,1195.979980,1184.410034,1184.630005,1184.630005,1498700000\n2005-01-20,1184.630005,1184.630005,1173.420044,1175.410034,1175.410034,1692000000\n2005-01-21,1175.410034,1179.449951,1167.819946,1167.869995,1167.869995,1643500000\n2005-01-24,1167.869995,1173.030029,1163.750000,1163.750000,1163.750000,1494600000\n2005-01-25,1163.750000,1174.300049,1163.750000,1168.410034,1168.410034,1610400000\n2005-01-26,1168.410034,1175.959961,1168.410034,1174.069946,1174.069946,1635900000\n2005-01-27,1174.069946,1177.500000,1170.150024,1174.550049,1174.550049,1600600000\n2005-01-28,1174.550049,1175.609985,1166.250000,1171.359985,1171.359985,1641800000\n2005-01-31,1171.359985,1182.069946,1171.359985,1181.270020,1181.270020,1679800000\n2005-02-01,1181.270020,1190.390015,1180.949951,1189.410034,1189.410034,1681980000\n2005-02-02,1189.410034,1195.250000,1188.920044,1193.189941,1193.189941,1561740000\n2005-02-03,1193.189941,1193.189941,1185.640015,1189.890015,1189.890015,1554460000\n2005-02-04,1189.890015,1203.469971,1189.670044,1203.030029,1203.030029,1648160000\n2005-02-07,1203.030029,1204.150024,1199.270020,1201.719971,1201.719971,1347270000\n2005-02-08,1201.719971,1205.109985,1200.160034,1202.300049,1202.300049,1416170000\n2005-02-09,1202.300049,1203.829956,1191.540039,1191.989990,1191.989990,1511040000\n2005-02-10,1191.989990,1198.750000,1191.540039,1197.010010,1197.010010,1491670000\n2005-02-11,1197.010010,1208.380005,1193.280029,1205.300049,1205.300049,1562300000\n2005-02-14,1205.300049,1206.930054,1203.589966,1206.140015,1206.140015,1290180000\n2005-02-15,1206.140015,1212.439941,1205.520020,1210.119995,1210.119995,1527080000\n2005-02-16,1210.119995,1212.439941,1205.060059,1210.339966,1210.339966,1490100000\n2005-02-17,1210.339966,1211.329956,1200.739990,1200.750000,1200.750000,1580120000\n2005-02-18,1200.750000,1202.920044,1197.349976,1201.589966,1201.589966,1551200000\n2005-02-22,1201.589966,1202.479980,1184.160034,1184.160034,1184.160034,1744940000\n2005-02-23,1184.160034,1193.520020,1184.160034,1190.800049,1190.800049,1501090000\n2005-02-24,1190.800049,1200.420044,1187.800049,1200.199951,1200.199951,1518750000\n2005-02-25,1200.199951,1212.150024,1199.609985,1211.369995,1211.369995,1523680000\n2005-02-28,1211.369995,1211.369995,1198.130005,1203.599976,1203.599976,1795480000\n2005-03-01,1203.599976,1212.250000,1203.599976,1210.410034,1210.410034,1708060000\n2005-03-02,1210.410034,1215.790039,1204.219971,1210.079956,1210.079956,1568540000\n2005-03-03,1210.079956,1215.719971,1204.449951,1210.469971,1210.469971,1616240000\n2005-03-04,1210.469971,1224.760010,1210.469971,1222.119995,1222.119995,1636820000\n2005-03-07,1222.119995,1229.109985,1222.119995,1225.310059,1225.310059,1488830000\n2005-03-08,1225.310059,1225.689941,1218.569946,1219.430054,1219.430054,1523090000\n2005-03-09,1219.430054,1219.430054,1206.660034,1207.010010,1207.010010,1704970000\n2005-03-10,1207.010010,1211.229980,1201.410034,1209.250000,1209.250000,1604020000\n2005-03-11,1209.250000,1213.040039,1198.150024,1200.079956,1200.079956,1449820000\n2005-03-14,1200.079956,1206.829956,1199.510010,1206.829956,1206.829956,1437430000\n2005-03-15,1206.829956,1210.540039,1197.750000,1197.750000,1197.750000,1513530000\n2005-03-16,1197.750000,1197.750000,1185.609985,1188.069946,1188.069946,1653190000\n2005-03-17,1188.069946,1193.280029,1186.339966,1190.209961,1190.209961,1581930000\n2005-03-18,1190.209961,1191.979980,1182.780029,1189.650024,1189.650024,2344370000\n2005-03-21,1189.650024,1189.650024,1178.819946,1183.780029,1183.780029,1819440000\n2005-03-22,1183.780029,1189.589966,1171.630005,1171.709961,1171.709961,2114470000\n2005-03-23,1171.709961,1176.260010,1168.699951,1172.530029,1172.530029,2246870000\n2005-03-24,1172.530029,1180.109985,1171.420044,1171.420044,1171.420044,1721720000\n2005-03-28,1171.420044,1179.910034,1171.420044,1174.280029,1174.280029,1746220000\n2005-03-29,1174.280029,1179.390015,1163.689941,1165.359985,1165.359985,2223250000\n2005-03-30,1165.359985,1181.540039,1165.359985,1181.410034,1181.410034,2097110000\n2005-03-31,1181.410034,1184.530029,1179.489990,1180.589966,1180.589966,2214230000\n2005-04-01,1180.589966,1189.800049,1169.910034,1172.920044,1172.920044,2168690000\n2005-04-04,1172.790039,1178.609985,1167.719971,1176.119995,1176.119995,2079770000\n2005-04-05,1176.119995,1183.560059,1176.119995,1181.390015,1181.390015,1870800000\n2005-04-06,1181.390015,1189.339966,1181.390015,1184.069946,1184.069946,1797400000\n2005-04-07,1184.069946,1191.880005,1183.810059,1191.140015,1191.140015,1900620000\n2005-04-08,1191.140015,1191.750000,1181.130005,1181.199951,1181.199951,1661330000\n2005-04-11,1181.199951,1184.069946,1178.689941,1181.209961,1181.209961,1525310000\n2005-04-12,1181.209961,1190.170044,1170.849976,1187.760010,1187.760010,1979830000\n2005-04-13,1187.760010,1187.760010,1171.400024,1173.790039,1173.790039,2049740000\n2005-04-14,1173.790039,1174.670044,1161.699951,1162.050049,1162.050049,2355040000\n2005-04-15,1162.050049,1162.050049,1141.920044,1142.619995,1142.619995,2689960000\n2005-04-18,1142.619995,1148.920044,1139.800049,1145.979980,1145.979980,2180670000\n2005-04-19,1145.979980,1154.670044,1145.979980,1152.780029,1152.780029,2142700000\n2005-04-20,1152.780029,1155.500000,1136.150024,1137.500000,1137.500000,2217050000\n2005-04-21,1137.500000,1159.949951,1137.500000,1159.949951,1159.949951,2308560000\n2005-04-22,1159.949951,1159.949951,1142.949951,1152.119995,1152.119995,2045880000\n2005-04-25,1152.119995,1164.050049,1152.119995,1162.099976,1162.099976,1795030000\n2005-04-26,1162.099976,1164.800049,1151.829956,1151.829956,1151.829956,1959740000\n2005-04-27,1151.739990,1159.869995,1144.420044,1156.380005,1156.380005,2151520000\n2005-04-28,1156.380005,1156.380005,1143.219971,1143.219971,1143.219971,2182270000\n2005-04-29,1143.219971,1156.969971,1139.189941,1156.849976,1156.849976,2362360000\n2005-05-02,1156.849976,1162.869995,1154.709961,1162.160034,1162.160034,1980040000\n2005-05-03,1162.160034,1166.890015,1156.709961,1161.170044,1161.170044,2167020000\n2005-05-04,1161.170044,1176.010010,1161.170044,1175.650024,1175.650024,2306480000\n2005-05-05,1175.650024,1178.619995,1166.770020,1172.630005,1172.630005,1997100000\n2005-05-06,1172.630005,1177.750000,1170.500000,1171.349976,1171.349976,1707200000\n2005-05-09,1171.349976,1178.869995,1169.380005,1178.839966,1178.839966,1857020000\n2005-05-10,1178.839966,1178.839966,1162.979980,1166.219971,1166.219971,1889660000\n2005-05-11,1166.219971,1171.770020,1157.709961,1171.109985,1171.109985,1834970000\n2005-05-12,1171.109985,1173.369995,1157.760010,1159.359985,1159.359985,1995290000\n2005-05-13,1159.359985,1163.750000,1146.180054,1154.050049,1154.050049,2188590000\n2005-05-16,1154.050049,1165.750000,1153.640015,1165.689941,1165.689941,1856860000\n2005-05-17,1165.689941,1174.349976,1159.859985,1173.800049,1173.800049,1887260000\n2005-05-18,1173.800049,1187.900024,1173.800049,1185.560059,1185.560059,2266320000\n2005-05-19,1185.560059,1191.089966,1184.489990,1191.079956,1191.079956,1775860000\n2005-05-20,1191.079956,1191.219971,1185.189941,1189.280029,1189.280029,1631750000\n2005-05-23,1189.280029,1197.439941,1188.760010,1193.859985,1193.859985,1681170000\n2005-05-24,1193.859985,1195.290039,1189.869995,1194.069946,1194.069946,1681000000\n2005-05-25,1194.069946,1194.069946,1185.959961,1190.010010,1190.010010,1742180000\n2005-05-26,1190.010010,1198.949951,1190.010010,1197.619995,1197.619995,1654110000\n2005-05-27,1197.619995,1199.560059,1195.280029,1198.780029,1198.780029,1381430000\n2005-05-31,1198.780029,1198.780029,1191.500000,1191.500000,1191.500000,1840680000\n2005-06-01,1191.500000,1205.640015,1191.030029,1202.219971,1202.219971,1810100000\n2005-06-02,1202.270020,1204.670044,1198.420044,1204.290039,1204.290039,1813790000\n2005-06-03,1204.290039,1205.089966,1194.550049,1196.020020,1196.020020,1627520000\n2005-06-06,1196.020020,1198.780029,1192.750000,1197.510010,1197.510010,1547120000\n2005-06-07,1197.510010,1208.849976,1197.260010,1197.260010,1197.260010,1851370000\n2005-06-08,1197.260010,1201.969971,1193.329956,1194.670044,1194.670044,1715490000\n2005-06-09,1194.670044,1201.859985,1191.089966,1200.930054,1200.930054,1824120000\n2005-06-10,1200.930054,1202.790039,1192.640015,1198.109985,1198.109985,1664180000\n2005-06-13,1198.109985,1206.030029,1194.510010,1200.819946,1200.819946,1661350000\n2005-06-14,1200.819946,1207.530029,1200.180054,1203.910034,1203.910034,1698150000\n2005-06-15,1203.910034,1208.079956,1198.660034,1206.579956,1206.579956,1840440000\n2005-06-16,1206.550049,1212.099976,1205.469971,1210.959961,1210.959961,1776040000\n2005-06-17,1210.930054,1219.550049,1210.930054,1216.959961,1216.959961,2407370000\n2005-06-20,1216.959961,1219.099976,1210.650024,1216.099976,1216.099976,1714530000\n2005-06-21,1216.099976,1217.130005,1211.859985,1213.609985,1213.609985,1720700000\n2005-06-22,1213.609985,1219.589966,1211.689941,1213.880005,1213.880005,1823250000\n2005-06-23,1213.880005,1216.449951,1200.719971,1200.729980,1200.729980,2029920000\n2005-06-24,1200.729980,1200.900024,1191.449951,1191.569946,1191.569946,2418800000\n2005-06-27,1191.569946,1194.329956,1188.300049,1190.689941,1190.689941,1738620000\n2005-06-28,1190.689941,1202.540039,1190.689941,1201.569946,1201.569946,1772410000\n2005-06-29,1201.569946,1204.069946,1198.699951,1199.849976,1199.849976,1769280000\n2005-06-30,1199.849976,1203.270020,1190.510010,1191.329956,1191.329956,2109490000\n2005-07-01,1191.329956,1197.890015,1191.329956,1194.439941,1194.439941,1593820000\n2005-07-05,1194.439941,1206.339966,1192.489990,1204.989990,1204.989990,1805820000\n2005-07-06,1204.989990,1206.109985,1194.780029,1194.939941,1194.939941,1883470000\n2005-07-07,1194.939941,1198.459961,1183.550049,1197.869995,1197.869995,1952440000\n2005-07-08,1197.869995,1212.729980,1197.199951,1211.859985,1211.859985,1900810000\n2005-07-11,1211.859985,1220.030029,1211.859985,1219.439941,1219.439941,1846300000\n2005-07-12,1219.439941,1225.540039,1216.599976,1222.209961,1222.209961,1932010000\n2005-07-13,1222.209961,1224.459961,1219.640015,1223.290039,1223.290039,1812500000\n2005-07-14,1223.290039,1233.160034,1223.290039,1226.500000,1226.500000,2048710000\n2005-07-15,1226.500000,1229.530029,1223.500000,1227.920044,1227.920044,1716400000\n2005-07-18,1227.920044,1227.920044,1221.130005,1221.130005,1221.130005,1582100000\n2005-07-19,1221.130005,1230.339966,1221.130005,1229.349976,1229.349976,2041280000\n2005-07-20,1229.349976,1236.560059,1222.910034,1235.199951,1235.199951,2063340000\n2005-07-21,1235.199951,1235.829956,1224.699951,1227.040039,1227.040039,2129840000\n2005-07-22,1227.040039,1234.189941,1226.150024,1233.680054,1233.680054,1766990000\n2005-07-25,1233.680054,1238.359985,1228.150024,1229.030029,1229.030029,1717580000\n2005-07-26,1229.030029,1234.420044,1229.030029,1231.160034,1231.160034,1934180000\n2005-07-27,1231.160034,1237.640015,1230.150024,1236.790039,1236.790039,1945800000\n2005-07-28,1236.790039,1245.150024,1235.810059,1243.719971,1243.719971,2001680000\n2005-07-29,1243.719971,1245.040039,1234.180054,1234.180054,1234.180054,1789600000\n2005-08-01,1234.180054,1239.099976,1233.800049,1235.349976,1235.349976,1716870000\n2005-08-02,1235.349976,1244.689941,1235.349976,1244.119995,1244.119995,2043120000\n2005-08-03,1244.119995,1245.859985,1240.569946,1245.040039,1245.040039,1999980000\n2005-08-04,1245.040039,1245.040039,1235.150024,1235.859985,1235.859985,1981220000\n2005-08-05,1235.859985,1235.859985,1225.619995,1226.420044,1226.420044,1930280000\n2005-08-08,1226.420044,1232.280029,1222.670044,1223.130005,1223.130005,1804140000\n2005-08-09,1223.130005,1234.109985,1223.130005,1231.380005,1231.380005,1897520000\n2005-08-10,1231.380005,1242.689941,1226.579956,1229.130005,1229.130005,2172320000\n2005-08-11,1229.130005,1237.810059,1228.329956,1237.810059,1237.810059,1941560000\n2005-08-12,1237.810059,1237.810059,1225.869995,1230.390015,1230.390015,1709300000\n2005-08-15,1230.400024,1236.239990,1226.199951,1233.869995,1233.869995,1562880000\n2005-08-16,1233.869995,1233.869995,1219.050049,1219.339966,1219.339966,1820410000\n2005-08-17,1219.339966,1225.630005,1218.069946,1220.239990,1220.239990,1859150000\n2005-08-18,1220.239990,1222.640015,1215.930054,1219.020020,1219.020020,1808170000\n2005-08-19,1219.020020,1225.079956,1219.020020,1219.709961,1219.709961,1558790000\n2005-08-22,1219.709961,1228.959961,1216.469971,1221.729980,1221.729980,1621330000\n2005-08-23,1221.729980,1223.040039,1214.439941,1217.589966,1217.589966,1678620000\n2005-08-24,1217.569946,1224.150024,1209.369995,1209.589966,1209.589966,1930800000\n2005-08-25,1209.589966,1213.729980,1209.569946,1212.369995,1212.369995,1571110000\n2005-08-26,1212.400024,1212.400024,1204.229980,1205.099976,1205.099976,1541090000\n2005-08-29,1205.099976,1214.280029,1201.530029,1212.280029,1212.280029,1599450000\n2005-08-30,1212.280029,1212.280029,1201.069946,1208.410034,1208.410034,1916470000\n2005-08-31,1208.410034,1220.359985,1204.400024,1220.329956,1220.329956,2365510000\n2005-09-01,1220.329956,1227.290039,1216.180054,1221.589966,1221.589966,2229860000\n2005-09-02,1221.589966,1224.449951,1217.750000,1218.020020,1218.020020,1640160000\n2005-09-06,1218.020020,1233.609985,1218.020020,1233.390015,1233.390015,1932090000\n2005-09-07,1233.390015,1237.060059,1230.930054,1236.359985,1236.359985,2067700000\n2005-09-08,1236.359985,1236.359985,1229.510010,1231.670044,1231.670044,1955380000\n2005-09-09,1231.670044,1243.130005,1231.670044,1241.479980,1241.479980,1992560000\n2005-09-12,1241.479980,1242.599976,1239.150024,1240.560059,1240.560059,1938050000\n2005-09-13,1240.569946,1240.569946,1231.199951,1231.199951,1231.199951,2082360000\n2005-09-14,1231.199951,1234.739990,1226.160034,1227.160034,1227.160034,1986750000\n2005-09-15,1227.160034,1231.880005,1224.849976,1227.729980,1227.729980,2079340000\n2005-09-16,1228.420044,1237.949951,1228.420044,1237.910034,1237.910034,3152470000\n2005-09-19,1237.910034,1237.910034,1227.650024,1231.020020,1231.020020,2076540000\n2005-09-20,1231.020020,1236.489990,1220.069946,1221.339966,1221.339966,2319250000\n2005-09-21,1221.339966,1221.520020,1209.890015,1210.199951,1210.199951,2548150000\n2005-09-22,1210.199951,1216.640015,1205.349976,1214.619995,1214.619995,2424720000\n2005-09-23,1214.619995,1218.829956,1209.800049,1215.290039,1215.290039,1973020000\n2005-09-26,1215.290039,1222.560059,1211.839966,1215.630005,1215.630005,2022220000\n2005-09-27,1215.630005,1220.170044,1211.109985,1215.660034,1215.660034,1976270000\n2005-09-28,1215.660034,1220.979980,1212.719971,1216.890015,1216.890015,2106980000\n2005-09-29,1216.890015,1228.699951,1211.540039,1227.680054,1227.680054,2176120000\n2005-09-30,1227.680054,1229.569946,1225.219971,1228.810059,1228.810059,2097520000\n2005-10-03,1228.810059,1233.339966,1225.150024,1226.699951,1226.699951,2097490000\n2005-10-04,1226.699951,1229.880005,1214.020020,1214.469971,1214.469971,2341420000\n2005-10-05,1214.469971,1214.469971,1196.250000,1196.390015,1196.390015,2546780000\n2005-10-06,1196.390015,1202.140015,1181.920044,1191.489990,1191.489990,2792030000\n2005-10-07,1191.489990,1199.709961,1191.459961,1195.900024,1195.900024,2126080000\n2005-10-10,1195.900024,1196.520020,1186.119995,1187.329956,1187.329956,2195990000\n2005-10-11,1187.329956,1193.099976,1183.160034,1184.869995,1184.869995,2299040000\n2005-10-12,1184.869995,1190.020020,1173.650024,1177.680054,1177.680054,2491280000\n2005-10-13,1177.680054,1179.560059,1168.199951,1176.839966,1176.839966,2351150000\n2005-10-14,1176.839966,1187.130005,1175.439941,1186.569946,1186.569946,2188940000\n2005-10-17,1186.569946,1191.209961,1184.479980,1190.099976,1190.099976,2054570000\n2005-10-18,1190.099976,1190.099976,1178.130005,1178.140015,1178.140015,2197010000\n2005-10-19,1178.140015,1195.760010,1170.550049,1195.760010,1195.760010,2703590000\n2005-10-20,1195.760010,1197.300049,1173.300049,1177.800049,1177.800049,2617250000\n2005-10-21,1177.800049,1186.459961,1174.920044,1179.589966,1179.589966,2470920000\n2005-10-24,1179.589966,1199.390015,1179.589966,1199.380005,1199.380005,2197790000\n2005-10-25,1199.380005,1201.300049,1189.290039,1196.540039,1196.540039,2312470000\n2005-10-26,1196.540039,1204.010010,1191.380005,1191.380005,1191.380005,2467750000\n2005-10-27,1191.380005,1192.650024,1178.890015,1178.900024,1178.900024,2395370000\n2005-10-28,1178.900024,1198.410034,1178.900024,1198.410034,1198.410034,2379400000\n2005-10-31,1198.410034,1211.430054,1198.410034,1207.010010,1207.010010,2567470000\n2005-11-01,1207.010010,1207.339966,1201.660034,1202.760010,1202.760010,2457850000\n2005-11-02,1202.760010,1215.170044,1201.069946,1214.760010,1214.760010,2648090000\n2005-11-03,1214.760010,1224.699951,1214.760010,1219.939941,1219.939941,2716630000\n2005-11-04,1219.939941,1222.520020,1214.449951,1220.140015,1220.140015,2050510000\n2005-11-07,1220.140015,1224.180054,1217.290039,1222.810059,1222.810059,1987580000\n2005-11-08,1222.810059,1222.810059,1216.079956,1218.589966,1218.589966,1965050000\n2005-11-09,1218.589966,1226.589966,1216.530029,1220.650024,1220.650024,2214460000\n2005-11-10,1220.650024,1232.410034,1215.050049,1230.959961,1230.959961,2378460000\n2005-11-11,1230.959961,1235.699951,1230.719971,1234.719971,1234.719971,1773140000\n2005-11-14,1234.719971,1237.199951,1231.780029,1233.760010,1233.760010,1899780000\n2005-11-15,1233.760010,1237.939941,1226.410034,1229.010010,1229.010010,2359370000\n2005-11-16,1229.010010,1232.239990,1227.180054,1231.209961,1231.209961,2121580000\n2005-11-17,1231.209961,1242.959961,1231.209961,1242.800049,1242.800049,2298040000\n2005-11-18,1242.800049,1249.579956,1240.709961,1248.270020,1248.270020,2453290000\n2005-11-21,1248.270020,1255.890015,1246.900024,1254.849976,1254.849976,2117350000\n2005-11-22,1254.849976,1261.900024,1251.400024,1261.229980,1261.229980,2291420000\n2005-11-23,1261.229980,1270.640015,1259.510010,1265.609985,1265.609985,1985400000\n2005-11-25,1265.609985,1268.780029,1265.540039,1268.250000,1268.250000,724940000\n2005-11-28,1268.250000,1268.439941,1257.170044,1257.459961,1257.459961,2016900000\n2005-11-29,1257.459961,1266.180054,1257.459961,1257.479980,1257.479980,2268340000\n2005-11-30,1257.479980,1260.930054,1249.390015,1249.479980,1249.479980,2374690000\n2005-12-01,1249.479980,1266.170044,1249.479980,1264.670044,1264.670044,2614830000\n2005-12-02,1264.670044,1266.849976,1261.420044,1265.079956,1265.079956,2125580000\n2005-12-05,1265.079956,1265.079956,1258.119995,1262.089966,1262.089966,2325840000\n2005-12-06,1262.089966,1272.890015,1262.089966,1263.699951,1263.699951,2110740000\n2005-12-07,1263.699951,1264.849976,1253.020020,1257.369995,1257.369995,2093830000\n2005-12-08,1257.369995,1263.359985,1250.910034,1255.839966,1255.839966,2178300000\n2005-12-09,1255.839966,1263.079956,1254.239990,1259.369995,1259.369995,1896290000\n2005-12-12,1259.369995,1263.859985,1255.520020,1260.430054,1260.430054,1876550000\n2005-12-13,1260.430054,1272.109985,1258.560059,1267.430054,1267.430054,2390020000\n2005-12-14,1267.430054,1275.800049,1267.069946,1272.739990,1272.739990,2145520000\n2005-12-15,1272.739990,1275.170044,1267.739990,1270.939941,1270.939941,2180590000\n2005-12-16,1270.939941,1275.239990,1267.319946,1267.319946,1267.319946,2584190000\n2005-12-19,1267.319946,1270.510010,1259.280029,1259.920044,1259.920044,2208810000\n2005-12-20,1259.920044,1263.859985,1257.209961,1259.619995,1259.619995,1996690000\n2005-12-21,1259.619995,1269.369995,1259.619995,1262.790039,1262.790039,2065170000\n2005-12-22,1262.790039,1268.189941,1262.500000,1268.119995,1268.119995,1888500000\n2005-12-23,1268.119995,1269.760010,1265.920044,1268.660034,1268.660034,1285810000\n2005-12-27,1268.660034,1271.829956,1256.540039,1256.540039,1256.540039,1540470000\n2005-12-28,1256.540039,1261.099976,1256.540039,1258.170044,1258.170044,1422360000\n2005-12-29,1258.170044,1260.609985,1254.180054,1254.420044,1254.420044,1382540000\n2005-12-30,1254.420044,1254.420044,1246.589966,1248.290039,1248.290039,1443500000\n2006-01-03,1248.290039,1270.219971,1245.739990,1268.800049,1268.800049,2554570000\n2006-01-04,1268.800049,1275.369995,1267.739990,1273.459961,1273.459961,2515330000\n2006-01-05,1273.459961,1276.910034,1270.300049,1273.479980,1273.479980,2433340000\n2006-01-06,1273.479980,1286.089966,1273.479980,1285.449951,1285.449951,2446560000\n2006-01-09,1285.449951,1290.780029,1284.819946,1290.150024,1290.150024,2301490000\n2006-01-10,1290.150024,1290.150024,1283.760010,1289.689941,1289.689941,2373080000\n2006-01-11,1289.719971,1294.900024,1288.119995,1294.180054,1294.180054,2406130000\n2006-01-12,1294.180054,1294.180054,1285.040039,1286.060059,1286.060059,2318350000\n2006-01-13,1286.060059,1288.959961,1282.780029,1287.609985,1287.609985,2206510000\n2006-01-17,1287.609985,1287.609985,1278.609985,1282.930054,1282.930054,2179970000\n2006-01-18,1282.930054,1282.930054,1272.079956,1277.930054,1277.930054,2233200000\n2006-01-19,1277.930054,1287.790039,1277.930054,1285.040039,1285.040039,2444020000\n2006-01-20,1285.040039,1285.040039,1260.920044,1261.489990,1261.489990,2845810000\n2006-01-23,1261.489990,1268.189941,1261.489990,1263.819946,1263.819946,2256070000\n2006-01-24,1263.819946,1271.469971,1263.819946,1266.859985,1266.859985,2608720000\n2006-01-25,1266.859985,1271.869995,1259.420044,1264.680054,1264.680054,2617060000\n2006-01-26,1264.680054,1276.439941,1264.680054,1273.829956,1273.829956,2856780000\n2006-01-27,1273.829956,1286.380005,1273.829956,1283.719971,1283.719971,2623620000\n2006-01-30,1283.719971,1287.939941,1283.510010,1285.189941,1285.189941,2282730000\n2006-01-31,1285.199951,1285.199951,1276.849976,1280.079956,1280.079956,2708310000\n2006-02-01,1280.079956,1283.329956,1277.569946,1282.459961,1282.459961,2589410000\n2006-02-02,1282.459961,1282.459961,1267.719971,1270.839966,1270.839966,2565300000\n2006-02-03,1270.839966,1270.869995,1261.020020,1264.030029,1264.030029,2282210000\n2006-02-06,1264.030029,1267.040039,1261.619995,1265.020020,1265.020020,2132360000\n2006-02-07,1265.020020,1265.780029,1253.609985,1254.780029,1254.780029,2366370000\n2006-02-08,1254.780029,1266.469971,1254.780029,1265.650024,1265.650024,2456860000\n2006-02-09,1265.650024,1274.560059,1262.800049,1263.780029,1263.780029,2441920000\n2006-02-10,1263.819946,1269.890015,1254.979980,1266.989990,1266.989990,2290050000\n2006-02-13,1266.989990,1266.989990,1258.339966,1262.859985,1262.859985,1850080000\n2006-02-14,1262.859985,1278.209961,1260.800049,1275.530029,1275.530029,2437940000\n2006-02-15,1275.530029,1281.000000,1271.060059,1280.000000,1280.000000,2317590000\n2006-02-16,1280.000000,1289.390015,1280.000000,1289.380005,1289.380005,2251490000\n2006-02-17,1289.380005,1289.469971,1284.069946,1287.239990,1287.239990,2128260000\n2006-02-21,1287.239990,1291.920044,1281.329956,1283.030029,1283.030029,2104320000\n2006-02-22,1283.030029,1294.170044,1283.030029,1292.670044,1292.670044,2222380000\n2006-02-23,1292.670044,1293.839966,1285.140015,1287.790039,1287.790039,2144210000\n2006-02-24,1287.790039,1292.109985,1285.619995,1289.430054,1289.430054,1933010000\n2006-02-27,1289.430054,1297.569946,1289.430054,1294.119995,1294.119995,1975320000\n2006-02-28,1294.119995,1294.119995,1278.660034,1280.660034,1280.660034,2370860000\n2006-03-01,1280.660034,1291.800049,1280.660034,1291.239990,1291.239990,2308320000\n2006-03-02,1291.239990,1291.239990,1283.209961,1289.140015,1289.140015,2494590000\n2006-03-03,1289.140015,1297.329956,1284.199951,1287.229980,1287.229980,2152950000\n2006-03-06,1287.229980,1288.229980,1275.670044,1278.260010,1278.260010,2280190000\n2006-03-07,1278.260010,1278.260010,1271.109985,1275.880005,1275.880005,2268050000\n2006-03-08,1275.880005,1280.329956,1268.420044,1278.469971,1278.469971,2442870000\n2006-03-09,1278.469971,1282.739990,1272.229980,1272.229980,1272.229980,2140110000\n2006-03-10,1272.229980,1284.369995,1271.109985,1281.420044,1281.420044,2123450000\n2006-03-13,1281.579956,1287.369995,1281.579956,1284.130005,1284.130005,2070330000\n2006-03-14,1284.130005,1298.140015,1282.670044,1297.479980,1297.479980,2165270000\n2006-03-15,1297.479980,1304.400024,1294.969971,1303.020020,1303.020020,2293000000\n2006-03-16,1303.020020,1310.449951,1303.020020,1305.329956,1305.329956,2292180000\n2006-03-17,1305.329956,1309.790039,1305.319946,1307.250000,1307.250000,2549620000\n2006-03-20,1307.250000,1310.000000,1303.589966,1305.079956,1305.079956,1976830000\n2006-03-21,1305.079956,1310.880005,1295.819946,1297.229980,1297.229980,2147370000\n2006-03-22,1297.229980,1305.969971,1295.810059,1305.040039,1305.040039,2039810000\n2006-03-23,1305.040039,1305.040039,1298.109985,1301.670044,1301.670044,1980940000\n2006-03-24,1301.670044,1306.530029,1298.890015,1302.949951,1302.949951,2326070000\n2006-03-27,1302.949951,1303.739990,1299.089966,1301.609985,1301.609985,2029700000\n2006-03-28,1301.609985,1306.239990,1291.839966,1293.229980,1293.229980,2148580000\n2006-03-29,1293.229980,1305.599976,1293.229980,1302.890015,1302.890015,2143540000\n2006-03-30,1302.890015,1310.150024,1296.719971,1300.250000,1300.250000,2294560000\n2006-03-31,1300.250000,1303.000000,1294.869995,1294.869995,1294.869995,2236710000\n2006-04-03,1302.880005,1309.189941,1296.650024,1297.810059,1297.810059,2494080000\n2006-04-04,1297.810059,1307.550049,1294.709961,1305.930054,1305.930054,2147660000\n2006-04-05,1305.930054,1312.810059,1304.819946,1311.560059,1311.560059,2420020000\n2006-04-06,1311.560059,1311.989990,1302.439941,1309.040039,1309.040039,2281680000\n2006-04-07,1309.040039,1314.069946,1294.180054,1295.500000,1295.500000,2082470000\n2006-04-10,1295.510010,1300.739990,1293.170044,1296.619995,1296.619995,1898320000\n2006-04-11,1296.599976,1300.709961,1282.959961,1286.569946,1286.569946,2232880000\n2006-04-12,1286.569946,1290.930054,1286.449951,1288.119995,1288.119995,1938100000\n2006-04-13,1288.119995,1292.089966,1283.369995,1289.119995,1289.119995,1891940000\n2006-04-17,1289.119995,1292.449951,1280.739990,1285.329956,1285.329956,1794650000\n2006-04-18,1285.329956,1309.020020,1285.329956,1307.280029,1307.280029,2595440000\n2006-04-19,1307.650024,1310.390015,1302.790039,1309.930054,1309.930054,2447310000\n2006-04-20,1309.930054,1318.160034,1306.380005,1311.459961,1311.459961,2512920000\n2006-04-21,1311.459961,1317.670044,1306.589966,1311.280029,1311.280029,2392630000\n2006-04-24,1311.280029,1311.280029,1303.790039,1308.109985,1308.109985,2117330000\n2006-04-25,1308.109985,1310.790039,1299.170044,1301.739990,1301.739990,2366380000\n2006-04-26,1301.739990,1310.969971,1301.739990,1305.410034,1305.410034,2502690000\n2006-04-27,1305.410034,1315.000000,1295.569946,1309.719971,1309.719971,2772010000\n2006-04-28,1309.719971,1316.040039,1306.160034,1310.609985,1310.609985,2419920000\n2006-05-01,1310.609985,1317.209961,1303.459961,1305.189941,1305.189941,2437040000\n2006-05-02,1305.189941,1313.660034,1305.189941,1313.209961,1313.209961,2403470000\n2006-05-03,1313.209961,1313.469971,1303.920044,1308.119995,1308.119995,2395230000\n2006-05-04,1307.849976,1315.140015,1307.849976,1312.250000,1312.250000,2431450000\n2006-05-05,1312.250000,1326.530029,1312.250000,1325.760010,1325.760010,2294760000\n2006-05-08,1325.760010,1326.699951,1322.869995,1324.660034,1324.660034,2151300000\n2006-05-09,1324.660034,1326.599976,1322.479980,1325.140015,1325.140015,2157290000\n2006-05-10,1324.569946,1325.510010,1317.439941,1322.849976,1322.849976,2268550000\n2006-05-11,1322.630005,1322.630005,1303.449951,1305.920044,1305.920044,2531520000\n2006-05-12,1305.880005,1305.880005,1290.380005,1291.239990,1291.239990,2567970000\n2006-05-15,1291.189941,1294.810059,1284.510010,1294.500000,1294.500000,2505660000\n2006-05-16,1294.500000,1297.880005,1288.510010,1292.079956,1292.079956,2386210000\n2006-05-17,1291.729980,1291.729980,1267.310059,1270.319946,1270.319946,2830200000\n2006-05-18,1270.250000,1274.890015,1261.750000,1261.810059,1261.810059,2537490000\n2006-05-19,1261.810059,1272.150024,1256.280029,1267.030029,1267.030029,2982300000\n2006-05-22,1267.030029,1268.770020,1252.979980,1262.069946,1262.069946,2773010000\n2006-05-23,1262.060059,1273.670044,1256.150024,1256.579956,1256.579956,2605250000\n2006-05-24,1256.560059,1264.530029,1245.339966,1258.569946,1258.569946,2999030000\n2006-05-25,1258.410034,1273.260010,1258.410034,1272.880005,1272.880005,2372730000\n2006-05-26,1272.709961,1280.540039,1272.500000,1280.160034,1280.160034,1814020000\n2006-05-30,1280.040039,1280.040039,1259.869995,1259.869995,1259.869995,2176190000\n2006-05-31,1259.380005,1270.089966,1259.380005,1270.089966,1270.089966,2692160000\n2006-06-01,1270.050049,1285.709961,1269.189941,1285.709961,1285.709961,2360160000\n2006-06-02,1285.709961,1290.680054,1280.219971,1288.219971,1288.219971,2295540000\n2006-06-05,1288.160034,1288.160034,1264.660034,1265.290039,1265.290039,2313470000\n2006-06-06,1265.229980,1269.880005,1254.459961,1263.849976,1263.849976,2697650000\n2006-06-07,1263.609985,1272.469971,1255.770020,1256.150024,1256.150024,2644170000\n2006-06-08,1256.079956,1259.849976,1235.180054,1257.930054,1257.930054,3543790000\n2006-06-09,1257.930054,1262.579956,1250.030029,1252.300049,1252.300049,2214000000\n2006-06-12,1252.270020,1255.219971,1236.430054,1237.439941,1237.439941,2247010000\n2006-06-13,1236.079956,1243.369995,1222.520020,1223.689941,1223.689941,3215770000\n2006-06-14,1223.660034,1231.459961,1219.290039,1230.040039,1230.040039,2667990000\n2006-06-15,1230.010010,1258.640015,1230.010010,1256.160034,1256.160034,2775480000\n2006-06-16,1256.160034,1256.270020,1246.329956,1251.540039,1251.540039,2783390000\n2006-06-19,1251.540039,1255.930054,1237.170044,1240.130005,1240.130005,2517200000\n2006-06-20,1240.119995,1249.010010,1238.869995,1240.119995,1240.119995,2232950000\n2006-06-21,1240.089966,1257.959961,1240.089966,1252.199951,1252.199951,2361230000\n2006-06-22,1251.920044,1251.920044,1241.530029,1245.599976,1245.599976,2148180000\n2006-06-23,1245.589966,1253.130005,1241.430054,1244.500000,1244.500000,2017270000\n2006-06-26,1244.500000,1250.920044,1243.680054,1250.560059,1250.560059,1878580000\n2006-06-27,1250.550049,1253.369995,1238.939941,1239.199951,1239.199951,2203130000\n2006-06-28,1238.989990,1247.060059,1237.589966,1246.000000,1246.000000,2085490000\n2006-06-29,1245.939941,1272.880005,1245.939941,1272.869995,1272.869995,2621250000\n2006-06-30,1272.859985,1276.300049,1270.199951,1270.199951,1270.199951,3049560000\n2006-07-03,1270.060059,1280.380005,1270.060059,1280.189941,1280.189941,1114470000\n2006-07-05,1280.050049,1280.050049,1265.910034,1270.910034,1270.910034,2165070000\n2006-07-06,1270.579956,1278.319946,1270.579956,1274.079956,1274.079956,2009160000\n2006-07-07,1274.079956,1275.380005,1263.130005,1265.479980,1265.479980,1988150000\n2006-07-10,1265.459961,1274.060059,1264.459961,1267.339966,1267.339966,1854590000\n2006-07-11,1267.260010,1273.640015,1259.650024,1272.430054,1272.430054,2310850000\n2006-07-12,1272.390015,1273.310059,1257.290039,1258.599976,1258.599976,2250450000\n2006-07-13,1258.579956,1258.579956,1241.430054,1242.280029,1242.280029,2545760000\n2006-07-14,1242.290039,1242.699951,1228.449951,1236.199951,1236.199951,2467120000\n2006-07-17,1236.199951,1240.069946,1231.489990,1234.489990,1234.489990,2146410000\n2006-07-18,1234.479980,1239.859985,1224.540039,1236.859985,1236.859985,2481750000\n2006-07-19,1236.739990,1261.810059,1236.739990,1259.810059,1259.810059,2701980000\n2006-07-20,1259.810059,1262.560059,1249.130005,1249.130005,1249.130005,2345580000\n2006-07-21,1249.119995,1250.959961,1238.719971,1240.290039,1240.290039,2704090000\n2006-07-24,1240.250000,1262.500000,1240.250000,1260.910034,1260.910034,2312720000\n2006-07-25,1260.910034,1272.390015,1257.189941,1268.880005,1268.880005,2563930000\n2006-07-26,1268.869995,1273.890015,1261.939941,1268.400024,1268.400024,2667710000\n2006-07-27,1268.199951,1275.849976,1261.920044,1263.199951,1263.199951,2776710000\n2006-07-28,1263.150024,1280.420044,1263.150024,1278.550049,1278.550049,2480420000\n2006-07-31,1278.530029,1278.660034,1274.310059,1276.660034,1276.660034,2461300000\n2006-08-01,1278.530029,1278.660034,1265.709961,1270.920044,1270.920044,2527690000\n2006-08-02,1270.729980,1283.420044,1270.729980,1277.410034,1277.410034,2610750000\n2006-08-03,1278.219971,1283.959961,1271.250000,1280.270020,1280.270020,2728440000\n2006-08-04,1280.260010,1292.920044,1273.819946,1279.359985,1279.359985,2530970000\n2006-08-07,1279.310059,1279.310059,1273.000000,1275.770020,1275.770020,2045660000\n2006-08-08,1275.670044,1282.750000,1268.369995,1271.479980,1271.479980,2457840000\n2006-08-09,1271.130005,1283.739990,1264.729980,1265.949951,1265.949951,2555180000\n2006-08-10,1265.719971,1272.550049,1261.300049,1271.810059,1271.810059,2402190000\n2006-08-11,1271.640015,1271.640015,1262.079956,1266.739990,1266.739990,2004540000\n2006-08-14,1266.670044,1278.900024,1266.670044,1268.209961,1268.209961,2118020000\n2006-08-15,1268.189941,1286.229980,1268.189941,1285.579956,1285.579956,2334100000\n2006-08-16,1285.270020,1296.209961,1285.270020,1295.430054,1295.430054,2554570000\n2006-08-17,1295.369995,1300.780029,1292.709961,1297.479980,1297.479980,2458340000\n2006-08-18,1297.479980,1302.300049,1293.569946,1302.300049,1302.300049,2033910000\n2006-08-21,1302.300049,1302.300049,1295.510010,1297.520020,1297.520020,1759240000\n2006-08-22,1297.520020,1302.489990,1294.439941,1298.819946,1298.819946,1908740000\n2006-08-23,1298.729980,1301.500000,1289.819946,1292.989990,1292.989990,1893670000\n2006-08-24,1292.969971,1297.229980,1291.400024,1296.060059,1296.060059,1930320000\n2006-08-25,1295.920044,1298.880005,1292.390015,1295.089966,1295.089966,1667580000\n2006-08-28,1295.089966,1305.020020,1293.969971,1301.780029,1301.780029,1834920000\n2006-08-29,1301.569946,1305.020020,1295.290039,1304.280029,1304.280029,2093720000\n2006-08-30,1303.699951,1306.739990,1302.150024,1305.369995,1305.369995,2060690000\n2006-08-31,1304.250000,1306.109985,1302.449951,1303.819946,1303.819946,1974540000\n2006-09-01,1303.800049,1312.030029,1303.800049,1311.010010,1311.010010,1800520000\n2006-09-05,1310.939941,1314.670044,1308.819946,1313.250000,1313.250000,2114480000\n2006-09-06,1313.040039,1313.040039,1299.280029,1300.260010,1300.260010,2329870000\n2006-09-07,1300.209961,1301.250000,1292.130005,1294.020020,1294.020020,2325850000\n2006-09-08,1294.020020,1300.140015,1294.020020,1298.920044,1298.920044,2132890000\n2006-09-11,1298.859985,1302.359985,1290.930054,1299.540039,1299.540039,2506430000\n2006-09-12,1299.530029,1314.280029,1299.530029,1313.000000,1313.000000,2791580000\n2006-09-13,1312.739990,1319.920044,1311.119995,1318.069946,1318.069946,2597220000\n2006-09-14,1318.000000,1318.000000,1313.250000,1316.280029,1316.280029,2351220000\n2006-09-15,1316.280029,1324.650024,1316.280029,1319.660034,1319.660034,3198030000\n2006-09-18,1319.849976,1324.869995,1318.160034,1321.180054,1321.180054,2325080000\n2006-09-19,1321.170044,1322.040039,1312.170044,1317.640015,1317.640015,2390850000\n2006-09-20,1318.280029,1328.530029,1318.280029,1325.180054,1325.180054,2543070000\n2006-09-21,1324.890015,1328.189941,1315.449951,1318.030029,1318.030029,2627440000\n2006-09-22,1318.030029,1318.030029,1310.939941,1314.780029,1314.780029,2162880000\n2006-09-25,1314.780029,1329.349976,1311.579956,1326.369995,1326.369995,2710240000\n2006-09-26,1326.349976,1336.599976,1325.300049,1336.349976,1336.349976,2673350000\n2006-09-27,1336.119995,1340.079956,1333.540039,1336.589966,1336.589966,2749190000\n2006-09-28,1336.560059,1340.280029,1333.750000,1338.880005,1338.880005,2397820000\n2006-09-29,1339.150024,1339.880005,1335.640015,1335.849976,1335.849976,2273430000\n2006-10-02,1335.819946,1338.540039,1330.280029,1331.319946,1331.319946,2154480000\n2006-10-03,1331.319946,1338.310059,1327.099976,1334.109985,1334.109985,2682690000\n2006-10-04,1333.810059,1350.199951,1331.479980,1350.199951,1350.199951,3019880000\n2006-10-05,1349.839966,1353.790039,1347.750000,1353.219971,1353.219971,2817240000\n2006-10-06,1353.219971,1353.219971,1344.209961,1349.589966,1349.589966,2523000000\n2006-10-09,1349.579956,1352.689941,1346.550049,1350.660034,1350.660034,1935170000\n2006-10-10,1350.619995,1354.229980,1348.599976,1353.420044,1353.420044,2376140000\n2006-10-11,1353.280029,1353.969971,1343.569946,1349.949951,1349.949951,2521000000\n2006-10-12,1349.939941,1363.760010,1349.939941,1362.829956,1362.829956,2514350000\n2006-10-13,1362.819946,1366.630005,1360.500000,1365.619995,1365.619995,2482920000\n2006-10-16,1365.609985,1370.199951,1364.479980,1369.060059,1369.060059,2305920000\n2006-10-17,1369.050049,1369.050049,1356.869995,1364.050049,1364.050049,2519620000\n2006-10-18,1363.930054,1372.869995,1360.949951,1365.800049,1365.800049,2658840000\n2006-10-19,1365.949951,1368.089966,1362.060059,1366.959961,1366.959961,2619830000\n2006-10-20,1366.939941,1368.660034,1362.099976,1368.599976,1368.599976,2526410000\n2006-10-23,1368.579956,1377.400024,1363.939941,1377.020020,1377.020020,2480430000\n2006-10-24,1377.020020,1377.780029,1372.420044,1377.380005,1377.380005,2876890000\n2006-10-25,1377.359985,1383.609985,1376.000000,1382.219971,1382.219971,2953540000\n2006-10-26,1382.209961,1389.449951,1379.469971,1389.079956,1389.079956,2793350000\n2006-10-27,1388.890015,1388.890015,1375.849976,1377.339966,1377.339966,2458450000\n2006-10-30,1377.300049,1381.219971,1373.459961,1377.930054,1377.930054,2770440000\n2006-10-31,1377.930054,1381.209961,1372.189941,1377.939941,1377.939941,2803030000\n2006-11-01,1377.760010,1381.949951,1366.260010,1367.810059,1367.810059,2821160000\n2006-11-02,1367.439941,1368.390015,1362.209961,1367.339966,1367.339966,2646180000\n2006-11-03,1367.310059,1371.680054,1360.979980,1364.300049,1364.300049,2419730000\n2006-11-06,1364.270020,1381.400024,1364.270020,1379.780029,1379.780029,2533550000\n2006-11-07,1379.750000,1388.189941,1379.189941,1382.839966,1382.839966,2636390000\n2006-11-08,1382.500000,1388.609985,1379.329956,1385.719971,1385.719971,2814820000\n2006-11-09,1385.430054,1388.920044,1377.310059,1378.329956,1378.329956,3012050000\n2006-11-10,1378.329956,1381.040039,1375.599976,1380.900024,1380.900024,2290200000\n2006-11-13,1380.579956,1387.609985,1378.800049,1384.420044,1384.420044,2386340000\n2006-11-14,1384.359985,1394.489990,1379.069946,1393.219971,1393.219971,3027480000\n2006-11-15,1392.910034,1401.349976,1392.130005,1396.569946,1396.569946,2831130000\n2006-11-16,1396.530029,1403.760010,1396.530029,1399.760010,1399.760010,2835730000\n2006-11-17,1399.760010,1401.209961,1394.550049,1401.199951,1401.199951,2726100000\n2006-11-20,1401.170044,1404.369995,1397.849976,1400.500000,1400.500000,2546710000\n2006-11-21,1400.430054,1403.489990,1399.989990,1402.810059,1402.810059,2597940000\n2006-11-22,1402.689941,1407.890015,1402.260010,1406.089966,1406.089966,2237710000\n2006-11-24,1405.939941,1405.939941,1399.250000,1400.949951,1400.949951,832550000\n2006-11-27,1400.949951,1400.949951,1381.439941,1381.959961,1381.959961,2711210000\n2006-11-28,1381.609985,1387.910034,1377.829956,1386.719971,1386.719971,2639750000\n2006-11-29,1386.109985,1401.140015,1386.109985,1399.479980,1399.479980,2790970000\n2006-11-30,1399.469971,1406.300049,1393.829956,1400.630005,1400.630005,4006230000\n2006-12-01,1400.630005,1402.459961,1385.930054,1396.709961,1396.709961,2800980000\n2006-12-04,1396.670044,1411.229980,1396.670044,1409.119995,1409.119995,2766320000\n2006-12-05,1409.099976,1415.270020,1408.780029,1414.760010,1414.760010,2755700000\n2006-12-06,1414.400024,1415.930054,1411.050049,1412.900024,1412.900024,2725280000\n2006-12-07,1412.859985,1418.270020,1406.800049,1407.290039,1407.290039,2743150000\n2006-12-08,1407.270020,1414.089966,1403.670044,1409.839966,1409.839966,2440460000\n2006-12-11,1409.810059,1415.599976,1408.560059,1413.040039,1413.040039,2289900000\n2006-12-12,1413.000000,1413.780029,1404.750000,1411.560059,1411.560059,2738170000\n2006-12-13,1411.319946,1416.640015,1411.050049,1413.209961,1413.209961,2552260000\n2006-12-14,1413.160034,1427.229980,1413.160034,1425.489990,1425.489990,2729700000\n2006-12-15,1425.479980,1431.630005,1425.479980,1427.089966,1427.089966,3229580000\n2006-12-18,1427.079956,1431.810059,1420.650024,1422.479980,1422.479980,2568140000\n2006-12-19,1422.420044,1428.300049,1414.880005,1425.550049,1425.550049,2717060000\n2006-12-20,1425.510010,1429.050049,1423.510010,1423.530029,1423.530029,2387630000\n2006-12-21,1423.199951,1426.400024,1415.900024,1418.300049,1418.300049,2322410000\n2006-12-22,1418.099976,1418.819946,1410.280029,1410.760010,1410.760010,1647590000\n2006-12-26,1410.750000,1417.910034,1410.449951,1416.900024,1416.900024,1310310000\n2006-12-27,1416.630005,1427.719971,1416.630005,1426.839966,1426.839966,1667370000\n2006-12-28,1426.770020,1427.260010,1422.050049,1424.729980,1424.729980,1508570000\n2006-12-29,1424.709961,1427.000000,1416.839966,1418.300049,1418.300049,1678200000\n2007-01-03,1418.030029,1429.420044,1407.859985,1416.599976,1416.599976,3429160000\n2007-01-04,1416.599976,1421.839966,1408.430054,1418.339966,1418.339966,3004460000\n2007-01-05,1418.339966,1418.339966,1405.750000,1409.709961,1409.709961,2919400000\n2007-01-08,1409.260010,1414.979980,1403.969971,1412.839966,1412.839966,2763340000\n2007-01-09,1412.839966,1415.609985,1405.420044,1412.109985,1412.109985,3038380000\n2007-01-10,1408.699951,1415.989990,1405.319946,1414.849976,1414.849976,2764660000\n2007-01-11,1414.839966,1427.119995,1414.839966,1423.819946,1423.819946,2857870000\n2007-01-12,1423.819946,1431.229980,1422.579956,1430.729980,1430.729980,2686480000\n2007-01-16,1430.729980,1433.930054,1428.619995,1431.900024,1431.900024,2599530000\n2007-01-17,1431.770020,1435.270020,1428.569946,1430.619995,1430.619995,2690270000\n2007-01-18,1430.589966,1432.959961,1424.209961,1426.369995,1426.369995,2822430000\n2007-01-19,1426.349976,1431.569946,1425.189941,1430.500000,1430.500000,2777480000\n2007-01-22,1430.469971,1431.390015,1420.400024,1422.949951,1422.949951,2540120000\n2007-01-23,1422.949951,1431.329956,1421.660034,1427.989990,1427.989990,2975070000\n2007-01-24,1427.959961,1440.140015,1427.959961,1440.130005,1440.130005,2783180000\n2007-01-25,1440.119995,1440.689941,1422.339966,1423.900024,1423.900024,2994330000\n2007-01-26,1423.900024,1427.270020,1416.959961,1422.180054,1422.180054,2626620000\n2007-01-29,1422.030029,1426.939941,1418.459961,1420.619995,1420.619995,2730480000\n2007-01-30,1420.609985,1428.819946,1420.609985,1428.819946,1428.819946,2706250000\n2007-01-31,1428.650024,1441.609985,1424.780029,1438.239990,1438.239990,2976690000\n2007-02-01,1437.900024,1446.640015,1437.900024,1445.939941,1445.939941,2914890000\n2007-02-02,1445.939941,1449.329956,1444.489990,1448.390015,1448.390015,2569450000\n2007-02-05,1448.329956,1449.380005,1443.849976,1446.989990,1446.989990,2439430000\n2007-02-06,1446.979980,1450.189941,1443.400024,1448.000000,1448.000000,2608710000\n2007-02-07,1447.410034,1452.989990,1446.439941,1450.020020,1450.020020,2618820000\n2007-02-08,1449.989990,1450.449951,1442.810059,1448.310059,1448.310059,2816180000\n2007-02-09,1448.250000,1452.449951,1433.439941,1438.060059,1438.060059,2951810000\n2007-02-12,1438.000000,1439.109985,1431.439941,1433.369995,1433.369995,2395680000\n2007-02-13,1433.219971,1444.410034,1433.219971,1444.260010,1444.260010,2652150000\n2007-02-14,1443.910034,1457.650024,1443.910034,1455.300049,1455.300049,2699290000\n2007-02-15,1455.150024,1457.969971,1453.189941,1456.810059,1456.810059,2490920000\n2007-02-16,1456.770020,1456.770020,1451.569946,1455.540039,1455.540039,2399450000\n2007-02-20,1455.530029,1460.530029,1449.199951,1459.680054,1459.680054,2337860000\n2007-02-21,1459.599976,1459.599976,1452.020020,1457.630005,1457.630005,2606980000\n2007-02-22,1457.290039,1461.569946,1450.510010,1456.380005,1456.380005,1950770000\n2007-02-23,1456.219971,1456.219971,1448.359985,1451.189941,1451.189941,2579950000\n2007-02-26,1451.040039,1456.949951,1445.479980,1449.369995,1449.369995,2822170000\n2007-02-27,1449.250000,1449.250000,1389.420044,1399.040039,1399.040039,4065230000\n2007-02-28,1398.640015,1415.890015,1396.650024,1406.819946,1406.819946,3925250000\n2007-03-01,1406.800049,1409.459961,1380.869995,1403.170044,1403.170044,3874910000\n2007-03-02,1403.160034,1403.400024,1386.869995,1387.170044,1387.170044,3312260000\n2007-03-05,1387.109985,1391.859985,1373.969971,1374.119995,1374.119995,3480520000\n2007-03-06,1374.060059,1397.900024,1374.060059,1395.410034,1395.410034,3358160000\n2007-03-07,1395.020020,1401.160034,1390.640015,1391.969971,1391.969971,3141350000\n2007-03-08,1391.880005,1407.930054,1391.880005,1401.890015,1401.890015,3014850000\n2007-03-09,1401.890015,1410.150024,1397.300049,1402.839966,1402.839966,2623050000\n2007-03-12,1402.800049,1409.339966,1398.400024,1406.599976,1406.599976,2664000000\n2007-03-13,1406.229980,1406.229980,1377.709961,1377.949951,1377.949951,3485570000\n2007-03-14,1377.859985,1388.089966,1363.979980,1387.170044,1387.170044,3758350000\n2007-03-15,1387.109985,1395.729980,1385.160034,1392.280029,1392.280029,2821900000\n2007-03-16,1392.280029,1397.510010,1383.630005,1386.949951,1386.949951,3393640000\n2007-03-19,1386.949951,1403.199951,1386.949951,1402.060059,1402.060059,2777180000\n2007-03-20,1402.040039,1411.530029,1400.699951,1410.939941,1410.939941,2795940000\n2007-03-21,1410.920044,1437.770020,1409.750000,1435.040039,1435.040039,3184770000\n2007-03-22,1435.040039,1437.660034,1429.880005,1434.540039,1434.540039,3129970000\n2007-03-23,1434.540039,1438.890015,1433.209961,1436.109985,1436.109985,2619020000\n2007-03-26,1436.109985,1437.650024,1423.280029,1437.500000,1437.500000,2754660000\n2007-03-27,1437.489990,1437.489990,1425.540039,1428.609985,1428.609985,2673040000\n2007-03-28,1428.349976,1428.349976,1414.069946,1417.229980,1417.229980,3000440000\n2007-03-29,1417.170044,1426.239990,1413.270020,1422.530029,1422.530029,2854710000\n2007-03-30,1422.520020,1429.219971,1408.900024,1420.859985,1420.859985,2903960000\n2007-04-02,1420.829956,1425.489990,1416.369995,1424.550049,1424.550049,2875880000\n2007-04-03,1424.270020,1440.569946,1424.270020,1437.770020,1437.770020,2921760000\n2007-04-04,1437.750000,1440.160034,1435.079956,1439.369995,1439.369995,2616320000\n2007-04-05,1438.939941,1444.880005,1436.670044,1443.760010,1443.760010,2357230000\n2007-04-09,1443.770020,1448.099976,1443.280029,1444.609985,1444.609985,2349410000\n2007-04-10,1444.579956,1448.729980,1443.989990,1448.390015,1448.390015,2510110000\n2007-04-11,1448.229980,1448.390015,1436.150024,1438.869995,1438.869995,2950190000\n2007-04-12,1438.869995,1448.020020,1433.910034,1447.800049,1447.800049,2770570000\n2007-04-13,1447.800049,1453.109985,1444.150024,1452.849976,1452.849976,2690020000\n2007-04-16,1452.839966,1468.619995,1452.839966,1468.329956,1468.329956,2870140000\n2007-04-17,1468.469971,1474.349976,1467.150024,1471.479980,1471.479980,2920570000\n2007-04-18,1471.469971,1476.569946,1466.410034,1472.500000,1472.500000,2971330000\n2007-04-19,1472.479980,1474.229980,1464.469971,1470.729980,1470.729980,2913610000\n2007-04-20,1470.689941,1484.739990,1470.689941,1484.349976,1484.349976,3329940000\n2007-04-23,1484.329956,1487.319946,1480.189941,1480.930054,1480.930054,2575020000\n2007-04-24,1480.930054,1483.819946,1473.739990,1480.410034,1480.410034,3119750000\n2007-04-25,1480.280029,1496.589966,1480.280029,1495.420044,1495.420044,3252590000\n2007-04-26,1495.270020,1498.020020,1491.170044,1494.250000,1494.250000,3211800000\n2007-04-27,1494.209961,1497.319946,1488.670044,1494.069946,1494.069946,2732810000\n2007-04-30,1494.069946,1497.160034,1482.290039,1482.369995,1482.369995,3093420000\n2007-05-01,1482.369995,1487.270020,1476.699951,1486.300049,1486.300049,3400350000\n2007-05-02,1486.130005,1499.099976,1486.130005,1495.920044,1495.920044,3189800000\n2007-05-03,1495.560059,1503.339966,1495.560059,1502.390015,1502.390015,3007970000\n2007-05-04,1502.349976,1510.339966,1501.800049,1505.619995,1505.619995,2761930000\n2007-05-07,1505.569946,1511.000000,1505.540039,1509.479980,1509.479980,2545090000\n2007-05-08,1509.359985,1509.359985,1500.660034,1507.719971,1507.719971,2795720000\n2007-05-09,1507.319946,1513.800049,1503.770020,1512.579956,1512.579956,2935550000\n2007-05-10,1512.329956,1512.329956,1491.420044,1491.469971,1491.469971,3031240000\n2007-05-11,1491.469971,1506.239990,1491.469971,1505.849976,1505.849976,2720780000\n2007-05-14,1505.760010,1510.900024,1498.339966,1503.150024,1503.150024,2776130000\n2007-05-15,1503.109985,1514.829956,1500.430054,1501.189941,1501.189941,3071020000\n2007-05-16,1500.750000,1514.150024,1500.750000,1514.140015,1514.140015,2915350000\n2007-05-17,1514.010010,1517.140015,1509.290039,1512.750000,1512.750000,2868640000\n2007-05-18,1512.739990,1522.750000,1512.739990,1522.750000,1522.750000,2959050000\n2007-05-21,1522.750000,1529.869995,1522.709961,1525.099976,1525.099976,3465360000\n2007-05-22,1525.099976,1529.239990,1522.050049,1524.119995,1524.119995,2860500000\n2007-05-23,1524.089966,1532.430054,1521.900024,1522.280029,1522.280029,3084260000\n2007-05-24,1522.099976,1529.310059,1505.180054,1507.510010,1507.510010,3365530000\n2007-05-25,1507.500000,1517.410034,1507.500000,1515.729980,1515.729980,2316250000\n2007-05-29,1515.550049,1521.800049,1512.020020,1518.109985,1518.109985,2571790000\n2007-05-30,1517.599976,1530.229980,1510.060059,1530.229980,1530.229980,2980210000\n2007-05-31,1530.189941,1535.560059,1528.260010,1530.619995,1530.619995,3335530000\n2007-06-01,1530.619995,1540.560059,1530.619995,1536.339966,1536.339966,2927020000\n2007-06-04,1536.280029,1540.530029,1532.310059,1539.180054,1539.180054,2738930000\n2007-06-05,1539.119995,1539.119995,1525.619995,1530.949951,1530.949951,2939450000\n2007-06-06,1530.569946,1530.569946,1514.130005,1517.380005,1517.380005,2964190000\n2007-06-07,1517.359985,1517.359985,1490.369995,1490.719971,1490.719971,3538470000\n2007-06-08,1490.709961,1507.760010,1487.410034,1507.670044,1507.670044,2993460000\n2007-06-11,1507.640015,1515.530029,1503.349976,1509.119995,1509.119995,2525280000\n2007-06-12,1509.119995,1511.329956,1492.969971,1493.000000,1493.000000,3056200000\n2007-06-13,1492.650024,1515.699951,1492.650024,1515.670044,1515.670044,3077930000\n2007-06-14,1515.579956,1526.449951,1515.579956,1522.969971,1522.969971,2813630000\n2007-06-15,1522.969971,1538.709961,1522.969971,1532.910034,1532.910034,3406030000\n2007-06-18,1532.900024,1535.439941,1529.310059,1531.050049,1531.050049,2480240000\n2007-06-19,1531.020020,1535.849976,1525.670044,1533.699951,1533.699951,2873590000\n2007-06-20,1533.680054,1537.319946,1512.359985,1512.839966,1512.839966,3286900000\n2007-06-21,1512.500000,1522.900024,1504.750000,1522.189941,1522.189941,3161110000\n2007-06-22,1522.189941,1522.189941,1500.739990,1502.560059,1502.560059,4284320000\n2007-06-25,1502.560059,1514.290039,1492.680054,1497.739990,1497.739990,3287250000\n2007-06-26,1497.680054,1506.119995,1490.540039,1492.890015,1492.890015,3398530000\n2007-06-27,1492.619995,1506.800049,1484.180054,1506.339966,1506.339966,3398150000\n2007-06-28,1506.319946,1514.839966,1503.410034,1505.709961,1505.709961,3006710000\n2007-06-29,1505.699951,1517.530029,1493.609985,1503.349976,1503.349976,3165410000\n2007-07-02,1504.660034,1519.449951,1504.660034,1519.430054,1519.430054,2648990000\n2007-07-03,1519.119995,1526.010010,1519.119995,1524.869995,1524.869995,1560790000\n2007-07-05,1524.859985,1526.569946,1517.719971,1525.400024,1525.400024,2622950000\n2007-07-06,1524.959961,1532.400024,1520.469971,1530.439941,1530.439941,2441520000\n2007-07-09,1530.430054,1534.260010,1527.449951,1531.849976,1531.849976,2715330000\n2007-07-10,1531.849976,1531.849976,1510.010010,1510.119995,1510.119995,3244280000\n2007-07-11,1509.930054,1519.339966,1506.099976,1518.760010,1518.760010,3082920000\n2007-07-12,1518.739990,1547.920044,1518.739990,1547.699951,1547.699951,3489600000\n2007-07-13,1547.680054,1555.099976,1544.849976,1552.500000,1552.500000,2801120000\n2007-07-16,1552.500000,1555.900024,1546.689941,1549.520020,1549.520020,2704110000\n2007-07-17,1549.520020,1555.319946,1547.739990,1549.369995,1549.369995,3007140000\n2007-07-18,1549.199951,1549.199951,1533.670044,1546.170044,1546.170044,3609220000\n2007-07-19,1546.130005,1555.199951,1546.130005,1553.079956,1553.079956,3251450000\n2007-07-20,1553.189941,1553.189941,1529.199951,1534.099976,1534.099976,3745780000\n2007-07-23,1534.060059,1547.229980,1534.060059,1541.569946,1541.569946,3102700000\n2007-07-24,1541.569946,1541.569946,1508.619995,1511.040039,1511.040039,4115830000\n2007-07-25,1511.030029,1524.310059,1503.729980,1518.089966,1518.089966,4283200000\n2007-07-26,1518.089966,1518.089966,1465.300049,1482.660034,1482.660034,4472550000\n2007-07-27,1482.439941,1488.530029,1458.949951,1458.949951,1458.949951,4784650000\n2007-07-30,1458.930054,1477.880005,1454.319946,1473.910034,1473.910034,4128780000\n2007-07-31,1473.900024,1488.300049,1454.250000,1455.270020,1455.270020,4524520000\n2007-08-01,1455.180054,1468.380005,1439.589966,1465.810059,1465.810059,5256780000\n2007-08-02,1465.459961,1476.430054,1460.579956,1472.199951,1472.199951,4368850000\n2007-08-03,1472.180054,1473.229980,1432.800049,1433.060059,1433.060059,4272110000\n2007-08-06,1433.040039,1467.670044,1427.390015,1467.670044,1467.670044,5067200000\n2007-08-07,1467.619995,1488.300049,1455.800049,1476.709961,1476.709961,4909390000\n2007-08-08,1476.219971,1503.890015,1476.219971,1497.489990,1497.489990,5499560000\n2007-08-09,1497.209961,1497.209961,1453.089966,1453.089966,1453.089966,5889600000\n2007-08-10,1453.089966,1462.020020,1429.739990,1453.640015,1453.640015,5345780000\n2007-08-13,1453.420044,1466.290039,1451.540039,1452.920044,1452.920044,3696280000\n2007-08-14,1452.869995,1456.739990,1426.199951,1426.540039,1426.540039,3814630000\n2007-08-15,1426.150024,1440.780029,1404.359985,1406.699951,1406.699951,4290930000\n2007-08-16,1406.640015,1415.969971,1370.599976,1411.270020,1411.270020,6509300000\n2007-08-17,1411.260010,1450.329956,1411.260010,1445.939941,1445.939941,3570040000\n2007-08-20,1445.939941,1451.750000,1430.540039,1445.550049,1445.550049,3321340000\n2007-08-21,1445.550049,1455.319946,1439.760010,1447.119995,1447.119995,3012150000\n2007-08-22,1447.030029,1464.859985,1447.030029,1464.069946,1464.069946,3309120000\n2007-08-23,1464.050049,1472.060059,1453.880005,1462.500000,1462.500000,3084390000\n2007-08-24,1462.339966,1479.400024,1460.540039,1479.369995,1479.369995,2541400000\n2007-08-27,1479.359985,1479.359985,1465.979980,1466.790039,1466.790039,2406180000\n2007-08-28,1466.719971,1466.719971,1432.010010,1432.359985,1432.359985,3078090000\n2007-08-29,1432.010010,1463.760010,1432.010010,1463.760010,1463.760010,2824070000\n2007-08-30,1463.670044,1468.430054,1451.250000,1457.640015,1457.640015,2582960000\n2007-08-31,1457.609985,1481.469971,1457.609985,1473.989990,1473.989990,2731610000\n2007-09-04,1473.959961,1496.400024,1472.150024,1489.420044,1489.420044,2766600000\n2007-09-05,1488.760010,1488.760010,1466.339966,1472.290039,1472.290039,2991600000\n2007-09-06,1472.030029,1481.489990,1467.410034,1478.550049,1478.550049,2459590000\n2007-09-07,1478.550049,1478.550049,1449.069946,1453.550049,1453.550049,3191080000\n2007-09-10,1453.500000,1462.250000,1439.290039,1451.699951,1451.699951,2835720000\n2007-09-11,1451.689941,1472.479980,1451.689941,1471.489990,1471.489990,3015330000\n2007-09-12,1471.099976,1479.500000,1465.750000,1471.560059,1471.560059,2885720000\n2007-09-13,1471.469971,1489.579956,1471.469971,1483.949951,1483.949951,2877080000\n2007-09-14,1483.949951,1485.989990,1473.180054,1484.250000,1484.250000,2641740000\n2007-09-17,1484.239990,1484.239990,1471.819946,1476.650024,1476.650024,2598390000\n2007-09-18,1476.630005,1519.890015,1476.630005,1519.780029,1519.780029,3708940000\n2007-09-19,1519.750000,1538.739990,1519.750000,1529.030029,1529.030029,3846750000\n2007-09-20,1528.689941,1529.140015,1516.420044,1518.750000,1518.750000,2957700000\n2007-09-21,1518.750000,1530.890015,1518.750000,1525.750000,1525.750000,3679460000\n2007-09-24,1525.750000,1530.180054,1516.150024,1517.729980,1517.729980,3131310000\n2007-09-25,1516.339966,1518.270020,1507.130005,1517.209961,1517.209961,3187770000\n2007-09-26,1518.619995,1529.390015,1518.619995,1525.420044,1525.420044,3237390000\n2007-09-27,1527.319946,1532.459961,1525.810059,1531.380005,1531.380005,2872180000\n2007-09-28,1531.239990,1533.739990,1521.989990,1526.750000,1526.750000,2925350000\n2007-10-01,1527.290039,1549.020020,1527.250000,1547.040039,1547.040039,3281990000\n2007-10-02,1546.959961,1548.010010,1540.369995,1546.630005,1546.630005,3101910000\n2007-10-03,1545.800049,1545.839966,1536.339966,1539.589966,1539.589966,3065320000\n2007-10-04,1539.910034,1544.020020,1537.630005,1542.839966,1542.839966,2690430000\n2007-10-05,1543.839966,1561.910034,1543.839966,1557.589966,1557.589966,2919030000\n2007-10-08,1556.510010,1556.510010,1549.000000,1552.579956,1552.579956,2040650000\n2007-10-09,1553.180054,1565.260010,1551.819946,1565.150024,1565.150024,2932040000\n2007-10-10,1564.979980,1565.420044,1555.459961,1562.469971,1562.469971,3044760000\n2007-10-11,1564.719971,1576.089966,1546.719971,1554.410034,1554.410034,3911260000\n2007-10-12,1555.410034,1563.030029,1554.089966,1561.800049,1561.800049,2788690000\n2007-10-15,1562.250000,1564.739990,1540.810059,1548.709961,1548.709961,3139290000\n2007-10-16,1547.810059,1547.810059,1536.290039,1538.530029,1538.530029,3234560000\n2007-10-17,1544.439941,1550.660034,1526.010010,1541.239990,1541.239990,3638070000\n2007-10-18,1539.290039,1542.790039,1531.760010,1540.079956,1540.079956,3203210000\n2007-10-19,1540.000000,1540.000000,1500.260010,1500.630005,1500.630005,4160970000\n2007-10-22,1497.790039,1508.060059,1490.400024,1506.329956,1506.329956,3471830000\n2007-10-23,1509.300049,1520.010010,1503.609985,1519.589966,1519.589966,3309120000\n2007-10-24,1516.609985,1517.229980,1489.560059,1515.880005,1515.880005,4003300000\n2007-10-25,1516.150024,1523.239990,1500.459961,1514.400024,1514.400024,4183960000\n2007-10-26,1522.170044,1535.530029,1520.180054,1535.280029,1535.280029,3612120000\n2007-10-29,1536.920044,1544.670044,1536.430054,1540.979980,1540.979980,3124480000\n2007-10-30,1539.420044,1539.420044,1529.550049,1531.020020,1531.020020,3212520000\n2007-10-31,1532.150024,1552.760010,1529.400024,1549.380005,1549.380005,3953070000\n2007-11-01,1545.790039,1545.790039,1506.660034,1508.439941,1508.439941,4241470000\n2007-11-02,1511.069946,1513.150024,1492.530029,1509.650024,1509.650024,4285990000\n2007-11-05,1505.609985,1510.839966,1489.949951,1502.170044,1502.170044,3819330000\n2007-11-06,1505.329956,1520.770020,1499.069946,1520.270020,1520.270020,3879160000\n2007-11-07,1515.459961,1515.459961,1475.040039,1475.619995,1475.619995,4353160000\n2007-11-08,1475.270020,1482.500000,1450.310059,1474.770020,1474.770020,5439720000\n2007-11-09,1467.589966,1474.089966,1448.510010,1453.699951,1453.699951,4587050000\n2007-11-12,1453.660034,1464.939941,1438.530029,1439.180054,1439.180054,4192520000\n2007-11-13,1441.349976,1481.369995,1441.349976,1481.050049,1481.050049,4141310000\n2007-11-14,1483.400024,1492.140015,1466.469971,1470.579956,1470.579956,4031470000\n2007-11-15,1468.040039,1472.670044,1443.489990,1451.150024,1451.150024,3941010000\n2007-11-16,1453.089966,1462.180054,1443.989990,1458.739990,1458.739990,4168870000\n2007-11-19,1456.699951,1456.699951,1430.420044,1433.270020,1433.270020,4119650000\n2007-11-20,1434.510010,1452.640015,1419.280029,1439.699951,1439.699951,4875150000\n2007-11-21,1434.709961,1436.400024,1415.640015,1416.770020,1416.770020,4076230000\n2007-11-23,1417.619995,1440.859985,1417.619995,1440.699951,1440.699951,1612720000\n2007-11-26,1440.739990,1446.089966,1406.099976,1407.219971,1407.219971,3706470000\n2007-11-27,1409.589966,1429.489990,1407.430054,1428.229980,1428.229980,4320720000\n2007-11-28,1432.949951,1471.619995,1432.949951,1469.020020,1469.020020,4508020000\n2007-11-29,1467.410034,1473.810059,1458.359985,1469.719971,1469.719971,3524730000\n2007-11-30,1471.829956,1488.939941,1470.890015,1481.140015,1481.140015,4422200000\n2007-12-03,1479.630005,1481.160034,1470.079956,1472.420044,1472.420044,3323250000\n2007-12-04,1471.339966,1471.339966,1460.660034,1462.790039,1462.790039,3343620000\n2007-12-05,1465.219971,1486.089966,1465.219971,1485.010010,1485.010010,3663660000\n2007-12-06,1484.589966,1508.020020,1482.189941,1507.339966,1507.339966,3568570000\n2007-12-07,1508.599976,1510.630005,1502.660034,1504.660034,1504.660034,3177710000\n2007-12-10,1505.109985,1518.270020,1504.959961,1515.959961,1515.959961,2911760000\n2007-12-11,1516.680054,1523.569946,1475.989990,1477.650024,1477.650024,4080180000\n2007-12-12,1487.579956,1511.959961,1468.229980,1486.589966,1486.589966,4482120000\n2007-12-13,1483.270020,1489.400024,1469.209961,1488.410034,1488.410034,3635170000\n2007-12-14,1486.189941,1486.670044,1467.780029,1467.949951,1467.949951,3401050000\n2007-12-17,1465.050049,1465.050049,1445.430054,1445.900024,1445.900024,3569030000\n2007-12-18,1445.920044,1460.160034,1435.650024,1454.979980,1454.979980,3723690000\n2007-12-19,1454.699951,1464.420044,1445.310059,1453.000000,1453.000000,3401300000\n2007-12-20,1456.420044,1461.530029,1447.219971,1460.119995,1460.119995,3526890000\n2007-12-21,1463.189941,1485.400024,1463.189941,1484.459961,1484.459961,4508590000\n2007-12-24,1484.550049,1497.630005,1484.550049,1496.449951,1496.449951,1267420000\n2007-12-26,1495.119995,1498.849976,1488.199951,1497.660034,1497.660034,2010500000\n2007-12-27,1495.050049,1495.050049,1475.859985,1476.270020,1476.270020,2365770000\n2007-12-28,1479.829956,1488.010010,1471.699951,1478.489990,1478.489990,2420510000\n2007-12-31,1475.250000,1475.829956,1465.130005,1468.359985,1468.359985,2440880000\n2008-01-02,1467.969971,1471.770020,1442.069946,1447.160034,1447.160034,3452650000\n2008-01-03,1447.550049,1456.800049,1443.729980,1447.160034,1447.160034,3429500000\n2008-01-04,1444.010010,1444.010010,1411.189941,1411.630005,1411.630005,4166000000\n2008-01-07,1414.069946,1423.869995,1403.449951,1416.180054,1416.180054,4221260000\n2008-01-08,1415.709961,1430.280029,1388.300049,1390.189941,1390.189941,4705390000\n2008-01-09,1390.250000,1409.189941,1378.699951,1409.130005,1409.130005,5351030000\n2008-01-10,1406.780029,1429.089966,1395.310059,1420.329956,1420.329956,5170490000\n2008-01-11,1419.910034,1419.910034,1394.829956,1401.020020,1401.020020,4495840000\n2008-01-14,1402.910034,1417.890015,1402.910034,1416.250000,1416.250000,3682090000\n2008-01-15,1411.880005,1411.880005,1380.599976,1380.949951,1380.949951,4601640000\n2008-01-16,1377.410034,1391.989990,1364.270020,1373.199951,1373.199951,5440620000\n2008-01-17,1374.790039,1377.719971,1330.670044,1333.250000,1333.250000,5303130000\n2008-01-18,1333.900024,1350.280029,1312.510010,1325.189941,1325.189941,6004840000\n2008-01-22,1312.939941,1322.089966,1274.290039,1310.500000,1310.500000,6544690000\n2008-01-23,1310.410034,1339.089966,1270.050049,1338.599976,1338.599976,3241680000\n2008-01-24,1340.130005,1355.150024,1334.310059,1352.069946,1352.069946,5735300000\n2008-01-25,1357.319946,1368.560059,1327.500000,1330.609985,1330.609985,4882250000\n2008-01-28,1330.699951,1353.969971,1322.260010,1353.959961,1353.959961,4100930000\n2008-01-29,1355.939941,1364.930054,1350.189941,1362.300049,1362.300049,4232960000\n2008-01-30,1362.219971,1385.859985,1352.949951,1355.810059,1355.810059,4742760000\n2008-01-31,1351.979980,1385.619995,1334.079956,1378.550049,1378.550049,4970290000\n2008-02-01,1378.599976,1396.020020,1375.930054,1395.420044,1395.420044,4650770000\n2008-02-04,1395.380005,1395.380005,1379.689941,1380.819946,1380.819946,3495780000\n2008-02-05,1380.280029,1380.280029,1336.640015,1336.640015,1336.640015,4315740000\n2008-02-06,1339.479980,1351.959961,1324.339966,1326.449951,1326.449951,4008120000\n2008-02-07,1324.010010,1347.160034,1316.750000,1336.910034,1336.910034,4589160000\n2008-02-08,1336.880005,1341.219971,1321.060059,1331.290039,1331.290039,3768490000\n2008-02-11,1331.920044,1341.400024,1320.319946,1339.130005,1339.130005,3593140000\n2008-02-12,1340.550049,1362.099976,1339.359985,1348.859985,1348.859985,4044640000\n2008-02-13,1353.119995,1369.229980,1350.780029,1367.209961,1367.209961,3856420000\n2008-02-14,1367.329956,1368.160034,1347.310059,1348.859985,1348.859985,3644760000\n2008-02-15,1347.520020,1350.000000,1338.130005,1349.989990,1349.989990,3583300000\n2008-02-19,1355.859985,1367.280029,1345.050049,1348.780029,1348.780029,3613550000\n2008-02-20,1348.390015,1363.709961,1336.550049,1360.030029,1360.030029,3870520000\n2008-02-21,1362.209961,1367.939941,1339.339966,1342.530029,1342.530029,3696660000\n2008-02-22,1344.219971,1354.300049,1327.040039,1353.109985,1353.109985,3572660000\n2008-02-25,1352.750000,1374.359985,1346.030029,1371.800049,1371.800049,3866350000\n2008-02-26,1371.760010,1387.339966,1363.290039,1381.290039,1381.290039,4096060000\n2008-02-27,1378.949951,1388.339966,1372.000000,1380.020020,1380.020020,3904700000\n2008-02-28,1378.160034,1378.160034,1363.160034,1367.680054,1367.680054,3938580000\n2008-02-29,1364.069946,1364.069946,1325.420044,1330.630005,1330.630005,4426730000\n2008-03-03,1330.449951,1335.130005,1320.040039,1331.339966,1331.339966,4117570000\n2008-03-04,1329.579956,1331.030029,1307.390015,1326.750000,1326.750000,4757180000\n2008-03-05,1327.689941,1344.189941,1320.219971,1333.699951,1333.699951,4277710000\n2008-03-06,1332.199951,1332.199951,1303.420044,1304.339966,1304.339966,4323460000\n2008-03-07,1301.530029,1313.239990,1282.430054,1293.369995,1293.369995,4565410000\n2008-03-10,1293.160034,1295.010010,1272.660034,1273.369995,1273.369995,4261240000\n2008-03-11,1274.400024,1320.650024,1274.400024,1320.650024,1320.650024,5109080000\n2008-03-12,1321.130005,1333.260010,1307.859985,1308.770020,1308.770020,4414280000\n2008-03-13,1305.260010,1321.680054,1282.109985,1315.479980,1315.479980,5073360000\n2008-03-14,1316.050049,1321.469971,1274.859985,1288.140015,1288.140015,5153780000\n2008-03-17,1283.209961,1287.500000,1256.979980,1276.599976,1276.599976,5683010000\n2008-03-18,1277.160034,1330.739990,1277.160034,1330.739990,1330.739990,5335630000\n2008-03-19,1330.969971,1341.510010,1298.420044,1298.420044,1298.420044,5358550000\n2008-03-20,1299.670044,1330.670044,1295.219971,1329.510010,1329.510010,6145220000\n2008-03-24,1330.290039,1359.680054,1330.290039,1349.880005,1349.880005,4499000000\n2008-03-25,1349.069946,1357.469971,1341.209961,1352.989990,1352.989990,4145120000\n2008-03-26,1352.449951,1352.449951,1336.410034,1341.130005,1341.130005,4055670000\n2008-03-27,1340.339966,1345.619995,1325.660034,1325.760010,1325.760010,4037930000\n2008-03-28,1327.020020,1334.869995,1312.949951,1315.219971,1315.219971,3686980000\n2008-03-31,1315.920044,1328.520020,1312.810059,1322.699951,1322.699951,4188990000\n2008-04-01,1326.410034,1370.180054,1326.410034,1370.180054,1370.180054,4745120000\n2008-04-02,1369.959961,1377.949951,1361.550049,1367.530029,1367.530029,4320440000\n2008-04-03,1365.689941,1375.660034,1358.680054,1369.310059,1369.310059,3920100000\n2008-04-04,1369.849976,1380.910034,1362.829956,1370.400024,1370.400024,3703100000\n2008-04-07,1373.689941,1386.739990,1369.020020,1372.540039,1372.540039,3747780000\n2008-04-08,1370.160034,1370.160034,1360.619995,1365.540039,1365.540039,3602500000\n2008-04-09,1365.500000,1368.390015,1349.969971,1354.489990,1354.489990,3556670000\n2008-04-10,1355.369995,1367.239990,1350.109985,1360.550049,1360.550049,3686150000\n2008-04-11,1357.979980,1357.979980,1331.209961,1332.829956,1332.829956,3723790000\n2008-04-14,1332.199951,1335.640015,1326.160034,1328.319946,1328.319946,3565020000\n2008-04-15,1331.719971,1337.719971,1324.349976,1334.430054,1334.430054,3581230000\n2008-04-16,1337.020020,1365.489990,1337.020020,1364.709961,1364.709961,4260370000\n2008-04-17,1363.369995,1368.599976,1357.250000,1365.560059,1365.560059,3713880000\n2008-04-18,1369.000000,1395.900024,1369.000000,1390.329956,1390.329956,4222380000\n2008-04-21,1387.719971,1390.229980,1379.250000,1388.170044,1388.170044,3420570000\n2008-04-22,1386.430054,1386.430054,1369.839966,1375.939941,1375.939941,3821900000\n2008-04-23,1378.400024,1387.869995,1372.239990,1379.930054,1379.930054,4103610000\n2008-04-24,1380.520020,1397.719971,1371.089966,1388.819946,1388.819946,4461660000\n2008-04-25,1387.880005,1399.109985,1379.979980,1397.839966,1397.839966,3891150000\n2008-04-28,1397.959961,1402.900024,1394.400024,1396.369995,1396.369995,3607000000\n2008-04-29,1395.609985,1397.000000,1386.699951,1390.939941,1390.939941,3815320000\n2008-04-30,1391.219971,1404.569946,1384.250000,1385.589966,1385.589966,4508890000\n2008-05-01,1385.969971,1410.069946,1383.069946,1409.339966,1409.339966,4448780000\n2008-05-02,1409.160034,1422.719971,1406.250000,1413.900024,1413.900024,3953030000\n2008-05-05,1415.339966,1415.339966,1404.369995,1407.489990,1407.489990,3410090000\n2008-05-06,1405.599976,1421.569946,1397.099976,1418.260010,1418.260010,3924100000\n2008-05-07,1417.489990,1419.540039,1391.160034,1392.569946,1392.569946,4075860000\n2008-05-08,1394.290039,1402.349976,1389.390015,1397.680054,1397.680054,3827550000\n2008-05-09,1394.900024,1394.900024,1384.109985,1388.280029,1388.280029,3518620000\n2008-05-12,1389.400024,1404.060059,1386.199951,1403.579956,1403.579956,3370630000\n2008-05-13,1404.400024,1406.300049,1396.260010,1403.040039,1403.040039,4018590000\n2008-05-14,1405.650024,1420.189941,1405.650024,1408.660034,1408.660034,3979370000\n2008-05-15,1408.359985,1424.400024,1406.869995,1423.569946,1423.569946,3836480000\n2008-05-16,1423.890015,1425.819946,1414.349976,1425.349976,1425.349976,3842590000\n2008-05-19,1425.280029,1440.239990,1421.630005,1426.630005,1426.630005,3683970000\n2008-05-20,1424.489990,1424.489990,1409.089966,1413.400024,1413.400024,3854320000\n2008-05-21,1414.060059,1419.119995,1388.810059,1390.709961,1390.709961,4517990000\n2008-05-22,1390.829956,1399.069946,1390.229980,1394.349976,1394.349976,3955960000\n2008-05-23,1392.199951,1392.199951,1373.719971,1375.930054,1375.930054,3516380000\n2008-05-27,1375.969971,1387.400024,1373.069946,1385.349976,1385.349976,3588860000\n2008-05-28,1386.540039,1391.250000,1378.160034,1390.839966,1390.839966,3927240000\n2008-05-29,1390.500000,1406.319946,1388.589966,1398.260010,1398.260010,3894440000\n2008-05-30,1398.359985,1404.459961,1398.079956,1400.380005,1400.380005,3845630000\n2008-06-02,1399.619995,1399.619995,1377.790039,1385.670044,1385.670044,3714320000\n2008-06-03,1386.420044,1393.119995,1370.119995,1377.650024,1377.650024,4396380000\n2008-06-04,1376.260010,1388.180054,1371.739990,1377.199951,1377.199951,4338640000\n2008-06-05,1377.479980,1404.050049,1377.479980,1404.050049,1404.050049,4350790000\n2008-06-06,1400.060059,1400.060059,1359.900024,1360.680054,1360.680054,4771660000\n2008-06-09,1360.829956,1370.630005,1350.619995,1361.760010,1361.760010,4404570000\n2008-06-10,1358.979980,1366.839966,1351.560059,1358.439941,1358.439941,4635070000\n2008-06-11,1357.089966,1357.089966,1335.469971,1335.489990,1335.489990,4779980000\n2008-06-12,1335.780029,1353.030029,1331.290039,1339.869995,1339.869995,4734240000\n2008-06-13,1341.810059,1360.030029,1341.709961,1360.030029,1360.030029,4080420000\n2008-06-16,1358.849976,1364.699951,1352.069946,1360.140015,1360.140015,3706940000\n2008-06-17,1360.709961,1366.589966,1350.540039,1350.930054,1350.930054,3801960000\n2008-06-18,1349.589966,1349.589966,1333.400024,1337.810059,1337.810059,4573570000\n2008-06-19,1336.890015,1347.660034,1330.500000,1342.829956,1342.829956,4811670000\n2008-06-20,1341.020020,1341.020020,1314.459961,1317.930054,1317.930054,5324900000\n2008-06-23,1319.770020,1323.780029,1315.310059,1318.000000,1318.000000,4186370000\n2008-06-24,1317.229980,1326.020020,1304.420044,1314.290039,1314.290039,4705050000\n2008-06-25,1314.540039,1335.630005,1314.540039,1321.969971,1321.969971,4825640000\n2008-06-26,1316.290039,1316.290039,1283.150024,1283.150024,1283.150024,5231280000\n2008-06-27,1283.599976,1289.449951,1272.000000,1278.380005,1278.380005,6208260000\n2008-06-30,1278.060059,1290.310059,1274.859985,1280.000000,1280.000000,5032330000\n2008-07-01,1276.689941,1285.310059,1260.680054,1284.910034,1284.910034,5846290000\n2008-07-02,1285.819946,1292.170044,1261.510010,1261.520020,1261.520020,5276090000\n2008-07-03,1262.959961,1271.479980,1252.010010,1262.900024,1262.900024,3247590000\n2008-07-07,1262.900024,1273.949951,1240.680054,1252.310059,1252.310059,5265420000\n2008-07-08,1251.839966,1274.170044,1242.839966,1273.699951,1273.699951,6034110000\n2008-07-09,1273.380005,1277.359985,1244.569946,1244.689941,1244.689941,5181000000\n2008-07-10,1245.250000,1257.650024,1236.760010,1253.390015,1253.390015,5840430000\n2008-07-11,1248.660034,1257.270020,1225.349976,1239.489990,1239.489990,6742200000\n2008-07-14,1241.609985,1253.500000,1225.010010,1228.300049,1228.300049,5434860000\n2008-07-15,1226.829956,1234.349976,1200.439941,1214.910034,1214.910034,7363640000\n2008-07-16,1214.650024,1245.520020,1211.390015,1245.359985,1245.359985,6738630000\n2008-07-17,1246.310059,1262.310059,1241.489990,1260.319946,1260.319946,7365210000\n2008-07-18,1258.219971,1262.229980,1251.810059,1260.680054,1260.680054,5653280000\n2008-07-21,1261.819946,1267.739990,1255.699951,1260.000000,1260.000000,4630640000\n2008-07-22,1257.079956,1277.420044,1248.829956,1277.000000,1277.000000,6180230000\n2008-07-23,1278.869995,1291.170044,1276.060059,1282.189941,1282.189941,6705830000\n2008-07-24,1283.219971,1283.219971,1251.479980,1252.540039,1252.540039,6127980000\n2008-07-25,1253.510010,1263.229980,1251.750000,1257.760010,1257.760010,4672560000\n2008-07-28,1257.760010,1260.089966,1234.369995,1234.369995,1234.369995,4282960000\n2008-07-29,1236.380005,1263.199951,1236.380005,1263.199951,1263.199951,5414240000\n2008-07-30,1264.520020,1284.329956,1264.520020,1284.260010,1284.260010,5631330000\n2008-07-31,1281.369995,1284.930054,1265.969971,1267.380005,1267.380005,5346050000\n2008-08-01,1269.420044,1270.520020,1254.540039,1260.310059,1260.310059,4684870000\n2008-08-04,1253.270020,1260.489990,1247.449951,1249.010010,1249.010010,4562280000\n2008-08-05,1254.869995,1284.880005,1254.670044,1284.880005,1284.880005,1219310000\n2008-08-06,1283.989990,1291.670044,1276.000000,1289.189941,1289.189941,4873420000\n2008-08-07,1286.510010,1286.510010,1264.290039,1266.069946,1266.069946,5319380000\n2008-08-08,1266.290039,1297.849976,1262.109985,1296.319946,1296.319946,4966810000\n2008-08-11,1294.420044,1313.150024,1291.410034,1305.319946,1305.319946,5067310000\n2008-08-12,1304.790039,1304.790039,1285.640015,1289.589966,1289.589966,4711290000\n2008-08-13,1288.640015,1294.030029,1274.859985,1285.829956,1285.829956,4787600000\n2008-08-14,1282.109985,1300.109985,1276.839966,1292.930054,1292.930054,4064000000\n2008-08-15,1293.849976,1302.050049,1290.739990,1298.199951,1298.199951,4041820000\n2008-08-18,1298.140015,1300.219971,1274.510010,1278.599976,1278.599976,3829290000\n2008-08-19,1276.650024,1276.650024,1263.109985,1266.689941,1266.689941,4159760000\n2008-08-20,1267.339966,1276.010010,1261.160034,1274.540039,1274.540039,4555030000\n2008-08-21,1271.069946,1281.400024,1265.219971,1277.719971,1277.719971,4032590000\n2008-08-22,1277.589966,1293.089966,1277.589966,1292.199951,1292.199951,3741070000\n2008-08-25,1290.469971,1290.469971,1264.869995,1266.839966,1266.839966,3420600000\n2008-08-26,1267.030029,1275.650024,1263.209961,1271.510010,1271.510010,3587570000\n2008-08-27,1271.290039,1285.050049,1270.030029,1281.660034,1281.660034,3499610000\n2008-08-28,1283.790039,1300.680054,1283.790039,1300.680054,1300.680054,3854280000\n2008-08-29,1296.489990,1297.589966,1282.739990,1282.829956,1282.829956,3288120000\n2008-09-02,1287.829956,1303.040039,1272.199951,1277.579956,1277.579956,4783560000\n2008-09-03,1276.609985,1280.599976,1265.589966,1274.979980,1274.979980,5056980000\n2008-09-04,1271.800049,1271.800049,1232.829956,1236.829956,1236.829956,5212500000\n2008-09-05,1233.209961,1244.939941,1217.229980,1242.310059,1242.310059,5017080000\n2008-09-08,1249.500000,1274.420044,1247.119995,1267.790039,1267.790039,7351340000\n2008-09-09,1267.979980,1268.660034,1224.510010,1224.510010,1224.510010,7380630000\n2008-09-10,1227.500000,1243.900024,1221.599976,1232.040039,1232.040039,6543440000\n2008-09-11,1229.040039,1249.979980,1211.540039,1249.050049,1249.050049,6869250000\n2008-09-12,1245.880005,1255.089966,1233.810059,1251.699951,1251.699951,6273260000\n2008-09-15,1250.920044,1250.920044,1192.699951,1192.699951,1192.699951,8279510000\n2008-09-16,1188.310059,1214.839966,1169.280029,1213.599976,1213.599976,9459830000\n2008-09-17,1210.339966,1210.339966,1155.880005,1156.390015,1156.390015,9431870000\n2008-09-18,1157.079956,1211.140015,1133.500000,1206.510010,1206.510010,10082690000\n2008-09-19,1213.109985,1265.119995,1213.109985,1255.079956,1255.079956,9387170000\n2008-09-22,1255.369995,1255.369995,1205.609985,1207.089966,1207.089966,5368130000\n2008-09-23,1207.609985,1221.150024,1187.060059,1188.219971,1188.219971,5185730000\n2008-09-24,1188.790039,1197.410034,1179.790039,1185.869995,1185.869995,4820360000\n2008-09-25,1187.869995,1220.030029,1187.869995,1209.180054,1209.180054,5877640000\n2008-09-26,1204.469971,1215.770020,1187.540039,1213.270020,1213.270020,5383610000\n2008-09-29,1209.069946,1209.069946,1106.420044,1106.420044,1106.420044,7305060000\n2008-09-30,1113.780029,1168.030029,1113.780029,1166.359985,1166.359985,4937680000\n2008-10-01,1164.170044,1167.030029,1140.770020,1161.060059,1161.060059,5782130000\n2008-10-02,1160.640015,1160.640015,1111.430054,1114.280029,1114.280029,6285640000\n2008-10-03,1115.160034,1153.819946,1098.140015,1099.229980,1099.229980,6716120000\n2008-10-06,1097.560059,1097.560059,1007.969971,1056.890015,1056.890015,7956020000\n2008-10-07,1057.599976,1072.910034,996.229980,996.229980,996.229980,7069210000\n2008-10-08,988.909973,1021.059998,970.969971,984.940002,984.940002,8716330000\n2008-10-09,988.419983,1005.250000,909.190002,909.919983,909.919983,6819000000\n2008-10-10,902.309998,936.359985,839.799988,899.219971,899.219971,11456230000\n2008-10-13,912.750000,1006.929993,912.750000,1003.349976,1003.349976,7263370000\n2008-10-14,1009.969971,1044.310059,972.070007,998.010010,998.010010,8161990000\n2008-10-15,994.599976,994.599976,903.989990,907.840027,907.840027,6542330000\n2008-10-16,909.530029,947.710022,865.830017,946.429993,946.429993,7984500000\n2008-10-17,942.289978,984.640015,918.739990,940.549988,940.549988,6581780000\n2008-10-20,943.510010,985.400024,943.510010,985.400024,985.400024,5175640000\n2008-10-21,980.400024,985.440002,952.469971,955.049988,955.049988,5121830000\n2008-10-22,951.669983,951.669983,875.809998,896.780029,896.780029,6147980000\n2008-10-23,899.080017,922.830017,858.440002,908.109985,908.109985,7189900000\n2008-10-24,895.219971,896.299988,852.849976,876.770020,876.770020,6550050000\n2008-10-27,874.280029,893.780029,846.750000,848.919983,848.919983,5558050000\n2008-10-28,848.919983,940.510010,845.270020,940.510010,940.510010,7096950000\n2008-10-29,939.510010,969.969971,922.260010,930.090027,930.090027,7077800000\n2008-10-30,939.380005,963.229980,928.500000,954.090027,954.090027,6175830000\n2008-10-31,953.109985,984.380005,944.590027,968.750000,968.750000,6394350000\n2008-11-03,968.669983,975.570007,958.820007,966.299988,966.299988,4492280000\n2008-11-04,971.309998,1007.510010,971.309998,1005.750000,1005.750000,5531290000\n2008-11-05,1001.840027,1001.840027,949.859985,952.770020,952.770020,5426640000\n2008-11-06,952.400024,952.400024,899.729980,904.880005,904.880005,6102230000\n2008-11-07,907.440002,931.460022,906.900024,930.989990,930.989990,4931640000\n2008-11-10,936.750000,951.950012,907.469971,919.210022,919.210022,4572000000\n2008-11-11,917.150024,917.150024,884.900024,898.950012,898.950012,4998340000\n2008-11-12,893.390015,893.390015,850.479980,852.299988,852.299988,5764180000\n2008-11-13,853.130005,913.010010,818.690002,911.289978,911.289978,7849120000\n2008-11-14,904.359985,916.880005,869.880005,873.289978,873.289978,5881030000\n2008-11-17,873.229980,882.289978,848.979980,850.750000,850.750000,4927490000\n2008-11-18,852.340027,865.900024,826.840027,859.119995,859.119995,6679470000\n2008-11-19,859.030029,864.570007,806.179993,806.580017,806.580017,6548600000\n2008-11-20,805.869995,820.520020,747.780029,752.440002,752.440002,9093740000\n2008-11-21,755.840027,801.200012,741.020020,800.030029,800.030029,9495900000\n2008-11-24,801.200012,865.599976,801.200012,851.809998,851.809998,7879440000\n2008-11-25,853.400024,868.940002,834.989990,857.390015,857.390015,6952700000\n2008-11-26,852.900024,887.679993,841.369995,887.679993,887.679993,5793260000\n2008-11-28,886.890015,896.250000,881.210022,896.239990,896.239990,2740860000\n2008-12-01,888.609985,888.609985,815.690002,816.210022,816.210022,6052010000\n2008-12-02,817.940002,850.539978,817.940002,848.809998,848.809998,6170100000\n2008-12-03,843.599976,873.119995,827.599976,870.739990,870.739990,6221880000\n2008-12-04,869.750000,875.599976,833.599976,845.219971,845.219971,5860390000\n2008-12-05,844.429993,879.419983,818.409973,876.070007,876.070007,6165370000\n2008-12-08,882.710022,918.570007,882.710022,909.700012,909.700012,6553600000\n2008-12-09,906.479980,916.260010,885.380005,888.669983,888.669983,5693110000\n2008-12-10,892.169983,908.270020,885.450012,899.239990,899.239990,5942130000\n2008-12-11,898.349976,904.630005,868.729980,873.590027,873.590027,5513840000\n2008-12-12,871.789978,883.239990,851.349976,879.729980,879.729980,5959590000\n2008-12-15,881.070007,884.630005,857.719971,868.570007,868.570007,4982390000\n2008-12-16,871.530029,914.659973,871.530029,913.179993,913.179993,6009780000\n2008-12-17,908.159973,918.849976,895.940002,904.419983,904.419983,5907380000\n2008-12-18,905.979980,911.020020,877.440002,885.280029,885.280029,5675000000\n2008-12-19,886.960022,905.469971,883.020020,887.880005,887.880005,6705310000\n2008-12-22,887.200012,887.369995,857.090027,871.630005,871.630005,4869850000\n2008-12-23,874.309998,880.440002,860.099976,863.159973,863.159973,4051970000\n2008-12-24,863.869995,869.789978,861.440002,868.150024,868.150024,1546550000\n2008-12-26,869.510010,873.739990,866.520020,872.799988,872.799988,1880050000\n2008-12-29,872.369995,873.700012,857.070007,869.419983,869.419983,3323430000\n2008-12-30,870.580017,891.119995,870.580017,890.640015,890.640015,3627800000\n2008-12-31,890.590027,910.320007,889.669983,903.250000,903.250000,4172940000\n2009-01-02,902.989990,934.729980,899.349976,931.799988,931.799988,4048270000\n2009-01-05,929.169983,936.630005,919.530029,927.450012,927.450012,5413910000\n2009-01-06,931.169983,943.849976,927.280029,934.700012,934.700012,5392620000\n2009-01-07,927.450012,927.450012,902.369995,906.650024,906.650024,4704940000\n2009-01-08,905.729980,910.000000,896.809998,909.729980,909.729980,4991550000\n2009-01-09,909.909973,911.929993,888.309998,890.349976,890.349976,4716500000\n2009-01-12,890.400024,890.400024,864.320007,870.260010,870.260010,4725050000\n2009-01-13,869.789978,877.020020,862.020020,871.789978,871.789978,5567460000\n2009-01-14,867.280029,867.280029,836.929993,842.619995,842.619995,5407880000\n2009-01-15,841.989990,851.590027,817.039978,843.739990,843.739990,7807350000\n2009-01-16,844.450012,858.130005,830.659973,850.119995,850.119995,6786040000\n2009-01-20,849.640015,849.640015,804.469971,805.219971,805.219971,6375230000\n2009-01-21,806.770020,841.719971,804.299988,840.239990,840.239990,6467830000\n2009-01-22,839.739990,839.739990,811.289978,827.500000,827.500000,5843830000\n2009-01-23,822.159973,838.609985,806.070007,831.950012,831.950012,5832160000\n2009-01-26,832.500000,852.530029,827.690002,836.570007,836.570007,6039940000\n2009-01-27,837.299988,850.450012,835.400024,845.710022,845.710022,5353260000\n2009-01-28,845.729980,877.859985,845.729980,874.090027,874.090027,6199180000\n2009-01-29,868.890015,868.890015,844.150024,845.140015,845.140015,5067060000\n2009-01-30,845.690002,851.659973,821.669983,825.880005,825.880005,5350580000\n2009-02-02,823.090027,830.780029,812.869995,825.440002,825.440002,5673270000\n2009-02-03,825.690002,842.599976,821.979980,838.510010,838.510010,5886310000\n2009-02-04,837.770020,851.849976,829.179993,832.229980,832.229980,6420450000\n2009-02-05,831.750000,850.549988,819.909973,845.849976,845.849976,6624030000\n2009-02-06,846.090027,870.750000,845.419983,868.599976,868.599976,6484100000\n2009-02-09,868.239990,875.010010,861.650024,869.890015,869.890015,5574370000\n2009-02-10,866.869995,868.049988,822.989990,827.159973,827.159973,6770170000\n2009-02-11,827.409973,838.219971,822.299988,833.739990,833.739990,5926460000\n2009-02-12,829.909973,835.479980,808.059998,835.190002,835.190002,6476460000\n2009-02-13,833.950012,839.429993,825.210022,826.840027,826.840027,5296650000\n2009-02-17,818.609985,818.609985,789.169983,789.169983,789.169983,5907820000\n2009-02-18,791.059998,796.169983,780.429993,788.419983,788.419983,5740710000\n2009-02-19,787.909973,797.580017,777.030029,778.940002,778.940002,5746940000\n2009-02-20,775.869995,778.690002,754.250000,770.049988,770.049988,8210590000\n2009-02-23,773.250000,777.849976,742.369995,743.330017,743.330017,6509300000\n2009-02-24,744.690002,775.489990,744.690002,773.140015,773.140015,7234490000\n2009-02-25,770.640015,780.119995,752.890015,764.900024,764.900024,7483640000\n2009-02-26,765.760010,779.419983,751.750000,752.830017,752.830017,7599970000\n2009-02-27,749.929993,751.270020,734.520020,735.090027,735.090027,8926480000\n2009-03-02,729.570007,729.570007,699.700012,700.820007,700.820007,7868290000\n2009-03-03,704.440002,711.669983,692.299988,696.330017,696.330017,7583230000\n2009-03-04,698.599976,724.119995,698.599976,712.869995,712.869995,7673620000\n2009-03-05,708.270020,708.270020,677.929993,682.549988,682.549988,7507250000\n2009-03-06,684.039978,699.090027,666.789978,683.380005,683.380005,7331830000\n2009-03-09,680.760010,695.270020,672.880005,676.530029,676.530029,7277320000\n2009-03-10,679.280029,719.599976,679.280029,719.599976,719.599976,8618330000\n2009-03-11,719.590027,731.919983,713.849976,721.359985,721.359985,7287810000\n2009-03-12,720.890015,752.630005,714.760010,750.739990,750.739990,7326630000\n2009-03-13,751.969971,758.289978,742.460022,756.549988,756.549988,6787090000\n2009-03-16,758.840027,774.530029,753.369995,753.890015,753.890015,7883540000\n2009-03-17,753.880005,778.119995,749.929993,778.119995,778.119995,6156800000\n2009-03-18,776.010010,803.039978,765.640015,794.349976,794.349976,9098450000\n2009-03-19,797.919983,803.239990,781.820007,784.039978,784.039978,9033870000\n2009-03-20,784.580017,788.909973,766.200012,768.539978,768.539978,7643720000\n2009-03-23,772.309998,823.369995,772.309998,822.919983,822.919983,7715770000\n2009-03-24,820.599976,823.650024,805.479980,806.119995,806.119995,6767980000\n2009-03-25,806.809998,826.780029,791.369995,813.880005,813.880005,7687180000\n2009-03-26,814.059998,832.979980,814.059998,832.859985,832.859985,6992960000\n2009-03-27,828.679993,828.679993,813.429993,815.940002,815.940002,5600210000\n2009-03-30,809.070007,809.070007,779.809998,787.530029,787.530029,5912660000\n2009-03-31,790.880005,810.479980,790.880005,797.869995,797.869995,6089100000\n2009-04-01,793.590027,813.619995,783.320007,811.080017,811.080017,6034140000\n2009-04-02,814.530029,845.609985,814.530029,834.380005,834.380005,7542810000\n2009-04-03,835.130005,842.500000,826.700012,842.500000,842.500000,5855640000\n2009-04-06,839.750000,839.750000,822.789978,835.479980,835.479980,6210000000\n2009-04-07,834.119995,834.119995,814.530029,815.549988,815.549988,5155580000\n2009-04-08,816.760010,828.419983,814.840027,825.159973,825.159973,5938460000\n2009-04-09,829.289978,856.909973,829.289978,856.559998,856.559998,7600710000\n2009-04-13,855.330017,864.309998,845.349976,858.729980,858.729980,6434890000\n2009-04-14,856.880005,856.880005,840.250000,841.500000,841.500000,7569840000\n2009-04-15,839.440002,852.929993,835.580017,852.059998,852.059998,6241100000\n2009-04-16,854.539978,870.349976,847.039978,865.299988,865.299988,6598670000\n2009-04-17,865.179993,875.630005,860.869995,869.599976,869.599976,7352010000\n2009-04-20,868.270020,868.270020,832.390015,832.390015,832.390015,6973960000\n2009-04-21,831.250000,850.090027,826.830017,850.080017,850.080017,7436490000\n2009-04-22,847.260010,861.780029,840.570007,843.549988,843.549988,7327860000\n2009-04-23,844.619995,852.869995,835.450012,851.919983,851.919983,6563100000\n2009-04-24,853.909973,871.799988,853.909973,866.229980,866.229980,7114440000\n2009-04-27,862.820007,868.830017,854.650024,857.510010,857.510010,5613460000\n2009-04-28,854.479980,864.479980,847.119995,855.159973,855.159973,6328000000\n2009-04-29,856.849976,882.059998,856.849976,873.640015,873.640015,6101620000\n2009-04-30,876.590027,888.700012,868.510010,872.809998,872.809998,6862540000\n2009-05-01,872.739990,880.479980,866.099976,877.520020,877.520020,5312170000\n2009-05-04,879.210022,907.849976,879.210022,907.239990,907.239990,7038840000\n2009-05-05,906.099976,907.700012,897.340027,903.799988,903.799988,6882860000\n2009-05-06,903.950012,920.280029,903.950012,919.530029,919.530029,8555040000\n2009-05-07,919.580017,929.580017,901.359985,907.390015,907.390015,9120100000\n2009-05-08,909.030029,930.169983,909.030029,929.229980,929.229980,8163280000\n2009-05-11,922.989990,922.989990,908.679993,909.239990,909.239990,6150600000\n2009-05-12,910.520020,915.570007,896.460022,908.349976,908.349976,6871750000\n2009-05-13,905.400024,905.400024,882.799988,883.919983,883.919983,7091820000\n2009-05-14,884.239990,898.359985,882.520020,893.070007,893.070007,6134870000\n2009-05-15,892.760010,896.969971,878.940002,882.880005,882.880005,5439720000\n2009-05-18,886.070007,910.000000,886.070007,909.710022,909.710022,5702150000\n2009-05-19,909.669983,916.390015,905.219971,908.130005,908.130005,6616270000\n2009-05-20,908.619995,924.599976,901.369995,903.469971,903.469971,8205060000\n2009-05-21,900.419983,900.419983,879.609985,888.330017,888.330017,6019840000\n2009-05-22,888.679993,896.650024,883.750000,887.000000,887.000000,5155320000\n2009-05-26,887.000000,911.760010,881.460022,910.330017,910.330017,5667050000\n2009-05-27,909.950012,913.840027,891.869995,893.059998,893.059998,5698800000\n2009-05-28,892.960022,909.450012,887.599976,906.830017,906.830017,5738980000\n2009-05-29,907.020020,920.020020,903.559998,919.140015,919.140015,6050420000\n2009-06-01,923.260010,947.770020,923.260010,942.869995,942.869995,6370440000\n2009-06-02,942.869995,949.380005,938.460022,944.739990,944.739990,5987340000\n2009-06-03,942.510010,942.510010,923.849976,931.760010,931.760010,5323770000\n2009-06-04,932.489990,942.469971,929.320007,942.460022,942.460022,5352890000\n2009-06-05,945.669983,951.690002,934.130005,940.090027,940.090027,5277910000\n2009-06-08,938.119995,946.330017,926.440002,939.140015,939.140015,4483430000\n2009-06-09,940.349976,946.919983,936.150024,942.429993,942.429993,4439950000\n2009-06-10,942.729980,949.770020,927.969971,939.150024,939.150024,5379420000\n2009-06-11,939.039978,956.229980,939.039978,944.890015,944.890015,5500840000\n2009-06-12,943.440002,946.299988,935.659973,946.210022,946.210022,4528120000\n2009-06-15,942.450012,942.450012,919.650024,923.719971,923.719971,4697880000\n2009-06-16,925.599976,928.000000,911.599976,911.969971,911.969971,4951200000\n2009-06-17,911.890015,918.440002,903.780029,910.710022,910.710022,5523650000\n2009-06-18,910.859985,921.929993,907.940002,918.369995,918.369995,4684010000\n2009-06-19,919.960022,927.090027,915.799988,921.229980,921.229980,5713390000\n2009-06-22,918.130005,918.130005,893.039978,893.039978,893.039978,4903940000\n2009-06-23,893.460022,898.690002,888.859985,895.099976,895.099976,5071020000\n2009-06-24,896.309998,910.849976,896.309998,900.940002,900.940002,4636720000\n2009-06-25,899.450012,921.419983,896.270020,920.260010,920.260010,4911240000\n2009-06-26,918.840027,922.000000,913.030029,918.900024,918.900024,6076660000\n2009-06-29,919.859985,927.989990,916.179993,927.229980,927.229980,4211760000\n2009-06-30,927.150024,930.010010,912.859985,919.320007,919.320007,4627570000\n2009-07-01,920.820007,931.919983,920.820007,923.330017,923.330017,3919400000\n2009-07-02,921.239990,921.239990,896.419983,896.419983,896.419983,3931000000\n2009-07-06,894.270020,898.719971,886.359985,898.719971,898.719971,4712580000\n2009-07-07,898.599976,898.599976,879.929993,881.030029,881.030029,4673300000\n2009-07-08,881.900024,886.799988,869.320007,879.559998,879.559998,5721780000\n2009-07-09,881.280029,887.859985,878.450012,882.679993,882.679993,4347170000\n2009-07-10,880.030029,883.570007,872.809998,879.130005,879.130005,3912080000\n2009-07-13,879.570007,901.049988,875.320007,901.049988,901.049988,4499440000\n2009-07-14,900.770020,905.840027,896.500000,905.840027,905.840027,4149030000\n2009-07-15,910.150024,933.950012,910.150024,932.679993,932.679993,5238830000\n2009-07-16,930.169983,943.960022,927.450012,940.739990,940.739990,4898640000\n2009-07-17,940.559998,941.890015,934.650024,940.380005,940.380005,5141380000\n2009-07-20,942.070007,951.619995,940.989990,951.130005,951.130005,4853150000\n2009-07-21,951.969971,956.530029,943.219971,954.580017,954.580017,5309300000\n2009-07-22,953.400024,959.830017,947.750000,954.070007,954.070007,4634100000\n2009-07-23,954.070007,979.419983,953.270020,976.289978,976.289978,5761650000\n2009-07-24,972.159973,979.789978,965.950012,979.260010,979.260010,4458300000\n2009-07-27,978.630005,982.489990,972.289978,982.179993,982.179993,4631290000\n2009-07-28,981.479980,982.349976,969.349976,979.619995,979.619995,5490350000\n2009-07-29,977.659973,977.760010,968.650024,975.150024,975.150024,5178770000\n2009-07-30,976.010010,996.679993,976.010010,986.750000,986.750000,6035180000\n2009-07-31,986.799988,993.179993,982.849976,987.479980,987.479980,5139070000\n2009-08-03,990.219971,1003.609985,990.219971,1002.630005,1002.630005,5603440000\n2009-08-04,1001.409973,1007.119995,996.679993,1005.650024,1005.650024,5713700000\n2009-08-05,1005.409973,1006.640015,994.309998,1002.719971,1002.719971,7242120000\n2009-08-06,1004.059998,1008.000000,992.489990,997.080017,997.080017,6753380000\n2009-08-07,999.830017,1018.000000,999.830017,1010.479980,1010.479980,6827090000\n2009-08-10,1008.890015,1010.119995,1000.989990,1007.099976,1007.099976,5406080000\n2009-08-11,1005.770020,1005.770020,992.400024,994.349976,994.349976,5773160000\n2009-08-12,994.000000,1012.780029,993.359985,1005.809998,1005.809998,5498170000\n2009-08-13,1005.859985,1013.140015,1000.820007,1012.729980,1012.729980,5250660000\n2009-08-14,1012.229980,1012.599976,994.599976,1004.090027,1004.090027,4940750000\n2009-08-17,998.179993,998.179993,978.510010,979.729980,979.729980,4088570000\n2009-08-18,980.619995,991.200012,980.619995,989.669983,989.669983,4198970000\n2009-08-19,986.880005,999.609985,980.619995,996.460022,996.460022,4257000000\n2009-08-20,996.409973,1008.919983,996.390015,1007.369995,1007.369995,4893160000\n2009-08-21,1009.059998,1027.589966,1009.059998,1026.130005,1026.130005,5885550000\n2009-08-24,1026.589966,1035.819946,1022.479980,1025.569946,1025.569946,6302450000\n2009-08-25,1026.630005,1037.750000,1026.209961,1028.000000,1028.000000,5768740000\n2009-08-26,1027.349976,1032.469971,1021.570007,1028.119995,1028.119995,5080060000\n2009-08-27,1027.810059,1033.329956,1016.200012,1030.979980,1030.979980,5785880000\n2009-08-28,1031.619995,1039.469971,1023.130005,1028.930054,1028.930054,5785780000\n2009-08-31,1025.209961,1025.209961,1014.619995,1020.619995,1020.619995,5004560000\n2009-09-01,1019.520020,1028.449951,996.280029,998.039978,998.039978,6862360000\n2009-09-02,996.070007,1000.340027,991.969971,994.750000,994.750000,5842730000\n2009-09-03,996.119995,1003.429993,992.250000,1003.239990,1003.239990,4624280000\n2009-09-04,1003.840027,1016.479980,1001.650024,1016.400024,1016.400024,4097370000\n2009-09-08,1018.669983,1026.069946,1018.669983,1025.390015,1025.390015,5235160000\n2009-09-09,1025.359985,1036.339966,1023.969971,1033.369995,1033.369995,5202550000\n2009-09-10,1032.989990,1044.140015,1028.040039,1044.140015,1044.140015,5191380000\n2009-09-11,1043.920044,1048.180054,1038.400024,1042.729980,1042.729980,4922600000\n2009-09-14,1040.150024,1049.739990,1035.000000,1049.339966,1049.339966,4979610000\n2009-09-15,1049.030029,1056.040039,1043.420044,1052.630005,1052.630005,6185620000\n2009-09-16,1053.989990,1068.760010,1052.869995,1068.760010,1068.760010,6793530000\n2009-09-17,1067.869995,1074.770020,1061.199951,1065.489990,1065.489990,6668110000\n2009-09-18,1066.599976,1071.520020,1064.270020,1068.300049,1068.300049,5607970000\n2009-09-21,1067.140015,1067.280029,1057.459961,1064.660034,1064.660034,4615280000\n2009-09-22,1066.349976,1073.810059,1066.349976,1071.660034,1071.660034,5246600000\n2009-09-23,1072.689941,1080.150024,1060.390015,1060.869995,1060.869995,5531930000\n2009-09-24,1062.560059,1066.290039,1045.849976,1050.780029,1050.780029,5505610000\n2009-09-25,1049.479980,1053.469971,1041.170044,1044.380005,1044.380005,4507090000\n2009-09-28,1045.380005,1065.130005,1045.380005,1062.979980,1062.979980,3726950000\n2009-09-29,1063.689941,1069.619995,1057.829956,1060.609985,1060.609985,4949900000\n2009-09-30,1061.020020,1063.400024,1046.469971,1057.079956,1057.079956,5998860000\n2009-10-01,1054.910034,1054.910034,1029.449951,1029.849976,1029.849976,5791450000\n2009-10-02,1029.709961,1030.599976,1019.950012,1025.209961,1025.209961,5583240000\n2009-10-05,1026.869995,1042.579956,1025.920044,1040.459961,1040.459961,4313310000\n2009-10-06,1042.020020,1060.550049,1042.020020,1054.719971,1054.719971,5029840000\n2009-10-07,1053.650024,1058.020020,1050.099976,1057.579956,1057.579956,4238220000\n2009-10-08,1060.030029,1070.670044,1060.030029,1065.479980,1065.479980,4988400000\n2009-10-09,1065.280029,1071.510010,1063.000000,1071.489990,1071.489990,3763780000\n2009-10-12,1071.630005,1079.459961,1071.630005,1076.189941,1076.189941,3710430000\n2009-10-13,1074.959961,1075.300049,1066.709961,1073.189941,1073.189941,4320480000\n2009-10-14,1078.680054,1093.170044,1078.680054,1092.020020,1092.020020,5406420000\n2009-10-15,1090.359985,1096.560059,1086.410034,1096.560059,1096.560059,5369780000\n2009-10-16,1094.670044,1094.670044,1081.530029,1087.680054,1087.680054,4894740000\n2009-10-19,1088.219971,1100.170044,1086.479980,1097.910034,1097.910034,4619240000\n2009-10-20,1098.640015,1098.640015,1086.160034,1091.060059,1091.060059,5396930000\n2009-10-21,1090.359985,1101.359985,1080.770020,1081.400024,1081.400024,5616290000\n2009-10-22,1080.959961,1095.209961,1074.310059,1092.910034,1092.910034,5192410000\n2009-10-23,1095.619995,1095.829956,1075.489990,1079.599976,1079.599976,4767460000\n2009-10-26,1080.359985,1091.750000,1065.229980,1066.949951,1066.949951,6363380000\n2009-10-27,1067.540039,1072.479980,1060.619995,1063.410034,1063.410034,5337380000\n2009-10-28,1061.510010,1063.260010,1042.189941,1042.630005,1042.630005,6600350000\n2009-10-29,1043.689941,1066.829956,1043.689941,1066.109985,1066.109985,5595040000\n2009-10-30,1065.410034,1065.410034,1033.380005,1036.189941,1036.189941,6512420000\n2009-11-02,1036.180054,1052.180054,1029.380005,1042.880005,1042.880005,6202640000\n2009-11-03,1040.920044,1046.359985,1033.939941,1045.410034,1045.410034,5487500000\n2009-11-04,1047.140015,1061.000000,1045.150024,1046.500000,1046.500000,5635510000\n2009-11-05,1047.300049,1066.650024,1047.300049,1066.630005,1066.630005,4848350000\n2009-11-06,1064.949951,1071.479980,1059.319946,1069.300049,1069.300049,4277130000\n2009-11-09,1072.310059,1093.189941,1072.310059,1093.079956,1093.079956,4460030000\n2009-11-10,1091.859985,1096.420044,1087.400024,1093.010010,1093.010010,4394770000\n2009-11-11,1096.040039,1105.369995,1093.810059,1098.510010,1098.510010,4286700000\n2009-11-12,1098.310059,1101.969971,1084.900024,1087.239990,1087.239990,4160250000\n2009-11-13,1087.589966,1097.790039,1085.329956,1093.479980,1093.479980,3792610000\n2009-11-16,1094.130005,1113.689941,1094.130005,1109.300049,1109.300049,4565850000\n2009-11-17,1109.219971,1110.520020,1102.189941,1110.319946,1110.319946,3824070000\n2009-11-18,1109.439941,1111.099976,1102.699951,1109.800049,1109.800049,4293340000\n2009-11-19,1106.439941,1106.439941,1088.400024,1094.900024,1094.900024,4178030000\n2009-11-20,1094.660034,1094.660034,1086.810059,1091.380005,1091.380005,3751230000\n2009-11-23,1094.859985,1112.380005,1094.859985,1106.239990,1106.239990,3827920000\n2009-11-24,1105.829956,1107.560059,1097.630005,1105.650024,1105.650024,3700820000\n2009-11-25,1106.489990,1111.180054,1104.750000,1110.630005,1110.630005,3036350000\n2009-11-27,1105.469971,1105.469971,1083.739990,1091.489990,1091.489990,2362910000\n2009-11-30,1091.069946,1097.239990,1086.250000,1095.630005,1095.630005,3895520000\n2009-12-01,1098.890015,1112.280029,1098.890015,1108.859985,1108.859985,4249310000\n2009-12-02,1109.030029,1115.579956,1105.290039,1109.239990,1109.239990,3941340000\n2009-12-03,1110.589966,1117.280029,1098.739990,1099.920044,1099.920044,4810030000\n2009-12-04,1100.430054,1119.130005,1096.520020,1105.979980,1105.979980,5781140000\n2009-12-07,1105.520020,1110.719971,1100.829956,1103.250000,1103.250000,4103360000\n2009-12-08,1103.040039,1103.040039,1088.609985,1091.939941,1091.939941,4748030000\n2009-12-09,1091.069946,1097.040039,1085.890015,1095.949951,1095.949951,4115410000\n2009-12-10,1098.689941,1106.250000,1098.689941,1102.349976,1102.349976,3996490000\n2009-12-11,1103.959961,1108.500000,1101.339966,1106.410034,1106.410034,3791090000\n2009-12-14,1107.839966,1114.760010,1107.839966,1114.109985,1114.109985,4548490000\n2009-12-15,1114.109985,1114.109985,1105.349976,1107.930054,1107.930054,5045100000\n2009-12-16,1108.609985,1116.209961,1107.959961,1109.180054,1109.180054,4829820000\n2009-12-17,1106.359985,1106.359985,1095.880005,1096.079956,1096.079956,7615070000\n2009-12-18,1097.859985,1103.739990,1093.880005,1102.469971,1102.469971,6325890000\n2009-12-21,1105.310059,1117.680054,1105.310059,1114.050049,1114.050049,3977340000\n2009-12-22,1114.510010,1120.270020,1114.510010,1118.020020,1118.020020,3641130000\n2009-12-23,1118.839966,1121.579956,1116.000000,1120.589966,1120.589966,3166870000\n2009-12-24,1121.079956,1126.479980,1121.079956,1126.479980,1126.479980,1267710000\n2009-12-28,1127.530029,1130.380005,1123.510010,1127.780029,1127.780029,2716400000\n2009-12-29,1128.550049,1130.380005,1126.079956,1126.199951,1126.199951,2491020000\n2009-12-30,1125.530029,1126.420044,1121.939941,1126.420044,1126.420044,2277300000\n2009-12-31,1126.599976,1127.640015,1114.810059,1115.099976,1115.099976,2076990000\n2010-01-04,1116.560059,1133.869995,1116.560059,1132.989990,1132.989990,3991400000\n2010-01-05,1132.660034,1136.630005,1129.660034,1136.520020,1136.520020,2491020000\n2010-01-06,1135.709961,1139.189941,1133.949951,1137.140015,1137.140015,4972660000\n2010-01-07,1136.270020,1142.459961,1131.319946,1141.689941,1141.689941,5270680000\n2010-01-08,1140.520020,1145.390015,1136.219971,1144.979980,1144.979980,4389590000\n2010-01-11,1145.959961,1149.739990,1142.020020,1146.979980,1146.979980,4255780000\n2010-01-12,1143.810059,1143.810059,1131.770020,1136.219971,1136.219971,4716160000\n2010-01-13,1137.310059,1148.400024,1133.180054,1145.680054,1145.680054,4170360000\n2010-01-14,1145.680054,1150.410034,1143.800049,1148.459961,1148.459961,3915200000\n2010-01-15,1147.719971,1147.770020,1131.390015,1136.030029,1136.030029,4758730000\n2010-01-19,1136.030029,1150.449951,1135.770020,1150.229980,1150.229980,4724830000\n2010-01-20,1147.949951,1147.949951,1129.250000,1138.040039,1138.040039,4810560000\n2010-01-21,1138.680054,1141.579956,1114.839966,1116.479980,1116.479980,6874290000\n2010-01-22,1115.489990,1115.489990,1090.180054,1091.760010,1091.760010,6208650000\n2010-01-25,1092.400024,1102.969971,1092.400024,1096.780029,1096.780029,4481390000\n2010-01-26,1095.800049,1103.689941,1089.859985,1092.170044,1092.170044,4731910000\n2010-01-27,1091.939941,1099.510010,1083.109985,1097.500000,1097.500000,5319120000\n2010-01-28,1096.930054,1100.219971,1078.459961,1084.530029,1084.530029,5452400000\n2010-01-29,1087.609985,1096.449951,1071.589966,1073.869995,1073.869995,5412850000\n2010-02-01,1073.890015,1089.380005,1073.890015,1089.189941,1089.189941,4077610000\n2010-02-02,1090.050049,1104.729980,1087.959961,1103.319946,1103.319946,4749540000\n2010-02-03,1100.670044,1102.719971,1093.969971,1097.280029,1097.280029,4285450000\n2010-02-04,1097.250000,1097.250000,1062.780029,1063.109985,1063.109985,5859690000\n2010-02-05,1064.119995,1067.130005,1044.500000,1066.189941,1066.189941,6438900000\n2010-02-08,1065.510010,1071.199951,1056.510010,1056.739990,1056.739990,4089820000\n2010-02-09,1060.060059,1079.280029,1060.060059,1070.520020,1070.520020,5114260000\n2010-02-10,1069.680054,1073.670044,1059.339966,1068.130005,1068.130005,4251450000\n2010-02-11,1067.099976,1080.040039,1060.589966,1078.469971,1078.469971,4400870000\n2010-02-12,1075.949951,1077.810059,1062.969971,1075.510010,1075.510010,4160680000\n2010-02-16,1079.130005,1095.670044,1079.130005,1094.869995,1094.869995,4080770000\n2010-02-17,1096.140015,1101.030029,1094.719971,1099.510010,1099.510010,4259230000\n2010-02-18,1099.030029,1108.239990,1097.479980,1106.750000,1106.750000,3878620000\n2010-02-19,1105.489990,1112.420044,1100.800049,1109.170044,1109.170044,3944280000\n2010-02-22,1110.000000,1112.290039,1105.380005,1108.010010,1108.010010,3814440000\n2010-02-23,1107.489990,1108.579956,1092.180054,1094.599976,1094.599976,4521050000\n2010-02-24,1095.890015,1106.420044,1095.500000,1105.239990,1105.239990,4168360000\n2010-02-25,1101.239990,1103.500000,1086.020020,1102.939941,1102.939941,4521130000\n2010-02-26,1103.099976,1107.239990,1097.560059,1104.489990,1104.489990,3945190000\n2010-03-01,1105.359985,1116.109985,1105.359985,1115.709961,1115.709961,3847640000\n2010-03-02,1117.010010,1123.459961,1116.510010,1118.310059,1118.310059,4134680000\n2010-03-03,1119.359985,1125.640015,1116.579956,1118.790039,1118.790039,3951320000\n2010-03-04,1119.119995,1123.729980,1116.660034,1122.969971,1122.969971,3945010000\n2010-03-05,1125.119995,1139.380005,1125.119995,1138.699951,1138.699951,4133000000\n2010-03-08,1138.400024,1141.050049,1136.770020,1138.500000,1138.500000,3774680000\n2010-03-09,1137.560059,1145.369995,1134.900024,1140.449951,1140.449951,5185570000\n2010-03-10,1140.219971,1148.260010,1140.089966,1145.609985,1145.609985,5469120000\n2010-03-11,1143.959961,1150.239990,1138.989990,1150.239990,1150.239990,4669060000\n2010-03-12,1151.709961,1153.410034,1146.969971,1149.989990,1149.989990,4928160000\n2010-03-15,1148.530029,1150.979980,1141.449951,1150.510010,1150.510010,4164110000\n2010-03-16,1150.829956,1160.280029,1150.349976,1159.459961,1159.459961,4369770000\n2010-03-17,1159.939941,1169.839966,1159.939941,1166.209961,1166.209961,4963200000\n2010-03-18,1166.130005,1167.770020,1161.160034,1165.829956,1165.829956,4234510000\n2010-03-19,1166.680054,1169.199951,1155.329956,1159.900024,1159.900024,5212410000\n2010-03-22,1157.250000,1167.819946,1152.880005,1165.810059,1165.810059,4261680000\n2010-03-23,1166.469971,1174.719971,1163.829956,1174.170044,1174.170044,4411640000\n2010-03-24,1172.699951,1173.040039,1166.010010,1167.719971,1167.719971,4705750000\n2010-03-25,1170.030029,1180.689941,1165.089966,1165.729980,1165.729980,5668900000\n2010-03-26,1167.579956,1173.930054,1161.479980,1166.589966,1166.589966,4708420000\n2010-03-29,1167.709961,1174.849976,1167.709961,1173.219971,1173.219971,4375580000\n2010-03-30,1173.750000,1177.829956,1168.920044,1173.270020,1173.270020,4085000000\n2010-03-31,1171.750000,1174.560059,1165.770020,1169.430054,1169.430054,4484340000\n2010-04-01,1171.229980,1181.430054,1170.689941,1178.099976,1178.099976,4006870000\n2010-04-05,1178.709961,1187.729980,1178.709961,1187.439941,1187.439941,3881620000\n2010-04-06,1186.010010,1191.800049,1182.770020,1189.439941,1189.439941,4086180000\n2010-04-07,1188.229980,1189.599976,1177.250000,1182.449951,1182.449951,5101430000\n2010-04-08,1181.750000,1188.550049,1175.119995,1186.439941,1186.439941,4726970000\n2010-04-09,1187.469971,1194.660034,1187.150024,1194.369995,1194.369995,4511570000\n2010-04-12,1194.939941,1199.199951,1194.709961,1196.479980,1196.479980,4607090000\n2010-04-13,1195.939941,1199.040039,1188.819946,1197.300049,1197.300049,5403580000\n2010-04-14,1198.689941,1210.650024,1198.689941,1210.650024,1210.650024,5760040000\n2010-04-15,1210.770020,1213.920044,1208.500000,1211.670044,1211.670044,5995330000\n2010-04-16,1210.170044,1210.170044,1186.770020,1192.130005,1192.130005,8108470000\n2010-04-19,1192.060059,1197.869995,1183.680054,1197.520020,1197.520020,6597740000\n2010-04-20,1199.040039,1208.579956,1199.040039,1207.170044,1207.170044,5316590000\n2010-04-21,1207.160034,1210.989990,1198.849976,1205.939941,1205.939941,5724310000\n2010-04-22,1202.520020,1210.270020,1190.189941,1208.670044,1208.670044,6035780000\n2010-04-23,1207.869995,1217.280029,1205.099976,1217.280029,1217.280029,5326060000\n2010-04-26,1217.069946,1219.800049,1211.069946,1212.050049,1212.050049,5647760000\n2010-04-27,1209.920044,1211.380005,1181.619995,1183.709961,1183.709961,7454540000\n2010-04-28,1184.589966,1195.050049,1181.810059,1191.359985,1191.359985,6342310000\n2010-04-29,1193.300049,1209.359985,1193.300049,1206.780029,1206.780029,6059410000\n2010-04-30,1206.770020,1207.989990,1186.319946,1186.689941,1186.689941,6048260000\n2010-05-03,1188.579956,1205.130005,1188.579956,1202.260010,1202.260010,4938050000\n2010-05-04,1197.500000,1197.500000,1168.119995,1173.599976,1173.599976,6594720000\n2010-05-05,1169.239990,1175.949951,1158.150024,1165.869995,1165.869995,6795940000\n2010-05-06,1164.380005,1167.579956,1065.790039,1128.150024,1128.150024,10617810000\n2010-05-07,1127.040039,1135.130005,1094.150024,1110.880005,1110.880005,9472910000\n2010-05-10,1122.270020,1163.849976,1122.270020,1159.729980,1159.729980,6893700000\n2010-05-11,1156.390015,1170.479980,1147.709961,1155.790039,1155.790039,5842550000\n2010-05-12,1155.430054,1172.869995,1155.430054,1171.670044,1171.670044,5225460000\n2010-05-13,1170.040039,1173.569946,1156.140015,1157.439941,1157.439941,4870640000\n2010-05-14,1157.189941,1157.189941,1126.140015,1135.680054,1135.680054,6126400000\n2010-05-17,1136.520020,1141.880005,1114.959961,1136.939941,1136.939941,5922920000\n2010-05-18,1138.780029,1148.660034,1117.199951,1120.800049,1120.800049,6170840000\n2010-05-19,1119.569946,1124.270020,1100.660034,1115.050049,1115.050049,6765800000\n2010-05-20,1107.339966,1107.339966,1071.579956,1071.589966,1071.589966,8328570000\n2010-05-21,1067.260010,1090.160034,1055.900024,1087.689941,1087.689941,5452130000\n2010-05-24,1084.780029,1089.949951,1072.699951,1073.650024,1073.650024,5224040000\n2010-05-25,1067.420044,1074.750000,1040.780029,1074.030029,1074.030029,7329580000\n2010-05-26,1075.510010,1090.750000,1065.589966,1067.949951,1067.949951,4521050000\n2010-05-27,1074.270020,1103.520020,1074.270020,1103.060059,1103.060059,5698460000\n2010-05-28,1102.589966,1102.589966,1084.780029,1089.410034,1089.410034,4871210000\n2010-06-01,1087.300049,1094.770020,1069.890015,1070.709961,1070.709961,5271480000\n2010-06-02,1073.010010,1098.560059,1072.030029,1098.380005,1098.380005,5026360000\n2010-06-03,1098.819946,1105.670044,1091.810059,1102.829956,1102.829956,4995970000\n2010-06-04,1098.430054,1098.430054,1060.500000,1064.880005,1064.880005,6180580000\n2010-06-07,1065.839966,1071.359985,1049.859985,1050.469971,1050.469971,5467560000\n2010-06-08,1050.810059,1063.150024,1042.170044,1062.000000,1062.000000,6192750000\n2010-06-09,1062.750000,1077.739990,1052.250000,1055.689941,1055.689941,5983200000\n2010-06-10,1058.770020,1087.849976,1058.770020,1086.839966,1086.839966,5144780000\n2010-06-11,1082.650024,1092.250000,1077.119995,1091.599976,1091.599976,4059280000\n2010-06-14,1095.000000,1105.910034,1089.030029,1089.630005,1089.630005,4425830000\n2010-06-15,1091.209961,1115.589966,1091.209961,1115.229980,1115.229980,4644490000\n2010-06-16,1114.020020,1118.739990,1107.130005,1114.609985,1114.609985,5002600000\n2010-06-17,1115.979980,1117.719971,1105.869995,1116.040039,1116.040039,4557760000\n2010-06-18,1116.160034,1121.010010,1113.930054,1117.510010,1117.510010,4555360000\n2010-06-21,1122.790039,1131.229980,1108.239990,1113.199951,1113.199951,4514360000\n2010-06-22,1113.900024,1118.500000,1094.180054,1095.310059,1095.310059,4514380000\n2010-06-23,1095.569946,1099.640015,1085.310059,1092.040039,1092.040039,4526150000\n2010-06-24,1090.930054,1090.930054,1071.599976,1073.689941,1073.689941,4814830000\n2010-06-25,1075.099976,1083.560059,1067.890015,1076.760010,1076.760010,5128840000\n2010-06-28,1077.500000,1082.599976,1071.449951,1074.569946,1074.569946,3896410000\n2010-06-29,1071.099976,1071.099976,1035.180054,1041.239990,1041.239990,6136700000\n2010-06-30,1040.560059,1048.079956,1028.329956,1030.709961,1030.709961,5067080000\n2010-07-01,1031.099976,1033.579956,1010.909973,1027.369995,1027.369995,6435770000\n2010-07-02,1027.650024,1032.949951,1015.929993,1022.580017,1022.580017,3968500000\n2010-07-06,1028.089966,1042.500000,1018.349976,1028.060059,1028.060059,4691240000\n2010-07-07,1028.540039,1060.890015,1028.540039,1060.270020,1060.270020,4931220000\n2010-07-08,1062.920044,1071.250000,1058.239990,1070.250000,1070.250000,4548460000\n2010-07-09,1070.500000,1078.160034,1068.099976,1077.959961,1077.959961,3506570000\n2010-07-12,1077.229980,1080.780029,1070.449951,1078.750000,1078.750000,3426990000\n2010-07-13,1080.650024,1099.459961,1080.650024,1095.339966,1095.339966,4640460000\n2010-07-14,1095.609985,1099.079956,1087.680054,1095.170044,1095.170044,4521050000\n2010-07-15,1094.459961,1098.660034,1080.530029,1096.479980,1096.479980,4552470000\n2010-07-16,1093.849976,1093.849976,1063.319946,1064.880005,1064.880005,5297350000\n2010-07-19,1066.849976,1074.699951,1061.109985,1071.250000,1071.250000,4089500000\n2010-07-20,1064.530029,1083.939941,1056.880005,1083.479980,1083.479980,4713280000\n2010-07-21,1086.670044,1088.959961,1065.250000,1069.589966,1069.589966,4747180000\n2010-07-22,1072.140015,1097.500000,1072.140015,1093.670044,1093.670044,4826900000\n2010-07-23,1092.170044,1103.729980,1087.880005,1102.660034,1102.660034,4524570000\n2010-07-26,1102.890015,1115.010010,1101.300049,1115.010010,1115.010010,4009650000\n2010-07-27,1117.359985,1120.949951,1109.780029,1113.839966,1113.839966,4725690000\n2010-07-28,1112.839966,1114.660034,1103.109985,1106.130005,1106.130005,4002390000\n2010-07-29,1108.069946,1115.900024,1092.819946,1101.530029,1101.530029,4612420000\n2010-07-30,1098.439941,1106.439941,1088.010010,1101.599976,1101.599976,4006450000\n2010-08-02,1107.530029,1127.300049,1107.530029,1125.859985,1125.859985,4144180000\n2010-08-03,1125.339966,1125.439941,1116.760010,1120.459961,1120.459961,4071820000\n2010-08-04,1121.060059,1128.750000,1119.459961,1127.239990,1127.239990,4057850000\n2010-08-05,1125.780029,1126.560059,1118.810059,1125.810059,1125.810059,3685560000\n2010-08-06,1122.069946,1123.060059,1107.170044,1121.640015,1121.640015,3857890000\n2010-08-09,1122.800049,1129.239990,1120.910034,1127.790039,1127.790039,3979360000\n2010-08-10,1122.920044,1127.160034,1111.579956,1121.060059,1121.060059,3979360000\n2010-08-11,1116.890015,1116.890015,1088.550049,1089.469971,1089.469971,4511860000\n2010-08-12,1081.479980,1086.719971,1076.689941,1083.609985,1083.609985,4521050000\n2010-08-13,1082.219971,1086.250000,1079.000000,1079.250000,1079.250000,3328890000\n2010-08-16,1077.489990,1082.619995,1069.489990,1079.380005,1079.380005,3142450000\n2010-08-17,1081.160034,1100.140015,1081.160034,1092.540039,1092.540039,3968210000\n2010-08-18,1092.079956,1099.770020,1085.760010,1094.160034,1094.160034,3724260000\n2010-08-19,1092.439941,1092.439941,1070.660034,1075.630005,1075.630005,4290540000\n2010-08-20,1075.630005,1075.630005,1063.910034,1071.689941,1071.689941,3761570000\n2010-08-23,1073.359985,1081.579956,1067.079956,1067.359985,1067.359985,3210950000\n2010-08-24,1063.199951,1063.199951,1046.680054,1051.869995,1051.869995,4436330000\n2010-08-25,1048.979980,1059.380005,1039.829956,1055.329956,1055.329956,4360190000\n2010-08-26,1056.280029,1061.449951,1045.400024,1047.219971,1047.219971,3646710000\n2010-08-27,1049.270020,1065.209961,1039.699951,1064.589966,1064.589966,4102460000\n2010-08-30,1062.900024,1064.400024,1048.790039,1048.920044,1048.920044,2917990000\n2010-08-31,1046.880005,1055.140015,1040.880005,1049.329956,1049.329956,4038770000\n2010-09-01,1049.719971,1081.300049,1049.719971,1080.290039,1080.290039,4396880000\n2010-09-02,1080.660034,1090.099976,1080.390015,1090.099976,1090.099976,3704210000\n2010-09-03,1093.609985,1105.099976,1093.609985,1104.510010,1104.510010,3534500000\n2010-09-07,1102.599976,1102.599976,1091.150024,1091.839966,1091.839966,3107380000\n2010-09-08,1092.359985,1103.260010,1092.359985,1098.869995,1098.869995,3224640000\n2010-09-09,1101.150024,1110.270020,1101.150024,1104.180054,1104.180054,3387770000\n2010-09-10,1104.569946,1110.880005,1103.920044,1109.550049,1109.550049,3061160000\n2010-09-13,1113.380005,1123.869995,1113.380005,1121.900024,1121.900024,4521050000\n2010-09-14,1121.160034,1127.359985,1115.579956,1121.099976,1121.099976,4521050000\n2010-09-15,1119.430054,1126.459961,1114.630005,1125.069946,1125.069946,3369840000\n2010-09-16,1123.890015,1125.439941,1118.880005,1124.660034,1124.660034,3364080000\n2010-09-17,1126.390015,1131.469971,1122.430054,1125.589966,1125.589966,4086140000\n2010-09-20,1126.569946,1144.859985,1126.569946,1142.709961,1142.709961,3364080000\n2010-09-21,1142.819946,1148.589966,1136.219971,1139.780029,1139.780029,4175660000\n2010-09-22,1139.489990,1144.380005,1131.579956,1134.280029,1134.280029,3911070000\n2010-09-23,1131.099976,1136.770020,1122.790039,1124.829956,1124.829956,3847850000\n2010-09-24,1131.689941,1148.900024,1131.689941,1148.670044,1148.670044,4123950000\n2010-09-27,1148.640015,1149.920044,1142.000000,1142.160034,1142.160034,3587860000\n2010-09-28,1142.310059,1150.000000,1132.089966,1147.699951,1147.699951,4025840000\n2010-09-29,1146.750000,1148.630005,1140.260010,1144.729980,1144.729980,3990280000\n2010-09-30,1145.969971,1157.160034,1136.079956,1141.199951,1141.199951,4284160000\n2010-10-01,1143.489990,1150.300049,1139.420044,1146.239990,1146.239990,4298910000\n2010-10-04,1144.959961,1148.160034,1131.869995,1137.030029,1137.030029,3604110000\n2010-10-05,1140.680054,1162.760010,1140.680054,1160.750000,1160.750000,4068840000\n2010-10-06,1159.810059,1162.329956,1154.849976,1159.969971,1159.969971,4073160000\n2010-10-07,1161.569946,1163.869995,1151.410034,1158.060059,1158.060059,3910550000\n2010-10-08,1158.359985,1167.729980,1155.579956,1165.150024,1165.150024,3871420000\n2010-10-11,1165.319946,1168.680054,1162.020020,1165.319946,1165.319946,2505900000\n2010-10-12,1164.280029,1172.579956,1155.709961,1169.770020,1169.770020,4076170000\n2010-10-13,1171.319946,1184.380005,1171.319946,1178.099976,1178.099976,4969410000\n2010-10-14,1177.819946,1178.890015,1166.709961,1173.810059,1173.810059,4969410000\n2010-10-15,1177.469971,1181.199951,1167.119995,1176.189941,1176.189941,5724910000\n2010-10-18,1176.829956,1185.530029,1174.550049,1184.709961,1184.709961,4450050000\n2010-10-19,1178.640015,1178.640015,1159.709961,1165.900024,1165.900024,5600120000\n2010-10-20,1166.739990,1182.939941,1166.739990,1178.170044,1178.170044,5027880000\n2010-10-21,1179.819946,1189.430054,1171.170044,1180.260010,1180.260010,4625470000\n2010-10-22,1180.520020,1183.930054,1178.989990,1183.079956,1183.079956,3177890000\n2010-10-25,1184.739990,1196.140015,1184.739990,1185.619995,1185.619995,4221380000\n2010-10-26,1184.880005,1187.109985,1177.719971,1185.640015,1185.640015,4203680000\n2010-10-27,1183.839966,1183.839966,1171.699951,1182.449951,1182.449951,4335670000\n2010-10-28,1184.469971,1189.530029,1177.099976,1183.780029,1183.780029,4283460000\n2010-10-29,1183.869995,1185.459961,1179.699951,1183.260010,1183.260010,3537880000\n2010-11-01,1185.709961,1195.810059,1177.650024,1184.380005,1184.380005,4129180000\n2010-11-02,1187.859985,1195.880005,1187.859985,1193.569946,1193.569946,3866200000\n2010-11-03,1193.790039,1198.300049,1183.560059,1197.959961,1197.959961,4665480000\n2010-11-04,1198.339966,1221.250000,1198.339966,1221.060059,1221.060059,5695470000\n2010-11-05,1221.199951,1227.079956,1220.290039,1225.849976,1225.849976,5637460000\n2010-11-08,1223.239990,1224.569946,1217.550049,1223.250000,1223.250000,3937230000\n2010-11-09,1223.589966,1226.839966,1208.939941,1213.400024,1213.400024,4848040000\n2010-11-10,1213.140015,1218.750000,1204.329956,1218.709961,1218.709961,4561300000\n2010-11-11,1213.040039,1215.449951,1204.489990,1213.540039,1213.540039,3931120000\n2010-11-12,1209.069946,1210.500000,1194.079956,1199.209961,1199.209961,4213620000\n2010-11-15,1200.439941,1207.430054,1197.150024,1197.750000,1197.750000,3503370000\n2010-11-16,1194.790039,1194.790039,1173.000000,1178.339966,1178.339966,5116380000\n2010-11-17,1178.329956,1183.560059,1175.819946,1178.589966,1178.589966,3904780000\n2010-11-18,1183.750000,1200.290039,1183.750000,1196.689941,1196.689941,4687260000\n2010-11-19,1196.119995,1199.969971,1189.439941,1199.729980,1199.729980,3675390000\n2010-11-22,1198.069946,1198.939941,1184.579956,1197.839966,1197.839966,3689500000\n2010-11-23,1192.510010,1192.510010,1176.910034,1180.729980,1180.729980,4133070000\n2010-11-24,1183.699951,1198.619995,1183.699951,1198.349976,1198.349976,3384250000\n2010-11-26,1194.160034,1194.160034,1186.930054,1189.400024,1189.400024,1613820000\n2010-11-29,1189.079956,1190.339966,1173.640015,1187.760010,1187.760010,3673450000\n2010-11-30,1182.959961,1187.400024,1174.140015,1180.550049,1180.550049,4284700000\n2010-12-01,1186.599976,1207.609985,1186.599976,1206.069946,1206.069946,4548110000\n2010-12-02,1206.810059,1221.890015,1206.810059,1221.530029,1221.530029,4970800000\n2010-12-03,1219.930054,1225.569946,1216.819946,1224.709961,1224.709961,3735780000\n2010-12-06,1223.869995,1225.800049,1220.670044,1223.119995,1223.119995,3527370000\n2010-12-07,1227.250000,1235.050049,1223.250000,1223.750000,1223.750000,6970630000\n2010-12-08,1225.020020,1228.930054,1219.500000,1228.280029,1228.280029,4607590000\n2010-12-09,1230.140015,1234.709961,1226.849976,1233.000000,1233.000000,4522510000\n2010-12-10,1233.849976,1240.400024,1232.579956,1240.400024,1240.400024,4547310000\n2010-12-13,1242.520020,1246.729980,1240.339966,1240.459961,1240.459961,4361240000\n2010-12-14,1241.839966,1246.589966,1238.170044,1241.589966,1241.589966,4132350000\n2010-12-15,1241.579956,1244.250000,1234.010010,1235.229980,1235.229980,4407340000\n2010-12-16,1236.339966,1243.750000,1232.849976,1242.869995,1242.869995,4736820000\n2010-12-17,1243.630005,1245.810059,1239.869995,1243.910034,1243.910034,4632470000\n2010-12-20,1245.760010,1250.199951,1241.510010,1247.079956,1247.079956,3548140000\n2010-12-21,1249.430054,1255.819946,1249.430054,1254.599976,1254.599976,3479670000\n2010-12-22,1254.939941,1259.390015,1254.939941,1258.839966,1258.839966,1285590000\n2010-12-23,1257.530029,1258.589966,1254.050049,1256.770020,1256.770020,2515020000\n2010-12-27,1254.660034,1258.430054,1251.479980,1257.540039,1257.540039,1992470000\n2010-12-28,1259.099976,1259.900024,1256.219971,1258.510010,1258.510010,2478450000\n2010-12-29,1258.780029,1262.599976,1258.780029,1259.780029,1259.780029,2214380000\n2010-12-30,1259.439941,1261.089966,1256.319946,1257.880005,1257.880005,1970720000\n2010-12-31,1256.760010,1259.339966,1254.189941,1257.640015,1257.640015,1799770000\n2011-01-03,1257.619995,1276.170044,1257.619995,1271.869995,1271.869995,4286670000\n2011-01-04,1272.949951,1274.119995,1262.660034,1270.199951,1270.199951,4796420000\n2011-01-05,1268.780029,1277.630005,1265.359985,1276.560059,1276.560059,4764920000\n2011-01-06,1276.290039,1278.170044,1270.430054,1273.849976,1273.849976,4844100000\n2011-01-07,1274.410034,1276.829956,1261.699951,1271.500000,1271.500000,4963110000\n2011-01-10,1270.839966,1271.520020,1262.180054,1269.750000,1269.750000,4036450000\n2011-01-11,1272.579956,1277.250000,1269.619995,1274.479980,1274.479980,4050750000\n2011-01-12,1275.650024,1286.869995,1275.650024,1285.959961,1285.959961,4226940000\n2011-01-13,1285.780029,1286.699951,1280.469971,1283.760010,1283.760010,4310840000\n2011-01-14,1282.900024,1293.239990,1281.239990,1293.239990,1293.239990,4661590000\n2011-01-18,1293.219971,1296.060059,1290.160034,1295.020020,1295.020020,5284990000\n2011-01-19,1294.520020,1294.599976,1278.920044,1281.920044,1281.920044,4743710000\n2011-01-20,1280.849976,1283.349976,1271.260010,1280.260010,1280.260010,4935320000\n2011-01-21,1283.630005,1291.209961,1282.069946,1283.349976,1283.349976,4935320000\n2011-01-24,1283.290039,1291.930054,1282.469971,1290.839966,1290.839966,3902470000\n2011-01-25,1288.170044,1291.260010,1281.069946,1291.180054,1291.180054,4595380000\n2011-01-26,1291.969971,1299.739990,1291.969971,1296.630005,1296.630005,4730980000\n2011-01-27,1297.510010,1301.290039,1294.410034,1299.540039,1299.540039,4309190000\n2011-01-28,1299.630005,1302.670044,1275.099976,1276.339966,1276.339966,5618630000\n2011-01-31,1276.500000,1287.170044,1276.500000,1286.119995,1286.119995,4167160000\n2011-02-01,1289.140015,1308.859985,1289.140015,1307.589966,1307.589966,5164500000\n2011-02-02,1305.910034,1307.609985,1302.619995,1304.030029,1304.030029,4098260000\n2011-02-03,1302.770020,1308.599976,1294.829956,1307.099976,1307.099976,4370990000\n2011-02-04,1307.010010,1311.000000,1301.670044,1310.869995,1310.869995,3925950000\n2011-02-07,1311.849976,1322.849976,1311.849976,1319.050049,1319.050049,3902270000\n2011-02-08,1318.760010,1324.869995,1316.030029,1324.569946,1324.569946,3881530000\n2011-02-09,1322.479980,1324.540039,1314.890015,1320.880005,1320.880005,3922240000\n2011-02-10,1318.130005,1322.780029,1311.739990,1321.869995,1321.869995,4184610000\n2011-02-11,1318.660034,1330.790039,1316.079956,1329.150024,1329.150024,4219300000\n2011-02-14,1328.729980,1332.959961,1326.900024,1332.319946,1332.319946,3567040000\n2011-02-15,1330.430054,1330.430054,1324.609985,1328.010010,1328.010010,3926860000\n2011-02-16,1329.510010,1337.609985,1329.510010,1336.319946,1336.319946,1966450000\n2011-02-17,1334.369995,1341.500000,1331.000000,1340.430054,1340.430054,1966450000\n2011-02-18,1340.380005,1344.069946,1338.119995,1343.010010,1343.010010,1162310000\n2011-02-22,1338.910034,1338.910034,1312.329956,1315.439941,1315.439941,1322780000\n2011-02-23,1315.439941,1317.910034,1299.550049,1307.400024,1307.400024,1330340000\n2011-02-24,1307.089966,1310.910034,1294.260010,1306.099976,1306.099976,1222900000\n2011-02-25,1307.339966,1320.609985,1307.339966,1319.880005,1319.880005,3836030000\n2011-02-28,1321.609985,1329.380005,1320.550049,1327.219971,1327.219971,1252850000\n2011-03-01,1328.640015,1332.089966,1306.140015,1306.329956,1306.329956,1180420000\n2011-03-02,1305.469971,1314.189941,1302.579956,1308.439941,1308.439941,1025000000\n2011-03-03,1312.369995,1332.280029,1312.369995,1330.969971,1330.969971,4340470000\n2011-03-04,1330.729980,1331.079956,1312.589966,1321.150024,1321.150024,4223740000\n2011-03-07,1322.719971,1327.680054,1303.989990,1310.130005,1310.130005,3964730000\n2011-03-08,1311.050049,1325.739990,1306.859985,1321.819946,1321.819946,4531420000\n2011-03-09,1319.920044,1323.209961,1312.270020,1320.020020,1320.020020,3709520000\n2011-03-10,1315.719971,1315.719971,1294.209961,1295.109985,1295.109985,4723020000\n2011-03-11,1293.430054,1308.349976,1291.989990,1304.280029,1304.280029,3740400000\n2011-03-14,1301.189941,1301.189941,1286.369995,1296.390015,1296.390015,4050370000\n2011-03-15,1288.459961,1288.459961,1261.119995,1281.869995,1281.869995,5201400000\n2011-03-16,1279.459961,1280.910034,1249.050049,1256.880005,1256.880005,5833000000\n2011-03-17,1261.609985,1278.880005,1261.609985,1273.719971,1273.719971,4134950000\n2011-03-18,1276.709961,1288.880005,1276.180054,1279.209961,1279.209961,4685500000\n2011-03-21,1281.650024,1300.579956,1281.650024,1298.380005,1298.380005,4223730000\n2011-03-22,1298.290039,1299.349976,1292.699951,1293.770020,1293.770020,3576550000\n2011-03-23,1292.189941,1300.510010,1284.050049,1297.540039,1297.540039,3842350000\n2011-03-24,1300.609985,1311.339966,1297.739990,1309.660034,1309.660034,4223740000\n2011-03-25,1311.800049,1319.180054,1310.150024,1313.800049,1313.800049,4223740000\n2011-03-28,1315.449951,1319.739990,1310.189941,1310.189941,1310.189941,3215170000\n2011-03-29,1309.369995,1319.449951,1305.260010,1319.439941,1319.439941,3482580000\n2011-03-30,1321.890015,1331.739990,1321.890015,1328.260010,1328.260010,3809570000\n2011-03-31,1327.439941,1329.770020,1325.030029,1325.829956,1325.829956,3566270000\n2011-04-01,1329.479980,1337.849976,1328.890015,1332.410034,1332.410034,4223740000\n2011-04-04,1333.560059,1336.739990,1329.099976,1332.869995,1332.869995,4223740000\n2011-04-05,1332.030029,1338.209961,1330.030029,1332.630005,1332.630005,3852280000\n2011-04-06,1335.939941,1339.380005,1331.089966,1335.540039,1335.540039,4223740000\n2011-04-07,1334.819946,1338.800049,1326.560059,1333.510010,1333.510010,4005600000\n2011-04-08,1336.160034,1339.459961,1322.939941,1328.170044,1328.170044,3582810000\n2011-04-11,1329.010010,1333.770020,1321.060059,1324.459961,1324.459961,3478970000\n2011-04-12,1321.959961,1321.959961,1309.510010,1314.160034,1314.160034,4275490000\n2011-04-13,1314.030029,1321.349976,1309.189941,1314.410034,1314.410034,3850860000\n2011-04-14,1311.130005,1316.790039,1302.420044,1314.520020,1314.520020,3872630000\n2011-04-15,1314.540039,1322.880005,1313.680054,1319.680054,1319.680054,4223740000\n2011-04-18,1313.349976,1313.349976,1294.699951,1305.140015,1305.140015,4223740000\n2011-04-19,1305.989990,1312.699951,1303.969971,1312.619995,1312.619995,3886300000\n2011-04-20,1319.119995,1332.660034,1319.119995,1330.359985,1330.359985,4236280000\n2011-04-21,1333.229980,1337.489990,1332.829956,1337.380005,1337.380005,3587240000\n2011-04-25,1337.140015,1337.550049,1331.469971,1335.250000,1335.250000,2142130000\n2011-04-26,1336.750000,1349.550049,1336.750000,1347.239990,1347.239990,3908060000\n2011-04-27,1348.430054,1357.489990,1344.250000,1355.660034,1355.660034,4051570000\n2011-04-28,1353.859985,1361.709961,1353.599976,1360.479980,1360.479980,4036820000\n2011-04-29,1360.140015,1364.560059,1358.689941,1363.609985,1363.609985,3479070000\n2011-05-02,1365.209961,1370.579956,1358.589966,1361.219971,1361.219971,3846250000\n2011-05-03,1359.760010,1360.839966,1349.520020,1356.619995,1356.619995,4223740000\n2011-05-04,1355.900024,1355.900024,1341.500000,1347.319946,1347.319946,4223740000\n2011-05-05,1344.160034,1348.000000,1329.170044,1335.099976,1335.099976,3846250000\n2011-05-06,1340.239990,1354.359985,1335.579956,1340.199951,1340.199951,4223740000\n2011-05-09,1340.199951,1349.439941,1338.640015,1346.290039,1346.290039,4265250000\n2011-05-10,1348.339966,1359.439941,1348.339966,1357.160034,1357.160034,4223740000\n2011-05-11,1354.510010,1354.510010,1336.359985,1342.079956,1342.079956,3846250000\n2011-05-12,1339.390015,1351.050049,1332.030029,1348.650024,1348.650024,3777210000\n2011-05-13,1348.689941,1350.469971,1333.359985,1337.770020,1337.770020,3426660000\n2011-05-16,1334.770020,1343.329956,1327.319946,1329.469971,1329.469971,3846250000\n2011-05-17,1326.099976,1330.420044,1318.510010,1328.979980,1328.979980,4053970000\n2011-05-18,1328.540039,1341.819946,1326.589966,1340.680054,1340.680054,3922030000\n2011-05-19,1342.400024,1346.819946,1336.359985,1343.599976,1343.599976,3626110000\n2011-05-20,1342.000000,1342.000000,1330.670044,1333.270020,1333.270020,4066020000\n2011-05-23,1333.069946,1333.069946,1312.880005,1317.369995,1317.369995,3255580000\n2011-05-24,1317.699951,1323.719971,1313.869995,1316.280029,1316.280029,3846250000\n2011-05-25,1316.359985,1325.859985,1311.800049,1320.469971,1320.469971,4109670000\n2011-05-26,1320.640015,1328.510010,1314.410034,1325.689941,1325.689941,3259470000\n2011-05-27,1325.689941,1334.619995,1325.689941,1331.099976,1331.099976,3124560000\n2011-05-31,1331.099976,1345.199951,1331.099976,1345.199951,1345.199951,4696240000\n2011-06-01,1345.199951,1345.199951,1313.709961,1314.550049,1314.550049,4241090000\n2011-06-02,1314.550049,1318.030029,1305.609985,1312.939941,1312.939941,3762170000\n2011-06-03,1312.939941,1312.939941,1297.900024,1300.160034,1300.160034,3505030000\n2011-06-06,1300.260010,1300.260010,1284.719971,1286.170044,1286.170044,3555980000\n2011-06-07,1286.310059,1296.219971,1284.739990,1284.939941,1284.939941,3846250000\n2011-06-08,1284.630005,1287.040039,1277.420044,1279.560059,1279.560059,3970810000\n2011-06-09,1279.630005,1294.540039,1279.630005,1289.000000,1289.000000,3332510000\n2011-06-10,1288.599976,1288.599976,1268.280029,1270.979980,1270.979980,3846250000\n2011-06-13,1271.310059,1277.040039,1265.640015,1271.829956,1271.829956,4132520000\n2011-06-14,1272.219971,1292.500000,1272.219971,1287.869995,1287.869995,3500280000\n2011-06-15,1287.869995,1287.869995,1261.900024,1265.420044,1265.420044,4070500000\n2011-06-16,1265.530029,1274.109985,1258.069946,1267.640015,1267.640015,3846250000\n2011-06-17,1268.579956,1279.819946,1267.400024,1271.500000,1271.500000,4916460000\n2011-06-20,1271.500000,1280.420044,1267.560059,1278.359985,1278.359985,3464660000\n2011-06-21,1278.400024,1297.619995,1278.400024,1295.520020,1295.520020,4056150000\n2011-06-22,1295.479980,1298.609985,1286.790039,1287.140015,1287.140015,3718420000\n2011-06-23,1286.599976,1286.599976,1262.869995,1283.500000,1283.500000,4983450000\n2011-06-24,1283.040039,1283.930054,1267.239990,1268.449951,1268.449951,3665340000\n2011-06-27,1268.439941,1284.910034,1267.530029,1280.099976,1280.099976,3479070000\n2011-06-28,1280.209961,1296.800049,1280.209961,1296.670044,1296.670044,3681500000\n2011-06-29,1296.849976,1309.209961,1296.849976,1307.410034,1307.410034,4347540000\n2011-06-30,1307.640015,1321.969971,1307.640015,1320.640015,1320.640015,4200500000\n2011-07-01,1320.640015,1341.010010,1318.180054,1339.670044,1339.670044,3796930000\n2011-07-05,1339.589966,1340.890015,1334.300049,1337.880005,1337.880005,3722320000\n2011-07-06,1337.560059,1340.939941,1330.920044,1339.219971,1339.219971,3564190000\n2011-07-07,1339.619995,1356.479980,1339.619995,1353.219971,1353.219971,4069530000\n2011-07-08,1352.390015,1352.390015,1333.709961,1343.800049,1343.800049,3594360000\n2011-07-11,1343.310059,1343.310059,1316.420044,1319.489990,1319.489990,3879130000\n2011-07-12,1319.609985,1327.170044,1313.329956,1313.640015,1313.640015,4227890000\n2011-07-13,1314.449951,1331.479980,1314.449951,1317.719971,1317.719971,4060080000\n2011-07-14,1317.739990,1326.880005,1306.510010,1308.869995,1308.869995,4358570000\n2011-07-15,1308.869995,1317.699951,1307.520020,1316.140015,1316.140015,4242760000\n2011-07-18,1315.939941,1315.939941,1295.920044,1305.439941,1305.439941,4118160000\n2011-07-19,1307.069946,1328.140015,1307.069946,1326.729980,1326.729980,4304600000\n2011-07-20,1328.660034,1330.430054,1323.650024,1325.839966,1325.839966,3767420000\n2011-07-21,1325.650024,1347.000000,1325.650024,1343.800049,1343.800049,4837430000\n2011-07-22,1343.800049,1346.099976,1336.949951,1345.020020,1345.020020,3522830000\n2011-07-25,1344.319946,1344.319946,1331.089966,1337.430054,1337.430054,3536890000\n2011-07-26,1337.390015,1338.510010,1329.589966,1331.939941,1331.939941,4007050000\n2011-07-27,1331.910034,1331.910034,1303.489990,1304.890015,1304.890015,3479040000\n2011-07-28,1304.839966,1316.319946,1299.160034,1300.670044,1300.670044,4951800000\n2011-07-29,1300.119995,1304.160034,1282.859985,1292.280029,1292.280029,5061190000\n2011-08-01,1292.589966,1307.380005,1274.729980,1286.939941,1286.939941,4967390000\n2011-08-02,1286.560059,1286.560059,1254.030029,1254.050049,1254.050049,5206290000\n2011-08-03,1254.250000,1261.199951,1234.560059,1260.339966,1260.339966,6446940000\n2011-08-04,1260.229980,1260.229980,1199.540039,1200.069946,1200.069946,4266530000\n2011-08-05,1200.280029,1218.109985,1168.089966,1199.380005,1199.380005,5454590000\n2011-08-08,1198.479980,1198.479980,1119.280029,1119.459961,1119.459961,2615150000\n2011-08-09,1120.229980,1172.880005,1101.540039,1172.530029,1172.530029,2366660000\n2011-08-10,1171.770020,1171.770020,1118.010010,1120.760010,1120.760010,5018070000\n2011-08-11,1121.300049,1186.290039,1121.300049,1172.640015,1172.640015,3685050000\n2011-08-12,1172.869995,1189.040039,1170.739990,1178.810059,1178.810059,5640380000\n2011-08-15,1178.859985,1204.489990,1178.859985,1204.489990,1204.489990,4272850000\n2011-08-16,1204.219971,1204.219971,1180.530029,1192.760010,1192.760010,5071600000\n2011-08-17,1192.890015,1208.469971,1184.359985,1193.890015,1193.890015,4388340000\n2011-08-18,1189.619995,1189.619995,1131.030029,1140.650024,1140.650024,3234810000\n2011-08-19,1140.469971,1154.540039,1122.050049,1123.530029,1123.530029,5167560000\n2011-08-22,1123.550049,1145.489990,1121.089966,1123.819946,1123.819946,5436260000\n2011-08-23,1124.359985,1162.349976,1124.359985,1162.349976,1162.349976,5013170000\n2011-08-24,1162.160034,1178.560059,1156.300049,1177.599976,1177.599976,5315310000\n2011-08-25,1176.689941,1190.680054,1155.469971,1159.270020,1159.270020,5748420000\n2011-08-26,1158.849976,1181.229980,1135.910034,1176.800049,1176.800049,5035320000\n2011-08-29,1177.910034,1210.280029,1177.910034,1210.079956,1210.079956,4228070000\n2011-08-30,1209.760010,1220.099976,1195.770020,1212.920044,1212.920044,4572570000\n2011-08-31,1213.000000,1230.709961,1209.349976,1218.890015,1218.890015,5267840000\n2011-09-01,1219.119995,1229.290039,1203.849976,1204.420044,1204.420044,4780410000\n2011-09-02,1203.900024,1203.900024,1170.560059,1173.969971,1173.969971,4401740000\n2011-09-06,1173.969971,1173.969971,1140.130005,1165.239990,1165.239990,5103980000\n2011-09-07,1165.849976,1198.619995,1165.849976,1198.619995,1198.619995,4441040000\n2011-09-08,1197.979980,1204.400024,1183.339966,1185.900024,1185.900024,4465170000\n2011-09-09,1185.369995,1185.369995,1148.369995,1154.229980,1154.229980,4586370000\n2011-09-12,1153.500000,1162.520020,1136.069946,1162.270020,1162.270020,5168550000\n2011-09-13,1162.589966,1176.410034,1157.439941,1172.869995,1172.869995,4681370000\n2011-09-14,1173.319946,1202.380005,1162.729980,1188.680054,1188.680054,4986740000\n2011-09-15,1189.439941,1209.109985,1189.439941,1209.109985,1209.109985,4479730000\n2011-09-16,1209.209961,1220.060059,1204.459961,1216.010010,1216.010010,5248890000\n2011-09-19,1214.989990,1214.989990,1188.359985,1204.089966,1204.089966,4254190000\n2011-09-20,1204.500000,1220.390015,1201.290039,1202.089966,1202.089966,4315610000\n2011-09-21,1203.630005,1206.300049,1166.209961,1166.760010,1166.760010,4728550000\n2011-09-22,1164.550049,1164.550049,1114.219971,1129.560059,1129.560059,6703140000\n2011-09-23,1128.819946,1141.719971,1121.359985,1136.430054,1136.430054,5639930000\n2011-09-26,1136.910034,1164.189941,1131.069946,1162.949951,1162.949951,4762830000\n2011-09-27,1163.319946,1195.859985,1163.319946,1175.380005,1175.380005,5548130000\n2011-09-28,1175.390015,1184.709961,1150.400024,1151.060059,1151.060059,4787920000\n2011-09-29,1151.739990,1175.869995,1139.930054,1160.400024,1160.400024,5285740000\n2011-09-30,1159.930054,1159.930054,1131.339966,1131.420044,1131.420044,4416790000\n2011-10-03,1131.209961,1138.989990,1098.920044,1099.229980,1099.229980,5670340000\n2011-10-04,1097.420044,1125.119995,1074.770020,1123.949951,1123.949951,3714670000\n2011-10-05,1124.030029,1146.069946,1115.680054,1144.030029,1144.030029,2510620000\n2011-10-06,1144.109985,1165.550049,1134.949951,1164.969971,1164.969971,5098330000\n2011-10-07,1165.030029,1171.400024,1150.260010,1155.459961,1155.459961,5580380000\n2011-10-10,1158.150024,1194.910034,1158.150024,1194.890015,1194.890015,4446800000\n2011-10-11,1194.599976,1199.239990,1187.300049,1195.540039,1195.540039,4424500000\n2011-10-12,1196.189941,1220.250000,1196.189941,1207.250000,1207.250000,5355360000\n2011-10-13,1206.959961,1207.459961,1190.579956,1203.660034,1203.660034,4436270000\n2011-10-14,1205.650024,1224.609985,1205.650024,1224.579956,1224.579956,4116690000\n2011-10-17,1224.469971,1224.469971,1198.550049,1200.859985,1200.859985,4300700000\n2011-10-18,1200.750000,1233.099976,1191.479980,1225.380005,1225.380005,4840170000\n2011-10-19,1223.459961,1229.640015,1206.310059,1209.880005,1209.880005,4846390000\n2011-10-20,1209.920044,1219.530029,1197.339966,1215.390015,1215.390015,4870290000\n2011-10-21,1215.390015,1239.030029,1215.390015,1238.250000,1238.250000,4980770000\n2011-10-24,1238.719971,1256.550049,1238.719971,1254.189941,1254.189941,4309380000\n2011-10-25,1254.189941,1254.189941,1226.790039,1229.050049,1229.050049,4473970000\n2011-10-26,1229.170044,1246.280029,1221.060059,1242.000000,1242.000000,4873530000\n2011-10-27,1243.969971,1292.660034,1243.969971,1284.589966,1284.589966,6367610000\n2011-10-28,1284.390015,1287.079956,1277.010010,1285.089966,1285.089966,4536690000\n2011-10-31,1284.959961,1284.959961,1253.160034,1253.300049,1253.300049,4310210000\n2011-11-01,1251.000000,1251.000000,1215.420044,1218.280029,1218.280029,5645540000\n2011-11-02,1219.619995,1242.479980,1219.619995,1237.900024,1237.900024,4110530000\n2011-11-03,1238.250000,1263.209961,1234.810059,1261.150024,1261.150024,4849140000\n2011-11-04,1260.819946,1260.819946,1238.920044,1253.229980,1253.229980,3830650000\n2011-11-07,1253.209961,1261.699951,1240.750000,1261.119995,1261.119995,3429740000\n2011-11-08,1261.119995,1277.550049,1254.989990,1275.920044,1275.920044,3908490000\n2011-11-09,1275.180054,1275.180054,1226.640015,1229.099976,1229.099976,4659740000\n2011-11-10,1229.589966,1246.219971,1227.699951,1239.699951,1239.699951,4002760000\n2011-11-11,1240.119995,1266.979980,1240.119995,1263.849976,1263.849976,3370180000\n2011-11-14,1263.849976,1263.849976,1246.680054,1251.780029,1251.780029,3219680000\n2011-11-15,1251.699951,1264.250000,1244.339966,1257.810059,1257.810059,3599300000\n2011-11-16,1257.810059,1259.609985,1235.670044,1236.910034,1236.910034,4085010000\n2011-11-17,1236.560059,1237.729980,1209.430054,1216.130005,1216.130005,4596450000\n2011-11-18,1216.189941,1223.510010,1211.359985,1215.650024,1215.650024,3827610000\n2011-11-21,1215.619995,1215.619995,1183.160034,1192.979980,1192.979980,4050070000\n2011-11-22,1192.979980,1196.810059,1181.650024,1188.040039,1188.040039,3911710000\n2011-11-23,1187.479980,1187.479980,1161.790039,1161.790039,1161.790039,3798940000\n2011-11-25,1161.410034,1172.660034,1158.660034,1158.670044,1158.670044,1664200000\n2011-11-28,1158.670044,1197.349976,1158.670044,1192.550049,1192.550049,3920750000\n2011-11-29,1192.560059,1203.670044,1191.800049,1195.189941,1195.189941,3992650000\n2011-11-30,1196.719971,1247.109985,1196.719971,1246.959961,1246.959961,5801910000\n2011-12-01,1246.910034,1251.089966,1239.729980,1244.579956,1244.579956,3818680000\n2011-12-02,1246.030029,1260.079956,1243.349976,1244.280029,1244.280029,4144310000\n2011-12-05,1244.329956,1266.729980,1244.329956,1257.079956,1257.079956,4148060000\n2011-12-06,1257.189941,1266.030029,1253.030029,1258.469971,1258.469971,3734230000\n2011-12-07,1258.140015,1267.060059,1244.800049,1261.010010,1261.010010,4160540000\n2011-12-08,1260.869995,1260.869995,1231.469971,1234.349976,1234.349976,4298370000\n2011-12-09,1234.479980,1258.250000,1234.479980,1255.189941,1255.189941,3830610000\n2011-12-12,1255.050049,1255.050049,1227.250000,1236.469971,1236.469971,3600570000\n2011-12-13,1236.829956,1249.859985,1219.430054,1225.729980,1225.729980,4121570000\n2011-12-14,1225.729980,1225.729980,1209.469971,1211.819946,1211.819946,4298290000\n2011-12-15,1212.119995,1225.599976,1212.119995,1215.750000,1215.750000,3810340000\n2011-12-16,1216.089966,1231.040039,1215.199951,1219.660034,1219.660034,5345800000\n2011-12-19,1219.739990,1224.569946,1202.369995,1205.349976,1205.349976,3659820000\n2011-12-20,1205.719971,1242.819946,1205.719971,1241.300049,1241.300049,4055590000\n2011-12-21,1241.250000,1245.089966,1229.510010,1243.719971,1243.719971,2959020000\n2011-12-22,1243.719971,1255.219971,1243.719971,1254.000000,1254.000000,3492250000\n2011-12-23,1254.000000,1265.420044,1254.000000,1265.329956,1265.329956,2233830000\n2011-12-27,1265.020020,1269.369995,1262.300049,1265.430054,1265.430054,2130590000\n2011-12-28,1265.380005,1265.849976,1248.640015,1249.640015,1249.640015,2349980000\n2011-12-29,1249.750000,1263.540039,1249.750000,1263.020020,1263.020020,2278130000\n2011-12-30,1262.819946,1264.119995,1257.459961,1257.599976,1257.599976,2271850000\n2012-01-03,1258.859985,1284.619995,1258.859985,1277.060059,1277.060059,3943710000\n2012-01-04,1277.030029,1278.729980,1268.099976,1277.300049,1277.300049,3592580000\n2012-01-05,1277.300049,1283.050049,1265.260010,1281.060059,1281.060059,4315950000\n2012-01-06,1280.930054,1281.839966,1273.339966,1277.810059,1277.810059,3656830000\n2012-01-09,1277.829956,1281.989990,1274.550049,1280.699951,1280.699951,3371600000\n2012-01-10,1280.770020,1296.459961,1280.770020,1292.079956,1292.079956,4221960000\n2012-01-11,1292.020020,1293.800049,1285.410034,1292.479980,1292.479980,3968120000\n2012-01-12,1292.479980,1296.819946,1285.770020,1295.500000,1295.500000,4019890000\n2012-01-13,1294.819946,1294.819946,1277.579956,1289.089966,1289.089966,3692370000\n2012-01-17,1290.219971,1303.000000,1290.219971,1293.670044,1293.670044,4010490000\n2012-01-18,1293.650024,1308.109985,1290.989990,1308.040039,1308.040039,4096160000\n2012-01-19,1308.069946,1315.489990,1308.069946,1314.500000,1314.500000,4465890000\n2012-01-20,1314.489990,1315.380005,1309.170044,1315.380005,1315.380005,3912620000\n2012-01-23,1315.290039,1322.280029,1309.890015,1316.000000,1316.000000,3770910000\n2012-01-24,1315.959961,1315.959961,1306.060059,1314.650024,1314.650024,3693560000\n2012-01-25,1314.400024,1328.300049,1307.650024,1326.060059,1326.060059,4410910000\n2012-01-26,1326.280029,1333.469971,1313.599976,1318.430054,1318.430054,4522070000\n2012-01-27,1318.250000,1320.060059,1311.719971,1316.329956,1316.329956,4007380000\n2012-01-30,1316.160034,1316.160034,1300.489990,1313.010010,1313.010010,3659010000\n2012-01-31,1313.530029,1321.410034,1306.689941,1312.410034,1312.410034,4235550000\n2012-02-01,1312.449951,1330.520020,1312.449951,1324.089966,1324.089966,4504360000\n2012-02-02,1324.239990,1329.189941,1321.569946,1325.540039,1325.540039,4120920000\n2012-02-03,1326.209961,1345.339966,1326.209961,1344.900024,1344.900024,4608550000\n2012-02-06,1344.319946,1344.359985,1337.520020,1344.329956,1344.329956,3379700000\n2012-02-07,1344.329956,1349.239990,1335.920044,1347.050049,1347.050049,3742460000\n2012-02-08,1347.040039,1351.000000,1341.949951,1349.959961,1349.959961,4096730000\n2012-02-09,1349.969971,1354.319946,1344.630005,1351.949951,1351.949951,4209890000\n2012-02-10,1351.209961,1351.209961,1337.349976,1342.640015,1342.640015,3877580000\n2012-02-13,1343.060059,1353.349976,1343.060059,1351.770020,1351.770020,3618040000\n2012-02-14,1351.300049,1351.300049,1340.829956,1350.500000,1350.500000,3889520000\n2012-02-15,1350.520020,1355.869995,1340.800049,1343.229980,1343.229980,4080340000\n2012-02-16,1342.609985,1359.020020,1341.219971,1358.040039,1358.040039,4108880000\n2012-02-17,1358.060059,1363.400024,1357.239990,1361.229980,1361.229980,3717640000\n2012-02-21,1361.219971,1367.760010,1358.109985,1362.209961,1362.209961,3795200000\n2012-02-22,1362.109985,1362.699951,1355.530029,1357.660034,1357.660034,3633710000\n2012-02-23,1357.530029,1364.239990,1352.280029,1363.459961,1363.459961,3786450000\n2012-02-24,1363.459961,1368.920044,1363.459961,1365.739990,1365.739990,3505360000\n2012-02-27,1365.199951,1371.939941,1354.920044,1367.589966,1367.589966,3648890000\n2012-02-28,1367.560059,1373.089966,1365.969971,1372.180054,1372.180054,3579120000\n2012-02-29,1372.199951,1378.040039,1363.810059,1365.680054,1365.680054,4482370000\n2012-03-01,1365.900024,1376.170044,1365.900024,1374.089966,1374.089966,3919240000\n2012-03-02,1374.089966,1374.530029,1366.420044,1369.630005,1369.630005,3283490000\n2012-03-05,1369.589966,1369.589966,1359.130005,1364.329956,1364.329956,3429480000\n2012-03-06,1363.630005,1363.630005,1340.030029,1343.359985,1343.359985,4191060000\n2012-03-07,1343.390015,1354.849976,1343.390015,1352.630005,1352.630005,3580380000\n2012-03-08,1352.650024,1368.719971,1352.650024,1365.910034,1365.910034,3543060000\n2012-03-09,1365.969971,1374.760010,1365.969971,1370.869995,1370.869995,3639470000\n2012-03-12,1370.780029,1373.040039,1366.689941,1371.089966,1371.089966,3081870000\n2012-03-13,1371.920044,1396.130005,1371.920044,1395.949951,1395.949951,4386470000\n2012-03-14,1395.949951,1399.420044,1389.969971,1394.280029,1394.280029,4502280000\n2012-03-15,1394.170044,1402.630005,1392.780029,1402.599976,1402.599976,4271650000\n2012-03-16,1402.550049,1405.880005,1401.469971,1404.170044,1404.170044,5163950000\n2012-03-19,1404.170044,1414.000000,1402.430054,1409.750000,1409.750000,3932570000\n2012-03-20,1409.589966,1409.589966,1397.680054,1405.520020,1405.520020,3695280000\n2012-03-21,1405.520020,1407.750000,1400.640015,1402.890015,1402.890015,3573590000\n2012-03-22,1402.890015,1402.890015,1388.729980,1392.780029,1392.780029,3740590000\n2012-03-23,1392.780029,1399.180054,1386.869995,1397.109985,1397.109985,3472950000\n2012-03-26,1397.109985,1416.579956,1397.109985,1416.510010,1416.510010,3576950000\n2012-03-27,1416.550049,1419.150024,1411.949951,1412.520020,1412.520020,3513640000\n2012-03-28,1412.520020,1413.650024,1397.199951,1405.540039,1405.540039,3892800000\n2012-03-29,1405.390015,1405.390015,1391.560059,1403.280029,1403.280029,3832000000\n2012-03-30,1403.310059,1410.890015,1401.420044,1408.469971,1408.469971,3676890000\n2012-04-02,1408.469971,1422.380005,1404.459961,1419.040039,1419.040039,3572010000\n2012-04-03,1418.979980,1419.000000,1404.619995,1413.380005,1413.380005,3822090000\n2012-04-04,1413.089966,1413.089966,1394.089966,1398.959961,1398.959961,3938290000\n2012-04-05,1398.790039,1401.599976,1392.920044,1398.079956,1398.079956,3303740000\n2012-04-09,1397.449951,1397.449951,1378.239990,1382.199951,1382.199951,3468980000\n2012-04-10,1382.180054,1383.010010,1357.380005,1358.589966,1358.589966,4631730000\n2012-04-11,1358.979980,1374.709961,1358.979980,1368.709961,1368.709961,3743040000\n2012-04-12,1368.770020,1388.130005,1368.770020,1387.569946,1387.569946,3618280000\n2012-04-13,1387.609985,1387.609985,1369.849976,1370.260010,1370.260010,3631160000\n2012-04-16,1370.270020,1379.660034,1365.380005,1369.569946,1369.569946,3574780000\n2012-04-17,1369.569946,1392.760010,1369.569946,1390.780029,1390.780029,3456200000\n2012-04-18,1390.780029,1390.780029,1383.290039,1385.140015,1385.140015,3463140000\n2012-04-19,1385.079956,1390.459961,1370.300049,1376.920044,1376.920044,4180020000\n2012-04-20,1376.959961,1387.400024,1376.959961,1378.530029,1378.530029,3833320000\n2012-04-23,1378.530029,1378.530029,1358.790039,1366.939941,1366.939941,3654860000\n2012-04-24,1366.969971,1375.569946,1366.819946,1371.969971,1371.969971,3617100000\n2012-04-25,1372.109985,1391.369995,1372.109985,1390.689941,1390.689941,3998430000\n2012-04-26,1390.640015,1402.089966,1387.280029,1399.979980,1399.979980,4034700000\n2012-04-27,1400.189941,1406.640015,1397.310059,1403.359985,1403.359985,3645830000\n2012-04-30,1403.260010,1403.260010,1394.000000,1397.910034,1397.910034,3574010000\n2012-05-01,1397.859985,1415.319946,1395.729980,1405.819946,1405.819946,3807950000\n2012-05-02,1405.500000,1405.500000,1393.920044,1402.310059,1402.310059,3803860000\n2012-05-03,1402.319946,1403.069946,1388.709961,1391.569946,1391.569946,4004910000\n2012-05-04,1391.510010,1391.510010,1367.959961,1369.099976,1369.099976,3975140000\n2012-05-07,1368.790039,1373.910034,1363.939941,1369.579956,1369.579956,3559390000\n2012-05-08,1369.160034,1369.160034,1347.750000,1363.719971,1363.719971,4261670000\n2012-05-09,1363.199951,1363.729980,1343.130005,1354.579956,1354.579956,4288540000\n2012-05-10,1354.579956,1365.880005,1354.579956,1357.989990,1357.989990,3727990000\n2012-05-11,1358.109985,1365.660034,1348.890015,1353.390015,1353.390015,3869070000\n2012-05-14,1351.930054,1351.930054,1336.609985,1338.349976,1338.349976,3688120000\n2012-05-15,1338.359985,1344.939941,1328.410034,1330.660034,1330.660034,4114040000\n2012-05-16,1330.780029,1341.780029,1324.790039,1324.800049,1324.800049,4280420000\n2012-05-17,1324.819946,1326.359985,1304.859985,1304.859985,1304.859985,4664280000\n2012-05-18,1305.050049,1312.239990,1291.979980,1295.219971,1295.219971,4512470000\n2012-05-21,1295.729980,1316.390015,1295.729980,1315.989990,1315.989990,3786750000\n2012-05-22,1316.089966,1328.489990,1310.040039,1316.630005,1316.630005,4123680000\n2012-05-23,1316.020020,1320.709961,1296.530029,1318.859985,1318.859985,4108330000\n2012-05-24,1318.719971,1324.140015,1310.500000,1320.680054,1320.680054,3937670000\n2012-05-25,1320.810059,1324.199951,1314.229980,1317.819946,1317.819946,2872660000\n2012-05-29,1318.900024,1334.930054,1318.900024,1332.420044,1332.420044,3441640000\n2012-05-30,1331.250000,1331.250000,1310.760010,1313.319946,1313.319946,3534290000\n2012-05-31,1313.089966,1319.739990,1298.900024,1310.329956,1310.329956,4557620000\n2012-06-01,1309.869995,1309.869995,1277.250000,1278.040039,1278.040039,4669350000\n2012-06-04,1278.290039,1282.550049,1266.739990,1278.180054,1278.180054,4011960000\n2012-06-05,1277.819946,1287.619995,1274.160034,1285.500000,1285.500000,3403230000\n2012-06-06,1285.609985,1315.130005,1285.609985,1315.130005,1315.130005,4268360000\n2012-06-07,1316.150024,1329.050049,1312.680054,1314.989990,1314.989990,4258140000\n2012-06-08,1314.989990,1325.810059,1307.770020,1325.660034,1325.660034,3497190000\n2012-06-11,1325.719971,1335.520020,1307.729980,1308.930054,1308.930054,3537530000\n2012-06-12,1309.400024,1324.310059,1306.619995,1324.180054,1324.180054,3442920000\n2012-06-13,1324.020020,1327.280029,1310.510010,1314.880005,1314.880005,3506510000\n2012-06-14,1314.880005,1333.680054,1314.140015,1329.099976,1329.099976,3687720000\n2012-06-15,1329.189941,1343.319946,1329.189941,1342.839966,1342.839966,4401570000\n2012-06-18,1342.420044,1348.219971,1334.459961,1344.780029,1344.780029,3259430000\n2012-06-19,1344.829956,1363.459961,1344.829956,1357.979980,1357.979980,3815350000\n2012-06-20,1358.040039,1361.569946,1346.449951,1355.689941,1355.689941,3695700000\n2012-06-21,1355.430054,1358.270020,1324.410034,1325.510010,1325.510010,4094470000\n2012-06-22,1325.920044,1337.819946,1325.920044,1335.020020,1335.020020,5271490000\n2012-06-25,1334.900024,1334.900024,1309.270020,1313.719971,1313.719971,3501820000\n2012-06-26,1314.089966,1324.239990,1310.300049,1319.989990,1319.989990,3412940000\n2012-06-27,1320.709961,1334.400024,1320.709961,1331.849976,1331.849976,3286910000\n2012-06-28,1331.520020,1331.520020,1313.290039,1329.040039,1329.040039,3969370000\n2012-06-29,1330.119995,1362.170044,1330.119995,1362.160034,1362.160034,4590480000\n2012-07-02,1362.329956,1366.349976,1355.699951,1365.510010,1365.510010,3301650000\n2012-07-03,1365.750000,1374.810059,1363.530029,1374.020020,1374.020020,2116390000\n2012-07-05,1373.719971,1373.849976,1363.020020,1367.579956,1367.579956,3041520000\n2012-07-06,1367.089966,1367.089966,1348.030029,1354.680054,1354.680054,2745140000\n2012-07-09,1354.660034,1354.869995,1346.650024,1352.459961,1352.459961,2904860000\n2012-07-10,1352.959961,1361.540039,1336.270020,1341.469971,1341.469971,3470600000\n2012-07-11,1341.400024,1345.000000,1333.250000,1341.449951,1341.449951,3426290000\n2012-07-12,1341.290039,1341.290039,1325.410034,1334.760010,1334.760010,3654440000\n2012-07-13,1334.810059,1357.699951,1334.810059,1356.780029,1356.780029,3212930000\n2012-07-16,1356.500000,1357.260010,1348.510010,1353.640015,1353.640015,2862720000\n2012-07-17,1353.680054,1365.359985,1345.069946,1363.670044,1363.670044,3566680000\n2012-07-18,1363.579956,1375.260010,1358.959961,1372.780029,1372.780029,3642630000\n2012-07-19,1373.010010,1380.390015,1371.209961,1376.510010,1376.510010,4043360000\n2012-07-20,1376.510010,1376.510010,1362.189941,1362.660034,1362.660034,3925020000\n2012-07-23,1362.339966,1362.339966,1337.560059,1350.520020,1350.520020,3717180000\n2012-07-24,1350.520020,1351.530029,1329.239990,1338.310059,1338.310059,3891290000\n2012-07-25,1338.349976,1343.979980,1331.500000,1337.890015,1337.890015,3719170000\n2012-07-26,1338.170044,1363.130005,1338.170044,1360.020020,1360.020020,4429300000\n2012-07-27,1360.050049,1389.189941,1360.050049,1385.969971,1385.969971,4399010000\n2012-07-30,1385.939941,1391.739990,1381.369995,1385.300049,1385.300049,3212060000\n2012-07-31,1385.270020,1387.160034,1379.170044,1379.319946,1379.319946,3821570000\n2012-08-01,1379.319946,1385.030029,1373.349976,1375.319946,1375.319946,4440920000\n2012-08-02,1375.130005,1375.130005,1354.650024,1365.000000,1365.000000,4193740000\n2012-08-03,1365.449951,1394.160034,1365.449951,1390.989990,1390.989990,3751170000\n2012-08-06,1391.040039,1399.630005,1391.040039,1394.229980,1394.229980,3122050000\n2012-08-07,1394.459961,1407.140015,1394.459961,1401.349976,1401.349976,3682490000\n2012-08-08,1401.229980,1404.140015,1396.130005,1402.219971,1402.219971,3221790000\n2012-08-09,1402.260010,1405.949951,1398.800049,1402.800049,1402.800049,3119610000\n2012-08-10,1402.579956,1405.979980,1395.619995,1405.869995,1405.869995,2767980000\n2012-08-13,1405.869995,1405.869995,1397.319946,1404.109985,1404.109985,2499990000\n2012-08-14,1404.359985,1410.030029,1400.599976,1403.930054,1403.930054,2930900000\n2012-08-15,1403.890015,1407.729980,1401.829956,1405.530029,1405.530029,2655750000\n2012-08-16,1405.569946,1417.439941,1404.150024,1415.510010,1415.510010,3114100000\n2012-08-17,1415.839966,1418.709961,1414.670044,1418.160034,1418.160034,2922990000\n2012-08-20,1417.849976,1418.130005,1412.119995,1418.130005,1418.130005,2766320000\n2012-08-21,1418.130005,1426.680054,1410.859985,1413.170044,1413.170044,3282950000\n2012-08-22,1413.089966,1416.119995,1406.780029,1413.489990,1413.489990,3062690000\n2012-08-23,1413.489990,1413.489990,1400.500000,1402.079956,1402.079956,3008240000\n2012-08-24,1401.989990,1413.459961,1398.040039,1411.130005,1411.130005,2598790000\n2012-08-27,1411.130005,1416.170044,1409.109985,1410.439941,1410.439941,2472500000\n2012-08-28,1410.439941,1413.630005,1405.589966,1409.300049,1409.300049,2629090000\n2012-08-29,1409.319946,1413.949951,1406.569946,1410.489990,1410.489990,2571220000\n2012-08-30,1410.079956,1410.079956,1397.010010,1399.479980,1399.479980,2530280000\n2012-08-31,1400.069946,1413.089966,1398.959961,1406.579956,1406.579956,2938250000\n2012-09-04,1406.540039,1409.310059,1396.560059,1404.939941,1404.939941,3200310000\n2012-09-05,1404.939941,1408.810059,1401.250000,1403.439941,1403.439941,3389110000\n2012-09-06,1403.739990,1432.119995,1403.739990,1432.119995,1432.119995,3952870000\n2012-09-07,1432.119995,1437.920044,1431.449951,1437.920044,1437.920044,3717620000\n2012-09-10,1437.920044,1438.739990,1428.979980,1429.079956,1429.079956,3223670000\n2012-09-11,1429.130005,1437.760010,1429.130005,1433.560059,1433.560059,3509630000\n2012-09-12,1433.560059,1439.150024,1432.989990,1436.560059,1436.560059,3641200000\n2012-09-13,1436.560059,1463.760010,1435.339966,1459.989990,1459.989990,4606550000\n2012-09-14,1460.069946,1474.510010,1460.069946,1465.770020,1465.770020,5041990000\n2012-09-17,1465.420044,1465.630005,1457.550049,1461.189941,1461.189941,3482430000\n2012-09-18,1461.189941,1461.469971,1456.130005,1459.319946,1459.319946,3377390000\n2012-09-19,1459.500000,1465.150024,1457.880005,1461.050049,1461.050049,3451360000\n2012-09-20,1461.050049,1461.229980,1449.979980,1460.260010,1460.260010,3382520000\n2012-09-21,1460.339966,1467.069946,1459.510010,1460.150024,1460.150024,4833870000\n2012-09-24,1459.760010,1460.719971,1452.060059,1456.890015,1456.890015,3008920000\n2012-09-25,1456.939941,1463.239990,1441.589966,1441.589966,1441.589966,3739900000\n2012-09-26,1441.599976,1441.599976,1430.530029,1433.319946,1433.319946,3565380000\n2012-09-27,1433.359985,1450.199951,1433.359985,1447.150024,1447.150024,3150330000\n2012-09-28,1447.130005,1447.130005,1435.599976,1440.670044,1440.670044,3509230000\n2012-10-01,1440.900024,1457.140015,1440.900024,1444.489990,1444.489990,3505080000\n2012-10-02,1444.989990,1451.520020,1439.010010,1445.750000,1445.750000,3321790000\n2012-10-03,1446.050049,1454.300049,1441.989990,1450.989990,1450.989990,3531640000\n2012-10-04,1451.079956,1463.140015,1451.079956,1461.400024,1461.400024,3615860000\n2012-10-05,1461.400024,1470.959961,1456.890015,1460.930054,1460.930054,3172940000\n2012-10-08,1460.930054,1460.930054,1453.099976,1455.880005,1455.880005,2328720000\n2012-10-09,1455.900024,1455.900024,1441.180054,1441.479980,1441.479980,3216320000\n2012-10-10,1441.479980,1442.520020,1430.640015,1432.560059,1432.560059,3225060000\n2012-10-11,1432.819946,1443.900024,1432.819946,1432.839966,1432.839966,3672540000\n2012-10-12,1432.839966,1438.430054,1425.530029,1428.589966,1428.589966,3134750000\n2012-10-15,1428.750000,1441.310059,1427.239990,1440.130005,1440.130005,3483810000\n2012-10-16,1440.310059,1455.510010,1440.310059,1454.920044,1454.920044,3568770000\n2012-10-17,1454.219971,1462.199951,1453.349976,1460.910034,1460.910034,3655320000\n2012-10-18,1460.939941,1464.020020,1452.630005,1457.339966,1457.339966,3880030000\n2012-10-19,1457.339966,1457.339966,1429.849976,1433.189941,1433.189941,3875170000\n2012-10-22,1433.209961,1435.459961,1422.060059,1433.819946,1433.819946,3216220000\n2012-10-23,1433.739990,1433.739990,1407.560059,1413.109985,1413.109985,3587670000\n2012-10-24,1413.199951,1420.040039,1407.099976,1408.750000,1408.750000,3385970000\n2012-10-25,1409.739990,1421.119995,1405.140015,1412.969971,1412.969971,3512640000\n2012-10-26,1412.969971,1417.089966,1403.280029,1411.939941,1411.939941,3284910000\n2012-10-31,1410.989990,1418.760010,1405.949951,1412.160034,1412.160034,3577110000\n2012-11-01,1412.199951,1428.349976,1412.199951,1427.589966,1427.589966,3929890000\n2012-11-02,1427.589966,1434.270020,1412.910034,1414.199951,1414.199951,3732480000\n2012-11-05,1414.020020,1419.900024,1408.130005,1417.260010,1417.260010,2921040000\n2012-11-06,1417.260010,1433.380005,1417.260010,1428.390015,1428.390015,3306970000\n2012-11-07,1428.270020,1428.270020,1388.140015,1394.530029,1394.530029,4356490000\n2012-11-08,1394.530029,1401.229980,1377.510010,1377.510010,1377.510010,3779520000\n2012-11-09,1377.550049,1391.390015,1373.030029,1379.849976,1379.849976,3647350000\n2012-11-12,1379.859985,1384.869995,1377.189941,1380.030029,1380.030029,2567540000\n2012-11-13,1380.030029,1388.810059,1371.390015,1374.530029,1374.530029,3455550000\n2012-11-14,1374.640015,1380.130005,1352.500000,1355.489990,1355.489990,4109510000\n2012-11-15,1355.410034,1360.619995,1348.050049,1353.329956,1353.329956,3928870000\n2012-11-16,1353.359985,1362.030029,1343.349976,1359.880005,1359.880005,4045910000\n2012-11-19,1359.880005,1386.890015,1359.880005,1386.890015,1386.890015,3374800000\n2012-11-20,1386.819946,1389.770020,1377.040039,1387.810059,1387.810059,3207160000\n2012-11-21,1387.790039,1391.250000,1386.390015,1391.030029,1391.030029,2667090000\n2012-11-23,1391.030029,1409.160034,1391.030029,1409.150024,1409.150024,1504960000\n2012-11-26,1409.150024,1409.150024,1397.680054,1406.290039,1406.290039,2948960000\n2012-11-27,1406.290039,1409.010010,1398.030029,1398.939941,1398.939941,3323120000\n2012-11-28,1398.770020,1410.310059,1385.430054,1409.930054,1409.930054,3359250000\n2012-11-29,1409.959961,1419.699951,1409.040039,1415.949951,1415.949951,3356850000\n2012-11-30,1415.949951,1418.859985,1411.630005,1416.180054,1416.180054,3966000000\n2012-12-03,1416.339966,1423.729980,1408.459961,1409.459961,1409.459961,3074280000\n2012-12-04,1409.459961,1413.140015,1403.650024,1407.050049,1407.050049,3247710000\n2012-12-05,1407.050049,1415.560059,1398.229980,1409.280029,1409.280029,4253920000\n2012-12-06,1409.430054,1413.949951,1405.930054,1413.939941,1413.939941,3229700000\n2012-12-07,1413.949951,1420.339966,1410.900024,1418.069946,1418.069946,3125160000\n2012-12-10,1418.069946,1421.640015,1415.640015,1418.550049,1418.550049,2999430000\n2012-12-11,1418.550049,1434.270020,1418.550049,1427.839966,1427.839966,3650230000\n2012-12-12,1427.839966,1438.589966,1426.760010,1428.479980,1428.479980,3709050000\n2012-12-13,1428.479980,1431.359985,1416.000000,1419.449951,1419.449951,3349960000\n2012-12-14,1419.449951,1419.449951,1411.880005,1413.579956,1413.579956,3210170000\n2012-12-17,1413.540039,1430.670044,1413.540039,1430.359985,1430.359985,3455610000\n2012-12-18,1430.469971,1448.000000,1430.469971,1446.790039,1446.790039,4302240000\n2012-12-19,1446.790039,1447.750000,1435.800049,1435.810059,1435.810059,3869800000\n2012-12-20,1435.810059,1443.699951,1432.819946,1443.689941,1443.689941,3686580000\n2012-12-21,1443.670044,1443.670044,1422.579956,1430.150024,1430.150024,5229160000\n2012-12-24,1430.150024,1430.150024,1424.660034,1426.660034,1426.660034,1248960000\n2012-12-26,1426.660034,1429.420044,1416.430054,1419.829956,1419.829956,2285030000\n2012-12-27,1419.829956,1422.800049,1401.800049,1418.099976,1418.099976,2830180000\n2012-12-28,1418.099976,1418.099976,1401.579956,1402.430054,1402.430054,2426680000\n2012-12-31,1402.430054,1426.739990,1398.109985,1426.189941,1426.189941,3204330000\n2013-01-02,1426.189941,1462.430054,1426.189941,1462.420044,1462.420044,4202600000\n2013-01-03,1462.420044,1465.469971,1455.530029,1459.369995,1459.369995,3829730000\n2013-01-04,1459.369995,1467.939941,1458.989990,1466.469971,1466.469971,3424290000\n2013-01-07,1466.469971,1466.469971,1456.619995,1461.890015,1461.890015,3304970000\n2013-01-08,1461.890015,1461.890015,1451.640015,1457.150024,1457.150024,3601600000\n2013-01-09,1457.150024,1464.729980,1457.150024,1461.020020,1461.020020,3674390000\n2013-01-10,1461.020020,1472.300049,1461.020020,1472.119995,1472.119995,4081840000\n2013-01-11,1472.119995,1472.750000,1467.579956,1472.050049,1472.050049,3340650000\n2013-01-14,1472.050049,1472.050049,1465.689941,1470.680054,1470.680054,3003010000\n2013-01-15,1470.670044,1473.310059,1463.760010,1472.339966,1472.339966,3135350000\n2013-01-16,1472.329956,1473.959961,1467.599976,1472.630005,1472.630005,3384080000\n2013-01-17,1472.630005,1485.160034,1472.630005,1480.939941,1480.939941,3706710000\n2013-01-18,1480.949951,1485.979980,1475.810059,1485.979980,1485.979980,3795740000\n2013-01-22,1485.979980,1492.560059,1481.160034,1492.560059,1492.560059,3570950000\n2013-01-23,1492.560059,1496.130005,1489.900024,1494.810059,1494.810059,3552010000\n2013-01-24,1494.810059,1502.270020,1489.459961,1494.819946,1494.819946,3699430000\n2013-01-25,1494.819946,1503.260010,1494.819946,1502.959961,1502.959961,3476290000\n2013-01-28,1502.959961,1503.229980,1496.329956,1500.180054,1500.180054,3388540000\n2013-01-29,1500.180054,1509.349976,1498.089966,1507.839966,1507.839966,3949640000\n2013-01-30,1507.839966,1509.939941,1500.109985,1501.959961,1501.959961,3726810000\n2013-01-31,1501.959961,1504.189941,1496.760010,1498.109985,1498.109985,3999880000\n2013-02-01,1498.109985,1514.410034,1498.109985,1513.170044,1513.170044,3836320000\n2013-02-04,1513.170044,1513.170044,1495.020020,1495.709961,1495.709961,3390000000\n2013-02-05,1495.709961,1514.959961,1495.709961,1511.290039,1511.290039,3618360000\n2013-02-06,1511.290039,1512.530029,1504.709961,1512.119995,1512.119995,3611570000\n2013-02-07,1512.119995,1512.900024,1498.489990,1509.390015,1509.390015,3614580000\n2013-02-08,1509.390015,1518.310059,1509.390015,1517.930054,1517.930054,2986150000\n2013-02-11,1517.930054,1518.310059,1513.609985,1517.010010,1517.010010,2684100000\n2013-02-12,1517.010010,1522.290039,1515.609985,1519.430054,1519.430054,3414370000\n2013-02-13,1519.430054,1524.689941,1515.930054,1520.329956,1520.329956,3385880000\n2013-02-14,1520.329956,1523.140015,1514.020020,1521.380005,1521.380005,3759740000\n2013-02-15,1521.380005,1524.239990,1514.140015,1519.790039,1519.790039,3838510000\n2013-02-19,1519.790039,1530.939941,1519.790039,1530.939941,1530.939941,3748910000\n2013-02-20,1530.939941,1530.939941,1511.410034,1511.949951,1511.949951,4240570000\n2013-02-21,1511.949951,1511.949951,1497.290039,1502.420044,1502.420044,4274600000\n2013-02-22,1502.420044,1515.640015,1502.420044,1515.599976,1515.599976,3419320000\n2013-02-25,1515.599976,1525.839966,1487.849976,1487.849976,1487.849976,4011050000\n2013-02-26,1487.849976,1498.989990,1485.010010,1496.939941,1496.939941,3975280000\n2013-02-27,1496.939941,1520.079956,1494.880005,1515.989990,1515.989990,3551850000\n2013-02-28,1515.989990,1525.339966,1514.459961,1514.680054,1514.680054,3912320000\n2013-03-01,1514.680054,1519.989990,1501.479980,1518.199951,1518.199951,3695610000\n2013-03-04,1518.199951,1525.270020,1512.290039,1525.199951,1525.199951,3414430000\n2013-03-05,1525.199951,1543.469971,1525.199951,1539.790039,1539.790039,3610690000\n2013-03-06,1539.790039,1545.250000,1538.109985,1541.459961,1541.459961,3676890000\n2013-03-07,1541.459961,1545.780029,1541.459961,1544.260010,1544.260010,3634710000\n2013-03-08,1544.260010,1552.479980,1542.939941,1551.180054,1551.180054,3652260000\n2013-03-11,1551.150024,1556.270020,1547.359985,1556.219971,1556.219971,3091080000\n2013-03-12,1556.219971,1556.770020,1548.239990,1552.479980,1552.479980,3274910000\n2013-03-13,1552.479980,1556.390015,1548.250000,1554.520020,1554.520020,3073830000\n2013-03-14,1554.520020,1563.319946,1554.520020,1563.229980,1563.229980,3459260000\n2013-03-15,1563.209961,1563.619995,1555.739990,1560.699951,1560.699951,5175850000\n2013-03-18,1560.699951,1560.699951,1545.130005,1552.099976,1552.099976,3164560000\n2013-03-19,1552.099976,1557.250000,1538.569946,1548.339966,1548.339966,3796210000\n2013-03-20,1548.339966,1561.560059,1548.339966,1558.709961,1558.709961,3349090000\n2013-03-21,1558.709961,1558.709961,1543.550049,1545.800049,1545.800049,3243270000\n2013-03-22,1545.900024,1557.739990,1545.900024,1556.890015,1556.890015,2948380000\n2013-03-25,1556.890015,1564.910034,1546.219971,1551.689941,1551.689941,3178170000\n2013-03-26,1551.689941,1563.949951,1551.689941,1563.770020,1563.770020,2869260000\n2013-03-27,1563.750000,1564.069946,1551.900024,1562.849976,1562.849976,2914210000\n2013-03-28,1562.859985,1570.280029,1561.079956,1569.189941,1569.189941,3304440000\n2013-04-01,1569.180054,1570.569946,1558.469971,1562.170044,1562.170044,2753110000\n2013-04-02,1562.170044,1573.660034,1562.170044,1570.250000,1570.250000,3312160000\n2013-04-03,1570.250000,1571.469971,1549.800049,1553.689941,1553.689941,4060610000\n2013-04-04,1553.689941,1562.599976,1552.520020,1559.979980,1559.979980,3350670000\n2013-04-05,1559.979980,1559.979980,1539.500000,1553.280029,1553.280029,3515410000\n2013-04-08,1553.260010,1563.069946,1548.630005,1563.069946,1563.069946,2887120000\n2013-04-09,1563.109985,1573.890015,1560.920044,1568.609985,1568.609985,3252780000\n2013-04-10,1568.609985,1589.069946,1568.609985,1587.729980,1587.729980,3453350000\n2013-04-11,1587.729980,1597.349976,1586.170044,1593.369995,1593.369995,3393950000\n2013-04-12,1593.300049,1593.300049,1579.969971,1588.849976,1588.849976,3206290000\n2013-04-15,1588.839966,1588.839966,1552.280029,1552.359985,1552.359985,4660130000\n2013-04-16,1552.359985,1575.349976,1552.359985,1574.569946,1574.569946,3654700000\n2013-04-17,1574.569946,1574.569946,1543.689941,1552.010010,1552.010010,4250310000\n2013-04-18,1552.030029,1554.380005,1536.030029,1541.609985,1541.609985,3890800000\n2013-04-19,1541.609985,1555.890015,1539.400024,1555.250000,1555.250000,3569870000\n2013-04-22,1555.250000,1565.550049,1548.189941,1562.500000,1562.500000,2979880000\n2013-04-23,1562.500000,1579.579956,1562.500000,1578.780029,1578.780029,3565150000\n2013-04-24,1578.780029,1583.000000,1575.800049,1578.790039,1578.790039,3598240000\n2013-04-25,1578.930054,1592.640015,1578.930054,1585.160034,1585.160034,3908580000\n2013-04-26,1585.160034,1585.780029,1577.560059,1582.239990,1582.239990,3198620000\n2013-04-29,1582.339966,1596.650024,1582.339966,1593.609985,1593.609985,2891200000\n2013-04-30,1593.579956,1597.569946,1586.500000,1597.569946,1597.569946,3745070000\n2013-05-01,1597.550049,1597.550049,1581.280029,1582.699951,1582.699951,3530320000\n2013-05-02,1582.770020,1598.599976,1582.770020,1597.589966,1597.589966,3366950000\n2013-05-03,1597.599976,1618.459961,1597.599976,1614.420044,1614.420044,3603910000\n2013-05-06,1614.400024,1619.770020,1614.209961,1617.500000,1617.500000,3062240000\n2013-05-07,1617.550049,1626.030029,1616.640015,1625.959961,1625.959961,3309580000\n2013-05-08,1625.949951,1632.780029,1622.699951,1632.689941,1632.689941,3554700000\n2013-05-09,1632.689941,1635.010010,1623.089966,1626.670044,1626.670044,3457400000\n2013-05-10,1626.689941,1633.699951,1623.709961,1633.699951,1633.699951,3086470000\n2013-05-13,1632.099976,1636.000000,1626.739990,1633.770020,1633.770020,2910600000\n2013-05-14,1633.750000,1651.099976,1633.750000,1650.339966,1650.339966,3457790000\n2013-05-15,1649.130005,1661.489990,1646.680054,1658.780029,1658.780029,3657440000\n2013-05-16,1658.069946,1660.510010,1648.599976,1650.469971,1650.469971,3513130000\n2013-05-17,1652.449951,1667.469971,1652.449951,1667.469971,1667.469971,3440710000\n2013-05-20,1665.709961,1672.839966,1663.520020,1666.290039,1666.290039,3275080000\n2013-05-21,1666.199951,1674.930054,1662.670044,1669.160034,1669.160034,3513560000\n2013-05-22,1669.390015,1687.180054,1648.859985,1655.349976,1655.349976,4361020000\n2013-05-23,1651.619995,1655.500000,1635.530029,1650.510010,1650.510010,3945510000\n2013-05-24,1646.670044,1649.780029,1636.880005,1649.599976,1649.599976,2758080000\n2013-05-28,1652.630005,1674.209961,1652.630005,1660.060059,1660.060059,3457400000\n2013-05-29,1656.569946,1656.569946,1640.050049,1648.359985,1648.359985,3587140000\n2013-05-30,1649.140015,1661.910034,1648.609985,1654.410034,1654.410034,3498620000\n2013-05-31,1652.130005,1658.989990,1630.739990,1630.739990,1630.739990,4099600000\n2013-06-03,1631.709961,1640.420044,1622.719971,1640.420044,1640.420044,3952070000\n2013-06-04,1640.729980,1646.530029,1623.619995,1631.380005,1631.380005,3653840000\n2013-06-05,1629.050049,1629.310059,1607.089966,1608.900024,1608.900024,3632350000\n2013-06-06,1609.290039,1622.560059,1598.229980,1622.560059,1622.560059,3547380000\n2013-06-07,1625.270020,1644.400024,1625.270020,1643.380005,1643.380005,3371990000\n2013-06-10,1644.670044,1648.689941,1639.260010,1642.810059,1642.810059,2978730000\n2013-06-11,1638.640015,1640.130005,1622.920044,1626.130005,1626.130005,3435710000\n2013-06-12,1629.939941,1637.709961,1610.920044,1612.520020,1612.520020,3202550000\n2013-06-13,1612.150024,1639.250000,1608.069946,1636.359985,1636.359985,3378620000\n2013-06-14,1635.520020,1640.800049,1623.959961,1626.729980,1626.729980,2939400000\n2013-06-17,1630.640015,1646.500000,1630.339966,1639.040039,1639.040039,3137080000\n2013-06-18,1639.770020,1654.189941,1639.770020,1651.810059,1651.810059,3120980000\n2013-06-19,1651.829956,1652.449951,1628.910034,1628.930054,1628.930054,3545060000\n2013-06-20,1624.619995,1624.619995,1584.319946,1588.189941,1588.189941,4858850000\n2013-06-21,1588.619995,1599.189941,1577.699951,1592.430054,1592.430054,5797280000\n2013-06-24,1588.770020,1588.770020,1560.329956,1573.089966,1573.089966,4733660000\n2013-06-25,1577.520020,1593.790039,1577.089966,1588.030029,1588.030029,3761170000\n2013-06-26,1592.270020,1606.829956,1592.270020,1603.260010,1603.260010,3558340000\n2013-06-27,1606.439941,1620.069946,1606.439941,1613.199951,1613.199951,3364540000\n2013-06-28,1611.119995,1615.939941,1601.060059,1606.280029,1606.280029,4977190000\n2013-07-01,1609.780029,1626.609985,1609.780029,1614.959961,1614.959961,3104690000\n2013-07-02,1614.290039,1624.260010,1606.770020,1614.079956,1614.079956,3317130000\n2013-07-03,1611.479980,1618.969971,1604.569946,1615.410034,1615.410034,1966050000\n2013-07-05,1618.650024,1632.069946,1614.709961,1631.890015,1631.890015,2634140000\n2013-07-08,1634.199951,1644.680054,1634.199951,1640.459961,1640.459961,3514590000\n2013-07-09,1642.890015,1654.180054,1642.890015,1652.319946,1652.319946,3155360000\n2013-07-10,1651.560059,1657.920044,1647.660034,1652.619995,1652.619995,3011010000\n2013-07-11,1657.410034,1676.630005,1657.410034,1675.020020,1675.020020,3446340000\n2013-07-12,1675.260010,1680.189941,1672.329956,1680.189941,1680.189941,3039070000\n2013-07-15,1679.589966,1684.510010,1677.890015,1682.500000,1682.500000,2623200000\n2013-07-16,1682.699951,1683.729980,1671.839966,1676.260010,1676.260010,3081710000\n2013-07-17,1677.910034,1684.750000,1677.910034,1680.910034,1680.910034,3153440000\n2013-07-18,1681.050049,1693.119995,1681.050049,1689.369995,1689.369995,3452370000\n2013-07-19,1686.150024,1692.089966,1684.079956,1692.089966,1692.089966,3302580000\n2013-07-22,1694.410034,1697.609985,1690.670044,1695.530029,1695.530029,2779130000\n2013-07-23,1696.630005,1698.780029,1691.130005,1692.390015,1692.390015,3096180000\n2013-07-24,1696.060059,1698.380005,1682.569946,1685.939941,1685.939941,3336120000\n2013-07-25,1685.209961,1690.939941,1680.069946,1690.250000,1690.250000,3322500000\n2013-07-26,1687.310059,1691.849976,1676.030029,1691.650024,1691.650024,2762770000\n2013-07-29,1690.319946,1690.920044,1681.859985,1685.329956,1685.329956,2840520000\n2013-07-30,1687.920044,1693.189941,1682.420044,1685.959961,1685.959961,3320530000\n2013-07-31,1687.760010,1698.430054,1684.939941,1685.729980,1685.729980,3847390000\n2013-08-01,1689.420044,1707.849976,1689.420044,1706.869995,1706.869995,3775170000\n2013-08-02,1706.099976,1709.670044,1700.680054,1709.670044,1709.670044,3136630000\n2013-08-05,1708.010010,1709.239990,1703.550049,1707.140015,1707.140015,2529300000\n2013-08-06,1705.790039,1705.790039,1693.290039,1697.369995,1697.369995,3141210000\n2013-08-07,1695.300049,1695.300049,1684.910034,1690.910034,1690.910034,3010230000\n2013-08-08,1693.349976,1700.180054,1688.380005,1697.479980,1697.479980,3271660000\n2013-08-09,1696.099976,1699.420044,1686.020020,1691.420044,1691.420044,2957670000\n2013-08-12,1688.369995,1691.489990,1683.349976,1689.469971,1689.469971,2789160000\n2013-08-13,1690.650024,1696.810059,1682.619995,1694.160034,1694.160034,3035560000\n2013-08-14,1693.880005,1695.520020,1684.829956,1685.390015,1685.390015,2871430000\n2013-08-15,1679.609985,1679.609985,1658.589966,1661.319946,1661.319946,3426690000\n2013-08-16,1661.219971,1663.599976,1652.609985,1655.829956,1655.829956,3211450000\n2013-08-19,1655.250000,1659.180054,1645.839966,1646.060059,1646.060059,2904530000\n2013-08-20,1646.810059,1658.920044,1646.079956,1652.349976,1652.349976,2994090000\n2013-08-21,1650.660034,1656.989990,1639.430054,1642.800049,1642.800049,2932180000\n2013-08-22,1645.030029,1659.550049,1645.030029,1656.959961,1656.959961,2537460000\n2013-08-23,1659.920044,1664.849976,1654.810059,1663.500000,1663.500000,2582670000\n2013-08-26,1664.290039,1669.510010,1656.020020,1656.780029,1656.780029,2430670000\n2013-08-27,1652.540039,1652.540039,1629.050049,1630.479980,1630.479980,3219190000\n2013-08-28,1630.250000,1641.180054,1627.469971,1634.959961,1634.959961,2784010000\n2013-08-29,1633.500000,1646.410034,1630.880005,1638.170044,1638.170044,2527550000\n2013-08-30,1638.890015,1640.079956,1628.050049,1632.969971,1632.969971,2734300000\n2013-09-03,1635.949951,1651.349976,1633.410034,1639.770020,1639.770020,3731610000\n2013-09-04,1640.719971,1655.719971,1637.410034,1653.079956,1653.079956,3312150000\n2013-09-05,1653.280029,1659.170044,1653.069946,1655.079956,1655.079956,2957110000\n2013-09-06,1657.439941,1664.829956,1640.619995,1655.170044,1655.170044,3123880000\n2013-09-09,1656.849976,1672.400024,1656.849976,1671.709961,1671.709961,3102780000\n2013-09-10,1675.109985,1684.089966,1675.109985,1683.989990,1683.989990,3691800000\n2013-09-11,1681.040039,1689.130005,1678.699951,1689.130005,1689.130005,3135460000\n2013-09-12,1689.209961,1689.969971,1681.959961,1683.420044,1683.420044,3106290000\n2013-09-13,1685.040039,1688.729980,1682.219971,1687.989990,1687.989990,2736500000\n2013-09-16,1691.699951,1704.949951,1691.699951,1697.599976,1697.599976,3079800000\n2013-09-17,1697.729980,1705.520020,1697.729980,1704.760010,1704.760010,2774240000\n2013-09-18,1705.739990,1729.439941,1700.349976,1725.520020,1725.520020,3989760000\n2013-09-19,1727.339966,1729.859985,1720.199951,1722.339966,1722.339966,3740130000\n2013-09-20,1722.439941,1725.229980,1708.890015,1709.910034,1709.910034,5074030000\n2013-09-23,1711.439941,1711.439941,1697.099976,1701.839966,1701.839966,3126950000\n2013-09-24,1702.599976,1707.630005,1694.900024,1697.420044,1697.420044,3268930000\n2013-09-25,1698.020020,1701.709961,1691.880005,1692.770020,1692.770020,3148730000\n2013-09-26,1694.050049,1703.849976,1693.109985,1698.670044,1698.670044,2813930000\n2013-09-27,1695.520020,1695.520020,1687.109985,1691.750000,1691.750000,2951700000\n2013-09-30,1687.260010,1687.260010,1674.989990,1681.550049,1681.550049,3308630000\n2013-10-01,1682.410034,1696.550049,1682.069946,1695.000000,1695.000000,3238690000\n2013-10-02,1691.900024,1693.869995,1680.339966,1693.869995,1693.869995,3148600000\n2013-10-03,1692.349976,1692.349976,1670.359985,1678.660034,1678.660034,3279650000\n2013-10-04,1678.790039,1691.939941,1677.329956,1690.500000,1690.500000,2880270000\n2013-10-07,1687.150024,1687.150024,1674.699951,1676.119995,1676.119995,2678490000\n2013-10-08,1676.219971,1676.790039,1655.030029,1655.449951,1655.449951,3569230000\n2013-10-09,1656.989990,1662.469971,1646.469971,1656.400024,1656.400024,3577840000\n2013-10-10,1660.880005,1692.560059,1660.880005,1692.560059,1692.560059,3362300000\n2013-10-11,1691.089966,1703.439941,1688.520020,1703.199951,1703.199951,2944670000\n2013-10-14,1699.859985,1711.030029,1692.130005,1710.140015,1710.140015,2580580000\n2013-10-15,1709.170044,1711.569946,1695.930054,1698.060059,1698.060059,3327740000\n2013-10-16,1700.489990,1721.760010,1700.489990,1721.540039,1721.540039,3486180000\n2013-10-17,1720.170044,1733.449951,1714.119995,1733.150024,1733.150024,3453590000\n2013-10-18,1736.719971,1745.310059,1735.739990,1744.500000,1744.500000,3664890000\n2013-10-21,1745.199951,1747.790039,1740.670044,1744.660034,1744.660034,3052710000\n2013-10-22,1746.479980,1759.329956,1746.479980,1754.670044,1754.670044,3850840000\n2013-10-23,1752.270020,1752.270020,1740.500000,1746.380005,1746.380005,3713380000\n2013-10-24,1747.479980,1753.939941,1745.500000,1752.069946,1752.069946,3671700000\n2013-10-25,1756.010010,1759.819946,1752.449951,1759.770020,1759.770020,3175720000\n2013-10-28,1759.420044,1764.989990,1757.670044,1762.109985,1762.109985,3282300000\n2013-10-29,1762.930054,1772.089966,1762.930054,1771.949951,1771.949951,3358460000\n2013-10-30,1772.270020,1775.219971,1757.239990,1763.310059,1763.310059,3523040000\n2013-10-31,1763.239990,1768.530029,1755.719971,1756.540039,1756.540039,3826530000\n2013-11-01,1758.699951,1765.670044,1752.699951,1761.640015,1761.640015,3686290000\n2013-11-04,1763.400024,1768.780029,1761.560059,1767.930054,1767.930054,3194870000\n2013-11-05,1765.670044,1767.030029,1755.760010,1762.969971,1762.969971,3516680000\n2013-11-06,1765.000000,1773.739990,1764.400024,1770.489990,1770.489990,3322100000\n2013-11-07,1770.739990,1774.540039,1746.199951,1747.150024,1747.150024,4143200000\n2013-11-08,1748.369995,1770.780029,1747.630005,1770.609985,1770.609985,3837170000\n2013-11-11,1769.959961,1773.439941,1767.849976,1771.890015,1771.890015,2534060000\n2013-11-12,1769.510010,1771.780029,1762.290039,1767.689941,1767.689941,3221030000\n2013-11-13,1764.369995,1782.000000,1760.640015,1782.000000,1782.000000,3327480000\n2013-11-14,1782.750000,1791.530029,1780.219971,1790.619995,1790.619995,3139060000\n2013-11-15,1790.660034,1798.219971,1790.660034,1798.180054,1798.180054,3254820000\n2013-11-18,1798.819946,1802.329956,1788.000000,1791.530029,1791.530029,3168520000\n2013-11-19,1790.790039,1795.510010,1784.719971,1787.869995,1787.869995,3224450000\n2013-11-20,1789.589966,1795.729980,1777.229980,1781.369995,1781.369995,3109140000\n2013-11-21,1783.520020,1797.160034,1783.520020,1795.849976,1795.849976,3256630000\n2013-11-22,1797.209961,1804.839966,1794.699951,1804.760010,1804.760010,3055140000\n2013-11-25,1806.329956,1808.099976,1800.579956,1802.479980,1802.479980,2998540000\n2013-11-26,1802.869995,1808.420044,1800.770020,1802.750000,1802.750000,3427120000\n2013-11-27,1803.479980,1808.270020,1802.770020,1807.229980,1807.229980,2613590000\n2013-11-29,1808.689941,1813.550049,1803.979980,1805.810059,1805.810059,1598300000\n2013-12-02,1806.550049,1810.020020,1798.599976,1800.900024,1800.900024,3095430000\n2013-12-03,1800.099976,1800.099976,1787.849976,1795.150024,1795.150024,3475680000\n2013-12-04,1793.150024,1799.800049,1779.089966,1792.810059,1792.810059,3610540000\n2013-12-05,1792.819946,1792.819946,1783.380005,1785.030029,1785.030029,3336880000\n2013-12-06,1788.359985,1806.040039,1788.359985,1805.089966,1805.089966,3150030000\n2013-12-09,1806.209961,1811.520020,1806.209961,1808.369995,1808.369995,3129500000\n2013-12-10,1807.599976,1808.520020,1801.750000,1802.619995,1802.619995,3117150000\n2013-12-11,1802.760010,1802.969971,1780.089966,1782.219971,1782.219971,3472240000\n2013-12-12,1781.709961,1782.989990,1772.280029,1775.500000,1775.500000,3306640000\n2013-12-13,1777.979980,1780.920044,1772.449951,1775.319946,1775.319946,3061070000\n2013-12-16,1777.479980,1792.219971,1777.479980,1786.540039,1786.540039,3209890000\n2013-12-17,1786.469971,1786.770020,1777.050049,1781.000000,1781.000000,3270030000\n2013-12-18,1781.459961,1811.079956,1767.989990,1810.650024,1810.650024,4327770000\n2013-12-19,1809.000000,1810.880005,1801.349976,1809.599976,1809.599976,3497210000\n2013-12-20,1810.390015,1823.750000,1810.250000,1818.319946,1818.319946,5097700000\n2013-12-23,1822.920044,1829.750000,1822.920044,1827.989990,1827.989990,2851540000\n2013-12-24,1828.020020,1833.319946,1828.020020,1833.319946,1833.319946,1307630000\n2013-12-26,1834.959961,1842.839966,1834.959961,1842.020020,1842.020020,1982270000\n2013-12-27,1842.969971,1844.890015,1839.810059,1841.400024,1841.400024,2052920000\n2013-12-30,1841.469971,1842.469971,1838.770020,1841.069946,1841.069946,2293860000\n2013-12-31,1842.609985,1849.439941,1842.410034,1848.359985,1848.359985,2312840000\n2014-01-02,1845.859985,1845.859985,1827.739990,1831.979980,1831.979980,3080600000\n2014-01-03,1833.209961,1838.239990,1829.130005,1831.369995,1831.369995,2774270000\n2014-01-06,1832.310059,1837.160034,1823.729980,1826.770020,1826.770020,3294850000\n2014-01-07,1828.709961,1840.099976,1828.709961,1837.880005,1837.880005,3511750000\n2014-01-08,1837.900024,1840.020020,1831.400024,1837.489990,1837.489990,3652140000\n2014-01-09,1839.000000,1843.229980,1830.380005,1838.130005,1838.130005,3581150000\n2014-01-10,1840.060059,1843.150024,1832.430054,1842.369995,1842.369995,3335710000\n2014-01-13,1841.260010,1843.449951,1815.520020,1819.199951,1819.199951,3591350000\n2014-01-14,1821.359985,1839.260010,1821.359985,1838.880005,1838.880005,3353270000\n2014-01-15,1840.520020,1850.839966,1840.520020,1848.380005,1848.380005,3777800000\n2014-01-16,1847.989990,1847.989990,1840.300049,1845.890015,1845.890015,3491310000\n2014-01-17,1844.229980,1846.040039,1835.229980,1838.699951,1838.699951,3626120000\n2014-01-21,1841.050049,1849.310059,1832.380005,1843.800049,1843.800049,3782470000\n2014-01-22,1844.709961,1846.869995,1840.880005,1844.859985,1844.859985,3374170000\n2014-01-23,1842.290039,1842.290039,1820.060059,1828.459961,1828.459961,3972250000\n2014-01-24,1826.959961,1826.959961,1790.290039,1790.290039,1790.290039,4618450000\n2014-01-27,1791.030029,1795.979980,1772.880005,1781.560059,1781.560059,4045200000\n2014-01-28,1783.000000,1793.869995,1779.489990,1792.500000,1792.500000,3437830000\n2014-01-29,1790.150024,1790.150024,1770.449951,1774.199951,1774.199951,3964020000\n2014-01-30,1777.170044,1798.770020,1777.170044,1794.189941,1794.189941,3547510000\n2014-01-31,1790.880005,1793.880005,1772.260010,1782.589966,1782.589966,4059690000\n2014-02-03,1782.680054,1784.829956,1739.660034,1741.890015,1741.890015,4726040000\n2014-02-04,1743.819946,1758.729980,1743.819946,1755.199951,1755.199951,4068410000\n2014-02-05,1753.380005,1755.790039,1737.920044,1751.640015,1751.640015,3984290000\n2014-02-06,1752.989990,1774.060059,1752.989990,1773.430054,1773.430054,3825410000\n2014-02-07,1776.010010,1798.030029,1776.010010,1797.020020,1797.020020,3775990000\n2014-02-10,1796.199951,1799.939941,1791.829956,1799.839966,1799.839966,3312160000\n2014-02-11,1800.449951,1823.540039,1800.410034,1819.750000,1819.750000,3699380000\n2014-02-12,1820.119995,1826.550049,1815.969971,1819.260010,1819.260010,3326380000\n2014-02-13,1814.819946,1830.250000,1809.219971,1829.829956,1829.829956,3289510000\n2014-02-14,1828.459961,1841.650024,1825.589966,1838.630005,1838.630005,3114750000\n2014-02-18,1839.030029,1842.869995,1835.010010,1840.760010,1840.760010,3421110000\n2014-02-19,1838.900024,1847.500000,1826.989990,1828.750000,1828.750000,3661570000\n2014-02-20,1829.239990,1842.790039,1824.579956,1839.780029,1839.780029,3404980000\n2014-02-21,1841.069946,1846.130005,1835.599976,1836.250000,1836.250000,3403880000\n2014-02-24,1836.780029,1858.709961,1836.780029,1847.609985,1847.609985,4014530000\n2014-02-25,1847.660034,1852.910034,1840.189941,1845.119995,1845.119995,3515560000\n2014-02-26,1845.790039,1852.650024,1840.660034,1845.160034,1845.160034,3716730000\n2014-02-27,1844.900024,1854.530029,1841.130005,1854.290039,1854.290039,3547460000\n2014-02-28,1855.119995,1867.920044,1847.670044,1859.449951,1859.449951,3917450000\n2014-03-03,1857.680054,1857.680054,1834.439941,1845.729980,1845.729980,3428220000\n2014-03-04,1849.229980,1876.229980,1849.229980,1873.910034,1873.910034,3765770000\n2014-03-05,1874.050049,1876.530029,1871.109985,1873.810059,1873.810059,3392990000\n2014-03-06,1874.180054,1881.939941,1874.180054,1877.030029,1877.030029,3360450000\n2014-03-07,1878.520020,1883.569946,1870.560059,1878.040039,1878.040039,3564740000\n2014-03-10,1877.859985,1877.869995,1867.040039,1877.170044,1877.170044,3021350000\n2014-03-11,1878.260010,1882.349976,1863.880005,1867.630005,1867.630005,3392400000\n2014-03-12,1866.150024,1868.380005,1854.380005,1868.199951,1868.199951,3270860000\n2014-03-13,1869.060059,1874.400024,1841.859985,1846.339966,1846.339966,3670990000\n2014-03-14,1845.069946,1852.439941,1839.569946,1841.130005,1841.130005,3285460000\n2014-03-17,1842.810059,1862.300049,1842.810059,1858.829956,1858.829956,2860490000\n2014-03-18,1858.920044,1873.760010,1858.920044,1872.250000,1872.250000,2930190000\n2014-03-19,1872.250000,1874.140015,1850.349976,1860.770020,1860.770020,3289210000\n2014-03-20,1860.089966,1873.489990,1854.630005,1872.010010,1872.010010,3327540000\n2014-03-21,1874.530029,1883.969971,1863.459961,1866.520020,1866.520020,5270710000\n2014-03-24,1867.670044,1873.339966,1849.689941,1857.439941,1857.439941,3409000000\n2014-03-25,1859.479980,1871.869995,1855.959961,1865.619995,1865.619995,3200560000\n2014-03-26,1867.089966,1875.920044,1852.560059,1852.560059,1852.560059,3480850000\n2014-03-27,1852.109985,1855.550049,1842.109985,1849.040039,1849.040039,3733430000\n2014-03-28,1850.069946,1866.630005,1850.069946,1857.619995,1857.619995,2955520000\n2014-03-31,1859.160034,1875.180054,1859.160034,1872.339966,1872.339966,3274300000\n2014-04-01,1873.959961,1885.839966,1873.959961,1885.520020,1885.520020,3336190000\n2014-04-02,1886.609985,1893.170044,1883.790039,1890.900024,1890.900024,3131660000\n2014-04-03,1891.430054,1893.800049,1882.650024,1888.770020,1888.770020,3055600000\n2014-04-04,1890.250000,1897.280029,1863.260010,1865.089966,1865.089966,3583750000\n2014-04-07,1863.920044,1864.040039,1841.479980,1845.040039,1845.040039,3801540000\n2014-04-08,1845.479980,1854.949951,1837.489990,1851.959961,1851.959961,3721450000\n2014-04-09,1852.640015,1872.430054,1852.380005,1872.180054,1872.180054,3308650000\n2014-04-10,1872.280029,1872.530029,1830.869995,1833.079956,1833.079956,3758780000\n2014-04-11,1830.650024,1835.069946,1814.359985,1815.689941,1815.689941,3743460000\n2014-04-14,1818.180054,1834.189941,1815.800049,1830.609985,1830.609985,3111540000\n2014-04-15,1831.449951,1844.020020,1816.290039,1842.979980,1842.979980,3736440000\n2014-04-16,1846.010010,1862.310059,1846.010010,1862.310059,1862.310059,3155080000\n2014-04-17,1861.729980,1869.630005,1856.719971,1864.849976,1864.849976,3341430000\n2014-04-21,1865.790039,1871.890015,1863.180054,1871.890015,1871.890015,2642500000\n2014-04-22,1872.569946,1884.890015,1872.569946,1879.550049,1879.550049,3215440000\n2014-04-23,1879.319946,1879.750000,1873.910034,1875.390015,1875.390015,3085720000\n2014-04-24,1881.969971,1884.060059,1870.239990,1878.609985,1878.609985,3191830000\n2014-04-25,1877.719971,1877.719971,1859.699951,1863.400024,1863.400024,3213020000\n2014-04-28,1865.000000,1877.010010,1850.609985,1869.430054,1869.430054,4034680000\n2014-04-29,1870.780029,1880.599976,1870.780029,1878.329956,1878.329956,3647820000\n2014-04-30,1877.099976,1885.199951,1872.689941,1883.949951,1883.949951,3779230000\n2014-05-01,1884.390015,1888.589966,1878.040039,1883.680054,1883.680054,3416740000\n2014-05-02,1885.300049,1891.329956,1878.500000,1881.140015,1881.140015,3159560000\n2014-05-05,1879.449951,1885.510010,1866.770020,1884.660034,1884.660034,2733730000\n2014-05-06,1883.689941,1883.689941,1867.719971,1867.719971,1867.719971,3327260000\n2014-05-07,1868.530029,1878.829956,1859.790039,1878.209961,1878.209961,3632950000\n2014-05-08,1877.390015,1889.069946,1870.050049,1875.630005,1875.630005,3393420000\n2014-05-09,1875.270020,1878.569946,1867.020020,1878.479980,1878.479980,3025020000\n2014-05-12,1880.030029,1897.130005,1880.030029,1896.650024,1896.650024,3005740000\n2014-05-13,1896.750000,1902.170044,1896.060059,1897.449951,1897.449951,2915680000\n2014-05-14,1897.130005,1897.130005,1885.770020,1888.530029,1888.530029,2822060000\n2014-05-15,1888.160034,1888.160034,1862.359985,1870.849976,1870.849976,3552640000\n2014-05-16,1871.189941,1878.280029,1864.819946,1877.859985,1877.859985,3173650000\n2014-05-19,1876.660034,1886.000000,1872.420044,1885.079956,1885.079956,2664250000\n2014-05-20,1884.880005,1884.880005,1868.140015,1872.829956,1872.829956,3007700000\n2014-05-21,1873.339966,1888.800049,1873.339966,1888.030029,1888.030029,2777140000\n2014-05-22,1888.189941,1896.329956,1885.390015,1892.489990,1892.489990,2759800000\n2014-05-23,1893.319946,1901.260010,1893.319946,1900.530029,1900.530029,2396280000\n2014-05-27,1902.010010,1912.280029,1902.010010,1911.910034,1911.910034,2911020000\n2014-05-28,1911.770020,1914.459961,1907.300049,1909.780029,1909.780029,2976450000\n2014-05-29,1910.599976,1920.030029,1909.819946,1920.030029,1920.030029,2709050000\n2014-05-30,1920.329956,1924.030029,1916.640015,1923.569946,1923.569946,3263490000\n2014-06-02,1923.869995,1925.880005,1915.979980,1924.969971,1924.969971,2509020000\n2014-06-03,1923.069946,1925.069946,1918.790039,1924.239990,1924.239990,2867180000\n2014-06-04,1923.060059,1928.630005,1918.599976,1927.880005,1927.880005,2793920000\n2014-06-05,1928.520020,1941.739990,1922.930054,1940.459961,1940.459961,3113270000\n2014-06-06,1942.410034,1949.439941,1942.410034,1949.439941,1949.439941,2864300000\n2014-06-09,1948.969971,1955.550049,1947.160034,1951.270020,1951.270020,2812180000\n2014-06-10,1950.339966,1950.859985,1944.640015,1950.790039,1950.790039,2702360000\n2014-06-11,1949.369995,1949.369995,1940.079956,1943.890015,1943.890015,2710620000\n2014-06-12,1943.349976,1943.349976,1925.780029,1930.109985,1930.109985,3040480000\n2014-06-13,1930.800049,1937.300049,1927.689941,1936.160034,1936.160034,2598230000\n2014-06-16,1934.839966,1941.150024,1930.910034,1937.780029,1937.780029,2926130000\n2014-06-17,1937.150024,1943.689941,1933.550049,1941.989990,1941.989990,2971260000\n2014-06-18,1942.729980,1957.739990,1939.290039,1956.979980,1956.979980,3065220000\n2014-06-19,1957.500000,1959.869995,1952.260010,1959.479980,1959.479980,2952150000\n2014-06-20,1960.449951,1963.910034,1959.170044,1962.869995,1962.869995,4336240000\n2014-06-23,1962.920044,1963.739990,1958.890015,1962.609985,1962.609985,2717630000\n2014-06-24,1961.969971,1968.170044,1948.339966,1949.979980,1949.979980,3089700000\n2014-06-25,1949.270020,1960.829956,1947.489990,1959.530029,1959.530029,3106710000\n2014-06-26,1959.890015,1959.890015,1944.689941,1957.219971,1957.219971,2778840000\n2014-06-27,1956.560059,1961.469971,1952.180054,1960.959961,1960.959961,4290590000\n2014-06-30,1960.790039,1964.239990,1958.219971,1960.229980,1960.229980,3037350000\n2014-07-01,1962.290039,1978.579956,1962.290039,1973.319946,1973.319946,3188240000\n2014-07-02,1973.060059,1976.670044,1972.579956,1974.619995,1974.619995,2851480000\n2014-07-03,1975.880005,1985.589966,1975.880005,1985.439941,1985.439941,1998090000\n2014-07-07,1984.219971,1984.219971,1974.880005,1977.650024,1977.650024,2681260000\n2014-07-08,1976.390015,1976.390015,1959.459961,1963.709961,1963.709961,3302430000\n2014-07-09,1965.099976,1974.150024,1965.099976,1972.829956,1972.829956,2858800000\n2014-07-10,1966.670044,1969.839966,1952.859985,1964.680054,1964.680054,3165690000\n2014-07-11,1965.760010,1968.670044,1959.630005,1967.569946,1967.569946,2684630000\n2014-07-14,1969.859985,1979.849976,1969.859985,1977.099976,1977.099976,2744920000\n2014-07-15,1977.359985,1982.520020,1965.339966,1973.280029,1973.280029,3328740000\n2014-07-16,1976.349976,1983.939941,1975.670044,1981.569946,1981.569946,3390950000\n2014-07-17,1979.750000,1981.800049,1955.589966,1958.119995,1958.119995,3381680000\n2014-07-18,1961.540039,1979.910034,1960.819946,1978.219971,1978.219971,3106060000\n2014-07-21,1976.930054,1976.930054,1965.770020,1973.630005,1973.630005,2611160000\n2014-07-22,1975.650024,1986.239990,1975.650024,1983.530029,1983.530029,2890480000\n2014-07-23,1985.319946,1989.229980,1982.439941,1987.010010,1987.010010,2869720000\n2014-07-24,1988.069946,1991.390015,1985.790039,1987.979980,1987.979980,3203530000\n2014-07-25,1984.599976,1984.599976,1974.369995,1978.339966,1978.339966,2638960000\n2014-07-28,1978.250000,1981.520020,1967.310059,1978.910034,1978.910034,2803320000\n2014-07-29,1980.030029,1984.849976,1969.949951,1969.949951,1969.949951,3183300000\n2014-07-30,1973.209961,1978.900024,1962.420044,1970.069946,1970.069946,3448250000\n2014-07-31,1965.140015,1965.140015,1930.670044,1930.670044,1930.670044,4193000000\n2014-08-01,1929.800049,1937.349976,1916.369995,1925.150024,1925.150024,3789660000\n2014-08-04,1926.619995,1942.920044,1921.199951,1938.989990,1938.989990,3072920000\n2014-08-05,1936.339966,1936.339966,1913.770020,1920.209961,1920.209961,3462520000\n2014-08-06,1917.290039,1927.910034,1911.449951,1920.239990,1920.239990,3539150000\n2014-08-07,1923.030029,1928.890015,1904.780029,1909.569946,1909.569946,3230520000\n2014-08-08,1910.349976,1932.380005,1909.010010,1931.589966,1931.589966,2902280000\n2014-08-11,1933.430054,1944.900024,1933.430054,1936.920044,1936.920044,2784890000\n2014-08-12,1935.729980,1939.650024,1928.290039,1933.750000,1933.750000,2611700000\n2014-08-13,1935.599976,1948.410034,1935.599976,1946.719971,1946.719971,2718020000\n2014-08-14,1947.410034,1955.229980,1947.410034,1955.180054,1955.180054,2609460000\n2014-08-15,1958.869995,1964.040039,1941.500000,1955.060059,1955.060059,3023380000\n2014-08-18,1958.359985,1971.989990,1958.359985,1971.739990,1971.739990,2638160000\n2014-08-19,1972.729980,1982.569946,1972.729980,1981.599976,1981.599976,2656430000\n2014-08-20,1980.459961,1988.569946,1977.680054,1986.510010,1986.510010,2579560000\n2014-08-21,1986.819946,1994.760010,1986.819946,1992.369995,1992.369995,2638920000\n2014-08-22,1992.599976,1993.540039,1984.760010,1988.400024,1988.400024,2301860000\n2014-08-25,1991.739990,2001.949951,1991.739990,1997.920044,1997.920044,2233880000\n2014-08-26,1998.589966,2005.040039,1998.589966,2000.020020,2000.020020,2451950000\n2014-08-27,2000.540039,2002.140015,1996.199951,2000.119995,2000.119995,2344350000\n2014-08-28,1997.420044,1998.550049,1990.520020,1996.739990,1996.739990,2282400000\n2014-08-29,1998.449951,2003.380005,1994.650024,2003.369995,2003.369995,2259130000\n2014-09-02,2004.069946,2006.119995,1994.849976,2002.280029,2002.280029,2819980000\n2014-09-03,2003.569946,2009.280029,1998.140015,2000.719971,2000.719971,2809980000\n2014-09-04,2001.670044,2011.170044,1992.540039,1997.650024,1997.650024,3072410000\n2014-09-05,1998.000000,2007.709961,1990.099976,2007.709961,2007.709961,2818300000\n2014-09-08,2007.170044,2007.170044,1995.599976,2001.540039,2001.540039,2789090000\n2014-09-09,2000.729980,2001.010010,1984.609985,1988.439941,1988.439941,2882830000\n2014-09-10,1988.410034,1996.660034,1982.989990,1995.689941,1995.689941,2912430000\n2014-09-11,1992.849976,1997.650024,1985.930054,1997.449951,1997.449951,2941690000\n2014-09-12,1996.739990,1996.739990,1980.260010,1985.540039,1985.540039,3206570000\n2014-09-15,1986.040039,1987.180054,1978.479980,1984.130005,1984.130005,2776530000\n2014-09-16,1981.930054,2002.280029,1979.060059,1998.979980,1998.979980,3160310000\n2014-09-17,1999.300049,2010.739990,1993.290039,2001.569946,2001.569946,3209420000\n2014-09-18,2003.069946,2012.339966,2003.069946,2011.359985,2011.359985,3235340000\n2014-09-19,2012.739990,2019.260010,2006.589966,2010.400024,2010.400024,4880220000\n2014-09-22,2009.079956,2009.079956,1991.010010,1994.290039,1994.290039,3349670000\n2014-09-23,1992.780029,1995.410034,1982.770020,1982.770020,1982.770020,3279350000\n2014-09-24,1983.339966,1999.790039,1978.630005,1998.300049,1998.300049,3313850000\n2014-09-25,1997.319946,1997.319946,1965.989990,1965.989990,1965.989990,3273050000\n2014-09-26,1966.219971,1986.369995,1966.219971,1982.849976,1982.849976,2929440000\n2014-09-29,1978.959961,1981.280029,1964.040039,1977.800049,1977.800049,3094440000\n2014-09-30,1978.209961,1985.170044,1968.959961,1972.290039,1972.290039,3951100000\n2014-10-01,1971.439941,1971.439941,1941.719971,1946.160034,1946.160034,4188590000\n2014-10-02,1945.829956,1952.319946,1926.030029,1946.170044,1946.170044,4012510000\n2014-10-03,1948.119995,1971.189941,1948.119995,1967.900024,1967.900024,3560970000\n2014-10-06,1970.010010,1977.839966,1958.430054,1964.819946,1964.819946,3358220000\n2014-10-07,1962.359985,1962.359985,1934.869995,1935.099976,1935.099976,3687870000\n2014-10-08,1935.550049,1970.359985,1925.250000,1968.890015,1968.890015,4441890000\n2014-10-09,1967.680054,1967.680054,1927.560059,1928.209961,1928.209961,4344020000\n2014-10-10,1925.630005,1936.979980,1906.050049,1906.130005,1906.130005,4550540000\n2014-10-13,1905.650024,1912.089966,1874.140015,1874.739990,1874.739990,4352580000\n2014-10-14,1877.109985,1898.709961,1871.790039,1877.699951,1877.699951,4812010000\n2014-10-15,1874.180054,1874.180054,1820.660034,1862.489990,1862.489990,6090800000\n2014-10-16,1855.949951,1876.010010,1835.020020,1862.760010,1862.760010,5073150000\n2014-10-17,1864.910034,1898.160034,1864.910034,1886.760010,1886.760010,4482120000\n2014-10-20,1885.619995,1905.030029,1882.300049,1904.010010,1904.010010,3331210000\n2014-10-21,1909.380005,1942.449951,1909.380005,1941.280029,1941.280029,3987090000\n2014-10-22,1941.290039,1949.310059,1926.829956,1927.109985,1927.109985,3761930000\n2014-10-23,1931.020020,1961.949951,1931.020020,1950.819946,1950.819946,3789250000\n2014-10-24,1951.589966,1965.270020,1946.270020,1964.579956,1964.579956,3078380000\n2014-10-27,1962.969971,1964.640015,1951.369995,1961.630005,1961.630005,3538860000\n2014-10-28,1964.140015,1985.050049,1964.140015,1985.050049,1985.050049,3653260000\n2014-10-29,1983.290039,1991.400024,1969.040039,1982.300049,1982.300049,3740350000\n2014-10-30,1979.489990,1999.400024,1974.750000,1994.650024,1994.650024,3586150000\n2014-10-31,2001.199951,2018.189941,2001.199951,2018.050049,2018.050049,4292290000\n2014-11-03,2018.209961,2024.459961,2013.680054,2017.810059,2017.810059,3555440000\n2014-11-04,2015.810059,2015.979980,2001.010010,2012.099976,2012.099976,3956260000\n2014-11-05,2015.290039,2023.770020,2014.420044,2023.569946,2023.569946,3766590000\n2014-11-06,2023.329956,2031.609985,2015.859985,2031.209961,2031.209961,3669770000\n2014-11-07,2032.359985,2034.260010,2025.069946,2031.920044,2031.920044,3704280000\n2014-11-10,2032.010010,2038.699951,2030.170044,2038.260010,2038.260010,3284940000\n2014-11-11,2038.199951,2041.280029,2035.280029,2039.680054,2039.680054,2958320000\n2014-11-12,2037.750000,2040.329956,2031.949951,2038.250000,2038.250000,3246650000\n2014-11-13,2039.209961,2046.180054,2030.439941,2039.329956,2039.329956,3455270000\n2014-11-14,2039.739990,2042.219971,2035.199951,2039.819946,2039.819946,3227130000\n2014-11-17,2038.290039,2043.069946,2034.459961,2041.319946,2041.319946,3152890000\n2014-11-18,2041.479980,2056.080078,2041.479980,2051.800049,2051.800049,3416190000\n2014-11-19,2051.159912,2052.139893,2040.369995,2048.719971,2048.719971,3390850000\n2014-11-20,2045.869995,2053.840088,2040.489990,2052.750000,2052.750000,3128290000\n2014-11-21,2057.459961,2071.459961,2056.750000,2063.500000,2063.500000,3916420000\n2014-11-24,2065.070068,2070.169922,2065.070068,2069.409912,2069.409912,3128060000\n2014-11-25,2070.149902,2074.209961,2064.750000,2067.030029,2067.030029,3392940000\n2014-11-26,2067.360107,2073.290039,2066.620117,2072.830078,2072.830078,2745260000\n2014-11-28,2074.780029,2075.760010,2065.060059,2067.560059,2067.560059,2504640000\n2014-12-01,2065.780029,2065.780029,2049.570068,2053.439941,2053.439941,4159010000\n2014-12-02,2053.770020,2068.770020,2053.770020,2066.550049,2066.550049,3686650000\n2014-12-03,2067.449951,2076.280029,2066.649902,2074.330078,2074.330078,3612680000\n2014-12-04,2073.639893,2077.340088,2062.340088,2071.919922,2071.919922,3408340000\n2014-12-05,2072.780029,2079.469971,2070.810059,2075.370117,2075.370117,3419620000\n2014-12-08,2074.840088,2075.780029,2054.270020,2060.310059,2060.310059,3800990000\n2014-12-09,2056.550049,2060.600098,2034.170044,2059.820068,2059.820068,3970150000\n2014-12-10,2058.860107,2058.860107,2024.260010,2026.140015,2026.140015,4114440000\n2014-12-11,2027.920044,2055.530029,2027.920044,2035.329956,2035.329956,3917950000\n2014-12-12,2030.359985,2032.250000,2002.329956,2002.329956,2002.329956,4157650000\n2014-12-15,2005.030029,2018.689941,1982.260010,1989.630005,1989.630005,4361990000\n2014-12-16,1986.709961,2016.890015,1972.560059,1972.739990,1972.739990,4958680000\n2014-12-17,1973.770020,2016.750000,1973.770020,2012.890015,2012.890015,4942370000\n2014-12-18,2018.979980,2061.229980,2018.979980,2061.229980,2061.229980,4703380000\n2014-12-19,2061.040039,2077.850098,2061.030029,2070.649902,2070.649902,6465530000\n2014-12-22,2069.280029,2078.760010,2069.280029,2078.540039,2078.540039,3369520000\n2014-12-23,2081.479980,2086.729980,2079.770020,2082.169922,2082.169922,3043950000\n2014-12-24,2083.250000,2087.560059,2081.860107,2081.879883,2081.879883,1416980000\n2014-12-26,2084.300049,2092.699951,2084.300049,2088.770020,2088.770020,1735230000\n2014-12-29,2087.629883,2093.550049,2085.750000,2090.570068,2090.570068,2452360000\n2014-12-30,2088.489990,2088.489990,2079.530029,2080.350098,2080.350098,2440280000\n2014-12-31,2082.110107,2085.580078,2057.939941,2058.899902,2058.899902,2606070000\n2015-01-02,2058.899902,2072.360107,2046.040039,2058.199951,2058.199951,2708700000\n2015-01-05,2054.439941,2054.439941,2017.339966,2020.579956,2020.579956,3799120000\n2015-01-06,2022.150024,2030.250000,1992.439941,2002.609985,2002.609985,4460110000\n2015-01-07,2005.550049,2029.609985,2005.550049,2025.900024,2025.900024,3805480000\n2015-01-08,2030.609985,2064.080078,2030.609985,2062.139893,2062.139893,3934010000\n2015-01-09,2063.449951,2064.429932,2038.329956,2044.810059,2044.810059,3364140000\n2015-01-12,2046.130005,2049.300049,2022.579956,2028.260010,2028.260010,3456460000\n2015-01-13,2031.579956,2056.929932,2008.250000,2023.030029,2023.030029,4107300000\n2015-01-14,2018.400024,2018.400024,1988.439941,2011.270020,2011.270020,4378680000\n2015-01-15,2013.750000,2021.349976,1991.469971,1992.670044,1992.670044,4276720000\n2015-01-16,1992.250000,2020.459961,1988.119995,2019.420044,2019.420044,4056410000\n2015-01-20,2020.760010,2028.939941,2004.489990,2022.550049,2022.550049,3944340000\n2015-01-21,2020.189941,2038.290039,2012.040039,2032.119995,2032.119995,3730070000\n2015-01-22,2034.300049,2064.620117,2026.380005,2063.149902,2063.149902,4176050000\n2015-01-23,2062.979980,2062.979980,2050.540039,2051.820068,2051.820068,3573560000\n2015-01-26,2050.419922,2057.620117,2040.969971,2057.090088,2057.090088,3465760000\n2015-01-27,2047.859985,2047.859985,2019.910034,2029.550049,2029.550049,3329810000\n2015-01-28,2032.339966,2042.489990,2001.489990,2002.160034,2002.160034,4067530000\n2015-01-29,2002.449951,2024.640015,1989.180054,2021.250000,2021.250000,4127140000\n2015-01-30,2019.349976,2023.319946,1993.380005,1994.989990,1994.989990,4568650000\n2015-02-02,1996.670044,2021.660034,1980.900024,2020.849976,2020.849976,4008330000\n2015-02-03,2022.709961,2050.300049,2022.709961,2050.030029,2050.030029,4615900000\n2015-02-04,2048.860107,2054.739990,2036.719971,2041.510010,2041.510010,4141920000\n2015-02-05,2043.449951,2063.550049,2043.449951,2062.520020,2062.520020,3821990000\n2015-02-06,2062.280029,2072.399902,2049.969971,2055.469971,2055.469971,4232970000\n2015-02-09,2053.469971,2056.159912,2041.880005,2046.739990,2046.739990,3549540000\n2015-02-10,2049.379883,2070.860107,2048.620117,2068.590088,2068.590088,3669850000\n2015-02-11,2068.550049,2073.479980,2057.989990,2068.530029,2068.530029,3596860000\n2015-02-12,2069.979980,2088.530029,2069.979980,2088.479980,2088.479980,3788350000\n2015-02-13,2088.780029,2097.030029,2086.699951,2096.989990,2096.989990,3527450000\n2015-02-17,2096.469971,2101.300049,2089.800049,2100.340088,2100.340088,3361750000\n2015-02-18,2099.159912,2100.229980,2092.149902,2099.679932,2099.679932,3370020000\n2015-02-19,2099.250000,2102.129883,2090.790039,2097.449951,2097.449951,3247100000\n2015-02-20,2097.649902,2110.610107,2085.439941,2110.300049,2110.300049,3281600000\n2015-02-23,2109.830078,2110.050049,2103.000000,2109.659912,2109.659912,3093680000\n2015-02-24,2109.100098,2117.939941,2105.870117,2115.479980,2115.479980,3199840000\n2015-02-25,2115.300049,2119.590088,2109.889893,2113.860107,2113.860107,3312340000\n2015-02-26,2113.909912,2113.909912,2103.760010,2110.739990,2110.739990,3408690000\n2015-02-27,2110.879883,2112.739990,2103.750000,2104.500000,2104.500000,3547380000\n2015-03-02,2105.229980,2117.520020,2104.500000,2117.389893,2117.389893,3409490000\n2015-03-03,2115.760010,2115.760010,2098.260010,2107.780029,2107.780029,3262300000\n2015-03-04,2107.719971,2107.719971,2094.489990,2098.530029,2098.530029,3421110000\n2015-03-05,2098.540039,2104.250000,2095.219971,2101.040039,2101.040039,3103030000\n2015-03-06,2100.909912,2100.909912,2067.270020,2071.260010,2071.260010,3853570000\n2015-03-09,2072.250000,2083.489990,2072.209961,2079.429932,2079.429932,3349090000\n2015-03-10,2076.139893,2076.139893,2044.160034,2044.160034,2044.160034,3668900000\n2015-03-11,2044.689941,2050.080078,2039.689941,2040.239990,2040.239990,3406570000\n2015-03-12,2041.099976,2066.409912,2041.099976,2065.949951,2065.949951,3405860000\n2015-03-13,2064.560059,2064.560059,2041.170044,2053.399902,2053.399902,3498560000\n2015-03-16,2055.350098,2081.409912,2055.350098,2081.189941,2081.189941,3295600000\n2015-03-17,2080.590088,2080.590088,2065.080078,2074.280029,2074.280029,3221840000\n2015-03-18,2072.840088,2106.850098,2061.229980,2099.500000,2099.500000,4128210000\n2015-03-19,2098.689941,2098.689941,2085.560059,2089.270020,2089.270020,3305220000\n2015-03-20,2090.320068,2113.919922,2090.320068,2108.100098,2108.100098,5554120000\n2015-03-23,2107.989990,2114.860107,2104.419922,2104.419922,2104.419922,3267960000\n2015-03-24,2103.939941,2107.629883,2091.500000,2091.500000,2091.500000,3189820000\n2015-03-25,2093.100098,2097.429932,2061.050049,2061.050049,2061.050049,3521140000\n2015-03-26,2059.939941,2067.149902,2045.500000,2056.149902,2056.149902,3510670000\n2015-03-27,2055.780029,2062.830078,2052.959961,2061.020020,2061.020020,3008550000\n2015-03-30,2064.110107,2088.969971,2064.110107,2086.239990,2086.239990,2917690000\n2015-03-31,2084.050049,2084.050049,2067.040039,2067.889893,2067.889893,3376550000\n2015-04-01,2067.629883,2067.629883,2048.379883,2059.689941,2059.689941,3543270000\n2015-04-02,2060.030029,2072.169922,2057.320068,2066.959961,2066.959961,3095960000\n2015-04-06,2064.870117,2086.989990,2056.520020,2080.620117,2080.620117,3302970000\n2015-04-07,2080.790039,2089.810059,2076.100098,2076.330078,2076.330078,3065510000\n2015-04-08,2076.939941,2086.689941,2073.300049,2081.899902,2081.899902,3265330000\n2015-04-09,2081.290039,2093.310059,2074.290039,2091.179932,2091.179932,3172360000\n2015-04-10,2091.510010,2102.610107,2091.510010,2102.060059,2102.060059,3156200000\n2015-04-13,2102.030029,2107.649902,2092.330078,2092.429932,2092.429932,2908420000\n2015-04-14,2092.280029,2098.620117,2083.239990,2095.840088,2095.840088,3301270000\n2015-04-15,2097.820068,2111.909912,2097.820068,2106.629883,2106.629883,4013760000\n2015-04-16,2105.959961,2111.300049,2100.020020,2104.989990,2104.989990,3434120000\n2015-04-17,2102.580078,2102.580078,2072.370117,2081.179932,2081.179932,3627600000\n2015-04-20,2084.110107,2103.939941,2084.110107,2100.399902,2100.399902,3000160000\n2015-04-21,2102.820068,2109.639893,2094.379883,2097.290039,2097.290039,3243410000\n2015-04-22,2098.270020,2109.979980,2091.050049,2107.959961,2107.959961,3348480000\n2015-04-23,2107.209961,2120.489990,2103.189941,2112.929932,2112.929932,3636670000\n2015-04-24,2112.800049,2120.919922,2112.800049,2117.689941,2117.689941,3375780000\n2015-04-27,2119.290039,2125.919922,2107.040039,2108.919922,2108.919922,3438750000\n2015-04-28,2108.350098,2116.040039,2094.889893,2114.760010,2114.760010,3546270000\n2015-04-29,2112.489990,2113.649902,2097.409912,2106.850098,2106.850098,4074970000\n2015-04-30,2105.520020,2105.520020,2077.590088,2085.510010,2085.510010,4509680000\n2015-05-01,2087.379883,2108.409912,2087.379883,2108.290039,2108.290039,3379390000\n2015-05-04,2110.229980,2120.949951,2110.229980,2114.489990,2114.489990,3091580000\n2015-05-05,2112.629883,2115.239990,2088.459961,2089.459961,2089.459961,3793950000\n2015-05-06,2091.260010,2098.419922,2067.929932,2080.149902,2080.149902,3792210000\n2015-05-07,2079.959961,2092.899902,2074.989990,2088.000000,2088.000000,3676640000\n2015-05-08,2092.129883,2117.659912,2092.129883,2116.100098,2116.100098,3399440000\n2015-05-11,2115.560059,2117.689941,2104.580078,2105.330078,2105.330078,2992670000\n2015-05-12,2102.870117,2105.060059,2085.570068,2099.120117,2099.120117,3139520000\n2015-05-13,2099.620117,2110.189941,2096.040039,2098.479980,2098.479980,3374260000\n2015-05-14,2100.429932,2121.449951,2100.429932,2121.100098,2121.100098,3225740000\n2015-05-15,2122.070068,2123.889893,2116.810059,2122.729980,2122.729980,3092080000\n2015-05-18,2121.300049,2131.780029,2120.010010,2129.199951,2129.199951,2888190000\n2015-05-19,2129.449951,2133.020020,2124.500000,2127.830078,2127.830078,3296030000\n2015-05-20,2127.790039,2134.719971,2122.590088,2125.850098,2125.850098,3025880000\n2015-05-21,2125.550049,2134.280029,2122.949951,2130.820068,2130.820068,3070460000\n2015-05-22,2130.360107,2132.149902,2126.060059,2126.060059,2126.060059,2571860000\n2015-05-26,2125.340088,2125.340088,2099.179932,2104.199951,2104.199951,3342130000\n2015-05-27,2105.129883,2126.219971,2105.129883,2123.479980,2123.479980,3127960000\n2015-05-28,2122.270020,2122.270020,2112.860107,2120.790039,2120.790039,2980350000\n2015-05-29,2120.659912,2120.659912,2104.889893,2107.389893,2107.389893,3927390000\n2015-06-01,2108.639893,2119.149902,2102.540039,2111.729980,2111.729980,3011710000\n2015-06-02,2110.409912,2117.590088,2099.139893,2109.600098,2109.600098,3049350000\n2015-06-03,2110.639893,2121.919922,2109.610107,2114.070068,2114.070068,3099980000\n2015-06-04,2112.350098,2112.889893,2093.229980,2095.840088,2095.840088,3200050000\n2015-06-05,2095.090088,2100.989990,2085.669922,2092.830078,2092.830078,3243690000\n2015-06-08,2092.340088,2093.010010,2079.110107,2079.280029,2079.280029,2917150000\n2015-06-09,2079.070068,2085.620117,2072.139893,2080.149902,2080.149902,3034580000\n2015-06-10,2081.120117,2108.500000,2081.120117,2105.199951,2105.199951,3414320000\n2015-06-11,2106.239990,2115.020020,2106.239990,2108.860107,2108.860107,3128600000\n2015-06-12,2107.429932,2107.429932,2091.330078,2094.110107,2094.110107,2719400000\n2015-06-15,2091.340088,2091.340088,2072.489990,2084.429932,2084.429932,3061570000\n2015-06-16,2084.260010,2097.399902,2082.100098,2096.290039,2096.290039,2919900000\n2015-06-17,2097.399902,2106.790039,2088.860107,2100.439941,2100.439941,3222240000\n2015-06-18,2101.580078,2126.649902,2101.580078,2121.239990,2121.239990,3520360000\n2015-06-19,2121.060059,2121.639893,2109.449951,2109.989990,2109.989990,4449810000\n2015-06-22,2112.500000,2129.870117,2112.500000,2122.850098,2122.850098,3030020000\n2015-06-23,2123.159912,2128.030029,2119.889893,2124.199951,2124.199951,3091190000\n2015-06-24,2123.649902,2125.100098,2108.580078,2108.580078,2108.580078,3102480000\n2015-06-25,2109.959961,2116.040039,2101.780029,2102.310059,2102.310059,3214610000\n2015-06-26,2102.620117,2108.919922,2095.379883,2101.489990,2101.489990,5025470000\n2015-06-29,2098.629883,2098.629883,2056.639893,2057.639893,2057.639893,3678960000\n2015-06-30,2061.189941,2074.280029,2056.320068,2063.110107,2063.110107,4078540000\n2015-07-01,2067.000000,2082.780029,2067.000000,2077.419922,2077.419922,3727260000\n2015-07-02,2078.030029,2085.060059,2071.020020,2076.780029,2076.780029,2996540000\n2015-07-06,2073.949951,2078.610107,2058.399902,2068.760010,2068.760010,3486360000\n2015-07-07,2069.520020,2083.739990,2044.020020,2081.340088,2081.340088,4458660000\n2015-07-08,2077.659912,2077.659912,2044.660034,2046.680054,2046.680054,3608780000\n2015-07-09,2049.729980,2074.280029,2049.729980,2051.310059,2051.310059,3446810000\n2015-07-10,2052.739990,2081.310059,2052.739990,2076.620117,2076.620117,3065070000\n2015-07-13,2080.030029,2100.669922,2080.030029,2099.600098,2099.600098,3096730000\n2015-07-14,2099.719971,2111.979980,2098.179932,2108.949951,2108.949951,3002120000\n2015-07-15,2109.010010,2114.139893,2102.489990,2107.399902,2107.399902,3261810000\n2015-07-16,2110.550049,2124.419922,2110.550049,2124.290039,2124.290039,3227080000\n2015-07-17,2126.800049,2128.909912,2119.879883,2126.639893,2126.639893,3362750000\n2015-07-20,2126.850098,2132.820068,2123.659912,2128.280029,2128.280029,3245870000\n2015-07-21,2127.550049,2128.489990,2115.399902,2119.209961,2119.209961,3343690000\n2015-07-22,2118.209961,2118.510010,2110.000000,2114.149902,2114.149902,3694070000\n2015-07-23,2114.159912,2116.870117,2098.629883,2102.149902,2102.149902,3772810000\n2015-07-24,2102.239990,2106.010010,2077.090088,2079.649902,2079.649902,3870040000\n2015-07-27,2078.189941,2078.189941,2063.520020,2067.639893,2067.639893,3836750000\n2015-07-28,2070.750000,2095.600098,2069.090088,2093.250000,2093.250000,4117740000\n2015-07-29,2094.699951,2110.600098,2094.080078,2108.570068,2108.570068,4038900000\n2015-07-30,2106.780029,2110.479980,2094.969971,2108.629883,2108.629883,3579410000\n2015-07-31,2111.600098,2114.239990,2102.070068,2103.840088,2103.840088,3681340000\n2015-08-03,2104.489990,2105.699951,2087.310059,2098.040039,2098.040039,3476770000\n2015-08-04,2097.679932,2102.510010,2088.600098,2093.320068,2093.320068,3546710000\n2015-08-05,2095.270020,2112.659912,2095.270020,2099.840088,2099.840088,3968680000\n2015-08-06,2100.750000,2103.320068,2075.530029,2083.560059,2083.560059,4246570000\n2015-08-07,2082.610107,2082.610107,2067.909912,2077.570068,2077.570068,3602320000\n2015-08-10,2080.979980,2105.350098,2080.979980,2104.179932,2104.179932,3514460000\n2015-08-11,2102.659912,2102.659912,2076.489990,2084.070068,2084.070068,3708880000\n2015-08-12,2081.100098,2089.060059,2052.090088,2086.050049,2086.050049,4269130000\n2015-08-13,2086.189941,2092.929932,2078.260010,2083.389893,2083.389893,3221300000\n2015-08-14,2083.149902,2092.449951,2080.610107,2091.540039,2091.540039,2795590000\n2015-08-17,2089.699951,2102.870117,2079.300049,2102.439941,2102.439941,2867690000\n2015-08-18,2101.989990,2103.469971,2094.139893,2096.919922,2096.919922,2949990000\n2015-08-19,2095.689941,2096.169922,2070.530029,2079.610107,2079.610107,3512920000\n2015-08-20,2076.610107,2076.610107,2035.729980,2035.729980,2035.729980,3922470000\n2015-08-21,2034.079956,2034.079956,1970.890015,1970.890015,1970.890015,5018240000\n2015-08-24,1965.150024,1965.150024,1867.010010,1893.209961,1893.209961,6612690000\n2015-08-25,1898.079956,1948.040039,1867.079956,1867.609985,1867.609985,5183560000\n2015-08-26,1872.750000,1943.089966,1872.750000,1940.510010,1940.510010,5338250000\n2015-08-27,1942.770020,1989.599976,1942.770020,1987.660034,1987.660034,5006390000\n2015-08-28,1986.060059,1993.479980,1975.189941,1988.869995,1988.869995,3949080000\n2015-08-31,1986.729980,1986.729980,1965.979980,1972.180054,1972.180054,3915100000\n2015-09-01,1970.089966,1970.089966,1903.069946,1913.849976,1913.849976,4371850000\n2015-09-02,1916.520020,1948.910034,1916.520020,1948.859985,1948.859985,3742620000\n2015-09-03,1950.790039,1975.010010,1944.719971,1951.130005,1951.130005,3520700000\n2015-09-04,1947.760010,1947.760010,1911.209961,1921.219971,1921.219971,3167090000\n2015-09-08,1927.300049,1970.420044,1927.300049,1969.410034,1969.410034,3548650000\n2015-09-09,1971.449951,1988.630005,1937.880005,1942.040039,1942.040039,3652120000\n2015-09-10,1941.589966,1965.290039,1937.189941,1952.290039,1952.290039,3626320000\n2015-09-11,1951.449951,1961.050049,1939.189941,1961.050049,1961.050049,3218590000\n2015-09-14,1963.060059,1963.060059,1948.270020,1953.030029,1953.030029,3000200000\n2015-09-15,1955.099976,1983.189941,1954.300049,1978.089966,1978.089966,3239860000\n2015-09-16,1978.020020,1997.260010,1977.930054,1995.310059,1995.310059,3630680000\n2015-09-17,1995.329956,2020.859985,1986.729980,1990.199951,1990.199951,4183790000\n2015-09-18,1989.660034,1989.660034,1953.449951,1958.030029,1958.030029,6021240000\n2015-09-21,1960.839966,1979.640015,1955.800049,1966.969971,1966.969971,3269350000\n2015-09-22,1961.390015,1961.390015,1929.219971,1942.739990,1942.739990,3808260000\n2015-09-23,1943.239990,1949.520020,1932.569946,1938.760010,1938.760010,3190530000\n2015-09-24,1934.810059,1937.170044,1908.920044,1932.239990,1932.239990,4091530000\n2015-09-25,1935.930054,1952.890015,1921.500000,1931.339966,1931.339966,3721870000\n2015-09-28,1929.180054,1929.180054,1879.209961,1881.770020,1881.770020,4326660000\n2015-09-29,1881.900024,1899.479980,1871.910034,1884.089966,1884.089966,4132390000\n2015-09-30,1887.140015,1920.530029,1887.140015,1920.030029,1920.030029,4525070000\n2015-10-01,1919.650024,1927.209961,1900.699951,1923.819946,1923.819946,3983600000\n2015-10-02,1921.770020,1951.359985,1893.699951,1951.359985,1951.359985,4378570000\n2015-10-05,1954.329956,1989.170044,1954.329956,1987.050049,1987.050049,4334490000\n2015-10-06,1986.630005,1991.619995,1971.989990,1979.920044,1979.920044,4202400000\n2015-10-07,1982.339966,1999.310059,1976.439941,1995.829956,1995.829956,4666470000\n2015-10-08,1994.010010,2016.500000,1987.530029,2013.430054,2013.430054,3939140000\n2015-10-09,2013.729980,2020.130005,2007.609985,2014.890015,2014.890015,3706900000\n2015-10-12,2015.650024,2018.660034,2010.550049,2017.459961,2017.459961,2893250000\n2015-10-13,2015.000000,2022.339966,2001.780029,2003.689941,2003.689941,3401920000\n2015-10-14,2003.660034,2009.560059,1990.729980,1994.239990,1994.239990,3644590000\n2015-10-15,1996.469971,2024.150024,1996.469971,2023.859985,2023.859985,3746290000\n2015-10-16,2024.369995,2033.540039,2020.459961,2033.109985,2033.109985,3595430000\n2015-10-19,2031.729980,2034.449951,2022.310059,2033.660034,2033.660034,3287320000\n2015-10-20,2033.130005,2039.119995,2026.609985,2030.770020,2030.770020,3331500000\n2015-10-21,2033.469971,2037.969971,2017.219971,2018.939941,2018.939941,3627790000\n2015-10-22,2021.880005,2055.199951,2021.880005,2052.510010,2052.510010,4430850000\n2015-10-23,2058.189941,2079.739990,2058.189941,2075.149902,2075.149902,4108460000\n2015-10-26,2075.080078,2075.139893,2066.530029,2071.179932,2071.179932,3385800000\n2015-10-27,2068.750000,2070.370117,2058.840088,2065.889893,2065.889893,4216880000\n2015-10-28,2066.479980,2090.350098,2063.110107,2090.350098,2090.350098,4698110000\n2015-10-29,2088.350098,2092.520020,2082.629883,2089.409912,2089.409912,4008940000\n2015-10-30,2090.000000,2094.320068,2079.340088,2079.360107,2079.360107,4256200000\n2015-11-02,2080.760010,2106.199951,2080.760010,2104.050049,2104.050049,3760020000\n2015-11-03,2102.629883,2116.479980,2097.510010,2109.790039,2109.790039,4272060000\n2015-11-04,2110.600098,2114.590088,2096.979980,2102.310059,2102.310059,4078870000\n2015-11-05,2101.679932,2108.780029,2090.409912,2099.929932,2099.929932,4051890000\n2015-11-06,2098.600098,2101.909912,2083.739990,2099.199951,2099.199951,4369020000\n2015-11-09,2096.560059,2096.560059,2068.239990,2078.580078,2078.580078,3882350000\n2015-11-10,2077.189941,2083.669922,2069.909912,2081.719971,2081.719971,3821440000\n2015-11-11,2083.409912,2086.939941,2074.850098,2075.000000,2075.000000,3692410000\n2015-11-12,2072.290039,2072.290039,2045.660034,2045.969971,2045.969971,4016370000\n2015-11-13,2044.640015,2044.640015,2022.020020,2023.040039,2023.040039,4278750000\n2015-11-16,2022.079956,2053.219971,2019.390015,2053.189941,2053.189941,3741240000\n2015-11-17,2053.669922,2066.689941,2045.900024,2050.439941,2050.439941,4427350000\n2015-11-18,2051.989990,2085.310059,2051.989990,2083.580078,2083.580078,3926390000\n2015-11-19,2083.699951,2086.739990,2078.760010,2081.239990,2081.239990,3628110000\n2015-11-20,2082.820068,2097.060059,2082.820068,2089.169922,2089.169922,3929600000\n2015-11-23,2089.409912,2095.610107,2081.389893,2086.590088,2086.590088,3587980000\n2015-11-24,2084.419922,2094.120117,2070.290039,2089.139893,2089.139893,3884930000\n2015-11-25,2089.300049,2093.000000,2086.300049,2088.870117,2088.870117,2852940000\n2015-11-27,2088.820068,2093.290039,2084.129883,2090.110107,2090.110107,1466840000\n2015-11-30,2090.949951,2093.810059,2080.409912,2080.409912,2080.409912,4275030000\n2015-12-01,2082.929932,2103.370117,2082.929932,2102.629883,2102.629883,3712120000\n2015-12-02,2101.709961,2104.270020,2077.110107,2079.510010,2079.510010,3950640000\n2015-12-03,2080.709961,2085.000000,2042.349976,2049.620117,2049.620117,4306490000\n2015-12-04,2051.239990,2093.840088,2051.239990,2091.689941,2091.689941,4214910000\n2015-12-07,2090.419922,2090.419922,2066.780029,2077.070068,2077.070068,4043820000\n2015-12-08,2073.389893,2073.850098,2052.320068,2063.590088,2063.590088,4173570000\n2015-12-09,2061.169922,2080.330078,2036.530029,2047.619995,2047.619995,4385250000\n2015-12-10,2047.930054,2067.649902,2045.670044,2052.229980,2052.229980,3715150000\n2015-12-11,2047.270020,2047.270020,2008.800049,2012.369995,2012.369995,4301060000\n2015-12-14,2013.369995,2022.920044,1993.260010,2021.939941,2021.939941,4612440000\n2015-12-15,2025.550049,2053.870117,2025.550049,2043.410034,2043.410034,4353540000\n2015-12-16,2046.500000,2076.719971,2042.430054,2073.070068,2073.070068,4635450000\n2015-12-17,2073.760010,2076.370117,2041.660034,2041.890015,2041.890015,4327390000\n2015-12-18,2040.810059,2040.810059,2005.329956,2005.550049,2005.550049,6683070000\n2015-12-21,2010.270020,2022.900024,2005.930054,2021.150024,2021.150024,3760280000\n2015-12-22,2023.150024,2042.739990,2020.489990,2038.969971,2038.969971,3520860000\n2015-12-23,2042.199951,2064.729980,2042.199951,2064.290039,2064.290039,3484090000\n2015-12-24,2063.520020,2067.360107,2058.729980,2060.989990,2060.989990,1411860000\n2015-12-28,2057.770020,2057.770020,2044.199951,2056.500000,2056.500000,2492510000\n2015-12-29,2060.540039,2081.560059,2060.540039,2078.360107,2078.360107,2542000000\n2015-12-30,2077.340088,2077.340088,2061.969971,2063.360107,2063.360107,2367430000\n2015-12-31,2060.590088,2062.540039,2043.619995,2043.939941,2043.939941,2655330000\n2016-01-04,2038.199951,2038.199951,1989.680054,2012.660034,2012.660034,4304880000\n2016-01-05,2013.780029,2021.939941,2004.170044,2016.709961,2016.709961,3706620000\n2016-01-06,2011.709961,2011.709961,1979.050049,1990.260010,1990.260010,4336660000\n2016-01-07,1985.319946,1985.319946,1938.829956,1943.089966,1943.089966,5076590000\n2016-01-08,1945.969971,1960.400024,1918.459961,1922.030029,1922.030029,4664940000\n2016-01-11,1926.119995,1935.650024,1901.099976,1923.670044,1923.670044,4607290000\n2016-01-12,1927.829956,1947.380005,1914.349976,1938.680054,1938.680054,4887260000\n2016-01-13,1940.339966,1950.329956,1886.410034,1890.280029,1890.280029,5087030000\n2016-01-14,1891.680054,1934.469971,1878.930054,1921.839966,1921.839966,5241110000\n2016-01-15,1916.680054,1916.680054,1857.829956,1880.329956,1880.329956,5468460000\n2016-01-19,1888.660034,1901.439941,1864.599976,1881.329956,1881.329956,4928350000\n2016-01-20,1876.180054,1876.180054,1812.290039,1859.329956,1859.329956,6416070000\n2016-01-21,1861.459961,1889.849976,1848.979980,1868.989990,1868.989990,5078810000\n2016-01-22,1877.400024,1908.849976,1877.400024,1906.900024,1906.900024,4901760000\n2016-01-25,1906.280029,1906.280029,1875.969971,1877.079956,1877.079956,4401380000\n2016-01-26,1878.790039,1906.729980,1878.790039,1903.630005,1903.630005,4357940000\n2016-01-27,1902.520020,1916.989990,1872.699951,1882.949951,1882.949951,4754040000\n2016-01-28,1885.219971,1902.959961,1873.650024,1893.359985,1893.359985,4693010000\n2016-01-29,1894.000000,1940.239990,1894.000000,1940.239990,1940.239990,5497570000\n2016-02-01,1936.939941,1947.199951,1920.300049,1939.380005,1939.380005,4322530000\n2016-02-02,1935.260010,1935.260010,1897.290039,1903.030029,1903.030029,4463190000\n2016-02-03,1907.069946,1918.010010,1872.229980,1912.530029,1912.530029,5172950000\n2016-02-04,1911.670044,1927.349976,1900.520020,1915.449951,1915.449951,5193320000\n2016-02-05,1913.069946,1913.069946,1872.650024,1880.050049,1880.050049,4929940000\n2016-02-08,1873.250000,1873.250000,1828.459961,1853.439941,1853.439941,5636460000\n2016-02-09,1848.459961,1868.250000,1834.939941,1852.209961,1852.209961,5183220000\n2016-02-10,1857.099976,1881.599976,1850.319946,1851.859985,1851.859985,4471170000\n2016-02-11,1847.000000,1847.000000,1810.099976,1829.079956,1829.079956,5500800000\n2016-02-12,1833.400024,1864.780029,1833.400024,1864.780029,1864.780029,4696920000\n2016-02-16,1871.439941,1895.770020,1871.439941,1895.579956,1895.579956,4570670000\n2016-02-17,1898.800049,1930.680054,1898.800049,1926.819946,1926.819946,5011540000\n2016-02-18,1927.569946,1930.000000,1915.089966,1917.829956,1917.829956,4436490000\n2016-02-19,1916.739990,1918.780029,1902.170044,1917.780029,1917.780029,4142850000\n2016-02-22,1924.439941,1946.699951,1924.439941,1945.500000,1945.500000,4054710000\n2016-02-23,1942.380005,1942.380005,1919.439941,1921.270020,1921.270020,3890650000\n2016-02-24,1917.560059,1932.079956,1891.000000,1929.800049,1929.800049,4317250000\n2016-02-25,1931.869995,1951.829956,1925.410034,1951.699951,1951.699951,4118210000\n2016-02-26,1954.949951,1962.959961,1945.780029,1948.050049,1948.050049,4348510000\n2016-02-29,1947.130005,1958.270020,1931.810059,1932.229980,1932.229980,4588180000\n2016-03-01,1937.089966,1978.349976,1937.089966,1978.349976,1978.349976,4819750000\n2016-03-02,1976.599976,1986.510010,1968.800049,1986.449951,1986.449951,4666610000\n2016-03-03,1985.599976,1993.689941,1977.369995,1993.400024,1993.400024,5081700000\n2016-03-04,1994.010010,2009.130005,1986.770020,1999.989990,1999.989990,6049930000\n2016-03-07,1996.109985,2006.119995,1989.380005,2001.760010,2001.760010,4968180000\n2016-03-08,1996.880005,1996.880005,1977.430054,1979.260010,1979.260010,4641650000\n2016-03-09,1981.439941,1992.689941,1979.839966,1989.260010,1989.260010,4038120000\n2016-03-10,1990.969971,2005.079956,1969.250000,1989.569946,1989.569946,4376790000\n2016-03-11,1994.709961,2022.369995,1994.709961,2022.189941,2022.189941,4078620000\n2016-03-14,2019.270020,2024.569946,2012.050049,2019.640015,2019.640015,3487850000\n2016-03-15,2015.270020,2015.939941,2005.229980,2015.930054,2015.930054,3560280000\n2016-03-16,2014.239990,2032.020020,2010.040039,2027.219971,2027.219971,4057020000\n2016-03-17,2026.900024,2046.239990,2022.160034,2040.589966,2040.589966,4530480000\n2016-03-18,2041.160034,2052.360107,2041.160034,2049.580078,2049.580078,6503140000\n2016-03-21,2047.880005,2053.909912,2043.140015,2051.600098,2051.600098,3376600000\n2016-03-22,2048.639893,2056.600098,2040.569946,2049.800049,2049.800049,3418460000\n2016-03-23,2048.550049,2048.550049,2034.859985,2036.709961,2036.709961,3639510000\n2016-03-24,2032.479980,2036.040039,2022.489990,2035.939941,2035.939941,3407720000\n2016-03-28,2037.890015,2042.670044,2031.959961,2037.050049,2037.050049,2809090000\n2016-03-29,2035.750000,2055.909912,2028.310059,2055.010010,2055.010010,3822330000\n2016-03-30,2058.270020,2072.209961,2058.270020,2063.949951,2063.949951,3590310000\n2016-03-31,2063.770020,2067.919922,2057.459961,2059.739990,2059.739990,3715280000\n2016-04-01,2056.620117,2075.070068,2043.979980,2072.780029,2072.780029,3749990000\n2016-04-04,2073.189941,2074.020020,2062.570068,2066.129883,2066.129883,3485710000\n2016-04-05,2062.500000,2062.500000,2042.560059,2045.170044,2045.170044,4154920000\n2016-04-06,2045.560059,2067.330078,2043.089966,2066.659912,2066.659912,3750800000\n2016-04-07,2063.010010,2063.010010,2033.800049,2041.910034,2041.910034,3801250000\n2016-04-08,2045.540039,2060.629883,2041.689941,2047.599976,2047.599976,3359530000\n2016-04-11,2050.229980,2062.929932,2041.880005,2041.989990,2041.989990,3567840000\n2016-04-12,2043.719971,2065.050049,2039.739990,2061.719971,2061.719971,4239740000\n2016-04-13,2065.919922,2083.179932,2065.919922,2082.419922,2082.419922,4191830000\n2016-04-14,2082.889893,2087.840088,2078.129883,2082.780029,2082.780029,3765870000\n2016-04-15,2083.100098,2083.219971,2076.310059,2080.729980,2080.729980,3701450000\n2016-04-18,2078.830078,2094.659912,2073.649902,2094.340088,2094.340088,3316880000\n2016-04-19,2096.050049,2104.050049,2091.679932,2100.800049,2100.800049,3896830000\n2016-04-20,2101.520020,2111.050049,2096.320068,2102.399902,2102.399902,4184880000\n2016-04-21,2102.090088,2103.780029,2088.520020,2091.479980,2091.479980,4175290000\n2016-04-22,2091.489990,2094.320068,2081.199951,2091.580078,2091.580078,3790580000\n2016-04-25,2089.370117,2089.370117,2077.520020,2087.790039,2087.790039,3319740000\n2016-04-26,2089.840088,2096.870117,2085.800049,2091.699951,2091.699951,3557190000\n2016-04-27,2092.330078,2099.889893,2082.310059,2095.149902,2095.149902,4100110000\n2016-04-28,2090.929932,2099.300049,2071.620117,2075.810059,2075.810059,4309840000\n2016-04-29,2071.820068,2073.850098,2052.280029,2065.300049,2065.300049,4704720000\n2016-05-02,2067.169922,2083.419922,2066.110107,2081.429932,2081.429932,3841110000\n2016-05-03,2077.179932,2077.179932,2054.889893,2063.370117,2063.370117,4173390000\n2016-05-04,2060.300049,2060.300049,2045.550049,2051.120117,2051.120117,4058560000\n2016-05-05,2052.949951,2060.229980,2045.770020,2050.629883,2050.629883,4008530000\n2016-05-06,2047.770020,2057.719971,2039.449951,2057.139893,2057.139893,3796350000\n2016-05-09,2057.550049,2064.149902,2054.310059,2058.689941,2058.689941,3788620000\n2016-05-10,2062.629883,2084.870117,2062.629883,2084.389893,2084.389893,3600200000\n2016-05-11,2083.290039,2083.290039,2064.459961,2064.459961,2064.459961,3821980000\n2016-05-12,2067.169922,2073.989990,2053.129883,2064.110107,2064.110107,3782390000\n2016-05-13,2062.500000,2066.790039,2043.130005,2046.609985,2046.609985,3579880000\n2016-05-16,2046.530029,2071.879883,2046.530029,2066.659912,2066.659912,3501360000\n2016-05-17,2065.040039,2065.689941,2040.819946,2047.209961,2047.209961,4108960000\n2016-05-18,2044.380005,2060.610107,2034.489990,2047.630005,2047.630005,4101320000\n2016-05-19,2044.209961,2044.209961,2025.910034,2040.040039,2040.040039,3846770000\n2016-05-20,2041.880005,2058.350098,2041.880005,2052.320068,2052.320068,3507650000\n2016-05-23,2052.229980,2055.580078,2047.260010,2048.040039,2048.040039,3055480000\n2016-05-24,2052.649902,2079.669922,2052.649902,2076.060059,2076.060059,3627340000\n2016-05-25,2078.929932,2094.729980,2078.929932,2090.540039,2090.540039,3859160000\n2016-05-26,2091.439941,2094.300049,2087.080078,2090.100098,2090.100098,3230990000\n2016-05-27,2090.060059,2099.060059,2090.060059,2099.060059,2099.060059,3079150000\n2016-05-31,2100.129883,2103.479980,2088.659912,2096.949951,2096.949951,4514410000\n2016-06-01,2093.939941,2100.969971,2085.100098,2099.330078,2099.330078,3525170000\n2016-06-02,2097.709961,2105.260010,2088.590088,2105.260010,2105.260010,3632720000\n2016-06-03,2104.070068,2104.070068,2085.360107,2099.129883,2099.129883,3627780000\n2016-06-06,2100.830078,2113.360107,2100.830078,2109.409912,2109.409912,3442020000\n2016-06-07,2110.179932,2119.219971,2110.179932,2112.129883,2112.129883,3534730000\n2016-06-08,2112.709961,2120.550049,2112.709961,2119.120117,2119.120117,3562060000\n2016-06-09,2115.649902,2117.639893,2107.729980,2115.479980,2115.479980,3290320000\n2016-06-10,2109.570068,2109.570068,2089.959961,2096.070068,2096.070068,3515010000\n2016-06-13,2091.750000,2098.120117,2078.459961,2079.060059,2079.060059,3392030000\n2016-06-14,2076.649902,2081.300049,2064.100098,2075.320068,2075.320068,3759770000\n2016-06-15,2077.600098,2085.649902,2069.800049,2071.500000,2071.500000,3544720000\n2016-06-16,2066.360107,2079.620117,2050.370117,2077.989990,2077.989990,3628280000\n2016-06-17,2078.199951,2078.199951,2062.840088,2071.219971,2071.219971,4952630000\n2016-06-20,2075.580078,2100.659912,2075.580078,2083.250000,2083.250000,3467440000\n2016-06-21,2085.189941,2093.659912,2083.020020,2088.899902,2088.899902,3232880000\n2016-06-22,2089.750000,2099.709961,2084.360107,2085.449951,2085.449951,3168160000\n2016-06-23,2092.800049,2113.320068,2092.800049,2113.320068,2113.320068,3297940000\n2016-06-24,2103.810059,2103.810059,2032.569946,2037.410034,2037.410034,7597450000\n2016-06-27,2031.449951,2031.449951,1991.680054,2000.540039,2000.540039,5431220000\n2016-06-28,2006.670044,2036.089966,2006.670044,2036.089966,2036.089966,4385810000\n2016-06-29,2042.689941,2073.129883,2042.689941,2070.770020,2070.770020,4241740000\n2016-06-30,2073.169922,2098.939941,2070.000000,2098.860107,2098.860107,4622820000\n2016-07-01,2099.340088,2108.709961,2097.899902,2102.949951,2102.949951,3458890000\n2016-07-05,2095.050049,2095.050049,2080.860107,2088.550049,2088.550049,3658380000\n2016-07-06,2084.429932,2100.719971,2074.020020,2099.729980,2099.729980,3909380000\n2016-07-07,2100.419922,2109.080078,2089.389893,2097.899902,2097.899902,3604550000\n2016-07-08,2106.969971,2131.709961,2106.969971,2129.899902,2129.899902,3607500000\n2016-07-11,2131.719971,2143.159912,2131.719971,2137.159912,2137.159912,3253340000\n2016-07-12,2139.500000,2155.399902,2139.500000,2152.139893,2152.139893,4097820000\n2016-07-13,2153.810059,2156.449951,2146.209961,2152.429932,2152.429932,3502320000\n2016-07-14,2157.879883,2168.989990,2157.879883,2163.750000,2163.750000,3465610000\n2016-07-15,2165.129883,2169.050049,2155.790039,2161.739990,2161.739990,3122600000\n2016-07-18,2162.040039,2168.350098,2159.629883,2166.889893,2166.889893,3009310000\n2016-07-19,2163.790039,2164.629883,2159.010010,2163.780029,2163.780029,2968340000\n2016-07-20,2166.100098,2175.629883,2164.889893,2173.020020,2173.020020,3211860000\n2016-07-21,2172.909912,2174.560059,2159.750000,2165.169922,2165.169922,3438900000\n2016-07-22,2166.469971,2175.110107,2163.239990,2175.030029,2175.030029,3023280000\n2016-07-25,2173.709961,2173.709961,2161.949951,2168.479980,2168.479980,3057240000\n2016-07-26,2168.969971,2173.540039,2160.179932,2169.179932,2169.179932,3442350000\n2016-07-27,2169.810059,2174.979980,2159.070068,2166.580078,2166.580078,3995500000\n2016-07-28,2166.050049,2172.850098,2159.739990,2170.060059,2170.060059,3664240000\n2016-07-29,2168.830078,2177.090088,2163.489990,2173.600098,2173.600098,4038840000\n2016-08-01,2173.149902,2178.290039,2166.209961,2170.840088,2170.840088,3505990000\n2016-08-02,2169.939941,2170.199951,2147.580078,2157.030029,2157.030029,3848750000\n2016-08-03,2156.810059,2163.790039,2152.560059,2163.790039,2163.790039,3786530000\n2016-08-04,2163.510010,2168.189941,2159.070068,2164.250000,2164.250000,3709200000\n2016-08-05,2168.790039,2182.870117,2168.790039,2182.870117,2182.870117,3663070000\n2016-08-08,2183.760010,2185.439941,2177.850098,2180.889893,2180.889893,3327550000\n2016-08-09,2182.239990,2187.659912,2178.610107,2181.739990,2181.739990,3334300000\n2016-08-10,2182.810059,2183.409912,2172.000000,2175.489990,2175.489990,3254950000\n2016-08-11,2177.969971,2188.449951,2177.969971,2185.790039,2185.790039,3423160000\n2016-08-12,2183.739990,2186.280029,2179.419922,2184.050049,2184.050049,3000660000\n2016-08-15,2186.080078,2193.810059,2186.080078,2190.149902,2190.149902,3078530000\n2016-08-16,2186.239990,2186.239990,2178.139893,2178.149902,2178.149902,3196400000\n2016-08-17,2177.840088,2183.080078,2168.500000,2182.219971,2182.219971,3388910000\n2016-08-18,2181.899902,2187.030029,2180.459961,2187.020020,2187.020020,3300570000\n2016-08-19,2184.239990,2185.000000,2175.129883,2183.870117,2183.870117,3084800000\n2016-08-22,2181.580078,2185.149902,2175.959961,2182.639893,2182.639893,2777550000\n2016-08-23,2187.810059,2193.419922,2186.800049,2186.899902,2186.899902,3041490000\n2016-08-24,2185.090088,2186.659912,2171.250000,2175.439941,2175.439941,3148280000\n2016-08-25,2173.290039,2179.000000,2169.739990,2172.469971,2172.469971,2969310000\n2016-08-26,2175.100098,2187.939941,2160.389893,2169.040039,2169.040039,3342340000\n2016-08-29,2170.189941,2183.479980,2170.189941,2180.379883,2180.379883,2654780000\n2016-08-30,2179.449951,2182.270020,2170.409912,2176.120117,2176.120117,3006800000\n2016-08-31,2173.560059,2173.790039,2161.350098,2170.949951,2170.949951,3766390000\n2016-09-01,2171.330078,2173.560059,2157.090088,2170.860107,2170.860107,3392120000\n2016-09-02,2177.489990,2184.870117,2173.590088,2179.979980,2179.979980,3091120000\n2016-09-06,2181.610107,2186.570068,2175.100098,2186.479980,2186.479980,3447650000\n2016-09-07,2185.169922,2187.870117,2179.070068,2186.159912,2186.159912,3319420000\n2016-09-08,2182.760010,2184.939941,2177.489990,2181.300049,2181.300049,3727840000\n2016-09-09,2169.080078,2169.080078,2127.810059,2127.810059,2127.810059,4233960000\n2016-09-12,2120.860107,2163.300049,2119.120117,2159.040039,2159.040039,4010480000\n2016-09-13,2150.469971,2150.469971,2120.270020,2127.020020,2127.020020,4141670000\n2016-09-14,2127.860107,2141.330078,2119.899902,2125.770020,2125.770020,3664100000\n2016-09-15,2125.360107,2151.310059,2122.360107,2147.260010,2147.260010,3373720000\n2016-09-16,2146.479980,2146.479980,2131.199951,2139.159912,2139.159912,5014360000\n2016-09-19,2143.989990,2153.610107,2135.909912,2139.120117,2139.120117,3163000000\n2016-09-20,2145.939941,2150.800049,2139.169922,2139.760010,2139.760010,3140730000\n2016-09-21,2144.580078,2165.110107,2139.570068,2163.120117,2163.120117,3712090000\n2016-09-22,2170.939941,2179.989990,2170.939941,2177.179932,2177.179932,3552830000\n2016-09-23,2173.290039,2173.750000,2163.969971,2164.689941,2164.689941,3317190000\n2016-09-26,2158.540039,2158.540039,2145.040039,2146.100098,2146.100098,3216170000\n2016-09-27,2146.040039,2161.129883,2141.550049,2159.929932,2159.929932,3437770000\n2016-09-28,2161.850098,2172.399902,2151.790039,2171.370117,2171.370117,3891460000\n2016-09-29,2168.899902,2172.669922,2145.199951,2151.129883,2151.129883,4249220000\n2016-09-30,2156.510010,2175.300049,2156.510010,2168.270020,2168.270020,4173340000\n2016-10-03,2164.330078,2164.409912,2154.770020,2161.199951,2161.199951,3137550000\n2016-10-04,2163.370117,2165.459961,2144.010010,2150.489990,2150.489990,3750890000\n2016-10-05,2155.149902,2163.949951,2155.149902,2159.729980,2159.729980,3906550000\n2016-10-06,2158.219971,2162.929932,2150.280029,2160.770020,2160.770020,3461550000\n2016-10-07,2164.189941,2165.860107,2144.850098,2153.739990,2153.739990,3619890000\n2016-10-10,2160.389893,2169.600098,2160.389893,2163.659912,2163.659912,2916550000\n2016-10-11,2161.350098,2161.560059,2128.840088,2136.729980,2136.729980,3438270000\n2016-10-12,2137.669922,2145.360107,2132.770020,2139.179932,2139.179932,2977100000\n2016-10-13,2130.260010,2138.189941,2114.719971,2132.550049,2132.550049,3580450000\n2016-10-14,2139.679932,2149.189941,2132.979980,2132.979980,2132.979980,3228150000\n2016-10-17,2132.949951,2135.610107,2124.429932,2126.500000,2126.500000,2830390000\n2016-10-18,2138.310059,2144.379883,2135.489990,2139.600098,2139.600098,3170000000\n2016-10-19,2140.810059,2148.439941,2138.149902,2144.290039,2144.290039,3362670000\n2016-10-20,2142.510010,2147.179932,2133.439941,2141.340088,2141.340088,3337170000\n2016-10-21,2139.429932,2142.629883,2130.090088,2141.159912,2141.159912,3448850000\n2016-10-24,2148.500000,2154.790039,2146.909912,2151.330078,2151.330078,3357320000\n2016-10-25,2149.719971,2151.439941,2141.929932,2143.159912,2143.159912,3751340000\n2016-10-26,2136.969971,2145.729980,2131.590088,2139.429932,2139.429932,3775200000\n2016-10-27,2144.060059,2147.129883,2132.520020,2133.040039,2133.040039,4204830000\n2016-10-28,2132.229980,2140.719971,2119.360107,2126.409912,2126.409912,4019510000\n2016-10-31,2129.780029,2133.250000,2125.530029,2126.149902,2126.149902,3922400000\n2016-11-01,2128.679932,2131.449951,2097.850098,2111.719971,2111.719971,4532160000\n2016-11-02,2109.429932,2111.760010,2094.000000,2097.939941,2097.939941,4248580000\n2016-11-03,2098.800049,2102.560059,2085.229980,2088.659912,2088.659912,3886740000\n2016-11-04,2083.790039,2099.070068,2083.790039,2085.179932,2085.179932,3837860000\n2016-11-07,2100.590088,2132.000000,2100.590088,2131.520020,2131.520020,3736060000\n2016-11-08,2129.919922,2146.870117,2123.560059,2139.560059,2139.560059,3916930000\n2016-11-09,2131.560059,2170.100098,2125.350098,2163.260010,2163.260010,6264150000\n2016-11-10,2167.489990,2182.300049,2151.169922,2167.479980,2167.479980,6451640000\n2016-11-11,2162.709961,2165.919922,2152.489990,2164.449951,2164.449951,4988050000\n2016-11-14,2165.639893,2171.360107,2156.080078,2164.199951,2164.199951,5367200000\n2016-11-15,2168.290039,2180.840088,2166.379883,2180.389893,2180.389893,4543860000\n2016-11-16,2177.530029,2179.219971,2172.199951,2176.939941,2176.939941,3830590000\n2016-11-17,2178.610107,2188.060059,2176.649902,2187.120117,2187.120117,3809160000\n2016-11-18,2186.850098,2189.889893,2180.379883,2181.899902,2181.899902,3572400000\n2016-11-21,2186.429932,2198.699951,2186.429932,2198.179932,2198.179932,3607010000\n2016-11-22,2201.560059,2204.800049,2194.510010,2202.939941,2202.939941,3957940000\n2016-11-23,2198.550049,2204.719971,2194.510010,2204.719971,2204.719971,3418640000\n2016-11-25,2206.270020,2213.350098,2206.270020,2213.350098,2213.350098,1584600000\n2016-11-28,2210.209961,2211.139893,2200.360107,2201.719971,2201.719971,3505650000\n2016-11-29,2200.760010,2210.459961,2198.149902,2204.659912,2204.659912,3706560000\n2016-11-30,2204.969971,2214.100098,2198.810059,2198.810059,2198.810059,5533980000\n2016-12-01,2200.169922,2202.600098,2187.439941,2191.080078,2191.080078,5063740000\n2016-12-02,2191.120117,2197.949951,2188.370117,2191.949951,2191.949951,3779500000\n2016-12-05,2200.649902,2209.419922,2199.969971,2204.709961,2204.709961,3895230000\n2016-12-06,2207.260010,2212.780029,2202.209961,2212.229980,2212.229980,3855320000\n2016-12-07,2210.719971,2241.629883,2208.929932,2241.350098,2241.350098,4501820000\n2016-12-08,2241.129883,2251.689941,2237.570068,2246.189941,2246.189941,4200580000\n2016-12-09,2249.729980,2259.800049,2249.229980,2259.530029,2259.530029,3884480000\n2016-12-12,2258.830078,2264.030029,2252.370117,2256.959961,2256.959961,4034510000\n2016-12-13,2263.320068,2277.530029,2263.320068,2271.719971,2271.719971,3857590000\n2016-12-14,2268.350098,2276.199951,2248.439941,2253.280029,2253.280029,4406970000\n2016-12-15,2253.770020,2272.120117,2253.770020,2262.030029,2262.030029,4168200000\n2016-12-16,2266.810059,2268.050049,2254.239990,2258.070068,2258.070068,5920340000\n2016-12-19,2259.239990,2267.469971,2258.209961,2262.530029,2262.530029,3248370000\n2016-12-20,2266.500000,2272.560059,2266.139893,2270.760010,2270.760010,3298780000\n2016-12-21,2270.540039,2271.229980,2265.149902,2265.179932,2265.179932,2852230000\n2016-12-22,2262.929932,2263.179932,2256.080078,2260.959961,2260.959961,2876320000\n2016-12-23,2260.250000,2263.790039,2258.840088,2263.790039,2263.790039,2020550000\n2016-12-27,2266.229980,2273.820068,2266.149902,2268.879883,2268.879883,1987080000\n2016-12-28,2270.229980,2271.310059,2249.110107,2249.919922,2249.919922,2392360000\n2016-12-29,2249.500000,2254.510010,2244.560059,2249.260010,2249.260010,2336370000\n2016-12-30,2251.610107,2253.580078,2233.620117,2238.830078,2238.830078,2670900000\n2017-01-03,2251.570068,2263.879883,2245.129883,2257.830078,2257.830078,3770530000\n2017-01-04,2261.600098,2272.820068,2261.600098,2270.750000,2270.750000,3764890000\n2017-01-05,2268.179932,2271.500000,2260.449951,2269.000000,2269.000000,3761820000\n2017-01-06,2271.139893,2282.100098,2264.060059,2276.979980,2276.979980,3339890000\n2017-01-09,2273.590088,2275.489990,2268.899902,2268.899902,2268.899902,3217610000\n2017-01-10,2269.719971,2279.270020,2265.270020,2268.899902,2268.899902,3638790000\n2017-01-11,2268.600098,2275.320068,2260.830078,2275.320068,2275.320068,3620410000\n2017-01-12,2271.139893,2271.780029,2254.250000,2270.439941,2270.439941,3462130000\n2017-01-13,2272.739990,2278.679932,2271.510010,2274.639893,2274.639893,3081270000\n2017-01-17,2269.139893,2272.080078,2262.810059,2267.889893,2267.889893,3584990000\n2017-01-18,2269.139893,2272.010010,2263.350098,2271.889893,2271.889893,3315250000\n2017-01-19,2271.899902,2274.330078,2258.409912,2263.689941,2263.689941,3165970000\n2017-01-20,2269.959961,2276.959961,2265.010010,2271.310059,2271.310059,3524970000\n2017-01-23,2267.780029,2271.780029,2257.020020,2265.199951,2265.199951,3152710000\n2017-01-24,2267.879883,2284.629883,2266.679932,2280.070068,2280.070068,3810960000\n2017-01-25,2288.879883,2299.550049,2288.879883,2298.370117,2298.370117,3846020000\n2017-01-26,2298.629883,2300.989990,2294.080078,2296.679932,2296.679932,3610360000\n2017-01-27,2299.020020,2299.020020,2291.620117,2294.689941,2294.689941,3135890000\n2017-01-30,2286.010010,2286.010010,2268.040039,2280.899902,2280.899902,3591270000\n2017-01-31,2274.020020,2279.090088,2267.209961,2278.870117,2278.870117,4087450000\n2017-02-01,2285.590088,2289.139893,2272.439941,2279.550049,2279.550049,3916610000\n2017-02-02,2276.689941,2283.969971,2271.649902,2280.850098,2280.850098,3807710000\n2017-02-03,2288.540039,2298.310059,2287.879883,2297.419922,2297.419922,3597970000\n2017-02-06,2294.280029,2296.179932,2288.570068,2292.560059,2292.560059,3109050000\n2017-02-07,2295.870117,2299.399902,2290.159912,2293.080078,2293.080078,3448690000\n2017-02-08,2289.550049,2295.909912,2285.379883,2294.669922,2294.669922,3609740000\n2017-02-09,2296.699951,2311.080078,2296.610107,2307.870117,2307.870117,3677940000\n2017-02-10,2312.270020,2319.229980,2311.100098,2316.100098,2316.100098,3475020000\n2017-02-13,2321.719971,2331.580078,2321.419922,2328.250000,2328.250000,3349730000\n2017-02-14,2326.120117,2337.580078,2322.169922,2337.580078,2337.580078,3520910000\n2017-02-15,2335.580078,2351.300049,2334.810059,2349.250000,2349.250000,3775590000\n2017-02-16,2349.639893,2351.310059,2338.870117,2347.219971,2347.219971,3672370000\n2017-02-17,2343.010010,2351.159912,2339.580078,2351.159912,2351.159912,3513060000\n2017-02-21,2354.909912,2366.709961,2354.909912,2365.379883,2365.379883,3579780000\n2017-02-22,2361.110107,2365.129883,2358.340088,2362.820068,2362.820068,3468670000\n2017-02-23,2367.500000,2368.260010,2355.090088,2363.810059,2363.810059,4015260000\n2017-02-24,2355.729980,2367.340088,2352.870117,2367.340088,2367.340088,3831570000\n2017-02-27,2365.229980,2371.540039,2361.870117,2369.750000,2369.750000,3582610000\n2017-02-28,2366.080078,2367.790039,2358.959961,2363.639893,2363.639893,4210140000\n2017-03-01,2380.129883,2400.979980,2380.129883,2395.959961,2395.959961,4345180000\n2017-03-02,2394.750000,2394.750000,2380.169922,2381.919922,2381.919922,3821320000\n2017-03-03,2380.919922,2383.889893,2375.389893,2383.120117,2383.120117,3555260000\n2017-03-06,2375.229980,2378.800049,2367.979980,2375.310059,2375.310059,3232700000\n2017-03-07,2370.739990,2375.120117,2365.510010,2368.389893,2368.389893,3518390000\n2017-03-08,2369.810059,2373.090088,2361.010010,2362.979980,2362.979980,3812100000\n2017-03-09,2363.489990,2369.080078,2354.540039,2364.870117,2364.870117,3716340000\n2017-03-10,2372.520020,2376.860107,2363.040039,2372.600098,2372.600098,3432950000\n2017-03-13,2371.560059,2374.419922,2368.520020,2373.469971,2373.469971,3133900000\n2017-03-14,2368.550049,2368.550049,2358.179932,2365.449951,2365.449951,3172630000\n2017-03-15,2370.340088,2390.010010,2368.939941,2385.260010,2385.260010,3906840000\n2017-03-16,2387.709961,2388.100098,2377.179932,2381.379883,2381.379883,3365660000\n2017-03-17,2383.709961,2385.709961,2377.639893,2378.250000,2378.250000,5178040000\n2017-03-20,2378.239990,2379.550049,2369.659912,2373.469971,2373.469971,3054930000\n2017-03-21,2379.320068,2381.929932,2341.899902,2344.020020,2344.020020,4265590000\n2017-03-22,2343.000000,2351.810059,2336.449951,2348.449951,2348.449951,3572730000\n2017-03-23,2345.969971,2358.919922,2342.129883,2345.959961,2345.959961,3260600000\n2017-03-24,2350.419922,2356.219971,2335.739990,2343.979980,2343.979980,2975130000\n2017-03-27,2329.110107,2344.899902,2322.250000,2341.590088,2341.590088,3240230000\n2017-03-28,2339.790039,2363.780029,2337.629883,2358.570068,2358.570068,3367780000\n2017-03-29,2356.540039,2363.360107,2352.939941,2361.129883,2361.129883,3106940000\n2017-03-30,2361.310059,2370.419922,2358.580078,2368.060059,2368.060059,3158420000\n2017-03-31,2364.820068,2370.350098,2362.600098,2362.719971,2362.719971,3354110000\n2017-04-03,2362.340088,2365.870117,2344.729980,2358.840088,2358.840088,3416400000\n2017-04-04,2354.760010,2360.530029,2350.719971,2360.159912,2360.159912,3206240000\n2017-04-05,2366.590088,2378.360107,2350.520020,2352.949951,2352.949951,3770520000\n2017-04-06,2353.790039,2364.159912,2348.899902,2357.489990,2357.489990,3201920000\n2017-04-07,2356.590088,2363.760010,2350.739990,2355.540039,2355.540039,3053150000\n2017-04-10,2357.159912,2366.370117,2351.500000,2357.159912,2357.159912,2785410000\n2017-04-11,2353.919922,2355.219971,2337.250000,2353.780029,2353.780029,3117420000\n2017-04-12,2352.149902,2352.719971,2341.179932,2344.929932,2344.929932,3196950000\n2017-04-13,2341.979980,2348.260010,2328.949951,2328.949951,2328.949951,3143890000\n2017-04-17,2332.620117,2349.139893,2332.510010,2349.010010,2349.010010,2824710000\n2017-04-18,2342.530029,2348.350098,2334.540039,2342.189941,2342.189941,3269840000\n2017-04-19,2346.790039,2352.629883,2335.050049,2338.169922,2338.169922,3519900000\n2017-04-20,2342.689941,2361.370117,2340.909912,2355.840088,2355.840088,3647420000\n2017-04-21,2354.739990,2356.179932,2344.510010,2348.689941,2348.689941,3503360000\n2017-04-24,2370.330078,2376.979980,2369.189941,2374.149902,2374.149902,3690650000\n2017-04-25,2381.510010,2392.479980,2381.149902,2388.610107,2388.610107,3995240000\n2017-04-26,2388.979980,2398.159912,2386.780029,2387.449951,2387.449951,4105920000\n2017-04-27,2389.699951,2392.100098,2382.679932,2388.770020,2388.770020,4098460000\n2017-04-28,2393.679932,2393.679932,2382.360107,2384.199951,2384.199951,3718270000\n2017-05-01,2388.500000,2394.489990,2384.830078,2388.330078,2388.330078,3199240000\n2017-05-02,2391.050049,2392.929932,2385.820068,2391.169922,2391.169922,3813680000\n2017-05-03,2386.500000,2389.820068,2379.750000,2388.129883,2388.129883,3893990000\n2017-05-04,2389.790039,2391.429932,2380.350098,2389.520020,2389.520020,4362540000\n2017-05-05,2392.370117,2399.290039,2389.379883,2399.290039,2399.290039,3540140000\n2017-05-08,2399.939941,2401.360107,2393.919922,2399.379883,2399.379883,3429440000\n2017-05-09,2401.580078,2403.870117,2392.439941,2396.919922,2396.919922,3653590000\n2017-05-10,2396.790039,2399.739990,2392.790039,2399.629883,2399.629883,3643530000\n2017-05-11,2394.840088,2395.719971,2381.739990,2394.439941,2394.439941,3727420000\n2017-05-12,2392.439941,2392.439941,2387.189941,2390.899902,2390.899902,3305630000\n2017-05-15,2393.979980,2404.050049,2393.939941,2402.320068,2402.320068,3473600000\n2017-05-16,2404.550049,2405.770020,2396.050049,2400.669922,2400.669922,3420790000\n2017-05-17,2382.949951,2384.870117,2356.209961,2357.030029,2357.030029,4163000000\n2017-05-18,2354.689941,2375.739990,2352.719971,2365.719971,2365.719971,4319420000\n2017-05-19,2371.370117,2389.060059,2370.429932,2381.729980,2381.729980,3825160000\n2017-05-22,2387.209961,2395.459961,2386.919922,2394.020020,2394.020020,3172830000\n2017-05-23,2397.040039,2400.850098,2393.879883,2398.419922,2398.419922,3213570000\n2017-05-24,2401.409912,2405.580078,2397.989990,2404.389893,2404.389893,3389900000\n2017-05-25,2409.540039,2418.709961,2408.010010,2415.070068,2415.070068,3535390000\n2017-05-26,2414.500000,2416.679932,2412.199951,2415.820068,2415.820068,2805040000\n2017-05-30,2411.669922,2415.260010,2409.429932,2412.909912,2412.909912,3203160000\n2017-05-31,2415.629883,2415.989990,2403.590088,2411.800049,2411.800049,4516110000\n2017-06-01,2415.649902,2430.060059,2413.540039,2430.060059,2430.060059,3857140000\n2017-06-02,2431.280029,2440.229980,2427.709961,2439.070068,2439.070068,3461680000\n2017-06-05,2437.830078,2439.550049,2434.320068,2436.100098,2436.100098,2912600000\n2017-06-06,2431.919922,2436.209961,2428.120117,2429.330078,2429.330078,3357840000\n2017-06-07,2432.030029,2435.280029,2424.750000,2433.139893,2433.139893,3572300000\n2017-06-08,2434.270020,2439.270020,2427.939941,2433.790039,2433.790039,3728860000\n2017-06-09,2436.389893,2446.199951,2415.699951,2431.770020,2431.770020,4027340000\n2017-06-12,2425.879883,2430.379883,2419.969971,2429.389893,2429.389893,4027750000\n2017-06-13,2434.149902,2441.489990,2431.280029,2440.350098,2440.350098,3275500000\n2017-06-14,2443.750000,2443.750000,2428.340088,2437.919922,2437.919922,3555590000\n2017-06-15,2424.139893,2433.949951,2418.530029,2432.459961,2432.459961,3353050000\n2017-06-16,2431.239990,2433.149902,2422.879883,2433.149902,2433.149902,5284720000\n2017-06-19,2442.550049,2453.820068,2441.790039,2453.459961,2453.459961,3264700000\n2017-06-20,2450.659912,2450.659912,2436.600098,2437.030029,2437.030029,3416510000\n2017-06-21,2439.310059,2442.229980,2430.739990,2435.610107,2435.610107,3594820000\n2017-06-22,2437.399902,2441.620117,2433.270020,2434.500000,2434.500000,3468210000\n2017-06-23,2434.649902,2441.399902,2431.110107,2438.300049,2438.300049,5278330000\n2017-06-26,2443.320068,2450.419922,2437.030029,2439.070068,2439.070068,3238970000\n2017-06-27,2436.340088,2440.149902,2419.379883,2419.379883,2419.379883,3563910000\n2017-06-28,2428.699951,2442.969971,2428.020020,2440.689941,2440.689941,3500800000\n2017-06-29,2442.379883,2442.729980,2405.699951,2419.699951,2419.699951,3900280000\n2017-06-30,2429.199951,2432.709961,2421.649902,2423.409912,2423.409912,3361590000\n2017-07-03,2431.389893,2439.169922,2428.689941,2429.010010,2429.010010,1962290000\n2017-07-05,2430.780029,2434.899902,2422.050049,2432.540039,2432.540039,3367220000\n2017-07-06,2423.439941,2424.280029,2407.699951,2409.750000,2409.750000,3364520000\n2017-07-07,2413.520020,2426.919922,2413.520020,2425.179932,2425.179932,2901330000\n2017-07-10,2424.510010,2432.000000,2422.270020,2427.429932,2427.429932,2999130000\n2017-07-11,2427.350098,2429.300049,2412.790039,2425.530029,2425.530029,3106750000\n2017-07-12,2435.750000,2445.760010,2435.750000,2443.250000,2443.250000,3171620000\n2017-07-13,2444.989990,2449.320068,2441.689941,2447.830078,2447.830078,3067670000\n2017-07-14,2449.159912,2463.540039,2446.689941,2459.270020,2459.270020,2736640000\n2017-07-17,2459.500000,2462.820068,2457.159912,2459.139893,2459.139893,2793170000\n2017-07-18,2455.879883,2460.919922,2450.340088,2460.610107,2460.610107,2962130000\n2017-07-19,2463.850098,2473.830078,2463.850098,2473.830078,2473.830078,3059760000\n2017-07-20,2475.560059,2477.620117,2468.429932,2473.449951,2473.449951,3182780000\n2017-07-21,2467.399902,2472.540039,2465.060059,2472.540039,2472.540039,3059570000\n2017-07-24,2472.040039,2473.100098,2466.320068,2469.909912,2469.909912,3010240000\n2017-07-25,2477.879883,2481.239990,2474.909912,2477.129883,2477.129883,4108060000\n2017-07-26,2479.969971,2481.689941,2474.939941,2477.830078,2477.830078,3557020000\n2017-07-27,2482.760010,2484.040039,2459.929932,2475.419922,2475.419922,3995520000\n2017-07-28,2469.120117,2473.530029,2464.659912,2472.100098,2472.100098,3294770000\n2017-07-31,2475.939941,2477.959961,2468.530029,2470.300049,2470.300049,3469210000\n2017-08-01,2477.100098,2478.510010,2471.139893,2476.350098,2476.350098,3460860000\n2017-08-02,2480.379883,2480.379883,2466.479980,2477.570068,2477.570068,3478580000\n2017-08-03,2476.030029,2476.030029,2468.850098,2472.159912,2472.159912,3645020000\n2017-08-04,2476.879883,2480.000000,2472.080078,2476.830078,2476.830078,3235140000\n2017-08-07,2477.139893,2480.949951,2475.879883,2480.909912,2480.909912,2931780000\n2017-08-08,2478.350098,2490.870117,2470.320068,2474.919922,2474.919922,3344640000\n2017-08-09,2465.350098,2474.409912,2462.080078,2474.020020,2474.020020,3308060000\n2017-08-10,2465.379883,2465.379883,2437.750000,2438.209961,2438.209961,3621070000\n2017-08-11,2441.040039,2448.090088,2437.850098,2441.320068,2441.320068,3159930000\n2017-08-14,2454.959961,2468.219971,2454.959961,2465.840088,2465.840088,2822550000\n2017-08-15,2468.659912,2468.899902,2461.610107,2464.610107,2464.610107,2913100000\n2017-08-16,2468.629883,2474.929932,2463.860107,2468.110107,2468.110107,2953650000\n2017-08-17,2462.949951,2465.020020,2430.010010,2430.010010,2430.010010,3142620000\n2017-08-18,2427.639893,2440.270020,2420.689941,2425.550049,2425.550049,3415680000\n2017-08-21,2425.500000,2430.580078,2417.350098,2428.370117,2428.370117,2788150000\n2017-08-22,2433.750000,2454.770020,2433.669922,2452.510010,2452.510010,2777490000\n2017-08-23,2444.879883,2448.909912,2441.419922,2444.040039,2444.040039,2785290000\n2017-08-24,2447.909912,2450.389893,2436.189941,2438.969971,2438.969971,2846590000\n2017-08-25,2444.719971,2453.959961,2442.219971,2443.050049,2443.050049,2588780000\n2017-08-28,2447.350098,2449.120117,2439.030029,2444.239990,2444.239990,2677700000\n2017-08-29,2431.939941,2449.189941,2428.199951,2446.300049,2446.300049,2737580000\n2017-08-30,2446.060059,2460.310059,2443.770020,2457.590088,2457.590088,2633660000\n2017-08-31,2462.649902,2475.010010,2462.649902,2471.649902,2471.649902,3348110000\n2017-09-01,2474.419922,2480.379883,2473.850098,2476.550049,2476.550049,2710730000\n2017-09-05,2470.350098,2471.969971,2446.550049,2457.850098,2457.850098,3490260000\n2017-09-06,2463.830078,2469.639893,2459.199951,2465.540039,2465.540039,3374410000\n2017-09-07,2468.060059,2468.620117,2460.290039,2465.100098,2465.100098,3353930000\n2017-09-08,2462.250000,2467.110107,2459.399902,2461.429932,2461.429932,3302490000\n2017-09-11,2474.520020,2488.949951,2474.520020,2488.110107,2488.110107,3291760000\n2017-09-12,2491.939941,2496.770020,2490.370117,2496.479980,2496.479980,3230920000\n2017-09-13,2493.889893,2498.370117,2492.139893,2498.370117,2498.370117,3368050000\n2017-09-14,2494.560059,2498.429932,2491.350098,2495.620117,2495.620117,3414460000\n2017-09-15,2495.669922,2500.229980,2493.159912,2500.229980,2500.229980,4853170000\n2017-09-18,2502.510010,2508.320068,2499.919922,2503.870117,2503.870117,3194300000\n2017-09-19,2506.290039,2507.840088,2503.189941,2506.649902,2506.649902,3249100000\n2017-09-20,2506.840088,2508.850098,2496.669922,2508.239990,2508.239990,3530010000\n2017-09-21,2507.159912,2507.159912,2499.000000,2500.600098,2500.600098,2930860000\n2017-09-22,2497.260010,2503.469971,2496.540039,2502.219971,2502.219971,2865960000\n2017-09-25,2499.389893,2502.540039,2488.030029,2496.659912,2496.659912,3297890000\n2017-09-26,2501.040039,2503.510010,2495.120117,2496.840088,2496.840088,3043110000\n2017-09-27,2503.300049,2511.750000,2495.909912,2507.040039,2507.040039,3456030000\n2017-09-28,2503.409912,2510.810059,2502.929932,2510.060059,2510.060059,3168620000\n2017-09-29,2509.959961,2519.439941,2507.989990,2519.360107,2519.360107,3211920000\n2017-10-02,2521.199951,2529.229980,2520.399902,2529.120117,2529.120117,3199730000\n2017-10-03,2530.340088,2535.129883,2528.850098,2534.580078,2534.580078,3068850000\n2017-10-04,2533.479980,2540.530029,2531.800049,2537.739990,2537.739990,3017120000\n2017-10-05,2540.860107,2552.510010,2540.020020,2552.070068,2552.070068,3045120000\n2017-10-06,2547.439941,2549.409912,2543.790039,2549.330078,2549.330078,2884570000\n2017-10-09,2551.389893,2551.820068,2541.600098,2544.729980,2544.729980,2483970000\n2017-10-10,2549.989990,2555.229980,2544.860107,2550.639893,2550.639893,2960500000\n2017-10-11,2550.620117,2555.239990,2547.949951,2555.239990,2555.239990,2976090000\n2017-10-12,2552.879883,2555.330078,2548.310059,2550.929932,2550.929932,3151510000\n2017-10-13,2555.659912,2557.649902,2552.090088,2553.169922,2553.169922,3149440000\n2017-10-16,2555.570068,2559.469971,2552.639893,2557.639893,2557.639893,2916020000\n2017-10-17,2557.169922,2559.709961,2554.689941,2559.360107,2559.360107,2889390000\n2017-10-18,2562.870117,2564.110107,2559.669922,2561.260010,2561.260010,2998090000\n2017-10-19,2553.389893,2562.360107,2547.919922,2562.100098,2562.100098,2990710000\n2017-10-20,2567.560059,2575.439941,2567.560059,2575.209961,2575.209961,3384650000\n2017-10-23,2578.080078,2578.290039,2564.330078,2564.979980,2564.979980,3211710000\n2017-10-24,2568.659912,2572.179932,2565.580078,2569.129883,2569.129883,3427330000\n2017-10-25,2566.520020,2567.399902,2544.000000,2557.149902,2557.149902,3874510000\n2017-10-26,2560.080078,2567.070068,2559.800049,2560.399902,2560.399902,3869050000\n2017-10-27,2570.260010,2582.979980,2565.939941,2581.070068,2581.070068,3887110000\n2017-10-30,2577.750000,2580.030029,2568.250000,2572.830078,2572.830078,3658870000\n2017-10-31,2575.989990,2578.290039,2572.149902,2575.260010,2575.260010,3827230000\n2017-11-01,2583.209961,2588.399902,2574.919922,2579.360107,2579.360107,3813180000\n2017-11-02,2579.459961,2581.110107,2566.169922,2579.850098,2579.850098,4048270000\n2017-11-03,2581.929932,2588.419922,2576.770020,2587.840088,2587.840088,3567710000\n2017-11-06,2587.469971,2593.379883,2585.659912,2591.129883,2591.129883,3539080000\n2017-11-07,2592.110107,2597.020020,2584.350098,2590.639893,2590.639893,3809650000\n2017-11-08,2588.709961,2595.469971,2585.020020,2594.379883,2594.379883,3899360000\n2017-11-09,2584.000000,2586.500000,2566.330078,2584.620117,2584.620117,3831610000\n2017-11-10,2580.179932,2583.810059,2575.570068,2582.300049,2582.300049,3486910000\n2017-11-13,2576.530029,2587.659912,2574.479980,2584.840088,2584.840088,3402930000\n2017-11-14,2577.750000,2579.659912,2566.560059,2578.870117,2578.870117,3641760000\n2017-11-15,2569.449951,2572.840088,2557.449951,2564.620117,2564.620117,3558890000\n2017-11-16,2572.949951,2590.090088,2572.949951,2585.639893,2585.639893,3312710000\n2017-11-17,2582.939941,2583.959961,2577.620117,2578.850098,2578.850098,3300160000\n2017-11-20,2579.489990,2584.639893,2578.239990,2582.139893,2582.139893,3003540000\n2017-11-21,2589.169922,2601.189941,2589.169922,2599.030029,2599.030029,3332720000\n2017-11-22,2600.310059,2600.939941,2595.229980,2597.080078,2597.080078,2762950000\n2017-11-24,2600.419922,2604.209961,2600.419922,2602.419922,2602.419922,1349780000\n2017-11-27,2602.659912,2606.409912,2598.870117,2601.419922,2601.419922,3006860000\n2017-11-28,2605.939941,2627.689941,2605.439941,2627.040039,2627.040039,3488420000\n2017-11-29,2627.820068,2634.889893,2620.320068,2626.070068,2626.070068,4078280000\n2017-11-30,2633.929932,2657.739990,2633.929932,2647.580078,2647.580078,4938490000\n2017-12-01,2645.100098,2650.620117,2605.520020,2642.219971,2642.219971,3942320000\n2017-12-04,2657.189941,2665.189941,2639.030029,2639.439941,2639.439941,4023150000\n2017-12-05,2639.780029,2648.719971,2627.729980,2629.570068,2629.570068,3539040000\n2017-12-06,2626.239990,2634.409912,2624.750000,2629.270020,2629.270020,3229000000\n2017-12-07,2628.379883,2640.989990,2626.530029,2636.979980,2636.979980,3292400000\n2017-12-08,2646.209961,2651.649902,2644.100098,2651.500000,2651.500000,3106150000\n2017-12-11,2652.189941,2660.330078,2651.469971,2659.989990,2659.989990,3091950000\n2017-12-12,2661.729980,2669.719971,2659.780029,2664.110107,2664.110107,3555680000\n2017-12-13,2667.590088,2671.879883,2662.850098,2662.850098,2662.850098,3542370000\n2017-12-14,2665.870117,2668.090088,2652.010010,2652.010010,2652.010010,3430030000\n2017-12-15,2660.629883,2679.629883,2659.139893,2675.810059,2675.810059,5723920000\n2017-12-18,2685.919922,2694.969971,2685.919922,2690.159912,2690.159912,3724660000\n2017-12-19,2692.709961,2694.439941,2680.739990,2681.469971,2681.469971,3368590000\n2017-12-20,2688.179932,2691.010010,2676.110107,2679.250000,2679.250000,3241030000\n2017-12-21,2683.020020,2692.639893,2682.399902,2684.570068,2684.570068,3273390000\n2017-12-22,2684.219971,2685.350098,2678.129883,2683.340088,2683.340088,2399830000\n2017-12-26,2679.090088,2682.739990,2677.959961,2680.500000,2680.500000,1968780000\n2017-12-27,2682.100098,2685.639893,2678.909912,2682.620117,2682.620117,2202080000\n2017-12-28,2686.100098,2687.659912,2682.689941,2687.540039,2687.540039,2153330000\n2017-12-29,2689.149902,2692.120117,2673.610107,2673.610107,2673.610107,2443490000\n2018-01-02,2683.729980,2695.889893,2682.360107,2695.810059,2695.810059,3367250000\n2018-01-03,2697.850098,2714.370117,2697.770020,2713.060059,2713.060059,3538660000\n2018-01-04,2719.310059,2729.290039,2719.070068,2723.989990,2723.989990,3695260000\n2018-01-05,2731.330078,2743.449951,2727.919922,2743.149902,2743.149902,3236620000\n2018-01-08,2742.669922,2748.510010,2737.600098,2747.709961,2747.709961,3242650000\n2018-01-09,2751.149902,2759.139893,2747.860107,2751.290039,2751.290039,3453480000\n2018-01-10,2745.550049,2750.800049,2736.060059,2748.229980,2748.229980,3576350000\n2018-01-11,2752.969971,2767.560059,2752.780029,2767.560059,2767.560059,3641320000\n2018-01-12,2770.179932,2787.850098,2769.639893,2786.239990,2786.239990,3573970000\n2018-01-16,2798.959961,2807.540039,2768.639893,2776.419922,2776.419922,4325970000\n2018-01-17,2784.989990,2807.040039,2778.379883,2802.560059,2802.560059,3778050000\n2018-01-18,2802.399902,2805.830078,2792.560059,2798.030029,2798.030029,3681470000\n2018-01-19,2802.600098,2810.330078,2798.080078,2810.300049,2810.300049,3639430000\n2018-01-22,2809.159912,2833.030029,2808.120117,2832.969971,2832.969971,3471780000\n2018-01-23,2835.050049,2842.239990,2830.590088,2839.129883,2839.129883,3519650000\n2018-01-24,2845.419922,2852.969971,2824.810059,2837.540039,2837.540039,4014070000\n2018-01-25,2846.239990,2848.560059,2830.939941,2839.250000,2839.250000,3835150000\n2018-01-26,2847.479980,2872.870117,2846.179932,2872.870117,2872.870117,3443230000\n2018-01-29,2867.229980,2870.620117,2851.479980,2853.530029,2853.530029,3573830000\n2018-01-30,2832.739990,2837.750000,2818.270020,2822.429932,2822.429932,3990650000\n2018-01-31,2832.409912,2839.260010,2813.040039,2823.810059,2823.810059,4261280000\n2018-02-01,2816.449951,2835.959961,2812.699951,2821.979980,2821.979980,3938450000\n2018-02-02,2808.919922,2808.919922,2759.969971,2762.129883,2762.129883,4301130000\n2018-02-05,2741.060059,2763.389893,2638.169922,2648.939941,2648.939941,5283460000\n2018-02-06,2614.780029,2701.040039,2593.070068,2695.139893,2695.139893,5891660000\n2018-02-07,2690.949951,2727.669922,2681.330078,2681.659912,2681.659912,4626570000\n2018-02-08,2685.010010,2685.270020,2580.560059,2581.000000,2581.000000,5305440000\n2018-02-09,2601.780029,2638.669922,2532.689941,2619.550049,2619.550049,5680070000\n2018-02-12,2636.750000,2672.610107,2622.449951,2656.000000,2656.000000,4055790000\n2018-02-13,2646.270020,2668.840088,2637.080078,2662.939941,2662.939941,3472870000\n2018-02-14,2651.209961,2702.100098,2648.870117,2698.629883,2698.629883,4003740000\n2018-02-15,2713.459961,2731.510010,2689.820068,2731.199951,2731.199951,3684910000\n2018-02-16,2727.139893,2754.419922,2725.110107,2732.219971,2732.219971,3637460000\n2018-02-20,2722.989990,2737.600098,2706.760010,2716.260010,2716.260010,3627610000\n2018-02-21,2720.530029,2747.750000,2701.290039,2701.330078,2701.330078,3779400000\n2018-02-22,2710.419922,2731.260010,2697.770020,2703.959961,2703.959961,3701270000\n2018-02-23,2715.800049,2747.760010,2713.739990,2747.300049,2747.300049,3189190000\n2018-02-26,2757.370117,2780.639893,2753.780029,2779.600098,2779.600098,3424650000\n2018-02-27,2780.449951,2789.149902,2744.219971,2744.280029,2744.280029,3745080000\n2018-02-28,2753.780029,2761.520020,2713.540039,2713.830078,2713.830078,4230660000\n2018-03-01,2715.219971,2730.889893,2659.649902,2677.669922,2677.669922,4503970000\n2018-03-02,2658.889893,2696.250000,2647.320068,2691.250000,2691.250000,3882450000\n2018-03-05,2681.060059,2728.090088,2675.750000,2720.939941,2720.939941,3710810000\n2018-03-06,2730.179932,2732.080078,2711.260010,2728.120117,2728.120117,3370690000\n2018-03-07,2710.179932,2730.600098,2701.739990,2726.800049,2726.800049,3393270000\n2018-03-08,2732.750000,2740.449951,2722.649902,2738.969971,2738.969971,3212320000\n2018-03-09,2752.909912,2786.570068,2751.540039,2786.570068,2786.570068,3364100000\n2018-03-12,2790.540039,2796.979980,2779.260010,2783.020020,2783.020020,3185020000\n2018-03-13,2792.310059,2801.899902,2758.679932,2765.310059,2765.310059,3301650000\n2018-03-14,2774.060059,2777.110107,2744.379883,2749.479980,2749.479980,3391360000\n2018-03-15,2754.270020,2763.030029,2741.469971,2747.330078,2747.330078,3500330000\n2018-03-16,2750.570068,2761.850098,2749.969971,2752.010010,2752.010010,5372340000\n2018-03-19,2741.379883,2741.379883,2694.590088,2712.919922,2712.919922,3302130000\n2018-03-20,2715.050049,2724.219971,2710.050049,2716.939941,2716.939941,3261030000\n2018-03-21,2714.989990,2739.139893,2709.790039,2711.929932,2711.929932,3415510000\n2018-03-22,2691.360107,2695.679932,2641.590088,2643.689941,2643.689941,3739800000\n2018-03-23,2646.709961,2657.669922,2585.889893,2588.260010,2588.260010,3815080000\n2018-03-26,2619.350098,2661.360107,2601.810059,2658.550049,2658.550049,3511100000\n2018-03-27,2667.570068,2674.780029,2596.120117,2612.620117,2612.620117,3706350000\n2018-03-28,2611.300049,2632.649902,2593.060059,2605.000000,2605.000000,3864500000\n2018-03-29,2614.409912,2659.070068,2609.719971,2640.870117,2640.870117,3565990000\n2018-04-02,2633.449951,2638.300049,2553.800049,2581.879883,2581.879883,3598520000\n2018-04-03,2592.169922,2619.139893,2575.489990,2614.449951,2614.449951,3392810000\n2018-04-04,2584.040039,2649.860107,2573.610107,2644.689941,2644.689941,3350340000\n2018-04-05,2657.360107,2672.080078,2649.580078,2662.840088,2662.840088,3178970000\n2018-04-06,2645.820068,2656.879883,2586.270020,2604.469971,2604.469971,3299700000\n2018-04-09,2617.179932,2653.550049,2610.790039,2613.159912,2613.159912,3062960000\n2018-04-10,2638.409912,2665.449951,2635.780029,2656.870117,2656.870117,3543930000\n2018-04-11,2643.889893,2661.429932,2639.250000,2642.189941,2642.189941,3020760000\n2018-04-12,2653.830078,2674.719971,2653.830078,2663.989990,2663.989990,3021320000\n2018-04-13,2676.899902,2680.260010,2645.050049,2656.300049,2656.300049,2960910000\n2018-04-16,2670.100098,2686.489990,2665.159912,2677.840088,2677.840088,3019700000\n2018-04-17,2692.739990,2713.340088,2692.050049,2706.389893,2706.389893,3234360000\n2018-04-18,2710.110107,2717.489990,2703.629883,2708.639893,2708.639893,3383410000\n2018-04-19,2701.159912,2702.840088,2681.899902,2693.129883,2693.129883,3349370000\n2018-04-20,2692.560059,2693.939941,2660.610107,2670.139893,2670.139893,3388590000\n2018-04-23,2675.399902,2682.860107,2657.989990,2670.290039,2670.290039,3017480000\n2018-04-24,2680.800049,2683.550049,2617.320068,2634.560059,2634.560059,3706740000\n2018-04-25,2634.919922,2645.300049,2612.669922,2639.399902,2639.399902,3499440000\n2018-04-26,2651.649902,2676.479980,2647.159912,2666.939941,2666.939941,3665720000\n2018-04-27,2675.469971,2677.350098,2659.010010,2669.909912,2669.909912,3219030000\n2018-04-30,2682.510010,2682.870117,2648.040039,2648.050049,2648.050049,3734530000\n2018-05-01,2642.959961,2655.270020,2625.409912,2654.800049,2654.800049,3559850000\n2018-05-02,2654.239990,2660.870117,2631.699951,2635.669922,2635.669922,4010770000\n2018-05-03,2628.080078,2637.139893,2594.620117,2629.729980,2629.729980,3851470000\n2018-05-04,2621.449951,2670.929932,2615.320068,2663.419922,2663.419922,3327220000\n2018-05-07,2680.340088,2683.350098,2664.699951,2672.629883,2672.629883,3237960000\n2018-05-08,2670.260010,2676.340088,2655.199951,2671.919922,2671.919922,3717570000\n2018-05-09,2678.120117,2701.270020,2674.139893,2697.790039,2697.790039,3909500000\n2018-05-10,2705.020020,2726.110107,2704.540039,2723.070068,2723.070068,3333050000\n2018-05-11,2722.699951,2732.860107,2717.449951,2727.719971,2727.719971,2862700000\n2018-05-14,2738.469971,2742.100098,2725.469971,2730.129883,2730.129883,2972660000\n2018-05-15,2718.590088,2718.590088,2701.909912,2711.449951,2711.449951,3290680000\n2018-05-16,2712.620117,2727.760010,2712.169922,2722.459961,2722.459961,3202670000\n2018-05-17,2719.709961,2731.959961,2711.360107,2720.129883,2720.129883,3475400000\n2018-05-18,2717.350098,2719.500000,2709.179932,2712.969971,2712.969971,3368690000\n2018-05-21,2735.389893,2739.189941,2725.699951,2733.010010,2733.010010,3019890000\n2018-05-22,2738.340088,2742.239990,2721.879883,2724.439941,2724.439941,3366310000\n2018-05-23,2713.979980,2733.330078,2709.540039,2733.290039,2733.290039,3326290000\n2018-05-24,2730.939941,2731.969971,2707.379883,2727.760010,2727.760010,3256030000\n2018-05-25,2723.600098,2727.360107,2714.989990,2721.330078,2721.330078,2995260000\n2018-05-29,2705.110107,2710.669922,2676.810059,2689.860107,2689.860107,3736890000\n2018-05-30,2702.429932,2729.340088,2702.429932,2724.010010,2724.010010,3561050000\n2018-05-31,2720.979980,2722.500000,2700.679932,2705.270020,2705.270020,4235370000\n2018-06-01,2718.699951,2736.929932,2718.699951,2734.620117,2734.620117,3684130000\n2018-06-04,2741.669922,2749.159912,2740.540039,2746.870117,2746.870117,3376510000\n2018-06-05,2748.459961,2752.610107,2739.510010,2748.800049,2748.800049,3517790000\n2018-06-06,2753.250000,2772.389893,2748.459961,2772.350098,2772.350098,3651640000\n2018-06-07,2774.840088,2779.899902,2760.159912,2770.370117,2770.370117,3711330000\n2018-06-08,2765.840088,2779.389893,2763.590088,2779.030029,2779.030029,3123210000\n2018-06-11,2780.179932,2790.209961,2780.169922,2782.000000,2782.000000,3232330000\n2018-06-12,2785.600098,2789.800049,2778.780029,2786.850098,2786.850098,3401010000\n2018-06-13,2787.939941,2791.469971,2774.649902,2775.629883,2775.629883,3779230000\n2018-06-14,2783.209961,2789.060059,2776.520020,2782.489990,2782.489990,3526890000\n2018-06-15,2777.780029,2782.810059,2761.729980,2779.659912,2779.659912,5428790000\n2018-06-18,2765.790039,2774.989990,2757.120117,2773.750000,2773.750000,3287150000\n2018-06-19,2752.010010,2765.050049,2743.189941,2762.590088,2762.590088,3661470000\n2018-06-20,2769.729980,2774.860107,2763.909912,2767.320068,2767.320068,3327600000\n2018-06-21,2769.280029,2769.280029,2744.389893,2749.760010,2749.760010,3300060000\n2018-06-22,2760.790039,2764.169922,2752.679932,2754.879883,2754.879883,5450550000\n2018-06-25,2742.939941,2742.939941,2698.669922,2717.070068,2717.070068,3655080000\n2018-06-26,2722.120117,2732.909912,2715.600098,2723.060059,2723.060059,3555090000\n2018-06-27,2728.449951,2746.090088,2699.379883,2699.629883,2699.629883,3776090000\n2018-06-28,2698.689941,2724.340088,2691.989990,2716.310059,2716.310059,3428140000\n2018-06-29,2727.129883,2743.260010,2718.030029,2718.370117,2718.370117,3565620000\n2018-07-02,2704.949951,2727.260010,2698.949951,2726.709961,2726.709961,3073650000\n2018-07-03,2733.270020,2736.580078,2711.159912,2713.219971,2713.219971,1911470000\n2018-07-05,2724.189941,2737.830078,2716.020020,2736.610107,2736.610107,2953420000\n2018-07-06,2737.679932,2764.409912,2733.520020,2759.820068,2759.820068,2554780000\n2018-07-09,2775.620117,2784.649902,2770.729980,2784.169922,2784.169922,3050040000\n2018-07-10,2788.560059,2795.580078,2786.239990,2793.840088,2793.840088,3063850000\n2018-07-11,2779.820068,2785.909912,2770.770020,2774.020020,2774.020020,2964740000\n2018-07-12,2783.139893,2799.219971,2781.530029,2798.290039,2798.290039,2821690000\n2018-07-13,2796.929932,2804.530029,2791.689941,2801.310059,2801.310059,2614000000\n2018-07-16,2797.360107,2801.189941,2793.389893,2798.429932,2798.429932,2812230000\n2018-07-17,2789.340088,2814.189941,2789.239990,2809.550049,2809.550049,3050730000\n2018-07-18,2811.350098,2816.760010,2805.889893,2815.620117,2815.620117,3089780000\n2018-07-19,2809.370117,2812.050049,2799.770020,2804.489990,2804.489990,3266700000\n2018-07-20,2804.550049,2809.699951,2800.010010,2801.830078,2801.830078,3230210000\n2018-07-23,2799.169922,2808.610107,2795.139893,2806.979980,2806.979980,2907430000\n2018-07-24,2820.679932,2829.989990,2811.120117,2820.399902,2820.399902,3417530000\n2018-07-25,2817.729980,2848.030029,2817.729980,2846.070068,2846.070068,3553010000\n2018-07-26,2835.489990,2845.570068,2835.260010,2837.439941,2837.439941,3653330000\n2018-07-27,2842.350098,2843.169922,2808.340088,2818.820068,2818.820068,3415710000\n2018-07-30,2819.000000,2821.739990,2798.110107,2802.600098,2802.600098,3245770000\n2018-07-31,2809.729980,2824.459961,2808.060059,2816.290039,2816.290039,3892100000\n2018-08-01,2821.169922,2825.830078,2805.850098,2813.360107,2813.360107,3496990000\n2018-08-02,2800.479980,2829.909912,2796.340088,2827.219971,2827.219971,3467380000\n2018-08-03,2829.620117,2840.379883,2827.370117,2840.350098,2840.350098,3030390000\n2018-08-06,2840.290039,2853.290039,2835.979980,2850.399902,2850.399902,2874540000\n2018-08-07,2855.919922,2863.429932,2855.919922,2858.449951,2858.449951,3162770000\n2018-08-08,2856.790039,2862.439941,2853.090088,2857.699951,2857.699951,2972200000\n2018-08-09,2857.189941,2862.479980,2851.979980,2853.580078,2853.580078,3047050000\n2018-08-10,2838.899902,2842.199951,2825.810059,2833.280029,2833.280029,3256040000\n2018-08-13,2835.459961,2843.399902,2819.879883,2821.929932,2821.929932,3158450000\n2018-08-14,2827.879883,2843.110107,2826.580078,2839.959961,2839.959961,2976970000\n2018-08-15,2827.949951,2827.949951,2802.489990,2818.370117,2818.370117,3645070000\n2018-08-16,2831.439941,2850.489990,2831.439941,2840.689941,2840.689941,3219880000\n2018-08-17,2838.320068,2855.629883,2833.729980,2850.129883,2850.129883,3024100000\n2018-08-20,2853.929932,2859.760010,2850.620117,2857.050049,2857.050049,2748020000\n2018-08-21,2861.510010,2873.229980,2861.320068,2862.959961,2862.959961,3147140000\n2018-08-22,2860.989990,2867.540039,2856.050049,2861.820068,2861.820068,2689560000\n2018-08-23,2860.290039,2868.780029,2854.030029,2856.979980,2856.979980,2713910000\n2018-08-24,2862.350098,2876.159912,2862.350098,2874.689941,2874.689941,2596190000\n2018-08-27,2884.689941,2898.250000,2884.689941,2896.739990,2896.739990,2854080000\n2018-08-28,2901.449951,2903.770020,2893.500000,2897.520020,2897.520020,2683190000\n2018-08-29,2900.620117,2916.500000,2898.399902,2914.040039,2914.040039,2791860000\n2018-08-30,2908.939941,2912.459961,2895.219971,2901.129883,2901.129883,2802180000\n2018-08-31,2898.370117,2906.320068,2891.729980,2901.520020,2901.520020,2880260000\n2018-09-04,2896.959961,2900.179932,2885.129883,2896.719971,2896.719971,3077060000\n2018-09-05,2891.590088,2894.209961,2876.919922,2888.600098,2888.600098,3241250000\n2018-09-06,2888.639893,2892.050049,2867.290039,2878.050049,2878.050049,3139590000\n2018-09-07,2868.260010,2883.810059,2864.120117,2871.679932,2871.679932,2946270000\n2018-09-10,2881.389893,2886.929932,2875.939941,2877.129883,2877.129883,2731400000\n2018-09-11,2871.570068,2892.520020,2866.780029,2887.889893,2887.889893,2899660000\n2018-09-12,2888.290039,2894.649902,2879.199951,2888.919922,2888.919922,3264930000\n2018-09-13,2896.850098,2906.760010,2896.389893,2904.179932,2904.179932,3254930000\n2018-09-14,2906.379883,2908.300049,2895.770020,2904.979980,2904.979980,3149800000\n2018-09-17,2903.830078,2904.649902,2886.159912,2888.800049,2888.800049,2947760000\n2018-09-18,2890.739990,2911.169922,2890.429932,2904.310059,2904.310059,3074610000\n2018-09-19,2906.600098,2912.360107,2903.820068,2907.949951,2907.949951,3280020000\n2018-09-20,2919.729980,2934.800049,2919.729980,2930.750000,2930.750000,3337730000\n2018-09-21,2936.760010,2940.909912,2927.110107,2929.669922,2929.669922,5607610000\n2018-09-24,2921.830078,2923.790039,2912.629883,2919.370117,2919.370117,3372210000\n2018-09-25,2921.750000,2923.949951,2913.699951,2915.560059,2915.560059,3285480000\n2018-09-26,2916.979980,2931.149902,2903.280029,2905.969971,2905.969971,3388620000\n2018-09-27,2911.649902,2927.219971,2909.270020,2914.000000,2914.000000,3060850000\n2018-09-28,2910.030029,2920.530029,2907.500000,2913.979980,2913.979980,3432300000\n2018-10-01,2926.290039,2937.060059,2917.909912,2924.590088,2924.590088,3364190000\n2018-10-02,2923.800049,2931.419922,2919.370117,2923.429932,2923.429932,3401880000\n2018-10-03,2931.689941,2939.860107,2921.360107,2925.510010,2925.510010,3598710000\n2018-10-04,2919.350098,2919.780029,2883.919922,2901.610107,2901.610107,3496860000\n2018-10-05,2902.540039,2909.639893,2869.290039,2885.570068,2885.570068,3328980000\n2018-10-08,2877.530029,2889.449951,2862.080078,2884.429932,2884.429932,3330320000\n2018-10-09,2882.510010,2894.830078,2874.270020,2880.340088,2880.340088,3520500000\n2018-10-10,2873.899902,2874.020020,2784.860107,2785.679932,2785.679932,4501250000\n2018-10-11,2776.870117,2795.139893,2710.510010,2728.370117,2728.370117,4890630000\n2018-10-12,2770.540039,2775.770020,2729.439941,2767.129883,2767.129883,3966040000\n2018-10-15,2763.830078,2775.989990,2749.030029,2750.790039,2750.790039,3300140000\n2018-10-16,2767.050049,2813.459961,2766.909912,2809.919922,2809.919922,3428340000\n2018-10-17,2811.669922,2816.939941,2781.810059,2809.209961,2809.209961,3321710000\n2018-10-18,2802.000000,2806.040039,2755.179932,2768.780029,2768.780029,3616440000\n2018-10-19,2775.659912,2797.770020,2760.270020,2767.780029,2767.780029,3566490000\n2018-10-22,2773.939941,2778.939941,2749.219971,2755.879883,2755.879883,3307140000\n2018-10-23,2721.030029,2753.590088,2691.429932,2740.689941,2740.689941,4348580000\n2018-10-24,2737.870117,2742.590088,2651.889893,2656.100098,2656.100098,4709310000\n2018-10-25,2674.879883,2722.699951,2667.840088,2705.570068,2705.570068,4634770000\n2018-10-26,2667.860107,2692.379883,2628.159912,2658.689941,2658.689941,4803150000\n2018-10-29,2682.649902,2706.850098,2603.540039,2641.250000,2641.250000,4673700000\n2018-10-30,2640.679932,2685.429932,2635.340088,2682.629883,2682.629883,5106380000\n2018-10-31,2705.600098,2736.689941,2705.600098,2711.739990,2711.739990,5112420000\n2018-11-01,2717.580078,2741.669922,2708.850098,2740.370117,2740.370117,4708420000\n2018-11-02,2745.449951,2756.550049,2700.439941,2723.060059,2723.060059,4237930000\n2018-11-05,2726.370117,2744.270020,2717.939941,2738.310059,2738.310059,3623320000\n2018-11-06,2738.399902,2756.820068,2737.080078,2755.449951,2755.449951,3510860000\n2018-11-07,2774.129883,2815.149902,2774.129883,2813.889893,2813.889893,3914750000\n2018-11-08,2806.379883,2814.750000,2794.989990,2806.830078,2806.830078,3630490000\n2018-11-09,2794.100098,2794.100098,2764.239990,2781.010010,2781.010010,4019090000\n2018-11-12,2773.929932,2775.989990,2722.000000,2726.219971,2726.219971,3670930000\n2018-11-13,2730.050049,2754.600098,2714.979980,2722.179932,2722.179932,4091440000\n2018-11-14,2737.899902,2746.800049,2685.750000,2701.580078,2701.580078,4402370000\n2018-11-15,2693.520020,2735.379883,2670.750000,2730.199951,2730.199951,4179140000\n2018-11-16,2718.540039,2746.750000,2712.159912,2736.270020,2736.270020,3975180000\n2018-11-19,2730.739990,2733.159912,2681.090088,2690.729980,2690.729980,3772900000\n2018-11-20,2654.600098,2669.439941,2631.520020,2641.889893,2641.889893,4357900000\n2018-11-21,2657.739990,2670.729980,2649.820068,2649.929932,2649.929932,3233550000\n2018-11-23,2633.360107,2647.550049,2631.090088,2632.560059,2632.560059,1651650000\n2018-11-26,2649.969971,2674.350098,2649.969971,2673.449951,2673.449951,3443950000\n2018-11-27,2663.750000,2682.530029,2655.889893,2682.169922,2682.169922,3485220000\n2018-11-28,2691.449951,2744.000000,2684.379883,2743.790039,2743.790039,3951670000\n2018-11-29,2736.969971,2753.750000,2722.939941,2737.800049,2737.800049,3560770000\n2018-11-30,2737.760010,2760.879883,2732.760010,2760.169922,2760.169922,4658580000\n2018-12-03,2790.500000,2800.179932,2773.379883,2790.370117,2790.370117,4186060000\n2018-12-04,2782.429932,2785.929932,2697.179932,2700.060059,2700.060059,4499840000\n2018-12-06,2663.510010,2696.149902,2621.530029,2695.949951,2695.949951,5141470000\n2018-12-07,2691.260010,2708.540039,2623.139893,2633.080078,2633.080078,4216690000\n"
  },
  {
    "path": "ch_inference_for_props/figures/geomFitPValueForSP500/geomFitPValueForSP500.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('geomFitPValueForSP500.pdf', 6.6, 2.387,\n      mar = c(2, 1, 1, 1),\n      mgp = c(2.1, 0.5, 0))\nChiSquareTail(4.61,\n              6,\n              c(0, 25),\n              col = COL[1])\narrows(15.1, 0.07,\n       10.5, 0.05,\n       length = 0.1,\n       col = COL[1])\ntext(15.1, 0.07, 'Area representing\\nthe p-value',\n     pos = 4,\n     col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/iPodChiSqTail/iPodChiSqTail.R",
    "content": "library(openintro)\n\nx <- print(chisq.test(table(ask[2:3])))$statistic\n\nmyPDF('iPodChiSqTail.pdf', 5, 2.25,\n    mar = c(2, 1, 1, 1),\n    mgp = c(2.1, 0.7, 0))\nChiSquareTail(x, 2,\n              c(0, 50),\n              col = COL[1])\ntext(x, 0, \"Tail area (1 / 500 million)\\nis too small to see\", pos = 3)\nlines(c(x, 1000 * x), rep(0, 2), col = COL[1], lwd = 3)\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/jurorHTPValueShown/jurorHTPValueShown.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('jurorHTPValueShown.pdf', 4.4, 1.87,\n      mar = c(1.5, 1, 0.2, 1),\n      mgp = c(2.1, 0.45, 0))\nChiSquareTail(5.89,\n              3,\n              c(0, 16),\n              col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/mammograms/mammograms.R",
    "content": "require(openintro)\ndata(COL)\n\nfn <- 'mammogramPValue.pdf'\nmyPDF(fn, 4, 1.2,\n      mar = c(1.5, 0, 0.1, 0),\n      mgp = c(3, 0.3, 0))\nnormTail(L = -0.17, U = 0.17,\n        col = COL[1],\n        axes = FALSE,\n        xlim = c(-3.2, 3.2))\nat <- c(-10, -2, 0, 2, 10)\nlabels <- c(0, -0.0014, 0, 0.0014, 0)\naxis(1, at, labels, cex.axis = 0.9)\n# lines(rep(0, 2), c(0, dnorm(0)), col = COL[4])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/paydayCC_norm_pvalue/paydayCC_norm_pvalue.R",
    "content": "require(openintro)\n\nfn <- 'paydayCC_norm_pvalue.pdf'\nmyPDF(fn, 4, 1.5,\n      mar = c(1.55, 0, 0.1, 0),\n      mgp = c(3, 0.5, 0))\nnormTail(0.5, 0.017, L = 0.49, U = 0.51, col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_inference_for_props/figures/quadcopter/quadcopter_attribution.txt",
    "content": "https://secure.flickr.com/photos/sebilden/14642916088\n\nPhotographer: David J\nLicense: CC BY 2.0\n"
  },
  {
    "path": "ch_intro_to_data/TeX/case_study_using_stents_to_prevent_strokes.tex",
    "content": "\\exercisesheader{}\n\n% 1\n\n\\eoce{\\qt{Migraine and acupuncture,\n    Part I\\label{migraine_and_acupuncture_intro}}\nA migraine is a particularly painful type of headache,\nwhich patients sometimes wish to treat with acupuncture.\nTo determine whether acupuncture relieves migraine \npain, researchers conducted a randomized controlled study\nwhere 89 females diagnosed with migraine headaches were\nrandomly assigned to one of two groups:\ntreatment or control.\n43 patients in the treatment group received acupuncture \nthat is specifically designed to treat migraines.\n46 patients in the control group received placebo acupuncture\n(needle insertion at non-acupoint locations).\n24 hours after patients received acupuncture, they were asked \nif they were pain free.\nResults are summarized in the contingency table\nbelow.\\footfullcite{Allais:2011}\n\n\\noindent\\begin{minipage}[l]{0.4\\textwidth}\n\\begin{tabular}{ll  cc c} \n\t\t\t                         \t\t&           & \\multicolumn{2}{c}{\\textit{Pain free}} \\\\\n\\cline{3-4}\n\t\t\t                        \t \t&\t\t\t& Yes \t& No \t                  & Total \\\\\n\\cline{2-5}\n\t\t\t\t\t\t\t& Treatment \t& 10\t \t& 33\t\t                  & 43 \\\\\n\\raisebox{1.5ex}[0pt]{\\emph{Group}} & Control\t \t& 2\t \t& 44 \t \t                  & 46 \\\\\n\\cline{2-5}\n\t\t\t\t\t\t\t& Total\t\t& 12\t\t& 77\t\t                  & 89\n\\end{tabular}\n\\end{minipage}\n\\begin{minipage}[c]{0.05\\textwidth}\n\\end{minipage}\n\\begin{minipage}[c]{0.27\\textwidth}\n\\begin{center}\n\\Figures[An ear is show, with an \"M\" shown near the front lower lobe of the ear and an \"S\" shown near the middle upper portion of the ear.]{0.75}{eoce/migraine_and_acupuncture_intro}{earacupuncture}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.25\\textwidth}\n{\\footnotesize Figure from the original paper displaying the appropriate area \n(M) versus the inappropriate area (S) used in the treatment of migraine attacks.}\n\\end{minipage}\n\\begin{parts}\n\\item What percent of patients in the treatment group were pain free 24 hours \nafter receiving acupuncture? \n\\item What percent were pain free in the control group?\n\\item In which group did a higher percent of patients become pain free 24 hours \nafter receiving acupuncture?\n\\item Your findings so far might suggest that acupuncture is an effective treatment \nfor migraines for all people who suffer from migraines. However, this is not the \nonly possible conclusion that can be drawn based on your findings so far. What is \none other possible explanation for the observed difference between the percentages \nof patients that are pain free 24 hours after receiving acupuncture in the two groups?\n\\end{parts}\n}{}\n\n% 2\n\n\\eoce{\\qt{Sinusitis and antibiotics,\n    Part I\\label{sinusitis_and_antibiotics_intro}} \nResearchers studying the effect of antibiotic treatment for acute sinusitis \ncompared to symptomatic treatments randomly assigned 166 adults diagnosed \nwith acute sinusitis to one of two groups: treatment or control. Study \nparticipants received either a 10-day course of amoxicillin (an antibiotic) \nor a placebo similar in appearance and taste. The placebo consisted of \nsymptomatic treatments such as acetaminophen, nasal decongestants, etc.\nAt the end of the 10-day period, patients were asked if\nthey experienced improvement in symptoms.\nThe distribution of responses is summarized below. \n\\footfullcite{Garbutt:2012}\n\\begin{center}\n\\begin{tabular}{ll  cc c} \n                                    \t\t\t&\t\t\t& \\multicolumn{2}{c}{\\textit{Self-reported improvement}} \\\\\n                                    \t\t\t&\t\t\t& \\multicolumn{2}{c}{\\textit{in symptoms}} \\\\\n\\cline{3-4}\n\t\t\t                        \t\t&\t\t\t& Yes \t& No \t& Total \\\\\n\\cline{2-5}\n\t\t\t\t\t\t\t& Treatment \t& 66\t\t& 19\t\t& 85 \\\\\n\\raisebox{1.5ex}[0pt]{\\emph{Group}}\t& Control\t\t& 65\t\t& 16 \t\t& 81 \\\\\n\\cline{2-5}\n\t\t\t\t\t\t\t& Total\t\t& 131\t& 35\t\t& 166\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item What percent of patients in the treatment group experienced improvement \nin symptoms? \n\\item What percent experienced improvement in symptoms in the \ncontrol group?\n\\item In which group did a higher percentage of patients experience improvement\nin symptoms?\n\\item\n    Your findings so far might suggest a real difference\n    in effectiveness of antibiotic and placebo treatments\n    for improving symptoms of sinusitis.\n    However, this is not the only possible conclusion that\n    can be drawn based on your findings so far.\n    What is one other possible explanation for the observed\n    difference between the percentages of patients in the\n    antibiotic and placebo treatment groups that experience\n    improvement in symptoms of sinusitis?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_intro_to_data/TeX/ch_intro_to_data.tex",
    "content": "\\begin{chapterpage}{Introduction to data}\n  \\chaptertitle{Introduction to data}\n  \\label{introductionToData}\n  \\label{ch_intro_to_data}\n  \\chaptersection{basicExampleOfStentsAndStrokes}\n  \\chaptersection{dataBasics}\n  \\chaptersection{overviewOfDataCollectionPrinciples}\n  % \\chaptersection{section_obs_data_sampling}\n  \\chaptersection{experimentsSection}\n\\end{chapterpage}\n\\renewcommand{\\chapterfolder}{ch_intro_to_data}\n\n%\\begin{tipBox}{\\tipBoxTitle[Chapter Goal:]{Thinking about data}\n%Understand basics about data organization, data types, numerical summaries of data, graphical summaries of data, and foundational techniques for data collection. We begin and end the chapter with case studies.}\n%\\end{tipBox}\n\n\\chapterintro{Scientists seek to answer questions\n  using rigorous methods and careful observations.\n  These observations -- collected from the likes of field notes,\n  surveys, and experiments -- form the backbone of a statistical\n  investigation and are called \\term{data}.\n  Statistics is the study of how best to collect, analyze,\n  and draw conclusions from data, %It is helpful to put statistics in the context of a general process of investigation:\n%\\begin{enumerate}\n%\\setlength{\\itemsep}{0mm}\n%\\item Identify a question or problem.\n%\\item Collect relevant data on the topic.\n%\\item Analyze the data.\n%\\item Form a conclusion.\n%%\\item Make decisions based on the conclusion.\n%\\end{enumerate}\n%Statistics as a subject focuses on making stages 2-4 objective, rigorous, and efficient. That~is, statistics has three primary components: How best can we collect data? How should it be analyzed? And what can we infer from the analysis?\n  and in this first chapter,\n  we focus on both the properties of data\n  and on the collection of data.}\n\n%The topics scientists investigate are as diverse as the questions they ask. However, many of these investigations can be addressed with a small number of data collection techniques, analytic tools, and fundamental concepts in statistical inference. This chapter provides a glimpse into these and other themes we will encounter throughout the rest of the book. We introduce the basic principles of each branch and learn some tools along the way. We will encounter applications from other fields, some of which are not typically associated with science but nonetheless can benefit from statistical study.\n\n\n\n\\section{Case study: using stents to prevent strokes}\n\\label{basicExampleOfStentsAndStrokes}\n\n\\index{data!stroke|(}\n\nSection~\\ref{basicExampleOfStentsAndStrokes} introduces a classic challenge in statistics: evaluating the efficacy of a medical treatment. Terms in this section, and indeed much of this chapter, will all be revisited later in the text. The plan for now is simply to get a sense of the role statistics can play in practice.\n\nIn this section we will consider an experiment that studies effectiveness of stents in treating patients at risk of stroke.\nStents are devices put inside blood vessels that assist in patient recovery after cardiac events and reduce the risk of an additional heart attack or death. Many doctors have hoped that there would be similar benefits for patients at risk of stroke. We start by writing the principal question the researchers hope to answer:\n\\begin{quote}\nDoes the use of stents reduce the risk of stroke?\n\\end{quote}\n\nThe researchers who asked this question conducted an experiment with 451 at-risk patients. Each volunteer patient was randomly assigned to one of two groups:\n\\begin{itemize}\n\\item[]\\termsub{Treatment group}{treatment group}. Patients in the treatment group received a stent and medical management. The medical management included medications, management of risk factors, and help in lifestyle modification.\n\\item[]\\termsub{Control group}{control group}. Patients in the control group received the same medical management as the treatment group, but they did not receive stents.\n\\end{itemize}\nResearchers randomly assigned 224 patients to the treatment group and 227 to the control group. In this study, the control group provides a reference point against which we can measure the medical impact of stents in the treatment group.\n\nResearchers studied the effect of stents at two time points: 30~days after enrollment and 365~days after enrollment. The results of 5 patients are summarized in Figure~\\ref{stentStudyResultsDF}. Patient outcomes are recorded as ``stroke'' or ``no event'', representing whether or not the patient had a stroke at the end of a time period.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l ccc}\n\\hline\nPatient\t&\tgroup\t&\t0-30 days \t&\t0-365 days \\\\\n\\hline\n1\t\t&\ttreatment &\tno event &\tno event \\\\\n2\t\t&\ttreatment &\tstroke & stroke \\\\\n3\t\t&\ttreatment &\tno event & no event \\\\\n$\\vdots$\t&\t$\\vdots$\t  &\t$\\vdots$ \\\\\n450\t&\tcontrol &\tno event &\tno event \\\\\n451\t&\tcontrol &\tno event &\tno event \\\\\n\\hline\n\\end{tabular}\n\\caption{Results for five patients from the stent study.}\n\\label{stentStudyResultsDF}\n% trmt <- c(rep('trmt', 224), rep('control', 227)); outcome30 <- c(rep(c('event', 'no_event'), c(33, 191)), rep(c('event', 'no_event'), c(13, 214))); outcome365 <- c(rep(c('event', 'no_event'), c(33, 191)), rep(c('event', 'no_event'), c(13, 214)))\n\\end{figure}\n\nConsidering data from each patient individually would be a long, cumbersome path towards answering the original research question. Instead, performing a statistical data analysis allows us to consider all of the data at once. Figure~\\ref{stentStudyResults} summarizes the raw data in a more helpful way. In this table, we can quickly see what happened over the entire study. For instance, to identify the number of patients in the treatment group who had a stroke within 30 days, we look on the left-side of the table at the intersection of the treatment and stroke: 33.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l cc c cc}\n& \\multicolumn{2}{c}{0-30 days} &\\hspace{5mm}\\ & \\multicolumn{2}{c}{0-365 days} \\\\\n  \\cline{2-3} \\cline{5-6}\n\t& \tstroke \t& no event && \tstroke \t& no event \\\\\n  \\hline\ntreatment \t& 33\t\t& 191\t&&\t45 \t& 179 \\\\\ncontrol \t\t& 13\t\t& 214\t&& \t28\t& 199 \\\\\n  \\hline\nTotal\t\t\t\t& 46\t\t& 405\t&&\t73\t& 378 \\\\\n  \\hline\n\\end{tabular}\n\\caption{Descriptive statistics for the stent study.}\n\\label{stentStudyResults}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nOf the 224 patients in the treatment group, 45 had a stroke by the end of the first year. Using these two numbers, compute the proportion of patients in the treatment group who had a stroke by the end of their first year. (Please note: answers to all Guided Practice exercises are provided using footnotes.)\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\\footnotetext{The proportion of the 224 patients who had a stroke within 365 days: $45/224 = 0.20$.}\n\nWe can compute summary statistics from the table.\nA \\term{summary statistic}%\n\\index{statistic|seealso{summary statistic}}\nis a single number summarizing\na large amount of data.\nFor instance, the primary results of the study after 1~year\ncould be described by two summary statistics:\nthe proportion of people who had a stroke in the treatment\nand control groups.\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[] Proportion who had a stroke in the treatment (stent) group: $45/224 = 0.20 = 20\\%$.\n\\item[] Proportion who had a stroke in the control group: $28/227 = 0.12 = 12\\%$.\n\\end{itemize}\nThese two summary statistics are useful in looking for differences in the groups, and we are in for a surprise: an additional 8\\% of patients in the treatment group had a stroke! This is important for two reasons. First, it is contrary to what doctors expected, which was that stents would \\emph{reduce} the rate of strokes. Second, it leads to a statistical question: do the data show a ``real'' difference between the groups?\n\nThis second question is subtle. Suppose you flip a coin 100 times. While the chance a coin lands heads in any given coin flip is 50\\%, we probably won't observe exactly 50 heads. This type of fluctuation is part of almost any type of data generating process. It is possible that the 8\\% difference in the stent study is due to this natural variation. However, the larger the difference we observe (for a particular sample size), the less believable it is that the difference is due to chance. So what we are really asking is the following: is the difference so large that we should reject the notion that it was due to chance?\n\nWhile we don't yet have our statistical tools to fully address this question on our own, we can comprehend the conclusions of the published analysis: there was compelling evidence of harm by stents in this study of stroke patients.\n\n\\textbf{Be careful:}\nDo not generalize the results of this study to all patients\nand all stents.\nThis study looked at patients with very specific characteristics\nwho volunteered to be a part of this study and who may not be\nrepresentative of all stroke patients.\nIn addition, there are many types of stents and this study only\nconsidered the self-expanding Wingspan stent (Boston Scientific).\nHowever, this study does leave us with an important lesson:\nwe should keep our eyes open for surprises.\n\n\\index{data!stroke|)}\n\n\n{\\input{ch_intro_to_data/TeX/case_study_using_stents_to_prevent_strokes.tex}}\n\n\n\n\\section{Data basics}\n\\label{dataBasics}\n\nEffective organization and description of data is a first\nstep in most analyses.\nThis section introduces the \\emph{data matrix} for organizing\ndata as well as some terminology about different forms of data\nthat will be used throughout this book.\n\n\\subsection{Observations, variables, and data matrices}\n\n\\index{data!loan50|(}\n\nFigure~\\ref{loan50DF} displays rows 1, 2, 3, and 50 of a data set\nfor 50 randomly sampled loans offered through Lending Club,\nwhich is a peer-to-peer lending company.\nThese observations will be referred to as the\n\\data{loan50} data set.\n\nEach row in the table represents a single loan.\nThe formal name for a row is a \\term{case}\nor \\term{observational unit}\\index{unit of observation}.\nThe columns represent characteristics,\ncalled \\termsub{variables}{variable},\nfor each of the loans.\nFor example, the first row represents a loan of \\$22,000 with an interest rate of 10.90\\%, where the borrower is based in New Jersey (NJ) and has an income of \\$59,000.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat is the grade of the first loan in Figure~\\ref{loan50DF}?\nAnd what is the home ownership status of the borrower\nfor that first loan?\nFor these Guided Practice questions, you can check your answer\nin the footnote.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The loan's grade is B,\n  and the borrower rents their residence.}\n\nIn practice, it is especially important to ask clarifying\nquestions to ensure important aspects of the data are understood.\nFor instance, it is always important to be sure we know what\neach variable means and the units of measurement.\nDescriptions of the \\data{loan50} variables are given\nin Figure~\\ref{loan50Variables}.\n\n\\begin{figure}[h]\n\\centering\n{\\small\n\\begin{tabular}{ccc ccc cc} %c}\n  \\hline\n   & \\var{loan\\us{}amount}\n   & \\var{interest\\us{}rate}\n   & \\var{term} & \\var{grade} & \\var{state}\n   & \\var{total\\us{}income}\n   & \\var{homeownership} \\\\\n  \\hline\n  1 & 22000 & 10.90 & 60.00 & B & NJ & 59000.00 & rent \\\\ \n  2 & 6000 & 9.92 & 36.00 & B & CA & 60000.00 & rent \\\\ \n  3 & 25000 & 26.30 & 36.00 & E & SC & 75000.00 & mortgage \\\\\n  %1 & 7500 & 7.34 & 36 & A & MD & 70000 & rent \\\\\n  %2 & 25000 & 9.43 & 60 & B & OH & 254000 & mortgage \\\\\n  %3 & 14500 & 6.08 & 36 & A & MO & 80000 & mortgage \\\\\n  $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$\n      & $\\vdots$ & $\\vdots$ \\\\\n  50 & 15000 & 6.08 & 36.00 & A & TX & 77500.00 & mortgage \\\\\n  %50 & 3000 & 7.96 & 36 & A & CA & 34000 & rent \\\\\n   \\hline\n\\end{tabular}\n}\n\\caption{Four rows from the \\data{loan50} data matrix.}\n\\label{loan50DF}\n\\end{figure}\n% Dropped: state, verified_income\n% library(openintro); vars <- c(\"loan_amount\", \"interest_rate\", \"term\", \"grade\", \"state\", \"total_income\", \"homeownership\"); library(xtable); data(loan50); loan50[c(1,2,3,50), vars]; xtable(loan50[c(1,2,3,50), vars])\n\n\\begin{figure}[h]\n\\centering\\small\n\\begin{tabular}{lp{10.5cm}}\n\\hline\n{\\bf variable} & {\\bf description} \\\\\n\\hline\n\\var{loan\\us{}amount} & Amount of the loan received,\n    in US dollars.  \\\\\n\\var{interest\\us{}rate} & Interest rate on the loan,\n    in an annual percentage.  \\\\\n\\var{term} & The length of the loan, which is always set\n    as a whole number of months. \\\\\n\\var{grade} & Loan grade, which takes values A through G\n    and represents the quality of the loan and its likelihood\n    of being repaid.  \\\\\n\\var{state} & US state where the borrower resides. \\\\\n\\var{total\\us{}income} & Borrower's total income,\n    including any second income, in US dollars.   \\\\\n\\var{homeownership} & Indicates whether the\n    person owns, owns but has a mortgage, or rents.  \\\\\n%\\var{verified\\us{}income} & Indicates whether the\n%    income is verified, its source is verified but not the amount,\n%    or it is not verified.   \\\\\n\\hline\n\\end{tabular}\n\\caption{Variables and their descriptions for the \\data{loan50} data set.}\n\\label{loan50Variables}\n\\end{figure}\n\n\\index{data!loan50|)}\n\nThe data in Figure~\\ref{loan50DF} represent a \\term{data matrix},\nwhich is a convenient and common way to organize data,\nespecially if collecting data in a spreadsheet.\nEach row of a data matrix corresponds to a unique case\n(observational unit),\nand each column corresponds to a variable.\n%A data matrix for the stroke study introduced in\n%Section~\\ref{basicExampleOfStentsAndStrokes} is shown\n%in Figure~\\vref{stentStudyResultsDF}, where the cases were\n%patients and three variables were recorded for each\n%patient.\n\n\\D{\\newpage}\n\nWhen recording data, use a data matrix unless you have\na very good reason to use a different structure.\nThis structure allows new cases to be added as rows\nor new variables as new columns.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe grades for assignments, quizzes, and exams in a course are\noften recorded in a gradebook that takes the form of a data matrix.\nHow might you organize grade data using a data\nmatrix?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\n\\index{data!county|(}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{desc_county_as_data_matrix}%\nWe consider data for 3,142 counties in the United States,\nwhich includes each county's name,\nthe state where it resides, its population in 2017,\nhow its population changed from 2010 to 2017,\npoverty rate,\nand six additional characteristics.\nHow might these data be organized in\na data matrix?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\addtocounter{footnote}{-1}\n\\footnotetext{There are multiple strategies that can be followed.\n  One common strategy is to have each student represented by a row,\n  and then add a column for each assignment, quiz, or exam.\n  Under this setup, it is easy to review a single line to understand\n  a student's grade history.\n  There should also be columns to include student information,\n  such as one column to list student names.}\n\\addtocounter{footnote}{1}\n\\footnotetext{Each county may be viewed as a case,\n  and there are eleven pieces of information recorded for\n  each case.\n  A table with 3,142 rows and 11 columns could hold these data,\n  where each row represents a county and each column represents\n  a particular piece of information.}\n\nThe data described in Guided\nPractice~\\ref{desc_county_as_data_matrix} represents the\n\\data{county} data set, which is shown as a data matrix\nin Figure~\\ref{countyDF}.\nThe variables are summarized in Figure~\\ref{countyVariables}.\n\n\\begin{landscape}\n\\begin{figure}\n\\centering\\small\n\\begin{tabular}{ccc ccc ccc ccc}\n  \\hline\n & \\var{name} & \\var{state} & \\var{pop} & \\var{pop\\us{}change} & \\var{poverty} & \\var{homeownership} & \\var{multi\\us{}unit} & \\var{unemp\\us{}rate} & \\var{metro} & \\var{median\\us{}edu} & \\var{median\\us{}hh\\us{}income} \\\\ \n  \\hline\n1 & Autauga  & Alabama &  55504 &  1.48 & 13.7 & 77.5 &  7.2 & 3.86 & yes & some\\us{}college & 55317 \\\\ \n  2 & Baldwin  & Alabama & 212628 &  9.19 & 11.8 & 76.7 & 22.6 & 3.99 & yes & some\\us{}college & 52562 \\\\ \n  3 & Barbour  & Alabama &  25270 & -6.22 & 27.2 & 68.0 & 11.1 & 5.90 & no  & hs\\us{}diploma   & 33368 \\\\ \n  4 & Bibb     & Alabama &  22668 &  0.73 & 15.2 & 82.9 &  6.6 & 4.39 & yes & hs\\us{}diploma   & 43404 \\\\ \n  5 & Blount   & Alabama &  58013 &  0.68 & 15.6 & 82.0 &  3.7 & 4.02 & yes & hs\\us{}diploma   & 47412 \\\\ \n  6 & Bullock  & Alabama &  10309 & -2.28 & 28.5 & 76.9 &  9.9 & 4.93 & no  & hs\\us{}diploma   & 29655 \\\\ \n  7 & Butler   & Alabama &  19825 & -2.69 & 24.4 & 69.0 & 13.7 & 5.49 & no  & hs\\us{}diploma   & 36326 \\\\ \n  8 & Calhoun  & Alabama & 114728 & -1.51 & 18.6 & 70.7 & 14.3 & 4.93 & yes & some\\us{}college & 43686 \\\\ \n  9 & Chambers & Alabama &  33713 & -1.20 & 18.8 & 71.4 &  8.7 & 4.08 & no  & hs\\us{}diploma   & 37342 \\\\ \n  10 & Cherokee & Alabama &  25857 & -0.60 & 16.1 & 77.5 &  4.3 & 4.05 & no  & hs\\us{}diploma   & 40041 \\\\ \n  $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ \\\\\n  3142 & Weston   & Wyoming &   6927 & -2.93 & 14.4 & 77.9 &  6.5 & 3.98 & no  & some\\us{}college & 59605 \\\\ \n   \\hline\n\\end{tabular}\n\\caption{Eleven rows from the \\data{county} data set.}\n\\label{countyDF}\n% library(openintro); data(county); county$name <- gsub(\" County$\", \"\", county$name); county$pop <- county$pop2017; county$unemp_rate = county$unemployment_rate; these <- c(\"name\", \"state\", \"pop\", \"pop_change\", \"poverty\", \"homeownership\", \"multi_unit\", \"unemp_rate\", \"metro\", \"median_edu\", \"median_hh_income\"); county <- county[, these]; library(xtable); xtable(as.data.frame(lapply(rbind.data.frame(head(county, 10), tail(county, 1)), function(x) { format(x) })))\n\\end{figure}\n\n\\begin{figure}\n\\centering\\small\n\\begin{tabular}{lp{11cm}}\n\\hline\n{\\bf variable} & {\\bf description} \\\\\n\\hline\n\\var{name} &\n    County name. \\\\\n\\var{state} &\n    State where the county resides,\n    or the District of Columbia. \\\\\n\\var{pop} &\n    Population in 2017. \\\\\n\\var{pop\\us{}change} &\n    Percent change in the population from 2010 to 2017.\n    For example, the value \\resp{1.48} in the first row\n    means the population for this county\n    increased by 1.48\\% from 2010 to 2017. \\\\\n\\var{poverty} &\n    Percent of the population in poverty. \\\\\n\\var{homeownership}  &\n    Percent of the population that lives in their own home\n    or lives with the owner, e.g. children living with parents\n    who own the home. \\\\\n\\var{multi\\us{}unit}  &\n    Percent of living units that are in multi-unit structures,\n    e.g. apartments. \\\\\n\\var{unemp\\us{}rate} &\n    Unemployment rate as a percent. \\\\\n\\var{metro} &\n    Whether the county contains a metropolitan area. \\\\\n\\var{median\\us{}edu} & Median education level, which\n    can take a value among\n    \\resp{below\\us{}hs},\n    \\resp{hs\\us{}diploma},\n    \\resp{some\\us{}college},\n    and \\resp{bachelors}. \\\\\n\\var{median\\us{}hh\\us{}income} &\n    Median household income for the county, where a household's\n    income equals the total income of its occupants who are\n    15~years or older. \\\\\n%\\var{per\\us{}capita\\us{}income} &\n%    Per capita (per person) income for the county. \\\\\n\\hline\n\\end{tabular}\n\\centering\n\\caption{Variables and their descriptions for the \\data{county} data set.}\n\\label{countyVariables}\n\\end{figure}\n\\end{landscape}\n\n\\subsection{Types of variables}\n\\label{variableTypes}\n\nExamine the \\var{unemp\\us{}rate}, \\var{pop}, \\var{state},\nand \\var{median\\us{}edu} variables in the \\data{county}\ndata set. Each of these variables is inherently different from the\nother three, yet some share certain characteristics.\n\nFirst consider \\var{unemp\\us{}rate},\nwhich is said to be a \\term{numerical} variable since\nit can take a wide range of numerical values,\nand it is sensible to add, subtract, or take averages\nwith those values.\nOn the other hand, we would not classify a variable\nreporting telephone area codes as numerical since the\naverage, sum, and difference of area codes doesn't have\nany clear meaning.\n\nThe \\var{pop} variable is also numerical, although it seems\nto be a little different than \\var{unemp\\us{}rate}.\nThis variable of the population count can only take whole\nnon-negative numbers (\\resp{0}, \\resp{1}, \\resp{2}, ...).\nFor~this reason, the population variable is said to be\n\\term{discrete} since it can only take numerical values\nwith jumps.\nOn the other hand, the unemployment rate variable is said\nto be \\term{continuous}.\n\nThe variable \\var{state} can take up to 51 values after\naccounting for Washington, DC: \\resp{AL}, \\resp{AK}, ...,\nand \\resp{WY}.\nBecause the responses themselves are categories,\n\\var{state} is called a \\term{categorical} variable,\nand the possible values are called the variable's \\term{levels}.\n\nFinally, consider the \\var{median\\us{}edu} variable,\nwhich describes the median education level of county\nresidents and takes values\n\\resp{below\\us{}hs}, \\resp{hs\\us{}diploma},\n\\resp{some\\us{}college}, or \\resp{bachelors}\nin each county.\nThis variable seems to be a hybrid: it is a categorical variable\nbut the levels have a natural ordering.\nA variable with these properties is called an \\term{ordinal}\nvariable, while a regular categorical variable without this\ntype of special ordering is called a \\term{nominal} variable.\nTo simplify analyses, any ordinal variable in this book will\nbe treated as a nominal (unordered) categorical variable.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure\n    [Breakdown of variables into their respective types, showing \"all variables\" breaking down into \"numeric\" and \"categorical\". Then \"numeric\" is divided into \"continuous\" and \"discrete\", and \"categorical\" is broken down into \"nominal (unordered categorical)\" and \"ordinal (ordered categorical)'' variables.]\n    {0.57}{variables}\n  \\caption{Breakdown of variables into their respective types.}\n  \\label{variables}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Data were collected about students\n    in a statistics course.\n    Three variables were recorded for each student:\n    number of siblings, student height, and whether\n    the student had previously taken a statistics course.\n    Classify each of the variables as continuous numerical,\n    discrete numerical, or categorical.}\n  The number of siblings and student height represent\n  numerical variables.\n  Because the number of siblings is a count, it is discrete.\n  Height varies continuously, so it is a continuous numerical\n  variable.\n  The last variable classifies students into two categories\n  -- those who have and those who have not taken a statistics\n  course -- which makes this variable categorical.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\index{data!stroke}%\nAn experiment is evaluating the effectiveness of a new drug\nin treating migraines.\nA \\var{group} variable is used to indicate the experiment group\nfor each patient: treatment or control.\nThe \\mbox{\\var{num\\us{}migraines}} variable represents the number\nof migraines the patient experienced during a 3-month period.\n\\mbox{Classify} each variable as either numerical or\ncategorical.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The\n  \\var{group} variable can take just one of two group names,\n  making it categorical.\n  The \\var{num\\us{}migraines} variable describes\n  a count of the number of migraines, which is an outcome where\n  basic arithmetic is sensible, which means this is numerical\n  outcome; more specifically, since it represents a count,\n  \\var{num\\us{}migraines} is a discrete numerical variable.}\n\n\n\\D{\\newpage}\n\n\\subsection{Relationships between variables}\n\\label{variableRelations}\n\nMany analyses are motivated by a researcher looking\nfor a relationship between two or more variables.\nA social scientist may like to answer some of the\nfollowing questions:\n\\newcommand{\\popchangevmedianhhincomequestion}[0]{\n    % Note that this question is used to introduce the\n    %explanatory / response variable topic.\n    Does a higher than average increase in county population\n    tend to correspond to counties with higher or lower median\n    household incomes?}%\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item[(1)]\\label{ownershipMultiUnitQuestion}\n    If homeownership is lower than the national average\n    in one county, will the percent of multi-unit structures\n    in that county tend to be above or below the national average?\n\\item[(2)]\\label{pop_change_v_median_hh_income_question}\n    \\popchangevmedianhhincomequestion{}\n    % Do counties with a higher median household income\n    % tend to be growing faster or slower than other counties?\n\\item[(3)]\\label{isAverageIncomeAssociatedWithSmokingBans}\n    How useful a predictor is median education level\n    for the median household income for US counties?\n\\end{enumerate}\n\nTo answer these questions, data must be collected, such\nas the \\data{county} data set shown in Figure~\\ref{countyDF}.\nExamining summary statistics \\index{summary statistic} could\nprovide insights for each of the three questions about counties.\nAdditionally, graphs can be used to visually explore data.\n\n\\indexthis{Scatterplots}{scatterplot} are one type of graph\nused to study the relationship between two numerical variables.\nFigure~\\ref{multiunitsVsOwnership} compares the variables\n\\var{homeownership} and\n\\var{multi\\us{}unit},\nwhich is the percent of units in multi-unit structures\n(e.g. apartments, condos).\nEach point on the plot represents a single county.\nFor instance, the highlighted dot corresponds to\nCounty~413 in the \\data{county} data set:\nChattahoochee County, Georgia, which has 39.4\\% of\nunits in multi-unit structures and a homeownership rate\nof 31.3\\%.\nThe scatterplot suggests a relationship between the\ntwo variables: counties with a higher rate of multi-units\ntend to have lower homeownership rates.\nWe might brainstorm as to why this relationship exists\nand investigate each idea to determine which are the most\nreasonable explanations.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure\n    [Scatterplot of thousands of counties with the percent of multiunit structures in each county shown on the horizontal axis and homeownership rate shown on the vertical axis. The data range from 0\\% to almost 100\\% for both variables. In general, the points are much more concentrated in the upper left corner of the graph and then trend downward for observations further to the right while also becoming more sparse. One point is annotated at the location (39.4\\%, 31.3.\\%).]\n    {0.79}{multiunitsVsOwnership}\n  \\caption{A scatterplot of homeownership versus the percent\n      of units that are in multi-unit structures for US counties.\n      The highlighted dot represents Chattahoochee County, Georgia,\n      which has a multi-unit rate of 39.4\\% and a homeownership rate\n      of 31.3\\%.}\n  \\label{multiunitsVsOwnership}\n\\end{figure}\n\nThe multi-unit and homeownership rates are said to be\nassociated because the plot shows a discernible pattern.\nWhen two variables show some connection with one another,\nthey are called \\term{associated} variables.\nAssociated variables can also be called \\term{dependent}\nvariables and vice-versa.\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nExamine the variables in the \\data{loan50} data set,\nwhich are described in Figure~\\vref{loan50Variables}.\nCreate two questions about possible relationships\nbetween variables in \\data{loan50} that are of interest\nto~you.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Two example questions:\n  (1)~What is the relationship between loan amount and\n      total income?\n  (2)~If someone's income is above the average, will their\n      interest rate tend to be above or below the average?}\n\n\\begin{examplewrap}\n\\begin{nexample}{This example examines the relationship\n    between a county's population change\n    from 2010 to 2017\n    and median household income,\n    which is visualized as a scatterplot in\n    Figure~\\ref{pop_change_v_med_income}.\n    Are these variables associated?}\n  The larger the median household income for a county,\n  the higher the population growth observed for the county.\n  While this trend isn't true for every county,\n  the trend in the plot is evident.\n  Since there is some relationship between the variables,\n  they are associated.\n\\end{nexample}\n\\end{examplewrap}\n\n%When two variables show some connection with one another,\n%they are called \\term{associated} variables.\n%Associated variables can also be called \\term{dependent}\n%variables and vice-versa.\n%When the variables increase together,\n%as they do in Figure~\\ref{loan_amount_vs_income},\n%they are said to be \\term{positively associated}.\n%When the trend in the scatterplot goes down to the right,\n%then they are described as \\term{negatively correlated}.\n\n%While we may find it interesting to consider the relationship\n%between two variables such as those in the scatterplot,\n%the relationship between those variables can be more complex.\n%For example, interest rates on loans tend to be chosen based\n%on the riskiness of the loan, i.e. how likely it is to be\n%paid back, and that is likely to depend on a variety of\n%details, such as what the loan is for, the person's\n%creditworthiness, whether their income is verified, etc.\n%We will begin exploring some of these more complex relationships\n%in graphs in Chapter~\\ref{ch_summarizing_data} and beyond.\n%\\Comment{Revise if we don't add these more rich plots...}\n\n%\\begin{example}{Figure~\\ref{interest_rate_vs_loan_amount}\n%    features a scatterplot of interest rate against loan amount.\n%    Are these variables associated?}\n%  There isn't an evident trend in the data,\n%  so we would say these two variables are not associated.\n%\\end{example}\n\n\\begin{figure}\n  \\centering\n  \\Figure\n    [Scatterplot of thousands of counties with the median household income along the horizontal axis (data ranging from \\$0 to \\$120,000) and population change over 7 years (data ranging from about -15\\% to 25\\%). There is a cloud of points centered around (\\$45,000, -1\\%), and the points show a slight trend upwards while also becoming more sparse and volatile for observations corresponding to higher median incomes. One point is annotated at the location (\\$22,736, -3.63\\%).]\n    {0.9}{pop_change_v_med_income}\n  \\caption{A scatterplot showing\n      \\var{pop\\us{}change}\n      against \\var{median\\us{}hh\\us{}income}.\n      Owsley County of Kentucky, is highlighted,\n      which lost 3.63\\% of its population from 2010 to 2017\n      and had median household income of \\$22,736.}\n  \\label{pop_change_v_med_income}\n\\end{figure}\n\nBecause there is a downward trend in\nFigure~\\ref{multiunitsVsOwnership} --\ncounties with more units in multi-unit structures\nare associated with lower homeownership --\nthese variables are said to be\n\\termsub{negatively associated}{negative association}.\nA~\\term{positive association} is shown in the relationship\nbetween the\n\\var{median\\us{}hh\\us{}income}\nand \\var{pop\\us{}change}\nin Figure~\\ref{pop_change_v_med_income},\nwhere counties with higher median household income tend\nto have higher rates of population growth.\n\nIf two variables are not associated,\nthen they are said to be \\term{independent}.\nThat is, two variables are independent if there\nis no evident relationship between the two.\n\n\\begin{onebox}{Associated or independent, not both}\nA pair of variables are either related in some way (associated) or not (independent). No pair of variables is both associated and independent.\n\\end{onebox}\n\n\n\\D{\\newpage}\n\n\\subsection{Explanatory and response variables}\n\\label{explanatoryAndResponse}\n\nWhen we ask questions about the relationship\nbetween two variables, we sometimes also want to determine\nif the change in one variable causes a change in the other.\nConsider the following rephrasing of an earlier question\nabout the \\data{county} data set:\n\\begin{quote}\\em\n  If there is an increase in the median household income\n  in a county, does this drive an increase in its population?\n\\end{quote}\nIn this question, we are asking whether one variable\naffects another.\nIf this is our underlying belief,\nthen \\emph{median household income} is the\n\\termsub{explanatory}{explanatory variable}\nvariable and the \\emph{population change} is the\n\\termsub{response}{response variable} variable\nin the hypothesized relationship.\\footnote{Sometimes\n  the explanatory variable is called the \\term{independent}\n  variable and the response variable is called the\n  \\term{dependent} variable.\n  However, this becomes confusing since a \\emph{pair}\n  of variables might be independent or dependent,\n  so we avoid this language.}\n\n\\index{data!county|)}\n\n\\begin{onebox}{Explanatory and response variables}\nWhen we suspect one variable might causally affect another,\nwe label the first variable the explanatory variable and the\nsecond the response variable.\n\\vspace{1mm}\n\n\\hspace{10mm}\\Figure\n    [Simple graphic shown the words \"explanatory variable\" pointing to \"response variable\", where the words \"might affect\" appear above the arrow.]\n    {0.53}{expResp}\n\nFor many pairs of variables, there is no hypothesized\nrelationship, and these labels would not be applied to\neither variable in such cases.\n\\end{onebox}\n\nBear in mind that the act of labeling the variables in this\nway does nothing to guarantee that a causal relationship exists.\nA formal evaluation to check whether one variable causes\na change in another requires an experiment.\n\n%\\begin{exercisewrap}\n%\\begin{nexercise}\n%Consider the earlier question:\n%\\begin{quote}\\em\n%  If a county has a higher median household income,\n%  does this drive an increase in its population?\n%\\end{quote}\n%We could have just as easily reframed the causal relationship\n%to be in the reverse direction:\n%\\begin{quote}\\em\n%  If a county more population growth, does this drive\n%  it to have a higher median household income?\n%\\end{quote}\n%What are the explanatory and response variables when framing\n%the variable relationship in the second question?\n%\\end{nexercise}\n%\\end{exercisewrap}\n%\\footnotetext{In this framing, we have hypothesized\n%  that population growth drives median household income.\n%  That is, population growth is the explanatory variable,\n%  and median household income is the response.\n%  This exercise should emphasize that these variable labels\n%  do not actually define whether one variable actually affects\n%  the other.}\n\n\n\\subsection{Introducing observational studies and experiments}\n\n\\noindent%\nThere are two primary types of data collection:\nobservational studies and experiments.\n\nResearchers perform an \\term{observational study} when they\ncollect data in a way that does not directly interfere with\nhow the data arise.\nFor instance, researchers may collect information via surveys,\nreview medical or company records, or follow a \\term{cohort}\nof many similar individuals to form hypotheses about why certain\ndiseases might develop.\nIn each of these situations, researchers merely observe the\ndata that arise.\nIn general, observational studies can provide evidence of\na naturally occurring association between variables, but they\ncannot by themselves show a causal connection.\n\nWhen researchers want to investigate the possibility of\na causal connection, they conduct an \\term{experiment}.\nUsually there will be both an explanatory and a response\nvariable.\nFor instance, we may suspect administering a drug will reduce\nmortality in heart attack patients over the following year.\nTo check if there really is a causal connection between the\nexplanatory variable and the response, researchers will collect\na sample of individuals and split them into groups.\nThe individuals in each group are \\emph{assigned} a treatment.\nWhen individuals are randomly assigned to a group,\nthe experiment is called a \\term{randomized experiment}.\nFor example, each heart attack patient in the drug trial\ncould be randomly assigned,  perhaps by flipping a coin,\ninto one of two groups:\nthe first group receives a \\term{placebo} (fake treatment)\nand the second group receives the drug.\nSee the case study in\nSection~\\ref{basicExampleOfStentsAndStrokes} for another\nexample of an experiment, though that study did not employ\na~placebo.\n\n\\begin{onebox}{Association $\\neq$ Causation}\n  In general, association does not imply causation,\n  and causation can only be inferred from a randomized experiment.\n\\end{onebox}\n\n\n{\\input{ch_intro_to_data/TeX/data_basics.tex}}\n\n\n\n\n%%%%%\n\\section{Sampling principles and strategies}\n\\label{overviewOfDataCollectionPrinciples}\n\\label{section_obs_data_sampling}\n\n\\index{sample|(}\n\\index{population|(}\n\nThe first step in conducting research is to identify topics\nor questions that are to be investigated.\nA clearly laid out research question is helpful in identifying\nwhat subjects or cases should be studied and what variables are\nimportant.\nIt is also important to consider \\emph{how} data are collected\nso that they are reliable and help achieve the research goals.\n\n\n\\subsection{Populations and samples}\n\\label{populationsAndSamples}\n\n\\noindent%\nConsider the following three research questions:\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item\n    What is the average mercury content in swordfish\n    in the Atlantic Ocean?\n\\item\n    \\label{timeToGraduationQuestionForUCLAStudents}%\n    Over the last 5 years, what is the average time\n    to complete a degree for Duke undergrads?\n\\item\n    \\label{identifyPopulationOfStentStudy}%\n    Does a new drug reduce the number of deaths in patients\n    with severe heart disease?\n\\end{enumerate}\nEach research question refers to a target \\term{population}. In the first question, the target population is all swordfish in the Atlantic ocean, and each fish represents a case. Often times, it is too expensive to collect data for every case in a population. Instead, a sample is taken. A \\term{sample} represents a subset of the cases and is often a small fraction of the population. For instance, 60 swordfish (or some other number) in the population might be selected, and this sample data may be used to provide an estimate of the population average and answer the research question.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{identifyingThePopulationForTwoQuestionsInPopAndSampSubsection}%\nFor the second and third questions above,\nidentify the target population and what\nrepresents an individual case.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(\\ref{timeToGraduationQuestionForUCLAStudents})\n    The first question is only relevant\n    to students who complete their degree;\n    the average cannot be computed using a student\n    who never finished her degree.\n    Thus, only Duke undergrads who\n    graduated in the last five years represent cases\n    in the population under consideration.\n    Each such student is an individual case.\n    (\\ref{identifyPopulationOfStentStudy})~A person with\n    severe heart disease represents a case.\n    The population includes all people with severe heart\n    disease.}\n\n\n\\subsection{Anecdotal evidence}\n\\label{anecdotalEvidenceSubsection}\n\n\\index{bias|(}\n\n\\noindent%\nConsider the following possible responses to the three research questions:\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item\n    A man on the news got mercury poisoning from eating swordfish,\n    so the average mercury concentration in swordfish must be\n    dangerously high.\n\\item\\label{iKnowThreeStudentsWhoTookMoreThan7YearsToGraduateAtDuke}\n    I met two students who took more than 7 years to graduate\n    from Duke, so it must take longer to graduate at Duke than\n    at many other colleges.\n\\item\\label{myFriendsDadDiedAfterSulphinpyrazon}\n    My friend's dad had a heart attack and died after they gave\n    him a new heart disease drug, so~the drug must not work.\n\\end{enumerate}\nEach conclusion is based on data.\nHowever, there are two problems.\nFirst, the data only represent one or two cases.\nSecond, and more importantly, it is unclear whether these cases\nare actually representative of the population.\nData collected in this haphazard fashion are called\n\\term{anecdotal evidence}.\n\n\\captionsetup{width=\\textwidth-75mm}\n\\begin{figure}[h]\n  \\centering\n  \\hspace{8mm}\\Figuress\n    [A wintery scene, where the trees are covered in snow, and there are large piles of snow on the sides of the roads. This particular photo was taken at the University of Minnesota campus following a storm after which tree branches were a particularly vibrant white color after the storm.]\n    {55mm}{mnWinter}{mnWinter}\\hspace{4mm}\n  \\begin{minipage}[b]{\\textwidth-75mm}\n    \\caption[anecdotal evidence]{In February 2010,\n        some media pundits cited one large snow storm\n        as valid evidence against global warming.\n        As comedian Jon Stewart pointed out,\n        ``It's one storm, in one region, of one country.''\n    \\label{mnWinter}}\n  \\end{minipage}\n\\end{figure}\n\\captionsetup{width=\\mycaptionwidth}\n\n\\begin{onebox}{Anecdotal evidence}\nBe careful of data collected in a haphazard fashion.\nSuch evidence may be true and verifiable, but it may\nonly represent extraordinary cases.\n\\end{onebox}\n\n\\D{\\newpage}\n\nAnecdotal evidence typically is composed of unusual cases that we recall based on their striking characteristics. For instance, we are more likely to remember the two people we met who took 7~years to graduate than the six others who graduated in four years. Instead of looking at the most unusual cases, we should examine a sample of many cases that represent the population.\n\n\\subsection{Sampling from a population}\n\n\\index{sample!random sample|(}\n\\index{sample!bias|(}\n\nWe might try to estimate the time to graduation for Duke\nundergraduates in the last 5 years by collecting a sample\nof students.\nAll graduates in the last 5 years represent the\n\\emph{population}\\index{population}, and graduates who are\nselected for review are collectively called the\n\\emph{sample}\\index{sample}.\nIn general, we always seek to \\emph{randomly} select a sample\nfrom a population.\nThe most basic type of random selection is equivalent to how\nraffles are conducted.\nFor example, in selecting graduates, we could write each\ngraduate's name on a raffle ticket and draw 100 tickets.\nThe selected names would represent a random sample of 100 graduates.\nWe pick samples randomly to reduce the chance we introduce biases.\n\n\\begin{figure}[ht]\n  \\centering\n  \\Figures\n    [Graphic showing a larger circle on the left for \"all graduate\" and a smaller circle on the right for \"sample\". There are a large number of dots randomly scattered around inside the left circle, and five of those dots have arrows originating from them and pointing to 5 dots inside the right circle. Besides those 5 dots, there are no other dots in the right circle.]\n    {0.5}{popToSample}{popToSampleGraduates}\n  \\caption{In this graphic, five graduates are randomly\n      selected from the population to be included in the\n      sample.}\n  \\label{popToSampleGraduates}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose we ask a student who happens to be\n    majoring in nutrition to select several graduates for\n    the study.\n    What kind of students do you think she might collect?\n    Do you think her sample would be representative of all\n    graduates?}\n  Perhaps she would pick a disproportionate number of graduates\n  from health-related fields.\n  Or~perhaps her selection would be a good representation\n  of the population.\n  When selecting samples by hand, we run the risk of picking\n  a \\termsub{biased}{sample!bias} sample, even if their bias\n  isn't intended.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}\n  \\centering\n  \\Figures\n    [Graphic showing a larger circle on the left for \"all graduate\" and a smaller circle on the right for \"sample\". There are a large number of dots randomly scattered around inside the left circle. A smaller circle annotated as \"graduates from health-related fields\" is inside this circle and contains a subset of those dots, among which five have arrows originating from them and pointing to 5 dots inside the right circle. Besides those 5 dots, there are no other dots in the right circle.]\n    {0.5}{popToSample}{popToSubSampleGraduates}\n  \\caption{Asked to pick a sample of graduates,\n      a nutrition major might inadvertently pick a\n      disproportionate number of graduates from\n      health-related majors.}\n  \\label{popToSubSampleGraduates}\n\\end{figure}\n\n\\D{\\newpage}\n\nIf someone was permitted to pick and choose exactly which\ngraduates were included in the sample, it is entirely possible\nthat the sample could be skewed to that person's interests,\nwhich may be entirely unintentional.\nThis introduces \\term{bias} into a sample.\nSampling randomly helps resolve this problem.\nThe most basic random sample is called a\n\\term{simple random sample}, and which is equivalent to using\na raffle to select cases.\nThis means that each case in the population has an equal chance\nof being included and there is no implied connection between\nthe cases in the sample.\n\nThe act of taking a simple random sample helps minimize bias.\nHowever, bias can crop up in other ways.\nEven when people are picked at random, e.g. for surveys,\ncaution must be exercised if the\n\\term{non-response rate}\n\\index{sample!non-response rate|textbf} is high.\nFor instance, if only 30\\% of the people randomly sampled\nfor a survey actually respond, then it is unclear whether\nthe results are \\term{representative} of the entire population.\nThis \\term{non-response bias}\n\\index{sample!non-response bias|textbf} can skew results.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures\n    [Graphic showing a larger circle on the left for \"population of interest\" and a smaller circle on the right for \"sample\". There are a large number of dots randomly scattered around inside the left circle. A smaller circle annotated as \"population actually sampled\" is inside this circle and contains a subset of those dots, among which five have arrows originating from them and pointing to 5 dots inside the right circle. Besides those 5 dots, there are no other dots in the right circle.]\n    {0.5}{popToSample}{surveySample}\n  \\caption{Due to the possibility of non-response,\n      surveys studies may only reach a certain group\n      within the population.\n      It is difficult, and often times impossible,\n      to completely fix this problem.}\n  \\label{surveySample}\n\\end{figure}\n\nAnother common downfall is a\n\\term{convenience sample}\\index{sample!convenience sample},\nwhere individuals who are easily accessible are more likely\nto be included in the sample.\nFor instance, if a political survey is done by stopping people\nwalking in the Bronx, this will not represent all of New York City.\nIt is often difficult to discern what sub-population a convenience\nsample represents.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWe can easily access ratings for products, sellers, and companies through websites. These ratings are based only on those people who go out of their way to provide a rating. If 50\\% of online reviews for a product are negative, do you think this means that 50\\% of buyers are dissatisfied with the product?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Answers will vary.\n    From our own anecdotal experiences, we believe people\n    tend to rant more about products that fell below\n    expectations than rave about those that perform as\n    expected.\n    For this reason, we suspect there is a negative bias\n    in product ratings on sites like Amazon.\n    However, since our experiences may not be\n    representative, we also keep an open mind.}\n\n\\index{sample!bias|)}\n\\index{sample!random sample|)}\n\\index{bias|)}\n\\index{population|)}\n\\index{sample|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Observational studies}\n\nData where no treatment has been explicitly applied\n(or explicitly withheld) is called \\term{observational data}.\nFor instance, the loan data and county data described in\nSection~\\ref{dataBasics}\nare both examples of observational data.\n%It is important to collect such data in\n%a thoughtful and rigorous manner so that statistical\n%analyses based on the data can have meaningful\n%and generalizable results.\nMaking causal conclusions based on experiments is often reasonable.\nHowever, making the same causal conclusions based on observational\ndata can be treacherous and is not recommended.\nThus, observational studies are generally only sufficient\nto show associations or form hypotheses that we later check\nusing experiments.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{sunscreenLurkingExample}%\nSuppose an observational study tracked sunscreen use and skin cancer, and it was found that the more sunscreen someone used, the more likely the person was to have skin cancer. Does this mean sunscreen \\emph{causes} skin cancer?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{No.\n    See the paragraph following the exercise for\n    an explanation.}\n\nSome previous research tells us that using sunscreen actually reduces skin cancer risk, so maybe there is another variable that can explain this hypothetical association between sunscreen usage and skin cancer. One important piece of information that is absent is sun exposure. If someone is out in the sun all day, she is more likely to use sunscreen \\emph{and} more likely to get skin cancer. Exposure to the sun is unaccounted for in the simple investigation.\n\\begin{center}\n  \\Figures\n    [There are three boxes with words positioned in a triangle. One box has \"sun exposure\" written in it, and that box has two arrows pointing from it to the two other boxes, which are labeled \"use sunscreen\" and \"skin cancer\". There is a third arrow more lightly colored and pointing from the \"use sunscreen\" box to the \"skin cancer\" box, where a question mark has been placed above that lightly-colored arrow.]\n    {0.55}{variables}{sunCausesCancer}\n\\end{center}\n% Some studies:\n% http://www.sciencedirect.com/science/article/pii/S0140673698121682\n% http://archderm.ama-assn.org/cgi/content/abstract/122/5/537\n% Study with a similar scenario to that described here:\n% http://onlinelibrary.wiley.com/doi/10.1002/ijc.22745/full\n\nSun exposure is what is called a \\term{confounding variable},\\footnote{Also called a \\term{lurking variable}, \\term{confounding factor}, or a \\term{confounder}.} which is a variable that is correlated with both the explanatory and response variables. While one method to justify making causal conclusions from observational studies is to exhaust the search for confounding variables, there is no guarantee that all confounding variables can be examined or measured.\n\n%In the same way, the \\data{county} data set is an observational study with confounding variables, and its data cannot easily be used to make causal conclusions.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nFigure~\\ref{multiunitsVsOwnership} shows a negative association\nbetween the homeownership rate and the percentage of multi-unit\nstructures in a county.\nHowever, it is unreasonable to conclude that there is a causal\nrelationship between the two variables.\nSuggest a variable that might explain the negative\nrelationship.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Answers will vary.\n    Population density may be important.\n    If a county is very dense, then this may require\n    a larger fraction of residents to live in\n    multi-unit structures.\n    Additionally, the high density may contribute\n    to increases in property value, making\n    homeownership infeasible for many residents.}\n\nObservational studies come in two forms:\nprospective and retrospective studies.\nA \\term{prospective study} identifies individuals\nand collects information as events unfold.\nFor instance, medical researchers may identify and follow\na group of patients over many years to assess\nthe possible influences of behavior on cancer risk.\nOne example of such a study is The Nurses' Health Study,\nstarted in 1976 and expanded in 1989.\nThis prospective study recruits registered nurses and then\ncollects data from them using questionnaires.\n\\termsub{Retrospective studies}{retrospective studies}\ncollect data after events have taken place,\ne.g. researchers may review past events in medical records.\nSome data sets may contain both prospectively- and\nretrospectively-collected variables.\n\n\n\\subsection{Four sampling methods}\n\\label{fourSamplingMethods}\n\\label{threeSamplingMethods}\n\nAlmost all statistical methods are based on the notion of implied randomness. If observational data are not collected in a random framework from a population, these statistical methods -- the estimates and errors associated with the estimates -- are not reliable. Here we consider four random sampling techniques: simple, stratified, cluster, and multistage sampling. Figures~\\ref{simple_stratified} and~\\ref{cluster_multistage} provide graphical representations of these techniques.\n\n\\begin{figure}\n  \\centering\n  \\Figures\n    [Two figures are shown, one positioned above the other. The first is a large rectangle containing many points, where 18 of those points are circled and are a different color than the other points. The lower figure is also a large rectangle, but this rectangle contains 6 larger circles that are labeled \"Stratum 1\" through \"Stratum 6\". In each of these circles are many points, and 3 points have been circled and are in a different color within each of the six circles, specially calling out those 3 points in each of the stratum circles in a similar way to how the 18 points were being called out in the upper figure.]\n    {}{samplingMethodsFigure}{simple_stratified}\n  \\caption{\n      Examples of simple random\\index{sample!simple random sampling}\n      and stratified sampling\\index{sample!stratified sampling}.\n      In the top panel, simple random sampling was used to randomly\n      select the 18 cases.\n      In the bottom panel, stratified sampling was used:\n      cases were grouped into strata, then simple random sampling\n      was employed within \\mbox{each stratum}.}\n  \\label{simple_stratified}\n\\end{figure}\n\n\\termsub{Simple random sampling}{sample!simple random sampling} is probably the most intuitive form of random sampling. Consider the salaries of Major League Baseball (MLB) players, where each player is a member of one of the league's 30 teams. To take a simple random sample of 120 baseball players and their salaries, we could write the names of that season's several hundreds of players onto slips of paper, drop the slips into a bucket, shake the bucket around until we are sure the names are all mixed up, then draw out slips until we have the sample of 120 players. In general, a sample is referred to as ``simple random'' if each case in the population has an equal chance of being included in the final sample \\emph{and} knowing that a case is included in a sample does not provide useful information about which other cases are included.\n\n\\termsub{Stratified sampling}{sample!stratified sampling}\nis a divide-and-conquer sampling strategy.\nThe population is divided into groups called\n\\term{strata}\\index{sample!strata|textbf}.\nThe strata are chosen so that similar cases are grouped\ntogether, then a second sampling method, usually simple\nrandom sampling, is employed within each stratum.\nIn~the baseball salary example, the teams could represent\nthe strata, since some teams have a lot more money\n(up to 4~times as much!).\nThen we might randomly sample 4 players from each team for\na total of 120 players.\n\nStratified sampling is especially useful when the cases in each stratum are very similar with respect to the outcome of interest. The downside is that analyzing data from a stratified sample is a more complex task than analyzing data from a simple random sample. The analysis methods introduced in this book would need to be extended to analyze data collected using stratified sampling.\n\n\\begin{examplewrap}\n\\begin{nexample}{Why would it be good for cases within\n    each stratum to be very similar?}\n  We might get a more stable estimate for the subpopulation\n  in a stratum if the cases are very similar,\n  leading to more precise estimates within each group.\n  When we combine these estimates into a single estimate\n  for the full population, that population estimate will\n  tend to be more precise since each individual group\n  estimate is itself more precise.\n\\end{nexample}\n\\end{examplewrap}\n\nIn a \\termsub{cluster sample}{sample!cluster sample}, we break up the population into many groups, called \\termsub{clusters}{sample!cluster}. Then we sample a fixed number of clusters and include all observations from each of those clusters in the sample. A \\termsub{multistage sample}{sample!multistage sample} is like a cluster sample, but rather than keeping all observations in each cluster, we collect a random sample within each selected cluster. %Multistage sampling is similar to stratified sampling in its process, except that stratified sampling requires observations be sampled from \\emph{every} stratum.\n\n\\begin{figure}\n  \\centering\n  \\Figures\n    [Two figures are shown, one positioned above the other. The first is a large rectangle containing 8 large circles with labels \"Cluster 1\" through \"Cluster 8\". All of these large circles contain points. However, three of the large circles (Cluster 3, Cluster 4, and Cluster 8) are colored differently than the other large circles and their contained points are also colored differently. The lower figure is the same as the upper figure, except that only 5 of the points are colored differently in each of the 3 large circles that have been colored differently.]\n    {}{samplingMethodsFigure}{cluster_multistage}\n  \\caption{Examples of cluster\\index{sample!cluster sampling}\n  and multistage sampling\\index{sample!multistage sampling}.\n  In the top panel, cluster sampling was used:\n  data were binned into nine clusters, three of these clusters\n  were sampled, and all observations within these three cluster\n  were included in the sample.\n  In the bottom panel, multistage sampling was used,\n  which differs from cluster sampling only in that we\n  randomly select a subset of each cluster to be included\n  in the sample rather than measuring every case in each\n  sampled cluster.}\n\\label{cluster_multistage}\n\\end{figure}\n\nSometimes cluster or multistage sampling can be more economical\nthan the alternative sampling techniques.\nAlso, unlike stratified sampling, these approaches are most\nhelpful when there is a lot of case-to-case variability within\na cluster but the clusters themselves don't look very different\nfrom one another.\nFor example, if neighborhoods represented clusters, then cluster\nor multistage sampling work best when the neighborhoods are very\ndiverse.\nA~downside of these methods is that more advanced techniques\nare typically required to analyze the data, though the methods\nin this book can be extended to handle such data.\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose we are interested in estimating\n    the malaria rate in a densely tropical portion of rural\n    Indonesia.\n    We learn that there are 30 villages in that part of the\n    Indonesian jungle, each more or less similar to the next.\n    Our goal is to test 150 individuals for malaria.\n    What sampling method should be employed?}\n  A simple random sample would likely draw individuals from\n  all 30 villages, which could make data collection extremely\n  expensive.\n  Stratified sampling would be a challenge since it is\n  unclear how we would build strata of similar individuals.\n  However, cluster sampling or multistage sampling seem like\n  very good ideas.\n  If we decided to use multistage sampling, we might randomly\n  select half of the villages, then randomly select\n  10 people from each.\n  This would probably reduce our data collection costs\n  substantially in comparison to a simple random sample,\n  and the cluster sample would still give us reliable\n  information, even if we would need to analyze the data\n  with slightly more advanced methods than we discuss\n  in this book.\n\\end{nexample}\n\\end{examplewrap}\n\n\n{\\input{ch_intro_to_data/TeX/sampling_principles_and_strategies.tex}}\n\n\n\n\n\n\n%%%%%\n\\section{Experiments}\n\\label{experimentsSection}\n\n%\\sectionintro{\nStudies where the researchers assign treatments to cases are called \\termsub{experiments}{experiment}. When this assignment includes randomization, e.g.~using a coin flip to decide which treatment a patient receives, it is called a \\term{randomized experiment}. Randomized experiments are fundamentally important when trying to show a causal connection between two variables.\n%}\\setstretch{1.0}\n\n\n\\subsection{Principles of experimental design}\n\\label{experimentalDesignPrinciples}\n\n\\noindent{}Randomized experiments are generally built on four principles.\n\n\\begin{description}\n\\item[Controlling.]\n    Researchers assign treatments to cases, and they do their\n    best to \\term{control} any other differences in the\n    groups.\\footnote{This is a different concept than\n      a \\emph{control group}, which we discuss in\n      the second principle and in\n      Section~\\ref{biasInHumanExperiments}.}\n    For example, when patients take a drug in pill form,\n    some patients take the pill with only a sip of water\n    while others may have it with an entire glass of water.\n    To control for the effect of water consumption,\n    a doctor may ask all patients to drink a 12 ounce glass\n    of water with the pill.\n\\item[Randomization.] Researchers randomize patients into treatment groups to account for variables that cannot be controlled. For example, some patients may be more susceptible to a disease than others due to their dietary habits. Randomizing patients into the treatment or control group helps even out such differences, and it also prevents accidental bias from entering the study.\n\\item[Replication.] The more cases researchers observe, the more accurately they can estimate the effect of the explanatory variable on the response. In a single study, we \\term{replicate} by collecting a sufficiently large sample. Additionally, a group of scientists may replicate an entire study to verify an earlier finding.\n\n\\begin{figure}\n  \\centering\n  \\Figure\n    [There are three main stages shown in this figure, from top to bottom. The upper stage shows the numbering of patients as a rectangle containing 54 dots in a grid that are labeled with numbers 1 through 54. The dots are one of two colors: blue (high risk) and red (low risk). The second stage shows the two colored dots broken into two blocks. On the left are the low-risk patients (red) and on the right are the high-risk patients (blue). Going into the bottom third stage, are two boxes labeled \"control\" and \"treatment\", where half of the low-risk (red) and half of the blue (high risk) points have been randomly placed into each of these two experiment groups.]\n    {0.82}{figureShowingBlocking}\n  \\caption{Blocking using a variable depicting patient risk.\n      Patients are first divided into low-risk and high-risk\n      blocks, then each block is evenly separated into the\n      treatment groups using randomization.\n      This strategy ensures an equal representation of patients\n      in each treatment group from both the low-risk and high-risk\n      categories.}\n  \\label{figureShowingBlocking}\n\\end{figure}\n\n\\item[Blocking.] Researchers sometimes know or suspect that variables, other than the treatment, influence the response. Under these circumstances, they may first group individuals based on this variable into \\term{blocks} and then randomize cases within each block to the treatment groups. This strategy is often referred to as \\term{blocking}. For instance, if we are looking at the effect of a drug on heart attacks, we might first split patients in the study into low-risk and high-risk blocks, then randomly assign half the patients from each block to the control group and the other half to the treatment group, as shown in Figure~\\ref{figureShowingBlocking}. This strategy ensures each treatment group has an equal number of low-risk and high-risk patients.\n\\end{description}\n\nIt is important to incorporate the first three experimental\ndesign principles into any study, and this book describes\napplicable methods for analyzing data from such experiments.\nBlocking is a slightly more advanced technique, and statistical\nmethods in this book may be extended to analyze data collected\nusing blocking.\n\n\\subsection{Reducing bias in human experiments}\n\\label{biasInHumanExperiments}\n\nRandomized experiments are the gold standard for data collection,\nbut they do not ensure an unbiased perspective into the cause and\neffect relationship in all cases.\nHuman studies are perfect examples where bias can unintentionally\narise.\nHere we reconsider a study where a new drug was used to treat\nheart attack patients.\nIn particular, researchers wanted to know if the drug reduced\ndeaths in patients.\n\nThese researchers designed a randomized experiment because they wanted to draw causal conclusions about the drug's effect. Study volunteers\\footnote{Human subjects are often called \\term{patients}, \\term{volunteers}, or \\term{study participants}.} were randomly placed into two study groups. One group, the \\term{treatment group}, received the drug. The other group, called the \\term{control group}, did not receive any drug treatment.\n\nPut yourself in the place of a person in the study. If you are in the treatment group, you are given a fancy new drug that you anticipate will help you. On the other hand, a person in the other group doesn't receive the drug and sits idly, hoping her participation doesn't increase her risk of death. These perspectives suggest there are actually two effects: the one of interest is the effectiveness of the drug, and the second is an emotional effect that is difficult to quantify.\n\nResearchers aren't usually interested in the emotional effect,\nwhich might bias the study.\nTo circumvent this problem, researchers do not want patients\nto know which group they are in.\nWhen researchers keep the patients uninformed about their\ntreatment, the study is said to be \\term{blind}.\nBut there is one problem:\nif a patient doesn't receive a treatment, she will know she\nis in the control group.\nThe solution to this problem is to give fake treatments to\npatients in the control group.\nA fake treatment is called a \\term{placebo}, and an effective\nplacebo is the key to making a study truly blind.\nA classic example of a placebo is a sugar pill that is made\nto look like the actual treatment pill.\nOften times, a placebo results in a slight but real\nimprovement in patients.\nThis effect has been dubbed the \\term{placebo~effect}.\n\nThe patients are not the only ones who should be blinded:\ndoctors and researchers can accidentally bias a study.\nWhen a doctor knows a patient has been given the real treatment,\nshe might inadvertently give that patient more attention or care\nthan a patient that she knows is on the placebo.\nTo guard against this bias, which again has been found to have\na measurable effect in some instances, most modern studies employ\na \\term{double-blind} setup where doctors or researchers who\ninteract with patients are, just like the patients,\nunaware of who is or is not receiving the\ntreatment.\\footnote{There are always some researchers involved\n  in the study who do know which patients are receiving which\n  treatment.\n  However, they do not interact with the study's patients and\n  do not tell the blinded health care professionals who is\n  receiving which treatment.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nLook back to the study in Section~\\ref{basicExampleOfStentsAndStrokes} where researchers were testing whether stents were effective at reducing strokes in at-risk patients. Is this an experiment? Was the study blinded? Was it double-blinded?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The researchers assigned the patients into their treatment groups, so this study was an experiment. However, the patients could distinguish what treatment they received, so this study was not blind. The study could not be double-blind since it was not blind.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{gp_sham_surgery}%\nFor the study in Section~\\ref{basicExampleOfStentsAndStrokes},\ncould the researchers have employed a placebo?\nIf so, what would that placebo have looked like?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Ultimately, can we make patients think they\n  got treated from a surgery?\n  In fact, we can, and some experiments use\n  what's called a \\term{sham surgery}.\n  In a sham surgery, the patient does undergo surgery,\n  but the patient does not receive the full treatment,\n  though they will still get a placebo effect.}\n\nYou may have many questions about the ethics of\nsham surgeries to create a placebo after reading\nGuided Practice~\\ref{gp_sham_surgery}.\nThese questions may have even arisen in your mind when\nin the general experiment context, where a possibly\nhelpful treatment was withheld from individuals in the\ncontrol group;\nthe main difference is that a sham surgery tends to\ncreate additional risk, while withholding a treatment\nonly maintains a person's risk.\n\nThere are always multiple viewpoints of experiments\nand placebos, and rarely is it obvious which is\nethically ``correct''.\nFor instance, is it ethical to use a sham surgery\nwhen it creates a risk to the patient?\nHowever, if we don't use sham surgeries,\nwe may promote the use of a costly treatment that\nhas no real effect;\nif this happens, money and other resources will be diverted\naway from other treatments that are known to be helpful.\nUltimately, this is a difficult situation where\nwe cannot perfectly protect both the patients\nwho have volunteered for the study and the patients who\nmay benefit (or not) from the treatment in the future.\n\n\n{\\input{ch_intro_to_data/TeX/experiments.tex}}\n"
  },
  {
    "path": "ch_intro_to_data/TeX/data_basics.tex",
    "content": "\\exercisesheader{}\n\n% 3\n\n\\eoce{\\qt{Air pollution and birth outcomes, study components\\label{study_components_airpoll}} \nResearchers collected data to examine the relationship between air pollutants \nand preterm births in Southern California. During the study air pollution levels \nwere measured by air quality monitoring stations. Specifically, levels of carbon \nmonoxide were recorded in parts per million, nitrogen dioxide and ozone in parts \nper hundred million, and coarse particulate matter (PM$_{10}$) in $\\mu g/m^3$. \nLength of gestation data were collected on 143,196 births between the years 1989 \nand 1993, and air pollution exposure during gestation was calculated for each \nbirth. The analysis suggested that increased ambient PM$_{10}$ and, to a lesser \ndegree, CO concentrations may be associated with the occurrence of preterm births.\\footfullcite{Ritz+Yu+Chapa+Fruin:2000}\n\\begin{parts}\n\\item Identify the main research question of the study.\n\\item Who are the subjects in this study, and how many are included?\n\\item What are the variables in the study? Identify each variable as numerical or \ncategorical. If numerical, state whether the variable is discrete or continuous.\nIf categorical, state whether the variable is ordinal.\n\\end{parts}\n}{}\n\n% 4\n\n\\eoce{\\qt{Buteyko method, study components\\label{study_components_buteyko}} \nThe Buteyko method is a shallow breathing technique developed by Konstantin \nButeyko, a Russian doctor, in 1952. Anecdotal evidence suggests that the Buteyko \nmethod can reduce asthma symptoms and improve quality of life. In a scientific \nstudy to determine the effectiveness of this method, researchers recruited 600 \nasthma patients aged 18-69 who relied on medication for asthma treatment. These \npatients were randomly split into two research groups: one practiced the Buteyko \nmethod and the other did not. Patients were scored on quality of life, activity, \nasthma symptoms, and medication reduction on a scale from 0 to 10. On average, \nthe participants in the Buteyko group experienced a significant reduction in \nasthma symptoms and an improvement in quality of life.\\footfullcite{McDowan:2003} \n\\begin{parts}\n\\item Identify the main research question of the study.\n\\item Who are the subjects in this study, and how many are included?\n\\item What are the variables in the study? Identify each variable as numerical or \ncategorical. If numerical, state whether the variable is discrete or continuous.\nIf categorical, state whether the variable is ordinal.\n\\end{parts}\n}{}\n\n% 5\n\n\\eoce{\\qt{Cheaters, study components\\label{study_components_cheaters}} \nResearchers studying the relationship between honesty, age and self-control \nconducted an experiment on 160 children between the ages of 5 and 15. \nParticipants reported their age, sex, and whether they were an only child \nor not. The researchers asked each child to toss a fair coin in private and \nto record the outcome (white or black) on a paper sheet, and said they \nwould only reward children who report white.\nThe study's findings can be summarized as follows:\n``Half the students were \nexplicitly told not to cheat and the others were not given any explicit \ninstructions.\nIn the no instruction group probability of cheating was found to \nbe uniform across groups based on child's characteristics.\nIn the group that was \nexplicitly told to not cheat, girls were less likely to cheat,\nand while rate \nof cheating didn't vary by age for boys, it decreased with age\nfor girls.''\\footfullcite{Bucciol:2011} \n\\begin{parts}\n\\item Identify the main research question of the study.\n\\item Who are the subjects in this study, and how many are included?\n\\item\n  How many variables were recorded for each subject in the study\n  in order to conclude these findings? State the variables and their\n  types.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 6\n\n\\eoce{\\qt{Stealers, study components\\label{study_components_stealers}} \nIn a study of the relationship between socio-economic class and unethical \nbehavior, 129 University of California undergraduates at Berkeley were asked \nto identify themselves as having low or high social-class by comparing \nthemselves to others with the most (least) money, most (least) education, and \nmost (least) respected jobs. They were also presented with a jar of \nindividually wrapped candies and informed that the candies were for children\nin a nearby laboratory, but that they could take some if they wanted. After \ncompleting some unrelated tasks, participants reported the number of candies \nthey had taken.\\footfullcite{Piff:2012} \n\\begin{parts}\n\\item Identify the main research question of the study.\n\\item Who are the subjects in this study, and how many are included?\n\\item The study found that students who were identified as upper-class took more \ncandy than others. How many variables were recorded for each subject in the study \nin order to conclude these findings? State the variables and their types.\n\\end{parts}\n}{}\n\n% 7\n\n\\eoce{\\qt{Migraine and acupuncture,\n    Part II\\label{migraine_and_acupuncture_exp_resp}}\nExercise~\\ref{migraine_and_acupuncture_intro}\nintroduced a study exploring whether acupuncture had any\neffect on migraines.\nResearchers conducted a randomized controlled study\nwhere patients were randomly assigned to one of two groups:\ntreatment or control.\nThe patients in the treatment group received acupuncture\nthat was specifically designed to treat migraines.\nThe patients in the control group received placebo acupuncture\n(needle insertion at non-acupoint locations).\n24 hours after patients received acupuncture, they were asked \nif they were pain free.\nWhat are the explanatory and response variables in this study?\n}{}\n\n% 8\n\n\\eoce{\\qt{Sinusitis and antibiotics,\n    Part II\\label{sinusitis_and_antibiotics_exp_resp}} \nExercise~\\ref{sinusitis_and_antibiotics_intro}\nintroduced a study exploring the effect of antibiotic treatment\nfor acute sinusitis.\nStudy participants either received either a 10-day course of\nan antibiotic (treatment)\nor a placebo similar in appearance and taste (control).\nAt the end of the 10-day period, patients were asked if\nthey experienced improvement in symptoms.\nWhat are the explanatory and response variables in this study?\n}{}\n\n% 9\n\n\\eoce{\\qt{Fisher's irises\\label{fisher_irises}} Sir Ronald Aylmer Fisher was an \nEnglish statistician, evolutionary biologist, and geneticist who worked on a \ndata set that contained sepal length and width, and petal length and width from \nthree species of iris flowers (\\textit{setosa}, \\textit{versicolor} and \n\\textit{virginica}). There were 50 flowers from each species in the data set. \n\\footfullcite{Fisher:1936} \\\\\n\\noindent\\begin{minipage}[c]{0.48\\textwidth}\n\\begin{parts}\n\\item How many cases were included in the data?\n\\item How many numerical variables are included in the data? Indicate what \nthey are, and if they are continuous or discrete.\n\\item How many categorical variables are included in the data, and what are \nthey? List the corresponding levels (categories).\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.01\\textwidth}\n\\ \n\\end{minipage}\n\\begin{minipage}[c]{0.2\\textwidth}\n\\begin{center}\n\\Figures[Photo of a purple iris flower.]{}{eoce/fisher_irises}{irisversicolor}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.01\\textwidth}\n\\ \n\\end{minipage}\n\\begin{minipage}[c]{0.23\\textwidth}\n{\\raggedright\\footnotesize Photo by Ryan Claussen \n(\\oiRedirect{textbook-flickr_ryan_claussen_iris_picture}{http://flic.kr/p/6QTcuX}) \n\\oiRedirect{textbook-CC_BY_SA_2}{CC~BY-SA~2.0~license}}\n\\end{minipage}\n}{}\n\n% 10\n\n\\eoce{\\qt{Smoking habits of UK residents\\label{smoking_habits_UK_datamatrix}} A survey \nwas conducted to study the smoking habits of UK residents. Below is a data \nmatrix displaying a portion of the data collected in this survey. Note that \n``$\\pounds$\" stands for British Pounds Sterling, ``cig\" stands for cigarettes, \nand ``N/A'' refers to a missing component of the data. \\footfullcite{data:smoking}\n\\begin{center}\n\\scriptsize{\n\\begin{tabular}{rccccccc}\n\\hline\n\t& sex \t & age \t& marital \t& grossIncome \t\t\t\t\t     & smoke & amtWeekends\t& amtWeekdays \\\\ \n\\hline\n1 \t& Female & 42 \t& Single \t& Under $\\pounds$2,600 \t\t\t     & Yes \t & 12 cig/day   & 12 cig/day \\\\ \n2 \t& Male\t & 44\t& Single \t& $\\pounds$10,400 to $\\pounds$15,600 & No\t & N/A \t\t\t& N/A \\\\ \n3 \t& Male \t & 53 \t& Married   & Above $\\pounds$36,400 \t\t     & Yes \t & 6 cig/day \t& 6 cig/day \\\\ \n\\vdots & \\vdots & \\vdots & \\vdots & \\vdots \t\t\t\t             & \\vdots & \\vdots \t    & \\vdots \\\\ \n1691 & Male  & 40   & Single \t& $\\pounds$2,600 to $\\pounds$5,200   & Yes \t & 8 cig/day \t& 8 cig/day \\\\   \n\\hline\n\\end{tabular}\n}\n\\end{center}\n\\begin{parts}\n\\item What does each row of the data matrix represent?\n\\item How many participants were included in the survey?\n\\item Indicate whether each variable in the study is numerical or categorical. If numerical, identify as \ncontinuous or discrete. If categorical, indicate if the variable is ordinal.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 11\n\n\\eoce{\\qt{US Airports\\label{US Airports}}\nThe visualization below shows the\ngeographical distribution of airports in the contiguous United States\nand Washington, DC.\nThis visualization was constructed based on a dataset where\neach observation is an airport.\\footfullcite{data:usairports}\n\\begin{center}\n\\Figures[Four copies of a map of the United States are shown in a 2-by-2 grid. For each map, the axis labels are longitude (130 degrees west to 60 degrees west) and latitude (20 degrees north to 50 degrees north). The first column of plots is labeled \"private use\" and the second column \"public use\". The first row of plots is labeled \"privately owned\" and the second is labeled \"publicly owned\". Points are shown on each of the four plots, where each point represents an airport. There appear to be many thousands of points shown in the upper-left map (private use, privately owned) and the lower-right map (public use, publicly owned), while there are relatively fewer points -- even if still numbering in the hundreds or low thousands -- in the other two plots. In all plots, there is a greater density of points shown in the Middle and Eastern portions of the United States, with a more sparse number of points over the mountain and desert areas, and then a higher concentration of points again around the states bordered by the Pacific Ocean, especially near large cities.]{0.9}{eoce/airports}{airports}\n\\end{center}\n\\begin{parts}\n\\item\n    List the variables used in creating this visualization.\n\\item\n    Indicate whether each variable in the study is numerical\n    or categorical.\n    If numerical, identify as continuous or discrete.\n    If categorical, indicate if the variable is ordinal.\n\\end{parts}\n}{}\n\n% 12\n\n\\eoce{\\qt{UN Votes\\label{unvotes}}\nThe visualization below shows voting patterns in \nthe United States, Canada, and Mexico in the United Nations General Assembly \non a variety of issues. Specifically, for a given year between 1946 and 2015, \nit displays the percentage of roll calls in which the country voted yes for \neach issue. This visualization was constructed based on a dataset where each \nobservation is a country/year pair.\\footfullcite{data:unvotes}\n\\begin{center}\n\\Figures[A grid of scatter plots with overlaid trend lines for each of three groups of points (colored green, blue, and red) per plot is shown. The grid of plots has 2 rows and 3 columns, and the plots in this description will be referenced by number, where the numbering runs from 1 to 3 in the first row and 4 to 6 in the second row. For all plots, the horizontal axis is for \"year\" (about 1945 to about 2018) and the vertical axis is for \"percent yes\" with values ranging from 0\\% to 100\\%. Each of the six plots summarizes voting patterns in response to a different topic at the UN General Assembly and for the countries Canada (blue), Mexico (green), and the United States (red). Each plot has points and flexible (nonlinear) trend lines fit to those points. In all cases except Plot 2 for \"Colonialism\", the points (data) are relatively sparse in 1940 to 1960 relative to later years. Plot 1 represents \"Arms control and disarmament\", which for all countries starts out low, between 0\\% and 25\\%, and then quickly rises by 1960 to between 25\\% to 95\\%, where the US remains the lowest (hovering around 25\\% to 40\\%), Canada a bit higher between 50\\% to 70\\%, and Mexico the highest and typically between 85\\% to 100\\%. Plot 2 is labeled \"Colonialism\", and the trend lines start out between 50\\% to 80\\%, with the US then descending close to 0\\% by 1980, while Canada fluctuates between 25\\% to 60\\% over the duration, and Mexico rises to close to 100\\% by 1980. Plot 3 represents \"Economic development\", where the three countries al start near 25\\% to 40\\%, with the US declining to about 5\\% by 1990 before rising up to 20\\%, Canada descending to about 25\\% by 1985 before rising to 50\\% by 2000 before descending again to 25\\%, and Mexico rising to about 100\\% by 1980 before descending to about 85\\%. Plot 4 represents \"Human rights\", with all countries being clustered near 65\\% in 1945, then the US descends to 25\\% by 1975 and fluctuates between 10\\% and 30\\% for the rest of the time, Canada slowly descends over time to about 15\\%, and Mexico rises to close to 100\\% by 1985 and then descends slowly to about 80\\%. Plot 5 represents \"Nuclear weapons and materials\", with all countries starting near 0\\% in 1945, with the US then rising a bit but generally fluctuating between 15\\% to 40\\%, Canada rising to about 60\\% by 1965 before descending to and fluctuating around 40\\% to 50\\%, and Mexico rising rapidly to about 90\\% by 1970 then approaching 100\\% over time. Plot 6 represents the \"Palestinian conflict\", where the countries all start between 50\\% to 75\\%, with the US declining steadily to about 10\\% by 1985 and then approaching 5\\% after that, Canada declines a bit to about 35\\% in 1970 before rising to about 70\\% in 2000 and then descending rapidly to close to 0\\%, and Mexico gradually increases to about 95\\% in 1985 and then holds roughly steady.]{0.9}{eoce/unvotes}{unvotes}\n\\end{center}\n\\begin{parts}\n\\item List the variables used in creating this visualization.\n\\item Indicate whether each variable in the study is numerical or categorical. \nIf numerical, identify as continuous or discrete. If categorical, indicate if \nthe variable is ordinal.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_intro_to_data/TeX/experiments.tex",
    "content": "\\exercisesheader{}\n\n% 29\n\n\\eoce{\\qt{Light and exam performance\\label{light_exam_performance}} A study is designed to \ntest the effect of light level on exam performance of students. The researcher believes \nthat light levels might have different effects on males and females, so wants to make \nsure both are equally represented in each treatment. The treatments are fluorescent \noverhead lighting, yellow overhead lighting, no overhead lighting (only desk lamps). \n\\begin{parts}\n\\item What is the response variable?\n\\item What is the explanatory variable? What are its levels?\n\\item What is the blocking variable? What are its levels?\n\\end{parts}\n}{}\n\n% 30\n\n\\eoce{\\qt{Vitamin supplements\\label{vitamin_supplement}}\nTo assess the effectiveness of taking large doses\nof vitamin C in reducing the duration of the common cold, \nresearchers recruited 400 healthy volunteers from staff\nand students at a university.\nA~quarter of the patients were assigned a placebo,\nand the rest were evenly divided between 1g Vitamin C,\n3g Vitamin C, or 3g Vitamin C plus additives to be\ntaken at onset of a cold for the following two days.\nAll tablets had identical appearance and packaging.\nThe nurses who handed the prescribed pills to the\npatients knew which patient received which treatment,\nbut the researchers assessing the patients when they\nwere sick did not. \nNo significant differences were observed in any measure\nof cold duration or severity between the four groups,\nand the placebo group had the shortest duration of \nsymptoms.\\footfullcite{Audera:2001}\n\\begin{parts}\n\\item Was this an experiment or an observational study? Why?\n\\item What are the explanatory and response variables in this study?\n\\item Were the patients blinded to their treatment?\n\\item Was this study double-blind?\n\\item Participants are ultimately able to choose whether or not to use the pills \nprescribed to them. We might expect that not all of them will adhere and take their \npills. Does this introduce a confounding variable to the study? Explain your reasoning.\n\\end{parts}\n}{}\n\n% 31\n\n\\eoce{\\qt{Light, noise, and exam performance\\label{light_noise_exam_performance}} A study is \ndesigned to test the effect of light level and noise level on exam performance of \nstudents. The researcher believes that light and noise levels might have different \neffects on males and females, so wants to make sure both are equally represented in each \ntreatment. The light treatments considered are fluorescent overhead lighting, yellow \noverhead lighting, no overhead lighting (only desk lamps). The noise treatments \nconsidered are no noise,  construction noise, and human chatter noise.\n\\begin{parts}\n\\item What type of study is this?\n\\item How many factors are considered in this study? Identify them, and describe their \nlevels.\n\\item What is the role of the sex variable in this study?\n\\end{parts}\n}{}\n\n% 32\n\n\\eoce{\\qt{Music and learning\\label{music_learning}} You would like to conduct an experiment in \nclass to see if students learn better if they study without any music, with music that \nhas no lyrics (instrumental), or with music that has lyrics. Briefly outline a design for \nthis study.\n}{}\n\n% 33\n\n\\eoce{\\qt{Soda preference\\label{soda_preference}} You would like to conduct an experiment in \nclass to see if your classmates prefer the taste of regular Coke or Diet Coke. Briefly \noutline a design for this study.\n}{}\n\n% 34\n\n\\eoce{\\qt{Exercise and mental health\\label{exercise_mental_health}} A researcher is interested \nin the effects of exercise on mental health and he proposes the following study: Use \nstratified random sampling to ensure representative proportions of 18-30, 31-40 and 41-\n55 year olds from the population. Next, randomly assign half the subjects from each age \ngroup to exercise twice a week, and instruct the rest not to exercise. Conduct a mental \nhealth exam at the beginning and at the end of the study, and compare the results.\n\\begin{parts}\n\\item What type of study is this? \n\\item What are the treatment and control groups in this study?\n\\item Does this study make use of blocking? If so, what is the blocking variable?\n\\item Does this study make use of blinding?\n\\item Comment on whether or not the results of the study can be used to establish a \ncausal relationship between exercise and mental health, and indicate whether or not the \nconclusions can be generalized to the population at large.\n\\item Suppose you are given the task of determining if this proposed study should get \nfunding. Would you have any reservations about the study proposal?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_intro_to_data/TeX/review_exercises.tex",
    "content": "\\reviewexercisesheader{}\n\n% 35\n\n\\eoce{% Replaces gpa_study_hours\n\\qt{Pet names\\label{seattle_pet_names}}\nThe city of Seattle, WA has an open data portal that\nincludes pets registered in the city.\nFor each registered pet,\nwe have information on the pet's name and species.\nThe following visualization plots the proportion of dogs\nwith a given name versus the proportion of cats with the\nsame name.\nThe 20 most common cat and dog names are displayed.\nThe diagonal line on the plot is the $x = y$ line;\nif a name appeared on this line, the name's popularity\nwould be exactly the same for dogs and cats.\n\n\\noindent\\begin{minipage}[c]{0.4\\textwidth}\n\\raggedright\\begin{parts}\n\\item\n    Are these data collected as part of an experiment\n    or an observational study?\n\\item\n    What is the most common dog name? What is the most\n    common cat name?\n\\item\n    What names are more common for cats than dogs?\n\\item\n    Is the relationship between the two variables\n    positive or negative? \n    What does this mean in context of the data?\n\\end{parts}\\vspace{5mm}\n\\end{minipage}\n\\begin{minipage}[c]{0.05\\textwidth}\n\\ \n\\end{minipage}\n\\begin{minipage}[c]{0.53\\textwidth}\n\\begin{center}\n\\Figures[A scatterplot is shown, where each point is labeled with a pet name. The horizontal axis represents \"Proportion of cats\" and runs from 0.002 to 0.010. The vertical axis represents \"Proportion of dogs\" and runs from 0.002 to 0.010. There is also a diagonal line (y = x), and only two points fall below this line: \"Oliver\" at about (0.0045, 0.004) and \"Lily\" at about (0.005, 0.004). There is a slightly positive trend in the data, the most extreme cases (highest proportions for dogs or cats) are \"Lucy\" at (0.006, 0.0095), \"Charlie\" at (0.005, 0.009), \"Luna\" at (0.0065, 0.007), and \"Bella\" at (0.005, 0.007).]{0.95}{eoce/seattle_pet_names}{seattle_pet_names}\n\\end{center}\n\\end{minipage}\n}{}\n\n% 36\n\n\\eoce{\\qt{Stressed out, Part II\\label{stressed_out_experiment}} In a study evaluating the \nrelationship between stress and muscle cramps, half the subjects are randomly assigned to be exposed to increased stress by being placed into an elevator that falls rapidly and stops abruptly and the other half are left at no or baseline stress.\n\\begin{parts}\n\\item What type of study is this?\n\\item Can this study be used to conclude a causal relationship between increased stress \nand muscle cramps?\n\\end{parts}\n}{}\n\n% 37\n\n\\eoce{\\qt{Chia seeds and weight loss\\label{chia_weight_lostt}} Chia Pets -- those terra-cotta \nfigurines that sprout fuzzy green hair -- made the chia plant a household name. But chia \nhas gained an entirely new reputation as a diet supplement.  In one 2009 study, a team \nof researchers recruited 38 men and divided them randomly into two groups: treatment or \ncontrol. They also recruited 38 women, and they randomly placed half of these \nparticipants into the treatment group and the other half into the control group. One \ngroup was given 25 grams of chia seeds twice a day, and the other was given a placebo. \nThe subjects volunteered to be a part of the study. After 12 weeks, the scientists found \nno significant difference between the groups in appetite or weight loss. \n\\footfullcite{Nieman:2009}\n\\begin{parts}\n\\item What type of study is this? \n\\item What are the experimental and control treatments in this study?\n\\item Has blocking been used in this study? If so, what is the blocking variable?\n\\item Has blinding been used in this study?\n\\item Comment on whether or not we can make a causal statement, and indicate whether or \nnot we can generalize the conclusion to the population at large.\n\\end{parts}\n}{}\n\n% 38\n\n\\eoce{\\qt{City council survey\\label{city_council_survey}}\nA city council has requested a household survey be conducted\nin a suburban area of their city.\nThe area is broken into many distinct and unique neighborhoods,\nsome including large homes, some with only apartments, and others\na diverse mixture of housing structures.\nFor each part below,\nidentify the sampling methods described,\nand describe the statistical pros and cons of the method\nin the city's context.\n\\begin{parts}\n\\item\n    Randomly sample 200 households from the city.\n\\item\n    Divide the city into 20 neighborhoods,\n    and sample 10 households from each neighborhood.\n\\item\n    Divide the city into 20 neighborhoods,\n    randomly sample 3 neighborhoods,\n    and then sample all households from those 3 neighborhoods.\n\\item\n    Divide the city into 20 neighborhoods,\n    randomly sample 8 neighborhoods,\n    and then randomly sample 50 households\n    from those neighborhoods.\n\\item\n    Sample the 200 households closest to the city council offices.\n\\end{parts}\n}{}\n\n% 39\n\n\\eoce{\\qt{Flawed reasoning\\label{flawed_reasoning}} Identify the flaw(s) in reasoning \nin the following scenarios. Explain what the individuals in the study should \nhave done differently if they wanted to make such strong conclusions.\n\\begin{parts}\n\\item Students at an elementary school are given a questionnaire that they \nare asked to return after their parents have completed it. One of the questions \nasked is, ``Do you find that your work schedule makes it difficult for you to \nspend time with your kids after school?\" Of the parents who replied, 85\\% said \n``no\". Based on these results, the school officials conclude that a great \nmajority of the parents have no difficulty spending time with their kids \nafter school.\n\\item A survey is conducted on a simple random sample of 1,000 women who \nrecently gave birth, asking them about whether or not they smoked during \npregnancy. A follow-up survey asking if the children have respiratory problems \nis conducted 3 years later.\nHowever, only 567 of these women are reached at the \nsame address. The researcher reports that these 567 women are representative \nof all mothers.\n\\item An orthopedist administers a questionnaire to 30 of his patients who do \nnot have any joint problems and finds that 20 of them regularly go running. \nHe concludes that running decreases the risk of joint problems.\n\\end{parts}\n}{}\n\n% 40\n\n\\eoce{\\qt{Income and education in US counties\\label{income_education_county}} \nThe scatterplot below shows the relationship between per capita income \n(in thousands of dollars) and percent of population with a bachelor's \ndegree in 3,143 counties in the US in 2010.\n\n\\noindent\\begin{minipage}[c]{0.44\\textwidth}\n\\begin{parts}\n\\item What are the explanatory and response variables?\n\\item Describe the relationship between the two variables. Make sure to discuss \nunusual observations, if any.\n\\item Can we conclude that having a bachelor's degree increases one's income?\n\\end{parts}\\vspace{8mm}\n\\end{minipage}\n\\begin{minipage}[c]{0.55\\textwidth}\n\\begin{center}\n\\Figures[A scatterplot is shown, with \"Percent with Bachelor's Degree\" on the horizontal axis (running 0\\% to 80\\%) and \"Per Capita Income\" on the vertical axis (running \\$0 to \\$65,000). Many thousands of points are shown. For those points with Percent with Bachelor's Degree between 0\\% to 20\\%, the points typically lie between the vertical ranges of \\$10,000 and \\$25,000. For those between 20\\% to 40\\% on the horizontal, the points lie mostly between \\$15,000 and \\$35,000 on the vertical. For those between 40\\% and 60\\%, the points mostly lie between \\$25,000 and \\$45,000. There are only about 5 points with percentages larger than 60\\%, and these all lie above \\$45,000 on the vertical.]{0.78}{eoce/county_income_education}{county_income_education_scatterplot}\n\\end{center}\n\\end{minipage}\n}{}\n\n% 41\n\n\\eoce{\\qt[?]{Eat better, feel better\\label{eat_better_feel_better}}\nIn a public health \nstudy on the effects of consumption of fruits and vegetables on psychological \nwell-being in young adults, participants were randomly assigned to three \ngroups: (1) diet-as-usual, (2) an ecological momentary intervention involving \ntext message reminders to increase their fruits and vegetable consumption plus \na voucher to purchase them, or (3) a fruit and vegetable intervention in \nwhich participants were given two additional daily servings of fresh fruits and \nvegetables to consume on top of their normal diet. Participants were asked to \ntake a nightly survey on their smartphones.\nParticipants were student volunteers at the University of \nOtago, New Zealand.\nAt the end of the 14-day study, only participants in the third\ngroup showed improvements to their psychological well-being across\nthe 14-days relative to the other groups.\\footfullcite{conner2017let}\n\\begin{parts}\n\\item\n    What type of study is this?\n\\item\n    Identify the explanatory and response variables.\n\\item\n    Comment on whether the results of the study can be generalized to\n    the population.\n\\item\n    Comment on whether the results of the study can be used to establish\n    causal relationships.\n\\item\n    A newspaper article reporting on the study states,\n    ``The results of this study provide proof that giving young adults\n    fresh fruits and vegetables to eat can have psychological benefits,\n    even over a brief period of time.''\n    How would you suggest revising this statement so that it can be\n    supported by the study?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 42\n\n\\eoce{\\qt{Screens, teens, and psychological well-being\\label{screen_time_well_being}}\nIn a study of three nationally representative large-scale data sets from Ireland, \nthe United States, and the United Kingdom (n = 17,247), teenagers between the \nages of 12 to 15 were asked to keep a diary of their screen time and answer questions about how they felt or acted.\nThe answers to these questions \nwere then used to compute a psychological well-being score.\nAdditional data were collected and included in the analysis,\nsuch as each child's sex and age, and on the mother’s education,\nethnicity, psychological distress, and employment.\nThe study concluded that there is little clear-cut evidence\nthat screen time decreases adolescent\nwell-being.\\footfullcite{orben2018screens}\n\\begin{parts}\n\\item\n    What type of study is this?\n\\item\n    Identify the explanatory variables.\n\\item\n    Identify the response variable.\n\\item\n    Comment on whether the results of the study can be generalized\n    to the population, and why.\n\\item\n    Comment on whether the results of the study can be used\n    to establish causal relationships.\n\\end{parts}\n}{}\n\n% 43\n\n\\eoce{\\qt{Stanford Open Policing\\label{stanford_open_policing}}\nThe Stanford Open Policing project gathers, analyzes, and\nreleases records from traffic stops by law enforcement \nagencies across the United States.\nTheir goal is to help researchers, journalists, and policymakers\ninvestigate and improve interactions between police and the\npublic.\\footfullcite{pierson2017large}\nThe following is an excerpt from a summary table created based off of the data \ncollected as part of this project.\n\\begin{center}\n\\begin{tabular}{lllrrr}\n\\hline\n               &           & Driver's  & No. of stops & \\multicolumn{2}{c}{\\% of stopped}  \\\\\nCounty         & State     & race      & per year     & cars searched & drivers arrested \\\\ \n\\hline\nApaice County  & Arizona   & Black     & 266          & 0.08          & 0.02 \\\\ \nApaice County  & Arizona   & Hispanic  & 1008         & 0.05          & 0.02 \\\\ \nApaice County  & Arizona   & White     & 6322         & 0.02          & 0.01 \\\\ \nCochise County & Arizona   & Black     & 1169         & 0.05          & 0.01 \\\\ \nCochise County & Arizona   & Hispanic  & 9453         & 0.04          & 0.01 \\\\ \nCochise County & Arizona   & White     & 10826        & 0.02          & 0.01 \\\\ \n$\\cdots$       & $\\cdots$  & $\\cdots$  & $\\cdots$     & $\\cdots$      & $\\cdots$ \\\\\nWood County    & Wisconsin & Black     & 16           & 0.24          & 0.10 \\\\ \nWood County    & Wisconsin & Hispanic  & 27           & 0.04          & 0.03 \\\\ \nWood County    & Wisconsin & White     & 1157         & 0.03          & 0.03 \\\\ \n\\hline \n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item\n    What variables were collected on each individual traffic stop\n    in order to create to the summary table above?\n\\item\n    State whether each variable is numerical or categorical.\n    If numerical, state whether it is continuous or discrete.\n    If categorical, state whether it is ordinal or not.\n\\item\n    Suppose we wanted to evaluate whether vehicle search rates\n    are different for drivers of different races.\n    In this analysis, which variable would be the response\n    variable and which variable would be the explanatory variable?\n\\end{parts}\n}{}\n\n% 44\n\n\\eoce{\\qt{Space launches\\label{space_launches}}\nThe following summary table shows the number of space\nlaunches in the US by the type of launching agency and\nthe outcome of the launch (success or\nfailure).\\footfullcite{data:spacelaunches}\n\\begin{center}\n\\begin{tabular}{l | rr | rr}\n\\hline\n        & \\multicolumn{2}{| c}{1957 - 1999} & \\multicolumn{2}{| c}{2000 - 2018} \\\\\n        & Failure & Success & Failure & Success \\\\ \n\\hline\nPrivate &      13 &     295 &      10 &     562 \\\\ \nState   &     281 &    3751 &      33 &     711 \\\\ \nStartup &       - &       - &       5 &      65 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item\n    What variables were collected on each launch in order\n    to create to the summary table above?\n\\item\n    State whether each variable is numerical or categorical.\n    If numerical, state whether it is continuous or discrete.\n    If categorical, state whether it is ordinal or not.\n\\item\n    Suppose we wanted to study how the success rate of\n    launches vary between launching agencies and over time.\n    In this analysis, which variable would be the response\n    variable and which variable would be the explanatory\n    variable?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_intro_to_data/TeX/sampling_principles_and_strategies.tex",
    "content": "\\exercisesheader{}\n\n% 13\n\n\\eoce{\\qt{Air pollution and birth outcomes, scope of inference\\label{scope_airpoll}} \nExercise~\\ref{study_components_airpoll} introduces a study where researchers \ncollected data to examine the relationship between air pollutants and preterm \nbirths in Southern California. During the study air pollution levels were \nmeasured by air quality monitoring stations. Length of gestation data were \ncollected on 143,196 births between the years 1989 and 1993, and air pollution \nexposure during gestation was calculated for each birth.\n\\begin{parts}\n\\item Identify the population of interest and the sample in this study.\n\\item Comment on whether or not the results of the study can be generalized to the \npopulation, and if the findings of the study can be used to establish causal relationships.\n\\end{parts}\n}{}\n\n% 14\n\n\\eoce{\\qt{Cheaters, scope of inference\\label{scope_cheaters}} \nExercise~\\ref{study_components_cheaters} introduces a study where researchers \nstudying the relationship between honesty, age, and self-control conducted an \nexperiment on 160 children between the ages of 5 and 15. The researchers asked \neach child to toss a fair coin in private and to record the outcome (white or black) \non a paper sheet, and said they would only reward children who report white. \nHalf the students were explicitly told not to cheat and the others were not given \nany explicit instructions. Differences were observed in the cheating rates in the\ninstruction and no instruction groups, as well as some differences across \nchildren's characteristics within each group.\n\\begin{parts}\n\\item Identify the population of interest and the sample in this study.\n\\item Comment on whether or not the results of the study can be generalized to the \npopulation, and if the findings of the study can be used to establish causal \nrelationships.\n\\end{parts}\n}{}\n\n% 15\n\n\\eoce{\\qt{Buteyko method, scope of inference\\label{scope_buteyko}} \nExercise~\\ref{study_components_buteyko} introduces a study on using the Buteyko \nshallow breathing technique to reduce asthma symptoms and improve quality of life.\nAs part of this study 600 asthma patients aged 18-69 who relied on medication for \nasthma treatment were recruited and randomly assigned to two groups: one practiced \nthe Buteyko method and the other did not. Those in the Buteyko group experienced,\non average, a significant reduction in asthma symptoms and an improvement in quality \nof life.\n\\begin{parts}\n\\item Identify the population of interest and the sample in this study.\n\\item Comment on whether or not the results of the study can be generalized to the \npopulation, and if the findings of the study can be used to establish causal \nrelationships.\n\\end{parts}\n}{}\n\n% 16\n\n\\eoce{\\qt{Stealers, scope of inference\\label{scope_stealers}} \nExercise~\\ref{study_components_stealers} introduces a study on the relationship \nbetween socio-economic class and unethical behavior. As part of this study 129 \nUniversity of California Berkeley undergraduates were asked to identify themselves \nas having low or high social-class by comparing themselves to others with the most \n(least) money, most (least) education, and most (least) respected jobs. They were \nalso presented  with a jar of individually wrapped candies and informed that the\ncandies were for children in a nearby laboratory, but that they could take some if \nthey wanted. After completing some unrelated tasks, participants reported the \nnumber of candies they had taken. It was found that those who were identified as \nupper-class took more candy than others.\n\\begin{parts}\n\\item Identify the population of interest and the sample in this study.\n\\item Comment on whether or not the results of the study can be generalized to the \npopulation, and if the findings of the study can be used to establish causal \nrelationships.\n\\end{parts}\n}{}\n\n% 17\n\n\\eoce{\\qt{Relaxing after work\\label{relax_after_work_definitions}} The General \nSocial Survey asked the question, ``After an average work day, about how many \nhours do you have to relax or pursue activities that you enjoy?\" to a random \nsample of 1,155 Americans. The average relaxing time was found to be 1.65 \nhours. Determine which of the following is an observation, a variable, a \nsample statistic (value calculated based on the observed sample), or a \npopulation parameter.\n\\begin{parts}\n\\item An American in the sample.\n\\item Number of hours spent relaxing after an average work day.\n\\item 1.65.\n\\item Average number of hours all Americans spend relaxing after an average \nwork day.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 18\n\n\\eoce{\\qt{Cats on YouTube\\label{cats_on_youtube_definitions}} Suppose you want to \nestimate the percentage of videos on YouTube that are cat videos. It is \nimpossible for you to watch all videos on YouTube so you use a random video \npicker to select 1000 videos for you. You find that 2\\% of these videos are \ncat videos.\nDetermine which of the following is an observation, a variable, \na sample statistic (value calculated based on the observed sample), \nor a population parameter.\n\\begin{parts}\n\\item Percentage of all videos on YouTube that are cat videos.\n\\item 2\\%.\n\\item A video in your sample.\n\\item Whether or not a video is a cat video.\n\\end{parts}\n}{}\n\n% 19\n\n\\eoce{\\qt{Course satisfaction across sections\\label{course_satisfaction_sections}} \nA large college class has 160 students. All 160 students attend the lectures \ntogether, but the students are divided into 4 groups, each of 40 students, \nfor lab sections administered by different teaching assistants. The professor \nwants to conduct a survey about how satisfied the students are with the course, \nand he believes that the lab section a student is in might affect the student's \noverall satisfaction with the course.\n\\begin{parts}\n\\item What type of study is this?\n\\item Suggest a sampling strategy for carrying out this study.\n\\end{parts}\n}{}\n\n% 20\n\n\\eoce{\\qt{Housing proposal across dorms\\label{housing_proposal_dorms}} On a large \ncollege campus first-year students and sophomores live in dorms located on \nthe eastern part of the campus and juniors and seniors live in dorms located \non the western part of the campus. Suppose you want to collect student opinions \non a new housing structure the college administration is proposing and you want \nto make sure your survey equally represents opinions from students from all years.\n\\begin{parts}\n\\item What type of study is this?\n\\item Suggest a sampling strategy for carrying out this study.\n\\end{parts}\n}{}\n\n% 21\n\n\\eoce{\\qt{Internet use and life expectancy\\label{internet_life_expectancy}} The \nfollowing scatterplot was created as part of a study evaluating the \nrelationship between estimated life expectancy at birth (as of 2014) and \npercentage of internet users (as of 2009) in 208 countries for which such \ndata were available.\\footfullcite{data:ciaFactbook}\n\n\\noindent\\begin{minipage}[c]{0.44\\textwidth}\n\\begin{parts}\n\\item Describe the relationship between life expectancy and percentage of \ninternet users.\n\\item What type of study is this?\n\\item State a possible confounding variable that might explain this relationship \nand describe its potential effect.\n\\end{parts} \\vspace{15mm}\n\\end{minipage}\n\\begin{minipage}[r]{0.55\\textwidth}\n\\hfill%\n\\Figures[Scatterplot with \"percent of internet users\" (0\\% to 100\\%) along the horizontal axis and \"life expectancy at birth\" (50 to 90) along the vertical axis. For 0\\% to 15\\%, about 100 points are evenly spread between 50 and 75. Then for 15\\% to 90\\%, the points are concentrated between about 70 and 85, and a slight upward trend is evident.]{0.87}{eoce/internet_life_expectancy}{internet_life_expectancy}\n\\end{minipage}\n}{}\n\n% 22\n\n\\eoce{\\qt{Stressed out, Part I\\label{stressed_out_observational}} A study that \nsurveyed a random sample of otherwise healthy high school students found that \nthey are more likely to get muscle cramps when they are stressed. The study \nalso noted that students drink more coffee and sleep less when they are \nstressed.\n\\begin{parts}\n\\item What type of study is this?\n\\item Can this study be used to conclude a causal relationship between \nincreased stress and muscle cramps?\n\\item State possible confounding variables that might explain the observed \nrelationship between increased stress and muscle cramps. \n\\end{parts}\n}{}\n\n% 23\n\n\\eoce{\\qt{Evaluate sampling methods\\label{evaluate_sampling_methods}} A university wants to \ndetermine what fraction of its undergraduate student body support a new \\$25 annual fee \nto improve the student union. For each proposed method below, indicate whether \nthe method is reasonable or not.\n\\begin{parts}\n\\item Survey a simple random sample of 500 students.\n\\item Stratify students by their field of study, then sample 10\\% of students from  \neach stratum.\n\\item Cluster students by their ages (e.g. 18 years old in one cluster, 19 years \nold in one cluster, etc.), then randomly sample three clusters and survey all \nstudents in those clusters.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 24\n\n\\eoce{\\qt{Random digit dialing\\label{random_digit_dialing}} The Gallup Poll uses a \nprocedure called random digit dialing, which creates phone numbers based on \na list of all area codes in America in conjunction with the associated number \nof residential households in each area code. Give a possible reason the Gallup \nPoll chooses to use random digit dialing instead of picking phone numbers \nfrom the phone book.\n}{}\n\n% 25\n\n\\eoce{\\qt{Haters are gonna hate, study confirms\\label{scope_haters}}\nA study published in the\n\\textit{Journal of Personality and Social Psychology}\nasked a group of 200 randomly sampled men and\nwomen to evaluate how they felt about various subjects,\nsuch as camping, health care, architecture, taxidermy, \ncrossword puzzles, and Japan in order to measure their\nattitude towards mostly independent stimuli.\nThen, they presented the participants with information\nabout a new product: a microwave oven. This microwave oven\ndoes not exist, but the participants didn't know this,\nand were given three positive and three negative fake reviews.\nPeople who reacted positively to the subjects on the\ndispositional attitude measurement also tended to react \npositively to the microwave oven, and those who reacted\nnegatively tended to react negatively to it.\nResearchers concluded that ``some people tend to \nlike things, whereas others tend to dislike things, and a more thorough \nunderstanding of this tendency will lead to a more thorough understanding of \nthe psychology of attitudes.\" \\footfullcite{Hepler:2013}\n\\begin{parts}\n\\item What are the cases?\n\\item What is (are) the response variable(s) in this study?\n\\item What is (are) the explanatory variable(s) in this study?\n\\item Does the study employ random sampling?\n\\item Is this an observational study or an experiment? Explain your reasoning.\n\\item Can we establish a causal link between the explanatory and response \nvariables?\n\\item Can the results of the study be generalized to the population at large?\n\\end{parts}\n}{}\n\n% 26\n\n\\eoce{\\qt{Family size\\label{family_size}} Suppose we want to estimate household \nsize, where a ``household\" is defined as people living together in the \nsame dwelling, and sharing living accommodations. If we select students \nat random at an elementary school and ask them what their family size is, \nwill this be a good measure of household size? Or will our average be \nbiased? If so, will it overestimate or underestimate the true value?\n}{}\n\n% 27\n\n\\eoce{\\qt{Sampling strategies\\label{sampling_strategies}} A statistics student who is curious about the relationship between the amount of time students spend on social networking sites and their performance at school decides to conduct a survey. Various research strategies for collecting data are described below. In each, name the sampling method proposed and any bias you might expect.\n\\begin{parts}\n\\item He randomly samples 40 students from the study's population, gives them the survey, asks them to fill it out and bring it back the next day.\n\\item He gives out the survey only to his friends, making sure each one of them fills out the survey.\n\\item He posts a link to an online survey on Facebook and asks his friends to fill out the survey.\n\\item He randomly samples 5 classes and asks a random sample of students from those classes to fill out the survey.\n\\end{parts}\n}{}\n\n% 28\n\n\\eoce{\\qt{Reading the paper\\label{reading_paper}} Below are excerpts from two \narticles published in the \\emph{NY Times}:\n\\begin{parts}\n\\item An article titled \\emph{Risks: Smokers Found More Prone to Dementia} \nstates the following: \\footfullcite{news:smokingDementia}\n\\begin{adjustwidth}{1em}{1em}\n{\\footnotesize ``Researchers analyzed data from 23,123 health plan members who \nparticipated in a voluntary exam and health behavior survey from 1978 to 1985, \nwhen they were 50-60 years old. 23 years later, about 25\\% of the group had \ndementia, including 1,136 with Alzheimer's disease and 416 with vascular \ndementia. After adjusting for other factors, the researchers concluded that \npack-a-day smokers were 37\\% more likely than nonsmokers to develop dementia, \nand the risks went up with increased smoking; 44\\% for one to two packs a day; \nand twice the risk for more than two packs.\"}\n\\end{adjustwidth}\nBased on this study, can we conclude that smoking causes dementia later in \nlife? Explain your reasoning.\n\\item Another article titled \\emph{The School Bully Is Sleepy} states the \nfollowing: \\footfullcite{news:bullySleep}\n\\begin{adjustwidth}{1em}{1em}\n{\\footnotesize ``The University of Michigan study, collected survey data from \nparents on each child's sleep habits and asked both parents and teachers to \nassess behavioral concerns. About a third of the students studied were \nidentified by parents or teachers as having problems with disruptive behavior \nor bullying. The researchers found that children who had behavioral issues and \nthose who were identified as bullies were twice as likely to have shown \nsymptoms of sleep disorders.\"}\n\\end{adjustwidth}\nA friend of yours who read the article says, ``The study shows that sleep \ndisorders lead to bullying in school children.\" Is this statement justified? \nIf not, how best can you describe the conclusion that can be drawn from this \nstudy?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_intro_to_data/figures/county_fed_spendVsPoverty/county_fed_spendVsPoverty.R",
    "content": "library(openintro)\ndata(county)\ndata(COL)\n\nmyPDF(\"county_fed_spendVsPoverty.pdf\", 6, 3.5,\n      mar = c(3, 3.5, 0.5, 0.5),\n      mgp = c(2.4, 0.5, 0))\nplot(county$poverty, county$fed_spend, \n     pch = 20,\n     cex = 0.7,\n     col = COL[1, 3],\n     ylim = c(0, 31.25),\n     xlab = \"\",\n     ylab = \"Federal Spending Per Capita\",\n     axes = FALSE)\naxis(1)\naxis(2, at = seq(0, 30, 10))\nbox()\npoints(county$poverty, county$fed_spend, pch = \".\")\nmtext(\"Poverty Rate (Percent)\", 1, 1.9)\nt1 <- county$poverty[1088]\nt2 <- county$fed_spend[1088]\nlines(c(t1, t1), c(-10, t2),\n      lty = 2,\n      col = COL[4])\nlines(c(-10, t1), c(t2, t2),\n      lty = 2,\n      col = COL[4])\npoints(t1, t2,\n       col = COL[4])\ntext(43, 29,\n     \"32 counties with higher\\nfederal spending are not shown\",\n     cex = 0.8)\ndev.off()\n\ncounty[1088, ]\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/air_quality_durham/air_quality_durham.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\npm25_durham = read.csv(\"pm25_2011_durham.csv\", \n                       na.strings = \".\", stringsAsFactors = FALSE)\n\n# calculate sample size ---------------------------------------------\nn = pm25_durham %>%\n  filter(!is.na(DAILY_AQI_VALUE)) %>%\n  nrow() # n = 91\n\n# histogram parameters ----------------------------------------------\nhisto = hist(pm25_durham$DAILY_AQI_VALUE, plot = FALSE)\nbreaks = histo$breaks\nwidth = breaks[2] - breaks[1]\ncounts = histo$counts\nrel_freqs = round(counts / n, 2)\n\nfive_perc = n * 0.05\n\n# relative frequency histogram --------------------------------------\npdf(\"air_quality_durham_rel_freq_hist.pdf\", 5.5, 4.3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nhist(pm25_durham$DAILY_AQI_VALUE, \n     main = \"\", xlab = \"Daily AQI\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0,five_perc*4))\naxis(1)\naxis(2, at = seq(0, five_perc*4, five_perc), label = round(seq(0, 0.20, 0.05),2))\nabline(h = seq(0, five_perc*4, five_perc), lty = 2, col = COL[6])\nhist(pm25_durham$DAILY_AQI_VALUE, \n     main = \"\", xlab = \"Daily AQI\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0,five_perc*4), add = TRUE)\ndev.off()\n\n# relative frequency histogram - solution ---------------------------\npdf(\"air_quality_durham_rel_freq_hist_soln.pdf\", 5.5, 4.3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nhist(pm25_durham$DAILY_AQI_VALUE, \n     main = \"\", xlab = \"Daily AQI\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0, five_perc*4 + 1))\naxis(1)\naxis(2, at = seq(0, five_perc*4, five_perc), label = round(seq(0, 0.20, 0.05),2))\nabline(h = seq(0, five_perc*4, five_perc), lty = 2, col = COL[6])\nhist(pm25_durham$DAILY_AQI_VALUE, \n     main = \"\", xlab = \"Daily AQI\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0, five_perc*4), add = TRUE)\ntext(x = breaks[-1] - width/2, y = counts + 1, \n     labels = paste(rel_freqs),\n     col = COL[4], cex = 1)\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/air_quality_durham/pm25_2011_durham.csv",
    "content": "Date,AQS_SITE_ID,POC,Daily Mean PM2.5 Concentration,UNITS,DAILY_AQI_VALUE,DAILY_OBS_COUNT,PERCENT_COMPLETE,AQS_PARAMETER_CODE,AQS_PARAMETER_DESC,CSA_CODE,CSA_NAME,CBSA_CODE,CBSA_NAME,STATE_CODE,STATE,COUNTY_CODE,COUNTY,SITE_LATITUDE,SITE_LONGITUDE\r1/3/11,37-063-0015,1,5.9,ug/m3 LC,19,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/6/11,37-063-0015,1,10.4,ug/m3 LC,34,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/9/11,37-063-0015,1,5.6,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/10/11,37-063-0015,1,6.2,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/18/11,37-063-0015,1,9.4,ug/m3 LC,31,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/21/11,37-063-0015,1,5,ug/m3 LC,16,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/24/11,37-063-0015,1,11.5,ug/m3 LC,37,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/27/11,37-063-0015,1,9.8,ug/m3 LC,32,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/30/11,37-063-0015,1,12.5,ug/m3 LC,41,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/2/11,37-063-0015,1,5.5,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/5/11,37-063-0015,1,5.3,ug/m3 LC,17,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/8/11,37-063-0015,1,5,ug/m3 LC,16,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/11/11,37-063-0015,1,11.3,ug/m3 LC,37,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/14/11,37-063-0015,1,5.9,ug/m3 LC,19,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/17/11,37-063-0015,1,17.2,ug/m3 LC,54,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/20/11,37-063-0015,1,5.3,ug/m3 LC,17,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/23/11,37-063-0015,1,7.5,ug/m3 LC,24,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/26/11,37-063-0015,1,7.6,ug/m3 LC,25,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/1/11,37-063-0015,1,3.7,ug/m3 LC,12,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/4/11,37-063-0015,1,8.9,ug/m3 LC,29,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/7/11,37-063-0015,1,4.5,ug/m3 LC,15,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/10/11,37-063-0015,1,2.7,ug/m3 LC,9,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/13/11,37-063-0015,1,10.5,ug/m3 LC,34,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/16/11,37-063-0015,1,6.1,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/19/11,37-063-0015,1,8.3,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/22/11,37-063-0015,1,13.8,ug/m3 LC,45,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/25/11,37-063-0015,1,9.1,ug/m3 LC,30,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/28/11,37-063-0015,1,10.6,ug/m3 LC,34,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/31/11,37-063-0015,1,4.8,ug/m3 LC,16,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/3/11,37-063-0015,1,6.1,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/6/11,37-063-0015,1,5.6,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/9/11,37-063-0015,1,9.1,ug/m3 LC,30,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/12/11,37-063-0015,1,7.2,ug/m3 LC,23,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/16/11,37-063-0015,1,6.6,ug/m3 LC,21,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/18/11,37-063-0015,1,8.6,ug/m3 LC,28,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/21/11,37-063-0015,1,8.6,ug/m3 LC,28,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/24/11,37-063-0015,1,11,ug/m3 LC,36,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/27/11,37-063-0015,1,5.6,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/30/11,37-063-0015,1,6.2,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/3/11,37-063-0015,1,8.5,ug/m3 LC,28,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/6/11,37-063-0015,1,9.3,ug/m3 LC,30,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/9/11,37-063-0015,1,8.8,ug/m3 LC,29,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/11/11,37-063-0015,1,18.6,ug/m3 LC,57,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/12/11,37-063-0015,1,20,ug/m3 LC,60,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/15/11,37-063-0015,1,8,ug/m3 LC,26,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/18/11,37-063-0015,1,6.3,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/21/11,37-063-0015,1,10.8,ug/m3 LC,35,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/27/11,37-063-0015,1,6.8,ug/m3 LC,22,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/30/11,37-063-0015,1,14.9,ug/m3 LC,48,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/31/11,37-063-0015,1,22.5,ug/m3 LC,65,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/2/11,37-063-0015,1,16.9,ug/m3 LC,54,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/5/11,37-063-0015,1,16.8,ug/m3 LC,54,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/8/11,37-063-0015,1,21.3,ug/m3 LC,62,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/11/11,37-063-0015,1,14.2,ug/m3 LC,46,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/14/11,37-063-0015,1,10.8,ug/m3 LC,35,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/17/11,37-063-0015,1,12,ug/m3 LC,39,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/20/11,37-063-0015,1,8.4,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/23/11,37-063-0015,1,4.3,ug/m3 LC,14,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/26/11,37-063-0015,1,14.1,ug/m3 LC,46,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/29/11,37-063-0015,1,8.4,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/2/11,37-063-0015,1,17.3,ug/m3 LC,55,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/5/11,37-063-0015,1,10,ug/m3 LC,32,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/8/11,37-063-0015,1,12.4,ug/m3 LC,40,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/14/11,37-063-0015,1,9.2,ug/m3 LC,30,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/17/11,37-063-0015,1,7.9,ug/m3 LC,26,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/18/11,37-063-0015,1,9.6,ug/m3 LC,31,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/21/11,37-063-0015,1,18,ug/m3 LC,56,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/23/11,37-063-0015,1,17.2,ug/m3 LC,54,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/26/11,37-063-0015,1,10,ug/m3 LC,32,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/29/11,37-063-0015,1,14.3,ug/m3 LC,46,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/1/11,37-063-0015,1,10.7,ug/m3 LC,35,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/4/11,37-063-0015,1,16.2,ug/m3 LC,52,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/7/11,37-063-0015,1,10.1,ug/m3 LC,33,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/10/11,37-063-0015,1,8.8,ug/m3 LC,29,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/13/11,37-063-0015,1,15.2,ug/m3 LC,49,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/16/11,37-063-0015,1,10.1,ug/m3 LC,33,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/19/11,37-063-0015,1,13.7,ug/m3 LC,44,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/22/11,37-063-0015,1,8.4,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/25/11,37-063-0015,1,6.6,ug/m3 LC,21,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/28/11,37-063-0015,1,15.2,ug/m3 LC,49,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/31/11,37-063-0015,1,8.7,ug/m3 LC,28,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/3/11,37-063-0015,1,15.8,ug/m3 LC,52,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/6/11,37-063-0015,1,3.8,ug/m3 LC,12,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/9/11,37-063-0015,1,10.7,ug/m3 LC,35,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/12/11,37-063-0015,1,11.7,ug/m3 LC,38,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/15/11,37-063-0015,1,13.2,ug/m3 LC,43,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/18/11,37-063-0015,1,2.9,ug/m3 LC,9,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/21/11,37-063-0015,1,4.6,ug/m3 LC,15,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/24/11,37-063-0015,1,5.6,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/27/11,37-063-0015,1,8.2,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/30/11,37-063-0015,1,5.7,ug/m3 LC,19,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/1/11,37-063-0015,3,16.7125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/2/11,37-063-0015,3,3.754166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/3/11,37-063-0015,3,4.855555556,ug/m3 LC,.,18,75,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/4/11,37-063-0015,3,8.6875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/5/11,37-063-0015,3,10.18333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/6/11,37-063-0015,3,8.495833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/7/11,37-063-0015,3,5.991666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/8/11,37-063-0015,3,5.320833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/9/11,37-063-0015,3,6.9125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/10/11,37-063-0015,3,6.604166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/11/11,37-063-0015,3,5.804166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/12/11,37-063-0015,3,7.808333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/13/11,37-063-0015,3,9.095833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/14/11,37-063-0015,3,10.45416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/15/11,37-063-0015,3,11.92916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/16/11,37-063-0015,3,14.01666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/17/11,37-063-0015,3,12.98333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/18/11,37-063-0015,3,8.579166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/19/11,37-063-0015,3,7.195833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/20/11,37-063-0015,3,6.9375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/21/11,37-063-0015,3,4.9125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/22/11,37-063-0015,3,7.183333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/23/11,37-063-0015,3,14.22916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/24/11,37-063-0015,3,10.61904762,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/25/11,37-063-0015,3,13.15833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/26/11,37-063-0015,3,3.95,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/27/11,37-063-0015,3,10.58333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/28/11,37-063-0015,3,12.18333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/29/11,37-063-0015,3,9.420833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/30/11,37-063-0015,3,14.25833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/31/11,37-063-0015,3,13.80833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/1/11,37-063-0015,3,10.25238095,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/2/11,37-063-0015,3,6.129166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/3/11,37-063-0015,3,6.7875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/4/11,37-063-0015,3,7.604166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/5/11,37-063-0015,3,4.320833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/6/11,37-063-0015,3,8.225,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/7/11,37-063-0015,3,10.31666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/8/11,37-063-0015,3,6.833333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/9/11,37-063-0015,3,5.6125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/10/11,37-063-0015,3,7.25,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/11/11,37-063-0015,3,11.30833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/12/11,37-063-0015,3,8.595833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/13/11,37-063-0015,3,5.2625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/14/11,37-063-0015,3,7.25,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/15/11,37-063-0015,3,7.070833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/16/11,37-063-0015,3,11.10416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/17/11,37-063-0015,3,21.9125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/18/11,37-063-0015,3,17.39166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/19/11,37-063-0015,3,2.683333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/20/11,37-063-0015,3,5.8875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/21/11,37-063-0015,3,7.485714286,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/22/11,37-063-0015,3,8.186363636,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/23/11,37-063-0015,3,7.770833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/24/11,37-063-0015,3,10.55833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/25/11,37-063-0015,3,7.416666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/26/11,37-063-0015,3,8.770833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/27/11,37-063-0015,3,15.825,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/28/11,37-063-0015,3,10.32380952,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/1/11,37-063-0015,3,3.5125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/2/11,37-063-0015,3,8.079166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/3/11,37-063-0015,3,4.595833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/4/11,37-063-0015,3,7.416666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/5/11,37-063-0015,3,5.041666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/6/11,37-063-0015,3,1.870833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/7/11,37-063-0015,3,4.6875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/8/11,37-063-0015,3,4.470833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/9/11,37-063-0015,3,5.904166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/10/11,37-063-0015,3,2.3875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/11/11,37-063-0015,3,4.395833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/12/11,37-063-0015,3,8.408333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/13/11,37-063-0015,3,11.71666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/14/11,37-063-0015,3,8.875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/15/11,37-063-0015,3,8.416666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/16/11,37-063-0015,3,6.279166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/17/11,37-063-0015,3,5.491666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/18/11,37-063-0015,3,12.34166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/19/11,37-063-0015,3,7.575,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/20/11,37-063-0015,3,6.166666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/21/11,37-063-0015,3,9.225,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/22/11,37-063-0015,3,11.16363636,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/23/11,37-063-0015,3,9.745454545,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/24/11,37-063-0015,3,3.9625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/25/11,37-063-0015,3,7.483333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/26/11,37-063-0015,3,6.354166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/27/11,37-063-0015,3,6.320833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/28/11,37-063-0015,3,9.5625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/29/11,37-063-0015,3,11.50416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/30/11,37-063-0015,3,5.7,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/31/11,37-063-0015,3,2.891666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/1/11,37-063-0015,3,9.195833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/2/11,37-063-0015,3,7.733333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/3/11,37-063-0015,3,5.570833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/4/11,37-063-0015,3,7.454166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/5/11,37-063-0015,3,3.566666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/6/11,37-063-0015,3,5.520833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/7/11,37-063-0015,3,7.783333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/8/11,37-063-0015,3,16.52083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/9/11,37-063-0015,3,7.883333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/10/11,37-063-0015,3,5.645833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/11/11,37-063-0015,3,12.15833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/12/11,37-063-0015,3,6.129166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/13/11,37-063-0015,3,4.266666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/14/11,37-063-0015,3,9.8625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/15/11,37-063-0015,3,8.891666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/16/11,37-063-0015,3,4.9875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/17/11,37-063-0015,3,4.983333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/18/11,37-063-0015,3,9.775,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/19/11,37-063-0015,3,15.72916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/20/11,37-063-0015,3,11,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/21/11,37-063-0015,3,8.641666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/22/11,37-063-0015,3,5.8625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/23/11,37-063-0015,3,8.85,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/24/11,37-063-0015,3,12.40833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/25/11,37-063-0015,3,10.1125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/26/11,37-063-0015,3,4.220833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/27/11,37-063-0015,3,5.514285714,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/28/11,37-063-0015,3,6.6375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/29/11,37-063-0015,3,5.904166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/30/11,37-063-0015,3,7.429166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/1/11,37-063-0015,3,9.325,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/2/11,37-063-0015,3,9.129166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/3/11,37-063-0015,3,8.104166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/4/11,37-063-0015,3,3.45,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/5/11,37-063-0015,3,5.541666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/6/11,37-063-0015,3,9.116666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/7/11,37-063-0015,3,8.679166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/8/11,37-063-0015,3,7.570833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/9/11,37-063-0015,3,8.645833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/10/11,37-063-0015,3,11.79166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/11/11,37-063-0015,3,16.47916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/12/11,37-063-0015,3,16.37083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/13/11,37-063-0015,3,11.47083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/14/11,37-063-0015,3,9.3875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/15/11,37-063-0015,3,5.691666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/16/11,37-063-0015,3,4.429166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/17/11,37-063-0015,3,5.366666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/18/11,37-063-0015,3,5.170833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/21/11,37-063-0015,3,9.9375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/22/11,37-063-0015,3,13.2625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/23/11,37-063-0015,3,14.3875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/24/11,37-063-0015,3,10.94166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/25/11,37-063-0015,3,8.961904762,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/26/11,37-063-0015,3,16.26666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/27/11,37-063-0015,3,3.995238095,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/28/11,37-063-0015,3,6.579166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/29/11,37-063-0015,3,11.26666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/30/11,37-063-0015,3,13.23333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/31/11,37-063-0015,3,19.67916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/1/11,37-063-0015,3,28.65,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/2/11,37-063-0015,3,15.675,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/3/11,37-063-0015,3,7.979166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/4/11,37-063-0015,3,14.50833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/7/11,37-063-0015,3,19.48333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/8/11,37-063-0015,3,23.2625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/9/11,37-063-0015,3,23.37083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/10/11,37-063-0015,3,20.39166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/11/11,37-063-0015,3,14.49583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/12/11,37-063-0015,3,15.61666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/15/11,37-063-0015,3,10.1625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/16/11,37-063-0015,3,14.79166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/17/11,37-063-0015,3,12.15,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/18/11,37-063-0015,3,12.39583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/19/11,37-063-0015,3,4.454545455,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/20/11,37-063-0015,3,9.2125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/21/11,37-063-0015,3,42.44583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/22/11,37-063-0015,3,8.245833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/23/11,37-063-0015,3,4.825,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/24/11,37-063-0015,3,9.716666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/25/11,37-063-0015,3,11.20416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/26/11,37-063-0015,3,15.7125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/27/11,37-063-0015,3,15.2,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/28/11,37-063-0015,3,9.85,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/29/11,37-063-0015,3,8.379166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/30/11,37-063-0015,3,12.5125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/1/11,37-063-0015,3,16.475,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/2/11,37-063-0015,3,18.1875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/3/11,37-063-0015,3,23.37916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/4/11,37-063-0015,3,19.64583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/5/11,37-063-0015,3,12.95833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/6/11,37-063-0015,3,19.87727273,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/7/11,37-063-0015,3,11.35833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/8/11,37-063-0015,3,11.95416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/9/11,37-063-0015,3,8.570833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/10/11,37-063-0015,3,17.77916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/11/11,37-063-0015,3,20.425,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/12/11,37-063-0015,3,18.9625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/13/11,37-063-0015,3,18.22083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/14/11,37-063-0015,3,9.9,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/15/11,37-063-0015,3,5.266666667,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/16/11,37-063-0015,3,6.266666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/17/11,37-063-0015,3,7.05,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/18/11,37-063-0015,3,10.12916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/19/11,37-063-0015,3,21.9,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/20/11,37-063-0015,3,19.525,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/21/11,37-063-0015,3,18.91666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/22/11,37-063-0015,3,21.9375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/23/11,37-063-0015,3,17.40416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/24/11,37-063-0015,3,13.30416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/25/11,37-063-0015,3,9.558333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/26/11,37-063-0015,3,10.93181818,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/27/11,37-063-0015,3,14.6,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/28/11,37-063-0015,3,18.75416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/29/11,37-063-0015,3,14.9,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/30/11,37-063-0015,3,19.44166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/31/11,37-063-0015,3,7.1375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/1/11,37-063-0015,3,9.475,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/2/11,37-063-0015,3,15.2875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/3/11,37-063-0015,3,19.225,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/4/11,37-063-0015,3,17.52083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/5/11,37-063-0015,3,15.625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/6/11,37-063-0015,3,8.879166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/7/11,37-063-0015,3,13.99166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/8/11,37-063-0015,3,12.27727273,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/9/11,37-063-0015,3,9.370833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/10/11,37-063-0015,3,9.38,ug/m3 LC,.,20,83,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/11/11,37-063-0015,3,12.19583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/12/11,37-063-0015,3,19.375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/13/11,37-063-0015,3,15.075,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/14/11,37-063-0015,3,6.225,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/15/11,37-063-0015,3,7.8625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/16/11,37-063-0015,3,12.025,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/17/11,37-063-0015,3,12.8,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/18/11,37-063-0015,3,13.99583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/19/11,37-063-0015,3,14.75454545,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/20/11,37-063-0015,3,12.20416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/21/11,37-063-0015,3,12.07083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/22/11,37-063-0015,3,8.283333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/23/11,37-063-0015,3,8.716666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/24/11,37-063-0015,3,9.663636364,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/25/11,37-063-0015,3,9.220833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/26/11,37-063-0015,3,8.695833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/27/11,37-063-0015,3,4.4375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/28/11,37-063-0015,3,15.875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/29/11,37-063-0015,3,13.91666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/30/11,37-063-0015,3,9.9625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/31/11,37-063-0015,3,8.920833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/1/11,37-063-0015,3,11.32083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/2/11,37-063-0015,3,17.72083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/3/11,37-063-0015,3,16.54166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/4/11,37-063-0015,3,13.375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/5/11,37-063-0015,3,11.91666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/6/11,37-063-0015,3,6.391666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/7/11,37-063-0015,3,5.941666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/8/11,37-063-0015,3,14.42916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/9/11,37-063-0015,3,14.83809524,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/10/11,37-063-0015,3,11.44166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/11/11,37-063-0015,3,9.333333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/12/11,37-063-0015,3,12.28333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/13/11,37-063-0015,3,14.55416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/14/11,37-063-0015,3,14.39166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/15/11,37-063-0015,3,13.4125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/17/11,37-063-0015,3,5.391666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/18/11,37-063-0015,3,3.333333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/19/11,37-063-0015,3,5.35,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/20/11,37-063-0015,3,7.620833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/21/11,37-063-0015,3,4.880952381,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/22/11,37-063-0015,3,6.152380952,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/23/11,37-063-0015,3,5.1,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/24/11,37-063-0015,3,7.070833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/25/11,37-063-0015,3,3.683333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/26/11,37-063-0015,3,5.120833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/27/11,37-063-0015,3,9.870833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/28/11,37-063-0015,3,7.375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/29/11,37-063-0015,3,8.533333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/30/11,37-063-0015,3,7.195833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/1/11,37-063-0015,3,2.145833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/2/11,37-063-0015,3,4.8875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/3/11,37-063-0015,3,5.329166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/4/11,37-063-0015,3,6.033333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/5/11,37-063-0015,3,8.304166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/6/11,37-063-0015,3,9.7875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/7/11,37-063-0015,3,7.325,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/8/11,37-063-0015,3,7.35,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/9/11,37-063-0015,3,5.775,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/10/11,37-063-0015,3,9.020833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/11/11,37-063-0015,3,10.58636364,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/12/11,37-063-0015,3,7.208333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/13/11,37-063-0015,3,6.2,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/14/11,37-063-0015,3,7.366666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/15/11,37-063-0015,3,7.15,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/16/11,37-063-0015,3,5.820833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/17/11,37-063-0015,3,11.775,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/18/11,37-063-0015,3,11.45238095,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/19/11,37-063-0015,3,1.5625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/20/11,37-063-0015,3,4.6875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/21/11,37-063-0015,3,6.641666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/22/11,37-063-0015,3,7.166666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/23/11,37-063-0015,3,9.904166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/24/11,37-063-0015,3,12.24583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/25/11,37-063-0015,3,10.27083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/26/11,37-063-0015,3,12.9625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/27/11,37-063-0015,3,12.44166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/28/11,37-063-0015,3,1.645833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/29/11,37-063-0015,3,2.108333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/30/11,37-063-0015,3,9.079166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/31/11,37-063-0015,3,6.483333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/1/11,37-063-0015,3,7.7625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/2/11,37-063-0015,3,9.508333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/3/11,37-063-0015,3,11.55416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/4/11,37-063-0015,3,8.425,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/5/11,37-063-0015,3,5.1625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/6/11,37-063-0015,3,5.983333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/7/11,37-063-0015,3,6.841666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/8/11,37-063-0015,3,9.458333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/9/11,37-063-0015,3,8.616666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/10/11,37-063-0015,3,7.115,ug/m3 LC,.,20,83,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/11/11,37-063-0015,3,6.475,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/12/11,37-063-0015,3,8.9125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/13/11,37-063-0015,3,9.204166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/14/11,37-063-0015,3,9.370833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/15/11,37-063-0015,3,8.975,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/16/11,37-063-0015,3,10.07916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/17/11,37-063-0015,3,3.408333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/18/11,37-063-0015,3,5.879166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/19/11,37-063-0015,3,11.85,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/20/11,37-063-0015,3,13.17083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/21/11,37-063-0015,3,8.421052632,ug/m3 LC,.,19,79,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/22/11,37-063-0015,3,14.00416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/23/11,37-063-0015,3,2.25,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/24/11,37-063-0015,3,6.575,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/25/11,37-063-0015,3,8.775,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/26/11,37-063-0015,3,9.8375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/27/11,37-063-0015,3,6.395833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/28/11,37-063-0015,3,3.883333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/29/11,37-063-0015,3,2.175,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/30/11,37-063-0015,3,4.208333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/1/11,37-063-0015,3,6.15,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/2/11,37-063-0015,3,10.625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/3/11,37-063-0015,3,9.533333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/4/11,37-063-0015,3,10.95416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/5/11,37-063-0015,3,9.2,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/6/11,37-063-0015,3,4.25,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/7/11,37-063-0015,3,1.9375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/8/11,37-063-0015,3,5.558333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/9/11,37-063-0015,3,10.625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/10/11,37-063-0015,3,9.554166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/11/11,37-063-0015,3,7.245833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/12/11,37-063-0015,3,8.633333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/13/11,37-063-0015,3,11.54583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/14/11,37-063-0015,3,10.37368421,ug/m3 LC,.,19,79,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/15/11,37-063-0015,3,10.6125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/16/11,37-063-0015,3,7.466666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/17/11,37-063-0015,3,7.541666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/18/11,37-063-0015,3,10.8375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/19/11,37-063-0015,3,12.025,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/20/11,37-063-0015,3,15.22916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/21/11,37-063-0015,3,8.275,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/22/11,37-063-0015,3,7.366666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/23/11,37-063-0015,3,3.15,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/24/11,37-063-0015,3,7.929166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/25/11,37-063-0015,3,10.7875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/26/11,37-063-0015,3,7.329166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/27/11,37-063-0015,3,4.120833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/28/11,37-063-0015,3,4.283333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/29/11,37-063-0015,3,8.4,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/30/11,37-063-0015,3,10.15833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/31/11,37-063-0015,3,8.616666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/airports/airports.R",
    "content": "# load packages ----------------------------------------------------------------\nlibrary(tidyverse)\nlibrary(sf)\nlibrary(openintro)\nlibrary(nycflights13)\nlibrary(janitor)\nlibrary(measurements)\n\n# data sources -----------------------------------------------------------------\n# shapefile: https://catalog.data.gov/dataset/2013-cartographic-boundary-file-state-for-united-states-1-20000000\n# Downloaded 2018-08-13\n\n# set colors -------------------------------------------------------------------\nlgray <- COL[7,4]\ndgray <- COL[6,1]\n\n# load spatial data ------------------------------------------------------------\n# and filter out non-contigious states\nusa_49 <- st_read(\"data/cb_2013_us_state_20m/cb_2013_us_state_20m.shp\") %>%\n  filter(!(NAME %in% c(\"Alaska\", \"Hawaii\", \"Puerto Rico\")))\n\n# load usairports data ------------------------------------------------------------\ndata(usairports, package = \"openintro\")\n\n# clean airport data -----------------------------------------------------------\nusairports <- usairports %>%\n  filter(\n    !str_detect(arp_latitude, \"S\"),\n    !str_detect(state, \"AK|HI|PR|MQ|GU|CQ|VI\")\n    ) %>%\n  mutate(\n    lat_dms = str_replace(arp_latitude, \"N\", \"\") %>%\n      str_replace_all(\"-\", \" \"),\n    lon_dms = str_replace(arp_longitude, \"W\", \"\") %>%\n      str_replace_all(\"-\", \" \"),\n    lat_dd = conv_unit(lat_dms, from = \"deg_min_sec\", to = \"dec_deg\") %>% as.numeric(),\n    lon_dd = -1 * (conv_unit(lon_dms, from = \"deg_min_sec\", to = \"dec_deg\") %>% as.numeric())\n  ) %>%\n  filter(ownership %in% c(\"PR\", \"PU\")) %>%   # only want public and private owned\n  mutate(\n    ownership = case_when(\n      ownership == \"PR\" ~ \"Privately owned\",\n      ownership == \"PU\" ~ \"Publicly owned\"\n    ),\n    use = case_when(\n      use == \"PR\" ~ \"Private use\",\n      use == \"PU\" ~ \"Public use\"\n    ),\n    region = case_when(\n      region == \"AAL\" ~ \"Alaska\",\n      region == \"ACE\" ~ \"Central\",\n      region == \"AEA\" ~ \"Eastern\",\n      region == \"AGL\" ~ \"Great Lakes\",\n      region == \"ANE\" ~ \"New England\",\n      region == \"ANM\" ~ \"Northwest Mountain\",\n      region == \"ASO\" ~ \"Southern\",\n      region == \"ASW\" ~ \"Southwest\",\n      region == \"AWP\" ~ \"Western-Pacific\"\n    )\n  )\n  \n# plot -------------------------------------------------------------------------\nggplot(data = usa_49) +\n  geom_sf(fill = lgray, color = dgray, size = 0.2) +\n  geom_point(data = usairports, \n             aes(x = lon_dd, y = lat_dd, color = region),\n             alpha = 0.3, show.legend = FALSE) +\n  #scale_colour_manual(values = c(blue, green)) +\n  coord_sf(xlim = c(-130, -60),\n           ylim = c(20, 50)) +\n  facet_grid(ownership ~ use) +\n  labs(x = \"\", y = \"\", color = \"Use\") +\n  theme_minimal()\n\n# save plot --------------------------------------------------------------------\nggsave(\"airports.png\", width = 7, height = 4)\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/airports/data/cb_2013_us_state_20m/cb_2013_us_state_20m.prj",
    "content": "GEOGCS[\"GCS_North_American_1983\",DATUM[\"D_North_American_1983\",SPHEROID[\"GRS_1980\",6378137,298.257222101]],PRIMEM[\"Greenwich\",0],UNIT[\"Degree\",0.017453292519943295]]"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/airports/data/cb_2013_us_state_20m/cb_2013_us_state_20m.shp.iso.xml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<gmi:MI_Metadata xmlns:gmi=\"http://www.isotc211.org/2005/gmi\">\n   <gmd:fileIdentifier xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">cb_2013_us_state_20m.shp.xml</gco:CharacterString>\n   </gmd:fileIdentifier>\n   <gmd:language xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">eng</gco:CharacterString>\n   </gmd:language>\n   <gmd:characterSet xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gmd:MD_CharacterSetCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_CharacterSetCode\"\n                               codeListValue=\"8859part1\"\n                               codeSpace=\"006\">8859part1</gmd:MD_CharacterSetCode>\n   </gmd:characterSet>\n   <!-- This part represents a link to the Series Collection File --><gmd:parentIdentifier xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Series Information for the 2013 Cartographic Boundary File, State , 1:20,000,000</gco:CharacterString>\n   </gmd:parentIdentifier>\n   <gmd:hierarchyLevel xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gmd:MD_ScopeCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_ScopeCode\"\n                        codeListValue=\"dataset\">\ndataset\n</gmd:MD_ScopeCode>\n   </gmd:hierarchyLevel>\n   <gmd:contact xmlns:gmd=\"http://www.isotc211.org/2005/gmd\"\n                xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n                xlink:href=\"https://www.ngdc.noaa.gov/docucomp/8dd6ee96-96e1-492c-be55-76cdde8f27f1\"\n                xlink:title=\"pointofContact - U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch\"/>\n   <gmd:dateStamp xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gco:Date xmlns:gco=\"http://www.isotc211.org/2005/gco\">2014-04</gco:Date>\n   </gmd:dateStamp>\n   <gmd:metadataStandardName xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">ISO 19115 Geographic Information - Metadata </gco:CharacterString>\n   </gmd:metadataStandardName>\n   <gmd:metadataStandardVersion xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">2009-02-15 </gco:CharacterString>\n   </gmd:metadataStandardVersion>\n   <gmd:dataSetURI xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">http://www2.census.gov/geo/tiger/GENZ2013/STATE/cb_2013_us_state_20m.zip</gco:CharacterString>\n   </gmd:dataSetURI>\n   <!-- This is the ptvctinf/sdtsterm/sdtstype from section 3 of the FGDC\n                        Standard (Spatial Data Organization) -->\n                <gmd:spatialRepresentationInfo xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gmd:MD_VectorSpatialRepresentation>\n         <gmd:geometricObjects>\n            <gmd:MD_GeometricObjects>\n               <gmd:geometricObjectType>\n                  <gmd:MD_GeometricObjectTypeCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_GeometricObjectTypeCode\"\n                                                  codeListValue=\"complex\"\n                                                  codeSpace=\"001\">\n                                                complex </gmd:MD_GeometricObjectTypeCode>\n               </gmd:geometricObjectType>\n               <gmd:geometricObjectCount>\n                  <gco:Integer xmlns:gco=\"http://www.isotc211.org/2005/gco\">52</gco:Integer>\n               </gmd:geometricObjectCount>\n            </gmd:MD_GeometricObjects>\n         </gmd:geometricObjects>\n      </gmd:MD_VectorSpatialRepresentation>\n   </gmd:spatialRepresentationInfo>\n   <!--This is the indirect spatial reference of section 3 and the Projection\n               Information from Section 4 of the FGDC Standard-->\n         \n      <gmd:referenceSystemInfo xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gmd:MD_ReferenceSystem>\n         <gmd:referenceSystemIdentifier>\n            <gmd:RS_Identifier>\n               <gmd:code xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gmd:codeSpace>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">INCITS (formerly FIPS) codes.</gco:CharacterString>\n               </gmd:codeSpace>\n            </gmd:RS_Identifier>\n         </gmd:referenceSystemIdentifier>\n      </gmd:MD_ReferenceSystem>\n   </gmd:referenceSystemInfo>\n   <!-- This part represents Section 1 of the FGDC Metadata Standard -->\n<gmd:identificationInfo xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gmd:MD_DataIdentification>\n         <gmd:citation>\n            <gmd:CI_Citation>\n               <gmd:title>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">2013 Cartographic Boundary File, State for United States, 1:20,000,000</gco:CharacterString>\n               </gmd:title>\n               <gmd:date>\n                  <gmd:CI_Date>\n<!-- This is the publication date -->\n<gmd:date>\n                        <gco:Date xmlns:gco=\"http://www.isotc211.org/2005/gco\">201404</gco:Date>\n                     </gmd:date>\n                     <gmd:dateType>\n                        <gmd:CI_DateTypeCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_DateTypeCode\"\n                                             codeListValue=\"publication\"\n                                             codeSpace=\"002\"> publication </gmd:CI_DateTypeCode>\n                     </gmd:dateType>\n                  </gmd:CI_Date>\n               </gmd:date>\n               <gmd:citedResponsibleParty xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n                                          xlink:href=\"https://www.ngdc.noaa.gov/docucomp/ddd21bfb-2229-465b-95b2-bee36200b0e5\"\n                                          xlink:title=\"originator - U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch\"/>\n            </gmd:CI_Citation>\n         </gmd:citation>\n         <gmd:abstract>\n            <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">The 2013 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based shapefiles while others are available only as state-based files.</gco:CharacterString>\n         </gmd:abstract>\n         <gmd:purpose>\n            <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">These files were specifically created to support small-scale thematic mapping.  To improve the appearance of shapes at small scales, areas are represented with fewer vertices than detailed TIGER/Line Shapefiles.  Cartographic boundary files take up less disk space than their ungeneralized counterparts.  Cartographic boundary files take less time to render on screen than TIGER/Line Shapefiles.  You can join this shapefile with table data downloaded from American FactFinder by using the AFFGEOID field in the cartographic boundary file.  If detailed boundaries are required, please use the TIGER/Line Shapefiles instead of the generalized cartographic boundary files.  </gco:CharacterString>\n         </gmd:purpose>\n         <gmd:pointOfContact xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n                             xlink:href=\"https://www.ngdc.noaa.gov/docucomp/09b8253a-e2dc-4a6b-a905-11f1e0e87b3b\"\n                             xlink:title=\"pointOfContact - U.S. Department of Commerce, U.S. Census Bureau, Geography Division\"/>\n\n         <gmd:resourceMaintenance>\n            <gmd:MD_MaintenanceInformation>\n               <gmd:maintenanceAndUpdateFrequency>\n                  <gmd:MD_MaintenanceFrequencyCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_MaintenanceFrequencyCode\"\n                                                   codeListValue=\"notPlanned\"\n                                                   codeSpace=\"011\">\n                         notPlanned\n                </gmd:MD_MaintenanceFrequencyCode>\n               </gmd:maintenanceAndUpdateFrequency>\n            </gmd:MD_MaintenanceInformation>\n         </gmd:resourceMaintenance>\n         <gmd:descriptiveKeywords>\n            <gmd:MD_Keywords><!--before the keyword--><gmd:keyword>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">2013</gco:CharacterString>\n               </gmd:keyword>\n               <gmd:keyword>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Cartographic Boundary</gco:CharacterString>\n               </gmd:keyword>\n               <gmd:keyword>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Generalized</gco:CharacterString>\n               </gmd:keyword>\n               <gmd:keyword>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Shapefile</gco:CharacterString>\n               </gmd:keyword>\n               <gmd:keyword>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">State</gco:CharacterString>\n               </gmd:keyword>\n               <gmd:type>\n                  <gmd:MD_KeywordTypeCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_KeywordTypeCode\"\n                                          codeListValue=\"theme\"\n                                          codeSpace=\"005\"> theme </gmd:MD_KeywordTypeCode>\n               </gmd:type>\n               <gmd:thesaurusName>\n                  <gmd:CI_Citation>\n                     <gmd:title>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">None</gco:CharacterString>\n                     </gmd:title>\n                     <gmd:date xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n                  </gmd:CI_Citation>\n               </gmd:thesaurusName>\n            </gmd:MD_Keywords>\n         </gmd:descriptiveKeywords>\n         <gmd:descriptiveKeywords>\n            <gmd:MD_Keywords>\n               <gmd:keyword>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">United States\n</gco:CharacterString>\n               </gmd:keyword>\n               <gmd:keyword>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">US\n</gco:CharacterString>\n               </gmd:keyword>\n               <gmd:type>\n                  <gmd:MD_KeywordTypeCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_KeywordTypeCode\"\n                                          codeListValue=\"place\"\n                                          codeSpace=\"002\"> place </gmd:MD_KeywordTypeCode>\n               </gmd:type>\n               <gmd:thesaurusName>\n                  <gmd:CI_Citation>\n                     <gmd:title>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">\nINCITS 38:2009\n  \n \n</gco:CharacterString>\n                     </gmd:title>\n                     <gmd:date xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n                  </gmd:CI_Citation>\n               </gmd:thesaurusName>\n            </gmd:MD_Keywords>\n         </gmd:descriptiveKeywords>\n         <gmd:resourceConstraints>\n            <gmd:MD_LegalConstraints>\n               <gmd:accessConstraints>\n                  <gmd:MD_RestrictionCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_RestrictionCode\"\n                                          codeListValue=\"otherRestrictions\"\n                                          codeSpace=\"008 \"> otherRestrictions </gmd:MD_RestrictionCode>\n               </gmd:accessConstraints>\n               <gmd:useConstraints>\n                  <gmd:MD_RestrictionCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_RestrictionCode\"\n                                          codeListValue=\"otherRestrictions\"\n                                          codeSpace=\"008 \"/>\n               </gmd:useConstraints>\n               <gmd:otherConstraints>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\"> Access Constraints: None</gco:CharacterString>\n               </gmd:otherConstraints>\n               <gmd:otherConstraints>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\"> Use Constraints:The intended display scale for this file is 1:20,000,000.  This file should not be displayed at scales larger than 1:20,000,000.\n\nThese products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source.  The boundary information is for visual display at appropriate small scales only.  Cartographic boundary files should not be used for geographic analysis including area or perimeter calculation.  Files should not be used for geocoding addresses.  Files should not be used for determining precise geographic area relationships.</gco:CharacterString>\n               </gmd:otherConstraints>\n            </gmd:MD_LegalConstraints>\n         </gmd:resourceConstraints>\n            <!-- This is from the Direct Spatial Reference from Chapter 3 -->\n            <gmd:spatialRepresentationType>\n            <gmd:MD_SpatialRepresentationTypeCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_SpatialRepresentationTypeCode\"\n                                                  codeListValue=\"vector\">vector</gmd:MD_SpatialRepresentationTypeCode>\n         </gmd:spatialRepresentationType>\n         <gmd:language>\n            <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">eng</gco:CharacterString>\n         </gmd:language>\n         <gmd:characterSet>\n            <gmd:MD_CharacterSetCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_CharacterSetCode\"\n                                     codeListValue=\"8859part1\"\n                                     codeSpace=\"006\">8859part1</gmd:MD_CharacterSetCode>\n         </gmd:characterSet>\n         <gmd:topicCategory>\n            <gmd:MD_TopicCategoryCode>boundaries</gmd:MD_TopicCategoryCode>\n         </gmd:topicCategory>\n         <gmd:environmentDescription>\n            <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">The cartographic boundary files contain geographic data only and do not include display mapping software or statistical data.  For information on how to use cartographic boundary file data with specific software package users shall contact the company that produced the software</gco:CharacterString>\n         </gmd:environmentDescription>\n         <gmd:extent>\n            <gmd:EX_Extent id=\"boundingExtent\">\n               <gmd:geographicElement>\n                  <gmd:EX_GeographicBoundingBox id=\"boundingGeographicBoundingBox\">\n                     <gmd:westBoundLongitude>\n                        <gco:Decimal xmlns:gco=\"http://www.isotc211.org/2005/gco\">172.000000</gco:Decimal>\n                     </gmd:westBoundLongitude>\n                     <gmd:eastBoundLongitude>\n                        <gco:Decimal xmlns:gco=\"http://www.isotc211.org/2005/gco\">-65.221527</gco:Decimal>\n                     </gmd:eastBoundLongitude>\n                     <gmd:southBoundLatitude>\n                        <gco:Decimal xmlns:gco=\"http://www.isotc211.org/2005/gco\">-14.605210\n</gco:Decimal>\n                     </gmd:southBoundLatitude>\n                     <gmd:northBoundLatitude>\n                        <gco:Decimal xmlns:gco=\"http://www.isotc211.org/2005/gco\">71.342941</gco:Decimal>\n                     </gmd:northBoundLatitude>\n                  </gmd:EX_GeographicBoundingBox>\n               </gmd:geographicElement>\n               <gmd:temporalElement>\n                  <gmd:EX_TemporalExtent id=\"boundingTemporalExtent\">\n                     <gmd:extent>\n                        <gml:TimePeriod xmlns:gml=\"http://www.opengis.net/gml/3.2\" gml:id=\"boundingTemporalExtentA\">\n                           <gml:description>publication date</gml:description>\n                           <gml:beginPosition>2014-04</gml:beginPosition>\n                           <gml:endPosition>2014-04</gml:endPosition>\n                        </gml:TimePeriod>\n                     </gmd:extent>\n                  </gmd:EX_TemporalExtent>\n               </gmd:temporalElement>\n            </gmd:EX_Extent>\n         </gmd:extent>\n      </gmd:MD_DataIdentification>\n   </gmd:identificationInfo>\n   <!--This section provides the link for the file containing the Entity and\n                        Attribute Information. -->\n<gmd:contentInfo xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gmd:MD_FeatureCatalogueDescription>\n         <gmd:includedWithDataset>\n            <gco:Boolean xmlns:gco=\"http://www.isotc211.org/2005/gco\">true</gco:Boolean>\n         </gmd:includedWithDataset>\n         <gmd:featureCatalogueCitation>\n            <gmd:CI_Citation>\n               <gmd:title>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Feature Catalog for the State for United States 2013 Cartographic Boundary File000</gco:CharacterString>\n               </gmd:title>\n               <gmd:date>\n                  <gmd:CI_Date>\n\n                     <gmd:date xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"missing \"/>\n                     <gmd:dateType>\n                        <gmd:CI_DateTypeCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_DateTypeCode\"\n                                             codeListValue=\"publication\"\n                                             codeSpace=\"002\"/>\n                     </gmd:dateType>\n                  </gmd:CI_Date>\n               </gmd:date>\n               <gmd:citedResponsibleParty xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n                                          xlink:href=\"https://www.ngdc.noaa.gov/docucomp/1df27e57-4768-42de-909b-52f530601fba\"\n                                          xlink:title=\"U.S Department of Commerce, U.S Census Bureau, Geography Division (distributor)\"/>\n               <gmd:otherCitationDetails>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">http://meta.geo.census.gov/data/existing/decennial/GEO/CPMB/boundary/2013gz/state_20m/2013_state_20m.ea.iso.xml</gco:CharacterString>\n               </gmd:otherCitationDetails>\n            </gmd:CI_Citation>\n         </gmd:featureCatalogueCitation>\n      </gmd:MD_FeatureCatalogueDescription>\n   </gmd:contentInfo>\n   <gmd:distributionInfo xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gmd:MD_Distribution>\n         <gmd:distributionFormat>\n            <gmd:MD_Format>\n               <gmd:name>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">SHP (compressed)</gco:CharacterString>\n               </gmd:name>\n               <gmd:version xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gmd:fileDecompressionTechnique>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">The cartographic boundary files contain geographic data only and do not include display mapping software or statistical data.  For information on how to use cartographic boundary file data with specific software package users shall contact the company that produced the software</gco:CharacterString>\n               </gmd:fileDecompressionTechnique>\n            </gmd:MD_Format>\n         </gmd:distributionFormat>\n         <gmd:distributor>\n            <gmd:MD_Distributor>\n               <gmd:distributorContact xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n                                       xlink:href=\"https://www.ngdc.noaa.gov/docucomp/1df27e57-4768-42de-909b-52f530601fba\"\n                                       xlink:title=\"U.S Department of Commerce, U.S Census Bureau, Geography Division (distributor)\"/>\n               <gmd:distributionOrderProcess>\n                  <gmd:MD_StandardOrderProcess>\n                     <gmd:fees>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">The online cartographic boundary files may be downloaded without charge.</gco:CharacterString>\n                     </gmd:fees>\n                  </gmd:MD_StandardOrderProcess>\n               </gmd:distributionOrderProcess>\n            </gmd:MD_Distributor>\n         </gmd:distributor>\n         <gmd:transferOptions>\n            <gmd:MD_DigitalTransferOptions>\n               <gmd:onLine>\n                  <gmd:CI_OnlineResource>\n                     <gmd:linkage>\n                        <gmd:URL>http://www.census.gov/geo/maps-data/data/tiger.html</gmd:URL>\n                     </gmd:linkage>\n                  </gmd:CI_OnlineResource>\n               </gmd:onLine>\n            </gmd:MD_DigitalTransferOptions>\n         </gmd:transferOptions>\n      </gmd:MD_Distribution>\n   </gmd:distributionInfo>\n   <gmd:dataQualityInfo xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gmd:DQ_DataQuality>\n         <gmd:scope>\n            <gmd:DQ_Scope>\n               <gmd:level>\n                  <gmd:MD_ScopeCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_ScopeCode\"\n                                    codeListValue=\"dataset\"\n                                    codeSpace=\"005\"> dataset </gmd:MD_ScopeCode>\n               </gmd:level>\n            </gmd:DQ_Scope>\n         </gmd:scope>\n         <gmd:report>\n            <gmd:DQ_AbsoluteExternalPositionalAccuracy>\n               <gmd:nameOfMeasure>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Horizontal Positional\n                           Accuracy</gco:CharacterString>\n               </gmd:nameOfMeasure>\n               <gmd:measureDescription>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\"/>\n               </gmd:measureDescription>\n               <gmd:evaluationMethodDescription>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Data are not accurate.  Data are generalized representations of geographic boundaries at 1:20,000,000.</gco:CharacterString>\n               </gmd:evaluationMethodDescription>\n               <gmd:result>\n                  <gmd:DQ_QuantitativeResult>\n                     <gmd:valueUnit>\n                        <gml:BaseUnit xmlns:gml=\"http://www.opengis.net/gml/3.2\" gml:id=\"meters\">\n                           <gml:identifier codeSpace=\"meters \">\n                                          meters</gml:identifier>\n                           <gml:unitsSystem xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n                                            xlink:href=\"http://www.bipm.org/en/si/ \"/>\n                        </gml:BaseUnit>\n                     </gmd:valueUnit>\n                     <gmd:value>\n                        <gco:Record xmlns:gco=\"http://www.isotc211.org/2005/gco\">Missing </gco:Record>\n                     </gmd:value>\n                  </gmd:DQ_QuantitativeResult>\n               </gmd:result>\n            </gmd:DQ_AbsoluteExternalPositionalAccuracy>\n         </gmd:report>\n         <gmd:report>\n            <gmd:DQ_CompletenessCommission>\n               <gmd:result xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n            </gmd:DQ_CompletenessCommission>\n         </gmd:report>\n         <gmd:report>\n            <gmd:DQ_CompletenessOmission>\n               <gmd:evaluationMethodDescription>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">The cartographic boundary files are generalized representations of extracts taken from the MAF/TIGER Database. Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some off-shore areas may be excluded from the generalized files. Some small holes or discontiguous parts of areas are not included in generalized files.</gco:CharacterString>\n               </gmd:evaluationMethodDescription>\n               <gmd:result xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n            </gmd:DQ_CompletenessOmission>\n         </gmd:report>\n         <gmd:report>\n            <gmd:DQ_ConceptualConsistency>\n               <gmd:measureDescription>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">The Census Bureau performed automated tests to ensure logical consistency of the source database.  Segments making up the outer and inner boundaries of a polygon tie end-to-end to completely enclose the area.  All polygons were tested for closure. The Census Bureau uses its internally developed geographic update system to enhance and modify spatial and attribute data in the Census MAF/TIGER database.  Standard geographic codes, such as FIPS codes for states, counties, municipalities, county subdivisions, places, American Indian/Alaska Native/Native Hawaiian areas, and congressional districts are used when encoding spatial entities.  The Census Bureau performed spatial data tests for logical consistency of the codes during the compilation of the original Census MAF/TIGER database files.  Feature attribute information has been examined but has not been fully tested for consistency.  \n\nFor the cartographic boundary shapefiles, the Point and Vector Object Count for the G-polygon SDTS Point and Vector Object Type reflects the number of records in the shapefile attribute table.  For multi-polygon features, only one attribute record exists for each multi-polygon rather than one attribute record per individual G-polygon component of the multi-polygon feature.  TIGER/Line Shapefile multi-polygons are an exception to the G-polygon object type classification.  Therefore, when multi-polygons exist in a shapefile, the object count will be less than the actual number of G-polygons.</gco:CharacterString>\n               </gmd:measureDescription>\n               <gmd:result xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n            </gmd:DQ_ConceptualConsistency>\n         </gmd:report>\n         <gmd:lineage>\n            <gmd:LI_Lineage>\n               <gmd:processStep>\n                  <gmd:LI_ProcessStep>\n                     <gmd:description>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Spatial data were extracted from the MAF/TIGER database and processed through a U.S. Census Bureau batch generalization system.</gco:CharacterString>\n                     </gmd:description>\n                     <gmd:dateTime>\n                        <gco:DateTime xmlns:gco=\"http://www.isotc211.org/2005/gco\">2014-04-01T00:00:00</gco:DateTime>\n                     </gmd:dateTime>\n                     <gmd:source>\n                        <gmd:LI_Source>\n                           <gmd:description>\n                              <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Geo-spatial Relational Database</gco:CharacterString>\n                           </gmd:description>\n                           <gmd:sourceCitation>\n                              <gmd:CI_Citation>\n                                 <gmd:title>\n                                    <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">MAF/TIGER</gco:CharacterString>\n                                 </gmd:title>\n                                 <gmd:date>\n                                    <gmd:CI_Date>\n                                       <gmd:date>\n                                          <gco:Date xmlns:gco=\"http://www.isotc211.org/2005/gco\">201404</gco:Date>\n                                       </gmd:date>\n                                       <gmd:dateType>\n                                          <gmd:CI_DateTypeCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_DateTypeCode\"\n                                                               codeListValue=\"revision\"\n                                                               codeSpace=\"003\">\n                                                revision </gmd:CI_DateTypeCode>\n                                       </gmd:dateType>\n                                    </gmd:CI_Date>\n                                 </gmd:date>\n                              </gmd:CI_Citation>\n                           </gmd:sourceCitation>\n                        </gmd:LI_Source>\n                     </gmd:source>\n                  </gmd:LI_ProcessStep>\n               </gmd:processStep>\n            </gmd:LI_Lineage>\n         </gmd:lineage>\n      </gmd:DQ_DataQuality>\n   </gmd:dataQualityInfo>\n   <gmd:metadataMaintenance xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n      <gmd:MD_MaintenanceInformation>\n         <gmd:maintenanceAndUpdateFrequency>\n            <gmd:MD_MaintenanceFrequencyCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_MaintenanceFrequencyCode\"\n                                             codeListValue=\"notPlanned\"\n                                             codeSpace=\"011\">\n                         notPlanned\n                </gmd:MD_MaintenanceFrequencyCode>\n         </gmd:maintenanceAndUpdateFrequency>\n         <gmd:maintenanceNote>\n            <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">This was transformed from the Census Metadata Import\n      Format</gco:CharacterString>\n         </gmd:maintenanceNote>\n         <gmd:contact xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n                      xlink:href=\"https://www.ngdc.noaa.gov/docucomp/e2a02c3c-01bf-42e7-bee9-0f64f2ef611c \"\n                      xlink:title=\"U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch  (custodian) \"/>\n      </gmd:MD_MaintenanceInformation>\n   </gmd:metadataMaintenance>\n</gmi:MI_Metadata>"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/airports/data/cb_2013_us_state_20m/cb_2013_us_state_20m.shp.xml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<metadata>\n   <idinfo>\n      <citation>\n         <citeinfo>\n            <origin>U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch</origin>\n            <pubdate>201404</pubdate>\n            <title>2013 Cartographic Boundary File, State for United States, 1:20,000,000</title>\n            <geoform>vector digital data</geoform>\n            <serinfo>\n               <sername>Cartographic Boundary Files</sername>\n               <issue>2013</issue>\n            </serinfo>\n            <onlink>http://www2.census.gov/geo/tiger/GENZ2013/STATE/cb_2013_us_state_20m.zip</onlink>\n         </citeinfo>\n      </citation>\n      <descript>\n         <abstract>The 2013 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based shapefiles while others are available only as state-based files.</abstract>\n         <purpose>These files were specifically created to support small-scale thematic mapping.  To improve the appearance of shapes at small scales, areas are represented with fewer vertices than detailed TIGER/Line Shapefiles.  Cartographic boundary files take up less disk space than their ungeneralized counterparts.  Cartographic boundary files take less time to render on screen than TIGER/Line Shapefiles.  You can join this shapefile with table data downloaded from American FactFinder by using the AFFGEOID field in the cartographic boundary file.  If detailed boundaries are required, please use the TIGER/Line Shapefiles instead of the generalized cartographic boundary files.  </purpose>\n      </descript>\n      <timeperd>\n         <timeinfo>\n            <rngdates>\n               <begdate>201404</begdate>\n               <enddate>201404</enddate>\n            </rngdates>\n         </timeinfo>\n         <current>publication date</current>\n      </timeperd>\n      <status>\n         <progress>Complete</progress>\n         <update>None planned.  No changes or updates will be made to this version of the cartographic boundary files.  New versions of the cartographic boundary files will be produced on an annual release schedule.  Types of geography released may vary from year to year.</update>\n      </status>\n      <spdom>\n         <bounding>\n            <westbc>172.000000</westbc>\n            <eastbc>-65.221527</eastbc>\n            <northbc>71.342941</northbc>\n            <southbc>-14.605210\n</southbc>\n         </bounding>\n      </spdom>\n      <keywords>\n         <theme>\n            <themekt>None</themekt>\n            <themekey>2013</themekey>\n            <themekey>Cartographic Boundary</themekey>\n            <themekey>Generalized</themekey>\n            <themekey>Shapefile</themekey>\n            <themekey>State</themekey>\n         </theme>\n         <theme>\n            <themekt>ISO 19115 Topic Categories</themekt>\n            <themekey>Boundaries</themekey>\n         </theme>\n         <place>\n            <placekt>INCITS 38:2009</placekt>\n            <placekey>United States</placekey>\n            <placekey>US</placekey>\n         </place>\n      </keywords>\n      <accconst>None</accconst>\n      <useconst>The intended display scale for this file is 1:20,000,000.  This file should not be displayed at scales larger than 1:20,000,000.\n\nThese products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source.  The boundary information is for visual display at appropriate small scales only.  Cartographic boundary files should not be used for geographic analysis including area or perimeter calculation.  Files should not be used for geocoding addresses.  Files should not be used for determining precise geographic area relationships.</useconst>\n      <ptcontac>\n         <cntinfo>\n            <cntorgp>\n               <cntorg>U.S. Department of Commerce, U.S. Census Bureau, Geography Division</cntorg>\n            </cntorgp>\n            <cntaddr>\n               <addrtype>mailing</addrtype>\n               <address>4600 Silver Hill Road</address>\n               <city>Washington</city>\n               <state>DC</state>\n               <postal>20233-7400</postal>\n               <country>United States</country>\n            </cntaddr>\n            <cntvoice>301.763.1128</cntvoice>\n            <cntfax>301.763.4710</cntfax>\n            <cntemail>geo.geography@census.gov</cntemail>\n         </cntinfo>\n      </ptcontac>\n   </idinfo>\n   <dataqual>\n      <attracc>\n         <attraccr>Accurate against American National Standards Institute (ANSI) Publication INCITS 446-2008 (Geographic Names Information System (GNIS)) at the 100% level for the codes and base names present in the file.  The remaining attribute information has been examined but has not been fully tested for accuracy.</attraccr>\n      </attracc>\n      <logic>The Census Bureau performed automated tests to ensure logical consistency of the source database.  Segments making up the outer and inner boundaries of a polygon tie end-to-end to completely enclose the area.  All polygons were tested for closure. The Census Bureau uses its internally developed geographic update system to enhance and modify spatial and attribute data in the Census MAF/TIGER database.  Standard geographic codes, such as FIPS codes for states, counties, municipalities, county subdivisions, places, American Indian/Alaska Native/Native Hawaiian areas, and congressional districts are used when encoding spatial entities.  The Census Bureau performed spatial data tests for logical consistency of the codes during the compilation of the original Census MAF/TIGER database files.  Feature attribute information has been examined but has not been fully tested for consistency.  \n\nFor the cartographic boundary shapefiles, the Point and Vector Object Count for the G-polygon SDTS Point and Vector Object Type reflects the number of records in the shapefile attribute table.  For multi-polygon features, only one attribute record exists for each multi-polygon rather than one attribute record per individual G-polygon component of the multi-polygon feature.  TIGER/Line Shapefile multi-polygons are an exception to the G-polygon object type classification.  Therefore, when multi-polygons exist in a shapefile, the object count will be less than the actual number of G-polygons.</logic>\n      <complete>The cartographic boundary files are generalized representations of extracts taken from the MAF/TIGER Database. Generalized boundary files are clipped to a simplified version of the U.S. outline. As a result, some off-shore areas may be excluded from the generalized files. Some small holes or discontiguous parts of areas are not included in generalized files.</complete>\n      <posacc>\n         <horizpa>\n            <horizpar>Data are not accurate.  Data are generalized representations of geographic boundaries at 1:20,000,000.</horizpar>\n         </horizpa>\n      </posacc>\n      <lineage>\n         <srcinfo>\n            <srccite>\n               <citeinfo>\n                  <origin>U.S. Department of Commerce, U.S. Census Bureau, Geography Division</origin>\n                  <pubdate>unpublished material</pubdate>\n                  <title>Census MAF/TIGER database</title>\n               </citeinfo>\n            </srccite>\n            <typesrc>Geo-spatial Relational Database</typesrc>\n            <srctime>\n               <timeinfo>\n                  <rngdates>\n                     <begdate>20130101</begdate>\n                     <enddate>20130101</enddate>\n                  </rngdates>\n               </timeinfo>\n               <srccurr>The dates describe the effective date of 2013 cartographic boundaries.</srccurr>\n            </srctime>\n            <srccitea>MAF/TIGER</srccitea>\n            <srccontr>All spatial and feature data</srccontr>\n         </srcinfo>\n         <procstep>\n            <procdesc>Spatial data were extracted from the MAF/TIGER database and processed through a U.S. Census Bureau batch generalization system.</procdesc>\n            <srcused>MAF/TIGER</srcused>\n            <procdate>201404</procdate>\n         </procstep>\n      </lineage>\n   </dataqual>\n   <spdoinfo>\n      <indspref>INCITS (formerly FIPS) codes.</indspref>\n      <direct>Vector</direct>\n      <ptvctinf>\n         <sdtsterm>\n            <sdtstype>G-polygon</sdtstype>\n            <ptvctcnt>52</ptvctcnt>\n         </sdtsterm>\n      </ptvctinf>\n   </spdoinfo>\n   <spref>\n      <horizsys>\n         <geograph>\n            <latres>0.000458</latres>\n            <longres>0.000458</longres>\n            <geogunit>Decimal degrees</geogunit>\n         </geograph>\n         <geodetic>\n            <horizdn>North American Datum of 1983</horizdn>\n            <ellips>Geodetic Reference System 80</ellips>\n            <semiaxis>6378137.000000</semiaxis>\n            <denflat>298.257222</denflat>\n         </geodetic>\n      </horizsys>\n   </spref>\n   <eainfo>\n      <detailed>\n         <enttyp>\n            <enttypl>cb_2013_us_state_20m.shp</enttypl>\n            <enttypd>Current Census State and Equivalent National entities</enttypd>\n            <enttypds>U.S. Census Bureau</enttypds>\n         </enttyp>\n         <attr>\n            <attrlabl>STATEFP</attrlabl>\n            <attrdef>Current state Federal Information Processing Series (FIPS) code</attrdef>\n            <attrdefs>U.S. Census Bureau</attrdefs>\n            <attrdomv>\n               <codesetd>\n                  <codesetn>National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents</codesetn>\n                  <codesets>U.S. Census Bureau</codesets>\n               </codesetd>\n            </attrdomv>\n         </attr>\n         <attr>\n            <attrlabl>STATENS</attrlabl>\n            <attrdef>Current state ANSI code</attrdef>\n            <attrdefs>U.S. Census Bureau</attrdefs>\n            <attrdomv>\n               <codesetd>\n                  <codesetn>INCITS 446:2008 (Geographic Names Information System (GNIS)), Identifying Attributes for Named Physical and Cultural Geographic Features (Except Roads and Highways) of the United States, Its Territories, Outlying Areas, and Freely Associated Areas, and the Waters of the Same to the Limit of the Twelve-Mile Statutory Zone</codesetn>\n                  <codesets>U.S. Geological Survey (USGS)</codesets>\n               </codesetd>\n            </attrdomv>\n         </attr>\n         <attr>\n            <attrlabl>AFFGEOID</attrlabl>\n            <attrdef>American FactFinder summary level code + geovariant code + '00US' + GEOID</attrdef>\n            <attrdefs>U.S. Census Bureau</attrdefs>\n            <attrdomv>\n               <codesetd>\n                  <codesetn>American FactFinder geographic identifier</codesetn>\n                  <codesets>U.S. Census Bureau</codesets>\n               </codesetd>\n            </attrdomv>\n         </attr>\n         <attr>\n            <attrlabl>GEOID</attrlabl>\n            <attrdef>State identifier; state FIPS code</attrdef>\n            <attrdefs>U.S. Census Bureau</attrdefs>\n            <attrdomv>\n               <codesetd>\n                  <codesetn>National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents</codesetn>\n                  <codesets>U.S. Census Bureau</codesets>\n               </codesetd>\n            </attrdomv>\n         </attr>\n         <attr>\n            <attrlabl>STUSPS</attrlabl>\n            <attrdef>Current United States Postal Service state abbreviation</attrdef>\n            <attrdefs>U.S. Postal Service</attrdefs>\n            <attrdomv>\n               <codesetd>\n                  <codesetn>Official USPS state abbreviations, as shown in Publication 65, National 5-Digit ZIP Code and Post Office Directory</codesetn>\n                  <codesets>U.S. Postal Service</codesets>\n               </codesetd>\n            </attrdomv>\n         </attr>\n         <attr>\n            <attrlabl>NAME</attrlabl>\n            <attrdef>Current state name</attrdef>\n            <attrdefs>U.S. Census Bureau</attrdefs>\n            <attrdomv>\n               <codesetd>\n                  <codesetn>National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents</codesetn>\n                  <codesets>U.S. Census Bureau</codesets>\n               </codesetd>\n            </attrdomv>\n         </attr>\n         <attr>\n            <attrlabl>LSAD</attrlabl>\n            <attrdef>Current legal/statistical area description code for state</attrdef>\n            <attrdefs>U.S. Census Bureau</attrdefs>\n            <attrdomv>\n               <edom>\n                  <edomv>00</edomv>\n                  <edomvd>Blank</edomvd>\n                  <edomvds>U.S. Census Bureau</edomvds>\n               </edom>\n            </attrdomv>\n         </attr>\n         <attr>\n            <attrlabl>ALAND</attrlabl>\n            <attrdef>Current land area (square meters)</attrdef>\n            <attrdefs>U.S. Census Bureau</attrdefs>\n            <attrdomv>\n               <rdom>\n                  <rdommin>0</rdommin>\n                  <rdommax>9,999,999,999,999</rdommax>\n                  <attrunit>square meters</attrunit>\n               </rdom>\n            </attrdomv>\n         </attr>\n         <attr>\n            <attrlabl>AWATER</attrlabl>\n            <attrdef>Current water area (square meters)</attrdef>\n            <attrdefs>U.S. Census Bureau</attrdefs>\n            <attrdomv>\n               <rdom>\n                  <rdommin>0</rdommin>\n                  <rdommax>9,999,999,999,999</rdommax>\n                  <attrunit>square meters</attrunit>\n               </rdom>\n            </attrdomv>\n         </attr>\n      </detailed>\n   </eainfo>\n   <distinfo>\n      <distrib>\n         <cntinfo>\n            <cntorgp>\n               <cntorg>U.S. Department of Commerce, U.S. Census Bureau, Geography Division</cntorg>\n            </cntorgp>\n            <cntaddr>\n               <addrtype>mailing</addrtype>\n               <address>4600 Silver Hill Road</address>\n               <city>Washington</city>\n               <state>DC</state>\n               <postal>20233-7400</postal>\n               <country>United States</country>\n            </cntaddr>\n            <cntvoice>301.763.1128</cntvoice>\n            <cntemail>geo.geography@census.gov</cntemail>\n         </cntinfo>\n      </distrib>\n      <distliab>No warranty, expressed or implied is made with regard to the accuracy of these data, and no liability is assumed by the U.S. Government in general or the U.S. Census Bureau in specific as to the spatial or attribute accuracy of the data.  The act of distribution shall not constitute any such warranty and no responsibility is assumed by the U.S. government in the use of these files.  The boundary information is for small-scale mapping purposes only; boundary depiction and designation for small-scale mapping purposes do not constitute a determination of jurisdictional authority or rights of ownership or entitlement and they are not legal land descriptions.</distliab>\n      <stdorder>\n         <digform>\n            <digtinfo>\n               <formname>SHP (compressed)</formname>\n               <filedec>The files were compressed using Linux-based Info-ZIP Zip 2.32. Files can be decompressed with PK-ZIP, version 1.93A or higher, WinZip or other decompression software packages.</filedec>\n            </digtinfo>\n            <digtopt>\n               <onlinopt>\n                  <computer>\n                     <networka>\n                        <networkr>http://www.census.gov/geo/maps-data/data/tiger.html</networkr>\n                     </networka>\n                  </computer>\n               </onlinopt>\n            </digtopt>\n         </digform>\n         <fees>The online cartographic boundary files may be downloaded without charge.</fees>\n      </stdorder>\n      <techpreq>The cartographic boundary files contain geographic data only and do not include display mapping software or statistical data.  For information on how to use cartographic boundary file data with specific software package users shall contact the company that produced the software</techpreq>\n   </distinfo>\n   <metainfo>\n      <metd>201404</metd>\n      <metc>\n         <cntinfo>\n            <cntorgp>\n               <cntorg>U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch</cntorg>\n            </cntorgp>\n            <cntaddr>\n               <addrtype>mailing</addrtype>\n               <address>4600 Silver Hill Road</address>\n               <city>Washington</city>\n               <state>DC</state>\n               <postal>20233-7400</postal>\n               <country>United States</country>\n            </cntaddr>\n            <cntvoice>301.763.1128</cntvoice>\n            <cntfax>301.763.4710</cntfax>\n            <cntemail>geo.geography@census.gov</cntemail>\n         </cntinfo>\n      </metc>\n      <metstdn>Content Standard for Digital Geospatial Metadata</metstdn>\n      <metstdv>FGDC-STD-001-1998</metstdv>\n   </metainfo>\n</metadata>"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/airports/data/cb_2013_us_state_20m/state_20m.ea.iso.xml",
    "content": "<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?>\n<gfc:FC_FeatureCatalogue xmlns:gfc=\"http://www.isotc211.org/2005/gfc\">\n   <gmx:name xmlns:gmx=\"http://www.isotc211.org/2005/gmx\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Feature Catalog for the 2013 State  1:20,000,000</gco:CharacterString>\n   </gmx:name>\n   <gmx:scope xmlns:gmx=\"http://www.isotc211.org/2005/gmx\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">The State at a scale of 1:20,000,000</gco:CharacterString>\n   </gmx:scope>\n   <gmx:versionNumber xmlns:gmx=\"http://www.isotc211.org/2005/gmx\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\"/>\n   </gmx:versionNumber>\n   <gmx:versionDate xmlns:gmx=\"http://www.isotc211.org/2005/gmx\">\n      <gco:Date xmlns:gco=\"http://www.isotc211.org/2005/gco\">2014-04-</gco:Date>\n   </gmx:versionDate>\n   <gmx:language xmlns:gmx=\"http://www.isotc211.org/2005/gmx\">\n      <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">eng</gco:CharacterString>\n   </gmx:language>\n   <gmx:characterSet xmlns:gmx=\"http://www.isotc211.org/2005/gmx\">\n      <gmd:MD_CharacterSetCode xmlns:gmd=\"http://www.isotc211.org/2005/gmd\"\n                               codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#MD_CharacterSetCode\"\n                               codeListValue=\"utf8\"\n                               codeSpace=\"004\">utf8\n            </gmd:MD_CharacterSetCode>\n   </gmx:characterSet>\n   <gfc:producer xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n                 xlink:href=\"https://www.ngdc.noaa.gov/docucomp/8dd6ee96-96e1-492c-be55-76cdde8f27f1\"\n                 xlink:title=\"pointofContact - U.S. Department of Commerce, U.S. Census Bureau, Geography Division/Cartographic Products Branch\"/>\n   <gfc:featureType>\n      <gfc:FC_FeatureType>\n         <gfc:typeName>\n            <gco:LocalName xmlns:gco=\"http://www.isotc211.org/2005/gco\">cb_2013_us_state_20m.shp</gco:LocalName>\n         </gfc:typeName>\n         <gfc:definition>\n            <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Current Census State and Equivalent National entities</gco:CharacterString>\n         </gfc:definition>\n         <gfc:isAbstract>\n            <gco:Boolean xmlns:gco=\"http://www.isotc211.org/2005/gco\">false</gco:Boolean>\n         </gfc:isAbstract>\n         <gfc:featureCatalogue uuidref=\"2013_state_20m.ea.iso.xml\"/>\n         <gfc:carrierOfCharacteristics>\n            <gfc:FC_FeatureAttribute>\n               <gfc:memberName>\n                  <gco:LocalName xmlns:gco=\"http://www.isotc211.org/2005/gco\">STATEFP</gco:LocalName>\n               </gfc:memberName>\n               <gfc:definition>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Current state Federal Information Processing Series (FIPS) code</gco:CharacterString>\n               </gfc:definition>\n               <gfc:cardinality xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                        xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n               <gfc:listedValue>\n                  <gfc:FC_ListedValue>\n                     <gfc:label>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents</gco:CharacterString>\n                     </gfc:label>\n                     <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                              xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n                  </gfc:FC_ListedValue>\n               </gfc:listedValue>\n            </gfc:FC_FeatureAttribute>\n         </gfc:carrierOfCharacteristics>\n         <gfc:carrierOfCharacteristics>\n            <gfc:FC_FeatureAttribute>\n               <gfc:memberName>\n                  <gco:LocalName xmlns:gco=\"http://www.isotc211.org/2005/gco\">STATENS</gco:LocalName>\n               </gfc:memberName>\n               <gfc:definition>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Current state ANSI code</gco:CharacterString>\n               </gfc:definition>\n               <gfc:cardinality xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                        xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n               <gfc:listedValue>\n                  <gfc:FC_ListedValue>\n                     <gfc:label>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">INCITS 446:2008 (Geographic Names Information System (GNIS)), Identifying Attributes for Named Physical and Cultural Geographic Features (Except Roads and Highways) of the United States, Its Territories, Outlying Areas, and Freely Associated Areas, and the Waters of the Same to the Limit of the Twelve-Mile Statutory Zone</gco:CharacterString>\n                     </gfc:label>\n                     <gfc:definitionReference>\n                        <gfc:FC_DefinitionReference>\n                           <gfc:definitionSource>\n                              <gfc:FC_DefinitionSource>\n                                 <gfc:source>\n                                    <gmd:CI_Citation xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n                                       <gmd:title xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"inapplicable\"/>\n                                       <gmd:date xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n                                       <gmd:citedResponsibleParty>\n                                          <gmd:CI_ResponsibleParty>\n                                             <gmd:organisationName>\n                                                <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">U.S. Geological Survey (USGS)</gco:CharacterString>\n                                             </gmd:organisationName>\n                                             <gmd:role>\n                                                <gmd:CI_RoleCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_RoleCode\"\n                                                                 codeListValue=\"resourceProvider\"\n                                                                 codeSpace=\"001\">resourceProvider\n                                                  </gmd:CI_RoleCode>\n                                             </gmd:role>\n                                          </gmd:CI_ResponsibleParty>\n                                       </gmd:citedResponsibleParty>\n                                    </gmd:CI_Citation>\n                                 </gfc:source>\n                              </gfc:FC_DefinitionSource>\n                           </gfc:definitionSource>\n                        </gfc:FC_DefinitionReference>\n                     </gfc:definitionReference>\n                  </gfc:FC_ListedValue>\n               </gfc:listedValue>\n            </gfc:FC_FeatureAttribute>\n         </gfc:carrierOfCharacteristics>\n         <gfc:carrierOfCharacteristics>\n            <gfc:FC_FeatureAttribute>\n               <gfc:memberName>\n                  <gco:LocalName xmlns:gco=\"http://www.isotc211.org/2005/gco\">AFFGEOID</gco:LocalName>\n               </gfc:memberName>\n               <gfc:definition>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">American FactFinder summary level code + geovariant code + '00US' + GEOID</gco:CharacterString>\n               </gfc:definition>\n               <gfc:cardinality xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                        xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n               <gfc:listedValue>\n                  <gfc:FC_ListedValue>\n                     <gfc:label>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">American FactFinder geographic identifier</gco:CharacterString>\n                     </gfc:label>\n                     <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                              xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n                  </gfc:FC_ListedValue>\n               </gfc:listedValue>\n            </gfc:FC_FeatureAttribute>\n         </gfc:carrierOfCharacteristics>\n         <gfc:carrierOfCharacteristics>\n            <gfc:FC_FeatureAttribute>\n               <gfc:memberName>\n                  <gco:LocalName xmlns:gco=\"http://www.isotc211.org/2005/gco\">GEOID</gco:LocalName>\n               </gfc:memberName>\n               <gfc:definition>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">State identifier; state FIPS code</gco:CharacterString>\n               </gfc:definition>\n               <gfc:cardinality xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                        xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n               <gfc:listedValue>\n                  <gfc:FC_ListedValue>\n                     <gfc:label>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents</gco:CharacterString>\n                     </gfc:label>\n                     <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                              xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n                  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<gmd:CI_Citation xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n                                 <gmd:title xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"inapplicable\"/>\n                                 <gmd:date xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n                                 <gmd:citedResponsibleParty>\n                                    <gmd:CI_ResponsibleParty>\n                                       <gmd:organisationName>\n                                          <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">U.S. Postal Service</gco:CharacterString>\n                                       </gmd:organisationName>\n                                       <gmd:role>\n                                          <gmd:CI_RoleCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_RoleCode\"\n                                                           codeListValue=\"resourceProvider\"\n                                                           codeSpace=\"001\">resourceProvider\n                                                  </gmd:CI_RoleCode>\n                                       </gmd:role>\n                                    </gmd:CI_ResponsibleParty>\n                                 </gmd:citedResponsibleParty>\n                              </gmd:CI_Citation>\n                           </gfc:source>\n                        </gfc:FC_DefinitionSource>\n                     </gfc:definitionSource>\n                  </gfc:FC_DefinitionReference>\n               </gfc:definitionReference>\n               <gfc:listedValue>\n                  <gfc:FC_ListedValue>\n                     <gfc:label>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Official USPS state abbreviations, as shown in Publication 65, National 5-Digit ZIP Code and Post Office Directory</gco:CharacterString>\n                     </gfc:label>\n                     <gfc:definitionReference>\n                        <gfc:FC_DefinitionReference>\n                           <gfc:definitionSource>\n                              <gfc:FC_DefinitionSource>\n                                 <gfc:source>\n                                    <gmd:CI_Citation xmlns:gmd=\"http://www.isotc211.org/2005/gmd\">\n                                       <gmd:title xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"inapplicable\"/>\n                                       <gmd:date xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n                                       <gmd:citedResponsibleParty>\n                                          <gmd:CI_ResponsibleParty>\n                                             <gmd:organisationName>\n                                                <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">U.S. Postal Service</gco:CharacterString>\n                                             </gmd:organisationName>\n                                             <gmd:role>\n                                                <gmd:CI_RoleCode codeList=\"http://www.isotc211.org/2005/resources/Codelist/gmxCodelists.xml#CI_RoleCode\"\n                                                                 codeListValue=\"resourceProvider\"\n                                                                 codeSpace=\"001\">resourceProvider\n                                                  </gmd:CI_RoleCode>\n                                             </gmd:role>\n                                          </gmd:CI_ResponsibleParty>\n                                       </gmd:citedResponsibleParty>\n                                    </gmd:CI_Citation>\n                                 </gfc:source>\n                              </gfc:FC_DefinitionSource>\n                           </gfc:definitionSource>\n                        </gfc:FC_DefinitionReference>\n                     </gfc:definitionReference>\n                  </gfc:FC_ListedValue>\n               </gfc:listedValue>\n            </gfc:FC_FeatureAttribute>\n         </gfc:carrierOfCharacteristics>\n         <gfc:carrierOfCharacteristics>\n            <gfc:FC_FeatureAttribute>\n               <gfc:memberName>\n                  <gco:LocalName xmlns:gco=\"http://www.isotc211.org/2005/gco\">NAME</gco:LocalName>\n               </gfc:memberName>\n               <gfc:definition>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Current state name</gco:CharacterString>\n               </gfc:definition>\n               <gfc:cardinality xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                        xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n               <gfc:listedValue>\n                  <gfc:FC_ListedValue>\n                     <gfc:label>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents</gco:CharacterString>\n                     </gfc:label>\n                     <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                              xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n                  </gfc:FC_ListedValue>\n               </gfc:listedValue>\n            </gfc:FC_FeatureAttribute>\n         </gfc:carrierOfCharacteristics>\n         <gfc:carrierOfCharacteristics>\n            <gfc:FC_FeatureAttribute>\n               <gfc:memberName>\n                  <gco:LocalName xmlns:gco=\"http://www.isotc211.org/2005/gco\">LSAD</gco:LocalName>\n               </gfc:memberName>\n               <gfc:definition>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Current legal/statistical area description code for state</gco:CharacterString>\n               </gfc:definition>\n               <gfc:cardinality xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                        xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n               <gfc:listedValue>\n                  <gfc:FC_ListedValue>\n                     <gfc:label>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">00</gco:CharacterString>\n                     </gfc:label>\n                     <gfc:definition>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Blank</gco:CharacterString>\n                     </gfc:definition>\n                     <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                              xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n                  </gfc:FC_ListedValue>\n               </gfc:listedValue>\n            </gfc:FC_FeatureAttribute>\n         </gfc:carrierOfCharacteristics>\n         <gfc:carrierOfCharacteristics>\n            <gfc:FC_FeatureAttribute>\n               <gfc:memberName>\n                  <gco:LocalName xmlns:gco=\"http://www.isotc211.org/2005/gco\">ALAND</gco:LocalName>\n               </gfc:memberName>\n               <gfc:definition>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Current land area (square meters)</gco:CharacterString>\n               </gfc:definition>\n               <gfc:cardinality xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                        xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n               <gfc:valueMeasurementUnit>\n                  <gml:DerivedUnit xmlns:gml=\"http://www.opengis.net/gml/3.2\" gml:id=\"areaInMetersSquaredforALAND\">\n                     <gml:identifier codeSpace=\"area\"/>\n                     <gml:derivationUnitTerm uom=\"m\" exponent=\"2\"/>\n                  </gml:DerivedUnit>\n               </gfc:valueMeasurementUnit>\n               <gfc:listedValue>\n                  <gfc:FC_ListedValue>\n                     <gfc:label xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"inapplicable\"/>\n                     <gfc:definition>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">\n                                            Range Domain Minimum: 0\n                                            Range Domain Maximum: 9,999,999,999,999</gco:CharacterString>\n                     </gfc:definition>\n                  </gfc:FC_ListedValue>\n               </gfc:listedValue>\n            </gfc:FC_FeatureAttribute>\n         </gfc:carrierOfCharacteristics>\n         <gfc:carrierOfCharacteristics>\n            <gfc:FC_FeatureAttribute>\n               <gfc:memberName>\n                  <gco:LocalName xmlns:gco=\"http://www.isotc211.org/2005/gco\">AWATER</gco:LocalName>\n               </gfc:memberName>\n               <gfc:definition>\n                  <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">Current water area (square meters)</gco:CharacterString>\n               </gfc:definition>\n               <gfc:cardinality xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"unknown\"/>\n               <gfc:definitionReference xmlns:xlink=\"http://www.w3.org/1999/xlink\" xlink:title=\"U.S. Census Bureau\"\n                                        xlink:href=\"http://www.ngdc.noaa.gov/docucomp/eb139e38-ee29-4d59-b157-5e874d4420c4\"/>\n               <gfc:valueMeasurementUnit>\n                  <gml:DerivedUnit xmlns:gml=\"http://www.opengis.net/gml/3.2\" gml:id=\"areaInMetersSquaredforAWATER\">\n                     <gml:identifier codeSpace=\"area\"/>\n                     <gml:derivationUnitTerm uom=\"m\" exponent=\"2\"/>\n                  </gml:DerivedUnit>\n               </gfc:valueMeasurementUnit>\n               <gfc:listedValue>\n                  <gfc:FC_ListedValue>\n                     <gfc:label xmlns:gco=\"http://www.isotc211.org/2005/gco\" gco:nilReason=\"inapplicable\"/>\n                     <gfc:definition>\n                        <gco:CharacterString xmlns:gco=\"http://www.isotc211.org/2005/gco\">\n                                            Range Domain Minimum: 0\n                                            Range Domain Maximum: 9,999,999,999,999</gco:CharacterString>\n                     </gfc:definition>\n                  </gfc:FC_ListedValue>\n               </gfc:listedValue>\n            </gfc:FC_FeatureAttribute>\n         </gfc:carrierOfCharacteristics>\n      </gfc:FC_FeatureType>\n   </gfc:featureType>\n</gfc:FC_FeatureCatalogue>"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/antibiotic_use_children/antibiotic_use_children.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\nconditions = c(rep(\"Prematurity\", 33),\n               rep(\"Neuromuscular\", 10),\n               rep(\"Cardiovascular\", 16),\n               rep(\"Genetic/metabolic\", 6),\n               rep(\"Respiratory\", 13),\n               rep(\"Trauma\", 10),\n               rep(\"Gastrointestinal\", 2),\n               rep(\"Immunocompromised\", 2)\n               )\n\n# summary table -----------------------------------------------------\nsummary_table = sort(table(conditions))/sum(table(conditions))\n\n# barplot -----------------------------------------------------------\npdf(\"antibiotic_use_children_bar.pdf\", height = 3, width = 6)\npar(mar = c(3.2, 10.5, 0, 0.5), las = 1, mgp = c(2, 0.45, 0),\n    cex.lab = 1.25, cex.axis = 1.25)\nbarplot(summary_table, ylab = \"\", xlab = \"Relative frequency\", \n        col = COL[1], horiz = TRUE)\ndev.off()\n\n# pie chart ---------------------------------------------------------\npdf(\"antibiotic_use_children_pie.pdf\", height = 3, width = 6)\npar(mar=c(0, 2.8, 0, 6), las = 1)\npie(summary_table, \n    col = c(COL[1,1], COL[1,4], COL[2,1], COL[2,4], \n            COL[3,1], COL[3,4], COL[4,1], COL[4,4]), \n    cex = 1, clockwise = FALSE,\n    labels = names(summary_table))\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/association_plots/association_plots.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# set seed ----------------------------------------------------------\nset.seed = 2306\n\n# create x ----------------------------------------------------------\nx = seq(0, 10, 0.1)\n\n# create y_poslin: positive linear with x ---------------------------\ny_poslin = x * runif(1, min = 0, max = 4) + \n  rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4)) - \n  runif(1, min = 0, max = 3)\n\n# create y_neglin: negative linear with x ---------------------------\ny_neglin = x * -runif(1, min = 0, max = 4) + \n  rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4)) - \n  runif(1, min = 0, max = 5)\n\n# create y_poscur: curved positive with x ---------------------------\ny_poscur = x^2 + rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4))\n\n# create y_none: no association with x ------------------------------\ny_none = x + rnorm(length(x), mean = 0, sd = runif(1, min = 30, max = 40))\n\n# plot the associations --------------------------------------------- \n\npdf(\"association_plots.pdf\", 5.5, 4.3)\npar(mar = c(2.5, 0.5, 0.5, 0.5), las = 1, mgp = c(1, 0.5, 0), \n    cex.lab = 1.75, pch = 20, mfrow = c(2,2), \n    yaxt = \"n\", xaxt = \"n\")\nplot(y_poslin ~ x,  xlab = \"(1)\", ylab = \"\", col = COL[1])\nplot(y_none ~ x, xlab = \"(2)\", ylab = \"\", col = COL[1])\nplot(y_poscur ~ x, xlab = \"(3)\", ylab = \"\", col = COL[1])\nplot(y_neglin ~ x, xlab = \"(4)\", ylab = \"\", col = COL[1])\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/cleveland_sacramento/cleveland_sacramento.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# take a sample -----------------------------------------------------\ncle_sac = cle_sac[!is.na(cle_sac$personal_income),]\n\nset.seed(8957)\nsac = sample(cle_sac$personal_income[cle_sac$city == \"Sacramento\"], 17)\ncle = sample(cle_sac$personal_income[cle_sac$city == \"Cleveland\"], 21)\n\n# plot of total personal income in Cle and Sac ----------------------\npdf(\"cleveland_sacramento_hist.pdf\", height = 5, width = 7)\n\npar(mar = c(3.7, 2, 1,1), las = 1, mgp = c(2.5, 0.7, 0), \n    mfrow = c(2,1), cex.lab = 1.25)\n\nhistPlot(cle, xlim = c(0, 180000), ylim = c(0,10),\n         ylab = \"\", xlab = \"\", col = COL[1], breaks = 8, axes = FALSE)\naxis(1, at = seq(0,180000,45000))\naxis(2, at = seq(0,10,5))\ntext(x = 120000, y = 8, labels = \"Cleveland, OH\", pos = 4, cex = 1.25)\n\nhistPlot(sac, xlim = c(0,180000), ylim = c(0,10), \n         ylab = \"\", xlab = \"Total personal income\", col = COL[1], breaks = 8,\n         axes = FALSE)\naxis(1, at = seq(0,180000,45000))\naxis(2, at = seq(0,10,5))\ntext(x = 120000, y = 8, labels = \"Sacramento, CA\", pos = 4, cex = 1.25)\n\ndev.off()\n\n# summary stats -----------------------------------------------------\nmean(cle, na.rm = TRUE)\nsd(cle, na.rm = TRUE)\nlength(cle)\n\nmean(sac, na.rm = TRUE)\nsd(sac, na.rm = TRUE)\nlength(sac)\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/county_commute_times/countyMap.R",
    "content": "library(maps)\ncountyMap <- function(values, FIPS,\n                      col = c(\"red\", \"green\", \"blue\"),\n                      varTrans = I,\n                      gtlt = \"\",\n                      ...){\n  if(missing(FIPS)){\n    stop(\"Must provide the county FIPS\")\n  }\n  \n  # _____ Drop NAs _____ #\n  FIPS   <- FIPS[!is.na(values)]\n  values <- values[!is.na(values)]\n  \n  # _____ Scale Values _____ #\n  MI  <- min(values)\n  MA  <- max(values)\n  Leg <- seq(MI, MA, length.out = 50)\n  if(identical(varTrans, log)){\n    VAL    <- varTrans(values+0.1)\n    valCol <- varTrans(values+0.1)\n    LegCol <- varTrans(Leg+0.1)\n  } else {\n    VAL    <- varTrans(values)\n    valCol <- varTrans(values)\n    LegCol <- varTrans(Leg)\n  }\n  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01\n  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01\n  \n  val.000 <- 0.500*(1-valCol)\n  val.114 <- 0.557*(1-valCol)\n  val.200 <- 0.600*(1-valCol)\n  val.298 <- 0.649*(1-valCol)\n  val.318 <- 0.659*(1-valCol)\n  val.337 <- 0.669*(1-valCol)\n  val.447 <- 0.724*(1-valCol)\n  val.608 <- 0.804*(1-valCol)\n  val.741 <- 0.871*(1-valCol)\n  val.863 <- 0.932*(1-valCol)\n  val.941 <- 0.971*(1-valCol)\n  val.957 <- 0.979*(1-valCol)\n  \n  Leg.000 <- 0.500*(1-LegCol)\n  Leg.114 <- 0.557*(1-LegCol)\n  Leg.200 <- 0.600*(1-LegCol)\n  Leg.298 <- 0.649*(1-LegCol)\n  Leg.318 <- 0.659*(1-LegCol)\n  Leg.337 <- 0.669*(1-LegCol)\n  Leg.447 <- 0.724*(1-LegCol)\n  Leg.608 <- 0.804*(1-LegCol)\n  Leg.741 <- 0.871*(1-LegCol)\n  Leg.863 <- 0.932*(1-LegCol)\n  Leg.941 <- 0.971*(1-LegCol)\n  Leg.957 <- 0.979*(1-LegCol)\n  \n  if(col[1] == \"red\"){\n    col <- rgb(val.941, val.318, val.200)\n    COL <- rgb(Leg.941, Leg.318, Leg.200)\n  } else if(col[1] == \"green\"){\n    col <- rgb(val.298, val.447, val.114)\n    COL <- rgb(Leg.298, Leg.447, Leg.114)\n  } else if(col[1] == \"bg\"){\n    col <- rgb(val.337, val.608, val.741)\n    COL <- rgb(Leg.337, Leg.608, Leg.741)\n  } else if(col[1] == \"ye\"){\n    col <- rgb(val.957, val.863, val.000)\n    COL <- rgb(Leg.957, Leg.863, Leg.000)\n  } else {\n    col <- rgb(val.06, val.06, val.10)\n    COL <- rgb(Leg.06, Leg.06, Leg.10)\n  }\n\n  # _____ Remove These _____ #\n  data(county.fips)\n  col <- col[match(county.fips$fips, FIPS)]\n  plot(0,0,type = \"n\", axes = FALSE, xlab = \"\", ylab = \"\")\n  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))\n  map(\"county\", col = col, fill = TRUE, resolution = 0,\n    lty = 0, projection = \"polyconic\", mar = rep(0.1,4), add = TRUE, ...)\n  \n  x1 <- 0.335\n  x2 <- 0.365\n  for(i in 1:50){\n    y1 <- i/50 * 0.25 + 0.5\n    y2 <- (i-1)/50 * 0.25 + 0.5\n    rect(x1, y1, x2, y2, border = \"#00000000\", col = COL[i])\n  }\n  \n  VR    <- range(VAL)\n  VR[3] <- VR[2]\n  VR[2] <- mean(VR[c(1,3)])\n  \n  VR1    <- c()\n  VR1[1] <- values[which.min(abs(VAL - VR[1]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[2]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[3]))]\n  \n  VR    <- round(VR)\n  if(gtlt %in% c(\">\", \"><\")){\n    VR[3] <- paste(\">\", VR[3], sep = \"\")\n  }\n  if(gtlt %in% c(\"<\", \"><\")){\n    VR[1] <- paste(\"<\", VR[1], sep = \"\")\n  }\n  text(0.365, 0.51, VR[1], pos = 4)\n  text(0.365, 0.625, VR[2], pos = 4)\n  text(0.365, 0.74, VR[3], pos = 4)\n}\n\n\n\n\n\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/county_commute_times/county_commute_times.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load mapproj package for map functions ----------------------------\nlibrary(mapproj)\n\n# load data ---------------------------------------------------------\ndata(countyComplete)\n\n# histogram of travel to work time ----------------------------------\npdf(\"county_commute_times_hist.pdf\", 7.5, 4)\n\npar(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(countyComplete$mean_work_travel, breaks = 40, \n         xlab = \"Mean work travel (in min)\", ylab = \"\", \n         col = COL[1], axes = FALSE)\naxis(1)\naxis(2, at = seq(0, 200, 100))\n\ndev.off()\n\n# source custom code for county maps --------------------------------\nsource(\"countyMap.R\")\n\n# map of travel to work time ----------------------------------------\n\npdf(\"county_commute_times_map.pdf\", 7.5, 4)\n\nval <- countyComplete$mean_work_travel\nval[val >= 33] <- 33\ncountyMap(val, countyComplete$FIPS, \"green\", gtlt = \">\")\n\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/county_hispanic_pop/countyMap.R",
    "content": "library(maps)\ncountyMap <- function(values, FIPS,\n                      col = c(\"red\", \"green\", \"blue\"),\n                      varTrans = I,\n                      gtlt = \"\",\n                      ...){\n  if(missing(FIPS)){\n    stop(\"Must provide the county FIPS\")\n  }\n  \n  # _____ Drop NAs _____ #\n  FIPS   <- FIPS[!is.na(values)]\n  values <- values[!is.na(values)]\n  \n  # _____ Scale Values _____ #\n  MI  <- min(values)\n  MA  <- max(values)\n  Leg <- seq(MI, MA, length.out = 50)\n  if(identical(varTrans, log)){\n    VAL    <- varTrans(values+0.1)\n    valCol <- varTrans(values+0.1)\n    LegCol <- varTrans(Leg+0.1)\n  } else {\n    VAL    <- varTrans(values)\n    valCol <- varTrans(values)\n    LegCol <- varTrans(Leg)\n  }\n  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01\n  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01\n  \n  val.000 <- 0.500*(1-valCol)\n  val.114 <- 0.557*(1-valCol)\n  val.200 <- 0.600*(1-valCol)\n  val.298 <- 0.649*(1-valCol)\n  val.318 <- 0.659*(1-valCol)\n  val.337 <- 0.669*(1-valCol)\n  val.447 <- 0.724*(1-valCol)\n  val.608 <- 0.804*(1-valCol)\n  val.741 <- 0.871*(1-valCol)\n  val.863 <- 0.932*(1-valCol)\n  val.941 <- 0.971*(1-valCol)\n  val.957 <- 0.979*(1-valCol)\n  \n  Leg.000 <- 0.500*(1-LegCol)\n  Leg.114 <- 0.557*(1-LegCol)\n  Leg.200 <- 0.600*(1-LegCol)\n  Leg.298 <- 0.649*(1-LegCol)\n  Leg.318 <- 0.659*(1-LegCol)\n  Leg.337 <- 0.669*(1-LegCol)\n  Leg.447 <- 0.724*(1-LegCol)\n  Leg.608 <- 0.804*(1-LegCol)\n  Leg.741 <- 0.871*(1-LegCol)\n  Leg.863 <- 0.932*(1-LegCol)\n  Leg.941 <- 0.971*(1-LegCol)\n  Leg.957 <- 0.979*(1-LegCol)\n  \n  if(col[1] == \"red\"){\n    col <- rgb(val.941, val.318, val.200)\n    COL <- rgb(Leg.941, Leg.318, Leg.200)\n  } else if(col[1] == \"green\"){\n    col <- rgb(val.298, val.447, val.114)\n    COL <- rgb(Leg.298, Leg.447, Leg.114)\n  } else if(col[1] == \"bg\"){\n    col <- rgb(val.337, val.608, val.741)\n    COL <- rgb(Leg.337, Leg.608, Leg.741)\n  } else if(col[1] == \"ye\"){\n    col <- rgb(val.957, val.863, val.000)\n    COL <- rgb(Leg.957, Leg.863, Leg.000)\n  } else {\n    col <- rgb(val.06, val.06, val.10)\n    COL <- rgb(Leg.06, Leg.06, Leg.10)\n  }\n\n  # _____ Remove These _____ #\n  data(county.fips)\n  col <- col[match(county.fips$fips, FIPS)]\n  plot(0,0,type = \"n\", axes = FALSE, xlab = \"\", ylab = \"\")\n  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))\n  map(\"county\", col = col, fill = TRUE, resolution = 0,\n    lty = 0, projection = \"polyconic\", mar = rep(0.1,4), add = TRUE, ...)\n  \n  x1 <- 0.335\n  x2 <- 0.365\n  for(i in 1:50){\n    y1 <- i/50 * 0.25 + 0.5\n    y2 <- (i-1)/50 * 0.25 + 0.5\n    rect(x1, y1, x2, y2, border = \"#00000000\", col = COL[i])\n  }\n  \n  VR    <- range(VAL)\n  VR[3] <- VR[2]\n  VR[2] <- mean(VR[c(1,3)])\n  \n  VR1    <- c()\n  VR1[1] <- values[which.min(abs(VAL - VR[1]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[2]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[3]))]\n  \n  VR    <- round(VR)\n  if(gtlt %in% c(\">\", \"><\")){\n    VR[3] <- paste(\">\", VR[3], sep = \"\")\n  }\n  if(gtlt %in% c(\"<\", \"><\")){\n    VR[1] <- paste(\"<\", VR[1], sep = \"\")\n  }\n  text(0.365, 0.51, VR[1], pos = 4)\n  text(0.365, 0.625, VR[2], pos = 4)\n  text(0.365, 0.74, VR[3], pos = 4)\n}\n\n\n\n\n\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/county_hispanic_pop/county_hispanic_pop.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load mapproj package for map functions ----------------------------\nlibrary(mapproj)\n\n# load data ---------------------------------------------------------\ndata(countyComplete)\n\n# histogram of hispanic % -------------------------------------------\npdf(\"county_hispanic_pop_hist.pdf\", 7.5, 4)\n\npar(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(countyComplete$hispanic, breaks = 25, \n         xlab = \"Hispanic %\", ylab = \"\", \n         col = COL[1])\n\ndev.off()\n\n# log of histogram of hispanic % ------------------------------------\npdf(\"county_hispanic_pop_log_hist.pdf\", 7.5, 4)\n\npar(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(log(countyComplete$hispanic), breaks = 25, \n         xlab = \"log(% Hispanic)\", ylab = \"\", \n         col = COL[1])\n\ndev.off()\n\n# source custom code for county maps --------------------------------\nsource(\"countyMap.R\")\n\n# map of travel to work time ----------------------------------------\n\npdf(\"county_hispanic_pop_map.pdf\", 7.5, 4)\n\nval <- countyComplete$hispanic\nval[val >= 40] <- 40\ncountyMap(val, countyComplete$FIPS, \"bg\", gtlt=\">\")\n\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/county_income_education/county_income_education.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# number of observations --------------------------------------------\nnrow(county_complete) # n = 3142\n\n# scatterplot of income vs. % with bachelor's degree ----------------\npdf(\"county_income_education_scatterplot.pdf\", 5, 4)\npar(mar = c(4, 4.6, 1, 1), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.axis = 1.25, cex.lab = 1.4)\nplot(county_complete$per_capita_income_2010 ~ county_complete$bachelors_2010, \n    xlab = \"Percent with Bachelor's Degree\", \n    ylab = \"\", \n    pch = 20, col = COL[1,3], axes = FALSE, \n    xlim = c(0,80), ylim = c(0, 65) * 1000)\nAxisInDollars(2, at = seq(0, 70, 20) * 1000)\nAxisInPercent(1, at = seq(0, 80, 20))\npar(las = 0)\nmtext(\"Per Capita Income\", 2, 3.4, cex = 1.4)\nbox()\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/dream_act_mosaic/dream_act_mosaic.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\n\nideology = c(rep(\"Conservative\", 372), rep(\"Moderate\", 363), rep(\"Liberal\", 175))\nideology = factor(ideology, levels = c(\"Conservative\", \"Moderate\", \"Liberal\"))\ndream = c(rep(\"Support\", 186), rep(\"Not support\", 151), rep(\"Not sure\", 35), \n          rep(\"Support\", 174), rep(\"Not support\", 161), rep(\"Not sure\", 28),\n          rep(\"Support\", 114), rep(\"Not support\", 52), rep(\"Not sure\", 9)\n)\ndream = factor(dream, levels = c(\"Support\", \"Not support\", \"Not sure\"))\n\n\n# mosaicplot --------------------------------------------------------\n\npdf(\"dream_act_mosaic.pdf\", 7, 3)\npar(mar=c(0.5,0,0.25,0.5), las=1, mgp=c(4,1,0))\n\nmosaicplot(ideology ~ dream, main = \"\", cex.axis = 1.1, \n           xlab = \"\", ylab = \"\", color = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/estimate_mean_median_simple/estimate_mean_median_simple.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\nset.seed(9823)\nx <- 100 * rbeta(400, 12, 3)\n\n# plot --------------------------------------------------------------\nmyPDF(\"estimate_mean_median_simple.pdf\", 6, 2,\n      mar = c(1.7, 2.2, 0.2, 0.4), cex = 1.1)\nh <- hist(\n    x,\n    col = COL[1],\n    xlab = \"\",\n    ylab = \"\",\n    main = \"\",\n    axes = FALSE)\naxis(1)\nat <- pretty(par(\"yaxp\")[1:2])\naxis(2)\nabline(h = at, col = COL[6, 2], lty = 2)\nhist(x, col = COL[1, 2], add = TRUE)\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/gpa_study_hours/gpa_study_hours.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\nload(\"gpa_study_hours.rda\")\n# this dataset will also be available in the openintro package\n# with the same name\n\n# number of observations --------------------------------------------\nnrow(survey) # n = 193\n\n# scatterplot of gpa vs. study hours --------------------------------\npdf(\"gpa_study_hours_scatterplot.pdf\", 5.5, 4.3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nset.seed(193) # for jitter below\nplot(jitter(gpa_study_hours$gpa) ~ gpa_study_hours$study_hours, \n     xlab=\"Study hours/week\", ylab = \"GPA\", \n     pch=20, col = COL[1,2], cex.lab = 1.5, axes = FALSE,\n     ylim = c(2.5, 4.4))\naxis(1, at = seq(0, 70, 20), cex.axis = 1.5)\naxis(2, at = c(2.5, 3, 3.5, 4), cex.axis = 1.5)\nbox()\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/gpa_study_hours/gpa_study_hours.csv",
    "content": "\"gpa\",\"study_hours\"\n4,10\n3.8,25\n3.93,45\n3.4,10\n3.2,4\n3.52,10\n3.68,24\n3.4,40\n3.7,10\n3.75,10\n3.3,30\n3.425,7\n3.795,15\n3.83,60\n3.3,10\n3.75,10\n3.15,6\n3.7,20\n3.8,8\n3.63,30\n3.9,35\n3.294,12\n3.7,6\n3.4,20\n4,10\n3.4,14\n3.7,10\n3.8,10\n3.4,30\n3.4,20\n3.4,7\n3,20\n3.6,16\n3.567,14\n3.3,21\n3.4,21\n3.6,11\n3.67,10\n3.82,10\n2.9,15\n3.9,10\n3.4,10\n3.6,20\n3.1,10\n3.4,10\n3.8,12\n3.7,25\n3.7,20\n3.8,25\n3.92,15\n3.8,10\n3.868,40\n3.35,15\n3.85,10\n3.55,10\n3.7,25\n3.65,25\n3.125,36\n4,30\n3.25,14\n3.86,2\n3.5,10\n3.45,5\n3.6,4\n3.866,20\n3.82,12\n3.2,15\n3.5,3\n3.8,10\n3.8,15\n3.7,25\n3.75,15\n3.3,10\n3.875,15\n3.7,7\n3.5,14\n3.2,7\n3.566,40\n3.5,6\n4.3,10\n3.6,10\n3.2,20\n3.825,20\n3.85,69\n3.75,8\n4,10\n3.4,3\n3.9,8\n3.825,15\n3.7,45\n3.8,10\n2.91,18\n3.559,10\n3.69,10\n3.3,35\n3.75,10\n3.9,8\n3.65,15\n3.5,30\n3.6,35\n3.675,20\n3.9,12\n3.6,35\n3.675,8\n3.7,30\n3.66,10\n3.733,14\n3.7,28\n2.6,7\n4,20\n3.2,15\n3.16,24\n3.5,20\n3.65,20\n3.9,20\n3.785,25\n3.1,15\n3.15,16\n3.61,10\n3.3,35\n3.7,15\n3.7,20\n3.75,40\n3.4,4\n3.6,12\n3.5,49\n3.8,20\n3.7,30\n3.84,12\n3.41,8\n3.825,60\n2.95,6.5\n3.925,20\n3.3,18\n3.3,10\n3.6,40\n4,21\n3.3,12.5\n3.89,12\n3.2,20\n3.97,10\n3.3,10\n3.86,20\n3.76,20\n3.5,10\n3.6,30\n3.55,15\n3.97,20\n3.925,15\n3.68,14\n3.25,5\n3.56,5\n2.85,8\n3.6,8\n3.45,14\n3.5,15\n3.15,20\n3.35,14\n3.5,14\n3.79,25\n3.022,30\n3.46,20\n3.55,30\n3.97,20\n3.925,7\n3.2,8\n3.4,20\n3.9,14\n3.6,20\n3.83,60\n3.8,15\n4,20\n3.5,15\n3.3,8\n4,15\n3.1,10\n3.5,7\n3.62,20\n3.6,10\n3.8,28\n3.2,12\n3.925,5\n3.84,30\n3.1,5\n4,6\n3.35,30\n3.925,15\n3,9\n3.6,24\n3.7,12\n3.84,15\n3.8,10\n3.1,15\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/hist_box_match/hist_box_match.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# generate data -----------------------------------------------------\nset.seed(7365)\n\nsym = rnorm(1000, mean = 60, sd = 3)\nuni = runif(1000, min = 0, max = 100)\nrs = rgamma(1000, shape = 3, rate = 2)\n\n# histograms and box plots ------------------------------------------\npdf(\"hist_box_match.pdf\", width = 10, height = 3)\npar(mar=c(4, 3.6, 0, 0), las = 1, mgp = c(2.7, 0.7, 0), \n    mfrow = c(1,6), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nhistPlot(sym, xlab = \"(a)\", ylab = \"\", col = COL[1], axes = FALSE)\naxis(1, seq(50,70,10))\n\nhistPlot(uni, xlab = \"(b)\", ylab = \"\", col = COL[1], axes = FALSE)\naxis(1, seq(0,100,50))\n\nhistPlot(rs, xlab = \"(c)\", ylab = \"\", col = COL[1], axes = FALSE)\naxis(1, seq(0,6,2))\n\nboxPlot(rs, xlab = \"(1)\", ylab = \"\", col = COL[1,3])\nboxPlot(sym, xlab = \"(2)\", ylab = \"\", col = COL[1,3])\nboxPlot(uni, xlab = \"(3)\", ylab = \"\", col = COL[1,3])\n\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/hist_vs_box/hist_vs_box.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# generate data -----------------------------------------------------\nset.seed(12345)\nbimod = c(rnorm(300, mean = 5, sd = 1), \n          rnorm(300, mean = 12, sd = 1), \n          runif(25, min = 13, max = 28))\n\n# histogram and box plot --------------------------------------------\npdf(\"hist_vs_box.pdf\", height = 2.2, width = 8)\npar(mar = c(2, 2.8, 0.2, 0.5), las = 1, mgp = c(2.9, 0.7, 0),\n    cex.axis = 1.5, cex.lab = 1.5)\nlayout(matrix(1:2, 1), 2:1)\nhistPlot(bimod, xlab = \"\", ylab = \"\", yaxt = \"n\", col = COL[1])\npar(mar = c(2, 2.8, 0.2, 0))\nboxPlot(bimod, col = COL[1,2], xlim = c(0.4, 1.6))\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/income_coffee_shop/income_coffee_shop.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# generate data -----------------------------------------------------\nset.seed(956)\n\nsal_symmetric = rnorm(40, mean = 65000, sd = 2000)\nsal_skewed = c(sal_symmetric, 225000, 250000)\n\noptions(scipen=2)\n\n# histograms --------------------------------------------------------\npdf(\"income_coffee_shop.pdf\", 5.5, 4.3)\npar(mar = c(3.6, 1, 0.5, 1), las = 1, mgp = c(2.5, 0.7, 0), \n    mfrow = c(2,1), cex.lab = 1.5, cex.axis = 1.5)\n\nhistPlot(sal_symmetric, xlim = c(60000, 70000), \n         xlab = \"(1)\", ylim = c(0,12), col = COL[1], \n         axes = FALSE, ylab = \"\")\naxis(1, at = seq(60000, 70000, 2500))\naxis(2, at = seq(0,12,4), labels = NA)\n\nhistPlot(sal_skewed, xlab = \"(2)\", ylim = c(0,12), \n         breaks = seq(0, 260000, by = 1000), col = COL[1], \n         axes = FALSE, xlim = c(60000,260000), ylab = \"\")\naxis(1, at = seq(60000, 260000, 50000))\naxis(2, at = seq(0,12,4), labels = NA)\n\ndev.off()\n\n# summary stats -----------------------------------------------------\nlibrary(xtable)\n\nsummary_table = as.data.frame(cbind(summary(sal_symmetric), summary(sal_skewed)))\nnames(summary_table) = c(\"(1)\",\"(2)\")\nsummary_table = rbind(c(length(sal_symmetric), length(sal_skewed)), \n                      summary_table, c(sd(sal_symmetric), sd(sal_skewed)))\nrownames(summary_table)[1] = \"n\"\nrownames(summary_table)[dim(summary_table)[1]] = \"SD\"\n\nxtable(summary_table, digits = 0)"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/infant_mortality_rel_freq/infant_mortality.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(dplyr)\n\n# load data ---------------------------------------------------------\nload(\"factbook.rda\")\n# this dataset will also be available in the cia_factbook package\n# with the same name\n\n# calculate # of countries with life exp. & internet data -----------\ncia_factbook %>%\n  filter(!is.na(infant_mortality_rate)) %>%\n  nrow() # n = 224\n\n# histogram parameters ----------------------------------------------\nhisto = hist(cia_factbook$infant_mortality_rate, plot = FALSE)\nbreaks = histo$breaks\nwidth = breaks[2] - breaks[1]\ncounts = histo$counts\nn = sum(counts)\nrel_freqs = round(counts / n, 2)\n\nfive_perc = n * 0.05\n\n# rel. freq. histogram of infant mortality --------------------------\npdf(\"infant_mortality_rel_freq_hist.pdf\", 5.5, 3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nhist(cia_factbook$infant_mortality_rate, \n     main = \"\", xlab = \"Infant mortality\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0,five_perc*8))\naxis(1)\naxis(2, at = seq(0, 8 * five_perc, 2 * five_perc),\n     labels = seq(0, 0.4, 0.1))\naxis(2, at = seq(five_perc, 7 * five_perc, 2 * five_perc),\n     labels = rep(\"\", 4), tcl = -0.25)\nabline(h = seq(0, five_perc*8, five_perc), lty = 2, col = COL[6])\nhist(cia_factbook$infant_mortality_rate, \n     main = \"\", xlab = \"\", ylab = \"\",\n     col = COL[1], axes = FALSE, add = TRUE)\ndev.off()\n\n# rel. freq. histogram of infant mortality  - solution --------------\nsummary(cia_factbook$infant_mortality_rate)\n\npdf(\"infant_mortality_rel_freq_hist_soln.pdf\", height = 4.3, width = 8)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nhist(cia_factbook$infant_mortality_rate, \n     main = \"\", xlab = \"Infant mortality\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0,five_perc*8))\naxis(1)\naxis(2, at = seq(0, five_perc*8, five_perc), label = c(0, NA, 0.1, NA, 0.2, NA, 0.3, NA, 0.4))\nabline(h = seq(0, five_perc*8, five_perc), lty = 2, col = COL[6])\nhist(cia_factbook$infant_mortality_rate, \n     main = \"\", xlab = \"\", ylab = \"\",\n     col = COL[1], axes = FALSE, add = TRUE)\ntext(x = breaks[-1] - width/2, y = counts + 5, \n     labels = paste(rel_freqs),\n     col = COL[4], cex = 1)\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/internet_life_expactancy/internet_life_expactancy.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\nload(\"factbook.rda\")\n# this dataset will also be available in the cia_factbook package\n# with the same name\n\n# calculate % of internet users -------------------------------------\ncia_factbook$internet_perc = cia_factbook$internet_users / cia_factbook$population * 100\n\n# calculate # of countries with life exp. & internet data -----------\ncia_factbook %>%\n  filter(!is.na(internet_perc)) %>%\n  filter(!is.na(life_exp_at_birth)) %>%\n  nrow() # n = 208\n\n# scatterplot of gpa vs. study hours --------------------------------\npdf(\"internet_life_expactancy.pdf\", 5.5, 4.3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nplot(cia_factbook$life_exp_at_birth ~ cia_factbook$internet_perc, \n     xlab = \"% Internet users\", ylab = \"Life expectancy at birth\", \n     pch = 20, col = COL[1,2], cex.lab = 1.5, cex.axis = 1.5, xlim = c(0,100))\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/internet_life_expectancy/internet_life_expectancy.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\nload(\"factbook.rda\")\n# this dataset will also be available in the cia_factbook package\n# with the same name\n\n# calculate % of internet users -------------------------------------\ncia_factbook$internet_perc = cia_factbook$internet_users / cia_factbook$population * 100\n\n# calculate # of countries with life exp. & internet data -----------\ncia_factbook %>%\n  subset(!is.na(internet_perc)) %>%\n  subset(!is.na(life_exp_at_birth)) %>%\n  nrow() # n = 208\n\n# scatterplot of gpa vs. study hours --------------------------------\npdf(\"internet_life_expectancy.pdf\", 6, 4.3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nplot(cia_factbook$life_exp_at_birth ~ cia_factbook$internet_perc, \n     xlab = \"Percent Internet Users\",\n     ylab = \"Life Expectancy at Birth\", \n     pch = 20, col = COL[1,2], cex.lab = 1.5, cex.axis = 1.5,\n     xlim = c(0,100),\n     axes = FALSE)\nAxisInPercent(1, at = seq(0, 100, 20))\naxis(2)\nbox()\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/mammal_life_spans/mammal_life_spans.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(mammals)\n\n# calculate # of countries with life exp. & internet data -----------\nnrow(mammals) # n = 62\n\n# scatterplot of gpa vs. study hours --------------------------------\npdf(\"mammal_life_spans_scatterplot.pdf\", 5.5, 4.3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nplot(mammals$LifeSpan ~ mammals$Gestation, \n     xlab = \"Gestation (days)\", ylab = \"Life Span (years)\", \n     pch = 20, col = COL[1], axes = FALSE)\naxis(1, at = seq(0, 600, 100), labels = c(0, NA, 200, NA, 400, NA, 600))\naxis(2, at = seq(0, 100, 25))\nbox()\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/marathon_winners/marathon_winners.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(marathon)\n\n# histogram and box plot of marathon finishing times of winners -----\npdf(\"marathon_winners_hist_box.pdf\", height = 2.2, width = 7)\n\npar(mar = c(2, 2.8, 0.5, 5), las = 1, mgp = c(2.9, 0.7, 0),\n    cex.axis = 1.5, cex.lab = 1.5)\nlayout(matrix(1:2, 1), 2:1)\nhistPlot(marathon$Time, col = COL[1], \n         xlab = \"Marathon times\", ylab = \"\", yaxt = \"n\", \n         axes = FALSE)\naxis(1, at = seq(2, 3.2, 0.4))\naxis(2, at = seq(0, 20, 10))\n\npar(mar = c(2, 2.8, 0.5, 0))\nboxPlot(marathon$Time, col = COL[1,2], ylim = c(2, 3.2),\n        ylab = \"Marathon times\",\n        axes = FALSE)\naxis(2, at = seq(2, 3.2, 0.4))\n\ndev.off()\n\n# finishing times vs. gender ----------------------------------------\npdf(\"marathon_winners_gender_box.pdf\", height = 1.5, width = 7)\n\npar(mar = c(2, 5.1, 0, 1), las = 1, mgp = c(2.5, 0.7, 0), \n    mfrow = c(1,1), cex.lab = 1.5, cex.axis = 1.5)\nboxPlot(marathon$Time, horiz = TRUE, fact = marathon$Gender, \n        xlim = c(2,3.2), ylim = c(0.5, 2.5),\n        axes = FALSE, col = COL[1,2])\naxis(1, at = seq(2,3.2,0.4))\naxis(2, at = c(1,2), labels = c(\"Women\", \"Men\"))\n\ndev.off()\n\n# times series by gender --------------------------------------------\npdf(\"marathon_winners_time_series.pdf\", height = 3, width = 9)\n\npar(mar = c(2, 4, 0.5, 1.3), las = 1, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nplot(marathon$Time[marathon$Gender == \"m\"] ~ marathon$Year[marathon$Gender == \"m\"],\n     xlab = \"Year\", ylab = \"Marathon times\", \n     pch = 19, col = COL[1], ylim = c(2, 3.2), axes = FALSE)\n\npoints(marathon$Time[marathon$Gender == \"f\"] ~ marathon$Year[marathon$Gender == \"f\"],\n       xlab = \"Year\", pch = 4, lwd = 1.7, col = COL[2])\naxis(1)\naxis(2, at = seq(2, 3.2, 0.4))\nlegend(\"topright\", inset = 0, pch = c(4, 19), col = c(COL[2], COL[1]), \n       legend = c(\"Women\", \"Men\"))\n\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/office_productivity/office_productivity.R",
    "content": "# set seed ------------------------------------------------\nset.seed(2406)\n\n# sketch --------------------------------------------------\npdf(\"office_productivity_sketch.pdf\", 5.5, 3)\npar(mar = c(1.5, 1.5, 0.5, 0.5), mgp = c(0.3, 0.7, 0), \n    mfrow = c(1,1), cex.lab = 1.5)\ncurve(rev(dgamma(x, 2.5,1/2)), 0, 14, \n      xlab = \"stress\", ylab = \"productivity\", lwd = 2, axes = FALSE)\nbox()\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/oscar_winners/oscar_winners.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(oscars)\n\n# plot of oscar winner women and men ages ---------------------------\np <- oscars %>%\n  ggplot(aes(x = age)) +\n    geom_histogram(binwidth = 10, fill = COL[1,1], color = COL[5,1], size = 0.3) +\n    facet_wrap(~fct_rev(award), ncol = 1) +\n    theme_minimal() +\n    theme(strip.text = element_text(hjust = 0)) +\n    labs(x = \"Age (in years)\", y = \"\")\n\nggsave(p, file = \"ch_intro_to_data/oscar_winners/figures/oscars_winners_hist.pdf\",\n       height = 6, width = 8)\n\n# summary stats -----------------------------------------------------\noscars %>%\n  group_by(award) %>%\n  summarise(\n    mean = mean(age),\n    sd = sd(age),\n    n = n()\n  )\n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/raise_taxes_mosaic/raise_taxes_mosaic.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\n# based on http://www.publicpolicypolling.com/pdf/2015/PPP_Release_National_30215.pdf\n\nn = 691\n\nn_dem = round(n * 0.40)\nn_rep = round(n * 0.34)\nn_indep = 691 - (n_dem + n_rep)\n\nparty =  c(rep(\"Democrat\", n_dem), rep(\"Republican\", n_rep), rep(\"Indep / Other\", n_indep))\nparty = factor(party, levels = c(\"Democrat\", \"Republican\", \"Indep / Other\"))\ntaxes = c(rep(\"Raise taxes on the rich\", round(n_dem * 0.91)), \n          rep(\"Raise taxes on the poor\", round(n_dem * 0.04)), \n          rep(\"Not sure\", round(n_dem * 0.05)),\n          rep(\"Raise taxes on the rich\", round(n_rep * 0.47)), \n          rep(\"Raise taxes on the poor\", round(n_rep * 0.10)), \n          rep(\"Not sure\", round(n_rep * 0.43)),\n          rep(\"Raise taxes on the rich\", round(n_indep * 0.49)), \n          rep(\"Raise taxes on the poor\", round(n_indep * 0.11)), \n          rep(\"Not sure\", round(n_indep * 0.40)) \n)\ntaxes = factor(taxes, levels = c(\"Raise taxes on the rich\", \"Raise taxes on the poor\", \"Not sure\"))\n\n\n# mosaicplot --------------------------------------------------------\n\npdf(\"raise_taxes_mosaic.pdf\", 7, 3)\npar(mar=c(0.5,0,0.2,0.5), las=1, mgp=c(4,1,0))\n\nmosaicplot(party ~ taxes, main = \"\", cex.axis = 1.1, \n           xlab = \"\", ylab = \"\", color = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/randomization_avandia/randomization_avandia.R",
    "content": "# load openintro package for colors -----------------------\nlibrary(openintro)\n\n# create data ---------------------------------------------\ngr <- c(rep(\"rosig\", 67593), rep(\"piog\",159978))\nout <- c(rep(c(\"y\", \"n\"), c(2593, 67593-2593)), \n         rep(c(\"y\", \"n\"), c(5386, 159978-5386)))\n\nset.seed(13)\nN <- 10^2\nrand_dist <- rep(NA, N)\nfor(i in 1:N){\n  rand_group <- sample(gr)\n  rand_dist[i] <- sum(out[rand_group == \"rosig\"] == \"y\")\n}\n\n# plot randomization distribution -----------------------------------\npdf(\"randomization_avandia.pdf\", 6, 4)\npar(mar = c(4,2.7,0,0), las = 1 , mgp = c(2.7, 0.9, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(rand_dist, main=\"\", \n         xlab = \"Simulated rosiglitazone cardiovascular events\", ylab=\"\", \n         col = COL[1], axes = FALSE)\naxis(1, at = seq(2250, 2550, 100))\naxis(2, at = (0:4)*N/20, labels = c(0, NA, 2, NA, 4)/20)\nabline(h = 0)\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/randomization_heart_transplants/randomization_heart_transplants.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(heartTr)\n\n# mosaic plot -------------------------------------------------------\npdf(\"randomization_heart_transplants_mosaic.pdf\", 5.5, 4.3)\npar(mar = c(0, 0, 0, 0), las = 1, mgp = c(2.7, 0.9, 0))\nmosaicplot(transplant ~ survived, data = heartTr, \n           main = \"\", xlab = \"\", ylab = \"\", color = COL[1],\n           cex.axis = 1.5)\ndev.off()\n\n# box plot ----------------------------------------------------------\npdf(\"randomization_heart_transplants_box.pdf\", 5.5, 4.3)\npar(mar = c(2, 4.8, 0, 0), las = 1, mgp = c(3.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nboxPlot(heartTr$survtime, fact = heartTr$transplant, \n        ylab = \"Survival Time (days)\", col = COL[1,2])\ndev.off()\n\n# randomization -----------------------------------------------------\nload(\"inference.RData\")\n\ndiffs = inference(heartTr$survived, heartTr$transplant, \n                  success = \"dead\", order = c(\"treatment\",\"control\"), \n                  est = \"proportion\", type = \"ht\", method = \"simulation\", \n                  nsim = 100, null = 0, alternative = \"twosided\", simdist = TRUE,\n                  seed = 95632)\n\n# plot randomization distribution -----------------------------------\npdf(\"randomization_heart_transplants_rando.pdf\", height = 3, width = 7)\n\npar(mar = c(3.6, 2.2, 1, 1), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\n\nvalues  <- table(diffs)\nplot(diffs, type = \"n\", xlim = c(-0.25, 0.25), \n     xlab = \"simulated differences in proportions\", \n     ylab = \"\", axes = FALSE, ylim = c(0, max(values)))\naxis(1, at = seq(-0.25, 0.25, 0.05), \n     labels = c(-0.25, NA,-0.15, NA,-0.05, NA, 0.05, NA, 0.15, NA, 0.25))\nfor(i in 1:length(diffs)){\n  x   <- diffs[i]\n  rec <- sum(diffs == x)\n  points(rep(x, rec), 1:rec, pch = 20, cex = 0.8, col = COL[1])\n}\n\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/reproducing_bacteria/reproducing_bacteria.R",
    "content": "# set seed ------------------------------------------------\nset.seed(2406)\n\n# sketch --------------------------------------------------\npdf(\"reproducing_bacteria_sketch.pdf\", 5.5, 3)\npar(mar = c(1.5, 1.5, 0.5, 0.5), mgp = c(0.3, 0.7, 0), \n    mfrow = c(1,1), cex.lab = 1.5)\ncurve(-1*dexp(x, rate = 4), lwd = 2,\n      xlab = \"time\", ylab = \"number of bacteria cells\", axes = FALSE)\nbox()\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/seattle_pet_names/seattle_pet_names.R",
    "content": "# load packages ----------------------------------------------------------------\nlibrary(tidyverse)\nlibrary(openintro)\nlibrary(ggimage)\n\n# load data --------------------------------------------------------------------\ndata(seattlepets)\n\n# create data for viz ----------------------------------------------------------\nname_props <- seattlepets %>% \n  filter(\n    !is.na(animals_name),\n    species %in% c(\"Dog\", \"Cat\")\n    ) %>%\n  group_by(species) %>% \n  count(animals_name, sort = TRUE) %>%\n  mutate(prop = n / sum(n))\n\ncat_name_props <- name_props %>%\n  filter(species == \"Cat\") %>%\n  rename(cat_prop = prop) %>%\n  slice(1:30)\n\ndog_name_props <- name_props %>%\n  filter(species == \"Dog\") %>%\n  rename(dog_prop = prop) %>%\n  slice(1:30)\n\ncomb_name_props <- inner_join(cat_name_props, dog_name_props, by = \"animals_name\") %>%\n  ungroup() %>%\n  select(animals_name, cat_prop, dog_prop)\n\n# create viz -------------------------------------------------------------------\np <- ggplot(comb_name_props, aes(x = cat_prop, y = dog_prop)) +\n  geom_abline(intercept = 0, color = COL[7,10], alpha = 0.8, size = 1.5) +\n  geom_text_repel(aes(label = animals_name), segment.color = COL[6,3], \n                   seed = 291252, max.iter = 10000) +\n  geom_point(color = COL[1,3]) +\n  theme_bw() +\n  labs(x = \"Proportion of cats\", y = \"Proportion of dogs\") +\n  xlim(0.002, 0.01) +\n  ylim(0.002, 0.01) +\n  ggimage::geom_emoji(image = \"1f436\", aes(x = 0.003, y = 0.009), size = 0.1) +\n  ggimage::geom_emoji(image = \"1f431\", aes(x = 0.009, y = 0.003), size = 0.1)\n\nggsave(filename = \"mine-new/ch_intro_to_data/seattle_pet_names/figures/seattle_pet_names.pdf\", p, width = 5.5, height = 4.3)\n  \n"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/stats_scores_box/stats_scores_box.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# data --------------------------------------------------------------\nstats_scores = c(79, 83, 57, 82, 94, 83, 72, 74, 73, 71, 66, 89, 78, \n                 81, 78, 81, 88, 69, 77, 79)\n\n# summary -----------------------------------------------------------\nsummary(stats_scores)\n\n# scatterplot of gpa vs. study hours --------------------------------\npdf(\"stats_scores_boxplot.pdf\", 5.5, 2)\npar(mar = c(3, 0.5, 0.5, 0.5), las = 1, mgp = c(1.75, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nboxplot(stats_scores, horizontal = TRUE, col = COL[1], xlab = \"Scores\")\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/eoce/unvotes/unvotes.R",
    "content": "# load packages ----------------------------------------------------------------\nlibrary(tidyverse)\nlibrary(openintro)\nlibrary(unvotes)\nlibrary(lubridate)\n\n# plot unvotes by issues -------------------------------------------------------\n\nun_votes %>%\n  mutate(country = ifelse(country == \"United States of America\", \"US\", country)) %>%\n  filter(country %in% c(\"US\", \"Mexico\", \"Canada\")) %>%\n  inner_join(un_roll_calls, by = \"rcid\") %>%\n  inner_join(un_roll_call_issues, by = \"rcid\") %>%\n  mutate(\n    issue = ifelse(issue == \"Nuclear weapons and nuclear material\", \"Nuclear weapons and materials\", issue),\n    vote = fct_relevel(vote, \"yes\", \"no\", \"abstain\")\n    ) %>%\n  group_by(country, year = year(date), issue) %>%\n  summarize(\n    votes = n(),\n    percent_yes = mean(vote == \"yes\")\n  ) %>%\n  filter(votes > 5) %>%  # only use records where there are more than 5 votes\n  ggplot(mapping = aes(x = year, y = percent_yes, color = country)) +\n    geom_point(alpha = 0.5) +\n    geom_smooth(method = \"loess\", se = FALSE) +\n    facet_wrap(~ issue) +\n    labs(\n      y = \"% Yes\",\n      x = \"Year\",\n      color = \"Country\"\n    ) +\n    theme_minimal() +\n    scale_color_manual(values = c(COL[1,1], COL[2,1], COL[4,1]))\n\n# save plot --------------------------------------------------------------------\nggsave(here::here(\"ch_intro_to_data/unvotes/figures/\", \"unvotes.png\"), width = 7, height = 4)\n"
  },
  {
    "path": "ch_intro_to_data/figures/expResp/expResp.R",
    "content": "\npdf(\"expResp.pdf\", 3.82, 0.44)\npar(mar = rep(0, 4))\nplot(0:1, 0:1, type = 'n', axes = FALSE)\narrows(0.3, 0.4, 0.7, 0.4, length = 0.1)\ntext(0.5, 0.3, 'might affect', pos = 3, cex = 0.8)\ntext(0.15, 0.5, 'explanatory\\nvariable')\ntext(0.85, 0.5, 'response\\nvariable')\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/figureShowingBlocking/figureShowingBlocking.R",
    "content": "library(openintro)\nset.seed(2)\nxlim     <- c(0, 1)\nslimBox3 <- 0.03\ndata(COL)\n\nmyPDF(\"figureShowingBlocking.pdf\",\n      4,\n      7,\n      mar = rep(0, 4))\nplot(c(0, 2.9),\n     type = \"n\",\n     axes = FALSE,\n     xlab = \"\",\n     ylab = \"\",\n     xlim = c(-0.1, 1.1))\nrect(0, 2.2, 1, 2.9)\ntext(0.5, 2.885, \"Numbered patients\",\n     pos = 3, cex = 0.9)\nrect(0, 1.2, 0.45, 1.9)\nrect(0.55, 1.2, 1, 1.9)\narrows(0.56, 2.17, 0.75, 2.02, length = 0.1, lwd = 1.37)\narrows(0.44, 2.17, 0.25, 2.02, length = 0.1, lwd = 1.37)\ntext(0.5, 2.07, \"create\\nblocks\", cex = 0.8)\ntext(0.2, 1.89, \"Low-risk patients\", pos = 3, cex = 0.7)\ntext(0.2+0.55, 1.89, \"High-risk patients\", pos = 3, cex = 0.7)\nrect(0, 0.48, 1, 0.9, border = COL[5])\nrect(0, 0.00, 1, 0.42, border = COL[5])\narrows(0.09, 1.16, y1 = 1, length = 0.1, lwd = 1.37)\ntext(0.1, 1.08, \"randomly\\nsplit in half\", cex = 0.7, pos = 4)\narrows(0.12 + 0.55, 1.16, y1 = 1, length = 0.1, lwd = 1.37)\ntext(0.13 + 0.55, 1.08, \"randomly\\nsplit in half\", cex = 0.7, pos = 4)\n\n# _____ Inner Box _____ #\nrect(0.02, 0.50, 0.41, 0.88, border = COL[5,4])\nrect(0.02, 0.02, 0.41, 0.40, border = COL[5,4])\nrect(0.57+slimBox3, 0.50, 0.98, 0.88, border = COL[5,4])\nrect(0.57+slimBox3, 0.02, 0.98, 0.40, border = COL[5,4])\n\n# _____ Labels _____ #\nrect(-0.05, 0.39 + 0.47, 0.14, 0.45 + 0.47,\n     col = \"#FFFFFF\",\n     border = COL[5])\ntext(0.02, 0.424 + 0.47,\n     \"Control\",\n     cex = 0.6,\n     col = COL[5])\n\nrect(-0.05, 0.39, 0.14, 0.45,\n     col = \"#FFFFFF\",\n     border = COL[5])\ntext(0.04, 0.424,\n     \"Treatment\",\n     cex = 0.6,\n     col = COL[5])\n\nn   <- 6 * 9\npch <- c(1, 20)[sample(2, n, TRUE, c(0.8, 1.2))]\ncex <- rnorm(n, 1, 0.001)\nk   <- 0\nfor (x in seq(0.1, 0.9, len = 9)) {\n  for (y in rev(seq(0.3, 0.8, len = 6))) {\n    k <- k + 1\n    col <- COL[ifelse(pch[k]==1, 4, 1)]\n    points(x, y + 2,\n           pch = pch[k],\n           cex = cex[k],\n           col = col)\n    text(x, y + 1.98,\n         k,\n         cex = 0.45,\n         pos = 3,\n         col = col)\n  }\n}\n\ntrmt  <- rep(NA, n)\n\nthese <- which(pch == 1)\ntrmt[sample(these, length(these)/2)] <- \"ctrl\"\ntrmt[is.na(trmt) & pch == 1] <- \"trmt\"\nk <- 0\nx <- 0.078\ny <- 1.83\nfor (i in these) {\n  k <- k+1\n  points(x, y,\n         pch = pch[i],\n         cex = cex[i],\n         col = COL[4])\n  text(x, y - 0.02,\n       i,\n       cex = 0.45,\n       pos = 3,\n       col = COL[4])\n  if(y < 1.3){\n    x <- x + 0.095\n    y <- 1.83\n  } else {\n    y <- y - 0.11\n  }\n}\nthese <- which(pch != 1)\ntrmt[sample(these, length(these)/2)] <- \"ctrl\"\ntrmt[is.na(trmt) & pch != 1] <- \"trmt\"\nk <- 0\nx <- 0.615\ny <- 1.82\nfor (i in these) {\n  k <- k+1\n  points(x, y,\n         pch = pch[i],\n         cex = cex[i],\n         col = COL[1])\n  text(x, y - 0.02,\n       i,\n       cex = 0.45,\n       pos = 3,\n       col = COL[1])\n  if(y < 1.3){\n    x <- x + 0.08\n    y <- 1.83\n  } else {\n    y <- y - 0.095\n  }\n}\n\n# _____ Low Risk _____ #\nk <- rep(0, 4)\nx <- c(0.10, 0.10, 0.665, 0.665)\ny <- c(0.35, 0.83, 0.35, 0.83) - 0.03\nfor (i in 1:n) {\n  j <- 1\n  if (trmt[i] == \"trmt\") {\n    j <- j + 1\n  }\n  if (pch[i] != 1) {\n    j <- j + 2\n  }\n  k[j] <- k[j]+1\n  col <- COL[ifelse(pch[i] == 1, 4, 1)]\n  points(x[j], y[j],\n         pch = pch[i],\n         cex = cex[i],\n         col = col)\n  text(x[j], y[j] - 0.02,\n       i,\n       cex = 0.45,\n       pos = 3,\n       col = col)\n  if (y[j] < 0.12 + 0.51 * (j %in% c(2, 4)) - 0.03) {\n    x[j] <- x[j] + 0.11 - ifelse(j > 2, 0.025, 0)\n    y[j] <- 0.35 + ifelse(j %in% c(2, 4), 0.48, 0) - 0.03\n  } else {\n    y[j] <- y[j] - 0.085\n  }\n}\n\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/interest_rate_vs_income/interest_rate_vs_loan_amount.R",
    "content": "library(openintro)\ndata(loan50)\ndata(COL)\n\nthe.index <- 40\n\nmyPDF(\"interest_rate_vs_income.pdf\", 6, 3.5,\n      mar = c(3, 3.5, 0.5, 0.5),\n      mgp = c(2.4, 0.5, 0))\nx <- loan50$total_income\ny <- loan50$interest_rate\nplot(x, y, \n     pch = 20,\n     cex = 1.5,\n     col = COL[1, 3],\n     xlim = c(0, max(x)),\n     ylim = c(0, max(y)),\n     xlab = \"\",\n     ylab = \"Interest Rate (%)\",\n     axes = FALSE)\nAxisInDollars(1, pretty(c(0, x)))\nAxisInPercent(2, pretty(c(0, y)))\nbox()\n# points(x, y, pch = \".\")\nmtext(\"Total Income\", 1, 1.9)\nt1 <- x[the.index]\nt2 <- y[the.index]\n# lines(c(t1, t1), c(-1e4, t2), lty = 2, col = COL[4])\n# lines(c(-1e4, t1), c(t2, t2), lty = 2, col = COL[4])\n# points(t1, t2, col = COL[4])\ndev.off()\n\nsummary(lm(y ~ x))\n\nloan50[the.index, ]\n"
  },
  {
    "path": "ch_intro_to_data/figures/interest_rate_vs_loan_amount/interest_rate_vs_loan_amount.R",
    "content": "library(openintro)\ndata(loan50)\ndata(COL)\n\nmyPDF(\"interest_rate_vs_loan_amount.pdf\", 6, 3.5,\n      mar = c(3, 3.5, 0.5, 0.5),\n      mgp = c(2.4, 0.5, 0))\nx <- loan50$loan_amount\ny <- loan50$interest_rate\nplot(x, y, \n     pch = 20,\n     cex = 1.5,\n     col = COL[1, 3],\n     xlim = c(0, max(x)),\n     ylim = c(0, max(y)),\n     xlab = \"\",\n     ylab = \"Interest Rate (%)\",\n     axes = FALSE)\nAxisInDollars(1, pretty(c(0, x)))\nAxisInPercent(2, pretty(c(0, y)))\nbox()\n# points(x, y, pch = \".\")\nmtext(\"Loan Amount\", 1, 1.9)\nt1 <- x[35]\nt2 <- y[35]\n# lines(c(t1, t1), c(-1e4, t2), lty = 2, col = COL[4])\n# lines(c(-1e4, t1), c(t2, t2), lty = 2, col = COL[4])\n# points(t1, t2, col = COL[4])\ndev.off()\n\nloan50[35, ]\n"
  },
  {
    "path": "ch_intro_to_data/figures/interest_rate_vs_loan_income_ratio/interest_rate_vs_loan_income_ratio.R",
    "content": "library(openintro)\ndata(loan50)\ndata(COL)\n\nmyPDF(\"interest_rate_vs_loan_income_ratio.pdf\", 6, 3.5,\n      mar = c(3, 3.5, 0.5, 0.5),\n      mgp = c(2.4, 0.5, 0))\nx <- 100 * loan50$loan_amount / loan50$total_income\ny <- loan50$interest_rate\nplot(x, y, \n     pch = 20,\n     cex = 1.5,\n     col = COL[1, 3],\n     xlim = c(0, max(x)),\n     ylim = c(0, max(y)),\n     xlab = \"\",\n     ylab = \"Interest Rate (%)\",\n     axes = FALSE)\nAxisInPercent(1, pretty(c(0, x)))\nAxisInPercent(2, pretty(c(0, y)))\nbox()\n# points(x, y, pch = \".\")\nmtext(\"Loan Amount\", 1, 1.9)\nt1 <- x[35]\nt2 <- y[35]\nlines(c(t1, t1), c(-1e4, t2),\n      lty = 2,\n      col = COL[4])\nlines(c(-1e4, t1), c(t2, t2),\n      lty = 2,\n      col = COL[4])\npoints(t1, t2,\n       col = COL[4])\ndev.off()\n\nloan50[35, ]\n"
  },
  {
    "path": "ch_intro_to_data/figures/loan_amount_vs_income/loan_amount_vs_income.R",
    "content": "library(openintro)\ndata(loan50)\ndata(COL)\n\nmyPDF(\"loan_amount_vs_income.pdf\", 6, 3.5,\n      mar = c(3, 3.5, 0.5, 0.5),\n      mgp = c(2.4, 0.5, 0))\nx <- loan50$total_income\ny <- loan50$loan_amount\nplot(x, y, \n     pch = 20,\n     cex = 1.5,\n     col = COL[1, 3],\n     xlim = c(0, max(x)),\n     ylim = c(0, max(y)),\n     xlab = \"\",\n     ylab = \"Loan Amount\",\n     axes = FALSE)\nAxisInDollars(1, pretty(c(0, x)))\nAxisInDollars(2, pretty(c(0, y)))\nbox()\n# points(x, y, pch = \".\")\nmtext(\"Total Income\", 1, 1.9)\nt1 <- x[35]\nt2 <- y[35]\nlines(c(t1, t1), c(-1e4, t2),\n      lty = 2,\n      col = COL[4])\nlines(c(-1e4, t1), c(t2, t2),\n      lty = 2,\n      col = COL[4])\npoints(t1, t2,\n       col = COL[4])\ndev.off()\n\nloan50[35, ]\n"
  },
  {
    "path": "ch_intro_to_data/figures/mnWinter/ReadMe.txt",
    "content": "\nThis photo was taken by David Diez. It is released under the same license as the textbook.\n"
  },
  {
    "path": "ch_intro_to_data/figures/multiunitsVsOwnership/multiunitsVsOwnership.R",
    "content": "library(openintro)\ndata(COL)\n\nw3  <- 1 == 0\nind <- 413\n\nif(w3){\n  myPNG(\"MHP.png\", 1200, 800,\n        mar = c(3, 3.5, 0.5, 0.5),\n        mgp = c(2.4, 0.5, 0),\n        cex = 2)\n} else {\n  myPDF(\"multiunitsVsOwnership.pdf\", 6, 3.5,\n        mar = c(3, 3.8, 0.5, 0.5),\n        mgp = c(2.7, 0.4, 0))\n}\npch    <- 1\ncex    <- sqrt(county$pop2017 / 1e6)\ncex[is.na(cex)] <- 0.1\ncolPop <- fadeColor(ifelse(cex > 0.35, COL[4], COL[1]),\n                    substr(gray(0.6 + cex * 0.1), 2, 3))\ncolSm  <- colPop\ncexF   <- 2\ngp1 <- cex < 0.32\nif(!w3){\n  cex <- 0.7\n  gp1 <- rep(TRUE, nrow(county))\n  pch <- 20\n  colSm  <- COL[1, 3]\n  colPop <- COL[1, 3]\n  cexF   <- 1\n}\nx <- county$multi_unit\ny <- county$homeownership\nplot(x[gp1], y[gp1],\n     pch = pch,\n     col = colSm,\n     xlab = \"\",\n     ylab = \"Homeownership Rate\",\n     axes = FALSE,\n     cex = ifelse(gp1 & cex < 0.32, 0.32, cex)[gp1],\n     xlim = c(0, 100), # range(x, na.rm = TRUE),\n     ylim = range(y, na.rm = TRUE))\nat  =  seq(0, 100, 20)\naxis(1, at, paste0(at, \"%\"))\naxis(2, at, paste0(at, \"%\"))\nabline(h = at, v = at, col = COL[7, 2])\nbox()\npoints(x[gp1],\n       y[gp1],\n       pch = '.')\npoints(x[!gp1], y[!gp1],\n       pch = pch,\n       col = colPop,\n       cex = ifelse(cex < 0.32, 0.32, cex)[!gp1])\npoints(x[!gp1],\n       y[!gp1],\n       pch = '.')\nt1 <- x[ind]\nt2 <- y[ind]\nlines(c(t1, t1), c(-1e5, t2),\n      lty = 2,\n      col = COL[4])\nlines(c(-1e5, t1), c(t2, t2),\n      lty = 2,\n      col = COL[4])\npoints(t1, t2,\n       col = COL[4])\nmtext(\"Percent of Units in Multi-Unit Structures\",\n      1,\n      1.9,\n      cex = ifelse(w3, 2, 1))\n\nif(w3){\n  usr <- par(\"usr\")\n  szs <- c(0.1, 0.4, 2, 5)\n  cex <- sqrt(szs) # *c(1.2, 1.1, 1, 1)\n  szs <- format(szs)\n  szs[1] <- paste(\"<\", szs[1])\n  text(102, 95-5, \"Population Size\", pos = 2)\n  colPop <- rgb(ifelse(cex > 0.35, 1, 0),\n                0.15 * cex, 0.05 * cex,\n                0.6 + cex * 0.1)\n  for(i in 1:4){\n    points(82, 89 - 5 * i,\n           cex = cex[i],\n           col = colPop[i])\n    txt <- paste(szs[i], \"million\")\n    text(101, 89 - 5 * i, txt, pos = 2)\n  }\n  rect(78, 63, 120, 120)\n  text(25, 10,\n       \"Counties with >100,000 people are colored red\")\n}\n\ndev.off()\n\ncounty[ind, ]\n"
  },
  {
    "path": "ch_intro_to_data/figures/popToSample/popToSampleGraduates.R",
    "content": "library(openintro)\ndata(COL)\n\nset.seed(52)\nmyPDF(\"popToSampleGraduates.pdf\",\n      4,\n      2.1,\n      mar = rep(0, 4))\n\nplot(c(0, 2),\n     c(0, 1.1),\n     type = 'n',\n     axes = FALSE)\ntemp <- seq(0, 2 * pi, 2 * pi / 100)\nx <- 0.5 + 0.5 * cos(temp)\ny <- 0.5 + 0.5 * sin(temp)\nlines(x, y)\n\ns <- matrix(runif(700), ncol = 2)\nS <- matrix(NA, 350, 2)\nj <- 0\nfor (i in 1:nrow(s)) {\n  if(sum((s[i, ] - 0.5)^2) < 0.23){\n    j <- j + 1\n    S[j, ] <- s[i, ]\n  }\n}\npoints(S, col = COL[1, 3], pch = 20)\ntext(0.5, 1, 'all graduates', pos = 3)\n\nset.seed(50)\nN <- sample(j, 25)\nlines((x - 0.5) / 2 + 1.5, (y - 0.5) / 2 +  0.5, pch = 20)\n\nSS <- (S[N, ] - 0.5) / 2 + 0.5\nthese <- c(2, 5, 11, 10, 12)\npoints(SS[these, 1] + 1,\n       SS[these, 2],\n       col = COL[4, 2],\n       pch = 20,\n       cex = 1.5)\ntext(1.5, 0.75, 'sample', pos = 3)\n\nfor (i in these) {\n  arrows(S[N[i], 1], S[N[i], 2],\n         SS[i, 1] + 1 - 0.03, SS[i, 2],\n         length = 0.08, col = COL[5], lwd = 1.5)\n}\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/popToSample/popToSubSampleGraduates.R",
    "content": "library(openintro)\ndata(COL)\n\nset.seed(52)\nmyPDF(\"popToSubSampleGraduates.pdf\",\n      4,\n      2.1,\n      mar = rep(0, 4))\n\nplot(c(0, 2),\n     c(0, 1.1),\n     type = 'n',\n     axes = FALSE)\ntemp <- seq(0, 2 * pi, 2 * pi / 100)\nx <- 0.5 + 0.5 * cos(temp)\ny <- 0.5 + 0.5 * sin(temp)\nlines(x, y)\n\ns <- matrix(runif(700), ncol = 2)\nS <- matrix(NA, 350, 2)\nj <- 0\nsub <- rep(FALSE, 1000)\nfor (i in 1:nrow(s)) {\n  if(sum((s[i,] - 0.5)^2) < 0.23){\n    j <- j+1\n    S[j,] <- s[i,]\n  }\n  if(sum((s[i, ] - c(0.05, 0.18) - 0.5)^2) < 0.07){\n    sub[j] <- TRUE\n  }\n}\npoints(S, col = COL[1, 4 - 2 * sub], pch = 20)\ntext(0.5, 1, 'all graduates', pos = 3)\nlines((x - 0.5) * 2 * sqrt(0.07) + 0.55,\n      (y - 0.5) * 2 * sqrt(0.07) + 0.68)\n\nset.seed(7)\nN <- sample((1:j)[sub], 25)\nlines((x - 0.5) / 2 + 1.5,\n      (y - 0.5) / 2 + 0.5,\n      pch = 20)\n\nSS <- (S[N, ] - 0.5) / 2 + 0.5\nthese <- c(2, 5, 7, 12, 15)\npoints(SS[these, 1] + 1,\n       SS[these, 2],\n       col = COL[4, 2],\n       pch = 20,\n       cex = 1.5)\ntext(1.5, 0.75, 'sample', pos = 3)\n\nfor (i in these)  {\n  arrows(S[N[i], 1], S[N[i], 2],\n         SS[i, 1] + 1 - 0.03, SS[i, 2],\n         length = 0.08,\n         col = COL[5],\n         lwd = 1.5)\n}\nrect(0.143, 0.2, 0.952, 0.301,\n     border = \"#00000000\",\n     col = \"#FFFFFF88\")\nrect(0.236, 0.301, 0.858, 0.403,\n     border = \"#00000000\",\n     col = \"#FFFFFF88\")\ntext(0.55, 0.5 + 0.18 - sqrt(0.07),\n     'graduates from\\nhealth-related fields',\n     pos = 1)\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/popToSample/surveySample.R",
    "content": "library(openintro)\ndata(COL)\n\nset.seed(52)\nmyPDF(\"surveySample.pdf\",\n      4,\n      2.1,\n      mar = rep(0, 4))\n\nplot(c(0, 2),\n     c(0, 1.1),\n     type='n',\n     axes=FALSE)\ntemp <- seq(0, 2 * pi, 2 * pi / 100)\nx <- 0.5 + 0.5 * cos(temp)\ny <- 0.5 + 0.5 * sin(temp)\nlines(x, y)\n\ns <- matrix(runif(700), ncol = 2)\nS <- matrix(NA, 350, 2)\nj <- 0\nsub <- rep(FALSE, 1000)\nfor (i in 1:nrow(s)) {\n  if (sum((s[i,] - 0.5)^2) < 0.23) {\n    j <- j + 1\n    S[j, ] <- s[i, ]\n  }\n  if (sum((s[i, ] - c(-0.15, 0.05) - 0.5)^2) < 0.115) {\n    sub[j] <- TRUE\n  }\n}\npoints(S, col = COL[1, 4 - 2 * sub], pch = 20)\ntext(0.5, 1, 'population of interest', pos = 3)\nlines((x - 0.5) * 2 * sqrt(0.115) + 0.35,\n      (y - 0.5) * 2 * sqrt(0.115) + 0.55)\n\nset.seed(7)\nN <- sample((1:j)[sub], 25)\nlines((x - 0.5) / 2 + 1.5,\n      (y - 0.5) / 2 + 0.5,\n      pch=20)\n\nSS <- (S[N, ] - 0.5) / 2 + 0.5\nthese <- c(2, 5, 6, 7, 15)\npoints(SS[these, 1] + 1,\n       SS[these, 2],\n       col = COL[4, 2],\n       pch = 20,\n       cex = 1.5)\ntext(1.5, 0.75, 'sample', pos=3)\n\nfor(i in these){\n\tarrows(S[N[i], 1],\n\t       S[N[i], 2],\n\t       SS[i, 1] + 1 - 0.03,\n\t       SS[i, 2],\n\t       length=0.08,\n\t       col=COL[5],\n\t       lwd=1.5)\n}\nrect(0.145, 0.195, 0.775, 0.11,\n     border=\"#00000000\",\n     col=\"#FFFFFF88\")\nrect(0.31, 0.018, 0.605, 0.11,\n     border=\"#00000000\",\n     col=\"#FFFFFF88\")\ntext(0.46, 0.5 + 0.06 - sqrt(0.115),\n     'population actually\\nsampled',\n     pos=1,\n     cex=0.8)\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/pop_change_v_med_income/pop_change_v_med_income.R",
    "content": "library(openintro)\ndata(county)\ndata(COL)\n\nind <- 1088\n\nmyPDF(\"pop_change_v_med_income.pdf\", 7, 3.5,\n      mar = c(3, 5.1, 0.5, 1),\n      mgp = c(2.4, 0.5, 0))\nx <- county$median_hh_income\ny <- county$pop_change\nylim <- c(-15, 25) # range(y, na.rm = TRUE)\nplot(x, y,\n     pch = 20,\n     cex = 0.7,\n     type = \"n\",\n     xlim = c(0, max(x, na.rm = TRUE)),\n     ylim = ylim,\n     xlab = \"\",\n     ylab = \"\",\n     axes = FALSE)\nAxisInDollars(1, pretty(c(0, x)))\nAxisInPercent(2, pretty(ylim))\nabline(h = pretty(ylim), v = pretty(c(0, x)), col = COL[7, 2])\nbox()\npoints(x, y, pch = 20, cex = 0.7, col = COL[1, 3])\npoints(x, y, pch = \".\")\nmtext(\"Median Household Income\", 1, 1.9)\npar(las = 0)\nmtext(\"Population Change\\nover 7 Years\", 2, 3)\nt1 <- x[ind]\nt2 <- y[ind]\nlines(c(t1, t1), c(-1e5, t2),\n      lty = 2,\n      col = COL[4])\nlines(c(-1e5, t1), c(t2, t2),\n      lty = 2,\n      col = COL[4])\npoints(t1, t2,\n       col = COL[4])\ndev.off()\n\ncounty[ind, ]\n"
  },
  {
    "path": "ch_intro_to_data/figures/pop_change_v_per_capita_income/pop_change_v_per_capita_income.R",
    "content": "library(openintro)\ndata(county)\ndata(COL)\n\nind <- 1088\n\nmyPDF(\"pop_change_v_per_capita_income.pdf\", 6, 3.5,\n      mar = c(3, 5.1, 0.5, 1),\n      mgp = c(2.4, 0.5, 0))\nx <- county$per_capita_income\ny <- county$pop_change\nylim <- c(-15, 25) # range(y, na.rm = TRUE)\nplot(x, y,\n     pch = 20,\n     cex = 0.7,\n     type = \"n\",\n     xlim = c(0, max(x, na.rm = TRUE)),\n     ylim = ylim,\n     xlab = \"\",\n     ylab = \"\",\n     axes = FALSE)\nAxisInDollars(1, pretty(c(0, x)))\nAxisInPercent(2, pretty(ylim))\nabline(h = pretty(ylim), v = pretty(c(0, x)), col = COL[7, 2])\nbox()\npoints(x, y, pch = 20, cex = 0.7, col = COL[1, 3])\npoints(x, y, pch = \".\")\nmtext(\"Per Capita Income\", 1, 1.9)\npar(las = 0)\nmtext(\"Population Change\\nover 7 Years (Percent)\", 2, 3)\nt1 <- x[ind]\nt2 <- y[ind]\nlines(c(t1, t1), c(-1e5, t2),\n      lty = 2,\n      col = COL[4])\nlines(c(-1e5, t1), c(t2, t2),\n      lty = 2,\n      col = COL[4])\npoints(t1, t2,\n       col = COL[4])\ndev.off()\n\ncounty[ind, ]\n"
  },
  {
    "path": "ch_intro_to_data/figures/samplingMethodsFigure/SamplingMethodsFunctions.R",
    "content": "# _____ Simple Random _____ #\nBuildSRS <- function() {\n  plot(0, xlim = c(0,2), ylim = 0:1, type = 'n', axes = FALSE)\n  box()\n  x   <- runif(N, 0, 2)\n  y   <- runif(N)\n  inc <- n\n  points(x, y, col = col, pch = pch)\n\n  these <- sample(N, n)\n  points(x[these], y[these], pch = 20, cex = 0.8, col = colSamp)\n  points(x[these], y[these], cex = 1.4, col = colSamp)\n}\n\n\n# _____ Systematic Sample _____ #\nBuildSystematic <- function() {\n  plot(0, xlim = c(0, 2), ylim = 0:1, type = 'n', axes = FALSE)\n  box()\n  nx  <- 17\n  ny  <- (nx + 1) / 2\n  x   <- rep(seq(0.02, 1.98, length.out = nx), ny)\n  y   <- rep(seq(0.05, 0.95, length.out = ny), rep(nx, ny))\n  points(x, y, col = col, pch = pch)\n  these <- 1:(nx * ny)\n  these <- these[(these + 3) %% 7 == 0]\n  points(x[these], y[these], pch = 20, cex = 0.8, col = colSamp)\n  points(x[these], y[these], cex = 1.4, col = colSamp)\n}\n\n\n# _____ Stratified _____ #\nBuildStratified <- function() {\n  PCH <- rep(c(1, 3, 20)[3], 3)\n  plot(0, xlim = c(0,2), ylim = 0:1 + 0.01,\n       type = 'n', axes = FALSE)\n  box()\n  X    <- c(0.18, 0.3, 0.68, 1.18, 1.4, 1.74)\n  Y    <- c(0.2, 0.78, 0.44, 0.7, 0.25, 0.65)\n  locs <- c(1, 4, 5, 3, 6, 2)\n  gps  <- list()\n  N    <- 1.1*c(15, 12, 35, 22, 13, 28)\n  R    <- sqrt(N/500)\n  p    <- matrix(c(12, 2, NA,\n  \t\t\t\t 1,  2, NA,\n  \t\t\t\t 4,  30, NA,\n  \t\t\t\t 19, 1, NA,\n  \t\t\t\t 11, 0, NA,\n  \t\t\t\t 3, 24, NA), 3)\n  p     <- round(p * 1.1)\n  p[3,] <- N - p[1,] - p[2,]\n  above <- c(1, 1, 1, 1, -1, 1)\n  for(i in 1:6){\n  \thold <- seq(0, 2 * pi, len = 99)\n  \tx    <- X[i] + (R[i]+0.01)*cos(hold)\n  \ty    <- Y[i] + (R[i]+0.01)*sin(hold)\n  \tpolygon(x, y, border = COL[5,4])\n  \tx    <- rep(NA, N[i])\n  \ty    <- rep(NA, N[i])\n  \tfor(j in 1:N[i]){\n  \t\tinside <- FALSE\n  \t\twhile(!inside){\n  \t\t\txx <- runif(1, -R[i], R[i])\n  \t\t\tyy <- runif(1, -R[i], R[i])\n  \t\t\tif(sqrt(xx^2 + yy^2) < R[i]){\n  \t\t\t\tinside <- TRUE\n  \t\t\t\tx[j]   <- xx\n  \t\t\t\ty[j]   <- yy\n  \t\t\t}\n  \t\t}\n  \t}\n  \ttype <- sample(1, N[i], TRUE)\n  \tpch  <- PCH[type]\n  \tcol  <- COL[type]\n  \tx    <- X[i]+x\n  \ty    <- Y[i]+y\n  \tpoints(x, y, pch = pch, col = col)\n  \tthese  <- sample(N[i], 3)\n  \tpoints(x[these], y[these],\n  \t       pch = 20, cex = 0.8, col = colSamp)\n  \tpoints(x[these], y[these], cex = 1.4, col = colSamp)\n  }\n  text(X, Y + above * (R),\n       paste(\"Stratum\", 1:6),\n       pos = 2 + above,\n       cex = 1.1)\n}\n\n\n# _____ Cluster _____ #\nBuildCluster <- function() {\n  PCH <- rep(c(1, 3, 20)[3], 3)\n  plot(0, xlim = c(0, 2), ylim  =  c(0.01, 1.04), type = 'n', axes = FALSE)\n  box()\n  X    <- c(0.17, 0.19, 0.52, 0.85, 1, 1.22, 1.49, 1.79, 1.85)\n  Y    <- c(0.3, 0.75, 0.5, 0.26, 0.73, 0.38, 0.67, 0.3, 0.8)\n  locs <- c(1, 4, 5, 3, 6, 2)\n  gps  <- list()\n  N    <- c(18, 12, 11, 13, 16, 14, 15, 16, 12)\n  R    <- sqrt(N/500)\n  p    <- matrix(c(6,  8, NA,\n  \t\t\t\t 4,  4, NA,\n  \t\t\t\t 4,  4, NA,\n  \t\t\t\t 5,  4, NA,\n  \t\t\t\t 8,  5, NA,\n  \t\t\t\t 4,  5, NA,\n  \t\t\t\t 5,  9, NA,\n  \t\t\t\t 6,  5, NA,\n  \t\t\t\t 4,  5, NA), 3)\n  p[3,] <- N - p[1,] - p[2,]\n  above <- c(-1, 1, 1, 1, 1, -1, 1, 1, 1)\n  for(i in 1:length(X)){\n  \thold <- seq(0, 2 * pi, len = 99)\n  \tx    <- X[i] + (R[i] + 0.02) * cos(hold)\n  \ty    <- Y[i] + (R[i] + 0.02) * sin(hold)\n  \tpolygon(x, y, border = COL[5,4])\n  \tif(i %in% c(3, 4, 8)){\n  \t\tpolygon(x, y, border = COL[4], lty = 2, lwd = 1.5)\n  \t}\n  \tx    <- rep(NA, N[i])\n  \ty    <- rep(NA, N[i])\n  \tfor(j in 1:N[i]){\n  \t\tinside <- FALSE\n  \t\twhile(!inside){\n  \t\t\txx <- runif(1, -R[i], R[i])\n  \t\t\tyy <- runif(1, -R[i], R[i])\n  \t\t\tif(sqrt(xx^2 + yy^2) < R[i]){\n  \t\t\t\tinside <- TRUE\n  \t\t\t\tx[j]   <- xx\n  \t\t\t\ty[j]   <- yy\n  \t\t\t}\n  \t\t}\n  \t}\n  \ttype <- sample(1, N[i], TRUE)\n  \tpch  <- PCH[type]\n  \tcol  <- COL[type]\n  \tx    <- X[i]+x\n  \ty    <- Y[i]+y\n  \tpoints(x, y, pch = pch, col = col)\n  \tthese  <- sample(N[i], N[i])\n  \tif(i %in% c(3, 4, 8)){\n  \tpoints(x[these], y[these], pch = 20, cex = 0.8, col = colSamp)\n  \tpoints(x[these], y[these], cex = 1.4, col = colSamp)\n  \t\t#points(x[these], y[these], pch = 19, col = colSamp)\n  \t}\n  }\n  text(X, Y + above * (R + 0.01),\n       paste(\"Cluster\", 1:length(X)),\n       pos = 2 + above,\n       cex = 1.1)\n}\n\n\n  \n# _____ Multistage Sampling _____ #\nBuildMultistage <- function() {\n  PCH <- rep(c(1, 3, 20)[3], 3)\n  plot(0, xlim = c(0, 2), ylim = 0:1 + 0.035,\n       type = 'n', axes = FALSE)\n  box()\n  X    <- c(0.17, 0.19, 0.52, 0.85, 1, 1.22, 1.49, 1.79, 1.85)\n  Y    <- c(0.3, 0.75, 0.5, 0.26, 0.73, 0.38, 0.67, 0.3, 0.8)\n  locs <- c(1, 4, 5, 3, 6, 2)\n  gps  <- list()\n  N    <- c(18, 12, 11, 13, 16, 14, 15, 16, 12)\n  R    <- sqrt(N/500)\n  p    <- matrix(c(6,  8, NA,\n  \t\t\t\t 4,  4, NA,\n  \t\t\t\t 4,  4, NA,\n  \t\t\t\t 5,  4, NA,\n  \t\t\t\t 8,  5, NA,\n  \t\t\t\t 4,  5, NA,\n  \t\t\t\t 5,  9, NA,\n  \t\t\t\t 6,  5, NA,\n  \t\t\t\t 4,  5, NA), 3)\n  p[3,] <- N - p[1,] - p[2,]\n  above <- c(-1, 1, 1, 1, 1, -1, 1, 1, 1)\n  for(i in 1:length(X)){\n  \thold <- seq(0, 2*pi, len = 99)\n  \tx    <- X[i] + (R[i]+0.02)*cos(hold)\n  \ty    <- Y[i] + (R[i]+0.02)*sin(hold)\n  \tpolygon(x, y, border = COL[5,4])\n  \tif(i %in% c(3, 4, 8)){\n  \t\tpolygon(x, y, border = COL[4], lty = 2, lwd = 1.5)\n  \t}\n  \tx    <- rep(NA, N[i])\n  \ty    <- rep(NA, N[i])\n  \tfor(j in 1:N[i]){\n  \t\tinside <- FALSE\n  \t\twhile(!inside){\n  \t\t\txx <- runif(1, -R[i], R[i])\n  \t\t\tyy <- runif(1, -R[i], R[i])\n  \t\t\tif(sqrt(xx^2 + yy^2) < R[i]){\n  \t\t\t\tinside <- TRUE\n  \t\t\t\tx[j]   <- xx\n  \t\t\t\ty[j]   <- yy\n  \t\t\t}\n  \t\t}\n  \t}\n  \ttype <- sample(1, N[i], TRUE)\n  \tpch  <- PCH[type]\n  \tcol  <- COL[type]\n  \tx    <- X[i]+x\n  \ty    <- Y[i]+y\n  \tpoints(x, y, pch = pch, col = col)\n  \tthese  <- sample(N[i], 6)\n  \tif(i %in% c(3, 4, 8)){\n  \tpoints(x[these], y[these], pch = 20, cex = 0.8, col = colSamp)\n  \tpoints(x[these], y[these], cex = 1.4, col = colSamp)\n  \t\t#points(x[these], y[these], pch = 19, col = colSamp)\n  \t}\n  }\n  text(X, Y + above * (R + 0.01),\n       paste(\"Cluster\", 1:length(X)),\n       pos = 2 + above, cex = 1.1)\n}\n"
  },
  {
    "path": "ch_intro_to_data/figures/samplingMethodsFigure/samplingMethodsFigure.R",
    "content": "library(openintro)\ndata(COL)\nset.seed(3)\nN   <- 108\nn   <- 18\ncolSamp <- COL[4]\nPCH <- rep(c(1, 3, 20)[3], 3)\n\ncol <- rep(COL[1], N)\npch <- PCH[match(col, COL)]\n\nmyPDF(\"samplingMethodsFigure.pdf\", 5.9, 9, mar=rep(0.5,4), mfrow=c(3,1))\n\n#=====> SRS <=====#\nplot(0, xlim=c(0,2), ylim=0:1, type='n', axes=FALSE)\nbox()\nx   <- runif(N, 0, 2)\ny   <- runif(N)\ninc <- n\npoints(x, y, col=col, pch=pch)\n\nthese <- sample(N, n)\npoints(x[these], y[these], pch=20, cex=0.8, col=colSamp)\npoints(x[these], y[these], cex=1.4, col=colSamp)\n\n\n#=====> Stratified <=====#\nPCH <- rep(c(1, 3, 20)[3], 3)\nplot(0, xlim=c(0,2), ylim=0:1, type='n', axes=FALSE)\nbox()\nX    <- c(0.18, 0.3, 0.68, 1.18, 1.4, 1.74)\nY    <- c(0.2, 0.78, 0.44, 0.7, 0.25, 0.65)\nlocs <- c(1, 4, 5, 3, 6, 2)\ngps  <- list()\nN    <- 1.1*c(15, 12, 35, 22, 13, 28)\nR    <- sqrt(N/500)\np    <- matrix(c(12, 2, NA,\n\t\t\t\t 1,  2, NA,\n\t\t\t\t 4,  30, NA,\n\t\t\t\t 19, 1, NA,\n\t\t\t\t 11, 0, NA,\n\t\t\t\t 3, 24, NA), 3)\np     <- round(p*1.1)\np[3,] <- N - p[1,] - p[2,]\nabove <- c(1, 1, 1, 1, -1, 1)\nfor(i in 1:6){\n\thold <- seq(0, 2*pi, len=99)\n\tx    <- X[i] + (R[i]+0.01)*cos(hold)\n\ty    <- Y[i] + (R[i]+0.01)*sin(hold)\n\tpolygon(x, y, border=COL[5,4])\n\tx    <- rep(NA, N[i])\n\ty    <- rep(NA, N[i])\n\tfor(j in 1:N[i]){\n\t\tinside <- FALSE\n\t\twhile(!inside){\n\t\t\txx <- runif(1, -R[i], R[i])\n\t\t\tyy <- runif(1, -R[i], R[i])\n\t\t\tif(sqrt(xx^2 + yy^2) < R[i]){\n\t\t\t\tinside <- TRUE\n\t\t\t\tx[j]   <- xx\n\t\t\t\ty[j]   <- yy\n\t\t\t}\n\t\t}\n\t}\n\ttype <- sample(1, N[i], TRUE)\n\tpch  <- PCH[type]\n\tcol  <- COL[type]\n\tx    <- X[i]+x\n\ty    <- Y[i]+y\n\tpoints(x, y, pch=pch, col=col)\n\tthese  <- sample(N[i], 3)\n\tpoints(x[these], y[these], pch=20, cex=0.8, col=colSamp)\n\tpoints(x[these], y[these], cex=1.4, col=colSamp)\n}\ntext(X, Y+above*(R+0.01), paste(\"Stratum\", 1:6), pos=2+above, cex=1.1)\n\n#=====> Cluster <=====#\nPCH <- rep(c(1, 3, 20)[3], 3)\nplot(0, xlim=c(0,2), ylim=0:1, type='n', axes=FALSE)\nbox()\nX    <- c(0.17, 0.19, 0.52, 0.85, 1, 1.22, 1.49, 1.79, 1.85)\nY    <- c(0.3, 0.75, 0.5, 0.26, 0.73, 0.38, 0.67, 0.3, 0.8)\nlocs <- c(1, 4, 5, 3, 6, 2)\ngps  <- list()\nN    <- c(18, 12, 11, 13, 16, 14, 15, 16, 12)\nR    <- sqrt(N/500)\np    <- matrix(c(6,  8, NA,\n\t\t\t\t 4,  4, NA,\n\t\t\t\t 4,  4, NA,\n\t\t\t\t 5,  4, NA,\n\t\t\t\t 8,  5, NA,\n\t\t\t\t 4,  5, NA,\n\t\t\t\t 5,  9, NA,\n\t\t\t\t 6,  5, NA,\n\t\t\t\t 4,  5, NA), 3)\np[3,] <- N - p[1,] - p[2,]\nabove <- c(-1, 1, 1, 1, 1, -1, 1, 1, 1)\nfor(i in 1:length(X)){\n\thold <- seq(0, 2*pi, len=99)\n\tx    <- X[i] + (R[i]+0.02)*cos(hold)\n\ty    <- Y[i] + (R[i]+0.02)*sin(hold)\n\tpolygon(x, y, border=COL[5,4])\n\tif(i %in% c(3, 4, 8)){\n\t\tpolygon(x, y, border=COL[4], lty=2, lwd=1.5)\n\t}\n\tx    <- rep(NA, N[i])\n\ty    <- rep(NA, N[i])\n\tfor(j in 1:N[i]){\n\t\tinside <- FALSE\n\t\twhile(!inside){\n\t\t\txx <- runif(1, -R[i], R[i])\n\t\t\tyy <- runif(1, -R[i], R[i])\n\t\t\tif(sqrt(xx^2 + yy^2) < R[i]){\n\t\t\t\tinside <- TRUE\n\t\t\t\tx[j]   <- xx\n\t\t\t\ty[j]   <- yy\n\t\t\t}\n\t\t}\n\t}\n\ttype <- sample(1, N[i], TRUE)\n\tpch  <- PCH[type]\n\tcol  <- COL[type]\n\tx    <- X[i]+x\n\ty    <- Y[i]+y\n\tpoints(x, y, pch=pch, col=col)\n\tthese  <- sample(N[i], 6)\n\tif(i %in% c(3, 4, 8)){\n\tpoints(x[these], y[these], pch=20, cex=0.8, col=colSamp)\n\tpoints(x[these], y[these], cex=1.4, col=colSamp)\n\t\t#points(x[these], y[these], pch=19, col=colSamp)\n\t}\n}\ntext(X, Y+above*(R+0.01), paste(\"Cluster\", 1:length(X)), pos=2+above, cex=1.1)\n\ndev.off()"
  },
  {
    "path": "ch_intro_to_data/figures/samplingMethodsFigure/samplingMethodsFigures.R",
    "content": "library(openintro)\nsource(\"SamplingMethodsFunctions.R\")\ndata(COL)\nset.seed(4)\nN <- 108\nn <- 18\ncolSamp <- COL[4]\nPCH <- rep(c(1, 3, 20)[3], 3)\n\ncol <- rep(COL[1], N)\npch <- PCH[match(col, COL)]\n\n# BuildSystematic()\n\n\nset.seed(4)\nmyPDF(\"simple_stratified.pdf\", 7.4, 7.5,\n      mar = rep(0.5,4), mfrow = c(2,1))\nBuildSRS()\nBuildStratified()\ndev.off()\n\n\nset.seed(4)\nmyPDF(\"cluster_multistage.pdf\", 7.4, 7.5,\n      mar = rep(0.5,4), mfrow = c(2,1))\nBuildCluster()\nBuildMultistage()\ndev.off()\n"
  },
  {
    "path": "ch_intro_to_data/figures/variables/sunCausesCancer.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"sunCausesCancer.pdf\", 4.7, 1.2,\n      mar = rep(0, 4))\nplot(c(-0.05, 1.2),\n     c(0.39, 1),\n     type = 'n',\n     axes = FALSE)\ntext(0.59, 0.89, 'sun exposure')\nrect(0.4, 0.8, 0.78, 1)\ntext(0.3, 0.49, 'use sunscreen')\nrect(0.1, 0.4, 0.48, 0.6)\narrows(0.49, 0.78, 0.38, 0.62,\n       length = 0.08, lwd = 1.5)\ntext(0.87, 0.5, 'skin cancer')\nrect(0.71,0.4, 1.01, 0.6)\narrows(0.67, 0.78, 0.8, 0.62,\n       length = 0.08, lwd = 1.5)\n\narrows(0.5, 0.5, 0.69, 0.5,\n       length = 0.08, col = COL[6,2])\ntext(0.595, 0.565, \"?\",\n     cex = 1.5, col = COL[4])\ndev.off()\n"
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    "path": "ch_intro_to_data/figures/variables/variables.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('variables.pdf', 4.2, 1.5, mar = rep(0,4))\nplot(c(-0.15, 1.3),\n     c(0, 1),\n     type = 'n',\n     axes = FALSE)\n\ntext(0.6, 0.9, 'all variables')\nrect(0.4, 0.8, 0.8, 1)\n\ntext(0.25, 0.5, 'numerical')\nrect(0.1, 0.4, 0.4, 0.6)\narrows(0.45, 0.78, 0.34, 0.62, length = 0.08)\n\ntext(0.9, 0.5, 'categorical')\nrect(0.73, 0.4, 1.07, 0.6)\narrows(0.76, 0.78, 0.85, 0.62, length = 0.08)\n\ntext(0, 0.1, 'continuous')\nrect(-0.17, 0, 0.17, 0.2)\narrows(0.13, 0.38, 0.05, 0.22, length = 0.08)\n\ntext(0.39, 0.1, 'discrete')\nrect(0.25, 0, 0.53, 0.2)\narrows(0.35, 0.38, 0.4, 0.22, length = 0.08)\n\ntext(0.77, 0.14, 'nominal', col = COL[6], cex = 0.7)\ntext(0.77, 0.05, '(unordered categorical)',\n     col = COL[6],\n     cex = 0.5)\nrect(0.6, 0, 0.94, 0.2, border = COL[6])\narrows(0.82, 0.38, 0.77, 0.22, length = 0.08, col = COL[6])\n\ntext(1.14, 0.14, 'ordinal', col = COL[6], cex = 0.7)\ntext(1.14, 0.05, '(ordered categorical)', col = COL[6], cex = 0.5)\nrect(0.98, 0, 1.3, 0.2, border = COL[6])\narrows(1.03, 0.38, 1.11, 0.22, length = 0.08, col = COL[6])\n\ndev.off()\n"
  },
  {
    "path": "ch_probability/TeX/ch_probability.tex",
    "content": "\\begin{chapterpage}{Probability}\n  \\chaptertitle{Probability}\n  \\label{probability}\n  \\label{ch_probability}\n  \\chaptersection{basicsOfProbability}\n  \\chaptersection{conditionalProbabilitySection}\n  \\chaptersection{smallPop}\n  \\chaptersection{randomVariablesSection}\n  \\chaptersection{contDist}\n\\end{chapterpage}\n\\renewcommand{\\chapterfolder}{ch_probability}\n\n\\index{probability|(}\n\n\\chapterintro{Probability forms the foundation of statistics,\n  and you're probably \\mbox{already}\n  aware of many of the ideas presented in this chapter.\n  However, formalization of probability concepts is likely\n  new for most readers. \\\\\n\n  \\noindent%\n  While this chapter provides a theoretical foundation\n  for the ideas in later chapters and provides a path\n  to a deeper understanding,\n  mastery of the concepts introduced in this chapter\n  is not required for applying the\n  methods introduced in the rest of this book.}\n\n%  This chapter provides a theoretical foundation for\n%  the ideas introduced in later chapters.\n%  However, this chapter is not strictly required to\n%  understand or apply the methods introduced in the\n%  rest of this book.}\n\n\n\n\n\\section{Defining probability}\n\\label{basicsOfProbability}\n\nStatistics is based on probability,\nand while probability is not required for the applied\ntechniques in this book, it may help you gain a deeper\nunderstanding of the methods and set a better foundation\nfor future courses.\n\n\n\\subsection{Introductory examples}\n\nBefore we get into technical ideas, let's walk through\nsome basic examples that may feel more familiar.\n\n\\begin{examplewrap}\n\\begin{nexample}{A ``die'', the singular of dice, is a cube with six faces numbered \\resp{1}, \\resp{2}, \\resp{3}, \\resp{4}, \\resp{5}, and \\resp{6}. What is the chance of getting \\resp{1} when rolling a die?}\\label{probOf1}\nIf the die is fair, then the chance of a \\resp{1} is as good as the chance of any other number. Since there are six outcomes, the chance must be 1-in-6 or, equivalently, $1/6$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the chance of getting a \\resp{1} or \\resp{2} in the next roll?}\\label{probOf1Or2}\n\\resp{1} and \\resp{2} constitute two of the six equally likely possible outcomes, so the chance of getting one of these two outcomes must be $2/6 = 1/3$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the chance of getting either \\resp{1}, \\resp{2}, \\resp{3}, \\resp{4}, \\resp{5}, or \\resp{6} on the next roll?}\\label{probOf123456}\n100\\%. The outcome must be one of these numbers.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the chance of not rolling a \\resp{2}?}\\label{probNot2}\nSince the chance of rolling a \\resp{2} is $1/6$ or $16.\\bar{6}\\%$, the chance of not rolling a \\resp{2} must be $100\\% - 16.\\bar{6}\\%=83.\\bar{3}\\%$ or $5/6$.\n\nAlternatively, we could have noticed that not rolling a \\resp{2} is the same as getting a \\resp{1}, \\resp{3}, \\resp{4}, \\resp{5}, or \\resp{6}, which makes up five of the six equally likely outcomes and has probability $5/6$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Consider rolling two dice. If $1/6$ of the time the first die is a \\resp{1} and $1/6$ of those times the second die is a \\resp{1}, what is the chance of getting two \\resp{1}s?}\\label{probOf2Ones}\nIf $16.\\bar{6}$\\% of the time the first die is a \\resp{1} and $1/6$ of \\emph{those} times the second die is also a \\resp{1}, then the chance that both dice are \\resp{1} is $(1/6)\\times (1/6)$ or $1/36$.\n\\end{nexample}\n\\end{examplewrap}\n\n\n\\D{\\newpage}\n\n\\subsection{Probability}\n\n\\index{random process|(}\n\nWe use probability to build tools to describe and understand apparent randomness. We often frame probability in terms of a \\term{random process} giving rise to an \\term{outcome}.\n\\begin{center}\n\\begin{tabular}{lll}\nRoll a die &$\\rightarrow$ & \\resp{1}, \\resp{2}, \\resp{3}, \\resp{4}, \\resp{5}, or \\resp{6} \\\\\nFlip a coin &$\\rightarrow$ & \\resp{H} or \\resp{T} \\\\\n\\end{tabular}\n\\end{center}\nRolling a die or flipping a coin is a seemingly random process and each gives rise to an outcome. \n\n\\begin{onebox}{Probability}\nThe \\term{probability} of an outcome is the proportion of times the outcome would occur if we observed the random process an infinite number of times.\n\\end{onebox}\n\nProbability is defined as a proportion, and it always takes values between 0~and~1 (inclusively). It may also be displayed as a percentage between 0\\% and 100\\%.\n\nProbability can be illustrated by rolling a die many times. Let $\\hat{p}_n$ be the proportion of outcomes that are \\resp{1} after the first $n$ rolls. As the number of rolls increases, $\\hat{p}_n$ will converge to the probability of rolling a \\resp{1}, $p = 1/6$. Figure~\\ref{dieProp} shows this convergence for 100,000 die rolls. The tendency of $\\hat{p}_n$ to stabilize around $p$ is described by the \\term{Law of Large Numbers}. \n\n\\begin{figure}[h]\n\\centering\n\\Figure[A line plot is shown. The horizontal axis is \"n (number of rolls)\", which increases exponentially in values from 1 to 10 to 100 to 1,000 to 10,000 and then to 100,000. The vertical axis is for \"p-hat sub n\" and has a range from 0.0 to about 0.35. A horizontal dashed line is also shown at one-sixth. The line representing the fraction of rolls that take a value of 1 starts at 0 with the first roll and stays there until it reaches about 4, then it jumps up to 0.25 and bounces around and then up around 0.35 at 10 rolls before decreasing close to one-sixth. Here it bounces between 0.13 and 0.22 up to 100 rolls, and it continues becoming more stable around one-sixth with more rolls, not deviating further than about 0.03 from one-sixth through 1,000 rolls. It continues to get even more stable, not deviating more than about 0.015 from the value of one-sixth through about 5,000 rolls, after which it is nearly indistinguishable from one-sixth for more than 5,000 rolls.]{0.85}{dieProp}\n\\caption{The fraction of die rolls that are 1 at each stage in a simulation. The proportion tends to get closer to the probability $1/6 \\approx 0.167$ as the number of rolls increases.}\n\\label{dieProp}\n\\end{figure}\n\n\\begin{onebox}{Law of Large Numbers}\nAs more observations are collected, the proportion $\\hat{p}_n$ of occurrences with a particular outcome converges to the probability $p$ of that outcome.\n\\end{onebox}\n\nOccasionally the proportion will veer off from the probability and appear to defy the Law of Large Numbers, as $\\hat{p}_n$ does many times in Figure~\\ref{dieProp}. However, these deviations become smaller as the number of rolls increases.\n\nAbove we write $p$ as the probability of rolling a \\resp{1}. We can also write this probability as\n\\begin{align*}\nP(\\text{rolling a \\resp{1}})\n\\end{align*}\nAs we become more comfortable with this notation, we will abbreviate it further. For instance, if it is clear that the process is ``rolling a die'', we could abbreviate $P($rolling a \\resp{1}$)$ as~$P($\\resp{1}$)$. \n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{randomProcessExercise}\nRandom processes include rolling a die and flipping a coin. (a) Think of another random process. (b) Describe all the possible outcomes of that process. For instance, rolling a die is a random process with possible outcomes \\mbox{\\resp{1}, \\resp{2}, ..., \\resp{6}}.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Here are four examples. (i) Whether someone gets sick in the next month or not is an apparently random process with outcomes \\resp{sick} and \\resp{not}. (ii) We can \\emph{generate} a random process by randomly picking a person and measuring that person's height. The outcome of this process will be a positive number. (iii) Whether the stock market goes up or down next week is a seemingly random process with possible outcomes \\resp{up}, \\resp{down}, and \\resp{no\\us{}change}. Alternatively, we could have used the percent change in the stock market as a numerical outcome. (iv) Whether your roommate cleans her dishes tonight probably seems like a random process with possible outcomes \\resp{cleans\\us{}dishes} and \\resp{leaves\\us{}dishes}.}\n\nWhat we think of as random processes are not necessarily random, but they may just be too difficult to understand exactly. The fourth example in the footnote solution to Guided Practice~\\ref{randomProcessExercise} suggests a roommate's behavior is a random process. However, even if a roommate's behavior is not truly random, modeling her behavior as a random process can still be useful. \n\n%\\begin{tipBox}{\\tipBoxTitle{Modeling a process as random}\n%It can be helpful to model a process as random even if it is not truly random.}\n%\\end{tipBox}\n\n\\index{random process|)}\n\n\\subsection{Disjoint or mutually exclusive outcomes}\n\n\\index{disjoint|(}\n\\index{mutually exclusive|(}\n\nTwo outcomes are called \\term{disjoint} or \\term{mutually exclusive} if they cannot both happen. For instance, if we roll a die, the outcomes \\resp{1} and \\resp{2} are disjoint since they cannot both occur. On the other hand, the outcomes \\resp{1} and ``rolling an odd number'' are not disjoint since both occur if the outcome of the roll is a \\resp{1}. The terms \\emph{disjoint} and \\emph{mutually exclusive} are equivalent and interchangeable.\n\nCalculating the probability of disjoint outcomes is easy. When rolling a die, the outcomes \\resp{1} and \\resp{2} are disjoint, and we compute the probability that one of these outcomes will occur by adding their separate probabilities:\n\\begin{align*}\nP(\\text{\\resp{1} or \\resp{2}})\n  = P(\\text{\\resp{1}})+P(\\text{\\resp{2}})\n  = 1/6 + 1/6\n  = 1/3\n\\end{align*}\nWhat about  the probability of rolling a \\resp{1}, \\resp{2}, \\resp{3}, \\resp{4}, \\resp{5}, or \\resp{6}? Here again, all of the outcomes are disjoint so we add the probabilities:\n\\begin{align*}\n&P(\\text{\\resp{1} or \\resp{2} or \\resp{3} or \\resp{4}\n    or \\resp{5} or \\resp{6}}) \\\\\n  &\\quad = P(\\text{\\resp{1}})+P(\\text{\\resp{2}})\n      + P(\\text{\\resp{3}})+P(\\text{\\resp{4}})\n      + P(\\text{\\resp{5}})+P(\\text{\\resp{6}}) \\\\\n  &\\quad = 1/6 + 1/6 + 1/6 + 1/6 + 1/6 + 1/6\n  = 1\n\\end{align*}\nThe \\term{Addition Rule} guarantees the accuracy of this approach when the outcomes are disjoint. \n\n\\begin{onebox}{Addition Rule of disjoint outcomes}\n  If $A_1$ and $A_2$ represent two disjoint outcomes,\n  then the probability that one of them occurs is given by\n  \\begin{align*}\n  P(A_1\\text{ or } A_2) = P(A_1) + P(A_2)\n  \\end{align*}\n  If there are many disjoint outcomes $A_1$, ..., $A_k$,\n  then the probability that one of these outcomes will occur is\n  \\begin{align*}\n  P(A_1) + P(A_2) + \\cdots + P(A_k)\n  \\end{align*}\n\\end{onebox}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWe are interested in the probability of rolling a \\resp{1}, \\resp{4}, or \\resp{5}. (a) Explain why the outcomes \\resp{1}, \\resp{4}, and \\resp{5} are disjoint. (b) Apply the Addition Rule for disjoint outcomes to determine $P($\\resp{1} or \\resp{4} or \\resp{5}$)$.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) The random process is a die roll, and at most one of these outcomes can come up. This means they are disjoint outcomes. (b)~$P($\\resp{1} or \\resp{4} or \\resp{5}$) = P($\\resp{1}$)+P($\\resp{4}$)+P($\\resp{5}$) = \\frac{1}{6} + \\frac{1}{6} + \\frac{1}{6} = \\frac{3}{6} = \\frac{1}{2}$}\n\n\\index{data!loans|(}\n\\begin{exercisewrap}\n\\begin{nexercise}\nIn the \\data{loans} data set in Chapter~\\ref{ch_summarizing_data},\nthe \\var{homeownership} variable described whether the borrower\nrents, has a mortgage, or owns her property.\nOf the 10,000 borrowers, 3858 rented, 4789 had a mortgage,\nand 1353 owned their home.\\footnotemark{}\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}\n\\item\n    Are the outcomes \\resp{rent}, \\resp{mortgage},\n    and \\resp{own} disjoint?\n\\item\n    Determine the proportion of loans with value \\resp{mortgage}\n    and \\resp{own} separately.\n\\item\n    Use the Addition Rule for disjoint outcomes to compute\n    the probability a randomly selected loan from the data set\n    is for someone who has a mortgage or owns her\n    home.\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~Yes. Each loan is categorized in only one\nlevel of \\var{homeownership}.\n(b)~Mortgage: $\\frac{4789}{10000} = 0.479$.\nOwn: $\\frac{1353}{10000} = 0.135$.\n(c)~$P($\\resp{mortgage} or \\resp{own}$) = P($\\resp{mortgage}$) + P($\\resp{own}$) = 0.479 + 0.135 = 0.614$.}\n\\index{data!loans|)}\n\n\\index{event|(}\n\nData scientists rarely work with individual outcomes and instead consider \\indexthis{\\emph{sets}}{sets} or \\indexthis{\\emph{collections}}{collections} of outcomes. Let $A$ represent the event where a die roll results in \\resp{1} or \\resp{2} and $B$~represent the event that the die roll is a \\resp{4} or a \\resp{6}. We write $A$ as the set of outcomes $\\{$\\resp{1},~\\resp{2}$\\}$ and $B=\\{$\\resp{4}, \\resp{6}$\\}$. These sets are commonly called \\termsub{events}{event}. Because $A$ and $B$ have no elements in common, they are disjoint events. $A$ and $B$ are represented in Figure~\\ref{disjointSets}.\n\n\\begin{figure}[hhh]\n  \\centering\n  \\Figure[Six numbers are shown in order: 1, 2, 3, 4, 5, and 6. The numbers 1 and 2 are circled and labeled with the letter \"A\", the numbers 2 and 3 are circled and labeled with the letter \"B\", and the numbers 4 and 6 are circled with a label of the letter \"C\". (This last circle is not an actual circle but is a drawn enclosure that omits the number 5.)]{0.45}{disjointSets}\n  \\caption{Three events, $A$, $B$, and $D$, consist of\n      outcomes from rolling a die.\n      $A$ and $B$ are disjoint since they do not have\n      any outcomes in common.}\n  \\label{disjointSets}\n\\end{figure}\n\nThe Addition Rule applies to both disjoint outcomes and disjoint events. The probability that one of the disjoint events $A$ or $B$ occurs is the sum of the separate probabilities:\n\\begin{align*}\nP(A\\text{ or }B) = P(A) + P(B) = 1/3 + 1/3 = 2/3\n\\end{align*}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n(a) Verify the probability of event $A$, $P(A)$,\nis $1/3$ using the Addition Rule.\n(b)~Do the same for event $B$.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~$P(A) = P($\\resp{1} or \\resp{2}$)\n    = P($\\resp{1}$) + P($\\resp{2}$)\n    = \\frac{1}{6} + \\frac{1}{6}\n    = \\frac{2}{6}\n    = \\frac{1}{3}$.\n    (b)~Similarly, $P(B) = 1/3$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{exerExaminingDisjointSetsABD}\n(a) Using Figure~\\ref{disjointSets} as a reference, what outcomes are represented by event $D$? (b) Are events $B$ and $D$ disjoint? (c) Are events $A$ and $D$ disjoint?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~Outcomes \\resp{2} and \\resp{3}. (b)~Yes, events $B$ and $D$ are disjoint because they share no outcomes. (c)~The events $A$ and $D$ share an outcome in common, \\resp{2}, and so are not disjoint.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIn Guided Practice~\\ref{exerExaminingDisjointSetsABD}, you confirmed $B$ and $D$ from Figure~\\ref{disjointSets} are disjoint. Compute the probability that event $B$ or event $D$~occurs.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Since $B$ and $D$ are disjoint events, use the Addition Rule: $P(B$ or $D) = P(B) + P(D) = \\frac{1}{3} + \\frac{1}{3} = \\frac{2}{3}$.}\n\n\\index{event|)}\n\\index{disjoint|)}\n\\index{mutually exclusive|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Probabilities when events are not disjoint}\n\nLet's consider calculations for two events that are not disjoint in the context of a \\indexthis{regular deck of 52 cards}{deck of cards}, represented in Figure~\\ref{deckOfCards}. If you are unfamiliar with the cards in a regular deck, please see the footnote.\\footnote{The 52 cards are split into four \\term{suits}: $\\clubsuit$ (club), {\\color{redcards}$\\diamondsuit$} (diamond), {\\color{redcards}$\\heartsuit$} (heart), $\\spadesuit$ (spade). Each suit has its 13 cards labeled: \\resp{2}, \\resp{3}, ..., \\resp{10}, \\resp{J} (jack), \\resp{Q} (queen), \\resp{K} (king), and \\resp{A} (ace). Thus, each card is a unique combination of a suit and a label, e.g. {\\color{redcards}\\resp{4$\\heartsuit$}} and \\resp{J$\\clubsuit$}. The 12 cards represented by the jacks, queens, and kings are called \\termsub{\\resp{face cards}}{face card}. The cards that are {\\color{redcards}$\\diamondsuit$} or {\\color{redcards}$\\heartsuit$} are typically colored {\\color{redcards}red} while the other two suits are typically colored black.}\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{lll lll lll lll l}\n\\resp{2$\\clubsuit$} & \\resp{3$\\clubsuit$} & \\resp{4$\\clubsuit$} & \\resp{5$\\clubsuit$} & \\resp{6$\\clubsuit$} & \\resp{7$\\clubsuit$} & \\resp{8$\\clubsuit$} & \\resp{9$\\clubsuit$} & \\resp{10$\\clubsuit$} & \\resp{J$\\clubsuit$} & \\resp{Q$\\clubsuit$} & \\resp{K$\\clubsuit$} & \\resp{A$\\clubsuit$}  \\\\\n\\color{redcards} \\resp{2$\\diamondsuit$} & \\color{redcards}\\resp{3$\\diamondsuit$} & \\color{redcards}\\resp{4$\\diamondsuit$} & \\color{redcards}\\resp{5$\\diamondsuit$} & \\color{redcards}\\resp{6$\\diamondsuit$} & \\color{redcards}\\resp{7$\\diamondsuit$} & \\color{redcards}\\resp{8$\\diamondsuit$} & \\color{redcards}\\resp{9$\\diamondsuit$} & \\color{redcards}\\resp{10$\\diamondsuit$} & \\color{redcards}\\resp{J$\\diamondsuit$} & \\color{redcards}\\resp{Q$\\diamondsuit$} & \\color{redcards}\\resp{K$\\diamondsuit$} & \\color{redcards}\\resp{A$\\diamondsuit$} \\\\\n\\color{redcards}\\resp{2$\\heartsuit$} & \\color{redcards}\\resp{3$\\heartsuit$} & \\color{redcards}\\resp{4$\\heartsuit$} & \\color{redcards}\\resp{5$\\heartsuit$} & \\color{redcards}\\resp{6$\\heartsuit$} & \\color{redcards}\\resp{7$\\heartsuit$} & \\color{redcards}\\resp{8$\\heartsuit$} & \\color{redcards}\\resp{9$\\heartsuit$} & \\color{redcards}\\resp{10$\\heartsuit$} & \\color{redcards}\\resp{J$\\heartsuit$} & \\color{redcards}\\resp{Q$\\heartsuit$} & \\color{redcards}\\resp{K$\\heartsuit$} & \\color{redcards}\\resp{A$\\heartsuit$} \\\\\n\\resp{2$\\spadesuit$} & \\resp{3$\\spadesuit$} & \\resp{4$\\spadesuit$} & \\resp{5$\\spadesuit$} & \\resp{6$\\spadesuit$} & \\resp{7$\\spadesuit$} & \\resp{8$\\spadesuit$} & \\resp{9$\\spadesuit$} & \\resp{10$\\spadesuit$} & \\resp{J$\\spadesuit$} & \\resp{Q$\\spadesuit$} & \\resp{K$\\spadesuit$} & \\resp{A$\\spadesuit$}\n\\end{tabular}\n\\caption{Representations of the 52 unique cards in a deck.}\n\\label{deckOfCards}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n(a) What is the probability that a randomly selected card is a diamond? (b)~What is the probability that a randomly selected card is a face card?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) There are 52 cards and 13 diamonds. If the cards are thoroughly shuffled, each card has an equal chance of being drawn, so the probability that a randomly selected card is a diamond is $P({\\color{redcards}\\diamondsuit}) = \\frac{13}{52} = 0.250$. (b)~Likewise, there are 12 face cards, so $P($face card$) = \\frac{12}{52} = \\frac{3}{13} = 0.231$.}\n\n\\term{Venn diagrams} are useful when outcomes can be categorized as ``in'' or ``out'' for two or three variables, attributes, or random processes. The Venn diagram in Figure~\\ref{cardsDiamondFaceVenn} uses a circle to represent diamonds and another to represent face cards. If a card is both a diamond and a face card, it falls into the intersection of the circles. If it is a diamond but not a face card, it will be in part of the left circle that is not in the right circle (and so on). The total number of cards that are diamonds is given by the total number of cards in the diamonds circle: $10+3=13$. The probabilities are also shown (e.g. $10/52 = 0.1923$).\n\n\\begin{figure}[h]\n\\centering\n\\Figure[A Venn diagram is shown. One circle is labeled \"Diamonds\" with a total proportion of 0.25 and a second circle is labeled \"Face cards\" with a total proportion 0.2308. The two circles overlap and share 3 cards, which have a proportion of 0.0577 of a deck of cards. The portion of the diamond cards circle that is not overlapping with the other circle is labeled with a \"10\" for 10 cards and a proportion of 0.1923. The portion of the face cards circle that is not overlapping the other circle is labeled with a \"9\" for 9 cards and a proportion of 0.2308. It is also noted in the figure that \"There are also 30 cards that are neither diamonds nor face cards\".]{0.65}{cardsDiamondFaceVenn}\n\\caption{A Venn diagram for diamonds and face cards.}\n\\label{cardsDiamondFaceVenn}\n\\end{figure}\n\nLet $A$ represent the event that a randomly selected card is a diamond and $B$ represent the event that it is a face card. How do we compute $P(A$ or $B)$? Events $A$ and $B$ are not disjoint -- the cards {\\color{redcards}$J\\diamondsuit$}, {\\color{redcards}$Q\\diamondsuit$}, and {\\color{redcards}$K\\diamondsuit$} fall into both categories -- so we cannot use the Addition Rule for disjoint events. Instead we use the Venn diagram. We start by adding the probabilities of the two events:\n\\begin{align*}\nP(A) + P(B)\n  = P({\\color{redcards}\\diamondsuit}) + P(\\text{face card})\n  = 13/52 + 12/52\n\\end{align*}\n\n\\D{\\newpage}\n\n\\noindent%\nHowever, the three cards that are in both events were counted twice, once in each probability. We must correct this double counting:\n\\begin{align*}\nP(A\\text{ or } B)\n  &= P({\\color{redcards}\\diamondsuit}\\text{ or face card}) \\\\\n  &= P({\\color{redcards}\\diamondsuit}) + P(\\text{face card})\n      - P({\\color{redcards}\\diamondsuit}\\text{ and face card}) \\\\\n  &= 13/52 + 12/52 - 3/52 \\\\\n  &= 22/52 = 11/26\n\\end{align*}\nThis equation is an example of the \\term{General Addition Rule}. \n\n\\begin{onebox}{General Addition Rule}\n  If $A$ and $B$ are any two events, disjoint or not, then\n  the probability that at least one of them will occur is\n  \\begin{align*}\n  P(A\\text{ or }B) = P(A) + P(B) - P(A\\text{ and }B)\n  \\end{align*}\n  where $P(A$ and $B)$ is the probability that both events occur.\n\\end{onebox}\n\n\\begin{tipBox}{\\tipBoxTitle{``or'' is inclusive}\nWhen we write ``or'' in statistics, we mean ``and/or'' unless we explicitly state otherwise. Thus, $A$ or $B$ occurs means $A$, $B$, or both $A$ and $B$ occur.}\n\\end{tipBox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n(a) If $A$ and $B$ are disjoint, describe why this implies $P(A$ and $B) = 0$. (b) Using part (a), verify that the General Addition Rule simplifies to the simpler Addition Rule for disjoint events if $A$ and $B$ are disjoint.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) If $A$ and $B$ are disjoint, $A$ and $B$ can never occur simultaneously. (b) If $A$ and $B$ are disjoint, then the last $P(A\\text{ and }B)$ term of in the General Addition Rule formula is 0 (see part (a)) and we are left with the Addition Rule for disjoint events.}\n\n\\index{data!loans|(}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{emailSpamNumberVennExer}\n% library(openintro); d <- loans_full_schema; table(d[,c(\"application_type\", \"homeownership\")]); table(d[,c(\"application_type\")]); table(d[,c(\"homeownership\")])\nIn the \\data{loans} data set describing 10,000 loans,\n1495 loans were from joint applications\n(e.g. a couple applied together),\n4789 applicants had a mortgage,\nand 950 had both of these characteristics.\nCreate a Venn diagram for this setup.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{%\n  \\begin{minipage}[t]{0.65\\textwidth}\n  Both the counts and corresponding {\\color{oiB}probabilities}\n  (e.g. $3839/10000 = 0.384$) are shown.\n  Notice that the number of loans represented in the left\n  circle corresponds to $3839 + 950 = 4789$, and the number\n  represented in the right circle is $950 + 545 = 1495$.\n  \\end{minipage}\\ %\n  \\begin{minipage}[c]{0.3\\textwidth}\n  \\hfill\\Figure[A Venn diagram is shown with two circles. The first is labeled with \"applicant had a mortgage\" and the second is labeled with \"joint application\", where the two circles partially overlap. For the \"applicant had a mortgage\" circle, the portion that is not overlapping the other circle shows a count of 3839 and a proportion of 0.384. The portion of the \"joint application\" circle that is not overlapping with the first circle is labeled 545 with a proportion 0.055. The overlapping portion of the circles is labeled with a count of 950 and a proportion of 0.095. The figure also notes, outside of either circle, that \"Other loans\" are represented by 10,000 minus 3,839 minus 950 minus 545, which calculates to 4666 and a proportion 0.467.]{}{loans_app_type_home_venn} \\vspace{-13mm}\n  \\end{minipage}}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n(a)~Use your Venn diagram from Guided Practice~\\ref{emailSpamNumberVennExer} to determine the\nprobability a randomly drawn loan from the \\data{loans}\ndata set is from a joint application where the couple had\na mortgage.\n(b)~What is the probability that the loan had either of\nthese attributes?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{%\n  (a)~The solution is represented by the intersection of\n  the two circles: 0.095.\n  (b)~This is the sum of the three disjoint probabilities shown\n  in the circles: $0.384 + 0.095 + 0.055 = 0.534$\n  (off by 0.001 due to a rounding error).}\n\n\\index{data!loans|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Probability distributions}\n\nA \\termsub{probability distribution}{probability!distribution} is a table of all disjoint outcomes and their associated probabilities. Figure~\\ref{diceProb} shows the probability distribution for the sum of two dice. \n\n\\begin{figure}[h] \\small\n\\centering\n\\begin{tabular}{l ccc ccc ccc cc}\n  \\hline\n  \\ \\vspace{-3mm} \\\\\nDice sum\\vspace{0.3mm} & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12  \\\\\nProbability & $\\frac{1}{36}$ & $\\frac{2}{36}$ & $\\frac{3}{36}$ & $\\frac{4}{36}$ & $\\frac{5}{36}$ & $\\frac{6}{36}$ & $\\frac{5}{36}$ & $\\frac{4}{36}$ & $\\frac{3}{36}$ & $\\frac{2}{36}$ & $\\frac{1}{36}$\\vspace{1mm} \\\\\n   \\hline\n\\end{tabular}\n\\caption{Probability distribution for the sum of two dice.}\n\\label{diceProb}\n\\end{figure}\n\n\\begin{onebox}{Rules for probability distributions}\nA probability distribution is a list of the possible outcomes with corresponding probabilities that satisfies three rules: \\vspace{-2mm}\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item The outcomes listed must be disjoint.\n\\item Each probability must be between 0 and 1.\n\\item The probabilities must total 1. \\vspace{1mm}\n\\end{enumerate}\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{usHouseholdIncomeDistsExercise}\nFigure~\\ref{usHouseholdIncomeDists} suggests three distributions for household income in the United States. Only one is correct. Which one must it be? What is wrong with the other two?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The probabilities of (a) do not sum to~1.\n    The second probability in (b) is negative.\n    This leaves~(c), which sure enough satisfies the\n    requirements of a distribution.\n    One of the three was said to be the actual\n    distribution of US household incomes,\n    so it must be~(c).}\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{r | cc cc}\n  \\hline\nIncome Range & \\$0-25k & \\$25k-50k & \\$50k-100k & \\$100k+ \\\\\n  \\hline\n(a)\\hspace{0.2mm}\t & 0.18 & 0.39 & 0.33 & 0.16 \\\\\n(b)\t\t\t\t & 0.38 & -0.27 & 0.52 & 0.37 \\\\\n(c)\\hspace{0.2mm}\t & 0.28 & 0.27 & 0.29 & 0.16 \\\\\n  \\hline\n\\end{tabular}\n\\caption{Proposed distributions of US household incomes (Guided Practice~\\ref{usHouseholdIncomeDistsExercise}).}\n\\label{usHouseholdIncomeDists}\n\\end{figure}\n\nChapter~\\ref{introductionToData} emphasized the importance of plotting data to provide quick summaries. Probability distributions can also be summarized in a bar plot. For instance, the distribution of US household incomes is shown in Figure~\\ref{usHouseholdIncomeDistBar} as a bar plot. %\\footnote{It is also possible to construct a distribution plot when income is not artificially binned into four groups. \\emph{Continuous} distributions are considered in Section~\\ref{contDist}.}\nThe probability distribution for the sum of two dice is shown in Figure~\\ref{diceProb} and plotted in Figure~\\ref{diceSumDist}.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A bar plot is shown for \"US Household Incomes\" with four income buckets. The vertical axis is labeled as \"Probability\". The first income bucket is \\$0 to \\$25,000 and the bar has a height corresponding to a proportion of about 0.28. The second income bucket is \\$25,000 to \\$50,000 and has a bar height corresponding to a proportion of about 0.27. The second income bucket is \\$50,000 to \\$100,000 and has a bar height corresponding to a proportion of about 0.28. The second income bucket is over \\$100,000 and has a bar height corresponding to a proportion of about 0.15.]{0.65}{usHouseholdIncomeDistBar}\n  \\caption{The probability distribution of US household income.}\n  \\label{usHouseholdIncomeDistBar}\n\\end{figure}\n\n\\begin{figure}\n  \\centering\n  \\Figure[A bar plot is shown for the sum of two dice, which can take values of 2, 3, 4, 5, and so on up to 12. The vertical axis is labeled as \"Probability\". The bar for 2 has a height of about 0.025, 3 a height of 0.055, 4 a height of 0.09, 5 a height of 0.115, 6 a height of 0.14, 7 a height of 0.165, 8 a height of 0.14, 9 a height of 0.115, 10 a height of 0.09, 11 a height of 0.055, and 12 a height of 0.025.]{0.67}{diceSumDist}\n  \\caption{The probability distribution of the sum of two dice.}\n  \\label{diceSumDist}\n\\end{figure}\n\nIn these bar plots, the bar heights represent the probabilities of outcomes. If the outcomes are numerical and discrete, it is usually (visually) convenient to make a bar plot that resembles a histogram, as in the case of the sum of two dice. Another example of plotting the bars at their respective locations is shown in Figure~\\ref{bookCostDist} on page~\\pageref{bookCostDist}.\n\n\\subsection{Complement of an event}\n\nRolling a die produces a value in the set $\\{$\\resp{1}, \\resp{2}, \\resp{3}, \\resp{4}, \\resp{5}, \\resp{6}$\\}$. This set of all possible outcomes is called the \\term{sample space} ($S$)\\index{S@$S$} for rolling a die. We often use the sample space to examine the scenario where an event does not occur.\n\nLet $D=\\{$\\resp{2}, \\resp{3}$\\}$ represent the event that the outcome of a die roll is \\resp{2} or \\resp{3}. Then the \\term{complement} of $D$ represents all outcomes in our sample space that are not in $D$, which is denoted by $D^c = \\{$\\resp{1}, \\resp{4}, \\resp{5}, \\resp{6}$\\}$. That is, $D^c$ is the set of all possible outcomes not already included in $D$. Figure~\\ref{complementOfD} shows the relationship between $D$, $D^c$, and the sample space $S$. \n\n\\begin{figure}[hht]\n  \\centering\n  \\Figure[The numbers of 1, 2, 3, 4, 5, and 6 are shown in order. The numbers 2 and 3 are encircled and labeled \"D\". The numbers 1, 4, 5, and 6 are encircled and labeled \"D-to-the-C\" for the complement of D. Then there is a larger encircling of all of the numbers that his labeled \"S\" for the sample space.]{0.55}{complementOfD}\n  \\caption{Event $D=\\{$\\resp{2}, \\resp{3}$\\}$ and its complement,\n      $D^c = \\{$\\resp{1}, \\resp{4}, \\resp{5}, \\resp{6}$\\}$.\n      $S$~represents the sample space, which is the set of\n      all possible outcomes.}\n  \\label{complementOfD}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n(a) Compute $P(D^c) = P($rolling a \\resp{1}, \\resp{4}, \\resp{5}, or \\resp{6}$)$. (b) What is $P(D) + P(D^c)$?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~The outcomes are disjoint and each has probability $1/6$, so the total probability is $4/6=2/3$. (b)~We can also see that $P(D)=\\frac{1}{6} + \\frac{1}{6} = 1/3$. Since $D$ and $D^c$ are disjoint, $P(D) + P(D^c) = 1$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nEvents $A=\\{$\\resp{1}, \\resp{2}$\\}$ and $B=\\{$\\resp{4}, \\resp{6}$\\}$ are shown in Figure~\\ref{disjointSets} on page~\\pageref{disjointSets}. (a) Write out what $A^c$ and $B^c$ represent. (b)~Compute $P(A^c)$ and $P(B^c)$. (c)~Compute $P(A)+P(A^c)$ and $P(B)+P(B^c)$.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Brief solutions: (a)~$A^c=\\{$\\resp{3}, \\resp{4}, \\resp{5}, \\resp{6}$\\}$ and $B^c=\\{$\\resp{1}, \\resp{2}, \\resp{3}, \\resp{5}$\\}$. (b)~Noting that each outcome is disjoint, add the individual outcome probabilities to get $P(A^c)=2/3$ and $P(B^c)=2/3$. (c)~$A$~and~$A^c$ are disjoint, and the same is true of $B$~and~$B^c$. Therefore, $P(A) + P(A^c) = 1$ and $P(B) + P(B^c) = 1$.}\n\n\\D{\\newpage}\n\nA complement of an event $A$ is constructed to have two very important properties: (i) every possible outcome not in $A$ is in $A^c$, and (ii) $A$ and $A^c$ are disjoint. Property (i) implies\n\\begin{align*}\nP(A\\text{ or }A^c) = 1\n\\end{align*}\nThat is, if the outcome is not in $A$, it must be represented in $A^c$. We use the Addition Rule for disjoint events to apply Property (ii):\n\\begin{align*}\nP(A\\text{ or }A^c) = P(A) + P(A^c)\n\\end{align*}\nCombining the last two equations yields a very useful\nrelationship between the probability of an event and\nits complement.\n\n\\begin{onebox}{Complement}\n  The complement of event $A$ is denoted $A^c$, and $A^c$\n  represents all outcomes not in~$A$. $A$ and $A^c$ are\n  mathematically related:\n  \\begin{align*}\n  P(A) + P(A^c) = 1, \\quad\\text{i.e.}\\quad P(A) = 1-P(A^c)\n  \\end{align*}\n\\end{onebox}\n\nIn simple examples, computing $A$ or $A^c$ is feasible in a few steps. However, using the complement can save a lot of time as problems grow in complexity.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nLet $A$ represent the event where we roll two dice and their total is less than \\resp{12}. (a)~What does the event $A^c$ represent? (b)~Determine $P(A^c)$ from Figure~\\ref{diceProb} on page~\\pageref{diceProb}. (c)~Determine $P(A)$.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~The complement of $A$: when the total is equal to \\resp{12}. (b)~$P(A^c) = 1/36$. (c)~Use the probability of the complement from part (b), $P(A^c) = 1/36$, and the equation for the complement: $P($less than \\resp{12}$) = 1 - P($\\resp{12}$) = 1 - 1/36 = 35/36$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nFind the following probabilities for rolling two dice:\\footnotemark\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}\n\\item The sum of the dice is \\emph{not} \\resp{6}. \n\\item The sum is at least \\resp{4}.\n    That is, determine the probability of the event\n    $B = \\{$\\resp{4}, \\resp{5}, ..., \\resp{12}$\\}$.\n\\item The sum is no more than \\resp{10}.\n    That is, determine the probability of the event\n    $D=\\{$\\resp{2}, \\resp{3}, ..., \\resp{10}$\\}$.\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~First find $P($\\resp{6}$)=5/36$, then use the complement: $P($not \\resp{6}$) = 1 - P($\\resp{6}$) = 31/36$.\n\n(b)~First find  the complement, which requires much less effort: $P($\\resp{2} or \\resp{3}$)=1/36+2/36=1/12$. Then calculate $P(B) = 1-P(B^c) = 1-1/12 = 11/12$.\n\n(c)~As before, finding the complement is the clever way to determine $P(D)$. First find $P(D^c) = P($\\resp{11} or \\resp{12}$)=2/36 + 1/36=1/12$. Then calculate $P(D) = 1 - P(D^c) = 11/12$.}\n\n\n\\subsection{Independence}\n\\label{probabilityIndependence}\n\nJust as variables and observations can be independent, random processes can be independent, too. Two processes are \\term{independent} if knowing the outcome of one provides no useful information about the outcome of the other. For instance, flipping a coin and rolling a die are two independent processes -- knowing the coin was heads does not help determine the outcome of a die roll. On the other hand, stock prices usually move up or down together, so they are not independent.\n\nExample~\\ref{probOf2Ones} provides a basic example of two independent processes: rolling two dice. We want to determine the probability that both will be \\resp{1}. Suppose one of the dice is red and the other white. If the outcome of the red die is a \\resp{1}, it provides no information about the outcome of the white die. We first encountered this same question in Example~\\ref{probOf2Ones} (page~\\pageref{probOf2Ones}), where we calculated the probability using the following reasoning: $1/6$ of the time the red die is a \\resp{1}, and $1/6$ of \\emph{those} times the white die will also be \\resp{1}. This is illustrated in Figure~\\ref{indepForRollingTwo1s}. Because the rolls are independent, the probabilities of the corresponding outcomes can be multiplied to get the final answer: $(1/6)\\times(1/6)=1/36$. This can be generalized to many independent processes. \n\n\\begin{figure}[hht]\n\\centering\n\\Figure[A black rectangle outlines the graphic and has a label of \"All rolls\". Inside that rectangle, a vertical strip of the rectangle about one-sixths wide is shaded and labeled with \"one-sixth of the first rolls are a 1\". A horizontal section representing about one-sixth of that vertical slice is shaded differently and labeled \"one-sixth of those times where the first roll is a 1 the second roll is also a 1\".]{0.6}{indepForRollingTwo1s}\n\\caption{$1/6$ of the time, the first roll is a \\resp{1}. Then $1/6$ of \\emph{those} times, the second roll will also be a \\resp{1}.}\n\\label{indepForRollingTwo1s}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{What if there was also a blue die independent of the other two? What is the probability of rolling the three dice and getting all \\resp{1}s?}\\label{threeDice}\nThe same logic applies from Example~\\ref{probOf2Ones}. If $1/36$ of the time the white and red dice are both \\resp{1}, then $1/6$ of \\emph{those} times the blue die will also be \\resp{1}, so multiply:\n{\\begin{align*}\nP(white=\\text{\\small\\resp{1} and } red=\\text{\\small\\resp{1} and } blue=\\text{\\small\\resp{1}})\n\t&= P(white=\\text{\\small\\resp{1}})\\times P(red=\\text{\\small\\resp{1}})\\times P(blue=\\text{\\small\\resp{1}}) \\\\\n\t&= (1/6)\\times (1/6)\\times (1/6)\n\t= 1/216\n\\end{align*}} \\vspace{-7mm}\n\\end{nexample}\n\\end{examplewrap}\n\nExample~\\ref{threeDice} illustrates what is called the Multiplication Rule for independent processes. \n\n\\begin{onebox}{Multiplication Rule for independent processes}\n  \\index{Multiplication Rule|textbf}%\n  If $A$ and $B$ represent events from two different and\n  independent processes, then the probability that both $A$\n  and $B$ occur can be calculated as the product of their\n  separate probabilities:\n  \\begin{align*}\n  P(A \\text{ and }B) = P(A) \\times  P(B)\n  \\end{align*}\n  Similarly, if there are $k$ events $A_1$, ..., $A_k$\n  from $k$ independent processes, then the probability\n  they all occur is\n  \\begin{align*}\n  P(A_1) \\times  P(A_2)\\times  \\cdots \\times  P(A_k)\n  \\end{align*}\\vspace{-6mm}\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{ex2Handedness}\nAbout 9\\% of people are left-handed. Suppose 2 people are selected at random from the U.S. population. Because the sample size of 2 is very small relative to the population, it is reasonable to assume these two people are independent. (a)~What is the probability that both are left-handed? (b)~What is the probability that both are right-handed?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) The probability the first person is left-handed is $0.09$, which is the same for the second person. We apply the Multiplication Rule for independent processes to determine the probability that both will be left-handed: $0.09\\times 0.09 = 0.0081$.\n\n(b) It is reasonable to assume the proportion of people who are ambidextrous (both right- and left-handed) is nearly 0, which results in $P($right-handed$)=1-0.09=0.91$. Using the same reasoning as in part~(a), the probability that both will be right-handed is $0.91\\times 0.91 = 0.8281$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{ex5Handedness}%\nSuppose 5 people are selected at random.\\footnotemark\\vspace{-1.5mm}\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item[(a)] What is the probability that all are right-handed?\n\\item[(b)] What is the probability that all are left-handed?\n\\item[(c)] What is the probability that not all of the people are right-handed?\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~The abbreviations \\resp{RH} and \\resp{LH} are used for right-handed and left-handed, respectively. Since each are independent, we apply the Multiplication Rule for independent processes:\n\\begin{align*}\nP(\\text{all five are \\resp{RH}})\n&= P(\\text{first = \\resp{RH}, second = \\resp{RH}, ..., fifth = \\resp{RH}}) \\\\\n&= P(\\text{first = \\resp{RH}})\\times P(\\text{second = \\resp{RH}})\\times  \\dots \\times P(\\text{fifth = \\resp{RH}}) \\\\\n&= 0.91\\times 0.91\\times 0.91\\times 0.91\\times 0.91 = 0.624\n\\end{align*}\n\n(b)~Using the same reasoning as in~(a), $0.09\\times 0.09\\times 0.09\\times 0.09\\times 0.09 = 0.0000059$\n\n(c)~Use the complement, $P($all five are \\resp{RH}$)$, to answer this question:\n\\begin{align*}\nP(\\text{not all \\resp{RH}})\n\t= 1 - P(\\text{all \\resp{RH}})\n\t= 1 - 0.624 = 0.376\n\\end{align*}}\n\nSuppose the variables \\var{handedness} and\n\\var{sex} are independent,\ni.e. knowing someone's \\var{sex} provides no useful\ninformation about their \\var{handedness} and vice-versa.\nThen we can compute whether a randomly selected person is\nright-handed and female\\footnote{The actual proportion of\n  the U.S. population that is \\resp{female} is about 50\\%,\n  and so we use 0.5 for the probability of sampling a woman.\n  However, this probability does differ in other countries.}\nusing the Multiplication Rule:\n\\begin{align*}\nP(\\text{right-handed and female})\n    &= P(\\text{right-handed}) \\times  P(\\text{female}) \\\\\n    &= 0.91 \\times  0.50 = 0.455\n\\end{align*}\n\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThree people are selected at random.\\footnotemark \\vspace{-1.5mm}\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item[(a)] What is the probability that the first person is male and right-handed?\n\\item[(b)] What is the probability that the first two people are male and right-handed?.\n\\item[(c)] What is the probability that the third person is female and left-handed?\n\\item[(d)] What is the probability that the first two people are male and right-handed and the third person is female and left-handed?\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Brief answers are provided. (a)~This can be written in probability notation as $P($a randomly selected person is male and right-handed$)=0.455$. (b)~0.207. (c)~0.045. (d)~0.0093.}\n\nSometimes we wonder if one outcome provides useful information about another outcome. The question we are asking is, are the occurrences of the two events independent? We say that two events $A$ and $B$ are independent if they satisfy\n$P(A \\text{ and }B) = P(A) \\times  P(B)$.\n\n\\begin{examplewrap}\n\\begin{nexample}{If we shuffle up a deck of cards and draw one, is the event that the card is a heart independent of the event that the card is an ace?}\nThe probability the card is a heart is $1/4$ and the probability that it is an ace is $1/13$. The probability the card is the ace of hearts is $1/52$.\nWe check whether $P(A \\text{ and }B) = P(A) \\times  P(B)$\nis satisfied:\n\\begin{align*}\nP({\\color{redcards}\\heartsuit})\\times P(\\text{ace}) = \\frac{1}{4}\\times \\frac{1}{13} = \\frac{1}{52} \n\t\t\t\t\t= P({\\color{redcards}\\heartsuit}\\text{ and ace})\n\\end{align*}\nBecause the equation holds, the event that the card is a heart and the event that the card is an ace are independent events.\n\\end{nexample}\n\\end{examplewrap}\n\n\n{\\input{ch_probability/TeX/defining_probability.tex}}\n\n\n\n\n%_________________\n\\section{Conditional probability}\n\\label{conditionalProbabilitySection}\n\nThere can be rich relationships between two or more\nvariables that are useful to understand.\nFor example a car insurance company will consider\ninformation about a person's driving history to assess\nthe risk that they will be responsible for an accident.\nThese types of relationships are the realm of conditional\nprobabilities.\n\n\n\\subsection{Exploring probabilities with a contingency table}\n\n\\index{data!photo\\_classify|(}\n\n\\newcommand{\\fashN}{1822}\n% In order of ML, then Human\n\\newcommand{\\fashYY}{197}\n\\newcommand{\\fashYN}{22}\n\\newcommand{\\fashYA}{219}\n\\newcommand{\\fashNY}{112}\n\\newcommand{\\fashNN}{1491}\n\\newcommand{\\fashNA}{1603}\n\\newcommand{\\fashAY}{309}\n\\newcommand{\\fashAN}{1513}\n\\newcommand{\\fashAA}{\\fashN{}}\n%\\newcommand{\\fashPYY}{}\n%\\newcommand{\\fashPYN}{}\n%\\newcommand{\\fashPNY}{}\n%\\newcommand{\\fashPNN}{}\n%\\newcommand{\\fashPYA}{0.12}\n%\\newcommand{\\fashPNA}{0.88}\n%\\newcommand{\\fashPAY}{}\n%\\newcommand{\\fashPAN}{}\n%\\newcommand{\\fashPYCY}{}\n%\\newcommand{\\fashPYCN}{}\n%\\newcommand{\\fashPNCY}{}\n%\\newcommand{\\fashPNCN}{}\n\\newcommand{\\fashCYPY}{0.96}\n\\newcommand{\\fashCYPN}{0.04}\n\\newcommand{\\fashCNPY}{0.07}\n\\newcommand{\\fashCNPN}{0.93}\n\nThe \\data{photo\\us{}classify} data set represents\na classifier a sample of \\fashN{} photos from a photo sharing website.\nData scientists have been working to improve a classifier for\nwhether the photo is about fashion or not, and these 1822 photos\nrepresent a test for their classifier.\nEach photo gets two classifications:\nthe first is called \\var{mach\\us{}learn} and gives\na classification from a machine\nlearning~(ML)\\index{machine learning (ML)} system of\neither \\resp{pred\\us{}fashion} or \\resp{pred\\us{}not}.\nEach of these \\fashN{} photos have also been classified carefully\nby a team of people, which we take to be the source of truth;\nthis variable is called \\var{truth} and takes values\n\\resp{fashion} and \\resp{not}.\nFigure~\\ref{contTableOfFashionPhotos} summarizes the results.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{ll ccc rr}\n&& \\multicolumn{2}{c}{\\var{truth}} & \\hspace{1cm} &  \\\\\n\\cline{3-4}\n&& \\resp{fashion} & \\resp{not} & Total  \\\\\n\\cline{2-5}\n& \\resp{pred\\us{}fashion} &\n    \\fashYY{} & \\fashYN{} & \\fashYA{} \\\\\n\\raisebox{1.5ex}[0pt]{\\var{mach\\us{}learn}}\n    & \\resp{pred\\us{}not} \\hspace{0.5cm} &\n    \\fashNY{} & \\fashNN{} & \\fashNA{}   \\\\\n\\cline{2-5}\n& Total & \\fashAY{} & \\fashAN{} & \\fashN{} \\\\\n\\end{tabular}\n\\caption{Contingency table summarizing the\n    \\data{photo\\us{}classify} data set.}\n\\label{contTableOfFashionPhotos}\n\\end{figure}\n% library(openintro); table(photo_classify)\n\n\\begin{figure}[ht]\n  \\centering\n  \\Figure[A Venn diagram is shown, using boxes instead of circles, for the two categories of \"ML Predicts Fashion\" and \"Fashion Photos\" that partially overlap. The section of the rectangle for ML Predicts Fashion that is non-overlapping is labeled with 0.01. The section of the rectangle for Fashion Photos that is non-overlapping is labeled with 0.06. The overlapping section is labeled with 0.11. Outside of the rectangles is a label for \"Neither\" with a value 0.82.]{0.65}{photoClassifyVenn}\n  \\caption{A Venn diagram using boxes for the\n      \\data{photo\\us{}classify} data set.}\n  \\label{photoClassifyVenn}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{If a photo is actually about fashion,\n    what is the chance the ML classifier correctly identified\n    the photo as being about fashion?}\n  We can estimate this probability using the data.\n  Of the \\fashAY{} fashion photos,\n  the ML algorithm correctly classified \\fashYY{} of the photos:\n\\begin{align*}\nP(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}\n    given \\var{truth} is \\resp{fashion}})\n  = \\frac{\\fashYY{}}{\\fashAY{}}\n  = 0.638\n\\end{align*}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{We sample a photo from the data set\n    and learn the ML algorithm predicted this photo\n    was not about fashion.\n    What is the probability that it was incorrect and\n    the photo is about fashion?}\n  If the ML classifier suggests a photo is not about fashion,\n  then it comes from the second row in the data set.\n  Of~these \\fashNA{} photos, \\fashNY{} were actually\n  about fashion:\n\\begin{align*}\nP(\\text{\\var{truth} is \\resp{fashion}\n    given \\var{mach\\us{}learn} is \\resp{pred\\us{}not}})\n  = \\frac{\\fashNY{}}{\\fashNA{}}\n  = 0.070\n\\end{align*}\n\\end{nexample}\n\\end{examplewrap}\n\n\\subsection{Marginal and joint probabilities}\n\\label{marginalAndJointProbabilities}\n\n\\index{marginal probability|(}\n\\index{joint probability|(}\n\nFigure~\\ref{contTableOfFashionPhotos} includes row and\ncolumn totals for each variable separately in the\n\\data{photo\\us{}classify} data set.\nThese totals represent\n\\termsub{marginal probabilities}{marginal probability}\nfor the sample, which are the probabilities based on a\nsingle variable without regard to any other variables.\nFor instance, a probability based solely on the\n\\var{mach\\us{}learn} variable is a marginal probability:\n\\begin{align*}\nP(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}})\n    = \\frac{\\fashYA{}}{\\fashN{}}\n    = 0.12\n\\end{align*}\nA probability of outcomes for two or more variables\nor processes is called a\n\\termsub{joint \\mbox{probability}}{joint probability}:\n\\begin{align*}\nP(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}\n    and \\var{truth} is \\resp{fashion}})\n  = \\frac{\\fashYY{}}{\\fashN{}}\n  = 0.11\n\\end{align*}\nIt is common to substitute a comma for ``and'' in a joint\nprobability, although using either the word ``and'' or a\ncomma is acceptable:\n\\begin{center}\n$P(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion},\n    \\var{truth} is \\resp{fashion}})$ \\\\[2mm]\nmeans the same thing as \\\\[2mm]\n$P(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}\n    and \\var{truth} is \\resp{fashion}})$\n\\end{center}\n\n\\begin{onebox}{Marginal and joint probabilities}\n  If a probability is based on a single variable,\n  it is a \\emph{\\hiddenterm{marginal probability}}.\n  The probability of outcomes for two or more variables\n  or processes is called a \\emph{\\hiddenterm{joint probability}}.\n\\end{onebox}\n\nWe use \\term{table proportions} to summarize joint probabilities\nfor the \\data{photo\\us{}classify} sample.\nThese proportions are computed by dividing each count in\nFigure~\\ref{contTableOfFashionPhotos} by the table's total,\n\\fashN{}, to obtain the proportions in\nFigure~\\ref{photoClassifyProbTable}.\nThe joint probability distribution of the \\var{mach\\us{}learn}\nand \\var{truth} variables is shown in\nFigure~\\ref{photoClassifyDistribution}.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l rr r}\n\\hline\n& \\var{truth}: \\resp{fashion} &\n    \\var{truth}: \\resp{not} & Total  \\\\\n\\hline\n\\var{mach\\us{}learn}: \\resp{pred\\us{}fashion} \\hspace{0.5cm}\n    & 0.1081 & 0.0121 & 0.1202 \\\\\n\\var{mach\\us{}learn}: \\resp{pred\\us{}not}\n    & 0.0615 & 0.8183 & 0.8798  \\\\\n\\hline\nTotal & 0.1696 & 0.8304 & 1.00 \\\\\n\\hline\n\\end{tabular}\n\\caption{Probability table summarizing the\n    \\var{photo\\us{}classify} data set.}\n\\label{photoClassifyProbTable}\n\\end{figure}\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l c}\n  \\hline\nJoint outcome & Probability \\\\\n  \\hline\n\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}\n    and \\var{truth} is \\resp{fashion} & 0.1081 \\\\\n\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}\n    and \\var{truth} is \\resp{not} & 0.0121 \\\\\n\\var{mach\\us{}learn} is \\resp{pred\\us{}not}\n    and \\var{truth} is \\resp{fashion} & 0.0615 \\\\\n\\var{mach\\us{}learn} is \\resp{pred\\us{}not}\n    and \\var{truth} is \\resp{not} & 0.8183 \\\\\n   \\hline\nTotal & 1.0000 \\\\\n\\hline\n\\end{tabular}\n\\caption{Joint probability distribution for the \\data{photo\\us{}classify} data set.}\n\\label{photoClassifyDistribution}\n\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nVerify Figure~\\ref{photoClassifyDistribution} represents\na probability distribution: events are disjoint,\nall probabilities are non-negative, and the probabilities\nsum to~1.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Each of the four outcome combination are disjoint,\n   all probabilities are indeed non-negative, and the sum of\n   the probabilities is $0.1081 + 0.0121 + 0.0615 + 0.8183 = 1.00$.}\n\nWe can compute marginal probabilities using joint probabilities\nin simple cases.\nFor example, the probability a randomly selected photo from the\ndata set is about fashion is found by summing the outcomes where\n\\var{truth} takes value \\resp{fashion}:%\n\\index{marginal probability|)}\\index{joint probability|)}\n\\newcommand{\\ultruthfashion}[0]\n    {\\underline{\\var{truth} is \\resp{fashion}}}%\n\\begin{align*}\nP(\\text{\\ultruthfashion{}})\n  &= P(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}\n        and \\ultruthfashion{}}) \\\\\n    & \\qquad + P(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}not}\n        and \\ultruthfashion{}}) \\\\\n  &= 0.1081 + 0.0615 \\\\\n  &= 0.1696\n\\end{align*}\n\n\n\\subsection{Defining conditional probability}\n\n\\index{conditional probability|(}\n\nThe ML classifier predicts whether a photo is about fashion,\neven if it is not perfect.\nWe would like to better understand how to use information\nfrom a variable like \\var{mach\\us{}learn} to improve our\nprobability estimation of a second variable, which in this\nexample is \\var{truth}.\n\nThe probability that a random photo from the data set is about\nfashion is about 0.17.\nIf we knew the machine learning classifier predicted the\nphoto was about fashion, could we get a better estimate of the\nprobability the photo is actually about fashion?\nAbsolutely.\nTo do so, we limit our view to only those \\fashYA{} cases\nwhere the ML classifier predicted that the photo was about\nfashion and look at the fraction where the photo was actually\nabout fashion:\n\\begin{align*}\nP(\\text{\\var{truth} is \\resp{fashion} given\n    \\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}})\n  = \\frac{\\fashYY{}}{\\fashYA{}}\n  = 0.900\n\\end{align*}\nWe call this a \\term{conditional probability} because\nwe computed the probability under a condition:\nthe ML classifier prediction said the photo was about fashion.\n\nThere are two parts to a conditional probability,\nthe \\term{outcome of interest} and the \\term{condition}.\nIt is useful to think of the condition as information we know\nto be true, and this information usually can be described as\na known outcome or~event.\nWe generally separate the text inside our probability notation\ninto the outcome of interest and the condition with a\nvertical bar:\n\\begin{align*}\n&& P(\\text{\\var{truth} is \\resp{fashion} given\n    \\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}}) \\\\\n&& \\quad = P(\\text{\\var{truth} is \\resp{fashion}\\ }|\n    \\text{\\ \\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}})\n  = \\frac{\\fashYY{}}{\\fashYA{}}\n  = 0.900\n\\end{align*}\nThe vertical bar ``$|$'' is read as \\emph{given}.\n\n\\D{\\newpage}\n\nIn the last equation, we computed the probability a photo\nwas about fashion based on the condition that the ML algorithm\npredicted it was about fashion as a fraction:\n\\begin{align*}\n& P(\\text{\\var{truth} is \\resp{fashion}\\ }|\n    \\text{\\ \\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}}) \\\\\n  &\\quad = \\frac{\\text{\\# cases where \\var{truth} is \\resp{fashion}\n       and \\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}}}\n     {\\text{\\# cases where \\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}}} \\\\\n  &\\quad = \\frac{\\fashYY{}}{\\fashYA{}}\n      = 0.900\n\\end{align*}\nWe considered only those cases that met the condition,\n\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}, and then\nwe computed the ratio of those cases that satisfied our\noutcome of interest, photo was actually about fashion.\n\nFrequently, marginal and joint probabilities are provided\ninstead of count data.\nFor example, disease rates are commonly listed in percentages\nrather than in a count format.\nWe would like to be able to compute conditional probabilities\neven when no counts are available, and we use the last equation\nas a template to understand this technique.\n\nWe considered only those cases that satisfied the condition,\nwhere the ML algorithm predicted fashion.\nOf these cases, the conditional probability was the\nfraction representing the outcome of interest, that the\nphoto was about fashion.\nSuppose we were provided only the information in\nFigure~\\ref{photoClassifyProbTable}, i.e. only probability data.\nThen if we took a sample of 1000 photos, we would anticipate\nabout 12.0\\% or $0.120\\times 1000 = 120$ would be predicted to be\nabout fashion (\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}).\nSimilarly, we would expect about 10.8\\% or\n$0.108\\times 1000 = 108$ to meet both the information criteria\nand represent our outcome of interest.\nThen the conditional probability can be computed as\n\\begin{align*}\n&P(\\text{\\var{truth} is \\resp{fashion}}\\ |\\ \n    \\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}}) \\\\\n  &= \\frac{\\text{\\# (\\var{truth} is \\resp{fashion}\n      and \\var{mach\\us{}learn} is \\resp{pred\\us{}fashion})}}\n    {\\text{\\# (\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion})}} \\\\\n  &= \\frac{108}{120}\n\t\t= \\frac{0.108}{0.120}\n\t\t= 0.90\n\\end{align*}\nHere we are examining exactly the fraction of two probabilities,\n0.108 and 0.120, which we can write as\n\\begin{align*}\nP(\\text{\\var{truth} is \\resp{fashion} and\n    \\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}})\n\\quad\\text{and}\\quad\nP(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}}).\n\\end{align*}\nThe fraction of these probabilities is an example of the\ngeneral formula for conditional probability.\n\n\\begin{onebox}{Conditional probability}\n  The conditional probability of outcome $A$\n  given condition $B$ is computed as the following:\n  \\begin{align*}\n  P(A | B) = \\frac{P(A\\text{ and }B)}{P(B)}\n  \\end{align*}\n\\end{onebox}\n\n%\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{fashionProbOfMLNotGivenTruthNot}%\n(a) Write out the following statement in conditional\nprobability notation:\n``\\emph{The probability that the ML prediction was correct,\nif the photo was about fashion}''.\nHere the condition is now based on the photo's\n\\var{truth} status, not the ML algorithm. \\\\[1mm]\n(b)~Determine the probability from part (a).\nTable~\\vref{photoClassifyProbTable} may be helpful.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) If the photo is about fashion and the\n  ML algorithm prediction was correct, then the ML algorithm\n  my have a value of \\resp{pred\\us{}fashion}:\n  \\begin{align*}\n  P(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}}\\ |\n      \\ \\text{\\var{truth} is \\resp{fashion}})\n  \\end{align*}\n  (b)~The equation for conditional probability indicates we\n  should first find \\\\\n  $P(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}\n    and \\var{truth} is \\resp{fashion}}) = 0.1081$\n  and $P(\\text{\\var{truth} is \\resp{fashion}}) = 0.1696$. \\\\\n  Then the ratio represents the conditional probability:\n  $0.1081 / 0.1696 = 0.6374$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{whyCondProbSumTo1}%\n(a)~Determine the probability that the algorithm is incorrect\nif it is known the photo is about fashion. \\\\[1mm]\n(b)~Using the answers from part~(a) and\nGuided Practice~\\ref{fashionProbOfMLNotGivenTruthNot}(b),\ncompute\n\\begin{align*}\n&P(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}fashion}}\n    \\ |\\ \\text{\\var{truth} is \\resp{fashion}}) \\\\\n&\\qquad  +\\ P(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}not}}\n    \\ |\\ \\text{\\var{truth} is \\resp{fashion}})\n\\end{align*}\n(c)~Provide an intuitive argument to explain why the sum\nin~(b) is~1.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~This probability is\n  $\\frac{P(\\text{\\var{mach\\us{}learn} is \\resp{pred\\us{}not},\n      \\var{truth} is \\resp{fashion}})}\n    {P(\\text{\\var{truth} is \\resp{fashion}})}\n  = \\frac{0.0615}{0.1696} = 0.3626$.\n  (b)~The total equals~1.\n  (c)~Under the condition the photo is about fashion,\n      the ML algorithm must have either predicted it was\n      about fashion or predicted it was not about fashion.\n      The complement still works for conditional probabilities,\n      provided the probabilities are conditioned on the same\n      information.}\n\n\\index{conditional probability|)}\n\\index{data!photo\\_classify|)}\n\n\n\\subsection{Smallpox in Boston, 1721}\n\n\\index{data!smallpox|(}\n\nThe \\data{smallpox} data set provides a sample of 6,224 individuals from the year 1721 who were exposed to smallpox in Boston.\nDoctors at the time believed that inoculation, which involves exposing a person to the disease in a controlled form, could reduce the likelihood of death.\n\nEach case represents one person with two variables: \\var{inoculated} and \\var{result}. The variable \\var{inoculated} takes two levels: \\resp{yes} or \\resp{no}, indicating whether the person was inoculated or not. The variable \\var{result} has outcomes \\resp{lived} or \\resp{died}. These data are summarized in Tables~\\ref{smallpoxContingencyTable} and~\\ref{smallpoxProbabilityTable}.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{ll rr r}\n& & \\multicolumn{2}{c}{inoculated} & \\\\\n\\cline{3-4}\n& & \\resp{yes} & \\resp{no} & Total  \\\\\n\\cline{2-5}\n\t\t& \\resp{lived}     & 238 & 5136 & 5374 \\\\\n\\raisebox{1.5ex}[0pt]{\\var{result}} &  \\resp{died} \\hspace{0.5cm} & 6 & 844 & 850  \\\\\n\\cline{2-5}\n\t& Total & 244 & 5980 & 6224 \\\\\n\\end{tabular}\n\\caption{Contingency table for the \\data{smallpox} data set.}\n\\label{smallpoxContingencyTable}\n\\end{figure}\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{ll rr r}\n& & \\multicolumn{2}{c}{inoculated} & \\\\\n\\cline{3-4}\n& & \\resp{yes} & \\resp{no} & Total  \\\\\n   \\cline{2-5}\n & \\resp{lived}     & 0.0382 & 0.8252 & 0.8634 \\\\\n\\raisebox{1.5ex}[0pt]{\\var{result}} & \\resp{died} \\hspace{0.5cm} & 0.0010 & 0.1356  & 0.1366  \\\\\n   \\cline{2-5}\n& Total & 0.0392 & 0.9608 & 1.0000 \\\\\n\\end{tabular}\n\\caption{Table proportions for the \\data{smallpox} data, computed by dividing each count by the table total, 6224.}\n\\label{smallpoxProbabilityTable}\n\\end{figure}\n\n%\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{probDiedIfNotInoculated}\nWrite out, in formal notation, the probability a randomly selected person who was not inoculated died from smallpox, and find this \\mbox{probability.}\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$P($\\var{result} = \\resp{died} $|$ \\var{inoculated} = \\resp{no}$) = \\frac{P(\\text{\\var{result} = \\resp{died} and \\var{inoculated} = \\resp{no}})}{P(\\text{\\var{inoculated} = \\resp{no}})} = \\frac{0.1356}{0.9608} = 0.1411$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nDetermine the probability that an inoculated person died from smallpox. How does this result compare with the result of Guided Practice~\\ref{probDiedIfNotInoculated}?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$P($\\var{result} = \\resp{died} $|$ \\var{inoculated} = \\resp{yes}$) = \\frac{P(\\text{\\var{result} = \\resp{died} and \\var{inoculated} = \\resp{yes}})}{P(\\text{\\var{inoculated} = \\resp{yes}})} = \\frac{0.0010}{0.0392} = 0.0255$ (if we avoided rounding errors, we'd get $6 / 244 = 0.0246$). The death rate for individuals who were inoculated is only about 1~in~40 while the death rate is about 1~in~7 for those who were not inoculated.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{SmallpoxInoculationObsExpExercise}\nThe people of Boston self-selected whether or not to be inoculated. (a) Is this study observational or was this an experiment? (b) Can we infer any causal connection using these data? (c) What are some potential confounding variables that might influence whether someone \\resp{lived} or \\resp{died} and also affect whether that person was inoculated?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Brief answers: (a)~Observational. (b)~No, we cannot infer causation from this observational study. (c)~Accessibility to the latest and best medical care. There are other valid answers for part~(c).}\n\n\n\\subsection{General multiplication rule}\n\nSection~\\ref{probabilityIndependence} introduced the Multiplication Rule for independent processes. Here we provide the \\term{General Multiplication Rule} for events that might not be independent.\n\n\\begin{onebox}{General Multiplication Rule}\nIf $A$ and $B$ represent two outcomes or events, then \\vspace{-1.5mm}\n\\begin{align*}\nP(A\\text{ and }B) = P(A | B)\\times P(B)\n\\end{align*} \\vspace{-6.5mm} \\par\nIt is useful to think of $A$ as the outcome of interest and $B$ as the condition.\n\\end{onebox}\n\n\\noindent%\nThis General Multiplication Rule is simply a rearrangement of the conditional probability equation.\n\n%\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{Consider the \\data{smallpox} data set. Suppose we are given only two pieces of information: 96.08\\% of residents were not inoculated, and 85.88\\% of the residents who were not inoculated ended up surviving. How could we compute the probability that a resident was not inoculated and lived?}\nWe will compute our answer using the General Multiplication Rule and then verify it using Figure~\\ref{smallpoxProbabilityTable}. We want to determine\n\\begin{align*}\nP(\\text{\\var{result}\n    = \\resp{lived} and \\var{inoculated} = \\resp{no}})\n\\end{align*}\nand we are given that\n\\begin{align*}\nP(\\text{\\var{result}\n    = \\resp{lived} }|\\text{ \\var{inoculated} = \\resp{no}})\n    &= 0.8588 %\\\\\n&&P(\\text{\\var{inoculated} = \\resp{no}})\n    = 0.9608\n\\end{align*}\nAmong the 96.08\\% of people who were not inoculated, 85.88\\% survived:\n\\begin{align*}\nP(\\text{\\var{result} = \\resp{lived}\n        and \\var{inoculated} = \\resp{no}})\n    = 0.8588 \\times 0.9608\n    = 0.8251\n\\end{align*}\nThis is equivalent to the General Multiplication Rule. We can confirm this probability in Figure~\\ref{smallpoxProbabilityTable} at the intersection of \\resp{no} and \\resp{lived} (with a small rounding error).\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nUse $P($\\var{inoculated} = \\resp{yes}$) = 0.0392$ and $P($\\var{result} = \\resp{lived} $|$ \\var{inoculated} = \\resp{yes}$) = 0.9754$ to determine the probability that a person was both inoculated and lived.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The answer is 0.0382, which can be verified using Figure~\\ref{smallpoxProbabilityTable}.}\n\n%\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIf 97.54\\% of the inoculated people lived,\nwhat proportion of inoculated people must have died?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{There were only two possible outcomes: \\resp{lived} or \\resp{died}. This means that 100\\% - 97.54\\% = 2.46\\% of the people who were inoculated died.}\n\n\\begin{onebox}{Sum of conditional probabilities}\nLet $A_1$, ..., $A_k$ represent all the disjoint outcomes for a variable or process. Then if $B$ is an event, possibly for another variable or process, we have: \\vspace{-1mm}\n\\begin{align*}\nP(A_1|B) + \\cdots + P(A_k|B) = 1\n\\end{align*}%\n\\vspace{-5.5mm} \\par\nThe rule for complements also holds when an event and its complement are conditioned on the same information: \\vspace{-1.5mm}\n\\begin{align*}\nP(A | B) = 1 - P(A^c | B)\n\\end{align*}\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nBased on the probabilities computed above, does it appear that inoculation is effective at reducing the risk of death from smallpox?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The samples are large relative to the difference in death rates for the ``inoculated'' and ``not inoculated'' groups, so it seems there is an association between \\var{inoculated} and \\var{outcome}. However, as noted in the solution to Guided Practice~\\ref{SmallpoxInoculationObsExpExercise}, this is an observational study and we cannot be sure if there is a causal connection. (Further research has shown that inoculation is effective at reducing death rates.)}\n\n\n%\\D{\\newpage}\n\n\\subsection{Independence considerations in conditional probability}\n\nIf two events are independent, then knowing the outcome of one should provide no information about the other. We can show this is mathematically true using conditional probabilities.\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{condProbOfRollingA1AfterOne1}\nLet $X$ and $Y$ represent the outcomes of rolling two dice.\\footnotemark\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}\n\\item What is the probability that the first die, $X$, is \\resp{1}?\n\\item What is the probability that both $X$ and $Y$ are \\resp{1}?\n\\item Use the formula for conditional probability to compute $P(Y =$ \\resp{1}$\\ |\\ X = $ \\resp{1}$)$.\n\\item What is $P(Y=1)$? Is this different from the answer from part (c)? Explain.\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Brief solutions: (a) $1/6$. (b) $1/36$. (c)~$\\frac{P(Y = \\text{ \\resp{1} and }X=\\text{ \\resp{1}})}{P(X=\\text{ \\resp{1}})} = \\frac{1/36}{1/6} = 1/6$. (d)~The probability is the same as in part~(c): $P(Y=1)=1/6$. The probability that $Y=1$ was unchanged by knowledge about $X$, which makes sense as $X$ and $Y$ are independent.}\n\nWe can show in Guided Practice~\\ref{condProbOfRollingA1AfterOne1}(c) that the conditioning information has no influence by using the Multiplication Rule for independence processes:\n\\begin{align*}\nP(Y=\\text{\\resp{1}}\\ |\\ X=\\text{\\resp{1}})\n    &= \\frac{P(Y=\\text{\\resp{1} and }X=\\text{\\resp{1}})}\n      {P(X=\\text{\\resp{1}})} \\\\\n    &= \\frac{P(Y=\\text{\\resp{1}}) \\times\n        \\color{oiGB}P(X=\\text{\\resp{1}})}\n      {\\color{oiGB}P(X=\\text{\\resp{1}})} \\\\\n    &= P(Y=\\text{\\resp{1}}) \\\\\n\\end{align*}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nRon is watching a roulette table in a casino and notices that the last five outcomes were \\resp{black}. He figures that the chances of getting \\resp{black} six times in a row is very small (about $1/64$) and puts his paycheck on red. What is wrong with his reasoning?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{He has forgotten that the next roulette spin is independent of the previous spins. Casinos do employ this practice, posting the last several outcomes of many betting games to trick unsuspecting gamblers into believing the odds are in their favor. This is called the \\term{gambler's fallacy}.}\n\n\n\\D{\\newpage}\n\n\\subsection{Tree diagrams}\n\n\\index{data!smallpox|)}\n\\index{tree diagram|(}\n\n\\termsub{Tree diagrams}{tree diagram} are a tool to organize outcomes and probabilities around the structure of the data. They are most useful when two or more processes occur in a sequence and each process is conditioned on its predecessors.\n\nThe \\data{smallpox} data fit this description. We see the population as split by \\var{inoculation}: \\resp{yes} and \\resp{no}. Following this split, survival rates were observed for each group. This structure is reflected in the \\term{tree diagram} shown in Figure~\\ref{smallpoxTreeDiagram}. The first branch for \\var{inoculation} is said to be the \\term{primary} branch while the other branches are \\termni{secondary}.\n\n\\begin{figure}[ht]\n\\centering\n\\Figure[A tree diagram with a primary branch \"Inoculated\" and a secondary branch \"Result\". The Inoculated primary branching leads to two options: \"Yes\" with a probability of 0.0392 and \"No\" with a probability of 0.9608. Each of these branches has secondary branches with conditional probabilities for the \"Result\" conditional on \"Inoculated\". The Inoculated Yes branch breaks into branches for \"Lived\" (0.9754) and \"Died\" (0.0246). These branches also provide the multiplied probabilities along the branches as well. For example, the Yes-and-Lived branching multiplies 0.0392 times 0.9754 to get 0.03824. The Yes-and-Died branching has a multiplied probability of 0.00096. Next, turning our attention to the \"No\" primary branch, it also has secondary branches of Lived and Died with conditional probabilities 0.8589 and 0.1411, respectively. It also shows the probabilities multiplied along each set of branches, with No-and-Lived as 0.82523 and No-and-Died as 0.13557.]{0.93}{smallpoxTreeDiagram}\n\\caption{A tree diagram of the \\data{smallpox} data set.}\n\\label{smallpoxTreeDiagram}\n\\end{figure}\n\nTree diagrams are annotated with marginal and conditional probabilities, as shown in Figure~\\ref{smallpoxTreeDiagram}. This tree diagram splits the smallpox data by \\var{inoculation} into the \\resp{yes} and \\resp{no} groups with respective marginal probabilities 0.0392 and 0.9608. The secondary branches are conditioned on the first, so we assign conditional probabilities to these branches. For example, the top branch in Figure~\\ref{smallpoxTreeDiagram} is the probability that \\var{result} = \\resp{lived} conditioned on the information that \\var{inoculated} = \\resp{yes}. We may (and usually do) construct joint probabilities at the end of each branch in our tree by multiplying the numbers we come across as we move from left to right. These joint probabilities are computed using the General Multiplication Rule:\n\\begin{align*}\n& P(\\text{\\var{inoculated} = \\resp{yes}\n    and \\var{result} = \\resp{lived}}) \\\\\n  &\\quad = P(\\text{\\var{inoculated} = \\resp{yes}})\\times\n      P(\\text{\\var{result} = \\resp{lived}}|\n          \\text{\\var{inoculated} = \\resp{yes}}) \\\\\n  &\\quad = 0.0392\\times 0.9754=0.0382\n\\end{align*}\n\n\\begin{examplewrap}\n\\begin{nexample}{Consider the midterm and final for a statistics class. Suppose 13\\% of students earned an \\resp{A} on the midterm. Of those students who earned an \\resp{A} on the midterm, 47\\% received an \\resp{A} on the final, and 11\\% of the students who earned lower than an \\resp{A} on the midterm received an \\resp{A} on the final. You randomly pick up a final exam and notice the student received an \\resp{A}. What is the probability that this student earned an \\resp{A} on the midterm?} \\label{exerciseForTreeDiagramOfStudentGettingAOnMidtermGivenThatSheGotAOnFinal}\nThe end-goal is to find $P(\\text{\\var{midterm} = \\resp{A}} | \\text{\\var{final} = \\resp{A}})$. To calculate this conditional probability, we need the following probabilities:\n\\begin{align*}\nP(\\text{\\var{midterm} = \\resp{A} and \\var{final} = \\resp{A}})\n  \\qquad\\text{and}\\qquad\n  P(\\text{\\var{final} = \\resp{A}})\n\\end{align*}\nHowever, this information is not provided, and it is not obvious how to calculate these probabilities. Since we aren't sure how to proceed, it is useful to organize the information into a tree diagram:\n\\begin{center}\n\\Figure[A tree diagram with a primary branch \"Midterm\" and a secondary branch \"Final\". The Midterm primary branching leads to two options: \"A\" with a probability of 0.13 and \"Other\" with a probability of 0.87. Each of these branches has secondary branches with conditional probabilities for the \"Final\" conditional on \"Midterm\". The Midterm-A branch breaks into branches for \"A\", with a conditional probability of 0.47 with an A-and-A final probability of 0.0611, and an \"other\" secondary branch, with a conditional probability of 0.53 with an other-and-other final probability of 0.0689. Next, turning our attention to the Midterm-Other primary branch, it also has secondary branches of Final-A with a conditional probability of 0.11 and final probability of 0.0957, and an \"Final-other\" branch with a conditional probability of 0.89 and final probability of 0.7743.]{0.85}{testTree}\n\\end{center}\nWhen constructing a tree diagram, variables provided with marginal probabilities are often used to create the tree's primary branches; in this case, the marginal probabilities are provided for midterm grades. The final grades, which correspond to the conditional probabilities provided, will be shown on the secondary branches.\n\nWith the tree diagram constructed, we may compute the required probabilities:\n\\begin{align*}\n&P(\\text{\\var{midterm} = \\resp{A} and \\var{final} = \\resp{A}}) = 0.0611 \\\\\n&P(\\text{\\underline{\\color{black}\\var{final} = \\resp{A}}})  \\\\\n& \\quad= P(\\text{\\var{midterm} = \\resp{other} and \\underline{\\color{black}\\var{final} = \\resp{A}}}) + P(\\text{\\var{midterm} = \\resp{A} and \\underline{\\color{black}\\var{final} = \\resp{A}}}) \\\\\n& \\quad= 0.0957 + 0.0611  = 0.1568\n\\end{align*}\nThe marginal probability, $P($\\var{final} = \\resp{A}$)$, was calculated by adding up all the joint probabilities on the right side of the tree that correspond to \\var{final} = \\resp{A}. We may now finally take the ratio of the two probabilities:\n\\begin{align*}\nP(\\text{\\var{midterm} = \\resp{A}} | \\text{\\var{final} = \\resp{A}})\n  &= \\frac{P(\\text{\\var{midterm} = \\resp{A}\n      and \\var{final} = \\resp{A}})}\n    {P(\\text{\\var{final} = \\resp{A}})} \\\\\n  &= \\frac{0.0611}{0.1568} = 0.3897\n\\end{align*}\nThe probability the student also earned an A on the midterm is about 0.39.\n\\end{nexample}\n\\end{examplewrap}\n\n%\\begin{figure}[ht]\n%  \\centering\n%  \\Figure{0.9}{testTree}\n%  \\caption{A tree diagram describing the \\var{midterm}\n%      and \\var{final} variables.}\n%  \\label{testTree}\n%\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nAfter an introductory statistics course, 78\\% of students can successfully construct tree diagrams. Of those who can construct tree diagrams, 97\\% passed, while only 57\\% of those students who could not construct tree diagrams passed. (a)~Organize this information into a tree diagram. (b)~What is the probability that a randomly selected student passed? (c)~Compute the probability a student is able to construct a tree diagram if it is known that she passed.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{%\\begin{minipage}[t]{0.27\\linewidth}\n(a) The tree diagram is shown to the right. \\\\\n(b)~Identify which two joint probabilities represent\n    students who passed, and add them:\n    $P($passed$) = 0.7566+0.1254= 0.8820$. \\\\\n(c)~$P($construct tree diagram $|$ passed$)\n   = \\frac{0.7566}{0.8820} = 0.8578$. \\\\ %\\vspace{15mm} \\\\\n%\\end{minipage}\n%\\begin{minipage}[c]{0.7\\linewidth}\n\\Figure[A tree diagram with a primary branch \"Able to construct tree diagrams\" and a secondary branch \"Pass class\". The Able-to-construct-tree-diagrams primary branching leads to two options: \"Yes\" with a probability of 0.78 and \"No\" with a probability of 0.22. Each of these branches has secondary branches with conditional probabilities for \"Pass Class\" conditional on \"Able to construct tree diagrams\". The Yes primary branch breaks into branches for \"Pass\", with a conditional probability of 0.97 with a Yes-and-Pass final probability of 0.7566, and a \"Fail\" secondary branch, with a conditional probability of 0.03 with a Yes-and-Fail final probability of 0.0234. Next, turning our attention to the No primary branch, it also has secondary branches of Pass with a conditional probability of 0.57 and final probability of 0.1254, and a Fail branch with a conditional probability of 0.43 and final probability of 0.0946.]{0.7}{treeDiagramAndPass}}% \\vspace{-25mm}\n%\\end{minipage}}\n\n\n\\subsection{Bayes' Theorem}\n\\label{bayesTheoremSubsection}\n\n\\index{Bayes' Theorem|(}\n\nIn many instances, we are given a conditional probability of the form\n\\begin{align*}\nP(\\text{statement about variable 1 } | \\text{ statement about variable 2})\n\\end{align*}\nbut we would really like to know the inverted conditional probability:\n\\begin{align*}\nP(\\text{statement about variable 2 } | \\text{ statement about variable 1})\n\\end{align*}\nTree diagrams can be used to find the second conditional probability when given the first. However, sometimes it is not possible to draw the scenario in a tree diagram. In these cases, we can apply a very useful and general formula: Bayes' Theorem.\n\nWe first take a critical look at an example of inverting conditional probabilities where we still apply a tree diagram.\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{In Canada, about 0.35\\% of women over 40\n    will develop breast cancer in any given year.\n    A common screening test for cancer is the mammogram,\n    but this test is not perfect.\n    In about 11\\% of patients with breast cancer, the test\n    gives a \\term{false negative}:\n    it indicates a woman does not have breast cancer when\n    she does have breast cancer.\n    Similarly, the test gives a \\term{false positive}\n    in 7\\% of patients who do not have breast cancer:\n    it indicates these patients have breast cancer when\n    they actually do not.\n    If we tested a random woman over 40 for breast cancer\n    using a mammogram and the test came back positive\n    -- that is, the test suggested the patient has cancer --\n    what is the probability that the patient actually has\n    breast cancer?}\n\n\\label{probBreastCancerGivenPositiveTestExample}\n\nNotice that we are given sufficient information to quickly compute the probability of testing positive if a woman has breast cancer ($1.00-0.11=0.89$). However, we seek the inverted probability of cancer given a positive test result. (Watch out for the non-intuitive medical language: a~\\emph{positive} test result suggests the possible presence of cancer in a mammogram screening.) This inverted probability may be broken into two pieces:\n\\begin{align*}\nP(\\text{has BC } | \\text{ mammogram$^+$}) = \\frac{P(\\text{has BC and mammogram$^+$})}{P(\\text{mammogram$^+$})}\n\\end{align*}\nwhere ``has BC'' is an abbreviation for the patient having\nbreast cancer and ``mammogram$^+$'' means the mammogram screening\nwas positive.\nWe can construct a tree diagram for these probabilities:\n\\begin{center}\n\\Figure[A tree diagram with a primary branch \"Truth\" and a secondary branch \"Mammogram\". The Truth primary branching leads to two options: \"Cancer\" with a probability of 0.0035 and \"No Cancer\" with a probability of 0.9965. Each of these branches has secondary branches with conditional probabilities for \"Positive\" and \"Negative\" mammogram outcomes conditional on whether the truth is having cancer or not. The Cancer primary branch breaks into branches for \"Positive\", with a conditional probability of 0.89 with a Cancer-and-Positive final probability of 0.00312, and a \"Negative\" secondary branch, with a conditional probability of 0.11 with a Cancer-and-Negative final probability of 0.00038. Next, turning our attention to the No-Cancer primary branch, it also has secondary branches of Positive with a conditional probability of 0.07 and final probability of 0.06976, and a Negative branch with a conditional probability of 0.93 and final probability of 0.92675.]{0.9}{BreastCancerTreeDiagram}\n\\end{center}\nThe probability the patient has breast cancer\nand the mammogram is positive is\n\\begin{align*}\nP(\\text{has BC and mammogram$^+$}) &= P(\\text{mammogram$^+$ } | \\text{ has BC})P(\\text{has BC}) \\\\\n\t&= 0.89\\times 0.0035 = 0.00312\n\\end{align*}\nThe probability of a positive test result is the sum of the two corresponding scenarios:\n\\begin{align*}\nP(\\text{\\underline{\\color{black}mammogram$^+$}})\n  &= P(\\text{\\underline{\\color{black}mammogram$^+$} and has BC}) \\\\\n  &\\qquad\\qquad\n      + P(\\text{\\underline{\\color{black}mammogram$^+$} and no BC})\\\\\n  &= P(\\text{has BC})P(\\text{mammogram$^+$ } | \\text{ has BC}) \\\\\n  &\\qquad\\qquad\n      + P(\\text{no BC})P(\\text{mammogram$^+$ } | \\text{ no BC}) \\\\\n  &= 0.0035\\times 0.89 + 0.9965\\times 0.07 = 0.07288\n\\end{align*}\nThen if the mammogram screening is positive for a patient, the probability the patient has breast cancer is\n\\begin{align*}\nP(\\text{has BC } | \\text{ mammogram$^+$})\n\t&= \\frac{P(\\text{has BC and mammogram$^+$})}{P(\\text{mammogram$^+$})}\\\\\n\t&= \\frac{0.00312}{0.07288} \\approx 0.0428\n\\end{align*}\nThat is, even if a patient has a positive mammogram screening, there is still only a~4\\%~chance that she has breast cancer.\n\\end{nexample}\n\\end{examplewrap}\n\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figure{0.75}{BreastCancerTreeDiagram}\n%  \\caption{Tree diagram for\n%      Example~\\ref{probBreastCancerGivenPositiveTestExample}.}%,\n%      %computing the probability a random patient who tests\n%      %positive on a mammogram actually has breast cancer.}\n%\\label{BreastCancerTreeDiagram}\n%\\end{figure}\n\n\\D{\\newpage}\n\nExample~\\ref{probBreastCancerGivenPositiveTestExample} highlights why doctors often run more tests regardless of a first positive test result. When a medical condition is rare, a single positive test isn't generally definitive.\n\nConsider again the last equation of Example~\\ref{probBreastCancerGivenPositiveTestExample}.\nUsing the tree diagram, we can see that the numerator (the top of the fraction) is equal to the following product:\n\\begin{align*}\nP(\\text{has BC and mammogram$^+$}) = P(\\text{mammogram$^+$ } | \\text{ has BC})P(\\text{has BC})\n\\end{align*}\nThe denominator -- the probability the screening was positive -- is equal to the sum of probabilities for each positive screening scenario:\n\\begin{align*}\nP(\\text{\\underline{\\color{black}mammogram$^+$}})\n\t&= P(\\text{\\underline{\\color{black}mammogram$^+$} and no BC})\n\t\t+ P(\\text{\\underline{\\color{black}mammogram$^+$} and has BC})\n\\end{align*}\nIn the example, each of the probabilities on the right side was broken down into a product of a conditional probability and marginal probability using the tree diagram.\n\\begin{align*}\nP(\\text{mammogram$^+$})\n\t&= P(\\text{mammogram$^+$ and no BC}) + P(\\text{mammogram$^+$ and has BC}) \\\\\n\t&= P(\\text{mammogram$^+$ } | \\text{ no BC})P(\\text{no BC}) \\\\\n\t\t\t   &\\qquad\\qquad + P(\\text{mammogram$^+$ } | \\text{ has BC})P(\\text{has BC})\n\\end{align*}\nWe can see an application of Bayes' Theorem by substituting the resulting probability expressions into the numerator and denominator of the original conditional probability.\n\\begin{align*}\n& P(\\text{has BC } | \\text{ mammogram$^+$})  \\\\\n& \\qquad= \\frac{P(\\text{mammogram$^+$ } | \\text{ has BC})P(\\text{has BC})}\n\t{P(\\text{mammogram$^+$ } | \\text{ no BC})P(\\text{no BC}) + P(\\text{mammogram$^+$ } | \\text{ has BC})P(\\text{has BC})}\n\\end{align*}\n\n\\begin{onebox}{Bayes' Theorem: inverting probabilities}\nConsider the following conditional probability for variable 1 and variable 2:\\vspace{-1.5mm}\n\\begin{align*}\nP(\\text{outcome $A_1$ of variable 1 } | \\text{ outcome $B$ of variable 2})\n\\end{align*}\nBayes' Theorem states that this conditional probability can be identified as the following fraction:\\vspace{-1.5mm}\n\\begin{align*}\n\\frac{P(B | A_1) P(A_1)}\n\t{P(B | A_1) P(A_1) + P(B | A_2) P(A_2) + \\cdots + P(B | A_k) P(A_k)}\n\\end{align*}\nwhere $A_2$, $A_3$, ..., and $A_k$ represent all other possible outcomes of the first variable.\\index{Bayes' Theorem|textbf}\n\\end{onebox}\n\nBayes' Theorem is a generalization of what we have done\nusing tree diagrams.\nThe numerator identifies the probability of getting both\n$A_1$ and~$B$.\nThe denominator is the marginal probability of getting~$B$.\nThis bottom component of the fraction appears long and\ncomplicated since we have to add up probabilities from all of the different ways to get $B$. We always completed this step when using tree diagrams. However, we usually did it in a separate step so it didn't seem as complex.\n\n\\noindent%\nTo apply Bayes' Theorem correctly, there are two preparatory steps:\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item[(1)] First identify the marginal probabilities of each possible outcome of the first variable: $P(A_1)$, $P(A_2)$, ..., $P(A_k)$.\n\\item[(2)] Then identify the probability of the outcome $B$, conditioned on each possible scenario for the first variable: $P(B | A_1)$, $P(B | A_2)$, ..., $P(B | A_k)$.\n\\end{enumerate}\nOnce each of these probabilities are identified, they can be applied directly within the formula.\nBayes' Theorem tends to be a good option when there are so many scenarios that drawing a tree diagram would be complex.\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsASportingEvent}\nJose visits campus every Thursday evening. However, some days the parking garage is full, often due to college events. There are academic events on 35\\% of evenings, sporting events on 20\\% of evenings, and no events on 45\\% of evenings. When there is an academic event, the garage fills up about 25\\% of the time, and it fills up 70\\% of evenings with sporting events. On evenings when there are no events, it only fills up about 5\\% of the time. If Jose comes to campus and finds the garage full, what is the probability that there is a sporting event? Use a tree diagram to solve this problem.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{\\begin{minipage}[t]{0.27\\linewidth}\nThe tree diagram, with three primary branches, is shown to the right. Next, we identify two probabilities from the tree diagram. (1) The probability that there is a sporting event and the garage is full: 0.14. (2) The probability the garage is full: $0.0875 + 0.14 + 0.0225 = 0.25$. Then the solution is the ratio of these probabilities: $\\frac{0.14}{0.25} = 0.56$. If the garage is full, there is a 56\\% probability that there is a sporting event. \\vspace{0.1mm} \\\\\\ \n\\end{minipage}\n\\begin{minipage}[c]{0.65\\linewidth}\n\\Figure[A tree diagram with a primary branch \"Event\" and a secondary branch \"Garage full\". The primary \"Event\" branching has three possibilities of \"Academic\" with probability 0.35, \"Sporting\" with probability 0.20, and \"None\" with probability 0.45. Each of these three branches has two secondary branches. The \"Academic\" primary branch breaks into branches for \"Full\" that has a conditional probability of 0.25 with an Academic-and-Full final probability of 0.0875, and a \"Spaces Available\" secondary branch with a conditional probability of 0.75 with an Academic-and-Spaces-Available final probability of 0.2625. The \"Sporting\" primary branch breaks into branches for \"Full\" that has a conditional probability of 0.7 with a Sporting-and-Full final probability of 0.14, and a \"Spaces Available\" secondary branch with a conditional probability of 0.3 with a Sporting-and-Spaces-Available final probability of 0.06. The \"None\" primary branch breaks into branches for \"Full\" that has a conditional probability of 0.05 with a None-and-Full final probability of 0.0225, and a \"Spaces Available\" secondary branch with a conditional probability of 0.95 with a None-and-Spaces-Available final probability of 0.4275.]{}{treeDiagramGarage}\\vspace{-45mm}\n\\end{minipage}}\n\n\\begin{examplewrap}\n\\begin{nexample}{Here we solve the same problem presented in Guided Practice~\\ref{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsASportingEvent}, except this time we use Bayes' Theorem.}\nThe outcome of interest is whether there is a sporting event (call this $A_1$), and the condition is that the lot is full ($B$). Let $A_2$ represent an academic event and $A_3$ represent there being no event on campus. Then the given probabilities can be written as\n\\begin{align*}\n&P(A_1) = 0.2 &&P(A_2) = 0.35 &&P(A_3) = 0.45 \\\\\n&P(B | A_1) = 0.7 &&P(B | A_2) = 0.25 &&P(B | A_3) = 0.05\n\\end{align*}\nBayes' Theorem can be used to compute the probability of a sporting event ($A_1$) under the condition that the parking lot is full ($B$):\n\\begin{align*}\nP(A_1 | B) &= \\frac{P(B | A_1) P(A_1)}{P(B | A_1) P(A_1) + P(B | A_2) P(A_2) + P(B | A_3) P(A_3)} \\\\\n\t\t&= \\frac{(0.7)(0.2)}{(0.7)(0.2) + (0.25)(0.35) + (0.05)(0.45)} \\\\\n\t\t&= 0.56 \n\\end{align*}\nBased on the information that the garage is full, there is a 56\\% probability that a sporting event is being held on campus that evening.\n\\end{nexample}\n\\end{examplewrap}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsAnAcademicEvent}\nUse the information in the previous exercise and example to verify the probability that there is an academic event conditioned on the parking lot being full is 0.35.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Short answer:\n\\begin{align*}\nP(A_2 | B) &= \\frac{P(B | A_2) P(A_2)}{P(B | A_1) P(A_1) + P(B | A_2) P(A_2) + P(B | A_3) P(A_3)} \\\\\n\t\t&= \\frac{(0.25)(0.35)}{(0.7)(0.2) + (0.25)(0.35) + (0.05)(0.45)} \\\\\n\t\t&= 0.35\n\\end{align*}}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsNoEvent}\nIn Guided Practice~\\ref{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsASportingEvent} and~\\ref{exerciseForParkingLotOnCampusBeingFullAndWhetherOrNotThereIsAnAcademicEvent}, you found that if the parking lot is full, the probability there is a sporting event is 0.56 and the probability there is an academic event is 0.35. Using this information, compute $P($no event $|$ the lot is full$)$.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Each probability is conditioned on the same information that the garage is full, so the complement may be used: $1.00 - 0.56 - 0.35 = 0.09$.}\n\nThe last several exercises offered a way to update our belief about whether there is a sporting event, academic event, or no event going on at the school based on the information that the parking lot was full. This strategy of \\emph{updating beliefs} using Bayes' Theorem is actually the foundation of an entire section of statistics called \\term{Bayesian statistics}. While Bayesian statistics is very important and useful, we will not have time to cover much more of it in this book.\n\n\\index{Bayes' Theorem|)}\n\\index{tree diagram|)}\n\\index{conditional probability|)}\n\\index{probability|)}\n\n\n{\\input{ch_probability/TeX/conditional_probability.tex}}\n\n\n\n\n\n%_________________\n\\section{Sampling from a small population}\n\\label{smallPop}\n\n\\noindent%\nWhen we sample observations from a population,\nusually we're only sampling a small fraction of\nthe possible individuals or cases.\nHowever, sometimes our sample size is large enough\nor the population is small enough that we\nsample more than 10\\% of a population\\footnote{The 10\\%\n  guideline is a rule of thumb cutoff for when these\n  considerations become more important.}\n\\emph{without\nreplacement} (meaning we do not have a chance of\nsampling the same cases twice).\nSampling such a notable fraction of a population\ncan be important for how we analyze the\nsample.\n\n\\begin{examplewrap}\n\\begin{nexample}{Professors sometimes select a student at random to answer a question. If each student has an equal chance of being selected and there are 15 people in your class, what is the chance that she will pick you for the next question?}\nIf there are 15 people to ask and none are skipping class, then the probability is $1/15$, or about $0.067$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{If the professor asks 3 questions, what is the probability that you will not be selected? Assume that she will not pick the same person twice in a given lecture.}\\label{3woRep}\nFor the first question, she will pick someone else with probability $14/15$. When she asks the second question, she only has 14 people who have not yet been asked. Thus, if you were not picked on the first question, the probability you are again not picked is $13/14$. Similarly, the probability you are again not picked on the third question is $12/13$, and the probability of not being picked for any of the three questions is\n\\begin{align*}\n&P(\\text{not picked in 3 questions}) \\\\\n&\\quad = P(\\text{\\var{Q1}} = \\text{\\resp{not\\us{}picked}, }\\text{\\var{Q2}} = \\text{\\resp{not\\us{}picked}, }\\text{\\var{Q3}} = \\text{\\resp{not\\us{}picked}.}) \\\\\n&\\quad = \\frac{14}{15}\\times\\frac{13}{14}\\times\\frac{12}{13} = \\frac{12}{15} = 0.80\n\\end{align*}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat rule permitted us to multiply the probabilities in Example~\\ref{3woRep}?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The three probabilities we computed were actually one marginal probability, $P($\\var{Q1}$ = $\\resp{not\\us{}picked}$)$, and two conditional probabilities:\n\\begin{align*}\n&P(\\text{\\var{Q2}} =  \\text{\\resp{not\\us{}picked} }|\n    \\text{ \\var{Q1}} = \\text{\\resp{not\\us{}picked}}) \\\\\n&P(\\text{\\var{Q3}} =  \\text{\\resp{not\\us{}picked} }|\n    \\text{ \\var{Q1}} = \\text{\\resp{not\\us{}picked}, }\n    \\text{\\var{Q2}} = \\text{\\resp{not\\us{}picked}})\n\\end{align*}\nUsing the General Multiplication Rule, the product of these three probabilities is the probability of not being picked in 3 questions.}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose the professor randomly picks without regard to who she already selected, i.e. students can be picked more than once. What is the probability that you will not be picked for any of the three questions?}\\label{3wRep}\nEach pick is independent, and the probability of not being picked for any individual question is $14/15$. Thus, we can use the Multiplication Rule for independent processes.\n\\begin{align*}\n&P(\\text{not picked in 3 questions}) \\\\\n&\\quad = P(\\text{\\var{Q1}} = \\text{\\resp{not\\us{}picked}, }\\text{\\var{Q2}} = \\text{\\resp{not\\us{}picked}, }\\text{\\var{Q3}} = \\text{\\resp{not\\us{}picked}.}) \\\\\n&\\quad = \\frac{14}{15}\\times\\frac{14}{15}\\times\\frac{14}{15} = 0.813\n\\end{align*}\nYou have a slightly higher chance of not being picked compared to when she picked a new person for each question. However, you now may be picked more than once.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nUnder the setup of Example~\\ref{3wRep}, what is the probability of being picked to answer all three questions?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$P($being picked to answer all three questions$) = \\left(\\frac{1}{15}\\right)^3 = 0.00030$.}\n\nIf we sample from a small population \\term{without replacement}, we no longer have independence between our observations. In Example~\\ref{3woRep}, the probability of not being picked for the second question was conditioned on the event that you were not picked for the first question. In Example~\\ref{3wRep}, the professor sampled her students \\term{with replacement}: she repeatedly sampled the entire class without regard to who she already picked. \n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{raffleOf30TicketsWWOReplacement}\nYour department is holding a raffle. They sell 30 tickets and offer seven prizes. (a) They place the tickets in a hat and draw one for each prize. The tickets are sampled without replacement, i.e. the selected tickets are not placed back in the hat. What is the probability of winning a prize if you buy one ticket? (b)~What if the tickets are sampled with replacement?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) First determine the probability of not winning. The tickets are sampled without replacement, which means the probability you do not win on the first draw is $29/30$, $28/29$ for the second, ..., and $23/24$ for the seventh. The probability you win no prize is the product of these separate probabilities: $23/30$. That is, the probability of winning a prize is $1 - 23/30 = 7/30 = 0.233$. (b)~When the tickets are sampled with replacement, there are seven independent draws. Again we first find the probability of not winning a prize: $(29/30)^7 = 0.789$. Thus, the probability of winning (at least) one prize when drawing with replacement is 0.211.}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{followUpToRaffleOf30TicketsWWOReplacement}\nCompare your answers in Guided Practice~\\ref{raffleOf30TicketsWWOReplacement}. How much influence does the sampling method have on your chances of winning a prize?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{There is about a 10\\% larger chance of winning a prize when using sampling without replacement. However, at most one prize may be won under this sampling procedure.}\n\nHad we repeated Guided Practice~\\ref{raffleOf30TicketsWWOReplacement} with 300 tickets instead of 30, we would have found something interesting: the results would be nearly identical. The probability would be 0.0233 without replacement and 0.0231 with replacement. When the sample size is only a small fraction of the population (under 10\\%), observations are nearly independent even when sampling without replacement.\n\n\n{\\input{ch_probability/TeX/sampling_from_a_small_population.tex}}\n\n\n\n\n\n%_________________\n\\section{Random variables}\n\\label{randomVariablesSection}\n\n\\index{random variable|(}\n\n\\noindent%\nIt's often useful to model a process using what's\ncalled a \\term{random variable}.\nSuch a model allows us to apply a mathematical\nframework and statistical principles for\nbetter understanding and predicting outcomes\nin the real world.\n\n\\begin{examplewrap}\n\\begin{nexample}{Two books are assigned for a statistics class: a textbook and its corresponding study guide. The university bookstore determined 20\\% of enrolled students do not buy either book, 55\\% buy the textbook only, and 25\\% buy both books, and these percentages are relatively constant from one term to another. If~there are 100 students enrolled, how many books should the bookstore expect to sell to this class?}\\label{bookStoreSales}\nAround 20 students will not buy either book (0 books total), about 55 will buy one book (55 books total), and approximately 25 will buy two books (totaling 50 books for these 25 students). The bookstore should expect to sell about 105 books for this class.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWould you be surprised if the bookstore sold slightly more or less than 105 books?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{If they sell a little more or a little less, this should not be a surprise. Hopefully Chapter~\\ref{introductionToData} helped make clear that there is natural variability in observed data. For example, if we would flip a coin 100 times, it will not usually come up heads exactly half the time, but it will probably be close.}\n\n\\begin{examplewrap}\n\\begin{nexample}{The textbook costs \\$137 and the study guide \\$33. How much revenue should the bookstore expect from this class of 100 students?}\\label{bookStoreRev}\nAbout 55 students will just buy a textbook, providing revenue of\n\\begin{align*}\n\\$137 \\times  55 = \\$7,535\n\\end{align*}\nThe roughly 25 students who buy both the textbook and the study guide would pay a total of\n\\begin{align*}\n(\\$137 + \\$33) \\times  25 = \\$170 \\times  25 = \\$4,250\n\\end{align*}\nThus, the bookstore should expect to generate about $\\$7,535 + \\$4,250 = \\$11,785$ from these 100 students for this one class. However, there might be some \\emph{sampling variability} so the actual amount may differ by a little bit.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A probability distribution, which appears similar to a histogram. The horizontal axis is \"Cost\" and runs from \\$0 to \\$170. The vertical axis is Probability. There are three bars: a bar with height 0.2 at \\$0, a bar with height 0.55 with height \\$137, and a bar with height 0.25 at \\$170. A red triangle is shown at the mean, located at \\$117.85.]{0.6}{bookCostDist}\n  \\caption{Probability distribution for the bookstore's\n      revenue from one student.\n      The triangle represents\n      the average revenue per student.}\n  \\label{bookCostDist}\n\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the average revenue per student for this course?}\\label{revFromStudent}\nThe expected total revenue is \\$11,785, and there are 100 students. Therefore the expected revenue per student is $\\$11,785/100 =  \\$117.85$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\subsection{Expectation}\n\n\\index{expectation|(}\n\nWe call a variable or process with a numerical outcome a \\term{random variable}, and we usually represent this random variable with a capital letter such as $X$, $Y$, or $Z$. The amount of money a single student will spend on her statistics books is a random variable, and we represent it by $X$.\n\n\\begin{onebox}{Random variable}\nA random process or variable with a numerical outcome.\n\\end{onebox}\n\nThe possible outcomes of $X$ are labeled with a corresponding lower case letter $x$ and subscripts. For example, we write $x_1=\\$0$, $x_2=\\$137$, and $x_3=\\$170$, which occur with probabilities $0.20$, $0.55$, and $0.25$. The distribution of $X$ is summarized in Figure~\\ref{bookCostDist} and Figure~\\ref{statSpendDist}.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l ccc r}\n\\hline\n$i$\t  & 1 & 2 & 3  & Total\\\\\n\\hline\n$x_i$ & \\$0 & \\$137 & \\$170 & --\\\\\n$P(X=x_i)$ & 0.20 & 0.55 & 0.25 & 1.00 \\\\\n\\hline\n\\end{tabular}\n\\caption{The probability distribution for the random variable $X$, representing the bookstore's revenue from a single student.}\n\\label{statSpendDist}\n\\end{figure}\n\nWe computed the average outcome of $X$ as \\$117.85 in Example~\\ref{revFromStudent}.\nWe call this average the \\term{expected value} of $X$, denoted by $E(X)$\\index{EX@$E(X)$}.\nThe expected value of a random variable is computed by adding each outcome weighted by its probability:\n\\begin{align*}\nE(X) &= 0 \\times  P(X=0) + 137 \\times  P(X=137) + 170 \\times  P(X=170) \\\\\n\t&= 0 \\times  0.20 + 137 \\times  0.55 + 170 \\times  0.25 = 117.85\n\\end{align*}\n\n\\begin{onebox}{Expected value of a Discrete Random Variable}\nIf $X$ takes outcomes $x_1$, ..., $x_k$ with probabilities $P(X=x_1)$, ..., $P(X=x_k)$, the expected value of $X$ is the sum of each outcome multiplied by its corresponding probability:\n\\begin{align*}\nE(X)\n  &= x_1 \\times P(X = x_1) + \\cdots + x_k\\times P(X = x_k) \\\\\n  &= \\sum_{i = 1}^{k} x_i P(X = x_i)\n\\end{align*}\nThe Greek letter $\\mu$\\index{Greek!mu@mu ($\\mu$)}\nmay be used in place of the notation $E(X)$.\n\\end{onebox}\n\n\\D{\\newpage}\n\nThe expected value for a random variable represents the average outcome. For example, $E(X)=117.85$ represents the average amount the bookstore expects to make from a single student, which we could also write as $\\mu=117.85$.\n\nIt is also possible to compute the expected value of a continuous random variable (see Section~\\ref{contDist}). However, it requires a little calculus and we save it for a later class.\\footnote{$\\mu = \\int xf(x)dx$ where $f(x)$ represents a function for the density curve.}\n\nIn physics, the expectation holds the same meaning as the center of gravity. The distribution can be represented by a series of weights at each outcome, and the mean represents the balancing point. This is represented in Figures~\\ref{bookCostDist} and~\\ref{bookWts}. The idea of a center of gravity also expands to continuous probability distributions. Figure~\\ref{contBalance} shows a continuous probability distribution balanced atop a wedge placed at the mean.\n\n\\begin{figure}\n\\centering\n\\Figure[A bar is hung by a string, and three weights are hanging on the bar at three different locations. The weights are located at the locations 0, 137, and 170 and have weights proportional to the probabilities 0.2, 0.55, and 0.25, respectively. The weights are balanced, because the string that is suspending the bar is located at the mean of the distribution, 117.85.]{0.72}{bookWts}\n\\caption{A weight system representing the probability distribution for $X$. The string holds the distribution at the mean to keep the system balanced.}\n\\label{bookWts}\n\\end{figure}\n\n\\begin{figure}\n\\centering\n\\Figure[A distribution that is skewed to the right is displayed, similar to how a histogram would appear if the bins were so small as to blend together and look continuous. This distribution is balancing atop a triangle located at the mean of the distribution.]{0.68}{contBalance}\n\\caption{A continuous distribution can also be balanced at its mean.}\n\\label{contBalance}\n\\end{figure}\n\n\\index{expectation|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Variability in random variables}\n\nSuppose you ran the university bookstore. Besides how much revenue you expect to generate, you might also want to know the volatility (variability) in your revenue. \n\nThe \\indexthis{variance}{variance} and \\indexthis{standard deviation}{standard deviation} can be used to describe the variability of a random variable. Section~\\ref{variability}\nintroduced a method for finding the variance and standard deviation for a data set. We first computed deviations from the mean ($x_i - \\mu$), squared those deviations, and took an average to get the variance. In the case of a random variable, we again compute squared deviations. However, we take their sum weighted by their corresponding probabilities, just like we did for the expectation. This weighted sum of squared deviations equals the variance, and we calculate the standard deviation by taking the square root of the variance, just as we did in Section~\\ref{variability}.\n\n\\begin{onebox}{General variance formula}\nIf $X$ takes outcomes $x_1$, ..., $x_k$ with probabilities $P(X=x_1)$, ..., $P(X=x_k)$ and expected value $\\mu=E(X)$, then the variance of $X$, denoted by $Var(X)$ or the symbol $\\sigma^2$, is\n\\begin{align*}\n\\sigma^2 &= (x_1-\\mu)^2\\times P(X=x_1) + \\cdots \\\\\n\t& \\qquad\\quad\\cdots+ (x_k-\\mu)^2\\times P(X=x_k) \\\\\n\t&= \\sum_{j=1}^{k} (x_j - \\mu)^2 P(X=x_j)\n\\end{align*}\nThe standard deviation of $X$, labeled\n$\\sigma$\\index{Greek!sigma@sigma ($\\sigma$)},\nis the square root of the variance.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{Compute the expected value, variance, and standard deviation of $X$, the revenue of a single statistics student for the bookstore.}\nIt is useful to construct a table that holds computations for each outcome separately, then add up the results.\n\\begin{center}\n\\begin{tabular}{l rrr r}\n\\hline\n$i$ & 1 & 2 & 3 & Total \\\\\n\\hline\n$x_i$ & \\$0 & \\$137 & \\$170 &  \\\\\n$P(X=x_i)$ & 0.20 & 0.55 & 0.25 &  \\\\\n$x_i \\times  P(X=x_i)$ & 0 & 75.35 & 42.50 & 117.85 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\nThus, the expected value is $\\mu=117.85$, which we computed earlier. The variance can be constructed by extending this table:\n\\begin{center}\n\\begin{tabular}{l rrr r}\n\\hline\n$i$ & 1 & 2 & 3 & Total \\\\\n\\hline\n$x_i$ & \\$0 & \\$137 & \\$170 &  \\\\\n$P(X=x_i)$ & 0.20 & 0.55 & 0.25 &  \\\\\n$x_i \\times  P(X=x_i)$ & 0 & 75.35 & 42.50 & 117.85 \\\\\n$x_i - \\mu$ & -117.85 & 19.15 & 52.15 &  \\\\\n$(x_i-\\mu)^2$ & 13888.62 &  366.72 & 2719.62 &  \\\\\n$(x_i-\\mu)^2\\times P(X=x_i)$ & 2777.7 & 201.7 & 679.9 & 3659.3 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\nThe variance of $X$ is $\\sigma^2 = 3659.3$, which means the standard deviation is $\\sigma = \\sqrt{3659.3} = \\$60.49$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe bookstore also offers a chemistry textbook for \\$159 and a book supplement for \\$41. From past experience, they know about 25\\% of chemistry students just buy the textbook while 60\\% buy both the textbook and supplement.\\footnotemark\n\\begin{enumerate}\n\\item[(a)] What proportion of students don't buy either book? Assume no students buy the supplement without the textbook.\n\\item[(b)] Let $Y$ represent the revenue from a single student. Write out the probability distribution of $Y$, i.e. a table for each outcome and its associated probability.\n\\item[(c)] Compute the expected revenue from a single chemistry student. \n\\item[(d)] Find the standard deviation to describe the variability associated with the revenue from a single student.\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a) 100\\% - 25\\% - 60\\% = 15\\% of students do not buy any books for the class. Part~(b) is represented by the first two lines in the table below. The expectation for part~(c) is given as the total on the line $y_i\\times P(Y=y_i)$. The result of part~(d) is the square-root of the variance listed on in the total on the last line: $\\sigma = \\sqrt{Var(Y)} = \\$69.28$.\n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n$i$ (scenario) & 1 (\\resp{noBook}) & 2 (\\resp{textbook}) & 3 (\\resp{both}) & Total \\\\\n  \\hline\n$y_i$ & 0.00 & 159.00 & 200.00 &  \\\\\n$P(Y=y_i)$ & 0.15 & 0.25 & 0.60 & \\\\\n$y_i\\times P(Y=y_i)$ & 0.00 & 39.75 & 120.00 & $E(Y) = 159.75$\\\\\n$y_i-E(Y)$ & -159.75 & -0.75 & 40.25 & \\\\\n$(y_i-E(Y))^2$ & 25520.06 & 0.56 & 1620.06 & \\\\\n$(y_i-E(Y))^2\\times P(Y)$ & 3828.0 & 0.1 & 972.0 & $Var(Y) \\approx 4800$ \\\\\n   \\hline\n\\end{tabular}\n\\end{center}}\n\n\\subsection{Linear combinations of random variables}\n\nSo far, we have thought of each variable as being a complete story in and of itself. Sometimes it is more appropriate to use a combination of variables. For instance, the amount of time a person spends commuting to work each week can be broken down into several daily commutes. Similarly, the total gain or loss in a stock portfolio is the sum of the gains and losses in its components.\n\n\\begin{examplewrap}\n\\begin{nexample}{John travels to work five days a week. We will use $X_1$ to represent his travel time on Monday, $X_2$ to represent his travel time on Tuesday, and so on. Write an equation using $X_1$, ..., $X_5$ that represents his travel time for the week, denoted by $W$.}\nHis total weekly travel time is the sum of the five daily values:\n\\begin{align*}\nW = X_1 + X_2 + X_3 + X_4 + X_5\n\\end{align*}\nBreaking the weekly travel time $W$ into pieces provides a framework for understanding each source of randomness and is useful for modeling $W$.\n\\end{nexample}\n\\end{examplewrap}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{It takes John an average of 18 minutes each day to commute to work. What would you expect his average commute time to be for the week?}\nWe were told that the average (i.e. expected value) of the commute time is 18 minutes per day: $E(X_i) = 18$. To get the expected time for the sum of the five days, we can add up the expected time for each individual day:\n\\begin{align*}\nE(W) &= E(X_1 + X_2 + X_3 + X_4 + X_5) \\\\\n\t&= E(X_1) + E(X_2) + E(X_3) + E(X_4) + E(X_5) \\\\\n\t&= 18 + 18 + 18 + 18 + 18 = 90\\text{ minutes}\n\\end{align*}\nThe expectation of the total time is equal to the sum of the expected individual times. More generally, the expectation of a sum of random variables is always the sum of the expectation for each random variable.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{elenaIsSellingATVAndBuyingAToasterOvenAtAnAuction}%\nElena is selling a TV at a cash auction and also intends to buy a toaster oven in the auction. If $X$ represents the profit for selling the TV and $Y$ represents the cost of the toaster oven, write an equation that represents the net change in Elena's cash.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{She will make $X$ dollars on the TV but spend $Y$ dollars on the toaster oven: $X-Y$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nBased on past auctions, Elena figures she should expect to make about \\$175 on the TV and pay about \\$23 for the toaster oven. In total, how much should she expect to make or spend?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$E(X-Y) = E(X) - E(Y) = 175 - 23 = \\$152$. She should expect to make about \\$152.}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{explainWhyThereIsUncertaintyInTheSum}\nWould you be surprised if John's weekly commute wasn't exactly 90 minutes or if Elena didn't make exactly \\$152? Explain.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{No, since there is probably some variability. For example, the traffic will vary from one day to next, and auction prices will vary depending on the quality of the merchandise and the interest of the attendees.}\n\nTwo important concepts concerning combinations of random variables have so far been introduced. First, a final value can sometimes be described as the sum of its parts in an equation. Second, intuition suggests that putting the individual average values into this equation gives the average value we would expect in total. This second point needs clarification -- it is guaranteed to be true in what are called \\emph{linear combinations of random variables}.\n\nA \\term{linear combination} of two random variables $X$ and $Y$ is a fancy phrase to describe a combination\n\\begin{align*}\naX + bY\n\\end{align*}\nwhere $a$ and $b$ are some fixed and known numbers. For John's commute time, there were five random variables -- one for each work day -- and each random variable could be written as having a fixed coefficient of 1:\n\\begin{align*}\n1X_1 + 1 X_2 + 1 X_3 + 1 X_4 + 1 X_5\n\\end{align*}\nFor Elena's net gain or loss, the $X$ random variable had\na coefficient of +1 and the $Y$ random variable had\na coefficient of~-1.\n\n\\D{\\newpage}\n\nWhen considering the average of a linear combination of random variables, it is safe to plug in the mean of each random variable and then compute the final result. For a few examples of nonlinear combinations of random variables -- cases where we cannot simply plug in the means -- see the footnote.\\footnote{If $X$ and $Y$ are random variables, consider the following combinations: $X^{1+Y}$, $X\\times Y$, $X/Y$. In such cases, plugging in the average value for each random variable and computing the result will not generally lead to an accurate average value for the end result.}\n\n\\begin{onebox}{Linear combinations of random variables and the average result}\nIf $X$ and $Y$ are random variables, then a linear combination of the random variables is given by\n\\begin{align*}\naX + bY\n\\end{align*}\nwhere $a$ and $b$ are some fixed numbers. To compute the average value of a linear combination of random variables, plug in the average of each individual random variable and compute the result:\n\\begin{align*}\na\\times E(X) + b\\times E(Y)\n\\end{align*}\nRecall that the expected value is the same as the mean, e.g. $E(X) = \\mu_X$.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{Leonard has invested \\$6000 in Caterpillar Inc\n    (stock ticker: CAT) and \\$2000 in Exxon Mobil Corp (XOM).\n    If $X$ represents the change in Caterpillar's stock next month\n    and $Y$ represents the change in Exxon Mobil's stock\n    next month, write an equation that describes how much\n    money will be made or lost in Leonard's stocks for the\n    month.}\n  For simplicity, we will suppose $X$ and $Y$ are not\n  in percents but are in decimal form\n  (e.g. if Caterpillar's stock increases 1\\%, then $X=0.01$;\n  or if it loses 1\\%, then $X=-0.01$).\n  Then we can write an equation for Leonard's gain as\n  \\begin{align*}\n  \\$6000\\times X + \\$2000\\times Y\n  \\end{align*}\n  If we plug in the change in the stock value for $X$ and $Y$,\n  this equation gives the change in value of Leonard's stock\n  portfolio for the month. A positive value represents a gain,\n  and a negative value represents a loss.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{expectedChangeInLeonardsStockPortfolio}\nCaterpillar stock has recently been rising\nat 2.0\\% and Exxon Mobil's at 0.2\\% per month, respectively.\nCompute the expected change in Leonard's stock portfolio\nfor next month.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n% library(openintro); d <- stocks_18; cols <- 2:4; apply(d[, cols], 2, mean); apply(d[, cols], 2, sd)\n\\footnotetext{%\n  $E(\\$6000\\times X + \\$2000\\times Y) =\n    \\$6000\\times 0.020 + \\$2000\\times 0.002 = \\$124$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nYou should have found that Leonard expects a positive gain\nin Guided Practice~\\ref{expectedChangeInLeonardsStockPortfolio}.\nHowever, would you be surprised if he actually had\na loss this month?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{No.\n  While stocks tend to rise over time, they are often\n  volatile in the short term.}\n\n\n\\D{\\newpage}\n\n\\subsection{Variability in linear combinations of random variables}\n\\label{var_lin_combo_of_RVs}\n\nQuantifying the average outcome from a linear combination\nof random variables is helpful, but it is also important\nto have some sense of the uncertainty associated with\nthe total outcome of that combination of random variables.\nThe expected net gain or loss of Leonard's stock portfolio\nwas considered in Guided Practice~\\ref{expectedChangeInLeonardsStockPortfolio}.\nHowever, there was no quantitative discussion of the\nvolatility of this portfolio.\nFor instance, while the average monthly gain might be\nabout \\$124 according to the data, that gain is not guaranteed.\nFigure~\\ref{changeInLeonardsStockPortfolioFor36Months}\nshows the monthly changes in a portfolio like Leonard's during\na three year period.\nThe gains and losses vary widely, and quantifying these\nfluctuations is important when investing in stocks.\n\n\\begin{figure}[ht]\n\\centering\n\\Figure[A dot plot is overlaid on a box plot for a variable \"Monthly Returns Over 3 Years\". The box portion spans about -200 to 450, with the median line at about 200. The whiskers extend to the lower end at about -600 and at the upper end at about 1050. There is one point beyond the lower whisker located at about -1400. The the dot plot, the points are pretty evenly distributed across the locations of the box-and-whiskers portion of the box plot, with the one exception being the point at -1400.]{0.6}{changeInLeonardsStockPortfolioFor36Months}\n\\caption{The change in a portfolio like Leonard's for 36 months,\n    where \\$6000 is in Caterpillar's stock and \\$2000 is in\n    Exxon Mobil's.}\n\\label{changeInLeonardsStockPortfolioFor36Months}\n\\end{figure}\n\nJust as we have done in many previous cases,\nwe use the variance and standard deviation to describe\nthe uncertainty associated with Leonard's monthly returns.\nTo do so, the variances of each stock's monthly return\nwill be useful, and these are shown in\nFigure~\\ref{sumStatOfCATXOM}.\nThe stocks' returns are nearly independent.\n\n\\begin{figure}\n\\centering\n\\begin{tabular}{lrrr}\n\\hline\n    & Mean ($\\bar{x}$) & Standard deviation ($s$) &\n\t    Variance ($s^2$) \\\\\n\\hline\nCAT & 0.0204  & 0.0757  & 0.0057 \\\\\nXOM & 0.0025  & 0.0455  & 0.0021 \\\\\n\\hline\n\\end{tabular}\n\\caption{The mean, standard deviation, and variance of the\n    CAT and XOM stocks.\n    These statistics were estimated from historical\n    stock data, so notation used for sample statistics\n    has been used.}\n\\label{sumStatOfCATXOM}\n\\end{figure}\n\nHere we use an equation from probability theory to\ndescribe the uncertainty of Leonard's monthly returns;\nwe leave the proof of this method to a dedicated\nprobability course.\nThe variance of a linear combination of random variables\ncan be computed by plugging in the variances of the\nindividual random variables and squaring the coefficients\nof the random variables:\n\\begin{align*}\nVar(aX + bY) = a^2\\times Var(X) + b^2\\times Var(Y)\n\\end{align*}\nIt is important to note that this equality assumes the\nrandom variables are independent;\n%\\Comment{new description here about if independence is broken}\nif independence doesn't hold, then a modification to\nthis equation would be required that we leave as a topic\nfor a future course to cover.\nThis equation can be used to compute the variance of\nLeonard's monthly return:\n\\begin{align*}\nVar(6000\\times X + 2000\\times Y)\n\t&= 6000^2\\times Var(X) + 2000^2\\times Var(Y) \\\\\n\t&= 36,000,000\\times 0.0057 + 4,000,000\\times 0.0021 \\\\\n\t&\\approx 213,600\n% sum(c(36e6, 4e6) * c(0.0057, 0.0021))\n\\end{align*}\nThe standard deviation is computed as the square root\nof the variance: $\\sqrt{213,600} = \\$463$.\nWhile an average monthly return of \\$124 on an\n\\$8000 investment is nothing to scoff at,\nthe monthly returns are so volatile that Leonard should\nnot expect this income to be very stable.\n\n\\begin{onebox}{Variability of linear combinations of random variables}\nThe variance of a linear combination of random variables may be computed by squaring the constants, substituting in the variances for the random variables, and computing the result:\n\\begin{align*}\nVar(aX + bY) = a^2\\times Var(X) + b^2\\times Var(Y)\n\\end{align*}\nThis equation is valid as long as the random variables are independent of each other. The standard deviation of the linear combination may be found by taking the square root of the variance.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose John's daily commute has a standard deviation of 4 minutes. What is the uncertainty in his total commute time for the week?} \\label{sdOfJohnsCommuteWeeklyTime}\nThe expression for John's commute time was\n\\begin{align*}\nX_1 + X_2 + X_3 + X_4 + X_5\n\\end{align*}\nEach coefficient is 1, and the variance of each day's time is $4^2=16$. Thus, the variance of the total weekly commute time is\n\\begin{align*}\n&\\text{variance }= 1^2 \\times  16 + 1^2 \\times  16 + 1^2 \\times  16 + 1^2 \\times  16 + 1^2 \\times  16 = 5\\times 16 = 80 \\\\\n&\\text{standard deviation } = \\sqrt{\\text{variance}} = \\sqrt{80} = 8.94\n\\end{align*}\nThe standard deviation for John's weekly work commute time is about 9 minutes.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe computation in Example~\\ref{sdOfJohnsCommuteWeeklyTime} relied on an important assumption: the commute time for each day is independent of the time on other days of that week. Do you think this is valid? Explain.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{One concern is whether traffic patterns tend to have a weekly cycle (e.g. Fridays may be worse than other days). If that is the case, and John drives, then the assumption is probably not reasonable. However, if John walks to work, then his commute is probably not affected by any weekly traffic cycle.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\\label{elenaIsSellingATVAndBuyingAToasterOvenAtAnAuctionVariability}\nConsider Elena's two auctions from Guided Practice~\\ref{elenaIsSellingATVAndBuyingAToasterOvenAtAnAuction} on page~\\pageref{elenaIsSellingATVAndBuyingAToasterOvenAtAnAuction}. Suppose these auctions are approximately independent and the variability in auction prices associated with the TV and toaster oven can be described using standard deviations of \\$25 and \\$8. Compute the standard deviation of Elena's net gain.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The equation for Elena can be written as\n\\begin{align*}\n(1)\\times X + (-1)\\times Y\n\\end{align*}\nThe variances of $X$ and $Y$ are 625 and 64. We square the coefficients and plug in the variances:\n\\begin{align*}\n(1)^2\\times Var(X) + (-1)^2\\times Var(Y) = 1\\times 625 + 1\\times 64 = 689\n\\end{align*}\nThe variance of the linear combination is 689, and the standard deviation is the square root of 689: about \\$26.25.}\n\nConsider again Guided Practice~\\ref{elenaIsSellingATVAndBuyingAToasterOvenAtAnAuctionVariability}. The negative coefficient for $Y$ in the linear combination was eliminated when we squared the coefficients. This generally holds true: negatives in a linear combination will have no impact on the variability computed for a linear combination, but they do impact the expected value computations.\n\n\\index{random variable|)}\n\n\n{\\input{ch_probability/TeX/random_variables.tex}}\n\n\n\n\n\n%_________________\n\\section{Continuous distributions}\n\\label{contDist}\n\n\\noindent%\nSo far in this chapter we've discussed cases\nwhere the outcome of a variable is discrete.\nIn this section, we consider a context where\nthe outcome is a continuous numerical variable.\n\n\\index{data!US adult heights|(}\n\\index{hollow histogram|(}\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{fdicHistograms} shows a few\n    different hollow histograms for the heights of US adults.\n    How does changing the number of bins allow you to make\n    different interpretations of the data?}\n  \\label{usHeights}%\n  Adding more bins provides greater detail.\n  This sample is extremely large, which is why much smaller\n  bins still work well.\n  Usually we do not use so many bins with smaller sample\n  sizes since small counts per bin mean the bin heights\n  are very volatile.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[ht]\n  \\centering\n  \\Figure[Four hollow histograms are shown for the US adult heights in centimeters with varying bin widths. The difference in appears will first be discussed, and then the shape of the last, most detailed histogram will be given. The first histogram has about 6 bins with values that appear to be non-zero, so the outline is very boxy. The second histogram has about 12 non-zero bins, and appears a bit more continuous and less boxy than the first histogram. The third histogram has about 25 non-zero bins, and the hollow histogram outline is starting to look somewhat smoother. The last histogram has about 50 bins, and due to the large number of bins, the distribution looks quite smooth, in that no steps from one bin to the next is a substantial jump or drop in height. Next, this last histogram is described: The bin heights are about zero until 147, then they steadily climb up to about 155 before steeply climbing a little until 157 and then steadily climb to a peak at about 165. From here the histogram declines about 10\\% from its peak at 170, at which point the decline is more gradual until about 183, at which point it descends rapidly until about 187 where it begins to descend more slowly as it approaches 0. At about 200, the bin heights have essentially hit zero and stay there.]{}{fdicHistograms}\n  \\caption{Four hollow histograms of US adults heights\n      with varying bin widths.}\n  \\label{fdicHistograms}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{What proportion of the sample is between \\resp{180} cm and \\resp{185} cm tall (about 5'11\" to 6'1\")?}\\label{contDistProb}\nWe can add up the heights of the bins in the range \\resp{180} cm and \\resp{185} and divide by the sample size. For instance, this can be done with the two shaded bins shown in Figure~\\ref{usHeightsHist180185}. The two bins in this region have counts of 195,307 and 156,239 people, resulting in the following estimate of the probability:\n\\begin{align*}\n\\frac{195307 + 156239}{\\text{3,000,000}} = 0.1172\n\\end{align*}\nThis fraction is the same as the proportion of the histogram's area that falls in the range \\resp{180} to \\resp{185} cm.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A histogram for heights is shown, with the two histogram bins between 180 and 185 centimeters are shaded, representing the individuals with heights between 180 and 185 centimeters.]{0.9}{usHeightsHist180185}\n  \\caption{A histogram with bin sizes of 2.5 cm.\n      The shaded region represents individuals with\n      heights between \\resp{180} and \\resp{185} cm.}\n  \\label{usHeightsHist180185}\n\\end{figure}\n\n\n\\D{\\newpage}\n\n\\subsection{From histograms to continuous distributions}\n\nExamine the transition from a boxy hollow histogram in the top-left of Figure~\\ref{fdicHistograms} to the much smoother plot in the lower-right. In this last plot, the bins are so slim that the hollow histogram is starting to resemble a smooth curve. This suggests the population height as a \\emph{continuous} numerical variable might best be explained by a curve that represents the outline of extremely slim bins.\n\nThis smooth curve represents a\n\\termsub{probability density function}\n    {probability!density function}\n(also called a \\term{density} or \\term{distribution}), and such a curve is shown in Figure~\\ref{fdicHeightContDist} overlaid on a histogram of the sample. A density has a special property: the total area under the density's curve is 1. \n\n\\begin{figure}[tbh]\n\\centering\n\\Figure[A histogram for heights of US adults is shown with an overlaid continuous line along the heights of the bins. This continuous line is smooth and would represent what a hollow histogram would look like if we had infinite data and the bin width was so small that the boxy outline of the histogram looks continuous. This is called a \"continuous probability density\".]{0.9}{fdicHeightContDist}\n\\caption{The continuous probability distribution of heights for US adults.}\n\\label{fdicHeightContDist}\n\\end{figure}\n\n\\index{hollow histogram|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Probabilities from continuous distributions}\n\nWe computed the proportion of individuals with heights \\resp{180} to \\resp{185} cm in Example~\\ref{contDistProb} as a fraction:\n\\begin{align*}\n\\frac{\\text{number of people between \\resp{180} and \\resp{185}}}{\\text{total sample size}}\n\\end{align*}\nWe found the number of people with heights between \\resp{180} and \\resp{185} cm by determining the fraction of the histogram's area in this region. Similarly, we can use the area in the shaded region under the curve to find a probability (with the help of a computer):\n\\begin{align*}\nP(\\text{\\var{height} between \\resp{180} and \\resp{185}})\n\t= \\text{area between \\resp{180} and \\resp{185}}\n\t= 0.1157\n\\end{align*}\nThe probability that a randomly selected person is between \\resp{180} and \\resp{185} cm is 0.1157. This is very close to the estimate from Example~\\ref{contDistProb}: 0.1172. \n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A density curve for heights is shown, with the region between 180 and 185 centimeters being shaded.]{0.7}{fdicHeightContDistFilled}\n  \\caption{Density for heights in the US adult population\n      with the area between 180 and 185 cm shaded.\n      Compare this plot with Figure~\\ref{usHeightsHist180185}.}\n  \\label{fdicHeightContDistFilled}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThree US adults are randomly selected. The probability a single adult is between \\resp{180} and \\resp{185} cm is 0.1157.\\footnotemark\\vspace{-1.5mm}\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item[(a)] What is the probability that all three are between \\resp{180} and \\resp{185} cm tall?\n\\item[(b)] What is the probability that none are between \\resp{180} and \\resp{185} cm?\n\\end{enumerate}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Brief answers:\n  (a) $0.1157 \\times 0.1157 \\times 0.1157 = 0.0015$.\n  (b) $(1-0.1157)^3 = 0.692$}\n\n\\begin{examplewrap}\n\\begin{nexample}{What is the probability that a randomly selected person is \\textbf{exactly} \\resp{180}~cm? Assume you can measure perfectly.}\n\\label{probabilityOfExactly180cm}\nThis probability is zero. A person might be close to \\resp{180} cm, but not exactly \\resp{180} cm tall. This also makes sense with the definition of probability as area; there is no area captured between \\resp{180}~cm and \\resp{180}~cm.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nSuppose a person's height is rounded to the nearest centimeter. Is there a chance that a random person's \\textbf{measured} height will be \\resp{180} cm?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{This has positive probability. Anyone between \\resp{179.5} cm and \\resp{180.5} cm will have a \\emph{measured} height of \\resp{180} cm. This is probably a more realistic scenario to encounter in practice versus Example~\\ref{probabilityOfExactly180cm}.}\n\n\\index{data!US adult heights|)}\n\n\n{\\input{ch_probability/TeX/continuous_distributions.tex}}\n"
  },
  {
    "path": "ch_probability/TeX/conditional_probability.tex",
    "content": "\\exercisesheader{}\n\n% 13\n\n\\eoce{\\qt{Joint and conditional probabilities\\label{joint_cond}} P(A) = 0.3, \nP(B) = 0.7\n\\begin{parts}\n\\item Can you compute P(A and B) if you only know P(A) and P(B)?\n\\item Assuming that events A and B arise from independent random processes,\n\\begin{subparts}\n\\item what is P(A and B)?\n\\item what is P(A or B)?\n\\item what is P(A$|$B)?\n\\end{subparts}\n\\item If we are given that P(A and B) = 0.1, are the random variables giving rise \nto events A and B independent?\n\\item If we are given that P(A and B) = 0.1, what is P(A$|$B)?\n\\end{parts}\n}{}\n\n% 14\n\n\\eoce{\\qt{PB \\& J\\label{pbj}} Suppose 80\\% of people like peanut butter, 89\\% \nlike jelly, and 78\\% like both. Given that a randomly sampled person likes peanut \nbutter, what's the probability that he also likes jelly?\n}{}\n\n% 15\n\n\\eoce{\\qt{Global warming\\label{global_warming}} A Pew Research poll asked \n1,306 Americans ``From what you've read and heard, is there solid evidence that \nthe average temperature on earth has been getting warmer over the past few \ndecades, or not?\". The table below shows the distribution of responses by party \nand ideology, where the counts have been replaced with relative frequencies.\n\\footfullcite{globalWarming}\n\\begin{center}\n\\begin{tabular}{ll  ccc c} \n                    &                           & \\multicolumn{3}{c}{\\textit{Response}} \\\\\n\\cline{3-5}\n                    &                           & Earth is  & Not       & Don't Know    &   \\\\\n                    &                           & warming   & warming   & Refuse        & Total\\\\\n\\cline{2-6}\n                    & Conservative Republican   & 0.11      & 0.20      & 0.02      & 0.33  \\\\\n\\textit{Party and}  & Mod/Lib Republican        & 0.06      & 0.06      & 0.01      & 0.13 \\\\\n\\textit{Ideology}   & Mod/Cons Democrat         & 0.25      & 0.07      & 0.02      & 0.34 \\\\\n                    & Liberal Democrat          & 0.18      & 0.01      & 0.01      & 0.20\\\\\n\\cline{2-6}\n                    &Total                      & 0.60      & 0.34      & 0.06      & 1.00\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Are believing that the earth is warming and being a liberal Democrat mutually \nexclusive?\n\\item What is the probability that a randomly chosen respondent believes the \nearth is warming or is a liberal Democrat?\n\\item What is the probability that a randomly chosen respondent believes the \nearth is warming given that he is a liberal Democrat?\n\\item What is the probability that a randomly chosen respondent believes the \nearth is warming given that he is a conservative Republican?\n\\item Does it appear that whether or not a respondent believes the earth is \nwarming is independent of their party and ideology? Explain your reasoning.\n\\item What is the probability that a randomly chosen respondent is a \nmoderate/liberal Republican given that he does not believe that the earth is \nwarming? \n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 16\n\n\\eoce{\\qt{Health coverage, relative frequencies\\label{health_coverage_rel_freqs}} \nThe Behavioral Risk Factor Surveillance System (BRFSS) is an annual telephone \nsurvey designed to identify risk factors in the adult population and report \nemerging health trends. The following table displays the distribution of health \nstatus of respondents to this survey (excellent, very good, good, fair, poor) \nand whether or not they have health insurance.\n\\begin{center}\n\\begin{tabular}{rrrrrrrr}\n& &  \\multicolumn{5}{c}{\\textit{Health Status}} &  \\\\ \n\\cline{3-7}\n                    &       & Excellent & Very good & Good      & Fair      & Poor      & Total \\\\ \n\\cline{2-8}\n\\textit{Health}     & No    & 0.0230    & 0.0364    & 0.0427    & 0.0192    & 0.0050    & 0.1262 \\\\ \n\\textit{Coverage}   & Yes   & 0.2099    & 0.3123    & 0.2410    & 0.0817    & 0.0289    & 0.8738 \\\\ \n\\cline{2-8}\n                    & Total & 0.2329    & 0.3486    & 0.2838    & 0.1009    & 0.0338    & 1.0000\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Are being in excellent health and having health coverage mutually \nexclusive?\n\\item What is the probability that a randomly chosen individual has excellent \nhealth?\n\\item What is the probability that a randomly chosen individual has excellent \nhealth given that he has health coverage?\n\\item What is the probability that a randomly chosen individual has excellent \nhealth given that he doesn't have health coverage?\n\\item Do having excellent health and having health coverage appear to be \nindependent?\n\\end{parts}\n}{}\n\n% 17\n\n\\eoce{\\qt{Burger preferences\\label{burger_preferences}} A 2010 SurveyUSA poll \nasked 500 Los Angeles residents, ``What is the best hamburger place in Southern \nCalifornia? Five Guys Burgers? In-N-Out Burger? Fat Burger? Tommy's Hamburgers? \nUmami Burger? Or somewhere else?'' The distribution of responses by gender is \nshown below. \\footfullcite{burgers}\n\\begin{center}\n\\begin{tabular}{l p{4cm} r r r }\n                    &                       & \\multicolumn{2}{c}{\\textit{Gender}} \\\\\n\\cline{3-4}\n                    &                       & Male  & Female    & Total \\\\\n\\cline{2-5}\n                    & Five Guys Burgers     & 5     & 6         & 11 \\\\\n                    & In-N-Out Burger       & 162   & 181       & 343 \\\\\n\\textit{Best}       & Fat Burger            & 10    & 12        & 22 \\\\\n\\textit{hamburger}  & Tommy's Hamburgers    & 27    & 27        & 54 \\\\ \n\\textit{place}      & Umami Burger          & 5     & 1         & 6 \\\\\n                    & Other                 & 26    & 20        & 46 \\\\\n                    & Not Sure              & 13    & 5         & 18 \\\\\n\\cline{2-5}  \n                    & Total                 & 248   & 252       & 500\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Are being female and liking Five Guys Burgers mutually exclusive?\n\\item What is the probability that a randomly chosen male likes In-N-Out the best?\n\\item What is the probability that a randomly chosen female likes In-N-Out the \nbest?\n\\item What is the probability that a man and a woman who are dating both like \nIn-N-Out the best? Note any assumption you make and evaluate whether you think \nthat assumption is reasonable.\n\\item What is the probability that a randomly chosen person likes Umami best or \nthat person is female?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 18\n\n\\eoce{\\qt{Assortative mating\\label{assortative_mating}} Assortative mating is a \nnonrandom mating pattern where individuals with similar genotypes and/or \nphenotypes mate with one another more frequently than what would be expected \nunder a random mating pattern. Researchers studying this topic collected data on \neye colors of 204 Scandinavian men and their female partners. The table below \nsummarizes the results.\\footfullcite{Laeng:2007}\n\\begin{center}\n\\begin{tabular}{ll  ccc c} \n                                        &           & \\multicolumn{3}{c}{\\textit{Partner (female)}} \\\\\n\\cline{3-5}\n                                        &           & Blue  & Brown     & Green     & Total \\\\\n\\cline{2-6}\n                                        & Blue      & 78    & 23        & 13        & 114 \\\\\n\\multirow{2}{*}{\\textit{Self (male)}}   & Brown     & 19    & 23        & 12        & 54 \\\\\n                                        & Green     & 11    & 9         & 16        & 36 \\\\\n\\cline{2-6}  \n                                        & Total     & 108   & 55        & 41        & 204\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item What is the probability that a randomly chosen male respondent or his \npartner has blue eyes?\n\\item What is the probability that a randomly chosen male respondent with blue \neyes has a partner with blue eyes? \n\\item What is the probability that a randomly chosen male respondent with brown \neyes has a partner with blue eyes? What about the probability of a randomly \nchosen male respondent with green eyes having a partner with blue eyes?\n\\item Does it appear that the eye colors of male respondents and their partners \nare independent? Explain your reasoning.\n\\end{parts}\n}{}\n\n% 19\n\n\\eoce{\\qt{Drawing box plots\\label{tree_drawing_box_plots}} After an introductory \nstatistics course, 80\\% of students can successfully construct box plots. Of \nthose who can construct box plots, 86\\% passed, while only 65\\% of those students \nwho could not construct box plots passed.\n\\begin{parts}\n\\item Construct a tree diagram of this scenario.\n\\item Calculate the probability that a student is able to construct a box plot \nif it is known that he passed.\n\\end{parts}\n}{}\n\n% 20\n\n\\eoce{\\qt{Predisposition for thrombosis\\label{tree_thrombosis}} A genetic test is \nused to determine if people have a predisposition for \\textit{thrombosis}, which \nis the formation of a blood clot inside a blood vessel that obstructs the flow of \nblood through the circulatory system. It is believed that 3\\% of people actually \nhave this predisposition. The genetic test is 99\\% accurate if a person actually \nhas the predisposition, meaning that the probability of a positive test result \nwhen a person actually has the predisposition is 0.99. The test is 98\\% accurate \nif a person does not have the predisposition. What is the probability that a \nrandomly selected person who tests positive for the predisposition by the test \nactually has the predisposition?\n}{}\n\n% 21\n\n\\eoce{\\qt{It's never lupus\\label{tree_lupus}} Lupus is a medical phenomenon where \nantibodies that are supposed to attack foreign cells to prevent infections \ninstead see plasma proteins as foreign bodies, leading to a high risk of blood \nclotting. It is believed that 2\\% of the population suffer from this disease. The \ntest is 98\\% accurate if a person actually has the disease. The test is 74\\% \naccurate if a person does not have the disease. There is a line from the Fox \ntelevision show \\emph{House} that is often used after a patient tests positive \nfor lupus: ``It's never lupus.\" Do you think there is truth to this statement? \nUse appropriate probabilities to support your answer.\n}{}\n\n% 22\n\n\\eoce{\\qt{Exit poll\\label{tree_exit_poll}} Edison Research gathered exit poll \nresults from several sources for the Wisconsin recall election of Scott Walker. \nThey found that 53\\% of the respondents voted in favor of Scott Walker. \nAdditionally, they estimated that of those who did vote in favor for Scott \nWalker, 37\\% had a college degree, while 44\\% of those who voted against Scott \nWalker had a college degree. Suppose we randomly sampled a person who \nparticipated in the exit poll and found that he had a college degree. What is the \nprobability that he voted in favor of Scott Walker?\n\\footfullcite{data:scott}\n}{}\n"
  },
  {
    "path": "ch_probability/TeX/continuous_distributions.tex",
    "content": "\\exercisesheader{}\n\n% 37\n\n\\eoce{\\qt{Cat weights\\label{cat_weights}} The histogram shown below represents \nthe weights (in kg) of 47 female and 97 male cats. \\footfullcite{cats} \\\\\n\\begin{minipage}[c]{0.47\\textwidth}\n\\begin{parts}\n\\item What fraction of these cats weigh less than 2.5 kg?\n\\item What fraction of these cats weigh between 2.5 and 2.75 kg?\n\\item What fraction of these cats weigh between 2.75 and 3.5 kg?\n\\end{parts} \\vspace{27mm}\n\\end{minipage}\n\\begin{minipage}[c]{0.05\\textwidth}\n$\\:$ \n\\end{minipage}\n\\begin{minipage}[c]{0.48\\textwidth}\n\\begin{center}\n\\Figures[A histogram of cat body weights in kilograms is shown. The weight range is from 2.0 to 4.0, and each histogram bin has a width of 0.25. The eight bin heights, from left to right, are 29, 32, 21, 25, 12, 15, 5, and 4.]{}{eoce/cat_weights}{cat_weights}\n\\end{center}\n\\end{minipage}\n}{}\n\n% 38\n\n\\eoce{\\qt{Income and gender\\label{income_gender}} The relative frequency table \nbelow displays the distribution of annual total personal income (in 2009 \ninflation-adjusted dollars) for a representative sample of 96,420,486 Americans. \nThese data come from the American Community Survey for 2005-2009. This sample is \ncomprised of 59\\% males and 41\\% females. \\footfullcite{acsIncome2005-2009} \\\\\n\n\\noindent\\begin{minipage}[c]{0.60\\textwidth}\n\\begin{parts}\n\\item Describe the distribution of total personal income.\n\\item What is the probability that a randomly chosen US resident makes less than \n\\$50,000 per year?\n\\item What is the probability that a randomly chosen US resident makes less than \n\\$50,000 per year and is female? Note any assumptions you make.\n\\item The same data source indicates that 71.8\\% of females make less than \n\\$50,000 per year. Use this value to determine whether or not the assumption you \nmade in part (c) is valid.\n\\end{parts} \n\\end{minipage}\n\\begin{minipage}[c]{0.4\\textwidth}\n{\\small\n\\begin{center}\n\\begin{tabular}{lr}\n  \\hline\n\\textit{Income}         & \\textit{Total} \\\\\n  \\hline\n\\$1 to \\$9,999 or loss  & 2.2\\% \\\\\n\\$10,000 to \\$14,999    & 4.7\\% \\\\\n\\$15,000 to \\$24,999    & 15.8\\% \\\\\n\\$25,000 to \\$34,999    & 18.3\\% \\\\\n\\$35,000 to \\$49,999    & 21.2\\% \\\\\n\\$50,000 to \\$64,999    & 13.9\\% \\\\\n\\$65,000 to \\$74,999    & 5.8\\% \\\\\n\\$75,000 to \\$99,999    & 8.4\\% \\\\\n\\$100,000 or more       & 9.7\\% \\\\\n   \\hline\n\\end{tabular}\n\\end{center}\n}\n\\end{minipage}\n}{}\n"
  },
  {
    "path": "ch_probability/TeX/defining_probability.tex",
    "content": "\\exercisesheader{}\n\n% 1\n\n\\eoce{\\qt{True or false\\label{tf_prob_definitions}} Determine if the statements \nbelow are true or false, and explain your reasoning.\n\\begin{parts}\n\\item If a fair coin is tossed many times and the last eight tosses are all heads, \nthen the chance that the next toss will be heads is somewhat less than 50\\%.\n\\item Drawing a face card (jack, queen, or king) and drawing a red card from a \nfull deck of playing cards are mutually exclusive events.\n\\item Drawing a face card and drawing an ace from a full deck of playing cards \nare mutually exclusive events.\n\\end{parts}\n}{}\n\n% 2\n\n\\eoce{\\qt{Roulette wheel\\label{roulette_wheel}} The game of roulette involves \nspinning a wheel with 38 slots: 18 red, 18 black, and 2 green. A ball is spun \nonto the wheel and will eventually land in a slot, where each slot has an equal \nchance of capturing the ball.\n\n\\noindent%\n\\begin{minipage}[c]{0.65\\textwidth}\n\\raggedright\\begin{parts}\n\\item You watch a roulette wheel spin 3 consecutive times and the ball lands on a \nred slot each time. What is the probability that the ball will land on a red slot \non the next spin?\n\\item You watch a roulette wheel spin 300 consecutive times and the ball lands on \na red slot each time. What is the probability that the ball will land on a red \nslot on the next spin?\n\\item Are you equally confident of your answers to parts~(a) and~(b)? Why or why \nnot?\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.05\\textwidth}\n\\ \n\\end{minipage}\n\\begin{minipage}[c]{0.28\\textwidth}\n\\begin{center}\n\\Figures[A photo of a roulette wheel.]{}{eoce/roulette_wheel}{roulette_wheel.jpg} \\\\\n{\\footnotesize Photo by H\\r{a}kan Dahlstr\\\"{o}m \\\\\n  (\\oiRedirect{textbook-flickr_hakan_dahlstrom_roulette_wheel}{http://flic.kr/p/93fEzp}) \\\\\n  \\oiRedirect{textbook-CC_BY_2}{CC~BY~2.0~license}}\n\\end{center}\n\\end{minipage}\n}{}\n\n% 3\n\n\\eoce{\\qt{Four games, one winner\\label{four_games_one_winner}} Below are four \nversions of the same game. Your archnemesis gets to pick the version of the game, \nand then you get to choose how many times to flip a coin: 10 times or 100 times. \nIdentify how many coin flips you should choose for each version of the game. It \ncosts \\$1 to play each game. Explain your reasoning.\n\\begin{parts}\n\\item If the proportion of heads is larger than 0.60, you win \\$1.\n\\item If the proportion of heads is larger than 0.40, you win \\$1.\n\\item If the proportion of heads is between 0.40 and 0.60, you win \\$1.\n\\item If the proportion of heads is smaller than 0.30, you win \\$1.\n\\end{parts}\n}{}\n\n% 4\n\n\\eoce{\\qt{Backgammon\\label{backgammon}} Backgammon is a board game for two \nplayers in which the playing pieces are moved according to the roll of two dice. \nPlayers win by removing all of their pieces from the board, so it is usually good \nto roll high numbers. You are playing backgammon with a friend and you roll two \n6s in your first roll and two 6s in your second roll. Your friend rolls two 3s in \nhis first roll and again in his second row. Your friend claims that you are \ncheating, because rolling double 6s twice in a row is very unlikely. Using \nprobability, show that your rolls were just as likely as~his.\n}{}\n\n% 5\n\n\\eoce{\\qt{Coin flips\\label{coin_flips}} If you flip a fair coin 10 times, what is \nthe probability of\n\\begin{parts}\n\\item getting all tails? \n\\item getting all heads? \n\\item getting at least one tails? \n\\end{parts}\n}{}\n\n% 6\n\n\\eoce{\\qt{Dice rolls\\label{dice_rolls}} If you roll a pair of fair dice, what is \nthe probability of\n\\begin{parts}\n\\item getting a sum of 1?\n\\item getting a sum of 5?\n\\item getting a sum of 12?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 7\n\n\\eoce{\\qt{Swing voters\\label{swing_voters}} A Pew Research survey asked 2,373 \nrandomly sampled registered voters their political affiliation (Republican, \nDemocrat, or Independent) and whether or not they identify as swing voters. 35\\% \nof respondents identified as Independent, 23\\% identified as swing voters, and \n11\\% identified as both.\\footfullcite{indepSwing}\n\\begin{parts}\n\\item Are being Independent and being a swing voter disjoint, i.e. mutually \nexclusive?\n\\item Draw a Venn diagram summarizing the variables and their associated \nprobabilities.\n\\item What percent of voters are Independent but not swing voters?\n\\item What percent of voters are Independent or swing voters?\n\\item What percent of voters are neither Independent nor swing voters?\n\\item Is the event that someone is a swing voter independent of the event that \nsomeone is a political Independent?\n\\end{parts}\n}{}\n\n% 8\n\n\\eoce{\\qt{Poverty and language\\label{poverty_language}} The American Community \nSurvey is an ongoing survey that provides data every year to give communities the \ncurrent information they need to plan investments and services. The 2010 American \nCommunity Survey estimates that 14.6\\% of Americans live below the poverty line, \n20.7\\% speak a language other than English (foreign language) at home, and 4.2\\% \nfall into both categories. \\footfullcite{poorLang}\n\\begin{parts}\n\\item Are living below the poverty line and speaking a foreign language at home \ndisjoint?\n\\item Draw a Venn diagram summarizing the variables and their associated \nprobabilities.\n\\item What percent of Americans live below the poverty line and only speak \nEnglish at home?\n\\item What percent of Americans live below the poverty line or speak a foreign \nlanguage at home?\n\\item What percent of Americans live above the poverty line and only speak \nEnglish at home? \n\\item Is the event that someone lives below the poverty line independent of the \nevent that the person speaks a foreign language at home?\n\\end{parts}\n}{}\n\n% 9\n\n\\eoce{\\qt{Disjoint vs. independent\\label{disjoint_indep}} In parts~(a) and~(b), \nidentify whether the events are disjoint, independent, or neither (events cannot \nbe both disjoint and independent).\n\\begin{parts}\n\\item You and a randomly selected student from your class both earn A's in this \ncourse. \n\\item You and your class study partner both earn A's in this course.\n\\item If two events can occur at the same time, must they be dependent?\n\\end{parts}\n}{}\n\n% 10\n\n\\eoce{\\qt{Guessing on an exam\\label{guessing_on_exam}} In a multiple choice exam, \nthere are 5 questions and 4 choices for each question (a, b, c, d). Nancy has not \nstudied for the exam at all and decides to randomly guess the answers. What is \nthe probability that:\n\\begin{parts}\n\\item the first question she gets right is the $5^{th}$ question?\n\\item she gets all of the questions right?\n\\item she gets at least one question right?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 11\n\n\\eoce{\\qt{Educational attainment of couples\\label{edu_attain_couples}} The table \nbelow shows the distribution of education level attained by US residents by \ngender based on data collected in the 2010 American Community Survey.\n\\footfullcite{eduSex}\n\\begin{center}\n\\begin{tabular}{l p{7cm} c c }\n&                                       & \\multicolumn{2}{c}{\\textit{Gender}} \\\\\n\\cline{3-4}\n&                                                   & Male  & Female \\\\\n\\cline{2-4}\n& Less than 9th grade                               & 0.07  & 0.13 \\\\\n& 9th to 12th grade, no diploma                     & 0.10  & 0.09 \\\\\n\\textit{Highest}    & HS graduate (or equivalent)   & 0.30  & 0.20 \\\\\n\\textit{education}  & Some college, no degree       & 0.22  & 0.24 \\\\ \n\\textit{attained}   & Associate's degree            & 0.06  & 0.08 \\\\\n& Bachelor's degree                                 & 0.16  & 0.17 \\\\\n& Graduate or professional degree                   & 0.09  & 0.09 \\\\\n\\cline{2-4} \n& Total                                             & 1.00  & 1.00\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item What is the probability that a randomly chosen man has at least a \nBachelor's degree?\n\\item What is the probability that a randomly chosen woman has at least a \nBachelor's degree?\n\\item What is the probability that a man and a woman getting married both have at \nleast a Bachelor's degree? Note any assumptions you must make to answer this \nquestion.\n\\item If you made an assumption in part~(c), do you think it was reasonable? If \nyou didn't make an assumption, double check your earlier answer and then return \nto this part.\n\\end{parts}\n}{}\n\n% 12\n\n\\eoce{\\qt{School absences\\label{school_absences}} Data collected at elementary \nschools in DeKalb County, GA suggest that each year roughly 25\\% of students miss \nexactly one day of school, 15\\% miss 2 days, and 28\\% miss 3 or more days due to \nsickness. \\footfullcite{Mizan:2011}\n\\begin{parts}\n\\item What is the probability that a student chosen at random doesn't miss any \ndays of school due to sickness this year?\n\\item What is the probability that a student chosen at random misses no more than \none day?\n\\item What is the probability that a student chosen at random misses at least one \nday?\n\\item If a parent has two kids at a DeKalb County elementary school, what is the \nprobability that neither kid will miss any school? Note any assumption you must \nmake to answer this question.\n\\item If a parent has two kids at a DeKalb County elementary school, what is the \nprobability that both kids will miss some school, i.e. at least one day? Note any \nassumption you make.\n\\item If you made an assumption in part~(d) or~(e), do you think it was \nreasonable? If you didn't make any assumptions, double check your earlier answers.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_probability/TeX/random_variables.tex",
    "content": "\\exercisesheader{}\n\n% 29\n\n\\eoce{\\qt{College smokers\\label{college_smokers}} At a university, 13\\% of \nstudents smoke.\n\\begin{parts}\n\\item Calculate the expected number of smokers in a random sample of 100 students \nfrom this university.\n\\item The university gym opens at 9 am on Saturday mornings. One Saturday morning \nat 8:55 am there are 27 students outside the gym waiting for it to open. Should \nyou use the same approach from part (a) to calculate the expected number of \nsmokers among these 27 students?\n\\end{parts}\n}{}\n\n% 30\n\n\\eoce{\\qt{Ace of clubs wins\\label{ace_of_clubs}} Consider the following card game \nwith a well-shuffled deck of cards. If you draw a red card, you win nothing. If \nyou get a spade, you win \\$5. For any club, you win \\$10 plus an extra \\$20 for \nthe ace of clubs.\n\\begin{parts}\n\\item Create a probability model for the amount you win at this game. Also, find \nthe expected winnings for a single game and the standard deviation of the \nwinnings.\n\\item What is the maximum amount you would be willing to pay to play this game? \nExplain your reasoning.\n\\end{parts}\n}{}\n\n% 31\n\n\\eoce{\\qt{Hearts win\\label{hearts}} In a new card game, you start\nwith a well-shuffled full deck and draw 3 cards without replacement.\nIf you draw 3 hearts, \nyou win \\$50. If you draw 3 black cards, you win \\$25. For any other draws, you \nwin nothing.\n\\begin{parts}\n\\item Create a probability model for the amount you win at this game, and find \nthe expected winnings. Also compute the standard deviation of this distribution.\n\\item If the game costs \\$5 to play, what would be the expected value and \nstandard deviation of the net profit (or loss)? \\textit{(Hint: \nprofit = winnings $-$ cost; $X-5$)}\n\\item If the game costs \\$5 to play, should you play this game? Explain.\n\\end{parts}\n}{}\n\n% 32\n\n\\eoce{\\qtq{Is it worth it\\label{worth_it}} Andy is always looking for ways to \nmake money fast. Lately, he has been trying to make money by gambling. Here is \nthe game he is considering playing: The game costs \\$2 to play. He draws a card \nfrom a deck. If he gets a number card (2-10), he wins nothing. For any face card (\njack, queen or king), he wins \\$3. For any ace, he wins \\$5, and he wins an \n\\textit{extra} \\$20 if he draws the ace of clubs.\n\\begin{parts}\n\\item Create a probability model and find Andy's expected profit per game.\n\\item Would you recommend this game to Andy as a good way to make money? Explain.\n\\end{parts}\n}{}\n\n% 33\n\n\\eoce{\\qt{Portfolio return\\label{portfolio_return}} A portfolio's value increases \nby 18\\% during a financial boom and by 9\\% during normal times. It decreases by \n12\\% during a recession. What is the expected return on this portfolio if each \nscenario is equally likely?\n}{}\n\n% 34\n\n\\eoce{\\qt{Baggage fees\\label{baggage_fees}} An airline charges the following \nbaggage fees: \\$25 for the first bag and \\$35 for the second. Suppose 54\\% of \npassengers have no checked luggage, 34\\% have one piece of checked luggage and \n12\\% have two pieces. We suppose a negligible portion of people check more than \ntwo bags.\n\\begin{parts}\n\\item Build a probability model, compute the average revenue per passenger, and \ncompute the corresponding standard deviation.\n\\item About how much revenue should the airline expect for a flight of 120 \npassengers? With what standard deviation? Note any assumptions you make and if \nyou think they are justified.\n\\end{parts}\n}{}\n\n% 35\n\n\\eoce{\\qt{American roulette\\label{roulette_american}} The game of American \nroulette involves spinning a wheel with 38 slots: 18 red, 18 black, and 2 green. \nA ball is spun onto the wheel and will eventually land in a slot, where each slot \nhas an equal chance of capturing the ball. Gamblers can place bets on red or \nblack. If the ball lands on their color, they double their money. If it lands on \nanother color, they lose their money. Suppose you bet \\$1 on red. What's the \nexpected value and standard deviation of your winnings?\n}{}\n\n% 36\n\n\\eoce{\\qt{European roulette\\label{roulette_european}} The game of European \nroulette involves spinning a wheel with 37 slots: 18 red, 18 black, and 1 green. \nA ball is spun onto the wheel and will eventually land in a slot, where each slot \nhas an equal chance of capturing the ball. Gamblers can place bets on red or \nblack. If the ball lands on their color, they double their money. If it lands on \nanother color, they lose their money.\n\\begin{parts}\n\\item Suppose you play roulette and bet \\$3 on a single round. What is the \nexpected value and standard deviation of your total winnings?\n\\item Suppose you bet \\$1 in three different rounds. What is the expected value \nand standard deviation of your total winnings?\n\\item How do your answers to parts (a) and (b) compare? What does this say about \nthe riskiness of the two games?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_probability/TeX/review_exercises.tex",
    "content": "\\reviewexercisesheader{}\n\n% 39\n\n\\eoce{\\qt{Grade distributions\\label{grade_dists}} Each row in the table below is \na proposed grade distribution for a class. Identify each as a valid or invalid \nprobability distribution, and explain your reasoning.\n\\begin{center}\n\\begin{tabular}{l  ccccc} \n    & \\multicolumn{5}{c}{\\textit{Grades}} \\\\\n\\cline{2-6}\n    & A     & B     & C     & D     & F  \\\\\n\\cline{2-6}\n(a) & 0.3   & 0.3   & 0.3   & 0.2   & 0.1\\\\\n(b) & 0     & 0     & 1     & 0     & 0 \\\\\n(c) & 0.3   & 0.3   & 0.3   & 0     & 0 \\\\\n(d) & 0.3   & 0.5   & 0.2   & 0.1   & -0.1 \\\\\n(e) & 0.2   & 0.4   & 0.2   & 0.1   & 0.1 \\\\\n(f) & 0     & -0.1  & 1.1   & 0     & 0 \\\\\n\\end{tabular}\n\\end{center}\n}{}\n\n% 40\n\n\\eoce{\\qt{Health coverage, frequencies\\label{health_coverage_freqs}} The \nBehavioral Risk Factor Surveillance System (BRFSS) is an annual telephone survey \ndesigned to identify risk factors in the adult population and report emerging \nhealth trends. The following table summarizes two variables for the respondents: \nhealth status and health coverage, which describes whether each respondent had \nhealth insurance. \\footfullcite{data:BRFSS2010}\n\\begin{center}\n\\begin{tabular}{rrrrrrrr}\n                    &       & \\multicolumn{5}{c}{\\textit{Health Status}} &  \\\\ \n\\cline{3-7}\n                    &       & Excellent & Very good & Good  & Fair  & Poor  & Total\\\\ \n\\cline{2-8}\n\\textit{Health}     & No    & 459       & 727       & 854   & 385   & 99    & 2,524 \\\\ \n\\textit{Coverage}   & Yes   & 4,198     & 6,245     & 4,821 & 1,634 & 578   & 17,476 \\\\ \n\\cline{2-8}\n                    & Total & 4,657     & 6,972     & 5,675 & 2,019 & 677   & 20,000\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item If we draw one individual at random, what is the probability that the \nrespondent has excellent health and doesn't have health coverage?\n\\item If we draw one individual at random, what is the probability that the \nrespondent has excellent health or doesn't have health coverage?\n\\end{parts}\n}{}\n\n% 41\n\n\\eoce{\\qt{HIV in Swaziland\\label{tree_hiv_swaziland}} Swaziland has the highest \nHIV prevalence in the world: 25.9\\% of this country's population is infected with \nHIV.\\footfullcite{ciaFactBookHIV:2012} The ELISA test is one of the first and \nmost accurate tests for HIV. For those who carry HIV, the ELISA test is 99.7\\% \naccurate. For those who do not carry HIV, the test is 92.6\\% accurate. If an \nindividual from Swaziland has tested positive, what is the probability that he \ncarries HIV?\n}{}\n\n% 42\n\n\\eoce{\\qt{Twins\\label{tree_twins}} About 30\\% of human twins are identical, and \nthe rest are fraternal. Identical twins are necessarily the same sex -- half are \nmales and the other half are females. One-quarter of fraternal twins are both \nmale, one-quarter both female, and one-half are mixes: one male, one female. You \nhave just become a parent of twins and are told they are both girls. Given this \ninformation, what is the probability that they are identical?\n}{}\n\n% 43\n\n\\eoce{\\qt{Cost of breakfast\\label{cost_of_breakfast}} Sally gets a cup of coffee \nand a muffin every day for breakfast from one of the many coffee shops in her \nneighborhood. She picks a coffee shop each morning at random and independently of \nprevious days. The average price of a cup of coffee is \\$1.40 with a standard \ndeviation of 30\\textcent{} (\\$0.30), the average price of a muffin is \\$2.50 with a \nstandard deviation of 15\\textcent{}, and the two prices are independent of each \nother.\n\\begin{parts}\n\\item What is the mean and standard deviation of the amount she spends on \nbreakfast daily?\n\\item What is the mean and standard deviation of the amount she spends on \nbreakfast weekly (7~days)?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 44\n\n\\eoce{\\qt{Scooping ice cream\\label{scoop_ice_cream}} Ice cream usually comes in 1.5 \nquart boxes (48 fluid ounces), and ice cream scoops hold about 2 ounces. \nHowever, there is some variability in the amount of ice cream in a box as well as \nthe amount of ice cream scooped out. We represent the amount of ice cream in the \nbox as $X$ and the amount scooped out as $Y$. Suppose these random variables have \nthe following means, standard deviations, and variances:\n\\begin{center}\n\\begin{tabular}{l ccc}\n\\hline\n    & mean & SD & variance \\\\\n\\hline\n$X$ & 48       & 1      & 1     \\\\\n$Y$ & 2    & 0.25   & 0.0625    \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item An entire box of ice cream, plus 3 scoops from a second box is served at a \nparty. How much ice cream do you expect to have been served at this party? What \nis the standard deviation of the amount of ice cream served?\n\\item How much ice cream would you expect to be left in the box after scooping \nout one scoop of ice cream? That is, find the expected value of $X-Y$. What is \nthe standard deviation of the amount left in the box?\n\\item Using the context of this exercise, explain why we add variances when we \nsubtract one random variable from another.\n\\end{parts}\n}{}\n\n% 45\n\n\\eoce{\\qt{Variance of a mean, Part I\\label{var_of_mean_1}}\nSuppose we have independent observations $X_1$ and $X_2$ from\na distribution with mean $\\mu$ and standard deviation $\\sigma$.\nWhat is the variance of the mean of the two values:\n$\\frac{X_1 + X_2}{2}$?\n}{}\n\n% 46\n\n\\eoce{\\qt{Variance of a mean, Part II\\label{var_of_mean_2}}\nSuppose we have 3 independent observations\n$X_1$, $X_2$, $X_3$ from\na distribution with mean $\\mu$ and standard deviation $\\sigma$.\nWhat is the variance of the mean of these 3 values:\n$\\frac{X_1 + X_2 + X_3}{3}$?\n}{}\n\n% 47\n\n\\eoce{\\qt{Variance of a mean, Part III\\label{var_of_mean_3}}\nSuppose we have $n$ independent observations\n$X_1$, $X_2$, ..., $X_n$ from\na distribution with mean $\\mu$ and standard deviation $\\sigma$.\nWhat is the variance of the mean of these $n$ values:\n$\\frac{X_1 + X_2 + \\dots + X_n}{n}$?\n}{}\n"
  },
  {
    "path": "ch_probability/TeX/sampling_from_a_small_population.tex",
    "content": "\\exercisesheader{}\n\n% 23\n\n\\eoce{\\qt{Marbles in an urn\\label{marbles_in_urn}} Imagine you have an urn \ncontaining 5 red, 3 blue, and 2 orange marbles in it. \n\\begin{parts}\n\\item What is the probability that the first marble you draw is blue?\n\\item Suppose you drew a blue marble in the first draw. If drawing with \nreplacement, what is the probability of drawing a blue marble in the second draw?\n\\item Suppose you instead drew an orange marble in the first draw. If drawing \nwith replacement, what is the probability of drawing a blue marble in the second \ndraw?\n\\item If drawing with replacement, what is the probability of drawing two blue \nmarbles in a row?\n\\item When drawing with replacement, are the draws independent? Explain.\n\\end{parts}\n}{}\n\n% 24\n\n\\eoce{\\qt{Socks in a drawer\\label{socks_in_drawer}} In your sock drawer you have \n4 blue, 5 gray, and 3 black socks. Half asleep one morning you grab 2 socks at \nrandom and put them on. Find the probability you end up wearing\n\\begin{parts}\n\\item 2 blue socks\n\\item no gray socks\n\\item at least 1 black sock\n\\item a green sock\n\\item matching socks\n\\end{parts}\n}{}\n\n% 25\n\n\\eoce{\\qt{Chips in a bag\\label{chips_in_bag}} Imagine you have a bag \ncontaining 5 red, 3 blue, and 2 orange chips.\n\\begin{parts}\n\\item Suppose you draw a chip and it is blue. If drawing without replacement, \nwhat is the probability the next is also blue?\n\\item Suppose you draw a chip and it is orange, and then you draw a second chip \nwithout replacement. What is the probability this second chip is blue?\n\\item If drawing without replacement, what is the probability of drawing two blue \nchips in a row?\n\\item When drawing without replacement, are the draws independent? Explain.\n\\end{parts}\n}{}\n\n% 26\n\n\\eoce{\\qt{Books on a bookshelf\\label{books_on_shelf}} The table below shows the \ndistribution of books on a bookcase based on whether they are nonfiction or \nfiction and hardcover or paperback.\n\\begin{center}\n\\begin{tabular}{ll  cc c} \n                                &           & \\multicolumn{2}{c}{\\textit{Format}} \\\\\n\\cline{3-4}\n                                &           & Hardcover     & Paperback     & Total \\\\\n\\cline{2-5}\n\\multirow{2}{*}{\\textit{Type}}  & Fiction   & 13            & 59            & 72 \\\\\n                                & Nonfiction& 15            & 8             & 23 \\\\\n\\cline{2-5} \n                                & Total     & 28            & 67            & 95 \\\\\n\\cline{2-5}\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Find the probability of drawing a hardcover book first then a paperback \nfiction book second when drawing without replacement.\n\\item Determine the probability of drawing a fiction book first and then a \nhardcover book second, when drawing without replacement.\n\\item Calculate the probability of the scenario in part~(b), except this time \ncomplete the calculations under the scenario where the first book is placed back \non the bookcase before randomly drawing the second book.\n\\item The final answers to parts~(b) and~(c) are very similar. Explain why this \nis the case.\n\\end{parts}\n}{}\n\n% 27\n\n\\eoce{\\qt{Student outfits\\label{student_outfits}} In a classroom with 24 \nstudents, 7 students are wearing jeans, 4 are wearing shorts, 8 are wearing \nskirts, and the rest are wearing leggings. If we randomly select 3 students \nwithout replacement, what is the probability that one of the selected students is \nwearing leggings and the other two are wearing jeans? Note that these are \nmutually exclusive clothing options.\n}{}\n\n% 28\n\n\\eoce{\\qt{The birthday problem\\label{birthday_problem}} Suppose we pick three \npeople at random. For each of the following questions, ignore the special case \nwhere someone might be born on February 29th, and assume that births are evenly \ndistributed throughout the year.\n\\begin{parts}\n\\item What is the probability that the first two people share a birthday? \n\\item What is the probability that at least two people share a birthday?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_probability/figures/BreastCancerTreeDiagram/BreastCancerTreeDiagram.R",
    "content": "library(openintro)\n\nmyPDF(\"BreastCancerTreeDiagram.pdf\", 7.5, 2.5)\ntreeDiag(c('Truth', 'Mammogram'),\n         c(0.0035, 0.9965),\n         list(c(0.89, 0.11),\n              c(0.07, 0.93)),\n         textwd = 0.2,\n         solwd = 0.35,\n         cex.main = 1.4,\n         c('cancer', 'no cancer'),\n         c('positive','negative'),\n         digits = 5,\n         col.main = COL[1],\n         showWork = TRUE)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/BreastCancerTreeDiagram/Mammogram Research.txt",
    "content": "\nTwo studies in Canada\nhttp://www.breastcancer.org/symptoms/testing/new_research/20090831b.jsp\n- Mammograms were 89% effective in detecting breast cancer\n- 7.4% of screenings using mammogram alone resulte in false positive\nhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC1173421/\n- About 0.35% of women have breast cancer\n\n\t\tMammogram\n\tCancer\t+, 0.89\n\tY, 0.0035\n\t\t-, 0.11\n1.00\n\t\t+, 0.07\n\tN, 0.9965\n\t\t-, 0.93\n\ntreeDiag(c(\"Cancer\", \"Mammogram\"), c(0.0035, 0.9965), list(c(0.89, 0.11), c(0.07, 0.93)), out2=c(\"Positive\", \"Negative\"), digits=6)\n\n\t\tCancer\n\tMamm.\tY, 0.04\n\t+, 0.07\n\t\tN, 0.96\n1.00\n\t\tY, 0.001\n\t-, 0.93\n\t\tN, 0.999\n\n\n\n\n\n\n\nWikipedia (no source)\n1000 -> 70 called back for diagnostic session -> 10 referred for biopsy -> 3.5 have cancer\n\n\nhttp://www.ucsf.edu/news/2011/10/10778/high-rate-false-positives-annual-mammogram\nOver 1 decade, age 50 and up\n61% of population has false positive\n\n\n\nhttp://ww5.komen.org/BreastCancer/AccuracyofMammograms.html\n\t\t\t1.00\nMammogram\t+, ?\t\n\t\n\n\n\n\n\nhttp://www.acponline.org/pressroom/mammo_study.htm\n\n\n\n\nhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC1173421/\n\n\n\n\n"
  },
  {
    "path": "ch_probability/figures/bookCostDist/bookCostDist.R",
    "content": "library(openintro)\ndata(COL)\n\nmake.bar <- function(at,\n                     height,\n                     thickness = NA,\n                     col = NA) {\n  if(is.na(thickness)){\n    R <- range(at)\n    minDiff <- min(diff(at))\n    thickness <- min(minDiff, diff(R) / 12)\n  }\n  x1 <- at - thickness / 2\n  x2 <- at + thickness / 2\n  if(is.na(col)) {\n    col <- 'grey'\n  }\n  for (i in 1:length(at)) {\n    rect(x1[i], 0,\n         x2[i], height[i],\n         col = col)\n  }\n}\n\nprobDist <- function(x,\n                     prob,\n                     labels1 = NA,\n                     labels2 = NA,\n                     thickness = NA,\n                     col = NA,\n                     ylim = NULL,\n                     ...) {\n  R <- range(x)\n  R <- R + c(-1, 1) * diff(R)/20\n  Ry <- c(0, range(prob)[2])\n  if(!is.null(ylim)[1]){\n    Ry <- ylim\n  }\n  plot(x, prob,\n       type = 'n',\n       axes = FALSE,\n       xlim = R,\n       ylim = Ry,\n       ...)\n  if (is.na(labels1)[1]) {\n    labels1 <- x\n  }\n  if (is.na(labels2)[1]) {\n    labels2 <- TRUE\n  }\n  axis(1, at = x, labels = paste0(\"$\", labels1))\n  make.bar(x, prob, thickness = thickness, col = col)\n}\n\nmyPDF('bookCostDist.pdf', 5, 2.3)\nat <- c(0, 137, 170)\nprob <- c(0.2, .55, .25)\n\npar(mar = c(2.9, 4, 0.1, 0.5),\n    mgp = c(1.7, 0.7, 0))\nprobDist(at, prob,\n         xlab = 'Cost',\n         ylab = '',\n         ylim = c(-0.02, 0.55),\n         col = COL[1])\naxis(2, at = seq(0, 0.4, 0.2))\nlines(c(-10, 180), c(0,0))\npolygon(117.85 + c(-17, 17, 0),\n        c(-0.08, -0.08, 0),\n        col = COL[4])\npar(las = 0)\nmtext('Probability', side = 2, line = 2.8)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/bookWts/bookWts.R",
    "content": "library(openintro)\ndata(COL)\n\nat <- c(0, 137, 170)\nwt <- c(0.2, 0.55, 0.25)\n\ncreateWtSystem <- function(at, wt, size = 1, label = TRUE){\n  R <- range(at)\n  r <- diff(R)\n  W <- range(wt)\n  M <- weighted.mean(at, wt)\n  par(mar = rep(0, 4))\n  plot(R + c(-1, 1) * r / 12,\n       0:1,\n       type = 'n')\n\n  # make hanger\n  x <- c(M, M)\n  y <- c(0.7, 1.0)\n  lines(x, y)\n\n  # make the board\n  rect(R[1],0.685,R[2],0.7)\n\n  # add weights\n  for(i in 1:length(at)) {\n    createWt(at[i],wt[i], size)\n  }\n\n  # label\n  if(label){\n    text(at, rep(0.74, length(at)), at)\n    text(M, 0.64, M)\n  }\n}\n\ncreateWt <- function(at, wt, size = 1){\n  # hook\n  x <- rep(at, 2)\n  y <- c(0.64, 0.6925)\n  lines(x, y)\n\n  # the weight\n  x <- x + c(-1, 1) * size\n  y <- c(0.64, 0.64 - wt)\n  rect(x[1], y[1],\n       x[2], y[2],\n       col = COL[1])\n}\n\nmyPDF('bookWts.pdf', 5.5, 3)\ncreateWtSystem(at, wt, 5, TRUE)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/cardsDiamondFaceVenn/cardsDiamondFaceVenn.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('cardsDiamondFaceVenn.pdf', 1.2 * 4.2, 1.2 * 1.7,\n      mar = c(0.2, 0.2, 0.2, 0.2))\nplot(c(0.2, 2.5),\n     c(-0.13, 1.15),\n     type = 'n',\n     axes = FALSE)\n\nz <- seq(0,2 * pi, len = 99)\nx2 <- cos(z) / 2 + 1.3\ny2 <- sin(z) / 3 + 0.5\npolygon(c(x2, x2[1]), c(y2, y2[1]), col = COL[3,3])\n\nx1 <- cos(z) / 2 + 0.7\ny1 <- sin(z) / 3 + 0.5\npolygon(c(x1, x1[1]),c(y1, y1[1]), col = COL[1,3])\n\ntext(c(0.55, 1, 1.45),\n     rep(0.57, 3),\n     c(10, 3, 9),\n     cex = c(1.3, 1.2, 1.3))\ntext(c(0.55, 1, 1.45),\n     c(0.41, 0.43, 0.41),\n     c('0.1923', '0.0577', '0.1731'),\n\t cex = c(1, 0.9, 1))\n# text(0.5, -0.25, 'Other cards: 30', cex = 0.8)\n# text(0.98, -0.26, '(0.5769)', cex = 0.8)\ntext(2.25, 0.55, cex = 0.8,\n     paste(\"There are also\", \"30 cards that are\",\n           \"neither diamonds\", \"nor face cards\", sep = \"\\n\"))\n# text(2.25, 0.28, '(0.5769)', cex = 0.8)\nBraces(0.7, 0.92, 3 * pi / 2, 0.98, 0.12)\ntext(0.7, 1.09, 'Diamonds, 0.2500')\nBraces(1.3, 0.08, pi / 2, 0.98, 0.12)\ntext(1.3, -0.08, 'Face cards, 0.2308')\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/changeInLeonardsStockPortfolioFor36Months/changeinleonardsstockportfoliofor36months.R",
    "content": "library(openintro)\n\nt <- c(\"cat\", \"xom\")\ns <- stocks_18[t]\napply(s, 2, mean)\napply(s, 2, sd)\napply(s, 2, var)\ncor(s)\nsummary(lm(s))\nret <- 6000 * s$cat + 2000 * s$xom\n# baselines <- c(cat = 65.39, goog = 742.60, xom = 72.33)\n# dates <- stocks_18$date\n\n\nmyPDF(\"changeInLeonardsStockPortfolioFor36Months.pdf\", 5, 2.15,\n      mar = c(3.5, 0.5, 0.5, 0.5),\n      mgp = c(2.3, 0.6, 0))\nboxPlot(ret,\n        main = \"\",\n        xlab = \"Monthly Returns Over 3 Years\",\n        ylab = \"\",\n        horiz = TRUE,\n        axes = FALSE,\n        ylim = c(0.6, 1.4))\npoints(ret,\n       rep(0.9, 36),\n       col = COL[1, 3],\n       pch = 19)\nbuildAxis(1, ret, 2, 4)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/complementOfD/complementOfD.R",
    "content": "library(openintro)\ndata(COL)\n\npdf('complementOfD.pdf', 4, 1.05)\npar(mar = rep(0, 4))\nplot(c(-0.05, 1), c(0.18, 0.92), type = 'n', axes = FALSE)\n\nfor(i in c(1,4,5,6)){\n  text(i / 7, 0.5, i)\n}\nfor(i in 2:3){\n  text(i / 7, 0.55, i)\n}\ntheta <- seq(0,2 * pi,length.out = 100)\n\n# _____ D _____ #\nlines(1 / 7 * cos(theta) + 2.5 / 7,\n      1 / 9 * sin(theta) + 0.55,\n      lty = 3,\n      col = COL[4],\n      lwd = 2.425)\ntext(2.5 / 7, 0.75, 'D', col = COL[4])\n\n# _____ D^c _____ #\nx <- 1 / 20 * cos(seq(0.5, 3 * pi / 2, length.out = 20)) + 1 / 7\ny <- 1 / 5 * sin(seq(0.2, 3 * pi / 2, length.out = 20)) + 0.5\nx <- c(x, 1 / 20 * cos(seq(-pi / 2, pi / 2, length.out = 20)) + 6 / 7)\ny <- c(y, 0.175 * sin(seq(-pi / 2, pi / 2, length.out = 20)) + 0.47)\nx <- c(x, 1 / 20 * cos(seq(pi / 2, pi, length.out = 10)) + 4 / 7)\ny <- c(y, 1 / 5 * sin(seq(pi / 2, pi-0.5, length.out = 10)) + .45)\nx <- c(x, seq(1 / 2, 3 / 14, length.out = 10))\ny <- c(y, seq(-0.35, 0.35, length.out = 10)^2 + 0.33)\nx <- c(x, x[1])\ny <- c(y, y[1])\nlines(x, y, lty = 2, col = COL[2])\ntext(5 / 7, 0.75, expression(D^C), col = COL[2])\n\n# _____ S _____ #\nx <- 1 / 10 * cos(seq(pi / 2, 3 * pi / 2, length.out = 20)) + 1 / 9\ny <- 1 / 3 * sin(seq(pi / 2, 3 * pi / 2, length.out = 20)) + 0.55\nx <- c(x, 1 / 10 * cos(seq(-pi / 2, pi / 2, length.out = 20)) + 8 / 9)\ny <- c(y, 1 / 3 * sin(seq(-pi / 2, pi / 2, length.out = 20)) + 0.55)\n#x <- c(x, 1 / 20 * cos(seq(pi / 2, pi, length.out = 10)) + 4 / 7)\n#y <- c(y, 1 / 5 * sin(seq(pi / 2, pi-0.5, length.out = 10)) + .45)\n#x <- c(x, seq(1 / 2, 3 / 14, length.out = 10))\n#y <- c(y, seq(-0.35, 0.35, length.out = 10)^2 + 0.33)\nx <- c(x, x[1])\ny <- c(y, y[1])\nlines(x, y, lty = 1, col = COL[1])\ntext(0, 0.55, expression(S), col = COL[1], pos = 2, cex = 1.3)\n\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/contBalance/contBalance.R",
    "content": "library(openintro)\ndata(COL)\n\nx <- seq(0, 22, 0.01)\ny <- dchisq(x, 5)\nM <- weighted.mean(x, y)\n\npdf('contBalance.pdf', 4, 2.2)\npar(mar = c(1.65, 0, 0, 0), mgp = c(5, 0.5, 0))\nplot(x, y + 0.035,\n     type = 'l',\n     ylim = range(c(0.025, y + 0.035)),\n     axes = FALSE)\naxis(1, at = c(-100, M, 100), labels = c('', expression(mu), ''))\nlines(c(0, 22), rep(0.035, 2))\npolygon(x, y + 0.035, col = COL[1])\npolygon(c(M - 20, M + 20, M),\n        c(-0.2, -0.2, 0.035),\n        col = COL[4])\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/diceSumDist/diceSumDist.R",
    "content": "library(openintro)\ndata(COL)\n\nprobDist <- function(x,\n                     prob,\n                     labels1 = NA,\n                     labels2 = NA,\n                     thickness = NA,\n                     col = NA,\n                     ylim = NULL,\n                     ...) {\n  R <- range(x)\n  R <- R + c(-1,1)*(R[2]-R[1])/20\n  Ry <- c(0, range(prob)[2])\n  if (!is.null(ylim)[1]) {\n    Ry <- ylim\n  }\n  plot(x, prob, type = 'n', axes = F, xlim = R, ylim = Ry, ...)\n  if(is.na(labels1)[1]) labels1 <- x\n  if(is.na(labels2)[1]) labels2 <- TRUE\n  axis(1, at = x, labels = labels1)\n  make.bar(x, prob, thickness = thickness, col = col)\n}\n\nmake.bar <- function(at,\n                     height,\n                     thickness = NA,\n                     col = NA) {\n  if (is.na(thickness)) {\n    R <- range(at)\n    minDiff <- min(diff(at))\n    thickness <- min(c(minDiff), (R[2]-R[1])/12)\n  }\n  x1 <- at - thickness/2\n  x2 <- at + thickness/2\n  if (is.na(col)) {\n    col <- 'grey'\n  }\n  for (i in 1:length(at)) {\n    rect(x1[i], 0, x2[i], height[i], col = col)\n  }\n}\n\nat = 2:12\nprob = c(1:6, 5:1)/36\n\nmyPDF('diceSumDist.pdf', 5.5, 3,\n      mar = c(3.3, 4.5, 0.8, 1),\n      mgp = c(2, 0.55, 0))\nprobDist(at, prob,\n         xlab = 'Dice Sum',\n         ylab = '',\n         thickness = 0.5,\n         col = COL[1])\nabline(h = 0)\naxis(2)\nmtext('Probability', side = 2, 3.3, las = 0)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/dieProp/dieProp.R",
    "content": "library(openintro)\ndata(COL)\n\n# _____ Simulate _____ #\nset.seed(51)\nn <- 10^5\nx <- sample(0:1, n, TRUE, p = c(5 / 6, 1 / 6))\ny <- cumsum(x) / 1:n\nX <- c(1:100, seq(102, 500, 2),\n\tseq(510, 1500, 10), seq(1550, 10000, 50),\n\tseq(10100, 25000, 100), seq(25250, 100000, 250))\nY <- y[X]\n\n# _____ Plotting _____ #\nmyPDF('dieProp.pdf', 6.5, 3,\n      mar = c(3.8, 3.8, 0.5, 1))\nplot(X, Y,\n     log = 'x',\n     type = 'l',\n     xlab = '',\n     ylab = '',\n     axes = FALSE,\n     col = COL[1],\n     lwd = 2)\nmtext('n (number of rolls)', side = 1, line = 2.5)\nabline(h = 1 / 6, lty = 2)\nat <- 10^(0:5)\nlabels <- c('1', '10', '100', '1,000', '10,000', '100,000')\naxis(1, at, labels)\naxis(2, at = seq(0, 0.3, 0.1))\naxis(2, at = seq(0.05, 0.3, 0.1), labels = rep(NA, 3), tcl = -0.15)\nat <- 1 / 6\nlabels <- expression(paste(hat(p)[n]))\naxis(2, at, labels,\n     line = 2.3,\n     tick = FALSE,\n     cex.axis = 1.1)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/disjointSets/disjointSets.R",
    "content": "library(openintro)\ndata(COL)\n\npdf('disjointSets.pdf', 3.35, 0.8)\npar(mar = rep(0, 4))\nplot(c(0.05, 0.95),\n     c(0.13, 0.82),\n     type = 'n',\n     axes = FALSE)\n\nfor(i in 1:6){\n  text(i / 7, 0.5, i)\n}\ntheta <- seq(0, 2 * pi, length.out = 100)\n\n# _____ A _____ #\nlines(1 / 7 * cos(theta) + 1.5 / 7,\n      1 / 6 * sin(theta) + 0.5,\n      col = COL[1])\ntext(1.5 / 7, 0.75, 'A', col = COL[1])\n\n# _____ B _____ #\nx <- 1 / 15 * cos(seq(3 * pi / 2, 3 * pi-0.3, length.out = 40)) + 6 / 7\ny <- 1 / 6 * sin(seq(3 * pi / 2, 3 * pi, length.out = 40)) + 0.5\nx <- c(x, seq(11 / 14, 9 / 14, length.out = 10))\ny <- c(y, seq(-0.3, 0.3, length.out = 10)^2 + 0.4)\nx <- c(x, 1 / 15 * cos(seq(0.3, 3 * pi / 2, length.out = 40)) + 4 / 7)\ny <- c(y, 1 / 6 * sin(seq(0, 3 * pi / 2, length.out = 40)) + 0.5)\nx <- c(x, x[1])\ny <- c(y, y[1])\nlines(x, y, lty = 2, col = COL[2])\ntext(5 / 7, 0.2, 'B', col = COL[2])\n\n# _____ D _____ #\nlines(1 / 7 * cos(theta) + 2.5 / 7,\n      1 / 6 * sin(theta) + 0.5,\n      lty = 3,\n      col = COL[4],\n      lwd = 2.425)\ntext(2.5 / 7, 0.75, 'D', col = COL[4])\n\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/eoce/cat_weights/cat_weights.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load MASS for data ------------------------------------------------\nlibrary(MASS)\ndata(cats)\n\n# histogram of weights ----------------------------------------------\npdf(\"cat_weights.pdf\", 5.5, 4.3)\npar(mar=c(3.7, 2.2, 0.5, 0.5), las=1, mgp=c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhist(cats$Bwt, breaks = seq(2, 4, 0.25), ylim = c(0, 35), \n     xlab = \"Body weight\", col = COL[1], main = \"\", \n     axes = FALSE)\naxis(1)\naxis(2, at = seq(0,40,10))\ndev.off()"
  },
  {
    "path": "ch_probability/figures/eoce/poverty_language/poverty_language.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(\"openintro\")\n\n# draw venn diagram -------------------------------------------------\nvenn.plot <- draw.pairwise.venn(\n  area1 = 146,\n  area2 = 207,\n  cross.area = 42,\n  category = c(\"below PL\", \"speak FL\"),\n  fill = c(COL[1,3], COL[2,3]),\n  lty = \"blank\",\n  cex = 2,\n  cat.cex = 2,\n  cat.pos = c(20, -30),\n  cat.dist = 0.09,\n  cat.just = list(c(-1, -1), c(1, 1)),\n  ext.pos = 30,\n  ext.dist = -0.05,\n  ext.length = 0.85,\n  ext.line.lwd = 2,\n  ext.line.lty = \"dashed\"\n);\ngrid.draw(venn.plot)\n\ntiff(filename = \"poverty_language.tiff\", compression = \"lzw\");\ngrid.draw(venn.plot);\ndev.off();"
  },
  {
    "path": "ch_probability/figures/eoce/swing_voters/swing_voters.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(\"openintro\")\n\n# draw venn diagram -------------------------------------------------\nvenn.plot <- draw.pairwise.venn(\n  area1 = 35,\n  area2 = 23,\n  cross.area = 11,\n  category = c(\"Independent\", \"Swing\"),\n  fill = c(COL[1,3], COL[2,3]),\n  lty = \"blank\",\n  cex = 2,\n  cat.cex = 2,\n  cat.pos = c(310, 105),\n  cat.dist = 0.09,\n  cat.just = list(c(-1, -1), c(1, 1)),\n  ext.pos = 30,\n  ext.dist = -0.05,\n  ext.length = 0.85,\n  ext.line.lwd = 2,\n  ext.line.lty = \"dashed\"\n);\ngrid.draw(venn.plot)\n\ntiff(filename = \"swing_voters.tiff\", compression = \"lzw\");\ngrid.draw(venn.plot);\ndev.off();"
  },
  {
    "path": "ch_probability/figures/eoce/tree_drawing_box_plots/tree_drawing_box_plots.R",
    "content": "# load openintro for treeDiag function ------------------------------\nlibrary(openintro)\n\n# tree --------------------------------------------------------------\npdf(\"tree_drawing_box_plots.pdf\", width = 6, height = 2.5)\ntreeDiag(c(\"\\nCan construct\\nbox plots?\", \"Passed?\"), \n         c(0.80, 0.20), list(c(0.86, 0.14), c(0.65, 0.35)), \n         c(\"yes\", \"no\"), textwd = 0.19, solwd = 0.25, showWork = TRUE,\n         col.main = COL[1])\ndev.off()"
  },
  {
    "path": "ch_probability/figures/eoce/tree_exit_poll/tree_exit_poll.R",
    "content": "# load openintro for treeDiag function ------------------------------\nlibrary(openintro)\n\n# tree --------------------------------------------------------------\npdf(\"tree_exit_poll.pdf\", width = 6, height = 3)\ntreeDiag(c(\"Support Walker\", \"College degree\"), \n         c(0.53, 0.47), p2=list(c(0.37, 0.63), c(0.44, 0.56)), \n         cex.main=1.1, col.main = COL[1])\ndev.off()"
  },
  {
    "path": "ch_probability/figures/eoce/tree_hiv_swaziland/tree_hiv_swaziland.R",
    "content": "# load openintro for treeDiag function ------------------------------\nlibrary(openintro)\n\n# tree --------------------------------------------------------------\npdf(\"tree_hiv_swaziland.pdf\", width = 7, height = 2.5)\ntreeDiag(c(\"HIV?\", \"Result\"), \n         c(0.259, 1-0.259), list(c(0.997, 0.003), c(1-0.926, 0.926)), \n         c(\"yes\",\"no\"), c(\"positive\",\"negative\"), \n         textwd=0.19, solwd=0.25, showWork=TRUE,\n         col.main = COL[1])\ndev.off()"
  },
  {
    "path": "ch_probability/figures/eoce/tree_lupus/tree_lupus.R",
    "content": "# load openintro for treeDiag function ------------------------------\nlibrary(openintro)\n\n# tree --------------------------------------------------------------\npdf(\"tree_lupus.pdf\", width = 6, height = 3)\ntreeDiag(c(\"Lupus?\", \"Result\"), \n         c(0.02, 0.98), list(c(0.98, 0.02), c(0.26, 0.74)), \n         c(\"yes\",\"no\"), c(\"positive\",\"negative\"), \n         textwd=0.19, solwd=0.25, showWork=TRUE,\n         col.main = COL[1])\ndev.off()"
  },
  {
    "path": "ch_probability/figures/eoce/tree_thrombosis/tree_thrombosis.R",
    "content": "# load openintro for treeDiag function ------------------------------\nlibrary(openintro)\n\n# tree --------------------------------------------------------------\npdf(\"tree_thrombosis.pdf\", width = 6, height = 2.5)\ntreeDiag(c(\"Predisposition?\", \"Result\"), \n         c(0.03, 0.97), list(c(0.99, 0.01), c(0.02, 0.98)), c(\"yes\",\"no\"), \n         c(\"positive\",\"negative\"), textwd=0.19, solwd=0.25, showWork=TRUE,\n         col.main = COL[1])\ndev.off()"
  },
  {
    "path": "ch_probability/figures/eoce/tree_twins/tree_twins.R",
    "content": "# load openintro for treeDiag function ------------------------------\nlibrary(openintro)\n\n# tree --------------------------------------------------------------\npdf(\"tree_twins.pdf\", width = 10, height = 3.5)\ntreeDiag(main = c(\"Type of twins\",\"Gender\"), \n         p1 = c(0.3, 0.7), p2 = list(c(0.5,0.5,0), c(0.25,0.25,0.5)), \n         out1 = c(\"identical\",\"fraternal\"), \n         out2 = c(\"males\",\"females\",\"male&female\"), \n         showWork = TRUE, textwd=0.19, solwd=0.25,\n         col.main = COL[1])\ndev.off()"
  },
  {
    "path": "ch_probability/figures/fdicHeightContDist/fdicHeightContDist.R",
    "content": "library(openintro)\ndata(COL)\n\n# _____ Load Data Set From fdicHistograms _____ #\nload(\"../fdicHistograms/fdicHistograms.rda\")\n\nBR <- list()\nMIDS <- br[-1]-0.25\nBR[[1]] <- seq(110, 210, 10)\nBR[[2]] <- seq(115, 210, 2.5)\nCOUNTS <- list()\nfor (i in 1:2) {\n  COUNTS[[i]] <- rep(0, length(BR[[i]])-1)\n  for (j in 1:(length(BR[[i]]) - 1)) {\n    these <- apply(cbind(MIDS < BR[[i]][j + 1],\n                         MIDS >= BR[[i]][j]),\n                   1,\n                   all)\n    if (any(these)) {\n      COUNTS[[i]][j] <- sum(counts[these])\n    }\n  }\n}\n\nhistTemp <- function(\n    BR, COUNTS, col = fadeColor(COL[1], \"10\"),\n    border = COL[1, 4], probability = TRUE,\n    xlab = '', ylab = NULL, xlim = NULL, ylim = NULL,\n    ...) {\n  br <- BR\n  h  <- COUNTS\n  if (probability) {\n    h <- h/sum(h)/diff(br)\n  }\n  if (is.null(ylab)) {\n    ylab <- 'frequency'\n    if (probability) {\n      ylab <- 'probability'\n    }\n  }\n  if (is.null(xlim)[1]) {\n    xR <- range(br)\n    xlim <- xR + c(-0.05, 0.05)*diff(xR)\n  }\n  if (is.null(ylim)[1]) {\n    ylim <- range(c(0,h))\n  }\n  plot(-1, -1,\n       xlab = xlab,\n       ylab = ylab,\n       xlim = xlim,\n       ylim = ylim,\n       type = 'n',\n       ...)\n  abline(h = 0)\n  lines(c(br[1], br[1]), c(0, h[1]), col = border)\n  for (i in 1:length(h)) {\n    if (i > 1) {\n      if (h[i] > h[i - 1]) {\n        lines(rep(br[i], 2), h[c(i - 1, i)], col = border)\n      }\n    }\n    lines(br[i + 0:1], rep(h[i], 2), col = border)\n    lines(rep(br[i + 1], 2), c(0, h[i]), col = border)\n    rect(br[i], 0, br[i + 1], h[i], col = col, border = border)\n  }\n}\n\n\npdf('fdicHeightContDist.pdf', 6.67, 3.22)\npar(mfrow = c(1, 1),\n    mar = c(3, 1, 0.1, 1),\n    mgp = c(1.8, 0.7, 0))\nhistTemp(BR[[2]],\n         COUNTS[[2]],\n         xlab = 'height (cm)',\n         axes = FALSE,\n         xlim = c(125, 210),\n         col = fadeColor(COL[1], \"10\"),\n         border = COL[1,4])\naxis(1)\nlines(dens$x, dens$y,\n      col = COL[1],\n      lwd = 2)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/fdicHeightContDistFilled/fdicHeightContDistFilled.R",
    "content": "library(openintro)\ndata(COL)\n\n# _____ Load Data Set From fdicHistograms _____ #\nload(\"../fdicHistograms/fdicHistograms.rda\")\n\nBR <- list()\nMIDS <- br[-1] - 0.25\nBR[[1]] <- seq(110, 210, 10)\nBR[[2]] <- seq(115, 210, 2.5)\nCOUNTS <- list()\nfor (i in 1:2) {\n  COUNTS[[i]] <- rep(0, length(BR[[i]]) - 1)\n  for (j in 1:(length(BR[[i]]) - 1)) {\n    these <- apply(cbind(MIDS < BR[[i]][j + 1],\n                         MIDS >= BR[[i]][j]),\n                   1,\n                   all)\n    if (any(these)) {\n      COUNTS[[i]][j] <- sum(counts[these])\n    }\n  }\n}\n\nBR <- list()\nMIDS <- br[-1] - 0.25\nBR[[1]] <- seq(110, 210, 10)\nBR[[2]] <- seq(115, 210, 2.5)\nCOUNTS <- list()\nfor (i in 1:2) {\n  COUNTS[[i]] <- rep(0, length(BR[[i]]) - 1)\n  for (j in 1:(length(BR[[i]]) - 1)) {\n    these <- apply(cbind(MIDS < BR[[i]][j + 1],\n                         MIDS >= BR[[i]][j]),\n                   1,\n                   all)\n    if (any(these)) {\n      COUNTS[[i]][j] <- sum(counts[these])\n    }\n  }\n}\n\n\nhistTemp <- function(\n    BR, COUNTS, col = fadeColor(COL[1], \"10\"),\n    border = COL[1, 4], probability = TRUE,\n    xlab = '', ylab = NULL,\n    xlim = NULL, ylim = NULL,\n    ...) {\n  br <- BR\n  h  <- COUNTS\n  if (probability) {\n    h <- h/sum(h)/diff(br)\n  }\n  if (is.null(ylab)) {\n    ylab <- 'frequency'\n    if (probability) {\n      ylab <- 'probability'\n    }\n  }\n  if (is.null(xlim)[1]) {\n    xR <- range(br)\n    xlim <- xR + c(-0.05, 0.05)*diff(xR)\n  }\n  if (is.null(ylim)[1]) {\n    ylim <- range(c(0,h))\n  }\n  plot(-1, -1,\n       xlab = xlab,\n       ylab = ylab,\n       xlim = xlim,\n       ylim = ylim,\n       type = 'n',\n       ...)\n  abline(h = 0)\n  lines(c(br[1],br[1]), c(0,h[1]), col = border)\n  for (i in 1:length(h)) {\n    if (i > 1) {\n      if (h[i] > h[i-1]) {\n        lines(rep(br[i],2), h[c(i-1,i)], col = border)\n      }\n    }\n    lines(br[i + 0:1],\n          rep(h[i], 2),\n          col = border)\n    lines(rep(br[i + 1], 2),\n          c(0, h[i]),\n          col = border)\n    rect(br[i], 0,\n         br[i + 1], h[i],\n         col = col,\n         border = border)\n  }\n}\n\npdf('fdicHeightContDistFilled.pdf', 5.7, 2.75)\npar(mfrow = c(1, 1),\n    mar = c(3, 1, 0.1, 1),\n    mgp = c(1.8, 0.7, 0))\nhistTemp(BR[[2]],\n         COUNTS[[2]],\n         col = fadeColor(COL[1], \"10\"),\n         border = COL[1,4],\n         xlim = c(125, 210),\n         axes = FALSE,\n         xlab = 'height (cm)',\n         ylab = '',\n         probability = TRUE)\naxis(1)\nlines(dens$x, dens$y, col = COL[1], lwd = 2)\nthese <- dens$x > 180 & dens$x < 185\npolygon(c(dens$x[these][1], dens$x[these], rev(dens$x[these])[1]),\n        c(0, dens$y[these], 0),\n        col = COL[1],\n        border = COL[1])\nsum(dens$y[these] * diff(dens$x[1:2]))\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/fdicHistograms/fdicHistograms.R",
    "content": "library(openintro)\ndata(COL)\nload(\"fdicHistograms.rda\")\n\nMIDS <- br[-1] - diff(br[1:2]) / 2\nBR <- list()\nBR[[1]] <- seq(110, 210, 10)\nBR[[2]] <- seq(115, 210, 5)\nBR[[3]] <- seq(110, 210, 2)\nBR[[4]] <- seq(110, 210, 1)\nCOUNTS <- list()\nfor (i in 1:4) {\n  COUNTS[[i]] <- rep(0, length(BR[[i]])-1)\n  for (j in 1:(length(BR[[i]])-1)) {\n    these <- apply(cbind(MIDS < BR[[i]][j+1],\n                         MIDS >= BR[[i]][j]),\n                   1,\n                   all)\n    if (any(these)) {\n      COUNTS[[i]][j] <- sum(counts[these])\n    }\n  }\n}\n\nhistTemp <- function(\n    BR, COUNTS, col = fadeColor(COL[1], \"10\"),\n    border = COL[1,4], probability = FALSE,\n    xlab = '', ylab = NULL,\n    xlim = NULL, ylim = NULL,\n    ...) {\n  br <- BR\n  h  <- COUNTS\n  if (probability) {\n    h <- h / sum(h) / diff(br)\n  }\n  if (is.null(ylab)) {\n    ylab <- 'frequency'\n    if (probability) {\n      ylab <- 'probability'\n    }\n  }\n  if (is.null(xlim)[1]) {\n    xR <- range(br)\n    xlim <- xR + c(-0.05, 0.05) * diff(xR)\n  }\n  if (is.null(ylim)[1]) {\n    ylim <- range(c(0, h))\n  }\n  plot(-1, -1,\n       xlab = xlab,\n       ylab = ylab,\n       xlim = xlim,\n       ylim = ylim,\n       type = 'n',\n       ...)\n  abline(h = 0)\n  lines(c(br[1], br[1]), c(0, h[1]), col = border)\n  for (i in 1:length(h)) {\n    if (i > 1) {\n      if (h[i] > h[i-1]) {\n        lines(rep(br[i], 2), h[c(i - 1, i)], col = border)\n      }\n    }\n    lines(br[i + 0:1], rep(h[i], 2), col = border)\n    lines(rep(br[i + 1], 2), c(0, h[i]), col = border)\n    rect(br[i], 0,\n         br[i + 1], h[i],\n         col = col,\n         border = '#00000000')\n  }\n}\n\n\n\nmyPDF('fdicHistograms.pdf', 6.2, 3.3,\n      mfrow = c(2, 2),\n      mar = c(2.7, 1, 1, 1),\n      mgp = c(1.6, 0.4, 0))\nfor (i in 1:4) {\n  histTemp(BR[[i]],\n           COUNTS[[i]],\n           xlim = c(125, 210),\n           axes = FALSE,\n           xlab = 'height (cm)')\n  lines(BR[[i]],\n        c(COUNTS[[i]], 0),\n        type = 's',\n        col = COL[1],\n        lwd = 2)\n  axis(1, cex.axis = 0.9)\n}\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/indepForRollingTwo1s/indepForRollingTwo1s.R",
    "content": "library(openintro)\ndata(COL)\n\npdf('indepForRollingTwo1s.pdf', 4.5, 2.7)\npar(mar = rep(0, 4))\nplot(0:1, c(0, 1.1), type = 'n', axes = FALSE)\nx <- cos(seq(0, 2 * pi, length.out = 99))\ny <- sin(seq(0, 2 * pi, length.out = 99))\n#lines(x / 2 + 0.5, y / 2 + 0.5)\ntext(0, 1.05, pos = 4, 'All rolls')\nrect(0, 0, 1, 1)\nrect(1/6, 0, 2/6, 1,\n     lty = 2,\n     border = COL[1],\n     col = COL[1,3])\ntext(2/6, 0.7,\n     '1/6th of the first\\nrolls are a 1.',\n     pos = 4,\n     col = COL[1])\nrect(1/6, 1/6, 2/6, 2/6,\n     lty = 3,\n     border = \"#00000000\",\n     col = COL[2])\nthe.text <- paste(\"1/6th of those times where\",\n                  \"the first roll is a 1 the\",\n                  \"second roll is also a 1.\",\n                  sep  =  \"\\n\")\ntext(2 / 6, 3 / 12 - 0.02,\n    the.text,\n    pos = 4,\n    col = COL[2])\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/loans_app_type_home_venn/loans_app_type_home_venn.R",
    "content": "library(openintro)\n\nd <- loans_full_schema\ntable(d[,c(\"application_type\", \"homeownership\")])\ntable(d[,c(\"application_type\")])\ntable(d[,c(\"homeownership\")])\n\nmyPDF('loans_app_type_home_venn.pdf', 5, 1.5,\n      mar = c(0.1, 1.5, 0.1, 0.1))\nplot(c(-0.2, 2.2),\n     c(0, 1),\n     type = 'n',\n     ylab = \"\",\n     axes = FALSE)\nbox()\n\nz <- seq(0, 2 * pi, len = 99)\nx1 <- cos(z) * 1.04 + 0.8\ny1 <- sin(z) / 3 + 0.5\nlines(c(x1, x1[1]), c(y1, y1[1]))\n\nx2 <- cos(z) / 1.8 + 1.65\ny2 <- sin(z) / 3 + 0.5\nlines(c(x2, x2[1]),c(y2, y2[1]))\n\ntext(0.6, 0.9, 'applicant had a mortgage')\ntext(1.9, 0.9, 'joint application')\ntext(c(0.6, 1.46, 2),\n     c(0.6, 0.58, 0.57),\n     c(3839, 950, 545),\n     cex = c(1.7, 1.2, 1.25))\ntext(c(0.6, 1.46, 2),\n     c(0.4, 0.44, 0.43),\n     format(c('0.384', '0.095', '0.055')),\n     cex = c(1.3, 0.95, 1),\n     col = COL[1])\ntext(0.77, 0.07, 'Other loans: 10000 - 3839 - 950 - 545 = 4666')\ntext(1.9, 0.06, '(0.467)', col = COL[1])\n\ndev.off()\n# table(email[,c(\"joint application\", \"number\")])\n"
  },
  {
    "path": "ch_probability/figures/photoClassifyVenn/photoClassifyVenn.R",
    "content": "library(openintro)\ndata(COL)\n\n# Proportions not exactly right. Adjusted slightly for aesthetics.\n\npdf('photoClassifyVenn.pdf', 4.5, 2.4)\npar(mar = rep(0, 4))\nplot(0:1, 0:1, type = 'n', axes = FALSE)\nrect(0, 0, 1, 1, lwd=2)\nrect(0.10, 0.35,\n     0.75, 0.58,\n     border = COL[4, 2],\n     col = paste0(COL[4], \"25\"),\n     lty = 3,\n     lwd = 2.512)\ntext(0.33, 0.28, 'ML Predicts Fashion', col=COL[4,2])\nrect(0.18, 0.34,\n     0.77, 0.69,\n     border = COL[1],\n     col = COL[1, 4],\n     lty = 2,\n     lwd = 2)\ntext(0.54, 0.68, 'Fashion Photos', col = COL[1], pos = 3)\ntext(0.45, 0.45, 0.11, col = COL[5]) # intersection\ntext(0.14, 0.49, 0.01, col = COL[4], cex = 0.9)\ntext(0.6, 0.63, 0.06, col = COL[1])\ntext(0.8, 0.11, 'Neither: 0.82', col = COL[6]) # outersection\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/smallpoxTreeDiagram/smallpoxTreeDiagram.R",
    "content": "library(openintro)\n\nmyPDF(\"smallpoxTreeDiagram.pdf\", 7, 3.5)\ntreeDiag(c('Inoculated', 'Result'),\n         c(0.0392, 0.9608),\n         list(c(0.9754, 0.0246),\n              c(0.8589, 0.1411)),\n         textwd = 0.2,\n         solwd = 0.35,\n         cex.main = 1.4,\n         c('yes', 'no'),\n         c('lived', 'died'),\n         digits = 5,\n         col.main = COL[1],\n         showWork = TRUE)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/testTree/testTree.R",
    "content": "library(openintro)\n\nmyPDF('testTree.pdf', 6.5, 3.4)\ntreeDiag(c('Midterm', 'Final'),\n         c(0.13, 0.87),\n         list(c(0.47, 0.53),\n              c(0.11, 0.89)),\n         textwd = 0.2,\n         solwd = 0.35,\n         cex.main = 1.4,\n         c('A', 'other'),\n         c('A', 'other'),\n         digits = 5,\n         col.main = COL[1],\n         showWork = TRUE)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/treeDiagramAndPass/treeDiagramAndPass.R",
    "content": "library(openintro)\n\n\nmyPDF('treeDiagramAndPass.pdf', 6, 2.7)\ntreeDiag(c('\\nAble to construct\\ntree diagrams', 'Pass class'),\n         c(0.78, 0.22),\n         list(c(0.97, 0.03),\n              c(0.57, 0.43)),\n         textwd = 0.2,\n         solwd = 0.35,\n         cex.main = 1.4,\n         c('yes', 'no'),\n         c('pass', 'fail'),\n         digits = 5,\n         col.main = COL[1],\n         showWork = TRUE)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/treeDiagramGarage/treeDiagramGarage.R",
    "content": "library(openintro)\n\n\nmyPDF('treeDiagramGarage.pdf', 7, 3.5)\ntreeDiag(c('Event', 'Garage full'),\n         c(0.35, 0.20, 0.45),\n         list(c(0.25, 0.75),\n              c(0.7, 0.3),\n              c(0.05, 0.95)),\n         textwd = 0.22,\n         solwd = 0.35,\n         cex.main = 1.4,\n         c('Academic', 'Sporting', 'None'),\n         c('Full', 'Spaces Available'),\n         digits = 5,\n         col.main = COL[1],\n         showWork = TRUE)\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/usHeightsHist180185/usHeightsHist180185.R",
    "content": "library(openintro)\ndata(COL)\n\n# _____ Load Data Set From fdicHistograms _____ #\nload(\"../fdicHistograms/fdicHistograms.rda\")\n\nBR      <- list()\nMIDS    <- br[-1] - 0.25\nBR[[1]] <- seq(110, 210, 10)\nBR[[2]] <- seq(115, 210, 2.5)\nCOUNTS  <- list()\nfor (i in 1:2) {\n  COUNTS[[i]] <- rep(0, length(BR[[i]])-1)\n  for (j in 1:(length(BR[[i]])-1)) {\n    these <- apply(cbind(MIDS < BR[[i]][j + 1],\n                         MIDS >= BR[[i]][j]),\n                   1,\n                   all)\n    if (any(these)) {\n      COUNTS[[i]][j] <- sum(counts[these])\n    }\n  }\n}\n\nhistTemp <- function(\n    BR, COUNTS, col = fadeColor(COL[1], \"10\"),\n    border = COL[1,4], probability = FALSE,\n    xlab = '', ylab = NULL,\n    xlim = NULL, ylim = NULL,\n    ...) {\n  br <- BR\n  h  <- COUNTS\n  if (probability) {\n    h <- h / sum(h) / diff(br)\n  }\n  if (is.null(ylab)) {\n    ylab <- 'frequency'\n    if (probability) {\n      ylab <- 'probability'\n    }\n  }\n  if (is.null(xlim)[1]) {\n    xR <- range(br)\n    xlim <- xR + c(-0.05, 0.05) * diff(xR)\n  }\n  if (is.null(ylim)[1]) {\n    ylim <- range(c(0,h))\n  }\n  plot(-1, -1,\n       xlab = xlab,\n       ylab = ylab,\n       xlim = xlim,\n       ylim = ylim,\n       type = 'n',\n       ...)\n  abline(h = 0)\n  lines(c(br[1], br[1]), c(0, h[1]), col = border)\n  for (i in 1:length(h)) {\n    if (i > 1) {\n      if (h[i] > h[i - 1]) {\n        lines(rep(br[i], 2), h[c(i - 1, i)], col = border)\n      }\n    }\n    lines(br[i + 0:1], rep(h[i], 2), col = border)\n    lines(rep(br[i + 1], 2), c(0, h[i]), col = border)\n    rect(br[i], 0, br[i + 1], h[i],\n         col = col,\n         border = '#00000000')\n  }\n}\n\n\nmyPDF('usHeightsHist180185.pdf', 6.9, 3.1625,\n      mar = c(3, 1, 0.1, 1),\n      mgp = c(1.8, 0.7, 0))\nhistTemp(BR[[2]],\n         COUNTS[[2]],\n         xlim = c(125, 210),\n         axes = FALSE,\n         xlab = 'height (cm)',\n         probability = FALSE)\nlines(BR[[i]],\n      c(COUNTS[[i]], 0),\n      type = 's',\n      col = COL[1],\n      lwd = 2)\naxis(1)\nrect(BR[[2]][27], 0,\n     BR[[2]][28], COUNTS[[2]][27],\n     col = COL[1],\n     border = COL[1])\nrect(BR[[2]][28], 0,\n     BR[[2]][29], COUNTS[[2]][28],\n     col = COL[1],\n     border = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_probability/figures/usHouseholdIncomeDistBar/usHouseholdIncomeDistBar.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('usHouseholdIncomeDistBar.pdf', 5.2, 3,\n      mar = c(3.4, 4.2, 0.8, 1))\np <- c(0.28, 0.27, 0.29, 0.16)\nnames(p) <- c('$0-25k', '$25k-50k', '$50k-100k', '$100k+')\nbarplot(p, xlab = '', col = COL[1])\npar(las = 0)\nmtext('US Household Incomes', side = 1, line = 2.3)\nmtext('Probability', side = 2, line = 3)\nabline(h = 0)\ndev.off()\n"
  },
  {
    "path": "ch_regr_mult_and_log/TeX/ch_regr_mult_and_log.tex",
    "content": "\\begin{chapterpage}{Multiple and logistic regression}\n  \\chaptertitle{Multiple and logistic \\titlebreak{} regression}\n  \\label{multipleRegressionAndANOVA}\n  \\label{multipleAndLogisticRegression}\n  \\label{ch_regr_mult_and_log}\n  \\chaptersection{introductionToMultipleRegression}\n  \\chaptersection{model_selection_section}\n  \\chaptersection{multipleRegressionModelAssumptions}\n  \\chaptersection{mario_kart_case_study}\n  \\chaptersection{logisticRegression}\n\\end{chapterpage}\n\\renewcommand{\\chapterfolder}{ch_regr_mult_and_log}\n\n\\chapterintro{The principles of simple linear regression\n  lay the foundation for more sophisticated regression\n  models used in a wide range of challenging settings.\n  In Chapter~\\ref{multipleAndLogisticRegression},\n  we explore multiple regression, which introduces the\n  possibility of more than one predictor in a linear model,\n  and logistic regression,\n  a technique for predicting categorical\n  outcomes with two levels.}\n\n\n\n\n\\section{Introduction to multiple regression}\n\\label{introductionToMultipleRegression}\n\n\\index{multiple regression|seealso{regression}}\n\\index{regression!multiple|(}\n\\index{regression|(}\n\nMultiple regression extends simple two-variable regression to the case that still has one response but many predictors (denoted $x_1$, $x_2$, $x_3$, ...). The method is motivated by scenarios where many variables may be simultaneously connected to an output.\n\n\\index{data!loans|(}\n\n\\newcommand{\\loNcomma}{10,000}\n\\newcommand{\\loN}{10000}\n\nWe will consider data about loans from the peer-to-peer lender,\nLending Club, which is a data set we first encountered in\nChapters~\\ref{ch_intro_to_data}\nand~\\ref{ch_summarizing_data}.\nThe loan data includes terms of the loan as well as\ninformation about the borrower.\nThe outcome variable we would like to better understand\nis the interest rate assigned to the loan.\nFor instance, all other characteristics held constant,\ndoes it matter how much debt someone already has?\nDoes it matter if their income has been verified?\nMultiple regression will help us answer these and other questions.\n\nThe data set \\data{loans} includes results from \\loNcomma{} loans,\nand we'll be looking at a subset of the available variables,\nsome of which will be new from those we saw in earlier chapters.\nThe first six observations in the data set are shown in\nFigure~\\ref{loansDataMatrix},\nand descriptions for each variable are shown in\nFigure~\\ref{loansVariables}.\nNotice that the past bankruptcy variable (\\var{bankruptcy})\nis an indicator variable\\index{indicator variable},\nwhere it takes the value 1 if the borrower had a past\nbankruptcy in their record and 0 if not.\nUsing an indicator variable in place of a category name\nallows for these variables to be directly used in regression.\nTwo of the other variables are\ncategorical\\index{categorical variable}\n(\\var{income\\us{}ver} and \\var{issued}), each of which\ncan take one of a few different non-numerical values;\nwe'll discuss how these are handled in the model in\nSection~\\ref{ind_and_cat_vars_as_predictors}.\n\n\\begin{figure}[h]\n\\centering\\footnotesize\n\\begin{tabular}{r ccc ccc cc}\n  \\hline\n   & interest\\us{}rate & income\\us{}ver\n       & debt\\us{}to\\us{}income & credit\\us{}util\n       & bankruptcy & term\n       & issued & credit\\us{}checks \\\\ \n  \\hline\n  1 & 14.07 & verified & 18.01 & 0.55 & 0 & 60 & Mar2018 & 6 \\\\ \n  2 & 12.61 & not & 5.04 & 0.15 & 1 & 36 & Feb2018 & 1 \\\\ \n  3 & 17.09 & source\\_only & 21.15 & 0.66 & 0 & 36 & Feb2018 & 4 \\\\ \n  4 & 6.72 & not & 10.16 & 0.20 & 0 & 36 & Jan2018 & 0 \\\\ \n  5 & 14.07 & verified & 57.96 & 0.75 & 0 & 36 & Mar2018 & 7 \\\\ \n  6 & 6.72 & not & 6.46 & 0.09 & 0 & 36 & Jan2018 & 6 \\\\\n  $\\vdots$ & $\\vdots$ & $\\vdots$ &\n      $\\vdots$ & $\\vdots$ & $\\vdots$ &\n      $\\vdots$ & $\\vdots$ & $\\vdots$ \\\\\n   \\hline\n\\end{tabular}\n\\caption{First six rows from the \\data{loans} data set.}\n\\label{loansDataMatrix}\n\\end{figure}\n%library(openintro)  # Run some example code from loans_full_schema\n%library(xtable); xtable(rbind.data.frame(head(d[, c(\"interest_rate\", co)], 6))) #, tail(d[, c(\"interest_rate\", co)], 2)))\n\n\\begin{figure}[h]\n\\centering\\small\n\\begin{tabular}{lp{11.5cm}}\n\\hline\n{\\bf variable} & {\\bf description} \\\\\n\\hline\n\\var{interest\\us{}rate} &\n    Interest rate for the loan. \\\\\n\\var{income\\us{}ver} &\n    Categorical variable describing whether the borrower's\n    income source and amount have been verified,\n    with levels \\resp{verified}, \\resp{source\\us{}only},\n    and \\resp{not}. \\\\\n\\var{debt\\us{}to\\us{}income} &\n    Debt-to-income ratio, which is the percentage of total debt\n    of the borrower divided by their total income. \\\\\n\\var{credit\\us{}util} &\n    Of all the credit available to the borrower,\n    what fraction are they utilizing.\n    For example, the credit utilization on a credit card would\n    be the card's balance divided by the card's credit limit. \\\\\n\\var{bankruptcy} &\n    An indicator variable for whether the borrower has a past\n    bankruptcy in her record. This variable takes a value of\n    \\resp{1} if the answer is ``yes''\n    and \\resp{0} if the answer is ``no''. \\\\\n\\var{term} &\n    The length of the loan, in months. \\\\\n\\var{issued} &\n    The month and year the loan was issued,\n    which for these loans is always during the first\n    quarter of 2018. \\\\\n\\var{credit\\us{}checks} &\n    Number of credit checks in the last 12 months.\n    For example, when filing an application for a credit card,\n    it is common for the company receiving the application\n    to run a credit check. \\\\\n\\hline\n\\end{tabular}\n\\caption{Variables and their descriptions for the\n    \\data{loans} data set.}\n\\label{loansVariables}\n\\end{figure}\n\n\n\\newpage\n\n\\subsection{Indicator and categorical variables as predictors}\n\\label{ind_and_cat_vars_as_predictors}\n\n\\newcommand{\\pastbankrACoef}{0.74}\n\\newcommand{\\pastbankrACoefSE}{0.15}\n\nLet's start by fitting a linear regression model for\ninterest rate with a single predictor indicating whether\nor not a person has a bankruptcy in their record:\n\\begin{align*}\n\\widehat{rate} &= 12.33 + \\pastbankrACoef{} \\times bankruptcy\n\\end{align*}\nResults of this model are shown in\nFigure~\\ref{intRateVsPastBankrModel}.\n%and a scatterplot for price\n%versus game condition is shown in\n%Figure~\\ref{intRateVsPastBankrScatter}.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l rrr r}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  (Intercept) & 12.3380 & 0.0533 & 231.49 & $<$0.0001 \\\\ \n  bankruptcy & 0.7368 & 0.1529 & 4.82 & $<$0.0001 \\\\ \n  \\hline\n  &&&\\multicolumn{2}{r}{$df=9998$}\n\\end{tabular}\n\\caption{Summary of a linear model for predicting\n    interest rate based on whether the borrower has\n    a bankruptcy in their record.}\n\\label{intRateVsPastBankrModel}\n\\end{figure}\n\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figures{0.45}{loansSingles}{intRateVsPastBankrScatter}\n%  \\caption{Scatterplot of interest rate against\n%      the past bankruptcy indicator variable.\n%      The least squares line is also shown,\n%      representing a relatively small difference\n%      between the two bankruptcy groups.}\n%  \\label{intRateVsPastBankrScatter}\n%\\end{figure}\n\n%\\begin{exercisewrap}\n%\\begin{nexercise}\n%Examine Figure~\\ref{intRateVsPastBankrScatter}.\n%Are the conditions for a linear model reasonable?\\footnotemark\n%\\end{nexercise}\n%\\end{exercisewrap}\n%\\footnotetext{Yes. Constant variability, nearly normal residuals, and linearity all appear reasonable.}\n\n\\begin{examplewrap}\n\\begin{nexample}{Interpret the coefficient for the\n     past bankruptcy variable in the model.\n     Is this coefficient significantly different from 0?}\n  The \\var{bankruptcy} variable takes one of two values:\n  1 when the borrower has a bankruptcy\n  in their history and 0 otherwise.\n  A slope of \\pastbankrACoef{} means that the model predicts a\n  \\pastbankrACoef{}\\% higher\n  interest rate for those borrowers with a bankruptcy in\n  their record.\n  (See Section~\\ref{categoricalPredictorsWithTwoLevels}\n  for a review of the interpretation for two-level\n  categorical predictor variables.)\n  Examining the regression output in\n  Figure~\\ref{intRateVsPastBankrModel},\n  we can see that the p-value for \\var{bankruptcy}\n  is very close to zero, indicating there is strong evidence\n  the coefficient is different from zero when using this\n  simple one-predictor model.\n\\end{nexample}\n\\end{examplewrap}\n\nSuppose we had fit a model using a 3-level categorical variable,\nsuch as \\var{income\\us{}ver}.\nThe output from software is shown in\nFigure~\\ref{intRateVsVerIncomeModel}.\nThis regression output provides multiple\nrows for the \\var{income\\us{}ver} variable.\nEach row represents the relative difference for\neach level of \\var{income\\us{}ver}.\nHowever, we are missing one of the levels:\n\\resp{not} (for \\emph{not verified}).\nThe missing level is called the \\term{reference level},\nand it represents the default level that\nother levels are measured against.\n%This will make more sense after we write out the equation.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l rrr r}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  (Intercept) &\n      11.0995 & 0.0809 & 137.18 & $<$0.0001 \\\\\n  income\\us{}ver\\lmlevel{source\\us{}only} &\n      1.4160 & 0.1107 & 12.79 & $<$0.0001 \\\\ \n  income\\us{}ver\\lmlevel{verified} &\n      3.2543 & 0.1297 & 25.09 & $<$0.0001 \\\\ \n  \\hline\n  &&&\\multicolumn{2}{r}{$df=9998$}\n\\end{tabular}\n\\caption{Summary of a linear model for predicting\n    interest rate based on whether the borrower's\n    income source and amount has been verified.\n    This predictor has three levels, which results\n    in 2 rows in the regression output.}\n\\label{intRateVsVerIncomeModel}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{How would we write an equation for\n    this regression model?}\n  \\label{verIncomeEquationExample}%\n  The equation for the regression model may be written as\n  a model with two predictors:\n  \\begin{align*}\n  \\widehat{rate} = 11.10 +\n      1.42 \\times\n          \\indfunc{income\\us{}ver}{source\\us{}only} +\n      3.25 \\times\n          \\indfunc{income\\us{}ver}{verified}\n  \\end{align*}\n  We use the notation $\\indfunc{variable}{level}$\n  to represent indicator variables\\index{indicator variable}\n  for when the categorical variable takes a particular value.\n  For example, $\\indfunc{income\\us{}ver}{source\\us{}only}$\n  would take a value of 1 if \\var{income\\us{}ver} was\n  \\resp{source\\us{}only} for a loan,\n  and it would take a value of 0 otherwise.\n  Likewise, $\\indfunc{income\\us{}ver}{verified}$ would take\n  a value of 1 if \\var{income\\us{}ver} took a value\n  of \\resp{verified} and 0 if it took any other value.\n  % In Example~\\ref{}, we'll run through a few examples\n  % of how we can use the equation for the model.\n\\end{nexample}\n\\end{examplewrap}\n\nThe notation used in Example~\\ref{verIncomeEquationExample}\nmay feel a bit confusing.\nLet's figure out how to use the equation for each level\nof the \\var{income\\us{}ver} variable.\n\n\\begin{examplewrap}\n\\begin{nexample}{Using the model from\n    Example~\\ref{verIncomeEquationExample},\n    compute the average interest rate for borrowers\n    whose income source and amount are both unverified.}\n  When \\var{income\\us{}ver} takes a value of \\resp{not},\n  then both indicator functions in the equation from\n  Example~\\ref{verIncomeEquationExample}\n  are set to zero:\n  \\begin{align*}\n  \\widehat{rate} &= 11.10 +\n      1.42 \\times 0 +\n      3.25 \\times 0 \\\\\n    &= 11.10\n  \\end{align*}\n  The average interest rate for these borrowers is 11.1\\%.\n  Because the \\resp{not} level does not have its own\n  coefficient and it is the reference value,\n  the indicators for the other levels for this variable\n  all drop out.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Using the model from\n    Example~\\ref{verIncomeEquationExample},\n    compute the average interest rate for borrowers\n    whose income source is verified but the amount is not.}\n  When \\var{income\\us{}ver} takes a value of\n  \\resp{source\\us{}only},\n  then the corresponding variable takes a value of 1\n  while the other ($\\indfunc{income\\us{}ver}{verified}$) is 0:\n  \\begin{align*}\n  \\widehat{rate} &= 11.10 +\n      1.42 \\times 1 +\n      3.25 \\times 0 \\\\\n    &= 12.52\n  \\end{align*}\n  The average interest rate for these borrowers is 12.52\\%.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nCompute the average interest rate for borrowers\nwhose income source and amount are both verified.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{When \\var{income\\us{}ver} takes a value of\n  \\resp{verified},\n  then the corresponding variable takes a value of 1\n  while the other ($\\indfunc{income\\us{}ver}{source\\us{}only}$)\n  is~0:\n  \\begin{align*}\n  \\widehat{rate} &= 11.10 +\n      1.42 \\times 0 +\n      3.25 \\times 1 \\\\\n    &= 14.35\n  \\end{align*}\n  The average interest rate for these borrowers is 14.35\\%.}\n\n\\begin{onebox}{Predictors with several categories}\nWhen fitting a regression model with a categorical variable\nthat has $k$ levels where $k > 2$, software will provide\na coefficient for $k - 1$ of those levels.\nFor the last level that does not receive a coefficient,\nthis is the \\term{reference level}, and the coefficients\nlisted for the other levels are all considered relative\nto this reference level.\n\\end{onebox}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nInterpret the coefficients in the \\var{income\\us{}ver}\nmodel.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Each of the coefficients gives the\n  incremental interest rate for the corresponding level\n  relative to the \\resp{not} level, which is the reference\n  level.\n  For example, for a borrower whose income source and\n  amount have been verified, the model predicts that\n  they will have a 3.25\\% higher interest rate than\n  a borrower who has not had their income source or\n  amount verified.}\n\nThe higher interest rate for borrowers who have verified\ntheir income source or amount is surprising.\nIntuitively, we'd think that a loan would look \\emph{less}\nrisky if the borrower's income has been verified.\nHowever, note that the situation may be more complex,\nand there may be confounding variables\nthat we didn't account for.\nFor example, perhaps lender require borrowers with\npoor credit to verify their income.\nThat is, verifying income in our data set might be\na signal of some concerns about the borrower\nrather than a reassurance that the borrower will pay\nback the loan.\nFor this reason, the borrower could be deemed higher\nrisk, resulting in a higher interest rate.\n(What other confounding variables might explain this\ncounter-intuitive relationship suggested by the model?)\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nHow much larger of an interest rate would we expect for\na borrower who has verified their income source and amount\nvs a borrower whose income source has only been\nverified?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Relative to the \\resp{not} category,\n  the \\resp{verified} category has an interest rate of\n  3.25\\% higher, while the \\resp{source\\us{}only}\n  category is only 1.42\\% higher.\n  Thus, \\resp{verified} borrowers will tend to get\n  an interest rate about $3.25\\% - 1.42\\% = 1.83\\%$\n  higher than \\resp{source\\us{}only} borrowers.}\n\n\n\\subsection{Including and assessing many variables in a model}\n\\label{includingAndAssessingManyVariablesInAModel}\n\nThe world is complex, and it can be helpful to\nconsider many factors at once in statistical modeling.\nFor example, we might like to use the full context of\nborrower to predict the interest rate they receive\nrather than using a single variable.\nThis is the strategy used in\n\\termsub{multiple regression}{regression!multiple}.\nWhile we remain cautious about making any causal\ninterpretations using multiple regression\non observational data,\nsuch models are a common first step in gaining insights\nor providing some evidence of a causal connection.\n\nWe want to construct a model that accounts not only\nfor any past bankruptcy or whether the borrower had\ntheir income source or amount verified,\nbut simultaneously accounts for all the variables\nin the data set:\n\\var{income\\us{}ver},\n\\var{debt\\us{}to\\us{}income},\n\\var{credit\\us{}util},\n\\var{bankruptcy},\n\\var{term},\n\\var{issued},\nand \\var{credit\\us{}checks}.\n\\begin{align*}\n\\widehat{\\var{rate}}\n\t&= \\beta_0 +\n\t    \\beta_1\\times \\indfunc{income\\us{}ver}{source\\us{}only} +\n\t    \\beta_2\\times \\indfunc{income\\us{}ver}{verified} +\n\t\t\\beta_3\\times \\var{debt\\us{}to\\us{}income} \\\\\n\t&\\qquad\\  +\n\t    \\beta_4 \\times \\var{credit\\us{}util} +\n\t    \\beta_5 \\times \\var{bankruptcy} +\n\t\t\\beta_6 \\times \\var{term} \\\\\n\t&\\qquad\\  +\n\t    \\beta_7 \\times \\indfunc{issued}{Jan2018} +\n\t    \\beta_8 \\times \\indfunc{issued}{Mar2018} +\n\t\t\\beta_9 \\times \\var{credit\\us{}checks}\n\\end{align*}\nThis equation represents a holistic approach for modeling\nall of the variables simultaneously.\nNotice that there are two coefficients for \\var{income\\us{}ver}\nand also two coefficients for \\var{issued}, since both are\n3-level categorical variables.\n\n%\\Comment{Work on this paragraph.}\n%A multiple regression model may be missing important components or it might not precisely represent the relationship between the outcome and the available explanatory variables. While no model is perfect, we wish to explore the possibility that this one may fit the data reasonably well.\n\n\nWe estimate the parameters\n$\\beta_0$, $\\beta_1$, $\\beta_2$, ..., $\\beta_9$\nin the same way as we did in the case of a single predictor.\nWe select $b_0$, $b_1$, $b_2$, ..., $b_9$ that minimize the\nsum of the squared residuals:\n\\begin{align}\\label{sumOfSqResInMultRegr}\nSSE = e_1^2 + e_2^2 + \\dots + e_{\\loN}^2\n\t= \\sum_{i=1}^{\\loN} e_i^2\n\t = \\sum_{i=1}^{\\loN} \\left(y_i - \\hat{y}_i\\right)^2\n\\end{align}\nwhere $y_i$ and $\\hat{y}_i$ represent the observed\ninterest rates and their estimated values according to\nthe model, respectively.\n\\loNcomma{} residuals are calculated, one for each observation.\nWe typically use a computer to minimize the sum of squares\nand compute point estimates, as shown in the sample output\nin Figure~\\ref{loansFullModelOutput}.\nUsing this output, we identify the point estimates $b_i$ of\neach $\\beta_i$, just as we did in the one-predictor case.\n\n\\newcommand{\\pastbankrFullCoef}{0.39}\n\\newcommand{\\pastbankrFullCoefSE}{0.13}\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{rrrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  \\vspace{-3.8mm} & & & & \\\\\n  (Intercept) & 1.9251 & 0.2102 & 9.16 & $<$0.0001 \\\\ \n  income\\us{}ver\\lmlevel{source\\us{}only} &\n      0.9750 & 0.0991 & 9.83 & $<$0.0001 \\\\ \n  income\\us{}ver\\lmlevel{verified} &\n      2.5374 & 0.1172 & 21.65 & $<$0.0001 \\\\ \n  debt\\us{}to\\us{}income & 0.0211 & 0.0029 & 7.18 & $<$0.0001 \\\\ \n  credit\\us{}util & 4.8959 & 0.1619 & 30.24 & $<$0.0001 \\\\ \n  bankruptcy & 0.3864 & 0.1324 & 2.92 & 0.0035 \\\\ \n  term & 0.1537 & 0.0039 & 38.96 & $<$0.0001 \\\\ \n  issued\\lmlevel{Jan2018} & 0.0276 & 0.1081 & 0.26 & 0.7981 \\\\ \n  issued\\lmlevel{Mar2018} & -0.0397 & 0.1065 & -0.37 & 0.7093 \\\\ \n  credit\\us{}checks & 0.2282 & 0.0182 & 12.51 & $<$0.0001 \\\\ \n   \\hline\n   &&&\\multicolumn{2}{r}{$df=9990$}\n\\end{tabular}\n\\caption{Output for the regression model, where\n    \\var{interest\\us{}rate} is the outcome and\n    the variables listed are the predictors.}\n\\label{loansFullModelOutput}\n\\end{figure}\n\n\\begin{onebox}{Multiple regression model}\n  A multiple regression model is a linear model\n  with many predictors.\n  In general, we write the model as\n  \\begin{align*}\n  \\hat{y} =\n      \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\cdots + \\beta_k x_k\n  \\end{align*}\n  when there are $k$ predictors.\n  We always estimate the $\\beta_i$ parameters using\n  statistical software.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{Write out the regression model using\n    the point estimates from\n    Figure~\\ref{loansFullModelOutput}.\n    How many predictors are there in this model?}\n  \\label{loansFullModelEqWCoef}%\n  The fitted model for the interest rate is given by:\n  {\\small\\begin{align*}\n  \\widehat{\\var{rate}}\n\t&= 1.925 +\n\t    0.975 \\times \\indfunc{income\\us{}ver}{source\\us{}only} +\n\t    2.537 \\times \\indfunc{income\\us{}ver}{verified} +\n\t\t0.021 \\times \\var{debt\\us{}to\\us{}income} \\\\\n\t&\\qquad\\  +\n\t    4.896 \\times \\var{credit\\us{}util} +\n\t    0.386 \\times \\var{bankruptcy} +\n\t\t0.154 \\times \\var{term} \\\\\n\t&\\qquad\\  +\n\t    0.028 \\times \\indfunc{issued}{Jan2018}\n\t    -0.040 \\times \\indfunc{issued}{Mar2018} +\n\t\t0.228 \\times \\var{credit\\us{}checks}\n  \\end{align*}}%\n  If we count up the number of predictor coefficients,\n  we get the \\emph{effective} number of predictors\n  in the model:~$k = 9$.\n  Notice that the \\var{issued} categorical predictor\n  counts as two, once for the two levels shown in the model.\n  In general, a categorical predictor with $p$ different\n  levels will be represented by $p - 1$ terms in a multiple\n  regression model.\n\\end{nexample}\n\\end{examplewrap}\n\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat does $\\beta_4$, the coefficient of variable\n\\var{credit\\us{}util}, represent?\nWhat is the point estimate of~$\\beta_4$?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$\\beta_4$ represents the change in\n   interest rate we would expect if someone's credit\n   utilization was 0 and went to 1,\n   all other factors held even.\n   The point estimate is $b_4 = 4.90\\%$.}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{Compute the residual of the first observation\n    in Figure~\\ref{loansDataMatrix} on\n    page~\\pageref{loansDataMatrix} using the equation identified\n    in Guided Practice~\\ref{loansFullModelEqWCoef}.}\n  To compute the residual, we first need the predicted value,\n  which we compute by plugging values into the equation from\n  Example~\\ref{loansFullModelEqWCoef}.\n  For example, $\\indfunc{income\\us{}ver}{source\\us{}only}$\n  takes a value of 0,\n  $\\indfunc{income\\us{}ver}{verified}$ takes a value of 1\n  (since the borrower's income source and amount were verified),\n  \\var{debt\\us{}to\\us{}income} was 18.01, and so on.\n  This leads to a prediction of $\\widehat{rate}_1 = 18.09$.\n  The observed interest rate was 14.07\\%, which leads to\n  a residual of $e_1 = 14.07 - 18.09 = -4.02$.\n\\end{nexample}\n\\end{examplewrap}\n% sum(model.matrix(m)[1, ] * round(m$coef, 3))\n\n\\begin{examplewrap}\n\\begin{nexample}{We estimated a coefficient for\n    \\var{bankruptcy} in\n    Section~\\ref{ind_and_cat_vars_as_predictors}\n    of $b_4 = \\pastbankrACoef{}$ with a standard error\n    of $SE_{b_1} = \\pastbankrACoefSE{}$ when using simple\n    linear regression.\n    Why is there a difference between that estimate\n    and the estimated coefficient of \\pastbankrFullCoef{}\n    in the multiple regression setting?}\n  \\label{pastBankrCoefDiffExplained}%\n  If we examined the data carefully, we would see that\n  some predictors are correlated.\n  For instance, when we estimated the connection of the\n  outcome \\var{interest\\us{}rate} and predictor\n  \\var{bankruptcy} using simple linear regression,\n  we were unable to control for other variables like\n  whether the borrower had her income verified,\n  the borrower's debt-to-income ratio, and other variables.\n  That original model was constructed in a vacuum and did\n  not consider the full context.\n  When we include all of the variables,\n  underlying and unintentional\n  bias that was missed by these other variables is reduced\n  or eliminated.\n  Of course, bias can still exist from other confounding\n  variables.\n\\end{nexample}\n\\end{examplewrap}\n\nExample~\\ref{pastBankrCoefDiffExplained} describes a common\nissue in multiple regression: correlation among predictor\nvariables.\nWe say the two predictor variables are \\term{collinear}\n(pronounced as \\emph{co-linear}) when they are correlated,\nand this collinearity complicates model estimation.\nWhile it is impossible to prevent collinearity from arising\nin observational data, experiments are usually designed to\nprevent predictors from being collinear.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe estimated value of the intercept is 1.925, and one might\nbe tempted to make some interpretation of this coefficient,\nsuch as, it is the model's predicted price when each of the\nvariables take value zero: income source is not verified,\nthe borrower has no debt (debt-to-income and credit\nutilization are zero), and so on.\nIs this reasonable?\nIs there any value gained by making this\ninterpretation?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Many of the variables do take a value 0\n  for at least one data point, and for those variables,\n  it is reasonable.\n  However, one variable never takes a value of zero:\n  \\var{term}, which describes the length of the loan,\n  in months.\n  If \\var{term} is set to zero, then the loan\n  must be paid back immediately; the borrower\n  must give the money back as soon as she receives it,\n  which means it is not a real loan.\n  Ultimately, the interpretation of the intercept in\n  this setting is not insightful.}\n\n\n\\D{\\newpage}\n\n\\subsection[Adjusted $R^2$ as a better tool\n    for multiple regression]\n    {Adjusted $\\pmb{R^2}$ as a better tool\n        for multiple regression}\n\n\\index{adjusted r squared@adjusted $R^2$ ($R_{adj}^2$)|(}\n\nWe first used $R^2$ in Section~\\ref{fittingALineByLSR}\nto determine the amount of variability in the response\nthat was explained by the model:\n\\begin{align*}\nR^2 =\n    1 - \\frac{\\text{variability in residuals}}\n        {\\text{variability in the outcome}}\n\t= 1 - \\frac{Var(e_i)}{Var(y_i)}\n\\end{align*}\nwhere $e_i$ represents the residuals of the model and\n$y_i$ the outcomes.\nThis equation remains valid in the multiple regression\nframework, but a small enhancement can make it even\nmore informative when comparing models.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{computeUnadjR2ForFullLoansModel}%\nThe variance of the residuals for the model given in\nGuided Practice~\\ref{loansFullModelEqWCoef}\nis 18.53, and the variance of the total price in all\nthe auctions is 25.01.\nCalculate $R^2$ for this model.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$R^2 = 1 - \\frac{18.53}{25.01} = 0.2591$.}\n\nThis strategy for estimating $R^2$ is acceptable when there\nis just a single variable.\nHowever, it becomes less helpful when there are many\nvariables.\nThe regular $R^2$ is a biased estimate of the amount of\nvariability explained by the model\nwhen applied to a new sample of data.\nTo get a better estimate, we use the adjusted $R^2$.\n\n\\begin{onebox}{Adjusted $\\pmb{R^2}$ as a tool for\n    model assessment}\n  The \\termsub{adjusted $\\pmb{R^2}$}\n      {adjusted r squared@adjusted $R^2$ ($R_{adj}^2$)}\n  is computed as\n  \\begin{align*}\n  R_{adj}^{2}\n    = 1 - \\frac{s_{\\text{residuals}}^2 / (n-k-1)}\n        {s_{\\text{outcome}}^2 / (n-1)}\n    = 1 - \\frac{s_{\\text{residuals}}^2}{s_{\\text{outcome}}^2}\n        \\times \\frac{n-1}{n-k-1}\n  \\end{align*}\n  where $n$ is the number of cases used to fit the model\n  and $k$ is the number of predictor variables in the model.\n  Remember that a categorical predictor with $p$ levels will\n  contribute $p - 1$ to the number of variables in the model.\n\\end{onebox}\n\nBecause $k$ is never negative, the adjusted $R^2$ will be\nsmaller -- often times just a little smaller -- than the\nunadjusted $R^2$.\nThe reasoning behind the adjusted $R^2$ lies in the\n\\termsub{degrees of freedom}{degrees of freedom (df)!regression}\nassociated with each variance,\nwhich is equal to $n - k - 1$ for the multiple regression\ncontext.\nIf we were to make predictions for \\emph{new data}\nusing our current model, we would find that the unadjusted\n$R^2$ would tend to be slightly overly optimistic, while\nthe adjusted $R^2$ formula helps correct this bias.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThere were $n=10000$ auctions in the \\data{loans} data set\nand $k=9$ predictor variables in the model.\nUse $n$, $k$, and the variances from\nGuided Practice~\\ref{computeUnadjR2ForFullLoansModel}\nto calculate $R_{adj}^2$ for the interest rate\nmodel.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$R_{adj}^2\n    = 1 - \\frac{18.53}{25.01}\\times \\frac{10000-1}{1000-9-1}\n    = 0.2584$.\n  While the difference is very small, it will be important\n  when we fine tune the model in the next section.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nSuppose you added another predictor to the model, but the\nvariance of the errors $Var(e_i)$ didn't go down.\nWhat would happen to the~$R^2$?\nWhat would happen to the\nadjusted~$R^2$?\\hspace{0.7mm}\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The unadjusted $R^2$ would stay the same\n    and the adjusted $R^2$ would go down.}\n\nAdjusted $R^2$ could have been used in\nChapter~\\ref{linRegrForTwoVar}.\nHowever, when there is only $k = 1$ predictors,\nadjusted $R^2$ is very close to regular $R^2$,\nso this nuance isn't typically important when\nthe model has only one predictor.\n\n\\index{adjusted r squared@adjusted $R^2$ ($R_{adj}^2$)|)}\n\n\n{\\input{ch_regr_mult_and_log/TeX/introduction_to_multiple_regression.tex}}\n\n\n\n\n\n\n%__________________\n\\section{Model selection}\n\\label{model_selection_section}\n\\label{modelSelection}\n\n\\index{model selection|(}\n\nThe best model is not always the most complicated.\nSometimes including variables that are not evidently\nimportant can actually reduce the accuracy of predictions.\nIn this section, we discuss model selection strategies,\nwhich will help us eliminate variables from the model that\nare found to be less important.\nIt's common (and hip, at least in the statistical world)\nto refer to models that have undergone such variable pruning\nas \\term{parsimonious}.\n\nIn practice, the model that includes all available explanatory\nvariables is often referred to as the \\term{full model}.\nThe full model may not be the best model, and if it isn't,\nwe want to identify a smaller model that is preferable.\n\n\n\\subsection{Identifying variables in the model that may\n    not be helpful}\n\nAdjusted $R^2$ describes the strength of a model fit,\nand it is a useful tool for evaluating which predictors\nare adding value to the model, where \\emph{adding value}\nmeans they are (likely) improving the accuracy in\npredicting future outcomes.\n\nLet's consider two models, which are shown in\nTables~\\ref{loansFullModelModelSelectionSection}\nand~\\ref{loansModelAllButIssued}.\nThe first table summarizes the full model since it includes\nall predictors, while the second does not include the\n\\var{issued} variable.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{rrrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  \\vspace{-3.8mm} & & & & \\\\\n  (Intercept) & 1.9251 & 0.2102 & 9.16 & $<$0.0001 \\\\ \n  income\\us{}ver\\lmlevel{source\\us{}only} &\n      0.9750 & 0.0991 & 9.83 & $<$0.0001 \\\\ \n  income\\us{}ver\\lmlevel{verified} &\n      2.5374 & 0.1172 & 21.65 & $<$0.0001 \\\\ \n  debt\\us{}to\\us{}income & 0.0211 & 0.0029 & 7.18 & $<$0.0001 \\\\ \n  credit\\us{}util & 4.8959 & 0.1619 & 30.24 & $<$0.0001 \\\\ \n  bankruptcy & 0.3864 & 0.1324 & 2.92 & 0.0035 \\\\ \n  term & 0.1537 & 0.0039 & 38.96 & $<$0.0001 \\\\ \n  issued\\lmlevel{Jan2018} & 0.0276 & 0.1081 & 0.26 & 0.7981 \\\\ \n  issued\\lmlevel{Mar2018} & -0.0397 & 0.1065 & -0.37 & 0.7093 \\\\ \n  credit\\us{}checks & 0.2282 & 0.0182 & 12.51 & $<$0.0001 \\\\ \n  \\hline\n  \\multicolumn{3}{l}{$R_{adj}^2 = 0.25843$}&\n      \\multicolumn{2}{r}{$df=9990$}\n\\end{tabular}\n\\caption{The fit for the full regression model,\n    including the adjusted $R^2$.}\n\\label{loansFullModelModelSelectionSection}\n\\end{figure}\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{rrrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  \\vspace{-3.8mm} & & & & \\\\\n  (Intercept) & 1.9213 & 0.1982 & 9.69 & $<$0.0001 \\\\ \n  income\\us{}ver\\lmlevel{source\\us{}only} &\n      0.9740 & 0.0991 & 9.83 & $<$0.0001 \\\\ \n  income\\us{}ver\\lmlevel{verified} &\n      2.5355 & 0.1172 & 21.64 & $<$0.0001 \\\\ \n  debt\\us{}to\\us{}income & 0.0211 & 0.0029 & 7.19 & $<$0.0001 \\\\ \n  credit\\us{}util & 4.8958 & 0.1619 & 30.25 & $<$0.0001 \\\\ \n  bankruptcy & 0.3869 & 0.1324 & 2.92 & 0.0035 \\\\ \n  term & 0.1537 & 0.0039 & 38.97 & $<$0.0001 \\\\ \n  credit\\us{}checks & 0.2283 & 0.0182 & 12.51 & $<$0.0001 \\\\ \n  \\hline\n  \\vspace{-3.6mm} & & & & \\\\\n  \\multicolumn{3}{l}{$R_{adj}^2 = 0.25854$}&\n      \\multicolumn{2}{r}{$df=9992$}\n\\end{tabular}\n\\caption{The fit for the regression model after dropping\n   the \\var{issued} variable.} %, which represented 3 categories\n   % and 2 degrees of freedom.}\n\\label{loansModelAllButIssued}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Which of the two models is better?}\n  We compare the adjusted $R^2$ of each model to determine\n  which to choose.\n  Since the first model has an $R^2_{adj}$ smaller than\n  the $R^2_{adj}$ of the second model, we prefer the second\n  model to the first.\n\\end{nexample}\n\\end{examplewrap}\n\nWill the model without \\var{issued} be better than the\nmodel with \\var{issued}?\nWe~cannot know for sure, but based on the adjusted $R^2$,\nthis is our best assessment.\n\n\n\\subsection{Two model selection strategies}\n\nTwo common strategies for adding or removing variables\nin a multiple regression model are called\n\\emph{backward elimination} and \\emph{forward selection}.\nThese techniques are often referred to as \\term{stepwise}\nmodel selection strategies, because they add or delete\none variable at a time as they ``step'' through the\ncandidate predictors.\n\n\\termsub{Backward elimination}{backward elimination}\nstarts with the model that includes all potential\npredictor variables.\nVariables are eliminated one-at-a-time from the model\nuntil we cannot improve the adjusted $R^2$.\nThe strategy within each elimination step is to eliminate\nthe variable that leads to the largest improvement in\nadjusted $R^2$.\n\n\\begin{examplewrap}\n\\begin{nexample}{Results corresponding to the \\emph{full model}\n    for the \\data{loans} data are shown in\n    Figure~\\ref{loansFullModelModelSelectionSection}.\n    How should we proceed under the backward elimination\n    strategy?}\n  \\label{loansBackwardElimEx}%\n  Our baseline adjusted $R^2$ from the full model is\n  $R^2_{adj} = 0.25843$, and we need to determine whether\n  dropping a predictor will improve the adjusted $R^2$.\n  To check, we fit models that each drop a different\n  predictor, and we record the adjusted $R^2$:\n  \\begin{center}\n  \\begin{tabular}{lllll}\n  Exclude ... &\n      \\var{income\\us{}ver} &\n      \\var{debt\\us{}to\\us{}income} &\n      \\var{credit\\us{}util} &\n      \\var{bankruptcy} \\\\\n  &\n      $R^2_{adj} = 0.22380$ &\n      $R^2_{adj} = 0.25468$ &\n      $R^2_{adj} = 0.19063$ &\n      $R^2_{adj} = 0.25787$ \\\\\n  \\\\\n  &\n      \\var{term} &\n      \\var{issued} &\n      \\var{credit\\us{}checks} \\\\\n  &\n      $R^2_{adj} = 0.14581$ &\n      $R^2_{adj} = 0.25854$ &\n      $R^2_{adj} = 0.24689$ \\\\\n  \\end{tabular}\n  \\end{center}\n  The model without \\var{issued} has the highest adjusted $R^2$\n  of 0.25854, higher than the adjusted $R^2$ for the full model.\n  Because eliminating \\var{issued} leads to a model with\n  a higher adjusted $R^2$, we drop \\var{issued} from the model.\n\n  Since we eliminated a predictor from the model in the first step,\n  we see whether we should eliminate any additional predictors.\n  Our baseline adjusted $R^2$ is now $R^2_{adj} = 0.25854$.\n  We now fit new models, which consider eliminating each of the\n  remaining predictors in addition to \\var{issued}:\n  \\begin{center}\n  \\begin{tabular}{llll}\n  Exclude \\var{issued} and ... &\n      \\var{income\\us{}ver} &\n      \\var{debt\\us{}to\\us{}income} &\n      \\var{credit\\us{}util} \\\\\n  &\n      $R^2_{adj} = 0.22395$ &\n      $R^2_{adj} = 0.25479$ &\n      $R^2_{adj} = 0.19074$ \\\\\n  \\\\\n  &\n      \\var{bankruptcy} &\n      \\var{term} &\n      \\var{credit\\us{}checks} \\\\\n  &\n      $R^2_{adj} = 0.25798$ &\n      $R^2_{adj} = 0.14592$ &\n      $R^2_{adj} = 0.24701$ \\\\\n  \\end{tabular}\n  \\end{center}\n  None of these models lead to an improvement in adjusted $R^2$,\n  so we do not eliminate any of the remaining predictors.\n  That is, after backward elimination, we are left with the\n  model that keeps all predictors except \\var{issued},\n  which we can summarize using the coefficients from\n  Figure~\\ref{loansModelAllButIssued}:\n  \\begin{align*}\n  \\widehat{rate} &= \\ 1.921\n      + 0.974 \\times \\indfunc{income\\us{}ver}{source\\us{}only}\n      + 2.535 \\times \\indfunc{income\\us{}ver}{verified} \\\\\n    &\\qquad\n      + 0.021 \\times \\var{debt\\us{}to\\us{}income}\n      + 4.896 \\times \\var{credit\\us{}util}\n      + 0.387 \\times \\var{bankruptcy} \\\\\n    &\\qquad\n      + 0.154 \\times \\var{term}\n      + 0.228 \\times \\var{credit\\us{}check}\n  \\end{align*}\n\\end{nexample}\n\\end{examplewrap}\n\nThe \\term{forward selection} strategy is the reverse of the backward elimination technique. Instead of eliminating variables one-at-a-time, we add variables one-at-a-time until we cannot find any variables that improve the model (as measured by adjusted $R^2$).\n\n\\begin{examplewrap}\n\\begin{nexample}{Construct a model for the \\data{loans} data\n    set using the forward selection strategy.}\n  \\label{loansForwardElimEx}%\n  We start with the model that includes no variables.\n  Then we fit each of the possible models with just one\n  variable.\n  That is, we fit the model including just \\var{income\\us{}ver},\n  then the model including just \\var{debt\\us{}to\\us{}income},\n  then a model with just \\var{credit\\us{}util}, and so on.\n  Then we examine the adjusted $R^2$ for each of these models:\n  \\begin{center}\n  \\begin{tabular}{lllll}\n  Add ... &\n      \\var{income\\us{}ver} &\n      \\var{debt\\us{}to\\us{}income} &\n      \\var{credit\\us{}util} &\n      \\var{bankruptcy} \\\\\n  &\n      $R^2_{adj} = 0.05926$ &\n      $R^2_{adj} = 0.01946$ &\n      $R^2_{adj} = 0.06452$ &\n      $R^2_{adj} = 0.00222$ \\\\\n  \\\\\n  &\n      \\var{term} &\n      \\var{issued} &\n      \\var{credit\\us{}checks} \\\\\n  &\n      $R^2_{adj} = 0.12855$ &\n      $R^2_{adj} = 0.00018$ &\n      $R^2_{adj} = 0.01711$ \\\\\n  \\end{tabular}\n  \\end{center}\n  % for (i in 1:7) { m <- lm(F(co, i), data = d);\n  %   cat(i, \" \", co[i], \" \", AdjR2(m), \"\\n\") }\n  In this first step, we compare the adjusted $R^2$ against\n  a baseline model that has no predictors.\n  The no-predictors model always has $R_{adj}^2 = 0$.\n  The model with one predictor that has the largest\n  adjusted $R^2$ is the model with the \\var{term} predictor,\n  and because this adjusted $R^2$ is larger than the\n  adjusted $R^2$ from the model with no predictors\n  ($R_{adj}^2 = 0$), we will add this variable to our model.\n\n  We repeat the process again, this time considering\n  2-predictor models where one of the predictors is\n  \\var{term} and with a new baseline of $R^2_{adj} = 0.12855$:\n  \\begin{center}\n  \\begin{tabular}{llll}\n  Add \\var{term} and ... &\n      \\var{income\\us{}ver} &\n      \\var{debt\\us{}to\\us{}income} &\n      \\var{credit\\us{}util} \\\\\n  &\n      $R^2_{adj} = 0.16851$ &\n      $R^2_{adj} = 0.14368$ &\n      $R^2_{adj} = 0.20046$ \\\\\n  \\\\\n  &\n      \\var{bankruptcy} &\n      \\var{issued} &\n      \\var{credit\\us{}checks} \\\\\n  &\n      $R^2_{adj} = 0.13070$ &\n      $R^2_{adj} = 0.12840$ &\n      $R^2_{adj} = 0.14294$ \\\\\n  \\end{tabular}\n  \\end{center}\n  The best second predictor, \\var{credit\\us{}util},\n  has a higher adjusted $R^2$ (0.20046) than the\n  baseline (0.12855), so we also add \\var{credit\\us{}util}\n  to the model.\n\n  Since we have again added a variable to the model,\n  we continue and see whether it would be beneficial\n  to add a third variable:\n  \\begin{center}\n  \\begin{tabular}{llll}\n  Add \\var{term}, \\var{credit\\us{}util}, and ... &\n      \\var{income\\us{}ver} &\n      \\var{debt\\us{}to\\us{}income} \\\\\n  &\n      $R^2_{adj} = 0.24183$ &\n      $R^2_{adj} = 0.20810$ \\\\\n  \\\\\n  &\n      \\var{bankruptcy} &\n      \\var{issued} &\n      \\var{credit\\us{}checks} \\\\\n  &\n      $R^2_{adj} = 0.20169$ &\n      $R^2_{adj} = 0.20031$ &\n      $R^2_{adj} = 0.21629$ \\\\\n  \\end{tabular}\n  \\end{center}\n  The model adding \\var{income\\us{}ver} improved adjusted $R^2$\n  (0.24183 to 0.20046), so we add \\var{income\\us{}ver} to the\n  model.\n\n  We continue on in this way,\n  next adding \\var{debt\\us{}to\\us{}income},\n  then \\var{credit\\us{}checks},\n  and \\var{bankruptcy}.\n  At this point, we come again to the \\var{issued} variable:\n  adding this variable leads to $R_{adj}^2 = 0.25843$,\n  while keeping all the other variables but excluding \\var{issued}\n  leads to a higher $R_{adj}^2 = 0.25854$.\n  This means we do not add \\var{issued}.\n  In this example, we have arrived at the same model that we\n  identified from backward elimination.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{Model selection strategies}\n  Backward elimination begins with the model\n  having the largest number of predictors\n  and eliminates variables one-by-one until we are satisfied\n  that all remaining variables are important to the model.\n  Forward selection starts with no variables included in\n  the model, then it adds in variables according to their\n  importance until no other important variables are found.\n\\end{onebox}\n\nBackward elimination and forward selection sometimes\narrive at different final models.\nIf trying both techniques and this happens, it's common\nto choose the model with the larger $R_{adj}^2$.\n\n\n\\subsection{The p-value approach,\n    an alternative to adjusted $\\pmb{R^2}$}\n\n\\noindent%\nThe p-value may be used as an alternative to $R_{adj}^2$\nfor model selection:\n\\begin{description}\n\\item[Backward elimination with the p-value approach.]\n    In backward elimination, we would identify the predictor\n    corresponding to the largest p-value.\n    If the p-value is above the significance level,\n    usually $\\alpha = 0.05$, then we would drop that variable,\n    refit the model, and repeat the process.\n    If the largest p-value is less than $\\alpha = 0.05$,\n    then we would not eliminate any predictors and the current\n    model would be our best-fitting model.\n\\item[Forward selection with the p-value approach.]\n    In forward selection with p-values, we reverse the process.\n    We begin with a model that has no predictors, then we fit\n    a model for each possible predictor, identifying the model\n    where the corresponding predictor's p-value is smallest.\n    If that p-value is smaller than $\\alpha = 0.05$, we add\n    it to the model and repeat the process, considering whether\n    to add more variables one-at-a-time.\n    When none of the remaining predictors can be added to the\n    model and have a p-value less than 0.05,\n    then we stop adding variables and the current model would\n    be our best-fitting model.\n\\end{description}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nExamine Figure~\\ref{loansModelAllButIssued} on\npage~\\pageref{loansModelAllButIssued}, which considers the\nmodel including all variables except the variable for the month\nthe loan was issued.\nIf we were using the p-value approach with backward elimination\nand we were considering this model, which of these variables\nwould be up for elimination?\nWould we drop that variable, or would we keep it in the\nmodel?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The \\var{bankruptcy} predictor is up for\n  elimination since it has the largest p-value.\n  However, since that p-value is smaller than 0.05,\n  we would still keep it in the model.}\n\nWhile the adjusted $R^2$ and p-value approaches are similar,\nthey sometimes lead to different models, with the $R_{adj}^2$\napproach tending to include more predictors in the final model.\n\n\\begin{onebox}{Adjusted $\\pmb{R^2}$ vs p-value approach}\n  When the sole goal is to improve prediction accuracy,\n  use $R_{adj}^2$.\n  This is commonly the case in machine learning\n  applications.\\vspace{3mm}\n\n  When we care about understanding which variables are\n  statistically significant predictors of the response,\n  or if there is interest in producing a simpler model\n  at the potential cost of a little prediction accuracy,\n  then the p-value approach is preferred.\n\\end{onebox}\n\nRegardless of whether you use $R_{adj}^2$ or the p-value approach,\nor if you use the backward elimination of forward selection\nstrategy, our job is not done after variable selection.\nWe must still verify the model conditions are reasonable.\n\n\\index{model selection|)}\n\n\n{\\input{ch_regr_mult_and_log/TeX/model_selection.tex}}\n\n\n\n\n\n\n\n%%%%%\n\\section{Checking model conditions using graphs}\n\\label{multipleRegressionModelAssumptions}\n\n\\index{regression!model assumptions|(}\n\\index{regression!model conditions|(}\n\\index{regression!technical conditions|(}\n\\index{regression!conditions|(}\n\n\\noindent%\nMultiple regression methods using the model\n\\begin{align*}\n\\hat{y} &= \\beta_0 + \\beta_1x_1 + \\beta_2x_2 + \\cdots + \\beta_kx_k\n\\end{align*}\ngenerally depend on the following four conditions:\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item the residuals of the model are nearly normal\n    (less important for larger data sets),\n\\item the variability of the residuals is nearly constant,\n\\item the residuals are independent, and\n\\item each variable is linearly related to the outcome.\n\\end{enumerate}\n\n\n\\subsection{Diagnostic plots}\n\\label{diagnostic_plots_subsection}\n\n\\termsub{Diagnostic plots}{diagnostic plots} can be used\nto check each of these conditions.\nWe will consider the\nmodel from the Lending Club loans data, and check whether\nthere are any notable concerns:\n\\begin{align*}\n\\widehat{rate} &= \\ 1.921\n    + 0.974 \\times \\indfunc{income\\us{}ver}{source\\us{}only}\n    + 2.535 \\times \\indfunc{income\\us{}ver}{verified} \\\\\n  &\\qquad\n    + 0.021 \\times \\var{debt\\us{}to\\us{}income}\n    + 4.896 \\times \\var{credit\\us{}util}\n    + 0.387 \\times \\var{bankruptcy} \\\\\n  &\\qquad\n    + 0.154 \\times \\var{term}\n    + 0.228 \\times \\var{credit\\us{}check}\n\\end{align*}\n\n\\begin{description}\n\\item[Check for outliers.]\n    In theory, the distribution of the residuals should\n    be nearly normal;\n    in practice, normality can be relaxed for most applications.\n    Instead, we examine a histogram of the residuals\n    to check if there are any outliers:\n    Figure~\\ref{loansDiagNormalHistogram}\n    is a histogram of these outliers.\n    Since this is a very large data set,\n    only particularly extreme observations would be a concern\n    in this particular case.\n    There are no extreme observations that might cause a~concern.\n\n    If we intended to construct what are called\n    \\termsub{prediction intervals}{prediction interval}\n    for future observations,\n    we would be more strict and\n    require the residuals to be nearly normal.\n    Prediction intervals are further discussed in\n    an online extra on the OpenIntro website:\\vspace{-2mm}\n    \\begin{center}\n      \\oiRedirect{stat_extra_linear_regression_supp}\n          {www.openintro.org/d?id=stat\\us{}extra\\us{}linear\\us{}regression\\us{}supp}\n    \\end{center}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[A histogram is shown for \"Debt to Income\" ratio, where values range from 0 to over 400. The data is extremely right skewed, where about 60\\% of the data is in the 0 to 20 bin, about 38\\% is in the 20 to 40 bin, 2\\% in the 40 to 60 bin, about half a percent in the 60 to 80 bin. All other bins are sufficiently small as to be indecipherable from a height of 0.]\n      {0.75}\n      {loansDiagnostics}\n      {loansDiagNormalHistogram}\n  \\caption{A histogram of the residuals.}\n  \\label{loansDiagNormalHistogram}\n\\end{figure}\n\n\\item[Absolute values of residuals against fitted values.]\n    A plot of the absolute value of the residuals against\n    their corresponding fitted values ($\\hat{y}_i$) is shown\n    in Figure~\\ref{loansDiagEvsAbsF}.\n    This plot is helpful to check the condition that the\n    variance of the residuals is approximately constant,\n    and a smoothed line has been added to represent the\n    approximate trend in this plot.\n    There is more evident variability for fitted values that are\n    larger, which we'll discuss further.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures{0.7}\n      {loansDiagnostics}\n      {loansDiagEvsAbsF}\n  \\caption{Comparing the absolute value of the residuals against the fitted values ($\\hat{y}_i$) is helpful in identifying deviations from the constant variance assumption.}\n  \\label{loansDiagEvsAbsF}\n\\end{figure}\n\n\\item[Residuals in order of their data collection.]\n    This type of plot can be helpful when observations were\n    collected in a sequence.\n    Such a plot is helpful in identifying any connection\n    between cases that are close to one another.\n    The loans in this data set were issued over a 3 month period,\n    and the month the loan was issued was not found to be important,\n    suggesting this is not a concern for this data set.\n    In cases where a data set does show some pattern\n    for this check, \\term{time series} methods may be useful.\n\n\\item[Residuals against each predictor variable.]\n    We consider a plot of the residuals against each of\n    the predictors in Figure~\\ref{loansDiagEvsVariables}.\n    For those instances where there are only 2-3 groups,\n    box plots are shown.\n    For the numerical outcomes, a smoothed line has been\n    fit to the data to make it easier to review.\n    Ultimately, we are looking for any notable change\n    in variability between groups or pattern in the data.\n\n    Here are the things of importance from these plots:\n    \\begin{itemize}\n    \\item\n        There is some minor differences in variability\n        between the verified income groups.\n    \\item\n        There is a very clear pattern for the\n        debt-to-income variable.\n        What also stands out is that this variable\n        is very strongly right skewed:\n        there are few observations with very high\n        debt-to-income ratios.\n    \\item\n        The downward curve on the right side of the\n        credit utilization and credit check plots suggests\n        some minor misfitting for those larger values.\n    \\end{itemize}\n\n\\begin{figure}\n  \\centering\n  \\Figures{}{loansDiagnostics}{loansDiagEvsVariables_1}\n  \\Figures{}{loansDiagnostics}{loansDiagEvsVariables_2}\n  \\Figures{}{loansDiagnostics}{loansDiagEvsVariables_3}\n  \\caption{Diagnostic plots for residuals against each of the\n      predictors.\n      For the box plots, we're looking for notable differences\n      in variability.\n      For numerical predictors, we also check for trends\n      or other structure in the data.}\n  \\label{loansDiagEvsVariables}\n\\end{figure}\n\n\\end{description}\n\nHaving reviewed the diagnostic plots, there are two options.\nThe first option is to, if we're not concerned about the issues\nobserved, use this as the final model;\nif going this route, it is important to still note any\nabnormalities observed in the diagnostics.\nThe second option is to try to improve the model,\nwhich is what we'll try to do with this particular model fit.\n\n\n\n\\D{\\newpage}\n\n\\subsection{Options for improving the model fit}\n\nThere are several options for improvement of a model,\nincluding transforming variables,\nseeking out additional variables to fill model gaps,\nor using more advanced methods that would account for\nchallenges around inconsistent variability or nonlinear\nrelationships between predictors and the outcome.\n\nThe main concern for the initial model is that\nthere is a notable nonlinear relationship\nbetween the debt-to-income variable observed in\nFigure~\\ref{loansDiagEvsVariables}.\nTo resolve this issue, we're going to consider\na couple strategies for adjusting the relationship\nbetween the predictor variable and the outcome.\n\nLet's start by taking a look at a histogram of\n\\var{debt\\us{}to\\us{}income} in\nFigure~\\ref{loansDebtToIncomeHist}.\nThe variable is extremely skewed,\nand upper values will have a lot of leverage\non the fit.\nBelow are several options:\n\\begin{itemize}\n\\item log transformation ($\\log{x}$),\n    \\index{transformation!log}\n\\item square root transformation ($\\sqrt{x}$),\n    \\index{transformation!square root}\n\\item inverse transformation ($1 / x$),\n    \\index{transformation!inverse}\n\\item truncation (cap the max value possible)\n    \\index{truncation}\\index{transformation!truncation}\n\\end{itemize}\nIf we inspected the data more closely, we'd observe\nsome instances where the variable takes a value of~0,\nand since $\\log(0)$ and $1 / x$ are undefined when $x = 0$,\nwe'll exclude these transformations from further\nconsideration.\\footnote{There are ways to make them work,\n   but we'll leave those options to a later course.}\nA square root transformation is valid for all values\nthe variable takes, and truncating some of the larger\nobservations is also a valid approach.\nWe'll consider both of these approaches.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures{0.62}{loansDiagnostics}{loansDebtToIncomeHist}\n  \\caption{Histogram of \\var{debt\\us{}to\\us{}income},\n      where extreme skew is evident.}\n  \\label{loansDebtToIncomeHist}\n\\end{figure}\n\nTo try transforming the variable, we make two new variables\nrepresenting the transformed versions:\n\\begin{description}\n\\item[Square root.]\n    We create a new variable,\n    \\var{sqrt\\us{}debt\\us{}to\\us{}income},\n    where all the values are simply the square roots of the\n    values in \\var{debt\\us{}to\\us{}income},\n    and then refit the model as before.\n    The result is shown in the left panel of\n    Figure~\\ref{loansDiagEvsTransformDebtToIncome}.\n    The square root pulled in the higher values\n    a bit, but the fit still doesn't look great\n    since the smoothed line is still wavy.\n\\item[Truncate at 50.]\n    We create a new variable,\n    \\var{debt\\us{}to\\us{}income\\us{}50},\n    where any values in \\var{debt\\us{}to\\us{}income}\n    that are greater than 50 are shrunk to exactly 50.\n    Refitting the model once more,\n    the diagnostic plot for this new variable is shown\n    in the right panel of\n    Figure~\\ref{loansDiagEvsTransformDebtToIncome}.\n    Here the fit looks much more reasonable,\n    so this appears to be a reasonable approach.\n    %If we inspected the data, we'd also observe that\n    %the debt-to-income ratio tends to be large when\n    %income is very small, so these values may also\n    %have been a bit inflated if someone was between jobs.\n\\end{description}\nThe downside of using transformations is that it reduces\nthe ease of interpreting the results.\nFortunately, since the truncation transformation only affects\na relatively small number of cases, the interpretation\nisn't dramatically impacted.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[Two residual plots are shown, each with a flexible trend line overlaid. The first residual plot is against the variable \"Square root of Debt to Income\", which shows relative stability of the trend line with some deviation downwards on the right where there are almost no values and so is less relevant. The second residual plot is against the variable \"Debt to Income, truncated at 50\", which shows general stability in the trend line throughout the plot.]\n      {0.9}\n      {loansDiagnostics}\n      {loansDiagEvsTransformDebtToIncome}\n  \\caption{Histogram of \\var{debt\\us{}to\\us{}income},\n      where extreme skew is evident.}\n  \\label{loansDiagEvsTransformDebtToIncome}\n\\end{figure}\n\n\\D{\\newpage}\n\nAs a next step, we'd evaluate the new model using\nthe truncated version of \\var{debt\\us{}to\\us{}income},\nwe would complete all the same procedures as before.\nThe other two issues noted while inspecting diagnostics\nin Section~\\ref{diagnostic_plots_subsection} are still\npresent in the updated model.\nIf we choose to report this model, we would want to also\ndiscuss these shortcomings to be transparent in our work.\nDepending on what the model will be used for, we could either\ntry to bring those under control, or we could stop since\nthose issues aren't severe.\nHad the non-constant variance been a little more dramatic,\nit would be a higher priority.\nUltimately we decided that the model was reasonable,\nand we report its final form here:\n\\begin{align*}\n\\widehat{rate} &= \\ 1.562\n    + 1.002 \\times \\indfunc{income\\us{}ver}{source\\us{}only}\n    + 2.436 \\times \\indfunc{income\\us{}ver}{verified} \\\\\n  &\\qquad\n    + 0.048 \\times \\var{debt\\us{}to\\us{}income\\us{}50}\n    + 4.694 \\times \\var{credit\\us{}util}\n    + 0.394 \\times \\var{bankruptcy} \\\\\n  &\\qquad\n    + 0.153 \\times \\var{term}\n    + 0.223 \\times \\var{credit\\us{}check}\n\\end{align*}\nA sharp eye would notice that the coefficient for\n\\var{debt\\us{}to\\us{}income\\us{}50} is more than twice\nas large as what the coefficient had been for the\n\\var{debt\\us{}to\\us{}income} variable in the earlier model.\nThis suggests those larger values not only were points\nwith high leverage, but they were influential points that\nwere dramatically impacting the coefficient.\n\n\\begin{onebox}{``All models are wrong,\n    but some are useful''~~~-George E.P. Box}\n  The truth is that no model is perfect.\n  However, even imperfect models can be useful.\n  Reporting a flawed model can be reasonable so long\n  as we are clear and report the model's shortcomings.\n\\end{onebox}\n\nDon't report results when conditions are grossly violated.\nWhile there is a little leeway in model conditions,\ndon't go too far.\nIf model conditions are very clearly violated,\nconsider a new model, even if it means learning more\nstatistical methods or hiring someone who can help.\nTo help you get started, we've developed a couple additional\nsections that you may find on OpenIntro's website.\nThese sections provide a light introduction to what are\ncalled \\termsub{interaction terms}{interaction term}\n\\index{regression!interaction term|textbf}\nand to fitting\n\\termsub{nonlinear curves}{nonlinear curve}%\n\\index{regression!nonlinear curve|textbf}\nto data, respectively:\n\\begin{center}\n\\oiRedirect{stat_extra_interaction_effects}\n    {www.openintro.org/d?file=stat\\_extra\\_interaction\\_effects}\n  \\\\[3mm]\n\\oiRedirect{stat_extra_nonlinear_relationships}\n    {www.openintro.org/d?file=stat\\_extra\\_nonlinear\\_relationships}\n\\end{center}\n\n\\index{regression!conditions|)}\n\\index{regression!technical conditions|)}\n\\index{regression!model conditions|)}\n\\index{regression!model assumptions|)}\n\\index{data!mario\\_kart|)}\n\\index{regression!multiple|)}\n\n\n{\\input{ch_regr_mult_and_log/TeX/checking_model_assumptions_using_graphs.tex}}\n\n\n\n\n\n\n%_____________________\n\\section{Multiple regression case study: Mario Kart}\n\\label{mario_kart_case_study}\n\n\\noindent%\nWe'll consider Ebay auctions of a video game called\n\\emph{Mario Kart} for the Nintendo Wii.\nThe outcome variable of interest is the total price of\nan auction, which is the highest bid plus the shipping cost.\nWe will try to determine how total price is related to each\ncharacteristic in an auction while simultaneously controlling\nfor other variables.\nFor instance, all other characteristics held constant,\nare longer auctions associated with higher or lower prices?\nAnd, on average, how much more do buyers tend to pay for\nadditional Wii wheels\n(plastic steering wheels that attach to the Wii controller)\nin auctions?\nMultiple regression will help us answer these and other questions.\n\n\\newcommand{\\mknum}{141}\n\n\n\\subsection{Data set and the full model}\n\nThe \\data{mariokart} data set includes results\nfrom \\mknum{}~auctions.\nFour observations from this data set are shown in\nFigure~\\ref{marioKartDataMatrix},\nand descriptions for each variable are shown in\nFigure~\\ref{marioKartVariables}. \nNotice that the condition and stock photo variables\nare indicator variables\\index{indicator variable},\nsimilar to \\var{bankruptcy} in the \\data{loan} data set.\n%For instance, the \\var{cond\\us{}new} variable takes value 1 if the game up for auction is new and 0 if it is used. Using indicator variables in place of category names allows for these variables to be directly used in regression.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{rrrrlr}\n  \\hline\n  & price & cond\\us{}new & stock\\us{}photo & duration & wheels \\\\ \n  \\hline\n  1 & 51.55 &   1 & 1 & 3 &   1 \\\\ \n  2 & 37.04 &  0 &  1 & 7 &   1 \\\\ \n  $\\vdots$ &$\\vdots$ &$\\vdots$ &$\\vdots$ &$\\vdots$ &$\\vdots$ \\\\\n  140 & 38.76 &  0 &  0 & 7 &   0 \\\\ \n  141 & 54.51 &  1 &  1 & 1 &   2 \\\\ \n  \\hline\n\\end{tabular}\n\\caption{Four observations from the \\data{mariokart}\n    data set.}\n\\label{marioKartDataMatrix}\n\\end{figure}\n%library(openintro); data(marioKart); d <- marioKart[marioKart$totalPr < 100,]; row.names(d) <- NULL; d\n\n\\begin{figure}[h]\n\\centering\\small\n\\begin{tabular}{lp{9.5cm}}\n\\hline\n{\\bf variable} & {\\bf description} \\\\\n\\hline\n\\var{price} &\n  Final auction price plus shipping costs, in US dollars. \\\\\n\\var{cond\\us{}new} &\n  Indicator variable for if the game is new (\\resp{1}) or used (\\resp{0}). \\\\\n\\var{stock\\us{}photo} &\n  Indicator variable for if the auction's main photo\n  is a stock photo. \\\\\n\\var{duration} &\n  The length of the auction, in days, taking values from 1 to 10. \\\\\n\\var{wheels} &\n  The number of Wii wheels included with the auction.\n  A \\emph{Wii wheel} is an optional steering wheel accessory\n  that holds the Wii controller. \\\\\n\\hline\n\\end{tabular}\n\\caption{Variables and their descriptions for the\n    \\data{mariokart} data set.}\n\\label{marioKartVariables}\n\\end{figure}\n\n% library(openintro); library(xtable); data(marioKart); d <- marioKart[marioKart$totalPr < 100,]; d$cond <- relevel(d$cond, \"used\"); xtable(lm(d$totalPr ~ d$cond)); xtable(lm(d$totalPr ~ d$duration))\n\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figure{0.5}{marioKartSingle}\n%  \\caption{Scatterplot of the total auction price against the\n%      game's condition.\n%      The least squares line is also shown.}\n%  \\label{marioKartSingle}\n%\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{condNewVarForMarioKartOnly}\nWe fit a linear regression model with\nthe game's condition as a predictor of auction price.\nResults of this model are summarized below:\n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  \\vspace{-3.8mm} & & & & \\\\\n(Intercept) & 42.8711 & 0.8140 & 52.67 & $<$0.0001 \\\\ \n  cond\\us{}new & 10.8996 & 1.2583 & 8.66 & $<$0.0001 \\\\ \n   \\hline\n   &&&\\multicolumn{2}{r}{$df=139$}\n\\end{tabular}\n\\end{center}\nWrite down the equation for the model,\nnote whether the slope is statistically different from zero,\nand interpret the coefficient.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The equation for the line may be written as\n  \\begin{align*}\n  \\widehat{price}\n      &= 42.87 + 10.90\\times cond\\us{}new\n  \\end{align*}\n  Examining the regression output in\n  Guided Practice~\\ref{condNewVarForMarioKartOnly},\n  we can see that the p-value for \\var{cond\\us{}new}\n  is very close to zero, indicating there is strong evidence\n  that the coefficient is different from zero when using this\n  simple one-variable model.\n\n  The \\var{cond\\us{}new} is a two-level\n  categorical variable that takes value 1 when the game is new\n  and value 0 when the game is used.\n  This means the 10.90 model coefficient predicts an extra\n  \\$10.90 for those games that are new versus those that are used.}\n\nSometimes there are underlying structures or relationships between predictor variables. For instance, new games sold on Ebay tend to come with more Wii wheels, which may have led to higher prices for those auctions. We would like to fit a model that includes all potentially important variables simultaneously. This would help us evaluate the relationship between a predictor variable and the outcome while controlling for the potential influence of other variables.\n\nWe want to construct a model that accounts for not only the game\ncondition, as in Guided Practice~\\ref{condNewVarForMarioKartOnly},\nbut simultaneously accounts for three other variables:\n\\begin{align*}\n\\widehat{\\var{price}}\n\t&= \\beta_0 + \\beta_1\\times \\var{cond\\us{}new} +\n\t\t\\beta_2\\times \\var{stock\\us{}photo} \\\\\n\t&\\qquad\\  + \\beta_3 \\times  \\var{duration} +\n\t\t\\beta_4 \\times  \\var{wheels}\n\\end{align*}\nFigure~\\ref{MarioKartFullModelOutput} summarizes the full model.\nUsing this output, we identify the point estimates of each\ncoefficient.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{rrrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  \\vspace{-3.8mm} & & & & \\\\\n(Intercept) & 36.2110 & 1.5140 & 23.92 & $<$0.0001 \\\\ \n  cond\\us{}new & 5.1306 & 1.0511 & 4.88 & $<$0.0001 \\\\ \n  stock\\us{}photo & 1.0803 & 1.0568 & 1.02 & 0.3085 \\\\ \n  duration & -0.0268 & 0.1904 & -0.14 & 0.8882 \\\\ \n  wheels & 7.2852 & 0.5547 & 13.13 & $<$0.0001 \\\\ \n   \\hline\n   &&&\\multicolumn{2}{r}{$df=136$}\n\\end{tabular}\n\\caption{Output for the regression model where \\var{price} is the outcome and \\var{cond\\us{}new}, \\var{stock\\us{}photo}, \\var{duration}, and \\var{wheels} are the predictors.}\n\\label{MarioKartFullModelOutput}\n\\end{figure}\n%library(openintro); library(xtable); data(marioKart); d <- marioKart[marioKart$totalPr < 100,]; d$cond <- relevel(d$cond, \"used\"); g <-lm(totalPr ~ cond + stockPhoto + duration + wheels, d)\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{eqForMultRegrOfTotalPrForAllPredWithCoef}%\nWrite out the model's equation using the point estimates from\nFigure~\\ref{MarioKartFullModelOutput}.\nHow many predictors are there in this model?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$\\widehat{price}\n    = 36.21\n        + 5.13 \\times \\var{cond\\us{}new}\n        + 1.08 \\times \\var{stock\\us{}photo}\n        - 0.03 \\times \\var{duration}\n        + 7.29 \\times \\var{wheels}$,\n  with the $k=4$ predictors.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat does $\\beta_4$, the coefficient of variable\n$x_4$ (Wii wheels), represent?\nWhat is the point estimate of $\\beta_4$?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{It is the average difference in auction price\n  for each additional Wii wheel included when holding the\n  other variables constant.\n  The point estimate is $b_4 = 7.29$.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{computeMultipleRegressionResidualForMarioKart}%\nCompute the residual of the first observation in\nFigure~\\ref{marioKartDataMatrix} using the equation identified\nin Guided Practice~\\ref{eqForMultRegrOfTotalPrForAllPredWithCoef}.%\n\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$e_i = y_i - \\hat{y_i} = 51.55 - 49.62 = 1.93$,\n  where 49.62 was computed using the variables values from the\n  observation and the equation identified in\n  Guided Practice~\\ref{eqForMultRegrOfTotalPrForAllPredWithCoef}.}\n\n\\begin{examplewrap}\n\\begin{nexample}{We estimated a coefficient for\n    \\var{cond\\us{}new} in\n    Section~\\ref{condNewVarForMarioKartOnly}\n    of $b_1 = 10.90$ with a standard error of $SE_{b_1} = 1.26$\n    when using simple linear regression.\n    Why might there be a difference between that estimate\n    and the one in the multiple regression setting?}\n  \\label{colinearityOfCondNewAndStockPhoto}%\n  If we examined the data carefully, we would see that\n  there is collinearity\\index{collinear} among some predictors.\n  For instance, when we estimated the connection of the outcome\n  \\var{price} and predictor \\var{cond\\us{}new} using simple linear\n  regression, we were unable to control for other variables like\n  the number of Wii wheels included in the auction.\n  That model was biased by the confounding variable \\var{wheels}.\n  When we use both variables, this particular underlying and\n  unintentional bias is reduced or eliminated (though bias\n  from other confounding variables may still remain).\n\\end{nexample}\n\\end{examplewrap}\n\n\n\\subsection{Model selection}\n\n\\noindent%\nLet's revisit the model for the Mario Kart auction and complete\nmodel selection using backwards selection.\nRecall that the full model took the following form:\n\\begin{align*}\n\\widehat{price} = 36.21\n    + 5.13 \\times \\var{cond\\us{}new}\n    + 1.08 \\times \\var{stock\\us{}photo}\n    - 0.03 \\times \\var{duration}\n    + 7.29 \\times \\var{wheels}\n\\end{align*}\n\n\n\\begin{examplewrap}\n\\begin{nexample}{Results corresponding to the full model\n    for the \\data{mariokart} data were shown\n    in Figure~\\vref{MarioKartFullModelOutput}.\n    For this model, we consider what would happen if dropping\n    each of the variables in the model:\n    \\begin{center}\n    \\begin{tabular}{lllll}\n    Exclude ... &\n      \\var{cond\\us{}new} &\n      \\var{stock\\us{}photo} &\n      \\var{duration} &\n      \\var{wheels} \\\\\n    &\n      $R^2_{adj} = 0.6626$ &\n      $R^2_{adj} = 0.7107$ &\n      $R^2_{adj} = 0.7128$ &\n      $R^2_{adj} = 0.3487$ \\\\\n    \\end{tabular}\n    \\end{center}\n    For the full model, $R_{adj}^2 = 0.7108$.\n    How should we proceed under the backward elimination strategy?}\n  \\label{backwardEliminationExampleWMarioKartData}%\n  The third model without \\var{duration} has the highest\n  $R_{adj}^2$ of 0.7128, so we compare it to\n  $R_{adj}^2$ for the full model.\n  Because eliminating \\var{duration} leads to a model with\n  a higher $R_{adj}^2$, we drop \\var{duration} from the model.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIn Example~\\ref{backwardEliminationExampleWMarioKartData},\nwe eliminated the \\var{duration} variable,\nwhich resulted in a model with $R_{adj}^2 = 0.7128$.\nLet's look at if we would eliminate another variable from the\nmodel using backwards elimination:\n\\begin{center}\n\\begin{tabular}{llll}\nExclude \\var{duration} and ... &\n\t\\var{cond\\us{}new} &\n\t\\var{stock\\us{}photo} &\n\t\\var{wheels} \\\\\n&\n\t$R^2_{adj} = 0.6587$ &\n\t$R^2_{adj} = 0.7124$ &\n\t$R^2_{adj} = 0.3414$ \\\\\n\\end{tabular}\n\\end{center}\nShould we eliminate any additional variable, and if so,\nwhich variable should we eliminate?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Removing any of the three remaining variables\n  would lead to a decrease in $R_{adj}^2$, so we should not\n  remove any additional variables from the model after we\n  removed \\var{duration}.}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{totPrPredictionUsedStockPhotoTwoWheels}%\nAfter eliminating the auction's duration from the model,\nwe are left with the following reduced model:\n\\begin{align*}\n\\widehat{price} &= \\ 36.05\n    + 5.18 \\times \\text{\\var{cond\\us{}new}}\n    + 1.12 \\times \\text{\\var{stock\\us{}photo}}\n    + 7.30 \\times \\text{\\var{wheels}}\n\\end{align*}\nHow much would you predict for the total price for\nthe Mario Kart game if it was used, used a stock photo,\nand included two wheels and put up for auction during\nthe time period that the Mario Kart data were\ncollected?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We would plug in \\resp{0} for \\var{cond\\us{}new}\n  \\resp{1} for \\var{stock\\us{}photo},\n  and \\resp{2} for \\var{wheels} into the equation,\n  which would return \\$51.77, which is the total price\n  we would expect for the auction.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWould you be surprised if the seller from\nGuided Practice~\\ref{totPrPredictionUsedStockPhotoTwoWheels}\ndidn't get the exact price predicted?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{No.\n  The model provides the \\emph{average} auction price\n  we would expect, and the price for one auction to the next\n  will continue to vary a bit\n  (but less than what our prediction would be without the model).}\n\n%If we continued the process, we would not eliminate any additional  of these models lead to an improvement in adjusted $R^2$, so we do not eliminate any of the remaining predictors. That is, after backward elimination, we are left with the model that keeps \\var{cond\\us{}new}, \\var{stock\\us{}photos}, and \\var{wheels}, which we can summarize using the coefficients from Table~\\ref{marioKartMultipleRegressionModelAllButDuration}:\n%\\begin{align*}\n%\\hat{y} \\ &= \\ b_0 + b_1x_1 + b_2x_2 + b_4x_4 \\\\\n%\\widehat{price} &= \\ 36.05 + 5.18 \\times \\text{\\var{cond\\us{}new}} + 1.12 \\times \\text{\\var{stock\\us{}photo}} + 7.30 \\times \\text{\\var{wheels}}\n%\\end{align*}\n\n\n\\subsection{Checking model conditions using graphs}\n\n\\noindent%\nLet's take a closer look at the diagnostics for the Mario Kart\nmodel to check if the model we have identified is reasonable.\n\n\\begin{description}\n\\item[Check for outliers.]\n    A histogram of the residuals is shown in\n    Figure~\\ref{mkDiagResHist}.\n    With a data set well over a hundred, we're primarily\n    looking for major outliers.\n    While one minor outlier appears on the upper end,\n    it is not a concern for this large of a data set.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[A histogram is shown for \"Residuals\". The distribution is centered at 0, is slightly right skewed, and has a standard deviation of about 4.]\n      {0.6}\n      {marioKartDiagnostics}\n      {mkDiagResHist}\n  \\caption{Histogram of the residuals.\n      No clear outliers are evident.}\n  \\label{mkDiagResHist}\n\\end{figure}\n\n\\item[Absolute values of residuals against fitted values.]\n    A plot of the absolute value of the residuals against\n    their corresponding fitted values ($\\hat{y}_i$) is shown\n    in Figure~\\ref{mkDiagnosticEvsAbsF}.\n    We don't see any obvious deviations from constant variance\n    in this example.\n\n\\begin{figure}\n  \\centering\n  \\Figures[Scatterplot of \"Absolute Value of Residuals\" (vertical axis) against \"Fitted Values\" (horizontal axis). The fitted values range from \\$35 to \\$65, and the absolute value of the residuals range from \\$0 to about \\$10, with no apparent pattern across the range of fitted values.]\n      {0.6}{marioKartDiagnostics}{mkDiagnosticEvsAbsF}\n  \\caption{Absolute value of the residuals against\n      the fitted values.\n      No patterns are evident.}\n  \\label{mkDiagnosticEvsAbsF}\n\\end{figure}\n\n\\item[Residuals in order of their data collection.]\n    A plot of the residuals in the order their corresponding\n    auctions were observed is shown in\n    Figure~\\ref{mkDiagnosticInOrder}.\n    Here we see no structure that indicates a problem.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[Scatterplot of \"Residuals\" (vertical axis) against \"Order of Collection\" (horizontal axis). The order of collection runs from 1 to about 140, and the residuals range from -\\$10 to about positive \\$10, with no apparent pattern across the range of fitted values.]\n      {0.55}{marioKartDiagnostics}{mkDiagnosticInOrder}\n  \\caption{Residuals in the order that their\n      corresponding observations were collected.\n      There are no evident patterns.}\n  \\label{mkDiagnosticInOrder}\n\\end{figure}\n\n\\item[Residuals against each predictor variable.]\n    We consider a plot of the residuals against the\n    \\var{cond\\us{}new} variable, the residuals against\n    the \\var{stock\\us{}photo} variable,\n    and the residuals against the \\var{wheels} variable.\n    These plots are shown in Figure~\\ref{mkDiagnosticEvsVariables}.\n    For the two-level condition variable, we are guaranteed not\n    to see any remaining trend, and instead we are checking that\n    the variability doesn't fluctuate across groups,\n    which it does not.\n    However, looking at the stock photo variable,\n    we find that there is some difference in the variability\n    of the residuals in the two groups.\n    Additionally, when we consider the residuals against the\n    \\var{wheels} variable, we see some possible structure.\n    There appears to be curvature in the residuals,\n    indicating the relationship is probably not linear.\n\n\\begin{figure}\n  \\centering\n  \\Figures[Three plots are shown for \"Residuals\" against different predictor variables \"Condition\", \"Photo Type\", and \"Number of Wheels\". Condition plot: A side-by-side box plot is shown for the condition levels of \"Used\" and \"New\". The median of \"Used\" is at \\$0 while the median of \"New\" is at about -\\$2. The boxes in each box plot ranges from about -\\$3 to positive \\$3, and the whiskers of each box plot runs from about -\\$10 to positive \\$10. There are a couple of points slightly beyond the whiskers. Photo Type plot: A side-by-side box plot is shown for the photo type levels of \"Unique Photo\" and \"Stock Photo\". The median of \"Unique Photos\" is at \\$0 while the median of \"Stock Photo\" is at about -\\$1. The boxes in each box plot ranges from about -\\$3 to positive \\$3. The whiskers of \"Unique Photo\" box plot ranges from about -\\$8 to positive \\$7, and the whiskers of \"Stock Photo\" box plot ranges from about -\\$11 to positive \\$11. There are a couple of points slightly beyond the whiskers. Number of Wheels plot: A scatterplot is shown for \"Residuals\" (vertical axis) against \"Number of Wheels\" (horizontal axis) with values from 0 to 4. For 0 wheels, the residuals largely range from -\\$8 to positive \\$10. For 1 wheel, the residuals largely range from -\\$10 to positive \\$5. For 2 wheels, the residuals largely range from -\\$8 to positive \\$10. There are two points with 3 wheels that have residuals of \\$6 and \\$11, and one point with 4 wheels that has a residual of about \\$0.]\n      {0.9}{marioKartDiagnostics}{mkDiagnosticEvsVariables}\n  \\caption{For the condition and stock photo variables,\n      we check\n      for differences in the distribution shape\n      or variability of\n      the residuals.\n      In the case of the stock photos variable,\n      we see a little\n      less variability in the unique photo group\n      than the stock\n      photo group.\n      For numerical predictors, we also check for\n      trends or other structure.\n      We see some slight bowing in the residuals against the\n      \\var{wheels} variable in the bottom plot.}\n  \\label{mkDiagnosticEvsVariables}\n\\end{figure}\n\n\\end{description}\n\nAs with the \\data{loans} analysis, we would summarize\ndiagnostics when reporting the model results.\nIn the case of this auction data,\nwe would report that there appears to be non-constant variance\nin the stock photo variable and that there may be a nonlinear\nrelationship between the total price and the number of wheels\nincluded for an auction.\nThis information would be important to buyers and sellers who\nmay review the analysis, and omitting this information could be\na setback to the very people who the model might assist. \\\\\n\n\\noindent%\n\\textbf{Note: there are no exercises for this section.}\n\n\n\n\n%__________________\n\\section{Introduction to logistic regression}\n\\label{logisticRegression}\n\n\\index{logistic regression|seealso{regression}}\n\\index{regression!logistic|(}\n\n\\noindent%\nIn this section we introduce\n\\termsub{logistic regression}{regression!logistic}\nas a tool for building models when there is a categorical\nresponse variable with two levels, e.g. yes and no.\nLogistic regression is a type of\n\\term{generalized linear model} (\\term{GLM})\nfor response variables\nwhere regular multiple regression does not work very well.\nIn particular, the response variable in these settings often\ntakes a form where residuals look completely different from\nthe normal distribution.\n\nGLMs can be thought of as a two-stage modeling approach.\nWe first model the response variable using a probability\ndistribution, such as the binomial or Poisson distribution.\nSecond, we model the parameter of the distribution using\na collection of predictors and a special form of multiple\nregression.\nUltimately, the application of a GLM will feel very similar\nto multiple regression, even if some of the details are\ndifferent.\n\n%In Section~\\ref{logisticRegression} we will revisit the \\data{email} data set from Chapter~\\ref{introductionToData}. These emails were collected from a single email account, and we will work on developing a basic spam filter using these data. The response variable, \\var{spam}, has been encoded to take value~0 when a message is not spam and~1 when it is spam. Our task will be to build an appropriate model that classifies messages as spam or not spam using email characteristics coded as predictor variables. While this model will not be the same as those used in large-scale spam filters, it shares many of the same features. \n\n\\subsection{Resume data}\n\n\\index{data!resume|(}\n\n\\newcommand{\\resN}{4870}\n\\newcommand{\\resCallbackProp}{0.0805}\n\\newcommand{\\resCallbackPerc}{8.05\\%}\n\\newcommand{\\resNumPred}{8}\n\\newcommand{\\resUniqueNames}{36}\n\\newcommand{\\resHonorsInt}{-2.4998}\n\\newcommand{\\resHonorsCoef}{0.8668}\n\\newcommand{\\resHonorsIntPlusCoef}{-1.6330}\n\\newcommand{\\resHonorsCoefSE}{0.1776}\n\\newcommand{\\resHonorsCoefZ}{4.88}\n\\newcommand{\\resHonorsProb}{0.163}\n\\newcommand{\\resHonorsPerc}{16.3\\%}\n\\newcommand{\\resHonorsNotProb}{0.076}\n\\newcommand{\\resHonorsNotPerc}{7.6\\%}\n\nWe will consider experiment data from a study that sought\nto understand the effect of race and sex on job application\ncallback rates;\ndetails of the study and a link to the data set may be\nfound in Appendix~\\ref{ch_regr_mult_and_log_data}.\nTo evaluate which factors were important,\njob postings were identified in Boston and Chicago\nfor the study,\nand researchers created many fake resumes to send off\nto these jobs to see which would elicit a callback.\nThe researchers enumerated important characteristics,\nsuch as years of\nexperience and education details, and they used these\ncharacteristics to randomly generate the resumes.\nFinally, they randomly assigned a name to each resume,\nwhere the name would imply the applicant's sex and race.\n\nThe first names that were used and randomly assigned\nin this experiment were selected so that they\nwould predominantly be recognized as belonging\nto Black or White individuals;\nother races were not considered in this study.\nWhile no name would definitively be inferred as pertaining\nto a Black individual or to a White individual,\nthe researchers conducted a survey to check for\nracial association of the names;\nnames that did not pass this survey check were excluded\nfrom usage in the experiment.\nYou can find the full set of names that did pass the\nsurvey test and were ultimately used in the study in\nFigure~\\ref{resumeFirstName}.\nFor example, Lakisha was a name that their survey indicated\nwould be interpreted as a Black woman, while Greg was a name\nthat would generally be interpreted to be associated with\na White male.\n\n\\begin{figure}[h]\n\\centering\\small\n\\begin{tabular}{lll c lll c lll}\n  \\cline{1-3} \\cline{5-7} \\cline{9-11}\n  first\\us{}name & race & sex\n      & \\ \\hspace{2mm}\\ &\n      first\\us{}name & race & sex\n      & \\ \\hspace{2mm}\\ &\n      first\\us{}name & race & sex\n      \\\\\n  \\cline{1-3} \\cline{5-7} \\cline{9-11}\n  Aisha & black & female &&\n      Hakim & black & male &&\n      Laurie & white & female \\\\\n  Allison & white & female &&\n      Jamal & black & male &&\n      Leroy & black & male \\\\\n  Anne & white & female &&\n      Jay & white & male &&\n      Matthew & white & male \\\\\n  Brad & white & male &&\n      Jermaine & black & male &&\n      Meredith & white & female \\\\\n  Brendan & white & male &&\n      Jill & white & female &&\n      Neil & white & male \\\\\n  Brett & white & male &&\n      Kareem & black & male &&\n      Rasheed & black & male \\\\\n  Carrie & white & female &&\n      Keisha & black & female &&\n      Sarah & white & female \\\\\n  Darnell & black & male &&\n      Kenya & black & female &&\n      Tamika & black & female \\\\\n  Ebony & black & female &&\n      Kristen & white & female &&\n      Tanisha & black & female \\\\\n  Emily & white & female &&\n      Lakisha & black & female &&\n      Todd & white & male \\\\\n  Geoffrey & white & male &&\n      Latonya & black & female &&\n      Tremayne & black & male \\\\\n  Greg & white & male &&\n      Latoya & black & female &&\n      Tyrone & black & male \\\\\n  \\cline{1-3} \\cline{5-7} \\cline{9-11}\n\\end{tabular}\n\\caption{List of all \\resUniqueNames{} unique names along\n    with the commonly inferred race and sex associated\n    with these names.}\n\\label{resumeFirstName}\n\\end{figure}\n% library(openintro); library(xtable); vars <- c(\"firstname\", \"race\", \"gender\"); d <- resume[, vars]; names(d)[1] <- \"first_name\"; d <- unique(d); d <- d[order(d$first_name), ]; rownames(d) <- NULL; d. <- cbind(d[1:12, ], d[13:24, ], d[25:36, ]); xtable(d.)\n\nThe response variable of interest is whether or not there\nwas a callback from the employer for the applicant,\nand there were \\resNumPred{} attributes that\nwere randomly assigned that we'll consider,\nwith special interest in the race and sex variables.\nRace and sex are \\term{protected classes} in the\nUnited States, meaning they are not legally permitted\nfactors for hiring or employment decisions.\nThe full set of attributes considered is provided in\nFigure~\\ref{resumeVariables}.\n\n\\D{\\newpage}\n\n\\begin{figure}[h]\n\\centering\\small\n\\begin{tabular}{lp{112mm}}\n\\hline\n{\\bf variable} & {\\bf description} \\\\\n\\hline\n\\var{callback} &\n    Specifies whether the employer called the applicant\n    following submission of the application for the job. \\\\\n%\\var{first\\us{}name} &\n%    First name of the applicant that is listed on the resume. \\\\\n\\var{job\\us{}city} &\n    City where the job was located: Boston or Chicago.\\\\\n%\\var{job\\us{}industry} &\n%    The job industry, e.g. manufacturing or transportation,\n%    for the job listing. \\\\\n%\\var{job\\us{}type} &\n%    The type of job, e.g. supervisor or sales representative,\n%    for the job listing. \\\\\n%\\var{job\\us{}req} &\n%    An indicator for if there were any job requirements listed\n%    in the job listing. \\\\\n\\var{college\\us{}degree} &\n    An indicator for whether the resume listed a college degree. \\\\\n\\var{years\\us{}experience} &\n    Number of years of experience listed on the resume. \\\\\n\\var{honors} &\n    Indicator for the resume listing some sort of honors,\n    e.g.~employee of the month. \\\\\n\\var{military} &\n    Indicator for if the resume listed any military experience. \\\\\n\\var{email\\us{}address} &\n    Indicator for if the resume listed an email address for\n    the applicant. \\\\\n\\var{race} &\n    Race of the applicant, implied by their first name\n    listed on the resume. \\\\\n\\var{sex} &\n    Sex of the applicant (limited to only \\resp{male}\n    and \\resp{female} in this study),\n    implied by the first name listed on the resume. \\\\\n\\hline\n\\end{tabular}\n\\caption{Descriptions for the \\var{callback} variable\n    along with \\resNumPred{} other variables\n    in the \\data{resume} data set.\n    Many of the variables are\n    indicator\\index{indicator variable} variables,\n    meaning they take the value 1 if the specified\n    characteristic is present and 0 otherwise.}\n\\label{resumeVariables}\n\\end{figure}\n\nAll of the attributes listed on each resume were\nrandomly assigned.\nThis means that no attributes that might be favorable\nor detrimental to employment would favor one demographic\nover another on these resumes.\nImportantly, due to the experimental nature of this study,\nwe can infer causation between these variables and the\ncallback rate, if the variable is statistically significant.\nOur analysis will allow us to compare the practical\nimportance of each of the variables relative to each other.\n\n\n\n\n\\subsection{Modeling the probability of an event}\n\\label{modelingTheProbabilityOfAnEvent}\n\nLogistic regression is a generalized linear model where\nthe outcome is a two-level categorical variable.\nThe outcome, $Y_i$, takes the value 1\n(in our application, this represents a callback\nfor the resume)\nwith probability $p_i$\nand the value 0 with probability $1 - p_i$.\nBecause each observation has a slightly different\ncontext, e.g. different education level or a different\nnumber of years of experience, the probability $p_i$\nwill differ for each observation.\nUltimately, it is this probability that we model\nin relation to the predictor variables:\nwe will examine which resume characteristics\ncorrespond to higher or lower callback rates.\n\n\\begin{onebox}{Notation for a logistic regression model}\nThe outcome variable for a GLM is denoted by $Y_i$,\nwhere the index $i$ is used to represent observation $i$.\nIn the resume application, $Y_i$ will be used to represent\nwhether resume $i$ received a callback ($Y_i=1$)\nor not ($Y_i=0$). \\vspace{3mm}\n\nThe predictor variables are represented as follows:\n$x_{1,i}$ is the value of variable 1 for observation $i$,\n$x_{2,i}$ is the value of variable 2 for observation $i$,\nand so on.\n\\end{onebox}\n\nThe logistic regression model relates the probability\na resume would receive a callback ($p_i$) to the predictors\n$x_{1,i}$, $x_{2,i}$, ..., $x_{k,i}$\nthrough a framework much like that of multiple regression:\n\\begin{align}\ntransformation(p_{i})\n  = \\beta_0 +\n      \\beta_1x_{1,i} +\n      \\beta_2 x_{2,i} +\n      \\cdots +\n      \\beta_k x_{k,i}\n\\label{linkTransformationEquation}\n\\end{align}\nWe want to choose a transformation in the equation\nthat makes practical and mathematical sense.\nFor example, we want a transformation that makes\nthe range of possibilities on the left hand side\nof the equation equal to the range of possibilities\nfor the right hand side;\nif there was no transformation for this equation,\nthe left hand side could only take values between 0 and 1,\nbut the right hand side could take values outside of this\nrange.\nA common transformation for $p_i$ is the \\term{logit transformation}, which may be written as\n\\begin{align*}\nlogit(p_i) = \\log_{e}\\left( \\frac{p_i}{1-p_i} \\right)\n\\end{align*}\nThe logit transformation is shown in\nFigure~\\ref{logitTransformationFigureHoriz}.\nBelow, we rewrite the equation relating $Y_i$ to its\npredictors using the logit transformation of $p_i$:\n\\begin{align*}\n\\log_{e}\\left( \\frac{p_i}{1-p_i} \\right)\n  = \\beta_0 +\n      \\beta_1 x_{1,i} +\n      \\beta_2 x_{2,i} +\n      \\cdots +\n      \\beta_k x_{k,i}\n\\end{align*}\nIn our resume example, there are \\resNumPred{} predictor\nvariables, so $k = \\resNumPred{}$.\nWhile the precise choice of a logit function isn't intuitive,\nit is based on theory that underpins generalized linear models,\nwhich is beyond the scope of this book.\nFortunately, once we fit a model using software,\nit will start to feel like we're back in the\nmultiple regression context, even if the\ninterpretation of the coefficients is more complex.\n\n\\begin{figure}\n  \\centering\n  \\Figure[The plot is shown showing the values of \"logit(p-sub-i)\" on the horizontal axis with values ranging from -6 to positive 6, and \"p-sub-i\" on the vertical axis with values ranging from 0 to 1. The shape of the curve is a sort of \"swoop\". It starts flat near 0, and curves upwards reaching a maximum slope as it crosses logit of 0 and a proportion of 0.5, at which point the slope starts tapering off again and flattening out as it approaches a value of 1. The following points are annotated on the curve, where the first value of each pair is for the logit value and the second is for the corresponding probability: (-5, 0.007), (-4, 0.018), (-3, 0.05), (-2, 0.12), (-1, 0.27), (0, 0.5), (1, 0.73), (2, 0.88), (3, 0.95), (4, 0.982), (5, 0.993), (6, 0.998).]\n      {}{logitTransformationFigureHoriz}\n  \\caption{Values of $p_i$ against values of $logit(p_i)$.}\n  \\label{logitTransformationFigureHoriz}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{We start by fitting a model with a single\n    predictor: \\var{honors}.\n    This variable indicates whether the applicant had any\n    type of honors listed on their resume,\n    such as employee of the month.\n    The following logistic regression model was fit using\n    statistical software:\n    \\begin{align*}\n    \\log_e \\left( \\frac{p_i}{1-p_i} \\right)\n      = \\resHonorsInt{} +\n          \\resHonorsCoef{} \\times\\text{\\var{honors}}\n    \\end{align*}\n    %library(openintro); m <- glm(received_callback ~ honors, data = resume, family=binomial); summary(m); co <- round(m$coefficients, 4); a <- exp(co[\"(Intercept)\"]); a/(1+a); a <- exp(sum(co)); a/(1+a)\n    (a) If a resume is randomly selected from the study\n    and it does not have any honors listed,\n    what is the probability resulted in a callback?\n    \n    (b) What would the probability be if the resume did\n    list some honors?}\n    \\label{logisticExampleWithHonors}%\n  (a) If a randomly chosen resume from those sent out is considered,\n  and it does not list honors, then \\var{honors} takes\n  value~0 and the right side of the model equation equals\n  \\resHonorsInt{}.\n  Solving for $p_i$:\n  $\\frac{e^{\\resHonorsInt{}}}{1 + e^{\\resHonorsInt{}}}\n      = \\resHonorsNotProb{}$.\n  Just as we labeled a fitted value of $y_i$ with a ``hat''\n  in single-variable and multiple regression, we do the same\n  for this probability: $\\hat{p}_i = \\resHonorsNotProb{}$.\n\n  (b) If the resume had listed some honors,\n  then the right side of the model equation is\n  $\\resHonorsInt{} + \\resHonorsCoef{} \\times 1\n      = \\resHonorsIntPlusCoef{}$,\n  which corresponds to a probability\n  $\\hat{p}_i = \\resHonorsProb{}$.\n\n  Notice that we could examine \\resHonorsInt{} and\n  \\resHonorsIntPlusCoef{} in\n  Figure~\\ref{logitTransformationFigureHoriz}\n  to estimate the probability before formally calculating\n  the value.\n\\end{nexample}\n\\end{examplewrap}\n\n\\D{\\newpage}\n\nTo convert from values on the logistic regression scale\n(e.g. \\resHonorsInt{} and \\resHonorsIntPlusCoef{} in\nExample~\\ref{logisticExampleWithHonors}),\nuse the following formula, which is the result\nof solving for $p_i$ in the regression model:\n\\newcommand{\\exponentialToSolveForPi}\n    {e^{\\beta_0 + \\beta_1 x_{1,i}+\\cdots+\\beta_k x_{k,i}}}%\n\\begin{align*}\np_i = \\frac{\\exponentialToSolveForPi{}}\n    {\\ 1\\ \\ +\\ \\ \\exponentialToSolveForPi{}\\ }\n\\end{align*}\nAs with most applied data problems,\nwe substitute the point estimates for the parameters\n(the $\\beta_i$) so that we can make use of this formula.\nIn Example~\\ref{logisticExampleWithHonors},\nthe probabilities were calculated as\n\\begin{align*}\n&\\frac{\\ e^{\\resHonorsInt{}}\\ }\n    {\\ 1\\ +\\ e^{\\resHonorsInt{}}\\ }\n  = \\resHonorsNotProb{} &&\n\\frac{\\ e^{\\resHonorsInt{} + \\resHonorsCoef{}}\\ }\n    {\\ 1\\ +\\ e^{\\resHonorsInt{} + \\resHonorsCoef{}}\\ }\n  = \\resHonorsProb{}\n\\end{align*}\nWhile knowing whether a resume listed honors provides\nsome signal when predicting whether or not the employer\nwould call, we would like to account for many different\nvariables at once to understand how each of the different\nresume characteristics affected the chance of a callback.\n\n\n\\subsection{Building the logistic model with many variables}\n\nWe used statistical software to fit the logistic regression\nmodel with all \\resNumPred{} predictors described in\nFigure~\\ref{resumeVariables}.\nLike multiple regression, the result may be presented\nin a summary table, which is shown in\nFigure~\\ref{resumeLogisticModelResults}.\nThe structure of this table is almost identical to that\nof multiple regression;\nthe only notable difference is that the p-values are\ncalculated using the normal distribution rather than\nthe $t$-distribution.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l rrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n  & Estimate & Std. Error & z value & Pr($>$$|$z$|$) \\\\\n  \\hline\n  \\vspace{-3.8mm} & & & & \\\\\n  (Intercept) & -2.6632 & 0.1820 & -14.64 & $<$0.0001 \\\\\n  job\\us{}city\\lmlevel{Chicago} &\n      -0.4403 & 0.1142 & -3.85 & 0.0001 \\\\\n  college\\us{}degree & -0.0666 & 0.1211 & -0.55 & 0.5821 \\\\\n  years\\us{}experience & 0.0200 & 0.0102 & 1.96 & 0.0503 \\\\\n  honors & 0.7694 & 0.1858 & 4.14 & $<$0.0001 \\\\\n  military & -0.3422 & 0.2157 & -1.59 & 0.1127 \\\\\n  email\\us{}address & 0.2183 & 0.1133 & 1.93 & 0.0541 \\\\\n  race\\lmlevel{white} & 0.4424 & 0.1080 & 4.10 & $<$0.0001 \\\\\n  sex\\lmlevel{male} & -0.1818 & 0.1376 & -1.32 & 0.1863 \\\\\n  \\hline\n\\end{tabular}\n\\caption{Summary table for the full logistic regression model\n    for the resume callback example.}\n\\label{resumeLogisticModelResults}\n\\end{figure}\n% library(openintro); library(dplyr); a <- resume; d <- data.frame(callback = a$received_callback, job_city = a$job_city, college_degree = a$college_degree, years_experience = a$years_experience, honors = a$honors, military = a$military, email_address = a$has_email_address, race = a$race, gender = ifelse(a$gender == \"m\", \"male\", \"female\"))\n% job_industry = a$job_industry, job_type = a$job_type, \n% m <- glm(callback ~ job_city + college_degree + years_experience + honors + military + email_address + race + gender, data = d, family = binomial); summary(m); xtable(m)\n\\newcommand{\\resRaceWhiteCoef}{0.4424}\n\nJust like multiple regression, we could trim some variables\nfrom the model.\nHere we'll use a statistic called\n\\term{Akaike information criterion (AIC)},\nwhich is an analog to how we used adjusted R-squared\nin multiple regression,\nand we look for models with a lower AIC\nthrough a backward elimination strategy.\nAfter using this criteria, the \\var{college\\us{}degree}\nvariable is eliminated, giving the smaller model summarized\nin Figure~\\ref{resumeLogisticReducedModel},\nwhich is what we'll rely on for the remainder\nof this section.\n%\\Comment{Do we want to discuss that one variable dropping out more?}\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l rrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n  & Estimate & Std. Error & z value & Pr($>$$|$z$|$) \\\\ \n  \\hline\n  \\vspace{-3.8mm} & & & & \\\\\n  (Intercept) & -2.7162 & 0.1551 & -17.51 & $<$0.0001 \\\\ \n  job\\us{}city\\lmlevel{Chicago} &\n      -0.4364 & 0.1141 & -3.83 & 0.0001 \\\\ \n  years\\us{}experience & 0.0206 & 0.0102 & 2.02 & 0.0430 \\\\ \n  honors & 0.7634 & 0.1852 & 4.12 & $<$0.0001 \\\\ \n  military & -0.3443 & 0.2157 & -1.60 & 0.1105 \\\\ \n  email\\us{}address & 0.2221 & 0.1130 & 1.97 & 0.0494 \\\\ \n  race\\lmlevel{white} & 0.4429 & 0.1080 & 4.10 & $<$0.0001 \\\\ \n  sex\\lmlevel{male} & -0.1959 & 0.1352 & -1.45 & 0.1473 \\\\ \n\\hline\n\\end{tabular}\n\\caption{Summary table for the logistic regression model\n    for the resume callback example, where variable selection\n    has been performed using AIC.}\n\\label{resumeLogisticReducedModel}\n\\end{figure}\n% # Run code for table above first\n% % m. <- step(m); summary(m.); xtable(m.)\n\\newcommand{\\resRaceWhiteCoefReduced}{0.4429}\n\n\\begin{examplewrap}\n\\begin{nexample}{The \\var{race} variable had taken\n    only two levels: \\resp{black} and \\resp{white}.\n    Based on the model results, was race a meaningful\n    factor for if a prospective employer would\n    call back?}\n  We see that the p-value for this coefficient is very\n  small (very nearly zero), which implies that race\n  played a statistically significant role in whether\n  a candidate received a callback.\n  Additionally, we see that the coefficient shown\n  corresponds to the level of \\resp{white},\n  and it is positive.\n  This positive coefficient reflects a positive gain\n  in callback rate for resumes where the candidate's\n  first name implied they were White.  \n  The data provide very strong evidence of racism\n  by prospective employers that favors resumes where the\n  first name is typically interpreted to be White.\n\\end{nexample}\n\\end{examplewrap}\n\n%We, the authors, found this conclusion saddening,\n%though not surprising.\n%It is also important to consider that this data only\n%highlights one stage of racial bias in employment --\n%when someone is trying to get hired --\n%and it does not consider racial bias during employment.\n%It does not scratch the surface of racial bias\n%for individuals who are hired.\n\n%\\begin{examplewrap}\n%\\begin{nexample}{Compare the coefficient of t.\n%    Why are the two estimated coefficients different?}\n%  We earlier discussed how the implied race on the resume\n%  was randomized and this variable is independent of\n%  other predictors.\n%  This means that the estimated effect will be virtually\n%  unchanged even after we add or remove other variables\n%  from the model.\n%  This property is the product of thoughtful experiment\n%  design by this study's researchers.\n%\\end{nexample}\n%\\end{examplewrap}\n\nThe coefficient of $\\indfunc{race}{white}$ in the full model in\nFigure~\\ref{resumeLogisticModelResults},\nis nearly identical to the model shown in\nFigure~\\ref{resumeLogisticReducedModel}.\nThe predictors in this experiment were thoughtfully\nlaid out so that the coefficient estimates would typically\nnot be much influenced by which other predictors were \nin the model,\nwhich aligned with the motivation of the study to tease\nout which effects were important to getting a callback.\nIn most observational data,\nit's common for point estimates to change a little,\nand sometimes a lot, depending on which other\nvariables are included in the model.\n%Collinearity can also occur in experiments,\n%but in this case the experiment was designed in such a way\n%that collinearity it was not an issue.\n%This might happen if predictor variables are correlated,\n%where the inclusion of one of the variables can influence.\n%\\Comment{Revisit the end of this paragraph,\n%  e.g. if removing the Ebay auction example.}\n%We previously saw this in the Ebay auction example when\n%we compared the coefficient of \\var{cond\\us{}new} in a\n%single-variable model and the corresponding coefficient\n%in the multiple regression model when including three\n%additional variables (see\n%Sections~\\ref{ind_and_cat_vars_as_predictors}\n%and~\\ref{includingAndAssessingManyVariablesInAModel}).\n\n\\begin{examplewrap}\n\\begin{nexample}{Use the model summarized in\n    Figure~\\ref{resumeLogisticReducedModel}\n    to estimate the probability\n    of receiving a callback for a job in Chicago\n    where the candidate lists 14 years experience,\n    no honors,\n    no military experience,\n    includes an email address,\n    and has a first name that implies they are a White male.}\n  \\label{exampleForResumeAndWhiteQuantified}%\n  We can start by writing out the equation using the\n  coefficients from the model, then we can\n  add in the corresponding values of each variable for this\n  individual:\n  \\begin{align*}\n  &log_e \\left(\\frac{p}{1 - p}\\right) \\\\\n    &\\quad= - 2.7162\n        - 0.4364 \\times \\indfunc{job\\us{}city}{Chicago}\n        + 0.0206 \\times \\var{years\\us{}experience}\n        + 0.7634 \\times \\var{honors} \\\\\n      &\\quad\\qquad\n          - 0.3443 \\times \\var{military}\n          + 0.2221 \\times \\var{email}\n          + 0.4429 \\times \\indfunc{race}{white}\n          - 0.1959 \\times \\indfunc{sex}{male} \\\\\n    &\\quad= - 2.7162\n        - 0.4364 \\times 1\n        + 0.0206 \\times 14\n        + 0.7634 \\times 0 \\\\\n      &\\quad\\qquad\n          - 0.3443 \\times 0\n          + 0.2221 \\times 1\n          + 0.4429 \\times 1\n          - 0.1959 \\times 1 \\\\\n    &\\quad= - 2.3955\n  \\end{align*}\n  We can now back-solve for $p$:\n  the chance such an individual will receive\n  a callback is about~8.35\\%.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Compute the probability of a callback\n    for an individual with a name commonly inferred\n    to be from a Black male but who otherwise\n    has the same characteristics as the one described\n    in Example~\\ref{exampleForResumeAndWhiteQuantified}.}\n  \\index{exampleForResumeAndBlackQuantified}%\n  We can complete the same steps for an individual\n  with the same characteristics who is Black,\n  where the only difference in the calculation is that\n  the indicator variable\n  $\\indfunc{race}{white}$ will take a value of \\resp{0}.\n  Doing so yields a probability of 0.0553.\n  Let's compare the results with those of\n  Example~\\ref{exampleForResumeAndWhiteQuantified}.\n\n  In practical terms, an individual perceived\n  as White based on their first name would need to\n  apply to $\\frac{1}{0.0835} \\approx 12$ jobs on average\n  to receive a callback,\n  while an individual perceived as Black\n  based on their first name\n  would need\n  to apply to $\\frac{1}{0.0553} \\approx 18$ jobs on average\n  to receive a callback.\n  That is, applicants who are perceived as\n  Black need to apply to 50\\% more employers\n  to receive a callback than someone who is perceived\n  as White based on their first name for jobs like\n  those in the study.\n\\end{nexample}\n\\end{examplewrap}\n\nWhat we've quantified in this section is alarming and disturbing.\nHowever, one aspect that makes this racism so difficult to\naddress is that the experiment, as well-designed as it is,\ncannot send us much signal about which employers are\ndiscriminating.\nIt is only possible to say that discrimination is happening,\neven if we cannot say which particular callbacks\n-- or non-callbacks -- represent discrimination.\nFinding strong evidence of racism for individual cases is\na persistent challenge in enforcing anti-discrimination laws.\n%For observational data on racial discrimination,\n%there are even more challenges:\n%some variables may be correlated with race\n%or there may be potential confounding variables that\n%cannot reasonably be modeled,\n%making the challenges even more profound in reliably\n%identifying racism.\n\n\n\n\\subsection{Diagnostics for the callback rate model}\n\\label{logistic_regr_diagnostics_subsection}\n\n\\begin{onebox}{Logistic regression conditions}\nThere are two key conditions for fitting a logistic regression model:\\vspace{-1mm}\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item\n    Each outcome $Y_i$ is independent of the other outcomes.\n\\item\n    Each predictor $x_i$ is linearly related to logit$(p_i)$\n    if all other predictors are held constant.\n\\end{enumerate}\n\\end{onebox}\n\nThe first logistic regression model condition\n-- independence of the outcomes --\nis reasonable for the experiment since characteristics\nof resumes were randomly assigned to the resumes that\nwere sent out.\n%This is further discussed in Appendix~\\ref{}.\n\nThe second condition of the logistic regression model is\nnot easily checked without a fairly sizable amount of data.\nLuckily, we have \\resN{} resume submissions in the data set!\nLet's first visualize these data by plotting the true\nclassification of the resumes against the model's fitted\nprobabilities, as shown in\nFigure~\\ref{logisticModelPredict}.\n%The vast majority of emails (spam or not) still have fitted probabilities below 0.5.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[Side-by-side dot plot of \"Predicted Probability\" for two levels of \"1 (Callback)\" and \"0 (No Callback)\". The points for each level has predicted probabilities concentrated between 0 and 0.2 with a small fraction of points taking larger values (with non above about 0.3 predicted probability). There is little evident difference between the two groups due to the very large number of points overlaying each other.]\n      {0.95}{logisticModel}{logisticModelPredict}\n  \\caption{The predicted probability that each of the\n      \\resN{} resumes results in a callback.\n      \\hiddenterm{Noise}\n      (small, random vertical shifts) have been added\n      to each point so points with nearly identical\n      values aren't plotted exactly on top of one another.}\n  \\label{logisticModelPredict}\n\\end{figure}\n\n\\D{\\newpage}\n\n%The probabilities predicted by the model fall between\n%4.3\\% and 29.9\\%.\nWe'd like to assess the quality of the model.\nFor example, we might ask:\nif we look at resumes that we modeled as having\na 10\\% chance of getting a callback, do we find\nabout 10\\% of them actually receive a callback?\nWe can check this for groups of the data by constructing\na plot as follows:\n\\begin{enumerate}\n\\item\n    Bucket the data into groups based on their\n    predicted probabilities.\n\\item\n    Compute the average predicted probability for each group.\n\\item\n    Compute the observed probability for each group,\n    along with a 95\\% confidence interval.\n\\item\n    Plot the observed probabilities\n    (with 95\\% confidence intervals)\n    against the average predicted probabilities for each group.\n\\end{enumerate}\nThe points plotted should fall close to the line $y = x$,\nsince the predicted probabilities should be similar to the\nobserved probabilities.\nWe can use the confidence intervals to roughly gauge whether\nanything might be amiss.\nSuch a plot is shown in Figure~\\ref{logisticModelBucketDiag}.\n\n%To help us out, we'll borrow an advanced statistical\n%method called \\term{natural splines} that estimates\n%the local probability over the region 0.04 to 0.30,\n%which is the range of the predicted probabilities.\n%All you need to know about natural splines to understand\n%what we are doing is that they are used to fit flexible\n%lines rather than straight lines.\n%\n%The curve fit using natural splines is shown in\n%Figure~\\ref{logisticModelSpline} as a solid black line.\n%If the logistic model fits well, the curve should closely\n%follow the dashed $y = x$ line.\n%We have added shading to represent the confidence bound for\n%the curved line to clarify what fluctuations might plausibly\n%be due to chance.\n%The dashed line generally stays within the error bound\n%of the solid curve, suggesting the fit is reasonable.\n\n\\begin{figure}\n  \\centering\n  \\Figures[A side-by-side dot plot of \"Predicted Probability\" for two levels of \"1 (Callback)\" and \"0 (No Callback)\" with some additional annotations. The data are bucketed into 10 groups based on their predicted probabilities. Each bucket has a 95\\% confidence interval plotted at the average value of the predicted probability in the buckets. The plot has an annotation explaining: \"Observations are bucketed, then we compute the observed probability in each bucket (y) against the average predicted probability (x) for each of the buckets with 95\\% confidence intervals.\" A \"y equals x\" line is plotted as well, and each of the ten confidence intervals overlaps this line.]\n      {0.95}{logisticModel}{logisticModelBucketDiag}\n  \\caption{The dashed line is within the confidence bound\n       of the 95\\% confidence intervals of each of the buckets,\n       suggesting the logistic fit is reasonable.}\n%  \\caption{The dashed line is within the confidence bound\n%       of the smoothed line, suggesting the logistic fit is\n%       reasonable.}\n  \\label{logisticModelBucketDiag}\n  %\\label{logisticModelSpline}\n\\end{figure}\n\nAdditional diagnostics may be created that are similar to those\nfeatured in Section~\\ref{multipleRegressionModelAssumptions}.\nFor instance, we could compute residuals as\nthe observed outcome minus the expected outcome\n($e_i = Y_i - \\hat{p}_i$),\nand then we could create plots of these residuals\nagainst each predictor.\nWe might also create a plot like that in\nFigure~\\ref{logisticModelBucketDiag}\nto better understand the deviations.\n%We might also create a smoothed average like that in\n%Figure~\\ref{logisticModelSpline} to better understand\n%deviations.\n\n\\index{data!resume|)}\n\\index{regression!logistic|)}\n\\index{regression|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Exploring discrimination between groups\n    of different sizes}\n    % An exercise in critical thinking around a hypothetical setting\n\n\\index{discrimination|(}\n\n%Discrimination is an incredibly important and complex societal issue, and this study only examined discrimination in a single aspect\nAny form of discrimination is concerning,\nand this is why we decided it was so important to discuss\nthis topic using data.\nThe resume study also only examined discrimination in a\nsingle aspect: whether a prospective employer would\ncall a candidate who submitted their resume.\nThere was a 50\\% higher barrier for resumes simply when\nthe candidate had a first name that was perceived to be\nfrom a Black individual.\nIt's unlikely that discrimination would stop there.\n\n%Of course, discrimination can happen to anyone.\n%Yet, discrimination against dominant groups is\n%considered to be much less impactful than\n%the discrimination experienced by oppressed groups.\n%\\emph{Why?}\n\n\\begin{examplewrap}\n\\begin{nexample}{Let's consider a sex-imbalanced\n    company that consists of 20\\% women\n    and 80\\% men,\\footnotemark{}\n    and we'll suppose that the\n    company is very large, consisting of perhaps\n    20,000 employees.\n    Suppose when someone goes up for promotion at this\n    company, 5~of their colleagues are randomly chosen\n    to provide feedback on their work.\n    \\exspace{}\n\n    Now let's imagine that 10\\% of the people in the\n    company are prejudiced against the other sex.\n    That~is, 10\\% of men are prejudiced against women,\n    and similarly, 10\\% of women are prejudiced against men.\n    \\exspace{}\n    \n    Who is discriminated against more at the company,\n    men or women?}\n  \\label{sex_imbalance_leads_to_discrimination}%\n  Let's suppose we took 100 men who have gone up for\n  promotion in the past few years.\n  For these men, $5 \\times 100 = 500$ random colleagues\n  will be tapped for their feedback, of which\n  about 20\\% will be women (100 women).\n  Of these 100 women, 10 are expected to be biased\n  against the man they are reviewing.\n  Then, of the 500 colleagues reviewing them,\n  men will experience\n  discrimination by about 2\\% of their colleagues when\n  they go up for promotion.\n\n  Let's do a similar calculation for 100 women\n  who have gone up for promotion in the last few years.\n  They will also have 500 random colleagues providing\n  feedback, of which about 400 (80\\%) will be men.\n  Of these 400 men, about 40 (10\\%) hold a bias against\n  women.\n  Of the 500 colleagues providing feedback on the\n  promotion packet for these women, 8\\% of the\n  colleagues hold a bias against the women.\n\\end{nexample}\n\\end{examplewrap}\n\\footnotetext{A more thoughtful example would include\n    non-binary individuals.}\n\nExample~\\ref{sex_imbalance_leads_to_discrimination}\nhighlights something profound:\neven in a hypothetical setting where each demographic\nhas the same degree of prejudice\nagainst the other demographic, the smaller group\nexperiences the negative effects more frequently.\nAdditionally, if we would complete a handful of examples\nlike the one above with different numbers,\nwe'd learn that the greater the imbalance\nin the population groups, the more the smaller group\nis disproportionately impacted.\\footnote{%\n  If a proportion $p$ of a company are\n  women and the rest of the company consists of men,\n  then under the hypothetical situation\n  the ratio of rates of discrimination against women\n  vs men would be given by $\\frac{1 - p}{p}$;\n  this ratio is always greater than 1 when $p < 0.5$.}%\n%That is, this mathematical property may lead\n%to more discrimination against a minority group,\n%and the degree of that discrimination\n%will be larger the greater the imbalance in the\n%population under the scenario described.\n\nOf course, there are other considerable real-world omissions\nfrom the hypothetical example.\nFor example, studies have found instances where people from an\noppressed group also discriminate against others within their\nown oppressed group.\nAs another example,\nthere are also instances where a majority group\ncan be oppressed, with apartheid in South Africa being one\nsuch historic example.\n%\\footnote{Two examples of majority groups\n%  being oppressed include Black slaves in some regions\n%  of southern states of early America,\n%  and apartheid in South Africa.}\nUltimately, discrimination is complex,\nand there are many factors at play beyond\nthe mathematics property we observed in\nExample~\\ref{sex_imbalance_leads_to_discrimination}.\n% That is, the mathematical property we've discussed\n%  here is far from the only factor in discrimination\n%  and oppression, yet it can be an important one\n%  in some settings.}\n%For one study on this topic, see\n%\\begin{quote}\\em\n%Milkman KL, Akinola M, Chugh D. 2015.\n%What Happens Before?\n%A Field Experiment Exploring How Pay and\n%Representation Differentially Shape Bias\n%on the Pathway Into Organizations.\n%Journal of Applied Psychology, 100:6, p1678-1712.\n%\\end{quote}\n%The paper's abstract summarizes the findings,\n%and substantial detail of the analysis is provided\n%within the paper.\n%We've also made the data set available,\n%which is noted in Appendix~\\ref{ch_regr_mult_and_log_data}\n%so that you may also explore it directly.\n%\\Comment{If we do not obtain the data from this study,\n%  then need to delete the last sentence.}\n%That is, discrimination isn't generally symmetric,\n%which makes this topic all the more complex.\n%For example, a study published in 2015 performed an\n%experiment similar to the job discrimination experiment\n%we analyzed earlier, but in this case an email was sent\n%to each of 6,500 faculty members at top US universities.\n%The emails sent were from fictional prospective students\n%seeking to discuss research opportunities prior to applying\n%to a doctoral program.\n%The emails were identical, except for the name of the\n%fictional student sending the message was randomly assigned,\n%and each name used was chosen to suggest a specific race\n%and sex.\n%Generally, White males were more likely to receive replies.\n%What was most profound was that female faculty members\n%were also more likely to reply to male students than their\n%female students.\n%Similarly, faculty members who were from oppressed groups\n%favored white \n%assistants than for male research assistants,\n%even though there was no difference in the fabricated\n%resumes;\n%this study was performed by surveying thousands of faculty\n%members, so while no faculty member could individually be\n%identified as being sexist, it was conclusive that the\n%females were being discriminated against in aggregate.\n\n%The 8\\%-to-2\\% is a direct result of the 80\\%-to-20\\% ratio\n%in Example~\\ref{sex_imbalance_leads_to_discrimination}.\n%More generally, if \n\n%No discrimination has a place in our society,\n%be it discrimination against a minority group\n%or a majority group.\n%Yet we cannot deny the mathematics behind\n%discrimination: minority groups may be more\n%prone to the negative impacts from discrimination\n%than majority groups.\n\n%Discrimination is a complex topic and discussed\n%thoughtfully by many others.\n%For further reading,\n%please consider the following excellent resources:\n%\\Comment{Need to identify appropriate resources.\n%  Suggestions welcome!}\n%\\begin{itemize}\n%\\item\n%     \\Add{https://www.theatlantic.com/education/archive/2017/08/myth-of-reverse-racism/535689/}\n%\\item\n%    \\Comment{Resource \\#1}\n%\\item\n%    \\Comment{Resource \\#2}\n%\\item\n%    \\Comment{Resource \\#3}\n%\\end{itemize}\n\nWe close this book on this serious topic,\nand we hope it inspires you to think about\nthe power of reasoning with data.\nWhether it is with a formal statistical model\nor by using critical thinking skills to structure\na problem, we hope the ideas you have learned will\nhelp you do more and do better in life.\n\n\\index{discrimination|)}\n\n\n{\\input{ch_regr_mult_and_log/TeX/introduction_to_logistic_regression.tex}}\n"
  },
  {
    "path": "ch_regr_mult_and_log/TeX/checking_model_assumptions_using_graphs.tex",
    "content": "\\exercisesheader{}\n\n% 13\n\n\\eoce{\\qt{Baby weights, Part VI\\label{baby_weights_conds}} \nExercise~\\ref{baby_weights_mlr} presents a regression model for predicting the \naverage birth weight of babies based on length of gestation, parity, height, \nweight, and smoking status of the mother. Determine if the model assumptions are \nmet using the plots below. If not, describe how to proceed with the analysis.\n\\begin{center}\n\\FigureFullPath[A histogram of residuals is shown, which has a bell-shaped distribution, is centered at 0, and has a standard deviation of about 12.]{0.4}{ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds_normal_hist}\\hspace{5mm}\n\\FigureFullPath[A scatterplot of \"residuals\" (vertical axis) against \"fitted values\". The residuals does not show any pattern for different fitted values.]{0.4}{ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds_abs_res_fitted}\\hspace{5mm}\n\\FigureFullPath[A scatterplot of \"residuals\" (vertical axis) against \"order of collection\". The residuals does not show any pattern across the order of collection variable.]{0.4}{ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds_res_order}\\hspace{5mm}\n\\FigureFullPath[A scatterplot of \"residuals\" (vertical axis) against \"length of gestation\". The residuals does not show any pattern for different lengths of gestation.]{0.4}{ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds_res_gestation}\\hspace{5mm}\n\\FigureFullPath[A scatterplot of \"residuals\" (vertical axis) against \"parity\", which only takes values 0 and 1. The residuals does not show any apparent patterns across the values 0 and 1 of parity.]{0.4}{ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds_res_parity}\\hspace{5mm}\n\\FigureFullPath[A scatterplot of \"residuals\" (vertical axis) against \"height of mother\". The residuals does not show any pattern for different values of \"height of mother\".]{0.4}{ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds_res_height}\\hspace{5mm}\n\\FigureFullPath[A scatterplot of \"residuals\" (vertical axis) against \"weight of mother\". The residuals does not show any pattern for different values of \"weight of mother\".]{0.4}{ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds_res_weight}\\hspace{5mm}\n\\FigureFullPath[A scatterplot of \"residuals\" (vertical axis) against \"smoke\", which only takes values 0 and 1. The residuals does not show any pattern for the 0 and 1 values of smoke.]{0.4}{ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds_res_smoke}\\hspace{5mm}\n\\end{center}\n}{}\n\n\\D{\\newpage}\n\n% 14\n\n\\eoce{\\qt{Movie returns, Part I\\label{movie_returns_altogether}}\nA FiveThirtyEight.com article reports that\n``Horror movies get nowhere near as much draw at the box \noffice as the big-time summer blockbusters or\naction/adventure movies ... but there’s a huge incentive\nfor studios to continue pushing them out.\nThe return-on-investment potential for horror movies\nis absurd.\"\nTo investigate how the return-on-investment compares\nbetween genres and how this relationship has changed over\ntime, an introductory statistics student fit a model\npredicting the ratio of gross revenue of movies from\ngenre and release year for 1,070 movies released between\n2000 and 2018.\nUsing the plots given below, determine if this regression\nmodel is appropriate for these\ndata.\\footfullcite{webpage:horrormovies}\n\\begin{center}\n\\FigureFullPath[A histogram is shown for \"Residuals\", which take values from about -15 to 100. The shape of the distribution is extremely right-skewed but centered at 0. The bin -15 to -10 represents about 1\\% of the values. The bin -10 to -5 represents about 1\\% of the values. The bin -5 to 0 represents about 65\\% of the values. The bin 0 to 5 represents about 25\\% of the values. The bin 5 to 10 represents about 2\\% of the values. The bin 10 to 15 represents about 1\\% of the values. The remaining bins above 15 have far less than 1\\% of the data.]{0.47}{ch_regr_mult_and_log/figures/eoce/movie_returns_altogether/horror_movies_conds_hist_res}\\hspace{3mm}\n\\FigureFullPath[A scatterplot is shown. The horizontal axis is for \"Fitted Values\", which takes values between 2.5 and 12. The vertical axis is for \"Residuals\" and takes values from -15 to about 90, though only about a dozen values have residuals larger than 25. The points are also colored for different genres: Action, Adventure, Comedy, Drama, and Horror. The points for Action, Adventure, Comedy, and Drama are clustered on the left with Fitted Values between 2.5 and 3.5, and the residuals for these points are largely between -5 and 12. The Horror points have Fitted Values between about 11 and 12, with residuals for these points largely between -10 and 25.]{0.47}{ch_regr_mult_and_log/figures/eoce/movie_returns_altogether/horror_movies_conds_res_genre_fitted}\\\\[5mm]\n\\FigureFullPath[A dot plot is shown for \"residuals\", where points are broken up into different genres: Action, Adventure, Comedy, Drama, and Horror. The residuals for Action, Adventure, Comedy, and Drama groups have residuals for these points largely between -5 and 12. The Horror genre residuals are largely between -10 and 25.]{0.47}{ch_regr_mult_and_log/figures/eoce/movie_returns_altogether/horror_movies_conds_res_genre}\\hspace{3mm}\n\\FigureFullPath[A scatterplot is shown for \"residuals\" (vertical axis) against \"order of collection\" (horizontal axis) from 1 to about 1100. The variability of residuals for the order of collection values from 0 to 600 largely range between -3 and positive 5. The variability of residuals for the order of collection values from 600 to 800 largely range between -5 and positive 10. The variability of residuals for the order of collection values above 800 largely range between -8 and positive 15.]{0.47}{ch_regr_mult_and_log/figures/eoce/movie_returns_altogether/horror_movies_conds_res_order}\\\\[5mm]\n\\FigureFullPath[A scatterplot is shown for \"residuals\" (vertical axis) against \"release year\" (horizontal axis) from 2010 to 2018. For each year in the range, the residuals largely range between roughly -10 and positive 12.]{0.47}{ch_regr_mult_and_log/figures/eoce/movie_returns_altogether/horror_movies_conds_res_year}\n\\end{center}\n}{}\n"
  },
  {
    "path": "ch_regr_mult_and_log/TeX/introduction_to_logistic_regression.tex",
    "content": "\\exercisesheader{}\n\n% 15\n\n\\eoce{\\qt{Possum classification, Part I\\label{possum_classification_model_select}} \nThe common brushtail possum of the Australia region is a bit cuter than its \ndistant cousin, the American opossum (see Figure~\\vref{brushtail_possum}). We \nconsider 104 brushtail possums from two regions in Australia, where the possums \nmay be considered a random sample from the population. The first region is \nVictoria, which is in the eastern half of Australia and traverses the southern \ncoast. The second region consists of New South Wales and Queensland, which make \nup eastern and northeastern Australia.\nWe use logistic regression to differentiate between possums in these two \nregions. The outcome variable, called \\var{population}, takes value 1 when a \npossum is from Victoria and 0 when it is from New South Wales or Queensland. We \nconsider five predictors: \\var{sex\\_\\hspace{0.3mm}male} (an indicator for a \npossum being male), \\var{head\\_\\hspace{0.3mm}length}, \\var{skull\\_\\hspace{0.3mm}\nwidth}, \\var{total\\_\\hspace{0.3mm}length}, and \\var{tail\\_\\hspace{0.3mm}length}. \nEach variable is summarized in a histogram. The full logistic regression model \nand a reduced model after variable selection are summarized in the table.\n\\begin{center}\n\\FigureFullPath[Six plots are shown for the distributions of each predictor variable. For the \"sex\\_male\" categorical variable, about 42 observations are \"0 (female)\" and 65 are \"1 (male)\". For the \"head\\_length (in mm)\" variable, a histogram is shown that is approximately bell-shaped, centered at about 93, and has a standard deviation of about 3. For the \"skull\\_width (in mm)\" variable, a histogram is shown for a slightly right-skewed distribution is shown with its peak at about 56 and a standard deviation of about 3. For the \"total\\_length (in cm)\" variable, a histogram is shown with most values ranging from about 80 to 95, with no major outliers. For the \"tail\\_length (in cm)\" variable, a histogram is shown with most data between about 33 and 42, with no major outliers. For the \"population\" categorical variable, about 58 observations are \"0 (Not Victoria)\" and 45 are \"1 (Victoria)\".]{}{ch_regr_mult_and_log/figures/eoce/possum_classification_model_select/possum_variables}\n\\end{center}\n\\begin{center}\\footnotesize\n\\begin{tabular}{r rrrr r rrrr}\n                            & \\multicolumn{4}{c}{\\emph{Full Model}} &\n                            & \\multicolumn{4}{c}{\\emph{Reduced Model}}  \\\\\n  \\cline{2-5}\\cline{7-10}\n\\vspace{-3.1mm} \\\\\n                            & Estimate & SE & Z & Pr($>$$|$Z$|$) &\n                            & Estimate & SE & Z & Pr($>$$|$Z$|$) \\\\ \n  \\hline\n\\vspace{-3.1mm} \\\\\n(Intercept)                 & 39.2349 & 11.5368 & 3.40  & 0.0007 &\n                            & 33.5095 & 9.9053  & 3.38  & 0.0007 \\\\ \nsex\\_\\hspace{0.3mm}male     & -1.2376 & 0.6662  & -1.86 & 0.0632 &\n                            & -1.4207 & 0.6457  & -2.20 & 0.0278 \\\\ \nhead\\_\\hspace{0.3mm}length  & -0.1601 & 0.1386  & -1.16 & 0.2480 \\\\ \nskull\\_\\hspace{0.3mm}width  & -0.2012 & 0.1327  & -1.52 & 0.1294 &\n                            & -0.2787 & 0.1226  & -2.27 & 0.0231 \\\\ \ntotal\\_\\hspace{0.3mm}length & 0.6488  & 0.1531  & 4.24  & 0.0000 &\n                            & 0.5687  & 0.1322  & 4.30  & 0.0000 \\\\ \ntail\\_\\hspace{0.3mm}length  & -1.8708 & 0.3741  & -5.00 & 0.0000 &\n                            & -1.8057 & 0.3599  & -5.02 & 0.0000 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Examine each of the predictors. Are there any outliers that are likely to \nhave a very large influence on the logistic regression model?\n\\item The summary table for the full model indicates that at least one variable \nshould be eliminated when using the p-value approach for variable selection: \n\\var{head\\_\\hspace{0.3mm}length}. The second component of the table summarizes \nthe reduced model following variable selection. Explain why the remaining estimates \nchange between the two models.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 16\n\n\\eoce{\\qt{Challenger disaster, Part I\\label{challenger_disaster_model_select}} \nOn January 28, 1986, a routine launch was anticipated for the Challenger space \nshuttle. Seventy-three seconds into the flight, disaster happened: the shuttle \nbroke apart, killing all seven crew members on board. An investigation into the \ncause of the disaster focused on a critical seal called an O-ring, and it is \nbelieved that damage to these O-rings during a shuttle launch may be related to \nthe ambient temperature during the launch. The table below summarizes \nobservational data on O-rings for 23 shuttle missions, where the mission order \nis based on the temperature at the time of the launch. \\emph{Temp} gives the \ntemperature in Fahrenheit, \\emph{Damaged} represents the number of damaged O-\nrings, and \\emph{Undamaged} represents the number of O-rings that were not \ndamaged.\n\\begin{center}\n\\begin{tabular}{l rrrrr rrrrr rrrrr rrrrr rrr}\n\\hline\n\\vspace{-3.1mm} \\\\\nShuttle Mission   & 1  & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 \\\\\n\\hline\n\\vspace{-3.1mm} \\\\\nTemperature       & 53 & 57 & 58 & 63 & 66 & 67 & 67 & 67 & 68 & 69 & 70 & 70  \\\\\nDamaged           & 5  & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\\\\nUndamaged         & 1  & 5 & 5 & 5 & 6 & 6 & 6 & 6 & 6 & 6 & 5 & 6 \\\\\n\\hline\n\\\\ \n\\cline{1-12}\n\\vspace{-3.1mm} \\\\\nShuttle Mission   & 13 & 14 & 15 & 16 & 17 & 18 & 19 & 20 & 21 & 22 & 23 \\\\\n\\cline{1-12}\n\\vspace{-3.1mm} \\\\\nTemperature       & 70 & 70 & 72 & 73 & 75 & 75 & 76 & 76 & 78 & 79 & 81 \\\\\nDamaged           & 1  & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\\\\nUndamaged         & 5  & 6 & 6 & 6 & 6 & 5 & 6 & 6 & 6 & 6 & 6 \\\\\n\\cline{1-12}\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Each column of the table above represents a different shuttle mission. \nExamine these data and describe what you observe with respect to the \nrelationship between temperatures and damaged O-rings.\n\\item Failures have been coded as 1 for a damaged O-ring and 0 for an undamaged \nO-ring, and a logistic regression model was fit to these data. A summary of this \nmodel is given below. Describe the key components of this summary table in words.\n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n            & Estimate & Std. Error & z value   & Pr($>$$|$z$|$) \\\\ \n  \\hline\n(Intercept) & 11.6630  & 3.2963     & 3.54      & 0.0004 \\\\ \nTemperature & -0.2162  & 0.0532     & -4.07     & 0.0000 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\item Write out the logistic model using the point estimates of the model \nparameters.\n\\item Based on the model, do you think concerns regarding O-rings are justified? \nExplain.\n\\end{parts}\n}{}\n\n% 17\n\n\\eoce{\\qt{Possum classification, Part II\\label{possum_classification_predict}} \nA logistic regression model was proposed for classifying common brushtail \npossums into their two regions in \nExercise~\\ref{possum_classification_model_select}. The outcome variable took \nvalue 1 if the possum was from Victoria and 0 otherwise.\n\\begin{center}\n\\begin{tabular}{r rrrr}\n  \\hline\n\\vspace{-3.1mm} \\\\\n                            & Estimate  & SE      & Z     & Pr($>$$|$Z$|$) \\\\ \n  \\hline\n\\vspace{-3.1mm} \\\\\n(Intercept)                 & 33.5095   & 9.9053  & 3.38  & 0.0007 \\\\ \nsex\\_\\hspace{0.3mm}male     & -1.4207   & 0.6457  & -2.20 & 0.0278 \\\\ \nskull\\_\\hspace{0.3mm}width  & -0.2787   & 0.1226  & -2.27 & 0.0231 \\\\ \ntotal\\_\\hspace{0.3mm}length & 0.5687    & 0.1322  & 4.30  & 0.0000 \\\\ \ntail\\_\\hspace{0.3mm}length  & -1.8057   & 0.3599  & -5.02 & 0.0000 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Write out the form of the model. Also identify which of the variables are \npositively associated when controlling for other variables.\n\\item Suppose we see a brushtail possum at a zoo in the US, and a sign says the \npossum had been captured in the wild in Australia, but it doesn't say which part \nof Australia. However, the sign does indicate that the possum is male, its skull \nis about 63 mm wide, its tail is 37 cm long, and its total length is 83 cm. What \nis the reduced model's computed probability that this possum is from Victoria? \nHow confident are you in the model's accuracy of this probability calculation?\n%logitp <- 33.5095 - 1.4207 - 0.2787*63 + 0.5687*83 - 1.8057*37; exp(logitp)/(1+exp(logitp))\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 18\n\n\\eoce{\\qt{Challenger disaster, Part II\\label{challenger_disaster_predict}} \nExercise~\\ref{challenger_disaster_model_select} introduced us to O-rings that \nwere identified as a plausible explanation for the breakup of the Challenger \nspace shuttle 73 seconds into takeoff in 1986. The investigation found that the \nambient temperature at the time of the shuttle launch was closely related to the \ndamage of O-rings, which are a critical component of the shuttle. See this \nearlier exercise if you would like to browse the original data.\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown. The horizontal axis is \"Temperature (Fahrenheit)\" with values ranging from about 53 to 82. The vertical axis is \"Probability of damage\" with values ranging from about 0 to 0.8. Only one point has a temperature below 55, which has a probability of damage at about 0.8. Three points have temperature between 55 and 65, and these have probabilities of about 0.2. For the couple dozen points with temperature between 65 and 82, probabilities are almost all 0 with only a few values at 0.2.]{0.6}{ch_regr_mult_and_log/figures/eoce/challenger_disaster_predict/challenger_disaster_damage_temp.pdf} \n\\end{center}\n\\begin{parts}\n\\item The data provided in the previous exercise are shown in the plot. The logistic \nmodel fit to these data may be written as\n\\begin{align*}\n\\log\\left( \\frac{\\hat{p}}{1 - \\hat{p}} \\right) = 11.6630 - 0.2162\\times Temperature\n\\end{align*}\nwhere $\\hat{p}$ is the model-estimated probability that an O-ring will become \ndamaged. Use the model to calculate the probability that an O-ring will become \ndamaged at each of the following ambient temperatures: 51, 53, and 55 degrees \nFahrenheit. The model-estimated probabilities for several additional ambient \ntemperatures are provided below, where subscripts indicate the temperature:\n\\begin{align*}\n&\\hat{p}_{57} = 0.341\n\t&& \\hat{p}_{59} = 0.251\n\t&& \\hat{p}_{61} = 0.179\n\t&& \\hat{p}_{63} = 0.124 \\\\\n&\\hat{p}_{65} = 0.084\n\t&& \\hat{p}_{67} = 0.056\n\t&& \\hat{p}_{69} = 0.037\n\t&& \\hat{p}_{71} = 0.024\n\\end{align*}\n\\item Add the model-estimated probabilities from part~(a) on the plot, then \nconnect these dots using a smooth curve to represent the model-estimated \nprobabilities.\n\\item Describe any concerns you may have regarding applying logistic regression \nin this application, and note any assumptions that are required to accept the \nmodel's validity.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_regr_mult_and_log/TeX/introduction_to_multiple_regression.tex",
    "content": "\\exercisesheader{}\n\n% 1\n\n\\eoce{\\qt{Baby weights, Part I\\label{baby_weights_smoke}} The Child Health \nand Development Studies investigate a range of topics. One study \nconsidered all pregnancies between 1960 and 1967 among women in the \nKaiser Foundation Health Plan in the San Francisco East Bay area. Here, \nwe study the relationship between smoking and weight of the baby. The \nvariable \\texttt{smoke} is coded 1 if the mother is a smoker, and 0 if \nnot. The summary table below shows the results of a linear regression \nmodel for predicting the average birth weight of babies, measured in \nounces, based on the smoking status of the mother. \n\\footfullcite{data:babies}\n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\\\ \n  \\hline\n(Intercept) & 123.05    & 0.65        & 189.60    & 0.0000 \\\\ \nsmoke       & -8.94     & 1.03        & -8.65     & 0.0000 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\nThe variability within the smokers and non-smokers are about equal and the \ndistributions are symmetric. With these conditions satisfied, it is reasonable \nto apply the model. (Note that we don't need to check linearity since the \npredictor has only two levels.)\n\\begin{parts}\n\\item Write the equation of the regression model.\n\\item Interpret the slope in this context, and calculate the predicted birth \nweight of babies born to smoker and non-smoker mothers.\n\\item Is there a statistically significant relationship between the average birth \nweight and smoking?\n\\end{parts}\n}{}\n\n% 2\n\n\\eoce{\\qt{Baby weights, Part II\\label{baby_weights_parity}} \nExercise~\\ref{baby_weights_smoke} introduces a data set\non birth weight of babies.\nAnother variable we consider is \\texttt{parity},\nwhich is 1 if the child is the first born,\nand 0 otherwise.\nThe summary table below shows the results of\na linear regression model for predicting the\naverage birth weight of babies, measured in ounces,\nfrom \\texttt{parity}. \n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n  \\hline\n(Intercept) & 120.07    & 0.60        & 199.94    & 0.0000 \\\\ \nparity      \t& -1.93     \t  & 1.19        & -1.62       & 0.1052 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Write the equation of the regression model.\n\\item Interpret the slope in this context, and calculate the predicted birth \nweight of first borns and others.\n\\item Is there a statistically significant relationship between the average \nbirth weight and parity?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 3\n\n\\eoce{\\qt{Baby weights, Part III\\label{baby_weights_mlr}} We considered the \nvariables \\texttt{smoke} and \\texttt{parity}, one at a time, in modeling birth \nweights of babies in Exercises~\\ref{baby_weights_smoke} and~\\ref{baby_weights_parity}. \nA more realistic approach to modeling infant \nweights is to consider all possibly related variables at once. Other variables \nof interest include length of pregnancy in days (\\texttt{gestation}), mother's \nage in years (\\texttt{age}), mother's height in inches (\\texttt{height}), and \nmother's pregnancy weight in pounds (\\texttt{weight}). Below are three \nobservations from this data set. \n\\begin{center}\n\\begin{tabular}{r c c c c c c c}\n  \\hline\n      & bwt & gestation & parity  & age   & height  & weight  & smoke \\\\ \n  \\hline\n1     & 120 & 284       & 0       & 27    &  62     & 100     &   0 \\\\ \n2     & 113 & 282       & 0       & 33    &  64     & 135     &   0 \\\\ \n$\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ &  $\\vdots$ & $\\vdots$ & $\\vdots$ &   $\\vdots$ \\\\ \n1236  & 117 & 297       & 0       & 38    &  65     & 129     &   0 \\\\ \n   \\hline\n\\end{tabular}\n\\end{center}\nThe summary table below shows the results of a regression model for predicting \nthe average birth weight of babies based on all of the variables included in \nthe data set.\n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\\\ \n  \\hline\n(Intercept) & -80.41    & 14.35       & -5.60     & 0.0000 \\\\ \ngestation   & 0.44      & 0.03        & 15.26     & 0.0000 \\\\ \nparity      & -3.33     & 1.13        & -2.95     & 0.0033 \\\\ \nage         & -0.01     & 0.09        & -0.10     & 0.9170 \\\\ \nheight      & 1.15      & 0.21        & 5.63      & 0.0000 \\\\ \nweight      & 0.05      & 0.03        & 1.99      & 0.0471 \\\\ \nsmoke       & -8.40     & 0.95        & -8.81     & 0.0000 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Write the equation of the regression model that includes all of the \nvariables.\n\\item Interpret the slopes of \\texttt{gestation} and \\texttt{age} in this \ncontext.\n\\item The coefficient for \\texttt{parity} is different than in the linear \nmodel shown in Exercise~\\ref{baby_weights_parity}. Why might there be a difference?\n\\item Calculate the residual for the first observation in the data set.\n\\item The variance of the residuals is 249.28, and the variance of the birth \nweights of all babies in the data set is 332.57. Calculate the $R^2$ and the \nadjusted $R^2$. Note that there are 1,236 observations in the data set.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 4\n\n\\eoce{\\qt{Absenteeism, Part I\\label{absent_from_school_mlr}} Researchers interested in the \nrelationship between absenteeism from school and certain demographic \ncharacteristics of children collected data from 146 randomly sampled students \nin rural New South Wales, Australia, in a particular school year. Below are \nthree observations from \nthis data set. \n\\begin{center}\n\\begin{tabular}{r c c c c}\n  \\hline\n \t  & eth \t& sex \t& lrn \t& days \\\\   \n  \\hline\n1 \t& 0 \t\t& 1 \t\t& 1 \t\t&   2 \\\\ \n2 \t& 0 \t\t& 1 \t\t& 1 \t\t&  11 \\\\ \n$\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ & $\\vdots$ \\\\ \n146 & 1 \t\t& 0 \t\t& 0 \t\t&  37 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\nThe summary table below shows the results of a linear regression model for \npredicting the average number of days absent based on ethnic background \n(\\texttt{eth}: 0 - aboriginal, 1 - not aboriginal), sex (\\texttt{sex}: 0 - \nfemale, 1 - male), and learner status (\\texttt{lrn}: 0 - average learner, 1 - \nslow learner). \\footfullcite{data:quine}\n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\\\ \n  \\hline\n(Intercept) & 18.93     & 2.57        & 7.37      & 0.0000 \\\\ \neth         & -9.11     & 2.60        & -3.51     & 0.0000 \\\\ \nsex         & 3.10      & 2.64        & 1.18      & 0.2411 \\\\ \nlrn         & 2.15      & 2.65        & 0.81      & 0.4177 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Write the equation of the regression model.\n\\item Interpret each one of the slopes in this context.\n\\item Calculate the residual for the first observation in the data set: a \nstudent who is aboriginal, male, a slow learner, and missed 2 days of school.\n\\item The variance of the residuals is 240.57, and the variance of the number of \nabsent days for all students in the data set is 264.17. Calculate the $R^2$ and \nthe adjusted $R^2$. Note that there are 146 observations in the data set.\n\\end{parts}\n}{}\n\n% 5\n\n\\eoce{\\qt{GPA\\label{gpa}} A survey of 55 Duke University students asked about their \nGPA, number of hours they study at night, number of nights they go out, and \ntheir gender. Summary output of the regression model is shown below. Note that \nmale is coded as 1. \n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\\\ \n  \\hline\n(Intercept) & 3.45      & 0.35        & 9.85      & 0.00 \\\\ \nstudyweek   & 0.00      & 0.00        & 0.27      & 0.79 \\\\ \nsleepnight  & 0.01      & 0.05        & 0.11      & 0.91 \\\\ \noutnight    & 0.05      & 0.05        & 1.01      & 0.32 \\\\ \ngender      & -0.08     & 0.12        & -0.68     & 0.50 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Calculate a 95\\% confidence interval for the coefficient of gender in the \nmodel, and interpret it in the context of the data.\n\\item Would you expect a 95\\% confidence interval for the slope of the remaining \nvariables to include 0? Explain\n\\end{parts}\n}{}\n\n% 6\n\n\\eoce{\\qt{Cherry trees\\label{cherry_trees}} Timber yield is approximately equal to the \nvolume of a tree, however, this value is difficult to measure without first \ncutting the tree down. Instead, other variables, such as height and diameter, \nmay be used to predict a tree's volume and yield. Researchers wanting to \nunderstand the relationship between these variables for black cherry trees \ncollected data from 31 such trees in the Allegheny National Forest, \nPennsylvania. Height is measured in feet, diameter in inches (at 54 inches above \nground), and volume in cubic feet.\\footfullcite{Hand:1994}\n\\begin{table}[ht]\n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n            & Estimate  & Std. Error  & t value   & Pr($>$$|$t$|$) \\\\ \n  \\hline\n(Intercept) & -57.99    & 8.64        & -6.71     & 0.00 \\\\ \nheight      & 0.34      & 0.13        & 2.61      & 0.01 \\\\ \ndiameter    & 4.71      & 0.26        & 17.82     & 0.00 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\end{table}\n\\begin{parts}\n\\item Calculate a 95\\% confidence interval for the coefficient of height, and \ninterpret it in the context of the data.\n\\item One tree in this sample is 79 feet tall, has a diameter of 11.3 inches, \nand is 24.2 cubic feet in volume. Determine if the model overestimates or \nunderestimates the volume of this tree, and by how much.\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_regr_mult_and_log/TeX/model_selection.tex",
    "content": "\\exercisesheader{}\n\n% 7\n\n\\eoce{\\qt{Baby weights, Part IV\\label{baby_weights_model_select_backward}} \nExercise~\\ref{baby_weights_mlr} considers a model that predicts a newborn's \nweight using several predictors (gestation length, parity, age of mother, height \nof mother, weight of mother, smoking status of mother). The table below shows \nthe adjusted R-squared for the full model as well as adjusted R-squared values \nfor all models we evaluate in the first step of the backwards elimination \nprocess. \n\\begin{center}\n\\begin{tabular}{rlr}\n  \\hline\n  & Model               & Adjusted $R^2$ \\\\ \n  \\hline\n1 & Full model          & 0.2541 \\\\ \n2 & No gestation        & 0.1031 \\\\ \n3 & No parity           & 0.2492 \\\\ \n4 & No age              & 0.2547 \\\\ \n5 & No height           & 0.2311 \\\\ \n6 & No weight           & 0.2536 \\\\ \n7 & No smoking status   & 0.2072 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\nWhich, if any, variable should be removed from the model first?\n}{}\n\n% 8\n\n\\eoce{\\qt{Absenteeism, Part II\\label{absent_from_school_model_select_backward}} \nExercise~\\ref{absent_from_school_mlr} considers a model that predicts the number \nof days absent using three predictors: ethnic background (\\var{eth}), \ngender (\\var{sex}), and learner status (\\var{lrn}). The table below shows the \nadjusted R-squared for the model as well as adjusted R-squared values for all \nmodels we evaluate in the first step of the backwards elimination process. \n\\begin{center}\n\\begin{tabular}{rlr}\n  \\hline\n  & Model               & Adjusted $R^2$ \\\\ \n  \\hline\n1 & Full model          & 0.0701 \\\\ \n2 & No ethnicity        & -0.0033 \\\\ \n3 & No sex              & 0.0676 \\\\ \n4 & No learner status   & 0.0723 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\nWhich, if any, variable should be removed from the model first?\n}{}\n\n% 9\n\n\\eoce{\\qt{Baby weights, Part V\\label{baby_weights_model_select_forward}} \nExercise~\\ref{baby_weights_mlr} provides regression output for the full \nmodel (including all explanatory variables available in the data set) for \npredicting birth weight of babies. In this exercise we consider a\nforward-selection algorithm and add variables to the model\none-at-a-time. The table \nbelow shows the p-value and adjusted $R^2$ of each model where we include only \nthe corresponding predictor. Based on this table, which variable should be added \nto the model first?\\vspace{0.5mm}\n\\begin{center}\n\\begin{tabular}{l c c c c c c}\n\\hline\nvariable    & gestation\t            & parity  & age\t    \n                & height          \n                    & weight              \n                        & smoke \\\\\n\\hline\np-value\t    & $2.2 \\times 10^{-16}$\t& 0.1052\t& 0.2375\t\n                & $2.97 \\times 10^{-12}$\n                    & $8.2 \\times 10^{-8}$\n                        & $2.2 \\times 10^{-16}$ \\\\\n$R_{adj}^2$\t& 0.1657\t\t\t\t        & 0.0013\t& 0.0003\t\n                & 0.0386\t\t\t\t\n                    & 0.0229\t\t\t\t\n                        & 0.0569 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n}{}\n\n% 10\n\n\\eoce{\\qt{Absenteeism, Part III\\label{absent_from_school_model_select_forward}} \nExercise~\\ref{absent_from_school_mlr} provides regression output for the full \nmodel, including all explanatory variables available in the data set, for \npredicting the number of days absent from school. In this exercise we consider a \nforward-selection algorithm and add variables to the model one-at-a-time. The \ntable below shows the p-value and adjusted $R^2$ of each model where we include \nonly the corresponding predictor. Based on this table, which variable should be \nadded to the model first?\\vspace{0.5mm}\n\\begin{center}\n\\begin{tabular}{l c c c}\n  \\hline\nvariable    & ethnicity  & sex\t   & learner status\t \\\\\n  \\hline\np-value\t\t& 0.0007     & 0.3142  & 0.5870\t \\\\\n$R_{adj}^2$\t& 0.0714     & 0.0001  & 0 \\\\\n  \\hline\n\\end{tabular}\n\\end{center}\n}{}\n\n% 11\n\n\\eoce{\\qt{Movie lovers, Part I\\label{movie_lovers_pval_select}} Suppose a social \nscientist is interested in studying what makes audiences love or hate a movie. \nShe collects a random sample of movies (genre, length, cast, director, budget, \netc.) as well as a measure of the success of the movie (score on a film review \naggregator website). If as part of her research she is interested in finding out\nwhich variables are significant predictors of movie success, what type of model \nselection method should she use?\n}{}\n\n% 12\n\n\\eoce{\\qt{Movie lovers, Part II\\label{movie_lovers_adjrsq_select}} Suppose an online \nmedia streaming company is interested in building a movie recommendation system. \nThe website maintains data on the movies in their database (genre, length, cast, \ndirector, budget, etc.) and additionally collects data from their subscribers (\ndemographic information, previously watched movies, how they rated previously \nwatched movies, etc.). The recommendation system will be deemed successful if \nsubscribers actually watch, and rate highly, the movies recommended to them. \nShould the company use the adjusted $R^2$ or the p-value approach in selecting\nvariables for their recommendation system?\n}{}\n"
  },
  {
    "path": "ch_regr_mult_and_log/TeX/mult_regr_case_study.tex",
    "content": "\n\n\n%_______________\n\\subsection*{Exercises}\n\nThere are no exercises for this section."
  },
  {
    "path": "ch_regr_mult_and_log/TeX/review_exercises.tex",
    "content": "\\reviewexercisesheader{}\n\n% 19\n\n\\eoce{\\qt{Multiple regression fact checking\\label{mult_regr_facts}}\nDetermine which of the following statements are\ntrue and false.\nFor each statement that is false, explain why it is false.\n\\begin{parts}\n\\item\n    If predictors are collinear, then removing\n    one variable will have no influence on the\n    point estimate of another variable's coefficient.\n\\item\n    Suppose a numerical variable $x$ has a coefficient of\n    $b_1 = 2.5$ in the multiple regression model.\n    Suppose also that the first observation has $x_1 = 7.2$,\n    the second observation has a value of $x_1 = 8.2$,\n    and these two observations have the same values\n    for all other predictors.\n    Then the predicted value of the second observation\n    will be 2.5 higher than the prediction of the first\n    observation based on the multiple regression model.\n\\item\n    If a regression model's first variable has\n    a coefficient of $b_1 = 5.7$, then if we are\n    able to influence the data so that an observation\n    will have its $x_1$ be 1 larger than it would\n    otherwise, the value $y_1$ for this observation\n    would increase by 5.7.\n\\item\n    Suppose we fit a multiple regression model\n    based on a data set of 472 observations.\n    We also notice that the distribution of the\n    residuals includes some skew but does not\n    include any particularly extreme outliers.\n    Because the residuals are not nearly normal,\n    we should not use this model and require\n    more advanced methods to model these data.\n\\end{parts}\n}{}\n\n% 20\n\n\\eoce{\\qt{Logistic regression fact checking\\label{log_regr_facts}}\nDetermine which of the following statements are\ntrue and false.\nFor each statement that is false, explain why it is false.\n\\begin{parts}\n\\item\n    Suppose we consider the first two observations\n    based on a logistic regression model,\n    where the first variable in observation~1\n    takes a value of $x_1 = 6$ and observation~2\n    has $x_1 = 4$.\n%    Each observation has all the same values for the\n%    other variables used in the model.\n    Suppose we realized we made an error for these\n    two observations, and the first observation\n    was actually $x_1 = 7$ (instead of~6)\n    and the second observation actually had\n    $x_1 = 5$ (instead of~4).\n    Then the predicted probability from the\n    logistic regression model would increase\n    the same amount for each observation after\n    we correct these variables.\n\\item\n    When using a logistic regression model,\n    it is impossible for the model to predict\n    a probability that is negative or a probability\n    that is greater than 1.\n\\item\n    Because logistic regression predicts probabilities\n    of outcomes, observations used to build a logistic\n    regression model need not be independent.\n\\item\n    When fitting logistic regression,\n    we typically complete model selection using\n    adjusted $R^2$.\n\\end{parts}\n}{}\n\n% 21\n\n\\eoce{\\qt{Spam filtering, Part I\\label{spam_filtering_model_sel}}\nSpam filters are built on principles similar to those\nused in logistic regression.\nWe fit a probability that each message is spam\nor not spam.\nWe have several email variables for this problem:\n\\resp{to\\us{}multiple},\n\\resp{cc},\n\\resp{attach},\n\\resp{dollar},\n\\resp{winner},\n\\resp{inherit},\n\\resp{password},\n\\resp{format},\n\\resp{re\\us{}subj},\n\\resp{exclaim\\us{}subj}, and\n\\resp{sent\\us{}email}.\nWe won't describe what each variable means\nhere for the sake of brevity, but each is\neither a numerical or indicator variable.\n\\begin{parts}\n\\item\n    For variable selection,\n    we fit the full model, which includes all\n    variables, and then we also fit each model\n    where we've dropped exactly one of the variables.\n    In each of these reduced models, the AIC value\n    for the model is reported below.\n    Based on these results, which variable,\n    if any, should we drop as part of model\n    selection?\n    Explain.\n    \\begin{center}\n    \\begin{tabular}{lc}\n      \\hline\n      Variable Dropped & AIC \\\\ \n      \\hline\n      None Dropped & 1863.50 \\\\ \n      \\resp{to\\us{}multiple} & 2023.50 \\\\ \n      \\resp{cc} & 1863.18 \\\\ \n      \\resp{attach} & 1871.89 \\\\ \n      \\resp{dollar} & 1879.70 \\\\ \n      \\resp{winner} & 1885.03 \\\\ \n      \\resp{inherit} & 1865.55 \\\\ \n      \\resp{password} & 1879.31 \\\\ \n      \\resp{format} & 2008.85 \\\\ \n      \\resp{re\\us{}subj} & 1904.60 \\\\ \n      \\resp{exclaim\\us{}subj} & 1862.76 \\\\ \n      \\resp{sent\\us{}email} & 1958.18 \\\\ \n      \\hline\n    \\end{tabular}\n    \\end{center}\n    \\textbf{See the next page for part~(b).}\n\n\\D{\\newpage}\n\n\\item\n    Consider the following model selection stage.\n    Here again we've computed the AIC\n    for each leave-one-variable-out model.\n    Based on the results, which variable,\n    if any, should we drop as part of model\n    selection?\n    Explain.\n    \\begin{center}\n    \\begin{tabular}{lc}\n      \\hline\n      Variable Dropped & AIC \\\\ \n      \\hline\n      None Dropped & 1862.41 \\\\ \n      \\resp{to\\us{}multiple} & 2019.55 \\\\ \n      \\resp{attach} & 1871.17 \\\\ \n      \\resp{dollar} & 1877.73 \\\\ \n      \\resp{winner} & 1884.95 \\\\ \n      \\resp{inherit} & 1864.52 \\\\ \n      \\resp{password} & 1878.19 \\\\ \n      \\resp{format} & 2007.45 \\\\ \n      \\resp{re\\us{}subj} & 1902.94 \\\\ \n      \\resp{sent\\us{}email} & 1957.56 \\\\ \n      \\hline\n    \\end{tabular}\n    \\end{center}\n\\end{parts}\n}{}\n\n% 22\n\n\\eoce{\\qt{Movie returns, Part II\\label{movie_returns_by_genre}}\nThe student from\nExercise~\\ref{movie_returns_altogether} analyzed\nreturn-on-investment (ROI) for movies based on\nrelease year and genre of movies.\nThe plots below show the predicted ROI vs. actual\nROI for each of the genres separately.\nDo these figures support the comment in the\nFiveThirtyEight.com article that states,\n``The return-on-investment potential for horror movies\nis absurd.''\nNote that the x-axis range varies for each plot.\n\\begin{center}\n\\FigureFullPath[Five scatterplots are shown, one for each of genre of Action, Adventure, Comedy, Drama, and Horror. Each plot has \"Actual ROI\" on the horizontal axis and \"Predicted ROI\" on the vertical axis. The Action and Adventure scatterplots have nearly all of their points with \"Actual ROI\" ranging from about 0 to 5 with a handful of points between 5 and 15, and in all cases the Predicted ROI is always between about 2 and 3. The Comedy and Drama scatterplots have nearly all of their points with \"Actual ROI\" ranging from about 0 to 12 with a handful of points above 12, and in all cases the Predicted ROI is always between about 2.5 and 3.5. The Horror scatterplot has nearly all of its points with \"Actual ROI\" ranging from about 0 to 50 with a handful of points above 50, and in all cases the Predicted ROI is always between about 11 and 12.]{0.6}{ch_regr_mult_and_log/figures/eoce/movie_returns_by_genre/horror_movies_by_genre}\n\\end{center}\n}{}\n\n% 23\n\n\\eoce{\\qt{Spam filtering, Part II\\label{spam_filtering_predict}}\nIn Exercise~\\ref{spam_filtering_model_sel},\nwe encountered a data set where we applied\nlogistic regression to aid in spam classification\nfor individual emails.\nIn this exercise, we've taken a small set of these\nvariables and fit a formal model with the following\noutput:\n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n  & Estimate & Std. Error & z value & Pr($>$$|$z$|$) \\\\ \n  \\hline\n  (Intercept) & -0.8124 & 0.0870 & -9.34 & 0.0000 \\\\ \n  to\\us{}multiple & -2.6351 & 0.3036 & -8.68 & 0.0000 \\\\ \n  winner & 1.6272 & 0.3185 & 5.11 & 0.0000 \\\\ \n  format & -1.5881 & 0.1196 & -13.28 & 0.0000 \\\\ \n  re\\us{}subj & -3.0467 & 0.3625 & -8.40 & 0.0000 \\\\ \n  \\hline\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item\n    Write down the model using the coefficients\n    from the model fit.\n\n\\item\n    Suppose we have an observation where\n    $\\var{to\\us{}multiple} = 0$,\n    $\\var{winner} = 1$,\n    $\\var{format} = 0$, and\n    $\\var{re\\us{}subj} = 0$.\n    What is the predicted probability that this message\n    is spam?\n\n\\item\n    Put yourself in the shoes of a data scientist\n    working on a spam filter.\n    For a given message, how high must the probability\n    a message is spam be before you think it would be\n    reasonable to put it in a \\emph{spambox}\n    (which the user is unlikely to check)?\n    What tradeoffs might you consider?\n    Any ideas about how you might make your spam-filtering\n    system even better from the perspective of someone\n    using your email service?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/absent_from_school_mlr/absent_from_school_mlr.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(xtable)\nlibrary(MASS)\n\n# load data ---------------------------------------------------------\ndata(quine)\n\n# convert categorical variables to 0/1 ------------------------------\n\nquine$Eth <- as.character(quine$Eth)\nquine$Eth[quine$Eth == \"A\"] <- 0\nquine$Eth[quine$Eth == \"N\"] <- 1\nquine$Eth <- as.factor(quine$Eth)\n\nquine$Sex <- as.character(quine$Sex)\nquine$Sex[quine$Sex == \"F\"] <- 0\nquine$Sex[quine$Sex == \"M\"] <- 1\nquine$Sex <- as.factor(quine$Sex)\n\nquine$Lrn <- as.character(quine$Lrn)\nquine$Lrn[quine$Lrn == \"AL\"] <- 0\nquine$Lrn[quine$Lrn == \"SL\"] <- 1\nquine$Lrn <- as.factor(quine$Lrn)\n\n# print out dataset -------------------------------------------------\n\nquine_sub <- quine[c(1,2,nrow(quine)), ]\nxtable(quine_sub[ ,c(1, 2, 4, 5)])\n\n# mlr for absent days  ----------------------------------------------\n\nmlr_absent_full <- lm(Days ~ Eth + Sex + Lrn, data = quine)\n\nxtable(summary(mlr_absent_full), digits = 2)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/absent_from_school_model_select_backward/absent_from_school_model_select_backward.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(xtable)\nlibrary(MASS)\n\n# load data ---------------------------------------------------------\ndata(quine)\n\n# convert categorical variables to 0/1 ------------------------------\n\nquine$Eth <- as.character(quine$Eth)\nquine$Eth[quine$Eth == \"A\"] <- 0\nquine$Eth[quine$Eth == \"N\"] <- 1\nquine$Eth <- as.factor(quine$Eth)\n\nquine$Sex <- as.character(quine$Sex)\nquine$Sex[quine$Sex == \"F\"] <- 0\nquine$Sex[quine$Sex == \"M\"] <- 1\nquine$Sex <- as.factor(quine$Sex)\n\nquine$Lrn <- as.character(quine$Lrn)\nquine$Lrn[quine$Lrn == \"AL\"] <- 0\nquine$Lrn[quine$Lrn == \"SL\"] <- 1\nquine$Lrn <- as.factor(quine$Lrn)\n\n# mlr for absent days  ----------------------------------------------\n\nmlr_absent_full <- lm(Days ~ Eth + Sex + Lrn, data = quine)\n\nround(summary(mlr_absent_full)$adj.r.squared, 4)\n\n# no Ethnicity ------------------------------------------------------\n\nmlr_absent_no_eth <- lm(Days ~ Sex + Lrn, data = quine)\n\nround(summary(mlr_absent_no_eth)$adj.r.squared, 4)\n\n# no Sex ------------------------------------------------------------\n\nmlr_absent_no_sex <- lm(Days ~ Eth + Lrn, data = quine)\n\nround(summary(mlr_absent_no_sex)$adj.r.squared, 4)\n\n# no Lrn ------------------------------------------------------------\n\nmlr_absent_no_lrn <- lm(Days ~ Eth + Sex, data = quine)\n\nround(summary(mlr_absent_no_lrn)$adj.r.squared, 4)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/absent_from_school_model_select_forward/absent_from_school_model_select_forward.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(xtable)\nlibrary(MASS)\n\n# load data ---------------------------------------------------------\ndata(quine)\n\n# convert categorical variables to 0/1 ------------------------------\n\nquine$Eth <- as.character(quine$Eth)\nquine$Eth[quine$Eth == \"A\"] <- 0\nquine$Eth[quine$Eth == \"N\"] <- 1\nquine$Eth <- as.factor(quine$Eth)\n\nquine$Sex <- as.character(quine$Sex)\nquine$Sex[quine$Sex == \"F\"] <- 0\nquine$Sex[quine$Sex == \"M\"] <- 1\nquine$Sex <- as.factor(quine$Sex)\n\nquine$Lrn <- as.character(quine$Lrn)\nquine$Lrn[quine$Lrn == \"AL\"] <- 0\nquine$Lrn[quine$Lrn == \"SL\"] <- 1\nquine$Lrn <- as.factor(quine$Lrn)\n\n# add Ethnicity -----------------------------------------------------\n\nmlr_absent_eth <- lm(Days ~ Eth, data = quine)\n\nround(summary(mlr_absent_eth)$coefficients[2,4], 4)\n\nround(summary(mlr_absent_eth)$adj.r.squared, 4)\n\n# add Sex -----------------------------------------------------------\n\nmlr_absent_sex <- lm(Days ~ Sex, data = quine)\n\nround(summary(mlr_absent_sex)$coefficients[2,4], 4)\n\nround(summary(mlr_absent_sex)$adj.r.squared, 4)\n\n# add Lrn -----------------------------------------------------------\n\nmlr_absent_lrn <- lm(Days ~ Lrn, data = quine)\n\nround(summary(mlr_absent_lrn)$coefficients[2,4], 4)\n\nround(summary(mlr_absent_lrn)$adj.r.squared, 4)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_conds/babies.csv",
    "content": 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  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_conds/baby_weights_conds.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nbabies <- read.csv(\"babies.csv\")\n\n# mlr for birth weight ----------------------------------------------\n\nm_bwt_mlr <- lm(bwt ~ gestation + parity + age + \n                  height + weight + smoke , data = babies)\n\n# complete cases data for plotting residuals plots ------------------\n\nbabies_cc <- na.omit(babies)\n\n# normal prob plot for residuals ------------------------------------\n\npdf(\"baby_weights_conds_normal_qq.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7,3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nqqnorm(m_bwt_mlr$residuals, \n       ylab = \"Residuals\", main = \"\",\n       pch = 19, col = COL[1,2],\n       ylim = c(-60,60), axes = FALSE)\naxis(1)\naxis(2, seq(-40, 40, 40))\nbox()\n\nqqline(m_bwt_mlr$residuals, col = COL[1])\n\ndev.off()\n\n# histogram for residuals ------------------------------------\n\npdf(\"baby_weights_conds_normal_hist.pdf\", 5.5, 4.3)\npar(mar = c(3.7,3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(m_bwt_mlr$residuals,\n    xlab = \"Residuals\",\n    ylab = \"\",\n    col = COL[1])\nbox()\ndev.off()\n\n# absolute values of residuals against fitted -----------------------\n\npdf(\"baby_weights_conds_abs_res_fitted.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_bwt_mlr$residuals ~ m_bwt_mlr$fitted.values, \n     ylab = \"Residuals\", xlab = \"Fitted values\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE)\naxis(1, seq(80, 160, 40))\naxis(2, seq(-40, 40, 40))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals in order of their data collection -----------------------\n\npdf(\"baby_weights_conds_res_order.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7, 3.9, 0.5, 1), las = 1, mgp = c(2.7,0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_bwt_mlr$residuals ~ c(1:length(m_bwt_mlr$residuals)), \n     ylab = \"Residuals\", xlab = \"Order of collection\",\n     pch = 19, col = COL[1,2], axes = FALSE)\naxis(1, seq(0,1200,400))\naxis(2, seq(-40,40,40))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals vs. gestation -------------------------------------------\n\npdf(\"baby_weights_conds_res_gestation.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_bwt_mlr$residuals ~ babies_cc$gestation, \n     ylab = \"Residuals\", xlab = \"Length of gestation\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE)\naxis(1, seq(150, 350, 50))\naxis(2, seq(-40, 40, 40))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals vs. parity -------------------------------------------\n\npdf(\"baby_weights_conds_res_parity.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_bwt_mlr$residuals ~ babies_cc$parity, \n     ylab = \"Residuals\", xlab = \"Parity\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE)\naxis(1, seq(0, 1, 1))\naxis(2, seq(-40, 40, 40))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals vs. height -------------------------------------------\n\npdf(\"baby_weights_conds_res_height.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_bwt_mlr$residuals ~ babies_cc$height, \n     ylab = \"Residuals\", xlab = \"Height of mother\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE)\naxis(1, at = seq(55, 70, 5))\naxis(2, at = seq(-40, 40, 40))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals vs. weight -------------------------------------------\n\npdf(\"baby_weights_conds_res_weight.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_bwt_mlr$residuals ~ babies_cc$weight, \n     ylab = \"Residuals\", xlab = \"Weight of mother\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE)\naxis(1, at = seq(100, 250, 50))\naxis(2, at = seq(-40, 40, 40))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals vs. smoke -------------------------------------------\n\npdf(\"baby_weights_conds_res_smoke.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_bwt_mlr$residuals ~ babies_cc$smoke, \n     ylab = \"Residuals\", xlab = \"Smoke\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE)\naxis(1, at = seq(0, 1, 1))\naxis(2, at = seq(-40, 40, 40))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_mlr/babies.csv",
    "content": 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20,280,0,30,60,115,0\r1118,121,281,0,29,63,108,0\r1119,113,282,0,30,64,118,1\r1120,117,270,0,23,58,115,0\r1121,158,267,0,35,64,125,0\r1122,128,277,0,39,61,120,0\r1123,158,289,0,30,66,140,0\r1124,133,289,0,22,65,123,1\r1125,163,298,0,37,61,98,0\r1126,128,282,1,19,66,118,0\r1127,126,271,1,21,60,105,0\r1128,127,283,0,42,62,154,1\r1129,134,287,0,40,63,118,0\r1130,140,274,0,41,63,122,0\r1131,102,285,0,29,63,117,1\r1132,100,252,0,24,61,150,0\r1133,120,295,0,29,59,100,1\r1134,98,279,1,18,65,115,1\r1135,130,246,0,19,62,118,0\r1136,104,280,0,41,63,118,1\r1137,122,285,0,31,62,102,1\r1138,137,276,1,25,64,127,0\r1139,114,285,1,20,61,104,0\r1140,63,236,1,24,58,99,0\r1141,98,318,0,23,63,107,0\r1142,99,268,0,32,63,124,1\r1143,89,238,1,26,64,136,0\r1144,117,283,0,22,65,142,1\r1145,143,281,0,29,67,132,0\r1146,106,279,0,29,63,125,1\r1147,99,246,0,35,62,106,0\r1148,156,300,0,27,65,120,1\r1149,72,266,1,25,66,200,1\r1150,75,266,0,37,61,113,1\r1151,97,285,0,35,61,112,1\r1152,106,264,0,41,64,114,0\r1153,91,225,0,18,68,117,1\r1154,117,269,1,28,61,99,0\r1155,117,284,0,25,66,177,1\r1156,112,291,0,23,66,145,0\r1157,112,270,0,29,61,124,0\r1158,141,293,0,28,61,125,0\r1159,131,259,0,19,63,134,0\r1160,130,290,0,19,65,123,1\r1161,132,270,0,26,67,140,0\r1162,114,265,0,23,67,130,1\r1163,160,291,0,34,64,110,1\r1164,106,283,0,24,63,119,0\r1165,84,260,1,20,64,104,1\r1166,112,268,1,25,59,103,0\r1167,139,311,0,37,66,135,0\r1168,104,267,0,30,63,180,0\r1169,130,294,0,32,63,110,1\r1170,71,254,0,19,61,145,1\r1171,82,270,0,21,65,150,1\r1172,119,280,1,21,64,128,0\r1173,123,353,0,26,63,115,0\r1174,115,278,0,27,59,95,0\r1175,124,289,1,21,67,145,1\r1176,138,292,0,25,65,130,1\r1177,88,276,0,25,63,103,1\r1178,146,305,0,23,NA,NA,0\r1179,128,241,1,17,64,126,0\r1180,82,274,0,31,64,101,1\r1181,100,274,0,24,63,113,0\r1182,114,271,0,32,61,130,0\r1183,97,269,0,20,65,137,1\r1184,126,298,0,24,61,112,0\r1185,122,275,1,20,65,127,0\r1186,152,295,0,39,62,140,0\r1187,116,274,0,21,62,110,1\r1188,132,302,0,36,63,145,1\r1189,84,260,1,37,66,140,0\r1190,119,277,1,18,61,89,1\r1191,104,275,0,24,NA,NA,0\r1192,106,312,0,24,62,135,1\r1193,124,NA,1,39,65,228,0\r1194,139,291,0,24,65,160,0\r1195,103,273,0,36,65,158,1\r1196,112,299,0,24,67,145,1\r1197,96,276,0,33,64,127,1\r1198,102,281,1,19,67,135,1\r1199,120,300,0,34,63,150,1\r1200,102,338,0,19,64,170,0\r1201,97,255,1,22,63,107,1\r1202,113,285,0,22,70,145,0\r1203,130,297,0,32,58,130,0\r1204,97,260,1,25,63,115,1\r1205,116,273,0,31,61,120,0\r1206,114,266,0,29,64,113,0\r1207,127,242,0,17,61,135,1\r1208,87,247,1,18,66,125,1\r1209,141,281,0,29,54,156,1\r1210,144,283,1,25,66,140,0\r1211,116,273,0,33,66,130,1\r1212,75,265,0,21,65,103,1\r1213,138,286,1,28,68,120,0\r1214,99,271,0,39,69,151,0\r1215,118,293,0,21,63,103,0\r1216,152,267,0,28,NA,119,1\r1217,97,266,0,24,62,109,0\r1218,146,319,0,28,66,145,0\r1219,81,285,0,19,63,150,1\r1220,110,321,0,28,66,180,0\r1221,135,284,1,19,60,95,0\r1222,114,290,1,21,65,120,1\r1223,124,288,1,21,64,116,1\r1224,115,262,1,23,64,136,1\r1225,143,281,0,28,65,135,1\r1226,113,287,1,29,70,145,1\r1227,109,244,1,21,63,102,1\r1228,103,278,0,30,60,87,1\r1229,118,276,0,34,64,116,0\r1230,127,290,0,27,65,121,0\r1231,132,270,0,27,65,126,0\r1232,113,275,1,27,60,100,0\r1233,128,265,0,24,67,120,0\r1234,130,291,0,30,65,150,1\r1235,125,281,1,21,65,110,0\r1236,117,297,0,38,65,129,0"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_mlr/baby_weights_mlr.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nbabies <- read.csv(\"babies.csv\")\n\n# print out dataset -------------------------------------------------\n\nbabies_sub <- babies[c(1,2,nrow(babies)), ]\nxtable(babies_sub)\n\n# mlr for birth weight ----------------------------------------------\n\nm_bwt_mlr <- lm(bwt ~ gestation + parity + age + \n                     height + weight + smoke , data = babies)\n\nxtable(summary(m_bwt_mlr), digits = 2)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_model_select_backward/babies.csv",
    "content": 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20,280,0,30,60,115,0\r1118,121,281,0,29,63,108,0\r1119,113,282,0,30,64,118,1\r1120,117,270,0,23,58,115,0\r1121,158,267,0,35,64,125,0\r1122,128,277,0,39,61,120,0\r1123,158,289,0,30,66,140,0\r1124,133,289,0,22,65,123,1\r1125,163,298,0,37,61,98,0\r1126,128,282,1,19,66,118,0\r1127,126,271,1,21,60,105,0\r1128,127,283,0,42,62,154,1\r1129,134,287,0,40,63,118,0\r1130,140,274,0,41,63,122,0\r1131,102,285,0,29,63,117,1\r1132,100,252,0,24,61,150,0\r1133,120,295,0,29,59,100,1\r1134,98,279,1,18,65,115,1\r1135,130,246,0,19,62,118,0\r1136,104,280,0,41,63,118,1\r1137,122,285,0,31,62,102,1\r1138,137,276,1,25,64,127,0\r1139,114,285,1,20,61,104,0\r1140,63,236,1,24,58,99,0\r1141,98,318,0,23,63,107,0\r1142,99,268,0,32,63,124,1\r1143,89,238,1,26,64,136,0\r1144,117,283,0,22,65,142,1\r1145,143,281,0,29,67,132,0\r1146,106,279,0,29,63,125,1\r1147,99,246,0,35,62,106,0\r1148,156,300,0,27,65,120,1\r1149,72,266,1,25,66,200,1\r1150,75,266,0,37,61,113,1\r1151,97,285,0,35,61,112,1\r1152,106,264,0,41,64,114,0\r1153,91,225,0,18,68,117,1\r1154,117,269,1,28,61,99,0\r1155,117,284,0,25,66,177,1\r1156,112,291,0,23,66,145,0\r1157,112,270,0,29,61,124,0\r1158,141,293,0,28,61,125,0\r1159,131,259,0,19,63,134,0\r1160,130,290,0,19,65,123,1\r1161,132,270,0,26,67,140,0\r1162,114,265,0,23,67,130,1\r1163,160,291,0,34,64,110,1\r1164,106,283,0,24,63,119,0\r1165,84,260,1,20,64,104,1\r1166,112,268,1,25,59,103,0\r1167,139,311,0,37,66,135,0\r1168,104,267,0,30,63,180,0\r1169,130,294,0,32,63,110,1\r1170,71,254,0,19,61,145,1\r1171,82,270,0,21,65,150,1\r1172,119,280,1,21,64,128,0\r1173,123,353,0,26,63,115,0\r1174,115,278,0,27,59,95,0\r1175,124,289,1,21,67,145,1\r1176,138,292,0,25,65,130,1\r1177,88,276,0,25,63,103,1\r1178,146,305,0,23,NA,NA,0\r1179,128,241,1,17,64,126,0\r1180,82,274,0,31,64,101,1\r1181,100,274,0,24,63,113,0\r1182,114,271,0,32,61,130,0\r1183,97,269,0,20,65,137,1\r1184,126,298,0,24,61,112,0\r1185,122,275,1,20,65,127,0\r1186,152,295,0,39,62,140,0\r1187,116,274,0,21,62,110,1\r1188,132,302,0,36,63,145,1\r1189,84,260,1,37,66,140,0\r1190,119,277,1,18,61,89,1\r1191,104,275,0,24,NA,NA,0\r1192,106,312,0,24,62,135,1\r1193,124,NA,1,39,65,228,0\r1194,139,291,0,24,65,160,0\r1195,103,273,0,36,65,158,1\r1196,112,299,0,24,67,145,1\r1197,96,276,0,33,64,127,1\r1198,102,281,1,19,67,135,1\r1199,120,300,0,34,63,150,1\r1200,102,338,0,19,64,170,0\r1201,97,255,1,22,63,107,1\r1202,113,285,0,22,70,145,0\r1203,130,297,0,32,58,130,0\r1204,97,260,1,25,63,115,1\r1205,116,273,0,31,61,120,0\r1206,114,266,0,29,64,113,0\r1207,127,242,0,17,61,135,1\r1208,87,247,1,18,66,125,1\r1209,141,281,0,29,54,156,1\r1210,144,283,1,25,66,140,0\r1211,116,273,0,33,66,130,1\r1212,75,265,0,21,65,103,1\r1213,138,286,1,28,68,120,0\r1214,99,271,0,39,69,151,0\r1215,118,293,0,21,63,103,0\r1216,152,267,0,28,NA,119,1\r1217,97,266,0,24,62,109,0\r1218,146,319,0,28,66,145,0\r1219,81,285,0,19,63,150,1\r1220,110,321,0,28,66,180,0\r1221,135,284,1,19,60,95,0\r1222,114,290,1,21,65,120,1\r1223,124,288,1,21,64,116,1\r1224,115,262,1,23,64,136,1\r1225,143,281,0,28,65,135,1\r1226,113,287,1,29,70,145,1\r1227,109,244,1,21,63,102,1\r1228,103,278,0,30,60,87,1\r1229,118,276,0,34,64,116,0\r1230,127,290,0,27,65,121,0\r1231,132,270,0,27,65,126,0\r1232,113,275,1,27,60,100,0\r1233,128,265,0,24,67,120,0\r1234,130,291,0,30,65,150,1\r1235,125,281,1,21,65,110,0\r1236,117,297,0,38,65,129,0"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_model_select_backward/baby_weights_model_select_backward.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nbabies <- read.csv(\"babies.csv\")\n\n# mlr for birth weight ----------------------------------------------\n\nm_bwt_mlr <- lm(bwt ~ gestation + parity + age + \n                     height + weight + smoke , data = babies)\n\nround(summary(m_bwt_mlr)$adj.r.squared, 4)\n\n# no gestation ------------------------------------------------------\n\nm_bwt_mlr_no_gestation <- lm(bwt ~ parity + age + \n                               height + weight + smoke , data = babies)\n\nround(summary(m_bwt_mlr_no_gestation)$adj.r.squared, 4)\n\n# no parity ---------------------------------------------------------\n\nm_bwt_mlr_no_parity <- lm(bwt ~ gestation + age + \n                            height + weight + smoke , data = babies)\n\nround(summary(m_bwt_mlr_no_parity)$adj.r.squared, 4)\n\n# no age ------------------------------------------------------------\n\nm_bwt_mlr_no_age <- lm(bwt ~ gestation + parity + \n                         height + weight + smoke , data = babies)\n\nround(summary(m_bwt_mlr_no_age)$adj.r.squared, 4)\n\n# no height ---------------------------------------------------------\n\nm_bwt_mlr_no_height <- lm(bwt ~ gestation + parity + \n                         age + weight + smoke , data = babies)\n\nround(summary(m_bwt_mlr_no_height)$adj.r.squared, 4)\n\n# no weight ---------------------------------------------------------\n\nm_bwt_mlr_no_weight <- lm(bwt ~ gestation + parity + \n                            age + height + smoke , data = babies)\n\nround(summary(m_bwt_mlr_no_weight)$adj.r.squared, 4)\n\n# no smoking --------------------------------------------------------\n\nm_bwt_mlr_no_smoking <- lm(bwt ~ gestation + parity + \n                            age + height + weight , data = babies)\n\nround(summary(m_bwt_mlr_no_smoking)$adj.r.squared, 4)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_model_select_forward/babies.csv",
    "content": 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20,280,0,30,60,115,0\r1118,121,281,0,29,63,108,0\r1119,113,282,0,30,64,118,1\r1120,117,270,0,23,58,115,0\r1121,158,267,0,35,64,125,0\r1122,128,277,0,39,61,120,0\r1123,158,289,0,30,66,140,0\r1124,133,289,0,22,65,123,1\r1125,163,298,0,37,61,98,0\r1126,128,282,1,19,66,118,0\r1127,126,271,1,21,60,105,0\r1128,127,283,0,42,62,154,1\r1129,134,287,0,40,63,118,0\r1130,140,274,0,41,63,122,0\r1131,102,285,0,29,63,117,1\r1132,100,252,0,24,61,150,0\r1133,120,295,0,29,59,100,1\r1134,98,279,1,18,65,115,1\r1135,130,246,0,19,62,118,0\r1136,104,280,0,41,63,118,1\r1137,122,285,0,31,62,102,1\r1138,137,276,1,25,64,127,0\r1139,114,285,1,20,61,104,0\r1140,63,236,1,24,58,99,0\r1141,98,318,0,23,63,107,0\r1142,99,268,0,32,63,124,1\r1143,89,238,1,26,64,136,0\r1144,117,283,0,22,65,142,1\r1145,143,281,0,29,67,132,0\r1146,106,279,0,29,63,125,1\r1147,99,246,0,35,62,106,0\r1148,156,300,0,27,65,120,1\r1149,72,266,1,25,66,200,1\r1150,75,266,0,37,61,113,1\r1151,97,285,0,35,61,112,1\r1152,106,264,0,41,64,114,0\r1153,91,225,0,18,68,117,1\r1154,117,269,1,28,61,99,0\r1155,117,284,0,25,66,177,1\r1156,112,291,0,23,66,145,0\r1157,112,270,0,29,61,124,0\r1158,141,293,0,28,61,125,0\r1159,131,259,0,19,63,134,0\r1160,130,290,0,19,65,123,1\r1161,132,270,0,26,67,140,0\r1162,114,265,0,23,67,130,1\r1163,160,291,0,34,64,110,1\r1164,106,283,0,24,63,119,0\r1165,84,260,1,20,64,104,1\r1166,112,268,1,25,59,103,0\r1167,139,311,0,37,66,135,0\r1168,104,267,0,30,63,180,0\r1169,130,294,0,32,63,110,1\r1170,71,254,0,19,61,145,1\r1171,82,270,0,21,65,150,1\r1172,119,280,1,21,64,128,0\r1173,123,353,0,26,63,115,0\r1174,115,278,0,27,59,95,0\r1175,124,289,1,21,67,145,1\r1176,138,292,0,25,65,130,1\r1177,88,276,0,25,63,103,1\r1178,146,305,0,23,NA,NA,0\r1179,128,241,1,17,64,126,0\r1180,82,274,0,31,64,101,1\r1181,100,274,0,24,63,113,0\r1182,114,271,0,32,61,130,0\r1183,97,269,0,20,65,137,1\r1184,126,298,0,24,61,112,0\r1185,122,275,1,20,65,127,0\r1186,152,295,0,39,62,140,0\r1187,116,274,0,21,62,110,1\r1188,132,302,0,36,63,145,1\r1189,84,260,1,37,66,140,0\r1190,119,277,1,18,61,89,1\r1191,104,275,0,24,NA,NA,0\r1192,106,312,0,24,62,135,1\r1193,124,NA,1,39,65,228,0\r1194,139,291,0,24,65,160,0\r1195,103,273,0,36,65,158,1\r1196,112,299,0,24,67,145,1\r1197,96,276,0,33,64,127,1\r1198,102,281,1,19,67,135,1\r1199,120,300,0,34,63,150,1\r1200,102,338,0,19,64,170,0\r1201,97,255,1,22,63,107,1\r1202,113,285,0,22,70,145,0\r1203,130,297,0,32,58,130,0\r1204,97,260,1,25,63,115,1\r1205,116,273,0,31,61,120,0\r1206,114,266,0,29,64,113,0\r1207,127,242,0,17,61,135,1\r1208,87,247,1,18,66,125,1\r1209,141,281,0,29,54,156,1\r1210,144,283,1,25,66,140,0\r1211,116,273,0,33,66,130,1\r1212,75,265,0,21,65,103,1\r1213,138,286,1,28,68,120,0\r1214,99,271,0,39,69,151,0\r1215,118,293,0,21,63,103,0\r1216,152,267,0,28,NA,119,1\r1217,97,266,0,24,62,109,0\r1218,146,319,0,28,66,145,0\r1219,81,285,0,19,63,150,1\r1220,110,321,0,28,66,180,0\r1221,135,284,1,19,60,95,0\r1222,114,290,1,21,65,120,1\r1223,124,288,1,21,64,116,1\r1224,115,262,1,23,64,136,1\r1225,143,281,0,28,65,135,1\r1226,113,287,1,29,70,145,1\r1227,109,244,1,21,63,102,1\r1228,103,278,0,30,60,87,1\r1229,118,276,0,34,64,116,0\r1230,127,290,0,27,65,121,0\r1231,132,270,0,27,65,126,0\r1232,113,275,1,27,60,100,0\r1233,128,265,0,24,67,120,0\r1234,130,291,0,30,65,150,1\r1235,125,281,1,21,65,110,0\r1236,117,297,0,38,65,129,0"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_model_select_forward/baby_weights_model_select_backward.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nbabies <- read.csv(\"babies.csv\")\n\n# birth weight vs. gestation ----------------------------------------\n\nm_bwt_gestation <- lm(bwt ~ gestation, data = babies)\n\nround(summary(m_bwt_gestation)$coefficients[2,4], 4) # p-val\nround(summary(m_bwt_gestation)$adj.r.squared, 4) # adj r-sq\n\n# birth weight vs. parity ----------------------------------------\n\nm_bwt_parity <- lm(bwt ~ parity, data = babies)\n\nround(summary(m_bwt_parity)$coefficients[2,4], 4) # p-val\nround(summary(m_bwt_parity)$adj.r.squared, 4) # adj r-sq\n\n# birth weight vs. age --------------------------------------------\n\nm_bwt_age <- lm(bwt ~ age, data = babies)\n\nround(summary(m_bwt_age)$coefficients[2,4], 4) # p-val\nround(summary(m_bwt_age)$adj.r.squared, 4) # adj r-sq\n\n# birth weight vs. height ------------------------------------------\n\nm_bwt_height <- lm(bwt ~ height, data = babies)\n\nround(summary(m_bwt_height)$coefficients[2,4], 4) # p-val\nround(summary(m_bwt_height)$adj.r.squared, 4) # adj r-sq\n\n# birth weight vs. weight ------------------------------------------\n\nm_bwt_weight <- lm(bwt ~ weight, data = babies)\n\nround(summary(m_bwt_weight)$coefficients[2,4], 4) # p-val\nround(summary(m_bwt_weight)$adj.r.squared, 4) # adj r-sq\n\n# birth weight vs. smoke ------------------------------------------\n\nm_bwt_smoke <- lm(bwt ~ smoke, data = babies)\n\nround(summary(m_bwt_smoke)$coefficients[2,4], 4) # p-val\nround(summary(m_bwt_smoke)$adj.r.squared, 4) # adj r-sq"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_parity/babies.csv",
    "content": 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20,280,0,30,60,115,0\r1118,121,281,0,29,63,108,0\r1119,113,282,0,30,64,118,1\r1120,117,270,0,23,58,115,0\r1121,158,267,0,35,64,125,0\r1122,128,277,0,39,61,120,0\r1123,158,289,0,30,66,140,0\r1124,133,289,0,22,65,123,1\r1125,163,298,0,37,61,98,0\r1126,128,282,1,19,66,118,0\r1127,126,271,1,21,60,105,0\r1128,127,283,0,42,62,154,1\r1129,134,287,0,40,63,118,0\r1130,140,274,0,41,63,122,0\r1131,102,285,0,29,63,117,1\r1132,100,252,0,24,61,150,0\r1133,120,295,0,29,59,100,1\r1134,98,279,1,18,65,115,1\r1135,130,246,0,19,62,118,0\r1136,104,280,0,41,63,118,1\r1137,122,285,0,31,62,102,1\r1138,137,276,1,25,64,127,0\r1139,114,285,1,20,61,104,0\r1140,63,236,1,24,58,99,0\r1141,98,318,0,23,63,107,0\r1142,99,268,0,32,63,124,1\r1143,89,238,1,26,64,136,0\r1144,117,283,0,22,65,142,1\r1145,143,281,0,29,67,132,0\r1146,106,279,0,29,63,125,1\r1147,99,246,0,35,62,106,0\r1148,156,300,0,27,65,120,1\r1149,72,266,1,25,66,200,1\r1150,75,266,0,37,61,113,1\r1151,97,285,0,35,61,112,1\r1152,106,264,0,41,64,114,0\r1153,91,225,0,18,68,117,1\r1154,117,269,1,28,61,99,0\r1155,117,284,0,25,66,177,1\r1156,112,291,0,23,66,145,0\r1157,112,270,0,29,61,124,0\r1158,141,293,0,28,61,125,0\r1159,131,259,0,19,63,134,0\r1160,130,290,0,19,65,123,1\r1161,132,270,0,26,67,140,0\r1162,114,265,0,23,67,130,1\r1163,160,291,0,34,64,110,1\r1164,106,283,0,24,63,119,0\r1165,84,260,1,20,64,104,1\r1166,112,268,1,25,59,103,0\r1167,139,311,0,37,66,135,0\r1168,104,267,0,30,63,180,0\r1169,130,294,0,32,63,110,1\r1170,71,254,0,19,61,145,1\r1171,82,270,0,21,65,150,1\r1172,119,280,1,21,64,128,0\r1173,123,353,0,26,63,115,0\r1174,115,278,0,27,59,95,0\r1175,124,289,1,21,67,145,1\r1176,138,292,0,25,65,130,1\r1177,88,276,0,25,63,103,1\r1178,146,305,0,23,NA,NA,0\r1179,128,241,1,17,64,126,0\r1180,82,274,0,31,64,101,1\r1181,100,274,0,24,63,113,0\r1182,114,271,0,32,61,130,0\r1183,97,269,0,20,65,137,1\r1184,126,298,0,24,61,112,0\r1185,122,275,1,20,65,127,0\r1186,152,295,0,39,62,140,0\r1187,116,274,0,21,62,110,1\r1188,132,302,0,36,63,145,1\r1189,84,260,1,37,66,140,0\r1190,119,277,1,18,61,89,1\r1191,104,275,0,24,NA,NA,0\r1192,106,312,0,24,62,135,1\r1193,124,NA,1,39,65,228,0\r1194,139,291,0,24,65,160,0\r1195,103,273,0,36,65,158,1\r1196,112,299,0,24,67,145,1\r1197,96,276,0,33,64,127,1\r1198,102,281,1,19,67,135,1\r1199,120,300,0,34,63,150,1\r1200,102,338,0,19,64,170,0\r1201,97,255,1,22,63,107,1\r1202,113,285,0,22,70,145,0\r1203,130,297,0,32,58,130,0\r1204,97,260,1,25,63,115,1\r1205,116,273,0,31,61,120,0\r1206,114,266,0,29,64,113,0\r1207,127,242,0,17,61,135,1\r1208,87,247,1,18,66,125,1\r1209,141,281,0,29,54,156,1\r1210,144,283,1,25,66,140,0\r1211,116,273,0,33,66,130,1\r1212,75,265,0,21,65,103,1\r1213,138,286,1,28,68,120,0\r1214,99,271,0,39,69,151,0\r1215,118,293,0,21,63,103,0\r1216,152,267,0,28,NA,119,1\r1217,97,266,0,24,62,109,0\r1218,146,319,0,28,66,145,0\r1219,81,285,0,19,63,150,1\r1220,110,321,0,28,66,180,0\r1221,135,284,1,19,60,95,0\r1222,114,290,1,21,65,120,1\r1223,124,288,1,21,64,116,1\r1224,115,262,1,23,64,136,1\r1225,143,281,0,28,65,135,1\r1226,113,287,1,29,70,145,1\r1227,109,244,1,21,63,102,1\r1228,103,278,0,30,60,87,1\r1229,118,276,0,34,64,116,0\r1230,127,290,0,27,65,121,0\r1231,132,270,0,27,65,126,0\r1232,113,275,1,27,60,100,0\r1233,128,265,0,24,67,120,0\r1234,130,291,0,30,65,150,1\r1235,125,281,1,21,65,110,0\r1236,117,297,0,38,65,129,0"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_parity/baby_weights_parity.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nbabies <- read.csv(\"babies.csv\")\n\n# model birth weight vs. parity -------------------------------------\n\nm_bwt_parity <- lm(bwt ~ as.factor(parity), data = babies)\n\nxtable(summary(m_bwt_parity), digits = 2)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_smoke/babies.csv",
    "content": 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20,280,0,30,60,115,0\r1118,121,281,0,29,63,108,0\r1119,113,282,0,30,64,118,1\r1120,117,270,0,23,58,115,0\r1121,158,267,0,35,64,125,0\r1122,128,277,0,39,61,120,0\r1123,158,289,0,30,66,140,0\r1124,133,289,0,22,65,123,1\r1125,163,298,0,37,61,98,0\r1126,128,282,1,19,66,118,0\r1127,126,271,1,21,60,105,0\r1128,127,283,0,42,62,154,1\r1129,134,287,0,40,63,118,0\r1130,140,274,0,41,63,122,0\r1131,102,285,0,29,63,117,1\r1132,100,252,0,24,61,150,0\r1133,120,295,0,29,59,100,1\r1134,98,279,1,18,65,115,1\r1135,130,246,0,19,62,118,0\r1136,104,280,0,41,63,118,1\r1137,122,285,0,31,62,102,1\r1138,137,276,1,25,64,127,0\r1139,114,285,1,20,61,104,0\r1140,63,236,1,24,58,99,0\r1141,98,318,0,23,63,107,0\r1142,99,268,0,32,63,124,1\r1143,89,238,1,26,64,136,0\r1144,117,283,0,22,65,142,1\r1145,143,281,0,29,67,132,0\r1146,106,279,0,29,63,125,1\r1147,99,246,0,35,62,106,0\r1148,156,300,0,27,65,120,1\r1149,72,266,1,25,66,200,1\r1150,75,266,0,37,61,113,1\r1151,97,285,0,35,61,112,1\r1152,106,264,0,41,64,114,0\r1153,91,225,0,18,68,117,1\r1154,117,269,1,28,61,99,0\r1155,117,284,0,25,66,177,1\r1156,112,291,0,23,66,145,0\r1157,112,270,0,29,61,124,0\r1158,141,293,0,28,61,125,0\r1159,131,259,0,19,63,134,0\r1160,130,290,0,19,65,123,1\r1161,132,270,0,26,67,140,0\r1162,114,265,0,23,67,130,1\r1163,160,291,0,34,64,110,1\r1164,106,283,0,24,63,119,0\r1165,84,260,1,20,64,104,1\r1166,112,268,1,25,59,103,0\r1167,139,311,0,37,66,135,0\r1168,104,267,0,30,63,180,0\r1169,130,294,0,32,63,110,1\r1170,71,254,0,19,61,145,1\r1171,82,270,0,21,65,150,1\r1172,119,280,1,21,64,128,0\r1173,123,353,0,26,63,115,0\r1174,115,278,0,27,59,95,0\r1175,124,289,1,21,67,145,1\r1176,138,292,0,25,65,130,1\r1177,88,276,0,25,63,103,1\r1178,146,305,0,23,NA,NA,0\r1179,128,241,1,17,64,126,0\r1180,82,274,0,31,64,101,1\r1181,100,274,0,24,63,113,0\r1182,114,271,0,32,61,130,0\r1183,97,269,0,20,65,137,1\r1184,126,298,0,24,61,112,0\r1185,122,275,1,20,65,127,0\r1186,152,295,0,39,62,140,0\r1187,116,274,0,21,62,110,1\r1188,132,302,0,36,63,145,1\r1189,84,260,1,37,66,140,0\r1190,119,277,1,18,61,89,1\r1191,104,275,0,24,NA,NA,0\r1192,106,312,0,24,62,135,1\r1193,124,NA,1,39,65,228,0\r1194,139,291,0,24,65,160,0\r1195,103,273,0,36,65,158,1\r1196,112,299,0,24,67,145,1\r1197,96,276,0,33,64,127,1\r1198,102,281,1,19,67,135,1\r1199,120,300,0,34,63,150,1\r1200,102,338,0,19,64,170,0\r1201,97,255,1,22,63,107,1\r1202,113,285,0,22,70,145,0\r1203,130,297,0,32,58,130,0\r1204,97,260,1,25,63,115,1\r1205,116,273,0,31,61,120,0\r1206,114,266,0,29,64,113,0\r1207,127,242,0,17,61,135,1\r1208,87,247,1,18,66,125,1\r1209,141,281,0,29,54,156,1\r1210,144,283,1,25,66,140,0\r1211,116,273,0,33,66,130,1\r1212,75,265,0,21,65,103,1\r1213,138,286,1,28,68,120,0\r1214,99,271,0,39,69,151,0\r1215,118,293,0,21,63,103,0\r1216,152,267,0,28,NA,119,1\r1217,97,266,0,24,62,109,0\r1218,146,319,0,28,66,145,0\r1219,81,285,0,19,63,150,1\r1220,110,321,0,28,66,180,0\r1221,135,284,1,19,60,95,0\r1222,114,290,1,21,65,120,1\r1223,124,288,1,21,64,116,1\r1224,115,262,1,23,64,136,1\r1225,143,281,0,28,65,135,1\r1226,113,287,1,29,70,145,1\r1227,109,244,1,21,63,102,1\r1228,103,278,0,30,60,87,1\r1229,118,276,0,34,64,116,0\r1230,127,290,0,27,65,121,0\r1231,132,270,0,27,65,126,0\r1232,113,275,1,27,60,100,0\r1233,128,265,0,24,67,120,0\r1234,130,291,0,30,65,150,1\r1235,125,281,1,21,65,110,0\r1236,117,297,0,38,65,129,0"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/baby_weights_smoke/baby_weights_smoke.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nbabies <- read.csv(\"babies.csv\")\n\n# model birth weight vs. smoking ------------------------------------\n\nm_bwt_smoke <- lm(bwt ~ as.factor(smoke), data = babies)\n\nxtable(summary(m_bwt_smoke), digits = 2)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/challenger_disaster_predict/challenger_disaster_predict.R",
    "content": "# load packages -----------------------------------------------------\n\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nload(\"orings.rda\")\nset.seed(17)\n\n# plot probability of damage vs. temperature ------------------------\n\nmyPDF(\"challenger_disaster_damage_temp.pdf\", 4.5, 2.7, \n      mar = c(3.2, 3.7, 0.8, 0.8), mgp = c(2.5, 0.55, 0))\n\nthese <- orings[,1] %in% c(67, 70, 76)\nplot(orings[,1] + \n       c(rep(0, 5), c(-0.1, 0, 0.1), 0, 0, -0.07, -0.07, 0.07, 0.07, \n         rep(0, 4), -0.07, 0.07, 0, 0, 0), \n     orings[,2]/6, \n     xlab = \"\", ylab = \"Probability of damage\", \n     xlim = c(50, 82), ylim = c(0,1), \n     col = COL[1,2], pch = 19)\nmtext(\"Temperature (Fahrenheit)\", 1, 2)\n\ndev.off()\n\n# probability calculations ------------------------------------------\n\ntemperature <- c(51, 53, 55)\nlogitp <- 11.6630 - 0.2162 * temperature\np <- exp(logitp) / (1+exp(logitp))\n\n# plot of predicted probabilities -----------------------------------\n\nmyPDF(\"challenger_disaster_pred_damage_temp.pdf\", 4.5, 2.7, \n      mar=c(3.2, 3.7, 0.8, 0.8), mgp = c(2.5, 0.55, 0))\n\nthese <- orings[,1] %in% c(67, 70, 76)\nplot(orings[,1] + \n       c(rep(0, 5), c(-0.1, 0, 0.1), 0, 0, -0.07, -0.07, 0.07, 0.07, \n         rep(0, 4), -0.07, 0.07, 0, 0, 0), \n     orings[,2]/6, \n     xlab = \"\", ylab = \"Probability of damage\", \n     xlim = c(50, 82), ylim = c(0,1), \n     col = COL[1,2], pch = 19)\nmtext(\"Temperature (Fahrenheit)\", 1, 2)\ntemperature <- seq(51, 75, 2)\nlogitp      <- 11.6630 - 0.2162*temperature\np           <- exp(logitp)/(1+exp(logitp))\npoints(temperature, p, col=COL[4], cex=0.7)\ntemperature <- seq(25, 100, 0.2)\nlogitp      <- 11.6630 - 0.2162 * temperature\np           <- exp(logitp) / (1+exp(logitp))\nlines(temperature, p, col = COL[4])\n\ndev.off()"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/gpa/gpa.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\ngpa_survey <- read.csv(\"gpa_survey.csv\")\n\n# gpa mlr -----------------------------------------------------------\n\nm_gpa <- lm(gpa ~ studyweek + sleepnight + outnight + gender,\n            data = gpa_survey)\n\nxtable(summary(m_gpa), digits = 2)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/gpa/gpa_survey.csv",
    "content": "gpa,studyweek,sleepnight,outnight,gender\r\n3.89,50,6,3,female\r\n3.9,15,6,1,female\r\n3.75,15,7,1,female\r\n3.6,10,6,4,male\r\n4,25,7,3,female\r\n3.15,20,7,3,male\r\n3.25,15,6,1,female\r\n3.925,10,8,3,female\r\n3.428,12,8,2,female\r\n3.8,2,8,4,male\r\n3.9,10,8,1,female\r\n2.9,30,6,2,female\r\n3.925,30,7,2,female\r\n3.65,21,9,3,female\r\n3.75,10,8.5,3.5,female\r\n4.67,14,6.5,3,male\r\n3.1,12,7.5,3.5,male\r\n3.8,12,8,1,female\r\n3.4,4,9,3,female\r\n3.575,45,6.5,1.5,female\r\n3.85,6,7,2.5,female\r\n3.4,10,7,3,female\r\n3.5,12,8,2,male\r\n3.6,13,6,3.5,female\r\n3.825,35,8,4,female\r\n3.925,10,8,3,female\r\n4,40,8,3,female\r\n3.425,14,9,3,female\r\n3.75,30,6,0,female\r\n3.15,8,6,0,female\r\n3.4,8,6.5,2,female\r\n3.7,20,7,1,female\r\n3.36,40,7,1,female\r\n3.7,15,7,1.5,male\r\n3.7,25,5,1,female\r\n3.6,10,7,2,female\r\n3.825,18,7,1.5,female\r\n3.2,15,6,1,female\r\n3.5,30,8,3,male\r\n3.5,11,7,1.5,female\r\n3,28,6,1.5,female\r\n3.98,4,7,1.5,female\r\n3.7,4,5,1,male\r\n3.81,25,7.5,2.5,female\r\n4,42,5,1,female\r\n3.1,3,7,2,male\r\n3.4,42,9,2,male\r\n3.5,25,8,2,male\r\n3.65,20,6,2,female\r\n3.7,7,8,2,female\r\n3.1,6,8,1,female\r\n4,20,7,3,female\r\n3.35,45,6,2,female\r\n3.541,30,7.5,1.5,female\r\n2.9,20,6,3,female"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/gpa_iq_conds/gpa_iq.csv",
    "content": "obs,gpa,iq,gender,concept\r1,7.94,111,2,67\r2,8.292,107,2,43\r3,4.643,100,2,52\r4,7.47,107,2,66\r5,8.882,114,1,58\r6,7.585,115,2,51\r7,7.65,111,2,71\r8,2.412,97,2,51\r9,6,100,1,49\r10,8.833,112,2,51\r11,7.47,104,1,35\r12,5.528,89,1,54\r13,7.167,104,2,54\r14,7.571,102,1,64\r15,4.7,91,1,56\r16,8.167,114,1,69\r17,7.822,114,1,55\r18,7.598,103,1,65\r19,4,106,2,40\r20,6.231,105,1,66\r21,7.643,113,2,55\r22,1.76,109,2,20\r24,6.419,108,1,56\r26,9.648,113,2,68\r27,10.7,130,1,69\r28,10.58,128,2,70\r29,9.429,128,2,80\r30,8,118,2,53\r31,9.585,113,2,65\r32,9.571,120,1,67\r33,8.998,132,1,62\r34,8.333,111,1,39\r35,8.175,124,2,71\r36,8,127,2,59\r37,9.333,128,1,60\r38,9.5,136,2,64\r39,9.167,106,2,71\r40,10.14,118,1,72\r41,9.999,119,1,54\r43,10.76,123,2,64\r44,9.763,124,2,58\r45,9.41,126,2,70\r46,9.167,116,2,72\r47,9.348,127,2,70\r48,8.167,119,2,47\r50,3.647,97,2,52\r51,3.408,86,1,46\r52,3.936,102,2,66\r53,7.167,110,2,67\r54,7.647,120,2,63\r55,0.53,103,2,53\r56,6.173,115,2,67\r57,7.295,93,2,61\r58,7.295,72,1,54\r59,8.938,111,1,60\r60,7.882,103,1,60\r61,8.353,123,2,63\r62,5.062,79,2,30\r63,8.175,119,2,54\r64,8.235,110,2,66\r65,7.588,110,2,44\r68,7.647,107,2,49\r69,5.237,74,1,44\r71,7.825,105,2,67\r72,7.333,112,1,64\r74,9.167,105,2,73\r76,7.996,110,2,59\r77,8.714,107,1,37\r78,7.833,103,1,63\r79,4.885,77,2,36\r80,7.998,98,1,64\r83,3.82,90,2,42\r84,5.936,96,1,28\r85,9,112,1,60\r86,9.5,112,1,70\r87,6.057,114,2,51\r88,6.057,93,1,21\r89,6.938,106,2,56"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/gpa_iq_conds/gpa_iq_conds.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\ngpa_iq_data <- read.csv(\"gpa_iq.csv\")\n\n# mlr for birth weight ----------------------------------------------\n\nm_gpa <- lm(gpa ~ iq + gender, data = gpa_iq_data)\n\n# normal prob plot for residuals ------------------------------------\n\npdf(\"gpa_iq_conds_normal_qq.pdf\", 5.5, 4.3)\npar(mar = c(3.7,3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\nqqnorm(m_gpa$residuals, \n       ylab = \"Residuals\", main = \"\",\n       pch = 19, col = COL[1,2])\nqqline(m_gpa$residuals, col = COL[1])\ndev.off()\n\n# Histogram for residuals ------------------------------------\n\npdf(\"gpa_iq_conds_normal_hist.pdf\", 5.5, 4.3)\npar(mar = c(3.7,3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(m_gpa$residuals, \n       xlab = \"Residuals\", ylab = \"\",\n       col = COL[1])\ndev.off()\n\n# absolute values of residuals against fitted -----------------------\n\npdf(\"gpa_iq_conds_abs_res_fitted.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7,0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_gpa$residuals ~ m_gpa$fitted.values, \n     ylab = \"Residuals\", xlab = \"Fitted values\", \n     pch = 19, col = COL[1,2])\n     \nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals in order of their data collection -----------------------\n\npdf(\"gpa_iq_conds_res_order.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7, 3.9, 0.5, 1), las = 1, mgp = c(2.7,0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_gpa$residuals ~ c(1:length(m_gpa$residuals)), \n     ylab = \"Residuals\", xlab = \"Order of collection\",\n     pch = 19, col = COL[1,2],\n     axes = FALSE)\naxis(1, at = seq(0, 80, 40))\naxis(2, at = seq(-6, 2, 4))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals vs. iq -------------------------------------------\n\npdf(\"gpa_iq_conds_res_iq.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_gpa$residuals ~ gpa_iq_data$iq, \n     ylab = \"Residuals\", xlab = \"IQ\", \n     pch = 19, col = COL[1,2],\n     axes = FALSE)\naxis(1, at = seq(75, 125, 25))\naxis(2, at = seq(-6, 2, 4))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals vs. gender -------------------------------------------\n\npdf(\"gpa_iq_conds_res_gender.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_gpa$residuals ~ gpa_iq_data$gender, \n     ylab = \"Residuals\", xlab = \"Gender\", \n     pch = 19, col = COL[1,2],\n     axes = FALSE)\naxis(1, at = c(1, 2), labels = c(0, 1))\naxis(2, at = seq(-6, 2, 4))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/log_regr_ex/log_regr_ex.R",
    "content": "library(openintro)\nlibrary(xtable)\nd <- email\nnames(d)\n\ntable(d$sent_email, d$spam)\nSGlm <- function(form, data = d) {\n  m <- glm(\n      form,\n      data = d,\n      family = binomial)\n  summary(m)\n}\n\nvars <- c(\n    \"to_multiple\", \"cc\", \"attach\", \"dollar\",\n    \"winner\", \"inherit\", \"password\", \"format\",\n    \"re_subj\", \"exclaim_subj\", \"sent_email\")\nform <- spam ~ 1\nfor (v in vars) {\n  form <- update(form, paste(\". ~ . +\", v))\n}\nm <- glm(\n    form,\n    data = d,\n    family = binomial)\nsummary(m)\n\n# form <- update(form, . ~ . - exclaim_subj - cc)\n\naic <- c(\"Drop None\" = SGlm(form))\nvars. <- names(unlist(sapply(vars, grep, x = as.character(form)[3], fixed = TRUE)))\nfor (v in vars.) {\n  m. <- update(form, paste(\". ~ . -\", v))\n  aic[v] <- SGlm(m.)$aic\n}\nwhich.min(aic)\n# xtable(data.frame(cbind(aic, aic[1] - aic)))\nxtable(data.frame(aic))\n\n\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/movie_returns_altogether/horror_movies_conds.R",
    "content": "# load packages ----------------------------------------------------------------\nlibrary(tidyverse)\nlibrary(lubridate)\nlibrary(openintro)\nlibrary(broom)\n\n# load data --------------------------------------------------------------------\nmovie_profit <- read_csv(\"mine-new/ch_regr_mult_and_log/horror_movies/figures/movie_profit.csv\") %>%\n  select(-X1)\n\n\n# fix dates --------------------------------------------------------------------\nmovie_profit <- movie_profit %>%\n  mutate(\n    release_date = mdy(release_date),\n    release_year = year(release_date),\n    oct_release = ifelse(month(release_date) == 10, \"yes\", \"no\"),\n    dom_gross_to_prod = domestic_gross / production_budget,\n    ww_gross_to_prod = worldwide_gross / production_budget\n    ) \n\n# subset for movies after 2000 -------------------------------------------------\nmovie_profit_2000 <- movie_profit %>%\n  filter(\n    release_year >= 2010,\n    release_year < 2019\n    )\n\n# mlr --------------------------------------------------------------------------\n\nm <- lm(ww_gross_to_prod ~ release_year + genre, data = movie_profit_2000)\nm_aug <- augment(m)\n\n# histogram of residuals -------------------------------------------------------\n\npdf(\"horror_movies_conds_hist_res.pdf\", 5.5, 4.3)\n\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nhistPlot(m_aug$.resid, breaks = seq(-25, 100, 5), \n         col = COL[1], \n         axes = FALSE, xlab=\"Residuals\", ylab=\"\")\naxis(1)\naxis(2, at = seq(0, 600, 200))\n\ndev.off()\n\n# residuals against fitted -----------------------------------------------------\n\ncols <- c(\n  \"Action\" = COL[1,1],\n  \"Adventure\" = COL[2,1],\n  \"Comedy\" = COL[3,1],\n  \"Drama\" = COL[4,1],\n  \"Horror\" = COL[5,1]\n)\n\nggplot(m_aug, aes(y = .resid, x = .fitted, color = genre)) + \n  geom_point(alpha = 0.7) +\n  theme_minimal() + \n  labs(x = \"Fitted values\", y = \"Residuals\", color = \"Genre\") +\n  scale_color_manual(values = cols) +\n  geom_hline(yintercept = 0, linetype = \"dashed\", size = 0.2)\n\nggsave(filename = \"horror_movies_conds_res_genre_fitted.pdf\",\n       width = 5.5, height = 4.3)\n\n# residuals in order of their data collection -----------------------\n\npdf(\"horror_movies_conds_res_order.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7, 3.9, 0.5, 1), las = 1, mgp = c(2.7,0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_aug$.resid ~ c(1:length(m$residuals)), \n     ylab = \"Residuals\", xlab = \"Order of collection\",\n     pch = 19, col = COL[1,2],\n     axes = FALSE)\naxis(1, at = seq(0, 1000, 200))\naxis(2, at = seq(-20, 80, 20))\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals vs. release year ---------------------------------------------------\n\npdf(\"horror_movies_conds_res_year.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7,0),\n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_aug$.resid ~ m_aug$release_year, \n     ylab = \"Residuals\", xlab = \"Release year\", \n     pch = 19, col = COL[1,2],\n     axes = FALSE)\naxis(1, at = seq(2010, 2018, 1))\naxis(2, at = seq(-20, 80, 20))\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# residuals vs. genre -------------------------------------------\n\nggplot(m_aug, aes(y = .resid, x = genre, color = genre)) +\n  geom_jitter(alpha = 0.7) + \n  guides(color = FALSE) +\n  scale_color_manual(values = cols) +\n  theme_minimal() + \n  labs(x = \"Genre\", y = \"Residuals\") +\n  geom_hline(yintercept = 0, linetype = \"dashed\", size = 0.2)\n\nggsave(filename = \"horror_movies_conds_res_genre.pdf\",\n       width = 5.5, height = 4.3)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/movie_returns_altogether/movie_profit.csv",
    "content": "\"\",\"release_date\",\"movie\",\"production_budget\",\"domestic_gross\",\"worldwide_gross\",\"distributor\",\"mpaa_rating\",\"genre\"\n\"1\",\"6/22/2007\",\"Evan Almighty\",1.75e+08,100289690,174131329,\"Universal\",\"PG\",\"Comedy\"\n\"2\",\"7/28/1995\",\"Waterworld\",1.75e+08,88246220,264246220,\"Universal\",\"PG-13\",\"Action\"\n\"3\",\"5/12/2017\",\"King Arthur: Legend of the Sword\",1.75e+08,39175066,139950708,\"Warner Bros.\",\"PG-13\",\"Adventure\"\n\"4\",\"12/25/2013\",\"47 Ronin\",1.75e+08,38362475,151716815,\"Universal\",\"PG-13\",\"Action\"\n\"5\",\"6/22/2018\",\"Jurassic World: Fallen Kingdom\",1.7e+08,416769345,1304866322,\"Universal\",\"PG-13\",\"Action\"\n\"6\",\"8/1/2014\",\"Guardians of the Galaxy\",1.7e+08,333172112,771051335,\"Walt Disney\",\"PG-13\",\"Action\"\n\"7\",\"5/7/2010\",\"Iron Man 2\",1.7e+08,312433331,621156389,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"8\",\"4/4/2014\",\"Captain America: The Winter Soldier\",1.7e+08,259746958,714401889,\"Walt Disney\",\"PG-13\",\"Action\"\n\"9\",\"7/11/2014\",\"Dawn of the Planet of the Apes\",1.7e+08,208545589,710644566,\"20th Century Fox\",\"PG-13\",\"Adventure\"\n\"10\",\"11/10/2004\",\"The Polar Express\",1.7e+08,186493587,310634169,\"Warner Bros.\",\"G\",\"Adventure\"\n\"11\",\"6/1/2012\",\"Snow White and the Huntsman\",1.7e+08,155136755,401021746,\"Universal\",\"PG-13\",\"Adventure\"\n\"12\",\"7/1/2003\",\"Terminator 3: Rise of the Machines\",1.7e+08,150358296,433058296,\"Warner Bros.\",\"R\",\"Action\"\n\"13\",\"5/7/2004\",\"Van Helsing\",1.7e+08,120150546,300150546,\"Universal\",\"PG-13\",\"Action\"\n\"14\",\"5/22/2015\",\"Tomorrowland\",1.7e+08,93436322,207283457,\"Walt Disney\",\"PG\",\"Adventure\"\n\"15\",\"5/27/2016\",\"Alice Through the Looking Glass\",1.7e+08,77042381,276934087,\"Walt Disney\",\"PG\",\"Adventure\"\n\"16\",\"5/21/2010\",\"Shrek Forever After\",1.65e+08,238736787,756244673,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"17\",\"11/4/2016\",\"Doctor Strange\",1.65e+08,232641920,676486457,\"Walt Disney\",\"PG-13\",\"Action\"\n\"18\",\"11/7/2014\",\"Big Hero 6\",1.65e+08,222527828,652127828,\"Walt Disney\",\"PG\",\"Adventure\"\n\"19\",\"3/26/2010\",\"How to Train Your Dragon\",1.65e+08,217581232,494870992,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"20\",\"11/2/2012\",\"Wreck-It Ralph\",1.65e+08,189412677,496511521,\"Walt Disney\",\"PG\",\"Adventure\"\n\"21\",\"11/5/2014\",\"Interstellar\",1.65e+08,188017894,667752422,\"Paramount Pictures\",\"PG-13\",\"Adventure\"\n\"22\",\"6/24/2016\",\"Independence Day: Resurgence\",1.65e+08,103144286,384413934,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"23\",\"7/29/2011\",\"Cowboys and Aliens\",1.63e+08,100368560,176038324,\"Universal\",\"PG-13\",\"Action\"\n\"24\",\"5/17/2007\",\"Shrek the Third\",1.6e+08,322719944,807330936,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"25\",\"5/24/2013\",\"Fast and Furious 6\",1.6e+08,238679850,789300444,\"Universal\",\"PG-13\",\"Action\"\n\"26\",\"6/3/2011\",\"X-Men: First Class\",1.6e+08,146408305,355408305,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"27\",\"12/25/2008\",\"The Curious Case of Benjamin Button\",1.6e+08,127509326,329631958,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"28\",\"7/14/2010\",\"The Sorcerer's Apprentice\",1.6e+08,63150991,217986320,\"Walt Disney\",\"PG\",\"Adventure\"\n\"29\",\"5/12/2006\",\"Poseidon\",1.6e+08,60674817,181674817,\"Warner Bros.\",\"PG-13\",\"Adventure\"\n\"30\",\"6/10/2016\",\"Warcraft\",1.6e+08,47225655,425547111,\"Universal\",\"PG-13\",\"Action\"\n\"31\",\"12/21/2018\",\"Aquaman\",1.6e+08,0,0,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"32\",\"9/30/2016\",\"Deepwater Horizon\",1.56e+08,61433527,122631306,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"33\",\"7/1/2015\",\"Terminator: Genisys\",1.55e+08,89760956,432150894,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"34\",\"3/23/2018\",\"Pacific Rim: Uprising\",1.55e+08,59185715,290241338,\"Universal\",\"PG-13\",\"Action\"\n\"35\",\"11/24/2004\",\"Alexander\",1.55e+08,34297191,167297191,\"Warner Bros.\",\"R\",\"Action\"\n\"36\",\"7/14/2017\",\"War for the Planet of the Apes\",1.52e+08,146880162,489592267,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"37\",\"5/25/2001\",\"Pearl Harbor\",151500000,198539855,449239855,\"Walt Disney\",\"PG-13\",\"Action\"\n\"38\",\"7/2/2007\",\"Transformers\",1.51e+08,319246193,708272592,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"39\",\"6/2/2017\",\"Wonder Woman\",1.5e+08,412563408,821133378,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"40\",\"3/4/2016\",\"Zootopia\",1.5e+08,341268248,1019706594,\"Walt Disney\",\"PG\",\"Adventure\"\n\"41\",\"11/18/2005\",\"Harry Potter and the Goblet of Fire\",1.5e+08,290013036,896911078,\"Warner Bros.\",\"PG-13\",\"Adventure\"\n\"42\",\"5/15/2003\",\"The Matrix Reloaded\",1.5e+08,281553689,738576929,\"Warner Bros.\",\"R\",\"Action\"\n\"43\",\"12/14/2007\",\"I am Legend\",1.5e+08,256393010,585532684,\"Warner Bros.\",\"PG-13\",\"Horror\"\n\"44\",\"7/1/2008\",\"Hancock\",1.5e+08,227946274,624234272,\"Sony Pictures\",\"PG-13\",\"Action\"\n\"45\",\"7/15/2005\",\"Charlie and the Chocolate Factory\",1.5e+08,206459076,475825484,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"46\",\"6/29/2007\",\"Ratatouille\",1.5e+08,206445654,626549695,\"Walt Disney\",\"G\",\"Adventure\"\n\"47\",\"11/8/2013\",\"Thor: The Dark World\",1.5e+08,206362140,644602516,\"Walt Disney\",\"PG-13\",\"Action\"\n\"48\",\"6/15/2005\",\"Batman Begins\",1.5e+08,205343774,359142722,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"49\",\"7/31/2015\",\"Mission: Impossible—Rogue Nation\",1.5e+08,195042377,689388363,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"50\",\"7/21/2017\",\"Dunkirk\",1.5e+08,190068280,499900860,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"51\",\"5/6/2011\",\"Thor\",1.5e+08,181030624,449326618,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"52\",\"11/7/2008\",\"Madagascar: Escape 2 Africa\",1.5e+08,180174880,599680774,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"53\",\"5/1/2009\",\"X-Men Origins: Wolverine\",1.5e+08,179883157,374825760,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"54\",\"5/26/2011\",\"Kung Fu Panda 2\",1.5e+08,165249063,664837547,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"55\",\"5/15/2015\",\"Mad Max: Fury Road\",1.5e+08,153636354,370651733,\"Warner Bros.\",\"R\",\"Action\"\n\"56\",\"8/10/2018\",\"The Meg\",1.5e+08,142700791,527100791,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"57\",\"11/5/2003\",\"The Matrix Revolutions\",1.5e+08,139270910,427300260,\"Warner Bros.\",\"R\",\"Action\"\n\"58\",\"3/29/2018\",\"Ready Player 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Daylights\",4e+07,51185000,191200000,\"MGM\",\"PG\",\"Action\"\n\"962\",\"12/8/2006\",\"Apocalypto\",4e+07,50866635,121032272,\"Walt Disney\",\"R\",\"Action\"\n\"963\",\"6/18/1986\",\"Legal Eagles\",4e+07,49851591,49851591,\"Universal\",\"PG\",\"Comedy\"\n\"964\",\"8/12/2005\",\"The Skeleton Key\",4e+07,47907715,92256918,\"Universal\",\"PG-13\",\"Horror\"\n\"965\",\"6/20/2014\",\"Jersey Boys\",4e+07,47047013,65282732,\"Warner Bros.\",\"R\",\"Drama\"\n\"966\",\"11/21/1997\",\"The Rainmaker\",4e+07,45916769,45916769,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"967\",\"2/7/1992\",\"Medicine Man\",4e+07,44948240,44948240,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"968\",\"12/12/1997\",\"Amistad\",4e+07,44212592,58250151,\"Dreamworks SKG\",\"R\",\"Drama\"\n\"969\",\"5/30/2014\",\"A Million Ways to Die in The West\",4e+07,42720965,86778557,\"Universal\",\"R\",\"Comedy\"\n\"970\",\"8/12/2011\",\"Final Destination 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Up\",4e+07,36037909,51867723,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"980\",\"3/1/1991\",\"The Doors\",4e+07,34416893,34416893,\"Sony Pictures\",\"R\",\"Drama\"\n\"981\",\"8/20/1999\",\"Mickey Blue Eyes\",4e+07,33864342,53864342,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"982\",\"10/20/2000\",\"Pay it Forward\",4e+07,33508922,55696705,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"983\",\"3/21/2008\",\"Drillbit Taylor\",4e+07,32862104,49686263,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"984\",\"12/25/2011\",\"Extremely Loud and Incredibly Close\",4e+07,31847881,55247881,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"985\",\"7/1/1994\",\"The Shadow\",4e+07,31835600,31835600,\"Universal\",\"PG-13\",\"Action\"\n\"986\",\"11/10/2010\",\"Morning Glory\",4e+07,31011732,59795070,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"987\",\"11/9/2005\",\"Get Rich or Die Tryin'\",4e+07,30981850,46666955,\"Paramount Pictures\",\"R\",\"Drama\"\n\"988\",\"12/25/2013\",\"Grudge Match\",4e+07,29807260,69807260,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"989\",\"4/2/1999\",\"The Out-of-Towners\",4e+07,28544120,28544120,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"990\",\"8/11/2017\",\"The Nut Job 2: Nutty by Nature\",4e+07,28370522,57465156,\"Open Road\",\"PG\",\"Adventure\"\n\"991\",\"8/23/1996\",\"The Island of Dr. Moreau\",4e+07,27682712,27682712,\"New Line\",\"PG-13\",\"Adventure\"\n\"992\",\"9/7/2001\",\"The Musketeer\",4e+07,27053815,27053815,\"Universal\",\"PG-13\",\"Adventure\"\n\"993\",\"1/27/2017\",\"Resident Evil: The Final Chapter\",4e+07,26844692,312825686,\"Sony Pictures\",\"R\",\"Action\"\n\"994\",\"2/29/2008\",\"The Other Boleyn Girl\",4e+07,26814957,78269970,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"995\",\"6/30/2017\",\"The House\",4e+07,25584504,31192743,\"Warner Bros.\",\"R\",\"Comedy\"\n\"996\",\"2/16/2001\",\"Sweet November\",4e+07,25288103,65754228,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"997\",\"4/5/2007\",\"The 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Impossible\",4e+07,19019882,169590606,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"1007\",\"3/9/2012\",\"A Thousand Words\",4e+07,18450127,20790486,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1008\",\"10/20/2006\",\"Marie Antoinette\",4e+07,15962471,60862471,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1009\",\"10/6/2000\",\"Get Carter\",4e+07,14967182,19417182,\"Warner Bros.\",\"R\",\"Drama\"\n\"1010\",\"4/21/1995\",\"Kiss of Death\",4e+07,14942422,14942422,\"20th Century Fox\",\"R\",\"Drama\"\n\"1011\",\"5/15/1987\",\"Ishtar\",4e+07,14375181,14375181,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1012\",\"2/28/1992\",\"Memoirs of an Invisible Man\",4e+07,14358033,14358033,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"1013\",\"10/23/2009\",\"Amelia\",4e+07,14279575,19756077,\"Fox Searchlight\",\"PG\",\"Drama\"\n\"1014\",\"5/7/2004\",\"New York Minute\",4e+07,14018364,21215882,\"Warner Bros.\",\"PG\",\"Comedy\"\n\"1015\",\"3/12/1999\",\"The Deep End of the Ocean\",4e+07,13508635,13508635,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1016\",\"8/30/2002\",\"FearDotCom\",4e+07,13208023,13208023,\"Warner Bros.\",\"R\",\"Horror\"\n\"1017\",\"11/7/2008\",\"Soul Men\",4e+07,12082391,12345883,\"MGM\",\"R\",\"Comedy\"\n\"1018\",\"8/20/1999\",\"Universal Soldier II: The Return\",4e+07,10447421,10717421,\"Sony Pictures\",\"R\",\"Action\"\n\"1019\",\"9/25/2009\",\"Pandorum\",4e+07,10330853,17033431,\"Overture Films\",\"R\",\"Horror\"\n\"1020\",\"9/26/2003\",\"Duplex\",4e+07,9652000,10070651,\"Miramax\",\"PG-13\",\"Comedy\"\n\"1021\",\"11/27/2002\",\"Extreme Ops\",4e+07,4835968,12624471,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"1022\",\"4/6/2001\",\"Just Visiting\",4e+07,4777007,16172200,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"1023\",\"3/11/1994\",\"The Hudsucker Proxy\",4e+07,2816518,14938149,\"Warner Bros.\",\"PG\",\"Comedy\"\n\"1024\",\"11/11/2016\",\"Billy Lynn’s Long Halftime Walk\",4e+07,1738477,30230402,\"Sony Pictures\",\"R\",\"Drama\"\n\"1025\",\"12/12/2008\",\"Delgo\",4e+07,915840,915840,\"Freestyle Releasing\",\"PG\",\"Adventure\"\n\"1026\",\"9/7/2007\",\"The Hunting Party\",4e+07,876671,7729552,\"Weinstein Co.\",\"R\",\"Adventure\"\n\"1027\",\"10/13/2006\",\"Alex Rider: Operation Stormbreaker\",4e+07,659210,20722450,\"Weinstein Co.\",\"PG\",\"Action\"\n\"1028\",\"11/20/2009\",\"Red Cliff\",4e+07,627047,119627047,\"Magnolia Pictures\",\"R\",\"Action\"\n\"1029\",\"9/24/2004\",\"The Last Shot\",4e+07,463730,463730,\"Walt Disney\",\"R\",\"Comedy\"\n\"1030\",\"3/16/2007\",\"Nomad\",4e+07,79123,79123,\"Weinstein Co.\",\"R\",\"Drama\"\n\"1031\",\"11/11/2016\",\"USS Indianapolis: Men of Courage\",4e+07,0,1641255,\"Saban Films\",\"R\",\"Drama\"\n\"1032\",\"8/14/2009\",\"The Time Traveler's Wife\",3.9e+07,63414846,102332135,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1033\",\"6/17/1983\",\"Superman III\",3.9e+07,59950623,59950623,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"1034\",\"2/2/2007\",\"Because I Said So\",3.9e+07,42674040,69538833,\"Universal\",\"PG-13\",\"Comedy\"\n\"1035\",\"10/5/2012\",\"Frankenweenie\",3.9e+07,35287788,81150788,\"Walt Disney\",\"PG\",\"Adventure\"\n\"1036\",\"3/29/1996\",\"Sgt. Bilko\",3.9e+07,30356589,37956589,\"Universal\",\"PG\",\"Comedy\"\n\"1037\",\"9/30/2005\",\"Serenity\",3.9e+07,25514517,40319440,\"Universal\",\"PG-13\",\"Action\"\n\"1038\",\"2/20/2004\",\"Against the Ropes\",3.9e+07,5881504,6429865,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"1039\",\"8/23/2013\",\"Yi dai zong shi\",38600000,6594959,57987299,\"Weinstein Co.\",\"PG-13\",\"Action\"\n\"1040\",\"6/22/2001\",\"The Fast and the Furious\",3.8e+07,144512310,206512310,\"Universal\",\"PG-13\",\"Action\"\n\"1041\",\"9/27/2002\",\"Sweet Home Alabama\",3.8e+07,127214072,182365114,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"1042\",\"11/18/1994\",\"Star Trek: Generations\",3.8e+07,75671262,1.2e+08,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"1043\",\"4/17/2015\",\"Paul Blart: Mall Cop 2\",3.8e+07,71091594,107650646,\"Sony Pictures\",\"PG\",\"Adventure\"\n\"1044\",\"12/19/1997\",\"Mouse Hunt\",3.8e+07,61894591,61894591,\"Dreamworks SKG\",\"PG\",\"Adventure\"\n\"1045\",\"12/23/2016\",\"Why Him?\",3.8e+07,60323786,117439538,\"20th Century Fox\",\"R\",\"Comedy\"\n\"1046\",\"4/22/2011\",\"Water for Elephants\",3.8e+07,58709717,116809717,\"20th Century Fox\",\"PG-13\",\"Drama\"\n\"1047\",\"12/29/1999\",\"The Hurricane\",3.8e+07,50699241,73956241,\"Universal\",\"R\",\"Drama\"\n\"1048\",\"9/6/2013\",\"Riddick\",3.8e+07,42025135,94763758,\"Universal\",\"R\",\"Action\"\n\"1049\",\"1/22/2016\",\"The 5th Wave\",3.8e+07,34912982,111336398,\"Sony Pictures\",\"PG-13\",\"Action\"\n\"1050\",\"9/20/2013\",\"Rush\",3.8e+07,26947624,98230839,\"Universal\",\"R\",\"Drama\"\n\"1051\",\"5/18/2001\",\"Angel Eyes\",3.8e+07,24044532,29544532,\"Warner Bros.\",\"R\",\"Drama\"\n\"1052\",\"12/21/2001\",\"Joe Somebody\",3.8e+07,22770864,24515990,\"20th Century Fox\",\"PG\",\"Comedy\"\n\"1053\",\"10/20/2017\",\"Only the Brave\",3.8e+07,18340051,24181629,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1054\",\"9/27/1996\",\"Extreme Measures\",3.8e+07,17378193,17378193,\"Sony Pictures\",\"R\",\"Drama\"\n\"1055\",\"9/7/2001\",\"Rock Star\",3.8e+07,16991902,19317765,\"Warner Bros.\",\"R\",\"Drama\"\n\"1056\",\"2/2/1996\",\"White Squall\",3.8e+07,10229300,10229300,\"Walt Disney\",\"PG-13\",\"Adventure\"\n\"1057\",\"10/10/2008\",\"City of Ember\",3.8e+07,7873007,17831558,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"1058\",\"10/31/1997\",\"Switchback\",3.8e+07,6504442,6504442,\"Paramount Pictures\",\"R\",\"Action\"\n\"1059\",\"9/14/2012\",\"The Master\",37500000,16247159,50647416,\"Weinstein Co.\",\"R\",\"Drama\"\n\"1060\",\"10/10/2008\",\"The Express\",37500000,9793406,9813309,\"Universal\",\"PG\",\"Drama\"\n\"1061\",\"8/7/2013\",\"We're the Millers\",3.7e+07,150394119,267816276,\"Warner Bros.\",\"R\",\"Comedy\"\n\"1062\",\"11/25/2015\",\"Creed\",3.7e+07,109767581,173567581,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1063\",\"9/17/2010\",\"The Town\",3.7e+07,92186262,152566881,\"Warner Bros.\",\"R\",\"Drama\"\n\"1064\",\"9/23/2011\",\"Dolphin Tale\",3.7e+07,72286779,96068724,\"Warner Bros.\",\"PG\",\"Drama\"\n\"1065\",\"2/23/2018\",\"Game Night\",3.7e+07,69001013,117201013,\"Warner Bros.\",\"R\",\"Comedy\"\n\"1066\",\"4/23/2004\",\"13 Going On 30\",3.7e+07,57139723,97658712,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1067\",\"4/4/2008\",\"Nim's Island\",3.7e+07,48006762,101857425,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"1068\",\"2/26/2010\",\"Cop Out\",3.7e+07,44875481,55909910,\"Warner Bros.\",\"R\",\"Comedy\"\n\"1069\",\"1/28/2011\",\"The Rite\",3.7e+07,33047633,97143987,\"Warner Bros.\",\"PG-13\",\"Horror\"\n\"1070\",\"7/18/2008\",\"Space Chimps\",3.7e+07,30105968,67029956,\"20th Century Fox\",\"G\",\"Adventure\"\n\"1071\",\"12/17/1999\",\"Magnolia\",3.7e+07,22450975,48446802,\"New Line\",\"R\",\"Drama\"\n\"1072\",\"5/29/2015\",\"Aloha\",3.7e+07,21052030,24935799,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1073\",\"10/5/2018\",\"A Star is Born\",3.6e+07,126181246,200881246,\"Warner Bros.\",\"R\",\"Drama\"\n\"1074\",\"2/11/2011\",\"Gnomeo and Juliet\",3.6e+07,99967670,193737977,\"Walt Disney\",\"G\",\"Comedy\"\n\"1075\",\"2/15/2002\",\"John Q\",3.6e+07,71026631,102226631,\"New Line\",\"PG-13\",\"Drama\"\n\"1076\",\"9/17/1999\",\"Blue Streak\",3.6e+07,68208190,117448157,\"Sony Pictures\",\"PG-13\",\"Action\"\n\"1077\",\"10/7/1983\",\"Never Say Never Again\",3.6e+07,55500000,1.6e+08,\"Warner Bros.\",\"PG\",\"Action\"\n\"1078\",\"3/26/2010\",\"Hot Tub Time Machine\",3.6e+07,50269859,65967750,\"MGM\",\"R\",\"Comedy\"\n\"1079\",\"9/12/2014\",\"Dolphin Tale 2\",3.6e+07,42024533,57824533,\"Warner Bros.\",\"PG\",\"Drama\"\n\"1080\",\"12/16/2016\",\"Collateral Beauty\",3.6e+07,31016021,85315070,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1081\",\"4/4/2003\",\"A Man Apart\",3.6e+07,26500000,43797731,\"New Line\",\"R\",\"Action\"\n\"1082\",\"2/25/2000\",\"Reindeer Games\",3.6e+07,23360779,23360779,\"Miramax\",\"R\",\"Action\"\n\"1083\",\"12/24/1999\",\"Snow Falling on Cedars\",3.6e+07,14378353,14378353,\"Universal\",\"PG-13\",\"Drama\"\n\"1084\",\"12/20/1996\",\"Ghosts of Mississippi\",3.6e+07,13052741,13052741,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1085\",\"10/24/1997\",\"Gattaca\",3.6e+07,12532777,12532777,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1086\",\"1/28/2000\",\"Isn't She Great\",3.6e+07,2954405,2954405,\"Universal\",\"R\",\"Comedy\"\n\"1087\",\"1/22/2016\",\"Yip Man 3\",3.6e+07,2679437,157300954,\"Well Go USA\",\"PG-13\",\"Action\"\n\"1088\",\"5/6/2011\",\"There Be Dragons\",3.6e+07,1069334,4020990,\"Samuel Goldwyn Films\",\"PG-13\",\"Drama\"\n\"1089\",\"4/14/2017\",\"Queen of the Desert\",3.6e+07,0,1578543,\"IFC Films\",\"PG-13\",\"Drama\"\n\"1090\",\"3/28/2003\",\"Head of State\",35200000,37788228,38283765,\"Dreamworks SKG\",\"PG-13\",\"Comedy\"\n\"1091\",\"9/8/2017\",\"It\",3.5e+07,327481748,697459228,\"Warner Bros.\",\"R\",\"Horror\"\n\"1092\",\"6/5/2009\",\"The Hangover\",3.5e+07,277322503,465764086,\"Warner Bros.\",\"R\",\"Comedy\"\n\"1093\",\"11/20/2009\",\"The Blind Side\",3.5e+07,255959475,305705794,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1094\",\"6/23/1989\",\"Batman\",3.5e+07,251188924,411348924,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"1095\",\"5/15/1992\",\"Lethal Weapon 3\",3.5e+07,144731527,319700000,\"Warner Bros.\",\"R\",\"Action\"\n\"1096\",\"9/18/1998\",\"Rush Hour\",3.5e+07,141186864,245300000,\"New Line\",\"PG-13\",\"Action\"\n\"1097\",\"2/8/2013\",\"Identity Thief\",3.5e+07,134506920,175361578,\"Universal\",\"R\",\"Comedy\"\n\"1098\",\"6/30/2006\",\"The Devil Wears Prada\",3.5e+07,124740460,326073155,\"20th Century Fox\",\"PG-13\",\"Comedy\"\n\"1099\",\"7/8/2011\",\"Horrible Bosses\",3.5e+07,117538559,212417601,\"Warner Bros.\",\"R\",\"Comedy\"\n\"1100\",\"3/30/2001\",\"Spy Kids\",3.5e+07,112692062,197692062,\"Miramax/Dimension\",\"PG\",\"Adventure\"\n\"1101\",\"7/17/2015\",\"Trainwreck\",3.5e+07,110212700,141123897,\"Universal\",\"R\",\"Comedy\"\n\"1102\",\"12/13/2013\",\"Saving Mr. Banks\",3.5e+07,83299761,114962525,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"1103\",\"12/7/1979\",\"Star Trek: The Motion Picture\",3.5e+07,82258456,1.39e+08,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"1104\",\"11/15/1996\",\"The English Patient\",3.5e+07,78716374,231710008,\"Miramax\",\"R\",\"Drama\"\n\"1105\",\"1/16/2009\",\"Hotel for Dogs\",3.5e+07,73178547,122357172,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"1106\",\"3/25/2005\",\"Guess Who\",3.5e+07,68915888,102115888,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1107\",\"12/21/2012\",\"This is 40\",3.5e+07,67544505,90221182,\"Universal\",\"R\",\"Comedy\"\n\"1108\",\"9/19/1997\",\"L.A. 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Alaska\",2.8e+07,8891623,8891623,\"Walt Disney\",\"R\",\"Comedy\"\n\"1403\",\"8/24/2001\",\"John Carpenter's Ghosts of Mars\",2.8e+07,8434601,8434601,\"Screen Media Films\",\"R\",\"Action\"\n\"1404\",\"7/11/1997\",\"A Simple Wish\",2.8e+07,8165213,8165213,\"Universal\",\"PG\",\"Comedy\"\n\"1405\",\"10/30/2015\",\"Our Brand is Crisis\",2.8e+07,7002261,8592432,\"Warner Bros.\",\"R\",\"Drama\"\n\"1406\",\"12/25/1997\",\"Kundun\",2.8e+07,5686694,5686694,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"1407\",\"6/10/1983\",\"Octopussy\",27500000,67900000,187500000,\"MGM\",\"PG\",\"Action\"\n\"1408\",\"6/26/2009\",\"My Sister's Keeper\",27500000,49200230,96673002,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1409\",\"2/8/2008\",\"Welcome Home Roscoe Jenkins\",27500000,42436517,43607627,\"Universal\",\"PG-13\",\"Comedy\"\n\"1410\",\"12/14/1984\",\"A Passage to India\",27500000,27187653,27187653,\"Sony Pictures\",NA,\"Drama\"\n\"1411\",\"12/25/2006\",\"Notes on a 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Boys\",2.7e+07,10341093,10341093,\"20th Century Fox\",\"PG-13\",\"Drama\"\n\"1421\",\"11/2/2007\",\"Martian Child\",2.7e+07,7500310,9352089,\"New Line\",\"PG\",\"Drama\"\n\"1422\",\"10/18/2002\",\"Formula 51\",2.7e+07,5204007,5204007,\"Screen Media Films\",\"R\",\"Action\"\n\"1423\",\"11/24/1999\",\"Flawless\",2.7e+07,4485485,4485485,\"MGM\",\"R\",\"Drama\"\n\"1424\",\"10/17/2008\",\"What Just Happened\",2.7e+07,1090947,2412123,\"Magnolia Pictures\",\"R\",\"Comedy\"\n\"1425\",\"1/16/2009\",\"Paul Blart: Mall Cop\",2.6e+07,146336178,185904750,\"Sony Pictures\",\"PG\",\"Adventure\"\n\"1426\",\"8/19/2005\",\"The 40 Year-old Virgin\",2.6e+07,109449237,177344230,\"Universal\",\"R\",\"Comedy\"\n\"1427\",\"12/21/1990\",\"Kindergarten Cop\",2.6e+07,91457688,2.02e+08,\"Universal\",\"PG-13\",\"Comedy\"\n\"1428\",\"8/6/2008\",\"Pineapple Express\",2.6e+07,87341380,102404019,\"Sony Pictures\",\"R\",\"Comedy\"\n\"1429\",\"12/22/1993\",\"Philadelphia\",2.6e+07,77324422,201324422,\"Sony/TriStar\",\"PG-13\",\"Drama\"\n\"1430\",\"7/31/1998\",\"Ever After: A Cinderella Story\",2.6e+07,65705772,65705772,\"20th Century Fox\",\"PG\",\"Drama\"\n\"1431\",\"6/15/1977\",\"A Bridge Too Far\",2.6e+07,50800000,50800000,\"United Artists\",\"PG\",\"Action\"\n\"1432\",\"4/26/2013\",\"Pain & Gain\",2.6e+07,49875291,81275291,\"Paramount Pictures\",\"R\",\"Action\"\n\"1433\",\"1/31/2003\",\"Final Destination 2\",2.6e+07,46896664,90396664,\"New Line\",\"R\",\"Horror\"\n\"1434\",\"12/22/2000\",\"O Brother, Where Art Thou?\",2.6e+07,45506619,75763814,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"1435\",\"12/29/2004\",\"In Good Company\",2.6e+07,45489752,63489752,\"Universal\",\"PG-13\",\"Comedy\"\n\"1436\",\"8/29/2012\",\"Lawless\",2.6e+07,37397291,54393637,\"Weinstein Co.\",\"R\",\"Drama\"\n\"1437\",\"3/29/2002\",\"Clockstoppers\",2.6e+07,36985501,38788828,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"1438\",\"12/4/2009\",\"Brothers\",2.6e+07,28544157,45043870,\"Lionsgate\",\"R\",\"Drama\"\n\"1439\",\"10/17/2014\",\"The Best of Me\",2.6e+07,26766213,41059418,\"Relativity\",\"PG-13\",\"Drama\"\n\"1440\",\"2/20/2004\",\"Welcome to Mooseport\",2.6e+07,14469428,14469428,\"20th Century Fox\",\"PG-13\",\"Comedy\"\n\"1441\",\"1/27/1995\",\"Highlander: The Final Dimension\",2.6e+07,13738574,13738574,\"Miramax\",\"PG-13\",\"Action\"\n\"1442\",\"8/24/2001\",\"The Curse of the Jade Scorpion\",2.6e+07,7496522,18496522,\"Dreamworks SKG\",\"PG-13\",\"Comedy\"\n\"1443\",\"10/18/2013\",\"The Fifth Estate\",2.6e+07,3254172,6154172,\"Walt Disney\",\"R\",\"Drama\"\n\"1444\",\"3/21/2014\",\"Blood 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Help\",2.5e+07,169705587,213120004,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"1454\",\"12/25/2016\",\"Hidden Figures\",2.5e+07,169607287,231771716,\"20th Century Fox\",\"PG\",\"Drama\"\n\"1455\",\"12/12/2008\",\"Gran Torino\",2.5e+07,148095302,274543085,\"Warner Bros.\",\"R\",\"Drama\"\n\"1456\",\"1/17/2014\",\"Ride Along\",2.5e+07,134202565,153733800,\"Universal\",\"PG-13\",\"Comedy\"\n\"1457\",\"12/15/1993\",\"Schindler’s List\",2.5e+07,96067179,321365567,\"Universal\",\"R\",\"Drama\"\n\"1458\",\"3/26/2004\",\"Scooby-Doo 2: Monsters Unleashed\",2.5e+07,84185387,181185387,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"1459\",\"8/15/2003\",\"Freddy vs. Jason\",2.5e+07,82622655,114576403,\"New Line\",\"R\",\"Horror\"\n\"1460\",\"2/16/2007\",\"Bridge to Terabithia\",2.5e+07,82234139,137984788,\"Walt Disney\",\"PG\",\"Drama\"\n\"1461\",\"12/21/2001\",\"Jimmy Neutron: Boy Genius\",2.5e+07,80936232,102992536,\"Paramount Pictures\",\"G\",\"Adventure\"\n\"1462\",\"1/18/2008\",\"Cloverfield\",2.5e+07,80048433,171302226,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"1463\",\"2/5/2010\",\"Dear John\",2.5e+07,80014842,142033509,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1464\",\"12/25/2012\",\"Parental Guidance\",2.5e+07,77267296,120832383,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"1465\",\"6/3/1987\",\"The Untouchables\",2.5e+07,76270454,76270454,\"Paramount Pictures\",\"R\",\"Action\"\n\"1466\",\"11/9/2007\",\"No Country for Old Men\",2.5e+07,74273505,164035753,\"Miramax\",\"R\",\"Action\"\n\"1467\",\"1/13/2012\",\"Contraband\",2.5e+07,66528000,98406855,\"Universal\",\"R\",\"Action\"\n\"1468\",\"1/27/2017\",\"A Dog’s Purpose\",2.5e+07,64321890,203731707,\"Universal\",\"PG\",\"Drama\"\n\"1469\",\"4/20/2012\",\"The Lucky One\",2.5e+07,60457138,96633833,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1470\",\"3/22/2000\",\"Romeo Must Die\",2.5e+07,55973336,91036760,\"Warner Bros.\",\"R\",\"Action\"\n\"1471\",\"2/10/2006\",\"Final Destination 3\",2.5e+07,54098051,112798051,\"New Line\",\"R\",\"Horror\"\n\"1472\",\"4/22/2011\",\"Madea's Big Happy Family\",2.5e+07,53345287,54160818,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"1473\",\"12/13/2013\",\"Tyler Perry's A Madea Christmas\",2.5e+07,52543354,52543354,\"Lionsgate\",\"PG-13\",\"Comedy\"\n\"1474\",\"11/12/2004\",\"Finding Neverland\",2.5e+07,51676606,115036108,\"Miramax\",\"PG\",\"Drama\"\n\"1475\",\"5/23/1986\",\"Cobra\",2.5e+07,49042224,49042224,\"Cannon\",\"R\",\"Action\"\n\"1476\",\"8/22/2008\",\"The House Bunny\",2.5e+07,48237389,71390601,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1477\",\"3/14/2003\",\"Agent Cody Banks\",2.5e+07,47545060,58240458,\"MGM\",\"PG\",\"Adventure\"\n\"1478\",\"1/27/2006\",\"Nanny McPhee\",2.5e+07,47279279,128745578,\"Universal\",\"PG\",\"Adventure\"\n\"1479\",\"9/19/1990\",\"Goodfellas\",2.5e+07,46743809,46777347,\"Warner Bros.\",\"R\",\"Drama\"\n\"1480\",\"8/15/2014\",\"The Giver\",2.5e+07,45090374,55090374,\"Weinstein Co.\",\"PG-13\",\"Drama\"\n\"1481\",\"7/18/1997\",\"Nothing To Lose\",2.5e+07,44480039,64594061,\"Walt Disney\",\"R\",\"Comedy\"\n\"1482\",\"11/20/1987\",\"The Last Emperor\",2.5e+07,43984987,44005073,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1483\",\"11/20/2015\",\"The Night Before\",2.5e+07,43035725,52427346,\"Sony Pictures\",\"R\",\"Comedy\"\n\"1484\",\"10/15/1993\",\"The Beverly Hillbillies\",2.5e+07,42222647,55598481,\"20th Century Fox\",\"PG\",\"Comedy\"\n\"1485\",\"12/27/2002\",\"The Hours\",2.5e+07,41675994,97030468,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"1486\",\"8/22/1997\",\"Money Talks\",2.5e+07,41076865,41076865,\"New Line\",\"R\",\"Action\"\n\"1487\",\"12/26/2007\",\"There Will Be Blood\",2.5e+07,40222514,77208711,\"Paramount Vantage\",\"R\",\"Drama\"\n\"1488\",\"12/20/2002\",\"The Wild Thornberrys Movie\",2.5e+07,40108697,60694737,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"1489\",\"6/13/2003\",\"Rugrats Go Wild\",2.5e+07,39402572,55443032,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"1490\",\"5/31/2002\",\"Undercover Brother\",2.5e+07,38230435,40796145,\"Universal\",\"PG-13\",\"Comedy\"\n\"1491\",\"7/6/2001\",\"Kiss of the Dragon\",2.5e+07,36833473,36833473,\"20th Century Fox\",\"R\",\"Action\"\n\"1492\",\"5/16/2014\",\"Million Dollar Arm\",2.5e+07,36447959,39217912,\"Walt Disney\",\"PG\",\"Drama\"\n\"1493\",\"1/1/2004\",\"Beauty Shop\",2.5e+07,36351350,38351350,\"MGM\",\"PG-13\",\"Comedy\"\n\"1494\",\"4/4/2003\",\"What a Girl Wants\",2.5e+07,35990505,35990505,\"Warner Bros.\",\"PG\",\"Comedy\"\n\"1495\",\"8/29/2003\",\"Jeepers Creepers II\",2.5e+07,35623801,119923801,\"MGM\",\"R\",\"Horror\"\n\"1496\",\"2/28/2003\",\"Cradle 2 the Grave\",2.5e+07,34657731,56434942,\"Warner Bros.\",\"R\",\"Action\"\n\"1497\",\"8/24/2007\",\"Mr. Bean’s Holiday\",2.5e+07,33302167,234981342,\"Universal\",\"G\",\"Adventure\"\n\"1498\",\"10/16/1998\",\"Bride of Chucky\",2.5e+07,32404188,50692188,\"Universal\",\"R\",\"Horror\"\n\"1499\",\"2/17/2017\",\"Fist Fight\",2.5e+07,32187017,40287017,\"Warner Bros.\",\"R\",\"Comedy\"\n\"1500\",\"11/21/2007\",\"August Rush\",2.5e+07,31664162,66015869,\"Warner Bros.\",\"PG\",\"Drama\"\n\"1501\",\"12/9/2011\",\"The Sitter\",2.5e+07,30542576,38749404,\"20th Century Fox\",\"R\",\"Comedy\"\n\"1502\",\"11/6/1998\",\"Elizabeth\",2.5e+07,30082699,82150642,\"Gramercy\",\"R\",\"Drama\"\n\"1503\",\"1/23/1998\",\"Spice World\",2.5e+07,29342592,56042592,\"Sony Pictures\",\"PG\",\"Comedy\"\n\"1504\",\"4/11/2014\",\"Draft Day\",2.5e+07,28842237,29847480,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"1505\",\"9/23/1994\",\"The Shawshank Redemption\",2.5e+07,28241469,28307092,\"Sony Pictures\",\"R\",\"Drama\"\n\"1506\",\"2/3/2017\",\"Rings\",2.5e+07,27793018,82933201,\"Paramount Pictures\",\"PG-13\",\"Horror\"\n\"1507\",\"5/22/2009\",\"Dance Flick\",2.5e+07,25794018,32224624,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1508\",\"4/20/2001\",\"Crocodile Dundee in Los Angeles\",2.5e+07,25590119,39393111,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"1509\",\"7/26/1996\",\"Kingpin\",2.5e+07,25023424,32223424,\"MGM\",\"R\",\"Comedy\"\n\"1510\",\"3/18/2005\",\"Ice Princess\",2.5e+07,24381334,25732334,\"Walt Disney\",\"G\",\"Comedy\"\n\"1511\",\"8/26/2011\",\"Don't Be Afraid of the Dark\",2.5e+07,24046682,39126427,\"FilmDistrict\",\"R\",\"Horror\"\n\"1512\",\"4/23/2010\",\"The Losers\",2.5e+07,23591432,29863840,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"1513\",\"8/24/2007\",\"War\",2.5e+07,22486409,40686409,\"Lionsgate\",\"R\",\"Action\"\n\"1514\",\"4/7/1995\",\"Don Juan DeMarco\",2.5e+07,22032635,22032635,\"New Line\",\"PG-13\",\"Drama\"\n\"1515\",\"4/22/2005\",\"A Lot Like Love\",2.5e+07,21835784,41921590,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"1516\",\"5/1/1998\",\"He Got Game\",2.5e+07,21567853,22411948,\"Walt Disney\",\"R\",\"Drama\"\n\"1517\",\"2/11/2011\",\"The Eagle\",2.5e+07,19490041,38993548,\"Focus Features\",\"PG-13\",\"Action\"\n\"1518\",\"8/5/2015\",\"Shaun the Sheep\",2.5e+07,19375982,101927062,\"Lionsgate\",\"PG\",\"Adventure\"\n\"1519\",\"9/2/2011\",\"Shark Night 3D\",2.5e+07,18877153,18877153,\"Relativity\",\"PG-13\",\"Horror\"\n\"1520\",\"3/24/2017\",\"CHiPS\",2.5e+07,18600152,23190697,\"Warner Bros.\",\"R\",\"Action\"\n\"1521\",\"10/11/2002\",\"Punch-Drunk Love\",2.5e+07,17791031,24591031,\"Sony Pictures\",\"R\",\"Comedy\"\n\"1522\",\"2/20/2004\",\"Eurotrip\",2.5e+07,17718223,20718223,\"Dreamworks SKG\",\"R\",\"Comedy\"\n\"1523\",\"12/22/2017\",\"Father Figures\",2.5e+07,17501244,21038826,\"Warner Bros.\",\"R\",\"Comedy\"\n\"1524\",\"4/4/2008\",\"The Ruins\",2.5e+07,17432844,22910563,\"Paramount Pictures\",\"R\",\"Horror\"\n\"1525\",\"12/8/2006\",\"Unaccompanied Minors\",2.5e+07,16655224,21970831,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"1526\",\"4/1/1988\",\"Bright Lights, Big City\",2.5e+07,16118077,16118077,\"United Artists\",\"R\",\"Drama\"\n\"1527\",\"11/15/2002\",\"Half Past Dead\",2.5e+07,15567860,19233280,\"Sony Pictures\",\"PG-13\",\"Action\"\n\"1528\",\"4/18/1986\",\"Legend\",2.5e+07,15502112,23506237,\"Universal\",\"PG\",\"Adventure\"\n\"1529\",\"7/26/1996\",\"The Adventures of Pinocchio\",2.5e+07,15382170,36682170,\"New Line\",\"G\",\"Adventure\"\n\"1530\",\"9/30/2005\",\"The Greatest Game Ever Played\",2.5e+07,15331289,15468266,\"Walt Disney\",\"PG\",\"Drama\"\n\"1531\",\"3/3/2000\",\"The Next Best Thing\",2.5e+07,14983572,24355762,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"1532\",\"10/8/2010\",\"My Soul to Take\",2.5e+07,14744435,16727470,\"Universal\",\"R\",\"Horror\"\n\"1533\",\"8/15/2008\",\"Fly Me To the Moon\",2.5e+07,14543943,43530281,\"Summit Entertainment\",\"G\",\"Adventure\"\n\"1534\",\"9/13/1996\",\"Maximum Risk\",2.5e+07,14102929,51702929,\"Sony Pictures\",\"R\",\"Action\"\n\"1535\",\"9/13/2002\",\"Stealing Harvard\",2.5e+07,13973532,13973532,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1536\",\"8/3/2007\",\"Hot Rod\",2.5e+07,13938332,14334401,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1537\",\"9/9/2011\",\"Warrior\",2.5e+07,13657115,24215385,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"1538\",\"12/24/1999\",\"Angela's Ashes\",2.5e+07,13038660,13038660,\"Paramount Pictures\",\"R\",\"Drama\"\n\"1539\",\"9/22/2017\",\"Battle of the Sexes\",2.5e+07,12638526,18445094,\"Fox Searchlight\",\"PG-13\",\"Drama\"\n\"1540\",\"12/21/2012\",\"Cirque du Soleil: Worlds Away\",2.5e+07,12512862,28012862,\"Paramount Pictures\",\"PG\",\"Drama\"\n\"1541\",\"11/13/2015\",\"The 33\",2.5e+07,12227722,28400715,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1542\",\"6/21/1985\",\"Lifeforce\",2.5e+07,11603545,11603545,\"Sony/TriStar\",\"R\",\"Horror\"\n\"1543\",\"4/15/2011\",\"The Conspirator\",2.5e+07,11538204,15907411,\"Roadside Attractions\",\"PG-13\",\"Drama\"\n\"1544\",\"7/3/2002\",\"The Powerpuff Girls\",2.5e+07,11411644,16425701,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"1545\",\"6/3/2005\",\"The Lords of Dogtown\",2.5e+07,11273517,13424365,\"Sony/TriStar\",\"PG-13\",\"Action\"\n\"1546\",\"7/1/1986\",\"Big Trouble in Little China\",2.5e+07,11100000,11100000,\"20th Century Fox\",NA,\"Action\"\n\"1547\",\"10/11/1996\",\"Michael Collins\",2.5e+07,11092559,27572844,\"Warner Bros.\",\"R\",\"Drama\"\n\"1548\",\"3/28/2008\",\"Stop-Loss\",2.5e+07,10915744,11229035,\"Paramount Pictures\",\"R\",\"Drama\"\n\"1549\",\"10/8/1993\",\"Gettysburg\",2.5e+07,10731997,10731997,\"New Line\",\"PG\",\"Drama\"\n\"1550\",\"8/13/1999\",\"Brokedown Palace\",2.5e+07,10115014,11115766,\"20th Century Fox\",\"PG-13\",\"Drama\"\n\"1551\",\"8/16/2002\",\"Possession\",2.5e+07,10103647,14805812,\"Focus Features\",\"PG-13\",\"Drama\"\n\"1552\",\"5/17/1991\",\"Stone Cold\",2.5e+07,9286314,9286314,\"Sony Pictures\",\"R\",\"Action\"\n\"1553\",\"11/25/2009\",\"The Road\",2.5e+07,8114270,29206732,\"Weinstein Co.\",\"R\",\"Drama\"\n\"1554\",\"4/6/2007\",\"The Hoax\",2.5e+07,7164995,7164995,\"Walt Disney\",\"R\",\"Drama\"\n\"1555\",\"8/17/1984\",\"Sheena\",2.5e+07,5778353,5778353,\"Sony Pictures\",NA,\"Adventure\"\n\"1556\",\"3/23/2001\",\"Say It Isn't So\",2.5e+07,5516708,5516708,\"20th Century Fox\",\"R\",\"Comedy\"\n\"1557\",\"12/7/2005\",\"The World's Fastest Indian\",2.5e+07,5128124,18991288,\"Magnolia Pictures\",\"PG-13\",\"Drama\"\n\"1558\",\"3/1/1995\",\"Tank Girl\",2.5e+07,4064333,4064333,\"MGM\",\"R\",\"Action\"\n\"1559\",\"4/22/2005\",\"King's 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Too?\",2e+07,60095852,60831067,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"1696\",\"10/23/1998\",\"La vita è bella\",2e+07,57598247,229385361,\"Miramax\",\"PG-13\",\"Drama\"\n\"1697\",\"10/18/2013\",\"12 Years a Slave\",2e+07,56671993,181025343,\"Fox Searchlight\",\"R\",\"Drama\"\n\"1698\",\"12/13/2002\",\"Drumline\",2e+07,56398162,56398162,\"20th Century Fox\",\"PG-13\",\"Comedy\"\n\"1699\",\"6/3/2016\",\"Me Before You\",2e+07,56245075,208314186,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1700\",\"4/15/2016\",\"Barbershop: The Next Cut\",2e+07,54030051,54404202,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"1701\",\"12/7/1990\",\"Edward Scissorhands\",2e+07,53976987,53976987,\"20th Century Fox\",\"PG-13\",\"Comedy\"\n\"1702\",\"1/9/2015\",\"Selma\",2e+07,52076908,66776576,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"1703\",\"2/17/2006\",\"Date Movie\",2e+07,48548426,85146165,\"20th Century Fox\",\"PG-13\",\"Comedy\"\n\"1704\",\"2/15/2002\",\"Peter Pan: Return to 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Girls\",1.8e+07,86047227,130953026,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1862\",\"6/1/1984\",\"Star Trek III: The Search for Spock\",1.8e+07,76471046,8.7e+07,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"1863\",\"9/9/2005\",\"The Exorcism of Emily Rose\",1.8e+07,75072454,144529078,\"Sony Pictures\",\"PG-13\",\"Horror\"\n\"1864\",\"12/10/1999\",\"Deuce Bigalow: Male Gigolo\",1.8e+07,65535067,92935067,\"Walt Disney\",\"R\",\"Comedy\"\n\"1865\",\"1/1/2004\",\"Barbershop 2: Back in Business\",1.8e+07,65070412,65842412,\"MGM\",\"PG-13\",\"Comedy\"\n\"1866\",\"12/16/2005\",\"The Family Stone\",1.8e+07,60062868,92357499,\"20th Century Fox\",\"PG-13\",\"Comedy\"\n\"1867\",\"6/12/1987\",\"Predator\",1.8e+07,59735548,98267558,\"20th Century Fox\",\"R\",\"Action\"\n\"1868\",\"3/25/2016\",\"My Big Fat Greek Wedding 2\",1.8e+07,59689605,92057814,\"Universal\",\"PG-13\",\"Comedy\"\n\"1869\",\"3/25/2011\",\"Diary of a Wimpy Kid: Rodrick Rules\",1.8e+07,52698535,73695194,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"1870\",\"9/19/1984\",\"Amadeus\",1.8e+07,51973029,51973029,\"Warner Bros.\",\"R\",\"Drama\"\n\"1871\",\"4/11/2008\",\"Prom Night\",1.8e+07,43869350,57193655,\"Sony Pictures\",\"PG-13\",\"Horror\"\n\"1872\",\"4/8/2011\",\"Soul Surfer\",1.8e+07,43853424,47158652,\"Sony Pictures\",\"PG\",\"Drama\"\n\"1873\",\"9/26/2003\",\"Under the Tuscan Sun\",1.8e+07,43601508,57490024,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"1874\",\"10/10/1986\",\"Peggy Sue Got Married\",1.8e+07,41382841,41382841,\"Sony/TriStar\",\"PG-13\",\"Comedy\"\n\"1875\",\"12/26/2001\",\"Gosford Park\",1.8e+07,41300105,41300105,\"USA Films\",\"R\",\"Comedy\"\n\"1876\",\"1/11/2002\",\"Orange County\",1.8e+07,41059716,43308707,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1877\",\"7/26/2013\",\"Blue Jasmine\",1.8e+07,33404871,102912961,\"Sony Pictures Classics\",\"PG-13\",\"Comedy\"\n\"1878\",\"4/28/2006\",\"United 93\",1.8e+07,31567134,77635035,\"Universal\",\"R\",\"Drama\"\n\"1879\",\"12/5/2003\",\"Honey\",1.8e+07,30272254,62646763,\"Universal\",\"PG-13\",\"Drama\"\n\"1880\",\"5/24/1996\",\"Spy Hard\",1.8e+07,26936265,26936265,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"1881\",\"8/7/2015\",\"Ricki and the Flash\",1.8e+07,26839498,41166033,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1882\",\"12/13/1989\",\"Glory\",1.8e+07,26593580,26593580,\"Sony Pictures\",\"R\",\"Action\"\n\"1883\",\"6/29/1984\",\"Conan the Destroyer\",1.8e+07,26400000,26400000,\"Universal\",NA,\"Action\"\n\"1884\",\"11/13/2015\",\"Love the Coopers\",1.8e+07,26302731,42227490,\"CBS Films\",\"PG-13\",\"Comedy\"\n\"1885\",\"6/24/1970\",\"Catch-22\",1.8e+07,24911670,24911670,\"Paramount Pictures\",NA,\"Comedy\"\n\"1886\",\"4/10/2009\",\"Observe and Report\",1.8e+07,24007324,27148898,\"Warner Bros.\",\"R\",\"Comedy\"\n\"1887\",\"9/18/2009\",\"Love Happens\",1.8e+07,22965110,36133014,\"Universal\",\"PG-13\",\"Drama\"\n\"1888\",\"12/4/1985\",\"Young Sherlock Holmes\",1.8e+07,19739000,19739000,\"Paramount Pictures\",\"PG-13\",\"Adventure\"\n\"1889\",\"11/5/2010\",\"127 Hours\",1.8e+07,18335230,60217171,\"Fox Searchlight\",\"R\",\"Drama\"\n\"1890\",\"5/19/2000\",\"Small Time Crooks\",1.8e+07,17266359,29934477,\"Dreamworks SKG\",\"PG\",\"Comedy\"\n\"1891\",\"5/12/2000\",\"Center Stage\",1.8e+07,17200925,21361109,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1892\",\"1/15/2016\",\"Norm of the North\",1.8e+07,17062499,30535660,\"Lionsgate\",\"PG\",\"Adventure\"\n\"1893\",\"2/6/2004\",\"Catch That Kid\",1.8e+07,16703799,16959614,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"1894\",\"8/16/2013\",\"Jobs\",1.8e+07,16131410,43402515,\"Open Road\",\"PG-13\",\"Drama\"\n\"1895\",\"10/26/2001\",\"Life as a House\",1.8e+07,15652637,23889158,\"New Line\",\"R\",\"Drama\"\n\"1896\",\"1/8/2010\",\"Youth in Revolt\",1.8e+07,15285588,19685588,\"Weinstein/Dimension\",\"R\",\"Comedy\"\n\"1897\",\"7/25/2014\",\"And So It Goes\",1.8e+07,15160801,17868801,\"Clarius Entertainment\",\"PG-13\",\"Comedy\"\n\"1898\",\"7/10/2009\",\"I Love You, Beth Cooper\",1.8e+07,14800725,16382538,\"20th Century Fox\",\"PG-13\",\"Comedy\"\n\"1899\",\"1/31/2014\",\"Labor Day\",1.8e+07,13371528,14189810,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"1900\",\"9/26/1997\",\"The Ice Storm\",1.8e+07,8038061,16011975,\"Fox Searchlight\",\"R\",\"Drama\"\n\"1901\",\"10/15/2004\",\"Being Julia\",1.8e+07,7739049,14488705,\"Sony Pictures\",\"R\",\"Drama\"\n\"1902\",\"3/22/1989\",\"Troop Beverly Hills\",1.8e+07,7190505,7190505,\"Sony Pictures\",NA,\"Comedy\"\n\"1903\",\"2/21/1986\",\"Nine 1/2 Weeks\",1.8e+07,6734844,6734844,\"MGM\",NA,\"Drama\"\n\"1904\",\"1/15/2010\",\"The Last Station\",1.8e+07,6617867,15696146,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"1905\",\"6/26/1981\",\"Dragonslayer\",1.8e+07,6e+06,6e+06,\"Paramount Pictures\",NA,\"Action\"\n\"1906\",\"9/30/1994\",\"Ed Wood\",1.8e+07,5828466,5828466,\"Walt Disney\",\"R\",\"Comedy\"\n\"1907\",\"6/6/2008\",\"Mongol\",1.8e+07,5705761,27147349,\"Picturehouse\",\"R\",\"Drama\"\n\"1908\",\"10/8/2008\",\"RocknRolla\",1.8e+07,5700626,27794339,\"Warner Bros.\",\"R\",\"Action\"\n\"1909\",\"6/25/1982\",\"Megaforce\",1.8e+07,5675599,5675599,\"20th Century Fox\",NA,\"Action\"\n\"1910\",\"8/20/2010\",\"Mao's Last Dancer\",1.8e+07,4806750,25941437,\"Samuel Goldwyn Films\",\"PG\",\"Drama\"\n\"1911\",\"4/11/2014\",\"The Railway Man\",1.8e+07,4438438,23910210,\"Weinstein Co.\",\"R\",\"Drama\"\n\"1912\",\"12/29/1995\",\"Restoration\",1.8e+07,4100000,4100000,\"Miramax\",\"R\",\"Drama\"\n\"1913\",\"3/18/2016\",\"Midnight Special\",1.8e+07,3712282,7680250,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1914\",\"11/25/2016\",\"Miss Sloane\",1.8e+07,3500605,7727952,\"EuropaCorp\",\"R\",\"Drama\"\n\"1915\",\"3/17/2017\",\"T2: Trainspotting\",1.8e+07,2402004,42091497,\"Sony Pictures\",\"R\",\"Drama\"\n\"1916\",\"4/25/1986\",\"8 Million Ways to Die\",1.8e+07,1305114,1305114,\"Sony Pictures\",NA,\"Action\"\n\"1917\",\"9/22/2006\",\"Renaissance\",1.8e+07,70644,2401413,\"Miramax\",\"R\",\"Action\"\n\"1918\",\"4/15/2016\",\"I Am Wrath\",1.8e+07,0,309608,\"Saban Films\",\"R\",\"Action\"\n\"1919\",\"8/22/2014\",\"The Prince\",1.8e+07,0,0,\"Lionsgate\",\"R\",\"Action\"\n\"1920\",\"6/28/1985\",\"Red Sonja\",17900000,6905861,6908640,\"MGM\",\"PG-13\",\"Action\"\n\"1921\",\"8/17/2007\",\"Superbad\",17500000,121463226,169955142,\"Sony Pictures\",\"R\",\"Comedy\"\n\"1922\",\"2/20/2009\",\"Madea Goes To Jail\",17500000,90508336,90508336,\"Lionsgate\",\"PG-13\",\"Comedy\"\n\"1923\",\"2/14/2008\",\"Step Up 2 the Streets\",17500000,58017783,148586910,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"1924\",\"1/13/2006\",\"Hoodwinked\",17500000,51386611,109843390,\"Weinstein Co.\",\"PG\",\"Adventure\"\n\"1925\",\"11/21/2007\",\"Hitman\",17500000,39687694,99135571,\"20th Century Fox\",\"R\",\"Action\"\n\"1926\",\"12/22/2004\",\"Hotel Rwanda\",17500000,23519128,36521223,\"MGM\",\"PG-13\",\"Drama\"\n\"1927\",\"8/25/2006\",\"Beerfest\",17500000,19185184,20159316,\"Warner Bros.\",\"R\",\"Comedy\"\n\"1928\",\"4/25/2003\",\"City of Ghosts\",17500000,325491,325491,\"MGM\",\"R\",\"Drama\"\n\"1929\",\"4/6/2018\",\"A Quiet Place\",1.7e+07,188024361,334524361,\"Paramount Pictures\",\"PG-13\",\"Horror\"\n\"1930\",\"8/10/2001\",\"The Others\",1.7e+07,96522687,207765056,\"Miramax\",\"PG-13\",\"Horror\"\n\"1931\",\"7/18/1986\",\"Aliens\",1.7e+07,85160248,183316455,\"20th Century Fox\",\"R\",\"Action\"\n\"1932\",\"8/13/2014\",\"Let’s Be Cops\",1.7e+07,82390774,136890774,\"20th Century Fox\",\"R\",\"Comedy\"\n\"1933\",\"10/17/1997\",\"I Know What You Did Last Summer\",1.7e+07,72250091,125250091,\"Sony Pictures\",\"R\",\"Horror\"\n\"1934\",\"10/22/2004\",\"Sideways\",1.7e+07,71502303,109793192,\"Fox Searchlight\",\"R\",\"Drama\"\n\"1935\",\"11/15/2013\",\"The Best Man Holiday\",1.7e+07,70525195,72835710,\"Universal\",\"R\",\"Comedy\"\n\"1936\",\"9/28/2012\",\"Pitch Perfect\",1.7e+07,65001093,116044347,\"Universal\",\"PG-13\",\"Comedy\"\n\"1937\",\"8/5/1998\",\"Halloween: H2O\",1.7e+07,55041738,55041738,\"Miramax\",\"R\",\"Horror\"\n\"1938\",\"4/5/2013\",\"Evil Dead\",1.7e+07,54239856,97778356,\"Sony Pictures\",\"R\",\"Horror\"\n\"1939\",\"8/27/2004\",\"Jet Li's Hero\",1.7e+07,53652140,177535958,\"Miramax\",\"PG-13\",\"Action\"\n\"1940\",\"10/29/2010\",\"Saw 3D\",1.7e+07,45710178,133735284,\"Lionsgate\",\"R\",\"Horror\"\n\"1941\",\"2/20/2015\",\"McFarland, USA\",1.7e+07,44480275,45707924,\"Walt Disney\",\"PG\",\"Drama\"\n\"1942\",\"11/11/2016\",\"Almost Christmas\",1.7e+07,42065185,42493506,\"Universal\",\"PG-13\",\"Drama\"\n\"1943\",\"3/10/2006\",\"The Hills Have Eyes\",1.7e+07,41778863,70355813,\"Fox Searchlight\",\"R\",\"Horror\"\n\"1944\",\"10/10/2003\",\"Good Boy!\",1.7e+07,37667746,45312217,\"MGM\",\"PG\",\"Adventure\"\n\"1945\",\"1/26/2007\",\"Smokin' Aces\",1.7e+07,35662731,57263440,\"Universal\",\"R\",\"Comedy\"\n\"1946\",\"10/2/1998\",\"A Night at the Roxbury\",1.7e+07,30331165,30331165,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1947\",\"3/4/2011\",\"Beastly\",1.7e+07,27865571,38028230,\"CBS Films\",\"PG-13\",\"Drama\"\n\"1948\",\"7/9/1982\",\"Tron\",1.7e+07,26918576,26918576,\"Walt Disney\",NA,\"Action\"\n\"1949\",\"8/20/2010\",\"Lottery Ticket\",1.7e+07,24719879,24719879,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"1950\",\"9/5/2003\",\"Dickie Roberts: Former Child Star\",1.7e+07,22734486,23734486,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1951\",\"3/31/2006\",\"ATL\",1.7e+07,21170563,21170563,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"1952\",\"8/24/2001\",\"Summer Catch\",1.7e+07,19693891,19693891,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"1953\",\"12/11/1998\",\"A Simple Plan\",1.7e+07,16316273,16316273,\"Paramount Pictures\",\"R\",\"Drama\"\n\"1954\",\"11/27/2002\",\"Wes Craven Presents: They\",1.7e+07,12840842,16140842,\"Miramax/Dimension\",\"PG-13\",\"Horror\"\n\"1955\",\"7/24/1987\",\"Superman IV: The Quest for Peace\",1.7e+07,11227824,11227824,\"Warner Bros.\",\"PG\",\"Action\"\n\"1956\",\"1/25/2008\",\"How She Move\",1.7e+07,7070641,8607815,\"Paramount Vantage\",\"PG-13\",\"Drama\"\n\"1957\",\"2/24/2006\",\"Running Scared\",1.7e+07,6855137,9729088,\"New Line\",\"R\",\"Action\"\n\"1958\",\"8/24/2012\",\"The Apparition\",1.7e+07,4936819,10637281,\"Warner Bros.\",\"PG-13\",\"Horror\"\n\"1959\",\"4/30/2004\",\"Bobby Jones: Stroke of Genius\",1.7e+07,2694071,2694071,\"Film Foundry\",\"PG\",\"Drama\"\n\"1960\",\"12/25/2010\",\"L'illusionniste\",1.7e+07,2231474,8609949,\"Sony Pictures Classics\",\"PG\",\"Adventure\"\n\"1961\",\"1/1/1981\",\"Roar\",1.7e+07,2110050,2110050,NA,\"PG\",\"Adventure\"\n\"1962\",\"10/17/2003\",\"Veronica Guerin\",1.7e+07,1569918,9438074,\"Walt Disney\",\"R\",\"Drama\"\n\"1963\",\"6/10/2016\",\"Genius\",1.7e+07,1361045,6942889,\"Roadside Attractions\",\"PG-13\",\"Drama\"\n\"1964\",\"6/26/2015\",\"Escobar: Paradise Lost\",1.7e+07,195792,3917679,\"RADiUS-TWC\",\"R\",\"Drama\"\n\"1965\",\"3/11/2016\",\"The Young Messiah\",16800000,6469813,7313697,\"Focus Features\",\"PG-13\",\"Drama\"\n\"1966\",\"11/27/1991\",\"My Girl\",16500000,58011485,58011485,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1967\",\"12/11/1987\",\"Wall Street\",16500000,43848100,43848100,\"20th Century Fox\",\"R\",\"Drama\"\n\"1968\",\"12/11/1995\",\"Sense and Sensibility\",16500000,42993774,134993774,\"Sony Pictures\",\"PG\",\"Drama\"\n\"1969\",\"8/18/2006\",\"The Illusionist\",16500000,39868642,83792062,\"Yari Film Group Rel…\",\"PG-13\",\"Drama\"\n\"1970\",\"12/19/2003\",\"House of Sand and Fog\",16500000,13005485,16157923,\"Dreamworks SKG\",\"R\",\"Drama\"\n\"1971\",\"9/21/2007\",\"Sydney White\",16500000,11892415,13636339,\"Universal\",\"PG-13\",\"Comedy\"\n\"1972\",\"6/2/1989\",\"Dead Poets Society\",16400000,95860116,239500000,\"Walt Disney\",\"PG\",\"Drama\"\n\"1973\",\"12/16/1994\",\"Dumb & Dumber\",1.6e+07,127175374,246400000,\"New Line\",\"PG-13\",\"Comedy\"\n\"1974\",\"5/19/2000\",\"Road Trip\",1.6e+07,68525609,119739110,\"Dreamworks SKG\",\"R\",\"Comedy\"\n\"1975\",\"12/8/1982\",\"The Verdict\",1.6e+07,53977250,53977250,\"20th Century Fox\",\"R\",\"Drama\"\n\"1976\",\"1/15/1999\",\"Varsity Blues\",1.6e+07,52894169,54294169,\"Paramount Pictures\",\"R\",\"Drama\"\n\"1977\",\"5/25/2012\",\"Moonrise Kingdom\",1.6e+07,45512466,68848446,\"Focus Features\",\"PG-13\",\"Drama\"\n\"1978\",\"11/25/2011\",\"The Artist\",1.6e+07,44667095,128256712,\"Weinstein Co.\",\"PG-13\",\"Drama\"\n\"1979\",\"8/2/2002\",\"The Master of Disguise\",1.6e+07,40363530,40363530,\"Sony Pictures\",\"PG\",\"Adventure\"\n\"1980\",\"12/29/2006\",\"El Laberinto del Fauno\",1.6e+07,37634615,87041569,\"Picturehouse\",\"R\",\"Horror\"\n\"1981\",\"2/2/2007\",\"The Messengers\",1.6e+07,35374833,53774833,\"Sony Pictures\",\"PG-13\",\"Horror\"\n\"1982\",\"3/2/2001\",\"See Spot Run\",1.6e+07,33357476,43057552,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"1983\",\"8/9/1991\",\"Double Impact\",1.6e+07,29090445,29090445,\"Sony Pictures\",\"R\",\"Action\"\n\"1984\",\"6/27/2001\",\"Baby Boy\",1.6e+07,28734552,28734552,\"Sony Pictures\",\"R\",\"Drama\"\n\"1985\",\"4/11/2001\",\"Joe Dirt\",1.6e+07,27087695,30987695,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1986\",\"9/12/2008\",\"The Women\",1.6e+07,26902075,50103808,\"Picturehouse\",\"PG-13\",\"Comedy\"\n\"1987\",\"4/20/2007\",\"Hot Fuzz\",1.6e+07,23618786,81742618,\"Focus Features\",\"R\",\"Comedy\"\n\"1988\",\"8/15/2008\",\"Vicky Cristina Barcelona\",1.6e+07,23216709,104504817,\"MGM\",\"PG-13\",\"Comedy\"\n\"1989\",\"6/13/2018\",\"Superfly\",1.6e+07,20537137,20723581,\"Sony Pictures\",\"R\",\"Action\"\n\"1990\",\"3/12/2010\",\"Remember Me\",1.6e+07,19068240,56506120,\"Summit Entertainment\",\"PG-13\",\"Drama\"\n\"1991\",\"10/11/2002\",\"White Oleander\",1.6e+07,16357770,21657770,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1992\",\"3/3/2000\",\"Drowning Mona\",1.6e+07,15427192,15980376,\"Destination Films\",\"PG-13\",\"Comedy\"\n\"1993\",\"1/30/1987\",\"Radio Days\",1.6e+07,14792779,14792779,\"Orion Pictures\",NA,\"Comedy\"\n\"1994\",\"7/18/2003\",\"How to Deal\",1.6e+07,14108518,14108518,\"New Line\",\"PG-13\",\"Drama\"\n\"1995\",\"5/28/2004\",\"Soul Plane\",1.6e+07,13922211,14553807,\"MGM\",\"R\",\"Comedy\"\n\"1996\",\"12/9/1988\",\"My Stepmother Is an Alien\",1.6e+07,13854000,13854000,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1997\",\"6/29/2012\",\"People Like Us\",1.6e+07,12431792,12617472,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"1998\",\"9/3/2004\",\"The Cookout\",1.6e+07,11540112,11540112,\"Lionsgate\",\"PG-13\",\"Comedy\"\n\"1999\",\"10/19/1979\",\"Meteor\",1.6e+07,8400000,8400000,\"American Internatio…\",NA,\"Action\"\n\"2000\",\"3/7/1986\",\"Highlander\",1.6e+07,5900000,12900000,\"20th Century Fox\",\"R\",\"Action\"\n\"2001\",\"11/18/2016\",\"Bleed for This\",1.6e+07,5083906,6603926,\"Open Road\",\"R\",\"Drama\"\n\"2002\",\"9/15/2000\",\"Duets\",1.6e+07,4734235,6615452,\"Walt Disney\",\"R\",\"Drama\"\n\"2003\",\"8/13/1999\",\"Detroit Rock City\",1.6e+07,4217115,5825314,\"New Line\",\"R\",\"Comedy\"\n\"2004\",\"10/19/2007\",\"Things We Lost in the Fire\",1.6e+07,3287315,8120148,\"Paramount Pictures\",\"R\",\"Drama\"\n\"2005\",\"5/16/2014\",\"The Immigrant\",1.6e+07,2013456,7585011,\"RADiUS-TWC\",\"R\",\"Drama\"\n\"2006\",\"8/15/1997\",\"Steel\",1.6e+07,1686429,1686429,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"2007\",\"12/21/2005\",\"The White Countess\",1.6e+07,1669971,2814566,\"Sony Pictures Classics\",\"PG-13\",\"Drama\"\n\"2008\",\"10/1/2014\",\"Men, Women and Children\",1.6e+07,705908,1685403,\"Paramount Pictures\",\"R\",\"Comedy\"\n\"2009\",\"12/31/2008\",\"Good\",1.6e+07,31631,31631,\"ThinkFilm\",\"R\",\"Drama\"\n\"2010\",\"6/21/2002\",\"Juwanna Mann\",15600000,13571817,13771817,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"2011\",\"6/8/2007\",\"La Môme\",15500000,10299782,88611837,\"Picturehouse\",\"PG-13\",\"Drama\"\n\"2012\",\"11/15/2002\",\"Ararat\",15500000,1693000,1693000,\"Miramax\",\"R\",\"Drama\"\n\"2013\",\"4/22/2005\",\"Madison\",15500000,517262,517262,\"MGM\",\"PG\",\"Drama\"\n\"2014\",\"2/26/2010\",\"The Yellow 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World\",5500000,6217849,8761608,\"MGM\",\"R\",\"Comedy\"\n\"2800\",\"12/14/2001\",\"Iris\",5500000,5580479,15155021,\"Miramax\",\"R\",\"Drama\"\n\"2801\",\"11/26/2004\",\"Les Choristes\",5500000,3629758,83529758,\"Miramax\",\"PG-13\",\"Drama\"\n\"2802\",\"10/3/2003\",\"Wonderland\",5500000,1060512,1060512,\"Lionsgate\",\"R\",\"Drama\"\n\"2803\",\"4/1/2011\",\"Haevnen\",5500000,1008098,15867314,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"2804\",\"5/17/2002\",\"Harvard Man\",5500000,56653,56653,NA,\"R\",\"Drama\"\n\"2805\",\"7/15/2011\",\"Salvation Boulevard\",5500000,28468,28468,\"IFC Films\",\"R\",\"Comedy\"\n\"2806\",\"8/3/2007\",\"The Ten\",5250000,769726,786677,\"ThinkFilm\",\"R\",\"Comedy\"\n\"2807\",\"2/24/2017\",\"The Girl with all the Gifts\",5250000,0,4802379,\"Saban Films\",\"R\",\"Horror\"\n\"2808\",\"8/5/2005\",\"Saint Ralph\",5200000,795126,1695126,\"Samuel Goldwyn Films\",\"PG-13\",\"Comedy\"\n\"2809\",\"4/22/2011\",\"Dum Maaro Dum\",5200000,564489,11633427,\"Fox Searchlight\",\"R\",\"Drama\"\n\"2810\",\"10/3/1980\",\"Somewhere in Time\",5100000,9709597,9709597,\"Universal\",NA,\"Drama\"\n\"2811\",\"2/24/2017\",\"Get Out\",5e+06,176040665,255363701,\"Universal\",\"R\",\"Horror\"\n\"2812\",\"1/20/2017\",\"Split\",5e+06,138141585,278306227,\"Universal\",\"PG-13\",\"Horror\"\n\"2813\",\"10/21/2011\",\"Paranormal Activity 3\",5e+06,104028807,202053386,\"Paramount Pictures\",\"R\",\"Horror\"\n\"2814\",\"10/28/2005\",\"Saw II\",5e+06,87025093,152925093,\"Lionsgate\",\"R\",\"Horror\"\n\"2815\",\"9/13/2013\",\"Insidious Chapter 2\",5e+06,83586447,161921515,\"FilmDistrict\",\"PG-13\",\"Horror\"\n\"2816\",\"7/22/2016\",\"Lights Out\",5e+06,67268835,148868835,\"Warner Bros.\",\"PG-13\",\"Horror\"\n\"2817\",\"10/25/2002\",\"Jackass: The Movie\",5e+06,64282312,79282312,\"Paramount Pictures\",\"R\",\"Comedy\"\n\"2818\",\"10/13/2017\",\"Happy Death Day\",5e+06,55683845,125013000,\"Universal\",\"PG-13\",\"Horror\"\n\"2819\",\"10/19/2012\",\"Paranormal Activity 4\",5e+06,53900335,140619520,\"Paramount Pictures\",\"R\",\"Horror\"\n\"2820\",\"10/24/2014\",\"Ouija\",5e+06,50856010,103300632,\"Universal\",\"PG-13\",\"Horror\"\n\"2821\",\"8/30/2013\",\"No se Aceptan Devoluciones\",5e+06,44467206,100486616,\"Lionsgate\",\"PG-13\",\"Comedy\"\n\"2822\",\"5/16/1975\",\"The Return of the Pink Panther\",5e+06,41833347,41833347,\"MGM\",NA,\"Comedy\"\n\"2823\",\"12/24/2003\",\"Monster\",5e+06,34469210,64240813,\"Newmarket Films\",\"R\",\"Drama\"\n\"2824\",\"12/23/1954\",\"20,000 Leagues Under the Sea\",5e+06,28200000,28200000,\"Walt Disney\",\"G\",\"Adventure\"\n\"2825\",\"4/11/2014\",\"Oculus\",5e+06,27695246,44115496,\"Relativity\",\"R\",\"Horror\"\n\"2826\",\"11/1/2013\",\"Dallas Buyers Club\",5e+06,27298285,60611845,\"Focus Features\",\"R\",\"Drama\"\n\"2827\",\"2/27/2015\",\"The Lazarus Effect\",5e+06,25801570,35341814,\"Lionsgate\",\"PG-13\",\"Horror\"\n\"2828\",\"3/16/2001\",\"Memento\",5e+06,25544867,39723096,\"Newmarket Films\",\"R\",\"Drama\"\n\"2829\",\"8/26/2011\",\"Our Idiot Brother\",5e+06,24814830,25861249,\"Weinstein Co.\",\"R\",\"Comedy\"\n\"2830\",\"7/21/2006\",\"Clerks II\",5e+06,24148068,27342246,\"MGM\",\"R\",\"Comedy\"\n\"2831\",\"4/8/1998\",\"The Players Club\",5e+06,23047939,23047939,\"New Line\",\"R\",\"Drama\"\n\"2832\",\"10/13/2000\",\"Billy Elliot\",5e+06,21995263,109263464,\"Focus Features\",\"PG-13\",\"Drama\"\n\"2833\",\"7/5/2013\",\"The Way Way Back\",5e+06,21502690,26853810,\"Fox Searchlight\",\"PG-13\",\"Comedy\"\n\"2834\",\"4/1/2016\",\"God’s Not Dead 2\",5e+06,20773069,23562057,\"Pure Flix Entertain…\",\"PG\",\"Drama\"\n\"2835\",\"12/17/1997\",\"The Apostle\",5e+06,20733485,21277770,\"October Films\",\"PG-13\",\"Drama\"\n\"2836\",\"11/3/1982\",\"The Man From Snowy 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at Night\",5e+06,13985117,19720203,\"A24\",\"R\",\"Horror\"\n\"2847\",\"3/15/2002\",\"Y Tu Mamá También\",5e+06,13649881,33649881,\"IFC Films\",\"R\",\"Drama\"\n\"2848\",\"9/24/2004\",\"Shaun of the Dead\",5e+06,13542874,30332385,\"Focus/Rogue Pictures\",\"R\",\"Comedy\"\n\"2849\",\"6/21/1996\",\"Lone Star\",5e+06,12961389,12961389,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"2850\",\"3/27/1986\",\"April Fool's Day\",5e+06,12947763,12947763,\"Paramount Pictures\",NA,\"Horror\"\n\"2851\",\"4/2/1982\",\"Diner\",5e+06,12592907,12592907,\"MGM\",NA,\"Comedy\"\n\"2852\",\"3/3/2017\",\"Before I Fall\",5e+06,12241072,18945682,\"Open Road\",\"PG-13\",\"Drama\"\n\"2853\",\"4/15/1983\",\"Lone Wolf McQuade\",5e+06,12232628,12232628,\"Orion Pictures\",NA,\"Action\"\n\"2854\",\"3/13/2009\",\"Sunshine Cleaning\",5e+06,12062558,17329337,\"Overture Films\",\"R\",\"Comedy\"\n\"2855\",\"1/29/2016\",\"Fifty Shades of 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Dieux\",5e+06,3954651,46263525,\"Sony Pictures Classics\",\"PG-13\",\"Drama\"\n\"2874\",\"8/27/1982\",\"Jekyll and Hyde... Together Again\",5e+06,3707583,3707583,\"Universal\",NA,\"Comedy\"\n\"2875\",\"3/3/2017\",\"Table 19\",5e+06,3614896,4620399,\"Fox Searchlight\",\"PG-13\",\"Comedy\"\n\"2876\",\"4/15/2016\",\"Green Room\",5e+06,3220371,3807503,\"A24\",\"R\",\"Horror\"\n\"2877\",\"11/16/1994\",\"Heavenly Creatures\",5e+06,3046086,5438120,\"Miramax\",\"R\",\"Drama\"\n\"2878\",\"5/13/2011\",\"Everything Must Go\",5e+06,2712131,2821010,\"Roadside Attractions\",\"PG\",\"Drama\"\n\"2879\",\"12/17/2010\",\"Rabbit Hole\",5e+06,2229058,6205034,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"2880\",\"12/28/2016\",\"Paterson\",5e+06,2141423,10761547,\"Bleecker Street\",\"R\",\"Comedy\"\n\"2881\",\"1/30/1998\",\"Zero Effect\",5e+06,2080693,2080693,\"Sony Pictures\",\"R\",\"Comedy\"\n\"2882\",\"9/12/2014\",\"Atlas Shrugged: Who Is John Galt?\",5e+06,851690,851690,\"Atlas Distribution\",\"PG-13\",\"Drama\"\n\"2883\",\"8/29/2003\",\"Party Monster\",5e+06,742898,894030,\"ContentFilm\",\"R\",\"Comedy\"\n\"2884\",\"2/21/1996\",\"Bottle Rocket\",5e+06,407488,407488,\"Sony Pictures\",\"R\",\"Action\"\n\"2885\",\"8/16/2013\",\"Ain't Them Bodies Saints\",5e+06,391611,1075009,\"IFC Films\",\"R\",\"Drama\"\n\"2886\",\"1/17/1997\",\"Albino Alligator\",5e+06,353480,353480,\"Miramax\",\"R\",\"Drama\"\n\"2887\",\"9/26/2014\",\"Jimi: All is By My Side\",5e+06,340911,927074,\"XLrator Media\",\"R\",\"Drama\"\n\"2888\",\"9/10/2010\",\"Lovely, Still\",5e+06,127564,282687,\"Monterey Media\",\"PG\",\"Drama\"\n\"2889\",\"11/16/2007\",\"Redacted\",5e+06,65388,861325,\"Magnolia Pictures\",\"R\",\"Drama\"\n\"2890\",\"10/17/2014\",\"Rudderless\",5e+06,56001,567219,\"Samuel Goldwyn Films\",\"R\",\"Drama\"\n\"2891\",\"8/14/2009\",\"Grace\",5e+06,8297,8297,\"Anchor Bay Entertai…\",\"R\",\"Horror\"\n\"2892\",\"9/2/2016\",\"Yoga Hosers\",5e+06,0,2199,\"Invincible Pictures\",\"PG-13\",\"Adventure\"\n\"2893\",\"11/21/2014\",\"Reach Me\",5e+06,0,0,\"Alchemy\",\"R\",\"Drama\"\n\"2894\",\"8/18/2014\",\"Henry & Me\",5e+06,0,0,\"Distrib Films\",\"PG\",\"Adventure\"\n\"2895\",\"1/23/2015\",\"Mommy\",4900000,3498695,17536004,\"Roadside Attractions\",\"R\",\"Drama\"\n\"2896\",\"11/20/1996\",\"Sling Blade\",4833610,24475416,34175000,\"Miramax\",\"R\",\"Drama\"\n\"2897\",\"1/6/2006\",\"Hostel\",4800000,47326473,82241110,\"Lionsgate\",\"R\",\"Horror\"\n\"2898\",\"9/30/2011\",\"Take Shelter\",4750000,1728953,4972016,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"2899\",\"8/22/1986\",\"The Texas Chainsaw Massacre 2\",4700000,8025872,8025872,\"Cannon\",NA,\"Horror\"\n\"2900\",\"4/22/1988\",\"Lady in White\",4700000,1705139,1705139,\"New Century Vista F…\",NA,\"Horror\"\n\"2901\",\"3/4/2005\",\"Dear Frankie\",4600000,1340891,3099369,\"Miramax\",\"PG-13\",\"Drama\"\n\"2902\",\"12/29/2004\",\"The Assassination of Richard Nixon\",4600000,708776,4880143,\"ThinkFilm\",\"R\",\"Drama\"\n\"2903\",\"6/24/2011\",\"Le nom des 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Trouble\",1500000,0,98806,\"Yash Raj Films\",NA,\"Action\"\n\"3224\",\"3/17/2006\",\"Fetching Cody\",1500000,0,0,NA,NA,\"Drama\"\n\"3225\",\"6/3/2011\",\"The Lion of Judah\",1500000,0,0,\"Rocky Mountain Pict…\",\"PG\",\"Adventure\"\n\"3226\",\"11/20/2015\",\"Mustang\",1400000,845464,5545484,\"Cohen Media Group\",\"PG-13\",\"Drama\"\n\"3227\",\"4/29/2005\",\"The Holy Girl\",1400000,304124,1261792,\"Fine Line\",\"R\",\"Drama\"\n\"3228\",\"10/9/1998\",\"Festen\",1300000,1647780,1647780,\"October Films\",\"R\",\"Comedy\"\n\"3229\",\"10/11/1996\",\"Trees Lounge\",1300000,749741,749741,\"Orion Classics\",\"R\",\"Drama\"\n\"3230\",\"3/23/2007\",\"Journey from the Fall\",1300000,635305,635305,\"Imaginasian\",\"R\",\"Drama\"\n\"3231\",\"5/5/2000\",\"The Basket\",1300000,609042,609042,\"MGM\",\"PG\",\"Drama\"\n\"3232\",\"3/15/1985\",\"Def-Con 4\",1300000,210904,210904,\"New World\",NA,\"Action\"\n\"3233\",\"4/30/1981\",\"Friday the 13th Part 2\",1250000,21722776,21722776,\"Paramount Pictures\",NA,\"Horror\"\n\"3234\",\"8/31/1984\",\"C.H.U.D.\",1250000,4700000,4700000,\"New World\",NA,\"Horror\"\n\"3235\",\"4/19/2013\",\"Filly Brown\",1250000,2850357,2940411,\"Lionsgate\",\"R\",\"Drama\"\n\"3236\",\"10/29/2004\",\"Saw\",1200000,55968727,103880027,\"Lionsgate\",\"R\",\"Horror\"\n\"3237\",\"8/4/1989\",\"Sex, Lies, and Videotape\",1200000,24741667,36741667,\"Miramax\",\"R\",\"Drama\"\n\"3238\",\"2/15/2002\",\"Super Troopers\",1200000,18492362,23046142,\"Fox Searchlight\",\"R\",\"Comedy\"\n\"3239\",\"2/22/2002\",\"Monsoon Wedding\",1200000,13876974,27025600,\"USA Films\",\"R\",\"Comedy\"\n\"3240\",\"11/10/2000\",\"You Can Count on Me\",1200000,9180275,10827356,\"Paramount Vantage\",\"R\",\"Drama\"\n\"3241\",\"4/19/2013\",\"Home Run\",1200000,2859955,2859955,\"Samuel Goldwyn Films\",\"PG-13\",\"Drama\"\n\"3242\",\"7/7/2000\",\"But I'm a Cheerleader\",1200000,2205627,2509344,\"Lionsgate\",\"R\",\"Comedy\"\n\"3243\",\"4/13/2012\",\"Blue Like Jazz\",1200000,595018,595018,\"Roadside Attractions\",\"PG-13\",\"Comedy\"\n\"3244\",\"8/28/2015\",\"Que Horas Ela Volta?\",1200000,376976,3247411,\"Oscilloscope Pictures\",\"R\",\"Drama\"\n\"3245\",\"11/19/1982\",\"Q\",1200000,255000,255000,\"United Film Distrib…\",NA,\"Horror\"\n\"3246\",\"6/18/2004\",\"Grand Theft Parsons\",1200000,0,0,\"Swipe Films\",\"PG-13\",\"Drama\"\n\"3247\",\"9/7/2012\",\"Crowsnest\",1200000,0,0,\"IFC Midnight\",\"R\",\"Horror\"\n\"3248\",\"9/14/2012\",\"Airborne\",1200000,0,0,\"Image Entertainment\",NA,\"Horror\"\n\"3249\",\"3/21/2014\",\"God’s Not Dead\",1150000,60755732,63777092,\"Pure Flix Entertain…\",\"PG\",\"Drama\"\n\"3250\",\"10/7/2005\",\"Waiting...\",1125000,16124543,18673274,\"Lionsgate\",\"R\",\"Comedy\"\n\"3251\",\"12/25/2005\",\"Wolf Creek\",1100000,16186348,29005064,\"Weinstein Co.\",\"R\",\"Horror\"\n\"3252\",\"2/11/2005\",\"Ong-Bak\",1100000,4563167,24062965,\"Magnolia Pictures\",\"R\",\"Action\"\n\"3253\",\"3/23/2012\",\"Serbuan maut\",1100000,4105123,9297407,\"Sony Pictures Classics\",\"R\",\"Action\"\n\"3254\",\"9/4/1987\",\"The Offspring\",1100000,1355728,1355728,\"Moviestore Entertai…\",\"R\",\"Horror\"\n\"3255\",\"5/18/2012\",\"Beyond the Black Rainbow\",1100000,56491,56491,\"Mongrel Media\",\"R\",\"Drama\"\n\"3256\",\"1/23/1943\",\"Casablanca\",1039000,10462500,10462500,\"Warner Bros.\",\"PG\",\"Drama\"\n\"3257\",\"11/21/1976\",\"Rocky\",1e+06,117235147,2.25e+08,\"United Artists\",\"PG\",\"Drama\"\n\"3258\",\"1/6/2012\",\"The Devil Inside\",1e+06,53262945,101759490,\"Paramount Pictures\",\"R\",\"Horror\"\n\"3259\",\"4/17/2015\",\"Unfriended\",1e+06,32789645,62869004,\"Universal\",\"R\",\"Horror\"\n\"3260\",\"2/8/1976\",\"Taxi Driver\",1e+06,28262574,28316211,\"Columbia\",\"R\",\"Drama\"\n\"3261\",\"2/1/1980\",\"The Fog\",1e+06,21378361,21378361,\"Avco Embassy\",NA,\"Horror\"\n\"3262\",\"8/23/2013\",\"You're Next\",1e+06,18494006,26887177,\"Lionsgate\",\"R\",\"Horror\"\n\"3263\",\"5/25/2012\",\"Chernobyl Diaries\",1e+06,18119640,42411721,\"Warner Bros.\",\"R\",\"Horror\"\n\"3264\",\"4/10/1981\",\"The Howling\",1e+06,17985000,17985000,\"Avco Embassy\",NA,\"Horror\"\n\"3265\",\"5/8/1963\",\"Dr. No\",1e+06,16067035,59567035,\"MGM\",\"PG\",\"Action\"\n\"3266\",\"9/18/1987\",\"Hellraiser\",1e+06,14564000,14575148,\"New World\",\"R\",\"Horror\"\n\"3267\",\"8/18/2000\",\"Godzilla 2000\",1e+06,10037390,10037390,\"Sony Pictures\",\"PG\",\"Action\"\n\"3268\",\"12/29/2010\",\"Blue Valentine\",1e+06,9737892,16566240,\"Weinstein Co.\",\"R\",\"Drama\"\n\"3269\",\"1/20/2006\",\"Transamerica\",1e+06,9015303,16553163,\"Weinstein Co.\",\"R\",\"Drama\"\n\"3270\",\"1/1/1970\",\"Beyond the Valley of the Dolls\",1e+06,9e+06,9e+06,\"20th Century Fox\",NA,\"Comedy\"\n\"3271\",\"7/20/2018\",\"Unfriended: Dark Web\",1e+06,8783985,9620953,\"OTL Releasing\",\"R\",\"Horror\"\n\"3272\",\"9/25/2015\",\"The Green Inferno\",1e+06,7192291,12931569,\"High Top Releasing\",\"R\",\"Horror\"\n\"3273\",\"10/19/2012\",\"The Sessions\",1e+06,6002451,11495204,\"Fox Searchlight\",\"R\",\"Drama\"\n\"3274\",\"3/23/2012\",\"October Baby\",1e+06,5355847,5391992,\"Five & Two Pictures\",\"PG-13\",\"Drama\"\n\"3275\",\"9/12/2014\",\"The Skeleton Twins\",1e+06,5284309,5797192,\"Lionsgate/Roadside …\",\"R\",\"Drama\"\n\"3276\",\"8/3/2005\",\"Junebug\",1e+06,2678010,3553253,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3277\",\"8/1/2008\",\"Frozen River\",1e+06,2511476,6030129,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3278\",\"11/21/2001\",\"Sidewalks of New York\",1e+06,2402459,3575308,\"Paramount Vantage\",\"R\",\"Comedy\"\n\"3279\",\"4/24/1998\",\"Two Girls and a Guy\",1e+06,2057193,2315026,\"Fox Searchlight\",\"R\",\"Drama\"\n\"3280\",\"9/18/2009\",\"The Secrets of Jonathan Sperry\",1e+06,1355079,1355079,\"Five & Two Pictures\",\"PG\",\"Drama\"\n\"3281\",\"9/19/2003\",\"Bubba Ho-Tep\",1e+06,1239183,1492895,\"Vitagraph Films\",\"R\",\"Comedy\"\n\"3282\",\"12/7/2001\",\"No Man's Land\",1e+06,1067481,2684207,\"MGM\",\"R\",\"Drama\"\n\"3283\",\"10/9/1998\",\"Slam\",1e+06,1009819,1087521,\"Trimark\",\"R\",\"Drama\"\n\"3284\",\"8/18/2017\",\"Patti Cake$\",1e+06,800148,1471090,\"Fox Searchlight\",\"R\",\"Comedy\"\n\"3285\",\"12/1/2000\",\"Panic\",1e+06,779137,1425707,\"Roxie Releasing\",\"R\",\"Drama\"\n\"3286\",\"5/9/2014\",\"Palo Alto\",1e+06,767732,1156309,\"TriBeca Films\",\"R\",\"Drama\"\n\"3287\",\"7/29/2011\",\"The Future\",1e+06,568662,1239174,\"Roadside Attractions\",\"R\",\"Drama\"\n\"3288\",\"2/14/2003\",\"All the Real Girls\",1e+06,549666,703020,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3289\",\"10/24/2014\",\"23 Blast\",1e+06,549185,549185,\"Abramorama Films\",\"PG-13\",\"Drama\"\n\"3290\",\"6/20/1997\",\"Dream With The Fishes\",1e+06,542909,542909,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3291\",\"5/2/2003\",\"Blue Car\",1e+06,464126,475367,\"Miramax\",\"R\",\"Drama\"\n\"3292\",\"10/19/2007\",\"Wristcutters: A Love Story\",1e+06,446165,473769,\"Autonomous Films\",\"R\",\"Comedy\"\n\"3293\",\"5/5/2000\",\"Luminarias\",1e+06,428535,428535,NA,\"R\",\"Comedy\"\n\"3294\",\"7/18/2014\",\"I Origins\",1e+06,336472,852399,\"Fox Searchlight\",\"R\",\"Drama\"\n\"3295\",\"8/22/2003\",\"The Battle of Shaker Heights\",1e+06,280351,839145,\"Miramax\",\"PG-13\",\"Comedy\"\n\"3296\",\"12/30/2002\",\"Love Liza\",1e+06,213137,213137,NA,\"R\",\"Drama\"\n\"3297\",\"8/22/2001\",\"Lisa Picard is Famous\",1e+06,113433,113433,NA,\"PG-13\",\"Comedy\"\n\"3298\",\"10/30/2009\",\"The House of the Devil\",1e+06,101215,102812,\"Magnolia Pictures\",\"R\",\"Horror\"\n\"3299\",\"6/1/2012\",\"Hardflip\",1e+06,96734,96734,\"Rocky Mountain Pict…\",\"PG-13\",\"Drama\"\n\"3300\",\"3/11/2016\",\"Creative Control\",1e+06,63014,63014,\"Magnolia Pictures\",\"R\",\"Drama\"\n\"3301\",\"10/17/2014\",\"Camp X-Ray\",1e+06,9837,9837,\"IFC Films\",\"R\",\"Drama\"\n\"3302\",\"11/21/2008\",\"Special\",1e+06,7202,26822,\"Revolver Entertainment\",\"R\",\"Drama\"\n\"3303\",\"4/10/2015\",\"The Sisterhood of Night\",1e+06,6870,6870,\"Freestyle Releasing\",\"PG-13\",\"Drama\"\n\"3304\",\"3/18/2005\",\"The Helix…Loaded\",1e+06,3700,3700,\"Romar\",\"R\",\"Comedy\"\n\"3305\",\"5/15/2015\",\"Childless\",1e+06,1036,1036,\"Monterey Media\",\"R\",\"Drama\"\n\"3306\",\"4/21/2006\",\"In Her Line of Fire\",1e+06,884,884,\"Regent Releasing\",\"R\",\"Action\"\n\"3307\",\"9/15/2006\",\"Jimmy and Judy\",1e+06,0,0,\"Outrider Pictures\",\"R\",\"Action\"\n\"3308\",\"7/17/2009\",\"The Poker House\",1e+06,0,0,\"Phase 4 Films\",\"R\",\"Drama\"\n\"3309\",\"9/23/2005\",\"Proud\",1e+06,0,0,\"Castle Hill Product…\",\"PG\",\"Drama\"\n\"3310\",\"12/31/2008\",\"Steppin: The Movie\",1e+06,0,0,\"Weinstein Co.\",\"PG-13\",\"Comedy\"\n\"3311\",\"1/29/2010\",\"Zombies of Mass Destruction\",1e+06,0,0,\"After Dark\",\"R\",\"Comedy\"\n\"3312\",\"4/14/2006\",\"Hard Candy\",950000,1024640,8267066,\"Lionsgate\",\"R\",\"Horror\"\n\"3313\",\"9/27/2002\",\"Charly\",950000,814666,814666,\"Excel Entertainment\",\"PG\",\"Comedy\"\n\"3314\",\"4/13/2012\",\"L!fe Happens\",930000,30905,30905,\"PMK*BNC\",\"R\",\"Comedy\"\n\"3315\",\"5/12/2017\",\"Lowriders\",916000,6179955,6188421,\"BH Tilt\",\"PG-13\",\"Drama\"\n\"3316\",\"7/12/2013\",\"Fruitvale Station\",9e+05,16098998,17549645,\"Weinstein Co.\",\"R\",\"Drama\"\n\"3317\",\"4/1/2016\",\"Meet the Blacks\",9e+05,9097072,9097072,\"Freestyle Releasing\",\"R\",\"Comedy\"\n\"3318\",\"8/26/2011\",\"Circumstance\",9e+05,454121,958978,\"Roadside Attractions\",\"R\",\"Drama\"\n\"3319\",\"8/25/2006\",\"The Quiet\",9e+05,381420,381420,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3320\",\"8/13/1942\",\"Bambi\",858000,102797000,2.68e+08,\"RKO Radio Pictures\",\"G\",\"Drama\"\n\"3321\",\"8/31/2012\",\"For a Good Time, Call\",850000,1251749,1386088,\"Focus Features\",\"R\",\"Comedy\"\n\"3322\",\"1/30/2004\",\"Latter Days\",850000,833118,865708,\"TLA Releasing\",\"R\",\"Drama\"\n\"3323\",\"10/25/2002\",\"Time Changer\",825000,1500711,1500711,\"Five & Two Pictures\",\"PG\",\"Drama\"\n\"3324\",\"12/30/2011\",\"Jodaeiye Nader az Simin\",8e+05,7098492,24426169,\"Sony Pictures Classics\",\"PG-13\",\"Drama\"\n\"3325\",\"5/10/1996\",\"Welcome to the Dollhouse\",8e+05,4198137,5034794,\"Sony Pictures Classics\",\"R\",\"Comedy\"\n\"3326\",\"3/28/2003\",\"Raising Victor Vargas\",8e+05,2073984,2900578,\"Samuel Goldwyn Films\",\"R\",\"Drama\"\n\"3327\",\"10/1/1993\",\"Ruby in Paradise\",8e+05,1001437,1001437,NA,\"R\",\"Drama\"\n\"3328\",\"5/7/2004\",\"The Mudge Boy\",8e+05,62544,62544,\"Strand\",\"R\",\"Drama\"\n\"3329\",\"8/6/2004\",\"Saints and Soldiers\",780000,1310470,1310470,\"Excel Entertainment\",\"PG-13\",\"Drama\"\n\"3330\",\"8/11/1973\",\"American Graffiti\",777000,1.15e+08,1.4e+08,\"Universal\",\"PG\",\"Drama\"\n\"3331\",\"6/8/2012\",\"Safety Not Guaranteed\",750000,4010957,4422318,\"FilmDistrict\",\"R\",\"Comedy\"\n\"3332\",\"2/3/2012\",\"The Innkeepers\",750000,78396,1011535,\"Magnolia Pictures\",\"R\",\"Horror\"\n\"3333\",\"8/29/2014\",\"Il conformista\",750000,59656,89609,\"Kino Lorber\",\"R\",\"Drama\"\n\"3334\",\"7/1/2005\",\"Undead\",750000,41196,229250,\"Lionsgate\",\"R\",\"Horror\"\n\"3335\",\"10/11/2013\",\"All the Boys Love Mandy Lane\",750000,0,1960521,\"Radius\",\"R\",\"Horror\"\n\"3336\",\"6/25/1968\",\"La mariée était en noir\",747000,44566,44566,\"Film Forum\",NA,\"Drama\"\n\"3337\",\"8/11/2006\",\"Half Nelson\",7e+05,2697938,4911725,\"ThinkFilm\",\"R\",\"Drama\"\n\"3338\",\"6/19/1998\",\"Hav Plenty\",650000,2301777,2301777,\"Miramax\",\"R\",\"Comedy\"\n\"3339\",\"7/14/1999\",\"The Blair Witch Project\",6e+05,140539099,248300000,\"Artisan\",\"R\",\"Horror\"\n\"3340\",\"8/10/1977\",\"The Kentucky Fried Movie\",6e+05,1.5e+07,2e+07,\"United Film Distrib…\",NA,\"Comedy\"\n\"3341\",\"10/31/2000\",\"Mercy Streets\",6e+05,173599,173599,NA,\"PG-13\",\"Drama\"\n\"3342\",\"7/2/1999\",\"Broken Vessels\",6e+05,15030,85343,NA,\"R\",\"Drama\"\n\"3343\",\"5/22/2015\",\"Drunk Wedding\",6e+05,3301,3301,\"Paramount Pictures\",\"R\",\"Comedy\"\n\"3344\",\"8/11/1964\",\"A Hard Day's Night\",560000,1537860,1626784,\"Universal\",\"G\",\"Comedy\"\n\"3345\",\"5/9/1980\",\"Friday the 13th\",550000,39754601,59754601,\"Paramount Pictures\",NA,\"Horror\"\n\"3346\",\"9/26/2008\",\"Fireproof\",5e+05,33456317,33473297,\"Samuel Goldwyn Films\",\"PG\",\"Drama\"\n\"3347\",\"11/15/1974\",\"Benji\",5e+05,31559560,31559560,NA,\"G\",\"Adventure\"\n\"3348\",\"10/3/2003\",\"The Station Agent\",5e+05,5801558,9470209,\"Miramax\",\"R\",\"Drama\"\n\"3349\",\"1/22/2010\",\"To Save a Life\",5e+05,3777210,3824868,\"Samuel Goldwyn Films\",\"PG-13\",\"Drama\"\n\"3350\",\"2/1/2002\",\"The Singles Ward\",5e+05,1250798,1250798,\"Halestorm Entertain…\",\"PG\",\"Comedy\"\n\"3351\",\"1/30/2004\",\"Osama\",5e+05,1127331,1971479,\"MGM\",\"PG-13\",\"Drama\"\n\"3352\",\"6/9/2000\",\"Groove\",5e+05,1115313,1167524,\"Sony Pictures Classics\",\"R\",\"Comedy\"\n\"3353\",\"1/31/2003\",\"The R.M.\",5e+05,1111615,1111615,\"Halestone\",\"PG\",\"Comedy\"\n\"3354\",\"7/30/1999\",\"Twin Falls Idaho\",5e+05,985341,1027228,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3355\",\"8/20/2004\",\"Mean Creek\",5e+05,603951,1348750,\"Paramount Vantage\",\"R\",\"Drama\"\n\"3356\",\"8/23/2013\",\"Drinking Buddies\",5e+05,343706,407100,\"Magnolia Pictures\",\"R\",\"Drama\"\n\"3357\",\"2/13/1998\",\"Hurricane Streets\",5e+05,334041,367582,\"MGM\",NA,\"Drama\"\n\"3358\",\"8/29/2003\",\"Civil Brand\",5e+05,254293,254293,\"Lionsgate\",\"R\",\"Drama\"\n\"3359\",\"10/29/2010\",\"Monsters\",5e+05,237301,5639730,\"Magnet Pictures\",\"R\",\"Drama\"\n\"3360\",\"3/24/2006\",\"Lonesome Jim\",5e+05,154187,602789,\"IFC Films\",\"R\",\"Comedy\"\n\"3361\",\"12/11/2015\",\"O Menino e o Mundo\",5e+05,129479,271893,\"GKIDS\",\"PG\",\"Adventure\"\n\"3362\",\"1/1/1991\",\"Johnny Suede\",5e+05,55000,55000,\"Miramax\",\"R\",\"Drama\"\n\"3363\",\"10/21/2005\",\"The Californians\",5e+05,4134,4134,\"Fabrication Films\",\"PG\",\"Drama\"\n\"3364\",\"11/2/2001\",\"Everything Put Together\",5e+05,0,7890,NA,\"R\",\"Drama\"\n\"3365\",\"9/25/2009\",\"Paranormal Activity\",450000,107918810,194183034,\"Paramount Pictures\",\"R\",\"Horror\"\n\"3366\",\"3/31/2006\",\"Brick\",450000,2075743,4243996,\"Focus/Rogue Pictures\",\"R\",\"Drama\"\n\"3367\",\"8/22/1997\",\"Sunday\",450000,410919,450349,NA,NA,\"Drama\"\n\"3368\",\"8/11/2006\",\"Conversations with Other Women\",450000,379418,1297745,\"Fabrication Films\",\"R\",\"Comedy\"\n\"3369\",\"8/3/1990\",\"Metropolitan\",430000,2938000,2938000,NA,\"PG-13\",\"Comedy\"\n\"3370\",\"6/11/2004\",\"Napoleon Dynamite\",4e+05,44540956,46122713,\"Fox Searchlight\",\"PG\",\"Comedy\"\n\"3371\",\"5/10/1975\",\"Monty Python and the Holy Grail\",4e+05,3427696,5028948,NA,NA,\"Comedy\"\n\"3372\",\"8/2/2006\",\"Quinceanera\",4e+05,1692693,2797199,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3373\",\"10/24/2008\",\"Heroes\",4e+05,655538,655538,\"Eros Entertainment\",\"R\",\"Adventure\"\n\"3374\",\"1/1/1983\",\"E tu vivrai nel terrore - L'aldilà\",4e+05,126387,126387,NA,NA,\"Horror\"\n\"3375\",\"7/27/2001\",\"Jackpot\",4e+05,44452,44452,NA,\"R\",\"Drama\"\n\"3376\",\"12/10/2004\",\"Fabled\",4e+05,31425,31425,\"Indican Pictures\",\"R\",\"Horror\"\n\"3377\",\"10/13/2005\",\"The Dark Hours\",4e+05,423,423,\"Freestyle Releasing\",\"R\",\"Horror\"\n\"3378\",\"4/1/1986\",\"My Beautiful Laundrette\",4e+05,0,0,\"Orion Classics\",NA,\"Drama\"\n\"3379\",\"1/1/1980\",\"Maniac\",350000,1e+07,1e+07,\"Analysis\",NA,\"Horror\"\n\"3380\",\"1/1/1987\",\"American Ninja 2: The Confrontation\",350000,4e+06,4e+06,NA,NA,\"Action\"\n\"3381\",\"4/13/1957\",\"12 Angry Men\",340000,0,0,\"United Artists\",NA,\"Drama\"\n\"3382\",\"10/17/1978\",\"Halloween\",325000,4.7e+07,7e+07,\"Compass International\",\"R\",\"Horror\"\n\"3383\",\"11/24/1999\",\"Tumbleweeds\",312000,1350248,1788168,\"Fine Line\",\"PG-13\",\"Drama\"\n\"3384\",\"3/10/2000\",\"God's Army\",3e+05,2637726,2652515,\"Excel Entertainment\",\"PG\",\"Drama\"\n\"3385\",\"10/17/2003\",\"Pieces of April\",3e+05,2528664,3571253,\"MGM\",\"PG-13\",\"Comedy\"\n\"3386\",\"9/20/1996\",\"When The Cat's Away\",3e+05,1652472,2525984,\"Sony Pictures Classics\",\"R\",\"Comedy\"\n\"3387\",\"12/10/2008\",\"Wendy and Lucy\",3e+05,865695,1416046,\"Oscilloscope Pictures\",\"R\",\"Drama\"\n\"3388\",\"9/11/1998\",\"Let's Talk About Sex\",3e+05,373615,373615,\"Fine Line\",NA,\"Comedy\"\n\"3389\",\"7/15/2005\",\"First Morning\",3e+05,87264,87264,\"Illuminare\",\"PG-13\",\"Drama\"\n\"3390\",\"3/11/2011\",\"3 Backyards\",3e+05,39475,39475,\"Screen Media Films\",\"R\",\"Drama\"\n\"3391\",\"8/7/1998\",\"First Love, Last Rites\",3e+05,10876,10876,\"Strand\",\"R\",\"Drama\"\n\"3392\",\"5/6/2005\",\"Fighting Tommy Riley\",3e+05,10514,10514,\"Freestyle Releasing\",\"R\",\"Drama\"\n\"3393\",\"8/17/2012\",\"Compliance\",270000,319285,830700,\"Magnolia Pictures\",\"R\",\"Drama\"\n\"3394\",\"6/28/2002\",\"Lovely and Amazing\",250000,4210379,4613482,\"Lionsgate\",\"R\",\"Drama\"\n\"3395\",\"4/28/2017\",\"Sleight\",250000,3930990,3934450,\"High Top Releasing\",\"R\",\"Action\"\n\"3396\",\"4/11/2003\",\"Better Luck Tomorrow\",250000,3802390,3809226,\"Paramount Pictures\",\"R\",\"Drama\"\n\"3397\",\"10/28/2011\",\"Like Crazy\",250000,3395391,3728400,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"3398\",\"7/14/2000\",\"Chuck&Buck\",250000,1055671,1157672,\"Artisan\",\"R\",\"Drama\"\n\"3399\",\"3/28/1997\",\"Love and Other Catastrophes\",250000,212285,743216,\"Fox Searchlight\",\"R\",\"Comedy\"\n\"3400\",\"8/28/1998\",\"I Married a Strange Person\",250000,203134,203134,\"Lionsgate\",NA,\"Comedy\"\n\"3401\",\"7/22/2005\",\"November\",250000,191862,191862,\"Sony Pictures Classics\",\"R\",\"Drama\"\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/movie_returns_by_genre/horror_movies_conds.R",
    "content": "# load packages ----------------------------------------------------------------\nlibrary(tidyverse)\nlibrary(lubridate)\nlibrary(openintro)\nlibrary(broom)\n\n# load data --------------------------------------------------------------------\nmovie_profit <- read_csv(\"movie_profit.csv\") %>%\n  select(-X1)\n\n\n# fix dates --------------------------------------------------------------------\nmovie_profit <- movie_profit %>%\n  mutate(\n    release_date = mdy(release_date),\n    release_year = year(release_date),\n    oct_release = ifelse(month(release_date) == 10, \"yes\", \"no\"),\n    dom_gross_to_prod = domestic_gross / production_budget,\n    ww_gross_to_prod = worldwide_gross / production_budget\n    ) \n\n# subset for movies after 2000 -------------------------------------------------\nmovie_profit_2000 <- movie_profit %>%\n  filter(\n    release_year >= 2010,\n    release_year < 2019\n    )\n\n# mlr --------------------------------------------------------------------------\n\nm <- lm(ww_gross_to_prod ~ release_year + genre, data = movie_profit_2000)\nm_aug <- augment(m)\n\n# residuals against fitted -----------------------------------------------------\n\ncols <- c(\n  \"Action\" = COL[1,1],\n  \"Adventure\" = COL[2,1],\n  \"Comedy\" = COL[3,1],\n  \"Drama\" = COL[4,1],\n  \"Horror\" = COL[5,1]\n)\n\nggplot(m_aug, aes(y = .fitted, x = ww_gross_to_prod, color = genre)) + \n  geom_point(alpha = 0.5) +\n  facet_wrap(~genre, scales = \"free_x\") +\n  theme_minimal() + \n  labs(x = \"Actual ROI\", y = \"Predicted ROI\", color = \"Genre\") +\n  scale_color_manual(values = cols) +\n  guides(color = FALSE) +\n  geom_abline(yintercept = 0, slope = 1)\n  \nggsave(filename = \"horror_movies_by_genre.pdf\",\n       width = 5.5, height = 4.3)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/movie_returns_by_genre/movie_profit.csv",
    "content": "\"\",\"release_date\",\"movie\",\"production_budget\",\"domestic_gross\",\"worldwide_gross\",\"distributor\",\"mpaa_rating\",\"genre\"\n\"1\",\"6/22/2007\",\"Evan Almighty\",1.75e+08,100289690,174131329,\"Universal\",\"PG\",\"Comedy\"\n\"2\",\"7/28/1995\",\"Waterworld\",1.75e+08,88246220,264246220,\"Universal\",\"PG-13\",\"Action\"\n\"3\",\"5/12/2017\",\"King Arthur: Legend of the Sword\",1.75e+08,39175066,139950708,\"Warner Bros.\",\"PG-13\",\"Adventure\"\n\"4\",\"12/25/2013\",\"47 Ronin\",1.75e+08,38362475,151716815,\"Universal\",\"PG-13\",\"Action\"\n\"5\",\"6/22/2018\",\"Jurassic World: Fallen Kingdom\",1.7e+08,416769345,1304866322,\"Universal\",\"PG-13\",\"Action\"\n\"6\",\"8/1/2014\",\"Guardians of the Galaxy\",1.7e+08,333172112,771051335,\"Walt Disney\",\"PG-13\",\"Action\"\n\"7\",\"5/7/2010\",\"Iron Man 2\",1.7e+08,312433331,621156389,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"8\",\"4/4/2014\",\"Captain America: The Winter Soldier\",1.7e+08,259746958,714401889,\"Walt Disney\",\"PG-13\",\"Action\"\n\"9\",\"7/11/2014\",\"Dawn of the Planet of the Apes\",1.7e+08,208545589,710644566,\"20th Century Fox\",\"PG-13\",\"Adventure\"\n\"10\",\"11/10/2004\",\"The Polar Express\",1.7e+08,186493587,310634169,\"Warner Bros.\",\"G\",\"Adventure\"\n\"11\",\"6/1/2012\",\"Snow White and the Huntsman\",1.7e+08,155136755,401021746,\"Universal\",\"PG-13\",\"Adventure\"\n\"12\",\"7/1/2003\",\"Terminator 3: Rise of the Machines\",1.7e+08,150358296,433058296,\"Warner Bros.\",\"R\",\"Action\"\n\"13\",\"5/7/2004\",\"Van Helsing\",1.7e+08,120150546,300150546,\"Universal\",\"PG-13\",\"Action\"\n\"14\",\"5/22/2015\",\"Tomorrowland\",1.7e+08,93436322,207283457,\"Walt Disney\",\"PG\",\"Adventure\"\n\"15\",\"5/27/2016\",\"Alice Through the Looking Glass\",1.7e+08,77042381,276934087,\"Walt Disney\",\"PG\",\"Adventure\"\n\"16\",\"5/21/2010\",\"Shrek Forever After\",1.65e+08,238736787,756244673,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"17\",\"11/4/2016\",\"Doctor Strange\",1.65e+08,232641920,676486457,\"Walt Disney\",\"PG-13\",\"Action\"\n\"18\",\"11/7/2014\",\"Big Hero 6\",1.65e+08,222527828,652127828,\"Walt Disney\",\"PG\",\"Adventure\"\n\"19\",\"3/26/2010\",\"How to Train Your Dragon\",1.65e+08,217581232,494870992,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"20\",\"11/2/2012\",\"Wreck-It Ralph\",1.65e+08,189412677,496511521,\"Walt Disney\",\"PG\",\"Adventure\"\n\"21\",\"11/5/2014\",\"Interstellar\",1.65e+08,188017894,667752422,\"Paramount Pictures\",\"PG-13\",\"Adventure\"\n\"22\",\"6/24/2016\",\"Independence Day: Resurgence\",1.65e+08,103144286,384413934,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"23\",\"7/29/2011\",\"Cowboys and Aliens\",1.63e+08,100368560,176038324,\"Universal\",\"PG-13\",\"Action\"\n\"24\",\"5/17/2007\",\"Shrek the Third\",1.6e+08,322719944,807330936,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"25\",\"5/24/2013\",\"Fast and Furious 6\",1.6e+08,238679850,789300444,\"Universal\",\"PG-13\",\"Action\"\n\"26\",\"6/3/2011\",\"X-Men: First Class\",1.6e+08,146408305,355408305,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"27\",\"12/25/2008\",\"The Curious Case of Benjamin Button\",1.6e+08,127509326,329631958,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"28\",\"7/14/2010\",\"The Sorcerer's Apprentice\",1.6e+08,63150991,217986320,\"Walt Disney\",\"PG\",\"Adventure\"\n\"29\",\"5/12/2006\",\"Poseidon\",1.6e+08,60674817,181674817,\"Warner Bros.\",\"PG-13\",\"Adventure\"\n\"30\",\"6/10/2016\",\"Warcraft\",1.6e+08,47225655,425547111,\"Universal\",\"PG-13\",\"Action\"\n\"31\",\"12/21/2018\",\"Aquaman\",1.6e+08,0,0,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"32\",\"9/30/2016\",\"Deepwater Horizon\",1.56e+08,61433527,122631306,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"33\",\"7/1/2015\",\"Terminator: Genisys\",1.55e+08,89760956,432150894,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"34\",\"3/23/2018\",\"Pacific Rim: Uprising\",1.55e+08,59185715,290241338,\"Universal\",\"PG-13\",\"Action\"\n\"35\",\"11/24/2004\",\"Alexander\",1.55e+08,34297191,167297191,\"Warner Bros.\",\"R\",\"Action\"\n\"36\",\"7/14/2017\",\"War for the Planet of the Apes\",1.52e+08,146880162,489592267,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"37\",\"5/25/2001\",\"Pearl Harbor\",151500000,198539855,449239855,\"Walt Disney\",\"PG-13\",\"Action\"\n\"38\",\"7/2/2007\",\"Transformers\",1.51e+08,319246193,708272592,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"39\",\"6/2/2017\",\"Wonder Woman\",1.5e+08,412563408,821133378,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"40\",\"3/4/2016\",\"Zootopia\",1.5e+08,341268248,1019706594,\"Walt Disney\",\"PG\",\"Adventure\"\n\"41\",\"11/18/2005\",\"Harry Potter and the Goblet of Fire\",1.5e+08,290013036,896911078,\"Warner Bros.\",\"PG-13\",\"Adventure\"\n\"42\",\"5/15/2003\",\"The Matrix Reloaded\",1.5e+08,281553689,738576929,\"Warner Bros.\",\"R\",\"Action\"\n\"43\",\"12/14/2007\",\"I am Legend\",1.5e+08,256393010,585532684,\"Warner Bros.\",\"PG-13\",\"Horror\"\n\"44\",\"7/1/2008\",\"Hancock\",1.5e+08,227946274,624234272,\"Sony Pictures\",\"PG-13\",\"Action\"\n\"45\",\"7/15/2005\",\"Charlie and the Chocolate Factory\",1.5e+08,206459076,475825484,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"46\",\"6/29/2007\",\"Ratatouille\",1.5e+08,206445654,626549695,\"Walt Disney\",\"G\",\"Adventure\"\n\"47\",\"11/8/2013\",\"Thor: The Dark World\",1.5e+08,206362140,644602516,\"Walt Disney\",\"PG-13\",\"Action\"\n\"48\",\"6/15/2005\",\"Batman Begins\",1.5e+08,205343774,359142722,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"49\",\"7/31/2015\",\"Mission: Impossible—Rogue Nation\",1.5e+08,195042377,689388363,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"50\",\"7/21/2017\",\"Dunkirk\",1.5e+08,190068280,499900860,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"51\",\"5/6/2011\",\"Thor\",1.5e+08,181030624,449326618,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"52\",\"11/7/2008\",\"Madagascar: Escape 2 Africa\",1.5e+08,180174880,599680774,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"53\",\"5/1/2009\",\"X-Men Origins: Wolverine\",1.5e+08,179883157,374825760,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"54\",\"5/26/2011\",\"Kung Fu Panda 2\",1.5e+08,165249063,664837547,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"55\",\"5/15/2015\",\"Mad Max: Fury Road\",1.5e+08,153636354,370651733,\"Warner Bros.\",\"R\",\"Action\"\n\"56\",\"8/10/2018\",\"The Meg\",1.5e+08,142700791,527100791,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"57\",\"11/5/2003\",\"The Matrix Revolutions\",1.5e+08,139270910,427300260,\"Warner Bros.\",\"R\",\"Action\"\n\"58\",\"3/29/2018\",\"Ready Player One\",1.5e+08,137018455,578621729,\"Warner Bros.\",\"PG-13\",\"Adventure\"\n\"59\",\"5/5/2006\",\"Mission: Impossible III\",1.5e+08,133501348,397501348,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"60\",\"5/14/2004\",\"Troy\",1.5e+08,133298577,484161265,\"Warner Bros.\",\"R\",\"Action\"\n\"61\",\"7/1/2010\",\"The Last Airbender\",1.5e+08,131772187,319713881,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"62\",\"11/2/2007\",\"Bee Movie\",1.5e+08,126631277,287594577,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"63\",\"7/24/2009\",\"G-Force\",1.5e+08,119436770,292817841,\"Walt Disney\",\"PG\",\"Adventure\"\n\"64\",\"11/21/2008\",\"Bolt\",1.5e+08,114053579,328015029,\"Walt Disney\",\"PG\",\"Adventure\"\n\"65\",\"3/30/2012\",\"Wrath of the Titans\",1.5e+08,83670083,305270083,\"Warner Bros.\",\"PG-13\",\"Adventure\"\n\"66\",\"11/16/2007\",\"Beowulf\",1.5e+08,82280579,195080579,\"Paramount Pictures\",\"PG-13\",\"Adventure\"\n\"67\",\"2/12/2010\",\"The Wolfman\",1.5e+08,62189884,142634358,\"Universal\",\"R\",\"Horror\"\n\"68\",\"2/17/2017\",\"The Great Wall\",1.5e+08,45157105,334550106,\"Universal\",\"PG-13\",\"Action\"\n\"69\",\"10/9/2015\",\"Pan\",1.5e+08,35088320,151543635,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"70\",\"3/11/2011\",\"Mars Needs Moms\",1.5e+08,21392758,39549758,\"Walt Disney\",\"PG\",\"Adventure\"\n\"71\",\"11/3/2006\",\"Flushed Away\",1.49e+08,64665672,179357126,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"72\",\"6/8/2012\",\"Madagascar 3: Europe's Most Wanted\",1.45e+08,216391482,746921271,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"73\",\"6/13/2014\",\"How to Train Your Dragon 2\",1.45e+08,177002924,614586270,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"74\",\"6/16/1999\",\"Tarzan\",1.45e+08,171091819,448191819,\"Walt Disney\",\"G\",\"Adventure\"\n\"75\",\"3/7/2014\",\"Mr. Peabody & Sherman\",1.45e+08,111506430,269806430,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"76\",\"11/21/2012\",\"Rise of the Guardians\",1.45e+08,103412758,306900902,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"77\",\"11/22/2002\",\"Die Another Day\",1.42e+08,160942139,431942139,\"MGM\",\"PG-13\",\"Action\"\n\"78\",\"5/8/2009\",\"Star Trek\",1.4e+08,257730019,385680446,\"Paramount Pictures\",\"PG-13\",\"Adventure\"\n\"79\",\"7/1/1998\",\"Armageddon\",1.4e+08,201578182,554600000,\"Walt Disney\",\"PG-13\",\"Adventure\"\n\"80\",\"7/3/2002\",\"Men in Black 2\",1.4e+08,190418803,441767803,\"Sony Pictures\",\"PG-13\",\"Action\"\n\"81\",\"7/22/2011\",\"Captain America: The First Avenger\",1.4e+08,176654505,370569776,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"82\",\"1/29/2016\",\"Kung Fu Panda 3\",1.4e+08,143528619,518418751,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"83\",\"7/10/1998\",\"Lethal Weapon 4\",1.4e+08,130444603,285400000,\"Warner Bros.\",\"R\",\"Action\"\n\"84\",\"3/27/2013\",\"G.I. Joe: Retaliation\",1.4e+08,122523060,375740705,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"85\",\"12/5/2003\",\"The Last Samurai\",1.4e+08,111110575,456810575,\"Warner Bros.\",\"R\",\"Action\"\n\"86\",\"12/21/2005\",\"Fun With Dick And Jane\",1.4e+08,110550000,203018919,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"87\",\"12/12/2014\",\"Exodus: Gods and Kings\",1.4e+08,65014513,268314513,\"20th Century Fox\",\"PG-13\",\"Drama\"\n\"88\",\"7/1/2016\",\"The BFG\",1.4e+08,55483770,199676255,\"Walt Disney\",\"PG\",\"Adventure\"\n\"89\",\"2/26/2016\",\"Gods of Egypt\",1.4e+08,31153464,138587563,\"Lionsgate\",\"PG-13\",\"Adventure\"\n\"90\",\"5/3/2002\",\"Spider-Man\",1.39e+08,403706375,821706375,\"Sony Pictures\",\"PG-13\",\"Adventure\"\n\"91\",\"3/6/2009\",\"Watchmen\",1.38e+08,107509799,186976250,\"Warner Bros.\",\"R\",\"Action\"\n\"92\",\"7/29/2005\",\"Stealth\",1.38e+08,32116746,76416746,\"Sony Pictures\",\"PG-13\",\"Action\"\n\"93\",\"6/13/2008\",\"The Incredible Hulk\",137500000,134806913,265573859,\"Universal\",\"PG-13\",\"Adventure\"\n\"94\",\"6/20/2003\",\"Hulk\",1.37e+08,132177234,245075434,\"Universal\",\"PG-13\",\"Action\"\n\"95\",\"7/11/2001\",\"Final Fantasy: The Spirits Within\",1.37e+08,32131830,85131830,\"Sony Pictures\",\"PG-13\",\"Adventure\"\n\"96\",\"3/22/2013\",\"The Croods\",1.35e+08,187168425,573068425,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"97\",\"12/25/2015\",\"The Revenant\",1.35e+08,183637894,532950503,\"20th Century Fox\",\"R\",\"Adventure\"\n\"98\",\"11/19/1999\",\"The World is Not Enough\",1.35e+08,126930660,361730660,\"MGM\",\"PG-13\",\"Action\"\n\"99\",\"3/4/2011\",\"Rango\",1.35e+08,123477607,245724600,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"100\",\"7/17/2013\",\"Turbo\",1.35e+08,83028130,286896578,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"101\",\"11/18/2011\",\"Happy Feet Two\",1.35e+08,64006466,157956466,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"102\",\"7/28/2006\",\"Miami Vice\",1.35e+08,63478838,163818556,\"Universal\",\"R\",\"Action\"\n\"103\",\"6/29/2005\",\"War of the Worlds\",1.32e+08,234280354,606836535,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"104\",\"11/26/2014\",\"Penguins of Madagascar\",1.32e+08,83350911,367650911,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"105\",\"11/22/2013\",\"The Hunger Games: Catching Fire\",1.3e+08,424668047,864868047,\"Lionsgate\",\"PG-13\",\"Adventure\"\n\"106\",\"7/6/2018\",\"Ant-Man and the Wasp\",1.3e+08,216565229,617176819,\"Walt Disney\",\"PG-13\",\"Action\"\n\"107\",\"6/6/2008\",\"Kung Fu Panda\",1.3e+08,215434591,631910531,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"108\",\"7/17/2015\",\"Ant-Man\",1.3e+08,180202163,518860086,\"Walt Disney\",\"PG-13\",\"Action\"\n\"109\",\"3/27/2015\",\"Home\",1.3e+08,177397510,386031994,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"110\",\"10/28/2011\",\"Puss in Boots\",1.3e+08,149260504,554987477,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"111\",\"11/5/2010\",\"Megamind\",1.3e+08,148415853,321887208,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"112\",\"7/18/2003\",\"Bad Boys II\",1.3e+08,138540870,273271982,\"Sony Pictures\",\"R\",\"Action\"\n\"113\",\"4/11/2014\",\"Rio 2\",1.3e+08,131538435,492846291,\"20th Century Fox\",\"G\",\"Adventure\"\n\"114\",\"3/28/2014\",\"Noah\",1.3e+08,101200044,352831065,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"115\",\"12/21/2011\",\"The Adventures of Tintin\",1.3e+08,77591831,373993951,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"116\",\"5/31/2013\",\"After Earth\",1.3e+08,60522097,251499665,\"Sony Pictures\",\"PG-13\",\"Action\"\n\"117\",\"11/26/2008\",\"Australia\",1.3e+08,49554002,215080810,\"20th Century Fox\",\"PG-13\",\"Drama\"\n\"118\",\"7/19/2013\",\"R.I.P.D.\",1.3e+08,33618855,79076678,\"Universal\",\"PG-13\",\"Action\"\n\"119\",\"5/19/2000\",\"Dinosaur\",127500000,137748063,356148063,\"Walt Disney\",\"PG\",\"Adventure\"\n\"120\",\"3/3/2017\",\"Logan\",1.27e+08,226277068,615476965,\"20th Century Fox\",\"R\",\"Action\"\n\"121\",\"5/2/2003\",\"X-Men 2\",1.25e+08,214949694,406875536,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"122\",\"4/29/2011\",\"Fast Five\",1.25e+08,210031325,630163454,\"Universal\",\"PG-13\",\"Action\"\n\"123\",\"12/16/2011\",\"Sherlock Holmes: A Game of Shadows\",1.25e+08,186848418,535663443,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"124\",\"5/28/2004\",\"The Day After Tomorrow\",1.25e+08,186740799,556319450,\"20th Century Fox\",\"PG-13\",\"Adventure\"\n\"125\",\"3/31/2017\",\"The Boss 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Bros.\",4.2e+07,20844907,20844907,\"Walt Disney\",\"PG\",\"Action\"\n\"909\",\"10/2/1992\",\"Hero\",4.2e+07,19487173,66787173,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"910\",\"4/18/1997\",\"McHale's Navy\",4.2e+07,4408420,4408420,\"Universal\",\"PG\",\"Comedy\"\n\"911\",\"5/28/2010\",\"Micmacs\",4.2e+07,1259693,11756922,\"Sony Pictures Classics\",\"R\",\"Comedy\"\n\"912\",\"11/8/2002\",\"8 Mile\",4.1e+07,116724075,245768384,\"Universal\",\"R\",\"Drama\"\n\"913\",\"5/11/2001\",\"A Knight’s Tale\",4.1e+07,56083966,100622586,\"Sony Pictures\",\"PG-13\",\"Adventure\"\n\"914\",\"8/22/2003\",\"The Medallion\",4.1e+07,22108977,22108977,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"915\",\"10/14/2011\",\"The Big Year\",4.1e+07,7204138,7684524,\"20th Century Fox\",\"PG\",\"Comedy\"\n\"916\",\"7/15/2005\",\"Wedding Crashers\",4e+07,209218368,283218368,\"New Line\",\"R\",\"Comedy\"\n\"917\",\"2/13/2015\",\"Fifty Shades of Grey\",4e+07,166167230,570998101,\"Universal\",\"R\",\"Drama\"\n\"918\",\"12/25/2003\",\"Cheaper by the Dozen\",4e+07,138614544,190212113,\"20th Century Fox\",\"PG\",\"Comedy\"\n\"919\",\"7/25/2014\",\"Lucy\",4e+07,126573960,457507776,\"Universal\",\"R\",\"Action\"\n\"920\",\"12/25/2013\",\"Lone Survivor\",4e+07,125095601,149804632,\"Universal\",\"R\",\"Action\"\n\"921\",\"11/22/1989\",\"Back to the Future Part II\",4e+07,118450002,3.32e+08,\"Universal\",\"PG\",\"Adventure\"\n\"922\",\"9/24/1999\",\"Double Jeopardy\",4e+07,116735231,177835231,\"Paramount Pictures\",\"R\",\"Action\"\n\"923\",\"7/25/2003\",\"Spy Kids 3-D: Game Over\",4e+07,111760631,167851995,\"Miramax/Dimension\",\"PG\",\"Adventure\"\n\"924\",\"7/24/1996\",\"A Time to Kill\",4e+07,108766007,152266007,\"Warner Bros.\",\"R\",\"Drama\"\n\"925\",\"7/1/1992\",\"A League of Their Own\",4e+07,107533925,132440066,\"Sony Pictures\",\"PG\",\"Comedy\"\n\"926\",\"10/1/2010\",\"The Social Network\",4e+07,96962694,224922135,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"927\",\"8/7/2009\",\"Julie & Julia\",4e+07,94125426,126646119,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"928\",\"2/10/2017\",\"John Wick: Chapter Two\",4e+07,92029184,171370497,\"Lionsgate\",\"R\",\"Action\"\n\"929\",\"1/15/2016\",\"Ride Along 2\",4e+07,90862685,124827316,\"Universal\",\"PG-13\",\"Comedy\"\n\"930\",\"4/14/2006\",\"Scary Movie 4\",4e+07,90710620,178710620,\"Weinstein/Dimension\",\"PG-13\",\"Comedy\"\n\"931\",\"3/27/2015\",\"Get Hard\",4e+07,90411453,106511453,\"Warner Bros.\",\"R\",\"Comedy\"\n\"932\",\"2/4/2000\",\"Scream 3\",4e+07,89138076,161838076,\"Miramax\",\"R\",\"Horror\"\n\"933\",\"5/24/1990\",\"Back to the Future Part III\",4e+07,88055283,244088654,\"Universal\",\"PG\",\"Adventure\"\n\"934\",\"11/14/2014\",\"Dumb and Dumber To\",4e+07,86208010,156553592,\"Universal\",\"PG-13\",\"Comedy\"\n\"935\",\"11/13/1992\",\"Bram Stoker's Dracula\",4e+07,82522790,215862692,\"Sony Pictures\",\"R\",\"Horror\"\n\"936\",\"2/17/2006\",\"Eight Below\",4e+07,81612565,120455994,\"Walt Disney\",\"PG\",\"Adventure\"\n\"937\",\"12/24/1999\",\"The Talented Mr. Ripley\",4e+07,81292135,128792135,\"Paramount Pictures\",\"R\",\"Drama\"\n\"938\",\"9/25/2015\",\"The Intern\",4e+07,75764672,197232734,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"939\",\"9/25/1992\",\"The Last of the Mohicans\",4e+07,75505856,75505856,\"20th Century Fox\",\"R\",\"Action\"\n\"940\",\"10/29/2004\",\"Ray\",4e+07,75305995,124823094,\"Universal\",\"PG-13\",\"Drama\"\n\"941\",\"4/1/2005\",\"Sin City\",4e+07,74103820,158527918,\"Miramax/Dimension\",\"R\",\"Action\"\n\"942\",\"3/20/2009\",\"I Love You, Man\",4e+07,72013010,92302502,\"Paramount Pictures\",\"R\",\"Comedy\"\n\"943\",\"12/20/1991\",\"JFK\",4e+07,70405498,205400000,\"Warner Bros.\",\"R\",\"Drama\"\n\"944\",\"1/27/2006\",\"Big Momma's House 2\",4e+07,70165972,137047376,\"20th Century Fox\",\"PG-13\",\"Comedy\"\n\"945\",\"11/4/2016\",\"Hacksaw Ridge\",4e+07,67209615,168940583,\"Lionsgate\",\"R\",\"Drama\"\n\"946\",\"3/2/2001\",\"The Mexican\",4e+07,66808615,145238250,\"Dreamworks SKG\",\"R\",\"Action\"\n\"947\",\"8/28/2009\",\"The Final Destination\",4e+07,66477700,187384627,\"Warner Bros.\",\"R\",\"Horror\"\n\"948\",\"4/17/2009\",\"17 Again\",4e+07,64167069,139474906,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"949\",\"6/4/2010\",\"Get Him to the Greek\",4e+07,61153526,91455875,\"Universal\",\"R\",\"Comedy\"\n\"950\",\"11/21/2003\",\"Gothika\",4e+07,59588068,141484812,\"Warner Bros.\",\"R\",\"Horror\"\n\"951\",\"11/30/2001\",\"Behind Enemy Lines\",4e+07,58855732,58855732,\"20th Century Fox\",\"PG-13\",\"Action\"\n\"952\",\"8/25/2006\",\"Invincible\",4e+07,57806952,58501127,\"Walt Disney\",\"PG\",\"Drama\"\n\"953\",\"2/15/2013\",\"Escape From Planet Earth\",4e+07,57012977,74156610,\"Weinstein Co.\",\"PG\",\"Adventure\"\n\"954\",\"7/10/1998\",\"Small Soldiers\",4e+07,55143823,71743823,\"Dreamworks SKG\",\"PG-13\",\"Adventure\"\n\"955\",\"7/31/1997\",\"Spawn\",4e+07,54979992,87949859,\"New Line\",\"PG-13\",\"Action\"\n\"956\",\"11/26/2014\",\"Horrible Bosses 2\",4e+07,54445357,105945357,\"Warner Bros.\",\"R\",\"Comedy\"\n\"957\",\"1/25/2002\",\"The Count of Monte Cristo\",4e+07,54228104,75389090,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"958\",\"6/16/2006\",\"The Lake House\",4e+07,52330111,114830111,\"Warner Bros.\",\"PG\",\"Drama\"\n\"959\",\"7/9/2010\",\"Predators\",4e+07,52000688,127234389,\"20th Century Fox\",\"R\",\"Action\"\n\"960\",\"8/15/2012\",\"The Odd Life of Timothy Green\",4e+07,51853450,55249159,\"Walt Disney\",\"PG\",\"Drama\"\n\"961\",\"7/31/1987\",\"The Living Daylights\",4e+07,51185000,191200000,\"MGM\",\"PG\",\"Action\"\n\"962\",\"12/8/2006\",\"Apocalypto\",4e+07,50866635,121032272,\"Walt Disney\",\"R\",\"Action\"\n\"963\",\"6/18/1986\",\"Legal Eagles\",4e+07,49851591,49851591,\"Universal\",\"PG\",\"Comedy\"\n\"964\",\"8/12/2005\",\"The Skeleton Key\",4e+07,47907715,92256918,\"Universal\",\"PG-13\",\"Horror\"\n\"965\",\"6/20/2014\",\"Jersey Boys\",4e+07,47047013,65282732,\"Warner Bros.\",\"R\",\"Drama\"\n\"966\",\"11/21/1997\",\"The Rainmaker\",4e+07,45916769,45916769,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"967\",\"2/7/1992\",\"Medicine Man\",4e+07,44948240,44948240,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"968\",\"12/12/1997\",\"Amistad\",4e+07,44212592,58250151,\"Dreamworks SKG\",\"R\",\"Drama\"\n\"969\",\"5/30/2014\",\"A Million Ways to Die in The West\",4e+07,42720965,86778557,\"Universal\",\"R\",\"Comedy\"\n\"970\",\"8/12/2011\",\"Final Destination 5\",4e+07,42587643,155011165,\"Warner Bros.\",\"R\",\"Horror\"\n\"971\",\"12/25/2007\",\"Aliens vs. Predator - Requiem\",4e+07,41797066,128884494,\"20th Century Fox\",\"R\",\"Action\"\n\"972\",\"12/25/2007\",\"The Water Horse: Legend of the Deep\",4e+07,40412817,103429755,\"Sony Pictures\",\"PG\",\"Drama\"\n\"973\",\"7/18/2014\",\"Sex Tape\",4e+07,38543473,126069509,\"Sony Pictures\",\"R\",\"Comedy\"\n\"974\",\"4/15/2011\",\"Scream 4\",4e+07,38180928,95989590,\"Weinstein/Dimension\",\"R\",\"Horror\"\n\"975\",\"12/21/1994\",\"Ri¢hie Ri¢h\",4e+07,38087756,38087756,\"Warner Bros.\",\"PG\",\"Comedy\"\n\"976\",\"8/11/2000\",\"Autumn in New York\",4e+07,37752931,90717684,\"MGM\",\"PG-13\",\"Drama\"\n\"977\",\"3/18/2011\",\"Paul\",4e+07,37412945,101162106,\"Universal\",\"R\",\"Comedy\"\n\"978\",\"12/19/2012\",\"The Guilt Trip\",4e+07,37134215,41294674,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"979\",\"2/18/2000\",\"Hanging Up\",4e+07,36037909,51867723,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"980\",\"3/1/1991\",\"The Doors\",4e+07,34416893,34416893,\"Sony Pictures\",\"R\",\"Drama\"\n\"981\",\"8/20/1999\",\"Mickey Blue Eyes\",4e+07,33864342,53864342,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"982\",\"10/20/2000\",\"Pay it Forward\",4e+07,33508922,55696705,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"983\",\"3/21/2008\",\"Drillbit Taylor\",4e+07,32862104,49686263,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"984\",\"12/25/2011\",\"Extremely Loud and Incredibly Close\",4e+07,31847881,55247881,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"985\",\"7/1/1994\",\"The Shadow\",4e+07,31835600,31835600,\"Universal\",\"PG-13\",\"Action\"\n\"986\",\"11/10/2010\",\"Morning Glory\",4e+07,31011732,59795070,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"987\",\"11/9/2005\",\"Get Rich or Die Tryin'\",4e+07,30981850,46666955,\"Paramount Pictures\",\"R\",\"Drama\"\n\"988\",\"12/25/2013\",\"Grudge Match\",4e+07,29807260,69807260,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"989\",\"4/2/1999\",\"The Out-of-Towners\",4e+07,28544120,28544120,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"990\",\"8/11/2017\",\"The Nut Job 2: Nutty by Nature\",4e+07,28370522,57465156,\"Open Road\",\"PG\",\"Adventure\"\n\"991\",\"8/23/1996\",\"The Island of Dr. Moreau\",4e+07,27682712,27682712,\"New Line\",\"PG-13\",\"Adventure\"\n\"992\",\"9/7/2001\",\"The Musketeer\",4e+07,27053815,27053815,\"Universal\",\"PG-13\",\"Adventure\"\n\"993\",\"1/27/2017\",\"Resident Evil: The Final Chapter\",4e+07,26844692,312825686,\"Sony Pictures\",\"R\",\"Action\"\n\"994\",\"2/29/2008\",\"The Other Boleyn Girl\",4e+07,26814957,78269970,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"995\",\"6/30/2017\",\"The House\",4e+07,25584504,31192743,\"Warner Bros.\",\"R\",\"Comedy\"\n\"996\",\"2/16/2001\",\"Sweet November\",4e+07,25288103,65754228,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"997\",\"4/5/2007\",\"The Reaping\",4e+07,25126214,62226214,\"Warner Bros.\",\"R\",\"Horror\"\n\"998\",\"6/3/1994\",\"Renaissance Man\",4e+07,24172899,24172899,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"999\",\"5/15/1998\",\"Quest for Camelot\",4e+07,22772500,38172500,\"Warner Bros.\",\"G\",\"Adventure\"\n\"1000\",\"9/6/2002\",\"City by the Sea\",4e+07,22433915,22433915,\"Warner Bros.\",\"R\",\"Drama\"\n\"1001\",\"1/15/1999\",\"At First Sight\",4e+07,22365133,22365133,\"MGM\",\"PG-13\",\"Drama\"\n\"1002\",\"1/16/2004\",\"Torque\",4e+07,21176322,46176322,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"1003\",\"11/13/2009\",\"Fantastic Mr. Fox\",4e+07,21002919,47083412,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"1004\",\"2/16/1996\",\"City Hall\",4e+07,20278055,20278055,\"Sony Pictures\",\"R\",\"Drama\"\n\"1005\",\"2/3/2012\",\"Big Miracle\",4e+07,20157300,25268680,\"Universal\",\"PG\",\"Drama\"\n\"1006\",\"12/21/2012\",\"The Impossible\",4e+07,19019882,169590606,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"1007\",\"3/9/2012\",\"A Thousand Words\",4e+07,18450127,20790486,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1008\",\"10/20/2006\",\"Marie Antoinette\",4e+07,15962471,60862471,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1009\",\"10/6/2000\",\"Get Carter\",4e+07,14967182,19417182,\"Warner Bros.\",\"R\",\"Drama\"\n\"1010\",\"4/21/1995\",\"Kiss of Death\",4e+07,14942422,14942422,\"20th Century Fox\",\"R\",\"Drama\"\n\"1011\",\"5/15/1987\",\"Ishtar\",4e+07,14375181,14375181,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1012\",\"2/28/1992\",\"Memoirs of an Invisible Man\",4e+07,14358033,14358033,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"1013\",\"10/23/2009\",\"Amelia\",4e+07,14279575,19756077,\"Fox Searchlight\",\"PG\",\"Drama\"\n\"1014\",\"5/7/2004\",\"New York Minute\",4e+07,14018364,21215882,\"Warner Bros.\",\"PG\",\"Comedy\"\n\"1015\",\"3/12/1999\",\"The Deep End of the Ocean\",4e+07,13508635,13508635,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"1016\",\"8/30/2002\",\"FearDotCom\",4e+07,13208023,13208023,\"Warner Bros.\",\"R\",\"Horror\"\n\"1017\",\"11/7/2008\",\"Soul Men\",4e+07,12082391,12345883,\"MGM\",\"R\",\"Comedy\"\n\"1018\",\"8/20/1999\",\"Universal Soldier II: The Return\",4e+07,10447421,10717421,\"Sony Pictures\",\"R\",\"Action\"\n\"1019\",\"9/25/2009\",\"Pandorum\",4e+07,10330853,17033431,\"Overture Films\",\"R\",\"Horror\"\n\"1020\",\"9/26/2003\",\"Duplex\",4e+07,9652000,10070651,\"Miramax\",\"PG-13\",\"Comedy\"\n\"1021\",\"11/27/2002\",\"Extreme Ops\",4e+07,4835968,12624471,\"Paramount Pictures\",\"PG-13\",\"Action\"\n\"1022\",\"4/6/2001\",\"Just Visiting\",4e+07,4777007,16172200,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"1023\",\"3/11/1994\",\"The Hudsucker Proxy\",4e+07,2816518,14938149,\"Warner Bros.\",\"PG\",\"Comedy\"\n\"1024\",\"11/11/2016\",\"Billy Lynn’s Long Halftime Walk\",4e+07,1738477,30230402,\"Sony Pictures\",\"R\",\"Drama\"\n\"1025\",\"12/12/2008\",\"Delgo\",4e+07,915840,915840,\"Freestyle Releasing\",\"PG\",\"Adventure\"\n\"1026\",\"9/7/2007\",\"The Hunting Party\",4e+07,876671,7729552,\"Weinstein Co.\",\"R\",\"Adventure\"\n\"1027\",\"10/13/2006\",\"Alex Rider: Operation Stormbreaker\",4e+07,659210,20722450,\"Weinstein Co.\",\"PG\",\"Action\"\n\"1028\",\"11/20/2009\",\"Red Cliff\",4e+07,627047,119627047,\"Magnolia Pictures\",\"R\",\"Action\"\n\"1029\",\"9/24/2004\",\"The Last Shot\",4e+07,463730,463730,\"Walt Disney\",\"R\",\"Comedy\"\n\"1030\",\"3/16/2007\",\"Nomad\",4e+07,79123,79123,\"Weinstein Co.\",\"R\",\"Drama\"\n\"1031\",\"11/11/2016\",\"USS Indianapolis: Men of Courage\",4e+07,0,1641255,\"Saban Films\",\"R\",\"Drama\"\n\"1032\",\"8/14/2009\",\"The Time Traveler's Wife\",3.9e+07,63414846,102332135,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1033\",\"6/17/1983\",\"Superman III\",3.9e+07,59950623,59950623,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"1034\",\"2/2/2007\",\"Because I Said So\",3.9e+07,42674040,69538833,\"Universal\",\"PG-13\",\"Comedy\"\n\"1035\",\"10/5/2012\",\"Frankenweenie\",3.9e+07,35287788,81150788,\"Walt Disney\",\"PG\",\"Adventure\"\n\"1036\",\"3/29/1996\",\"Sgt. 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Die\",2.5e+07,55973336,91036760,\"Warner Bros.\",\"R\",\"Action\"\n\"1471\",\"2/10/2006\",\"Final Destination 3\",2.5e+07,54098051,112798051,\"New Line\",\"R\",\"Horror\"\n\"1472\",\"4/22/2011\",\"Madea's Big Happy Family\",2.5e+07,53345287,54160818,\"Lionsgate\",\"PG-13\",\"Drama\"\n\"1473\",\"12/13/2013\",\"Tyler Perry's A Madea Christmas\",2.5e+07,52543354,52543354,\"Lionsgate\",\"PG-13\",\"Comedy\"\n\"1474\",\"11/12/2004\",\"Finding Neverland\",2.5e+07,51676606,115036108,\"Miramax\",\"PG\",\"Drama\"\n\"1475\",\"5/23/1986\",\"Cobra\",2.5e+07,49042224,49042224,\"Cannon\",\"R\",\"Action\"\n\"1476\",\"8/22/2008\",\"The House Bunny\",2.5e+07,48237389,71390601,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1477\",\"3/14/2003\",\"Agent Cody Banks\",2.5e+07,47545060,58240458,\"MGM\",\"PG\",\"Adventure\"\n\"1478\",\"1/27/2006\",\"Nanny 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Palace\",2.5e+07,10115014,11115766,\"20th Century Fox\",\"PG-13\",\"Drama\"\n\"1551\",\"8/16/2002\",\"Possession\",2.5e+07,10103647,14805812,\"Focus Features\",\"PG-13\",\"Drama\"\n\"1552\",\"5/17/1991\",\"Stone Cold\",2.5e+07,9286314,9286314,\"Sony Pictures\",\"R\",\"Action\"\n\"1553\",\"11/25/2009\",\"The Road\",2.5e+07,8114270,29206732,\"Weinstein Co.\",\"R\",\"Drama\"\n\"1554\",\"4/6/2007\",\"The Hoax\",2.5e+07,7164995,7164995,\"Walt Disney\",\"R\",\"Drama\"\n\"1555\",\"8/17/1984\",\"Sheena\",2.5e+07,5778353,5778353,\"Sony Pictures\",NA,\"Adventure\"\n\"1556\",\"3/23/2001\",\"Say It Isn't So\",2.5e+07,5516708,5516708,\"20th Century Fox\",\"R\",\"Comedy\"\n\"1557\",\"12/7/2005\",\"The World's Fastest Indian\",2.5e+07,5128124,18991288,\"Magnolia Pictures\",\"PG-13\",\"Drama\"\n\"1558\",\"3/1/1995\",\"Tank Girl\",2.5e+07,4064333,4064333,\"MGM\",\"R\",\"Action\"\n\"1559\",\"4/22/2005\",\"King's 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Mile\",2.1e+07,6830957,6830957,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"1671\",\"5/6/2011\",\"The Beaver\",2.1e+07,970816,5046038,\"Summit Entertainment\",\"PG-13\",\"Comedy\"\n\"1672\",\"2/24/2017\",\"Bitter Harvest\",2.1e+07,557241,606162,\"Roadside Attractions\",\"R\",\"Drama\"\n\"1673\",\"7/23/1982\",\"The Best Little Whorehouse in Texas\",20500000,69701637,69701637,\"Universal\",\"R\",\"Comedy\"\n\"1674\",\"8/11/2006\",\"Pulse\",20500000,20264436,30241435,\"Weinstein/Dimension\",\"R\",\"Horror\"\n\"1675\",\"6/12/1981\",\"Raiders of the Lost Ark\",2e+07,225686079,367452079,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"1676\",\"11/20/1992\",\"Home Alone 2: Lost in New York\",2e+07,173585516,358994850,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"1677\",\"11/16/1977\",\"Close Encounters of the Third Kind\",2e+07,169100479,340800479,\"Columbia\",\"PG\",\"Adventure\"\n\"1678\",\"5/20/1987\",\"Beverly Hills Cop 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USA\",1.7e+07,44480275,45707924,\"Walt Disney\",\"PG\",\"Drama\"\n\"1942\",\"11/11/2016\",\"Almost Christmas\",1.7e+07,42065185,42493506,\"Universal\",\"PG-13\",\"Drama\"\n\"1943\",\"3/10/2006\",\"The Hills Have Eyes\",1.7e+07,41778863,70355813,\"Fox Searchlight\",\"R\",\"Horror\"\n\"1944\",\"10/10/2003\",\"Good Boy!\",1.7e+07,37667746,45312217,\"MGM\",\"PG\",\"Adventure\"\n\"1945\",\"1/26/2007\",\"Smokin' Aces\",1.7e+07,35662731,57263440,\"Universal\",\"R\",\"Comedy\"\n\"1946\",\"10/2/1998\",\"A Night at the Roxbury\",1.7e+07,30331165,30331165,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1947\",\"3/4/2011\",\"Beastly\",1.7e+07,27865571,38028230,\"CBS Films\",\"PG-13\",\"Drama\"\n\"1948\",\"7/9/1982\",\"Tron\",1.7e+07,26918576,26918576,\"Walt Disney\",NA,\"Action\"\n\"1949\",\"8/20/2010\",\"Lottery Ticket\",1.7e+07,24719879,24719879,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"1950\",\"9/5/2003\",\"Dickie Roberts: Former Child Star\",1.7e+07,22734486,23734486,\"Paramount Pictures\",\"PG-13\",\"Comedy\"\n\"1951\",\"3/31/2006\",\"ATL\",1.7e+07,21170563,21170563,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"1952\",\"8/24/2001\",\"Summer Catch\",1.7e+07,19693891,19693891,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"1953\",\"12/11/1998\",\"A Simple Plan\",1.7e+07,16316273,16316273,\"Paramount Pictures\",\"R\",\"Drama\"\n\"1954\",\"11/27/2002\",\"Wes Craven Presents: They\",1.7e+07,12840842,16140842,\"Miramax/Dimension\",\"PG-13\",\"Horror\"\n\"1955\",\"7/24/1987\",\"Superman IV: The Quest for Peace\",1.7e+07,11227824,11227824,\"Warner Bros.\",\"PG\",\"Action\"\n\"1956\",\"1/25/2008\",\"How She Move\",1.7e+07,7070641,8607815,\"Paramount Vantage\",\"PG-13\",\"Drama\"\n\"1957\",\"2/24/2006\",\"Running Scared\",1.7e+07,6855137,9729088,\"New Line\",\"R\",\"Action\"\n\"1958\",\"8/24/2012\",\"The Apparition\",1.7e+07,4936819,10637281,\"Warner Bros.\",\"PG-13\",\"Horror\"\n\"1959\",\"4/30/2004\",\"Bobby Jones: Stroke of Genius\",1.7e+07,2694071,2694071,\"Film Foundry\",\"PG\",\"Drama\"\n\"1960\",\"12/25/2010\",\"L'illusionniste\",1.7e+07,2231474,8609949,\"Sony Pictures Classics\",\"PG\",\"Adventure\"\n\"1961\",\"1/1/1981\",\"Roar\",1.7e+07,2110050,2110050,NA,\"PG\",\"Adventure\"\n\"1962\",\"10/17/2003\",\"Veronica Guerin\",1.7e+07,1569918,9438074,\"Walt Disney\",\"R\",\"Drama\"\n\"1963\",\"6/10/2016\",\"Genius\",1.7e+07,1361045,6942889,\"Roadside Attractions\",\"PG-13\",\"Drama\"\n\"1964\",\"6/26/2015\",\"Escobar: Paradise Lost\",1.7e+07,195792,3917679,\"RADiUS-TWC\",\"R\",\"Drama\"\n\"1965\",\"3/11/2016\",\"The Young Messiah\",16800000,6469813,7313697,\"Focus Features\",\"PG-13\",\"Drama\"\n\"1966\",\"11/27/1991\",\"My Girl\",16500000,58011485,58011485,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1967\",\"12/11/1987\",\"Wall Street\",16500000,43848100,43848100,\"20th Century Fox\",\"R\",\"Drama\"\n\"1968\",\"12/11/1995\",\"Sense and Sensibility\",16500000,42993774,134993774,\"Sony Pictures\",\"PG\",\"Drama\"\n\"1969\",\"8/18/2006\",\"The Illusionist\",16500000,39868642,83792062,\"Yari Film Group Rel…\",\"PG-13\",\"Drama\"\n\"1970\",\"12/19/2003\",\"House of Sand and Fog\",16500000,13005485,16157923,\"Dreamworks SKG\",\"R\",\"Drama\"\n\"1971\",\"9/21/2007\",\"Sydney White\",16500000,11892415,13636339,\"Universal\",\"PG-13\",\"Comedy\"\n\"1972\",\"6/2/1989\",\"Dead Poets Society\",16400000,95860116,239500000,\"Walt Disney\",\"PG\",\"Drama\"\n\"1973\",\"12/16/1994\",\"Dumb & Dumber\",1.6e+07,127175374,246400000,\"New Line\",\"PG-13\",\"Comedy\"\n\"1974\",\"5/19/2000\",\"Road Trip\",1.6e+07,68525609,119739110,\"Dreamworks SKG\",\"R\",\"Comedy\"\n\"1975\",\"12/8/1982\",\"The Verdict\",1.6e+07,53977250,53977250,\"20th Century Fox\",\"R\",\"Drama\"\n\"1976\",\"1/15/1999\",\"Varsity Blues\",1.6e+07,52894169,54294169,\"Paramount Pictures\",\"R\",\"Drama\"\n\"1977\",\"5/25/2012\",\"Moonrise Kingdom\",1.6e+07,45512466,68848446,\"Focus Features\",\"PG-13\",\"Drama\"\n\"1978\",\"11/25/2011\",\"The Artist\",1.6e+07,44667095,128256712,\"Weinstein Co.\",\"PG-13\",\"Drama\"\n\"1979\",\"8/2/2002\",\"The Master of Disguise\",1.6e+07,40363530,40363530,\"Sony Pictures\",\"PG\",\"Adventure\"\n\"1980\",\"12/29/2006\",\"El Laberinto del Fauno\",1.6e+07,37634615,87041569,\"Picturehouse\",\"R\",\"Horror\"\n\"1981\",\"2/2/2007\",\"The Messengers\",1.6e+07,35374833,53774833,\"Sony Pictures\",\"PG-13\",\"Horror\"\n\"1982\",\"3/2/2001\",\"See Spot Run\",1.6e+07,33357476,43057552,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"1983\",\"8/9/1991\",\"Double Impact\",1.6e+07,29090445,29090445,\"Sony Pictures\",\"R\",\"Action\"\n\"1984\",\"6/27/2001\",\"Baby Boy\",1.6e+07,28734552,28734552,\"Sony Pictures\",\"R\",\"Drama\"\n\"1985\",\"4/11/2001\",\"Joe Dirt\",1.6e+07,27087695,30987695,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1986\",\"9/12/2008\",\"The Women\",1.6e+07,26902075,50103808,\"Picturehouse\",\"PG-13\",\"Comedy\"\n\"1987\",\"4/20/2007\",\"Hot Fuzz\",1.6e+07,23618786,81742618,\"Focus Features\",\"R\",\"Comedy\"\n\"1988\",\"8/15/2008\",\"Vicky Cristina Barcelona\",1.6e+07,23216709,104504817,\"MGM\",\"PG-13\",\"Comedy\"\n\"1989\",\"6/13/2018\",\"Superfly\",1.6e+07,20537137,20723581,\"Sony Pictures\",\"R\",\"Action\"\n\"1990\",\"3/12/2010\",\"Remember Me\",1.6e+07,19068240,56506120,\"Summit Entertainment\",\"PG-13\",\"Drama\"\n\"1991\",\"10/11/2002\",\"White Oleander\",1.6e+07,16357770,21657770,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"1992\",\"3/3/2000\",\"Drowning Mona\",1.6e+07,15427192,15980376,\"Destination Films\",\"PG-13\",\"Comedy\"\n\"1993\",\"1/30/1987\",\"Radio Days\",1.6e+07,14792779,14792779,\"Orion Pictures\",NA,\"Comedy\"\n\"1994\",\"7/18/2003\",\"How to Deal\",1.6e+07,14108518,14108518,\"New Line\",\"PG-13\",\"Drama\"\n\"1995\",\"5/28/2004\",\"Soul Plane\",1.6e+07,13922211,14553807,\"MGM\",\"R\",\"Comedy\"\n\"1996\",\"12/9/1988\",\"My Stepmother Is an Alien\",1.6e+07,13854000,13854000,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"1997\",\"6/29/2012\",\"People Like Us\",1.6e+07,12431792,12617472,\"Walt Disney\",\"PG-13\",\"Drama\"\n\"1998\",\"9/3/2004\",\"The Cookout\",1.6e+07,11540112,11540112,\"Lionsgate\",\"PG-13\",\"Comedy\"\n\"1999\",\"10/19/1979\",\"Meteor\",1.6e+07,8400000,8400000,\"American Internatio…\",NA,\"Action\"\n\"2000\",\"3/7/1986\",\"Highlander\",1.6e+07,5900000,12900000,\"20th Century Fox\",\"R\",\"Action\"\n\"2001\",\"11/18/2016\",\"Bleed for This\",1.6e+07,5083906,6603926,\"Open Road\",\"R\",\"Drama\"\n\"2002\",\"9/15/2000\",\"Duets\",1.6e+07,4734235,6615452,\"Walt Disney\",\"R\",\"Drama\"\n\"2003\",\"8/13/1999\",\"Detroit Rock City\",1.6e+07,4217115,5825314,\"New Line\",\"R\",\"Comedy\"\n\"2004\",\"10/19/2007\",\"Things We Lost in the Fire\",1.6e+07,3287315,8120148,\"Paramount Pictures\",\"R\",\"Drama\"\n\"2005\",\"5/16/2014\",\"The Immigrant\",1.6e+07,2013456,7585011,\"RADiUS-TWC\",\"R\",\"Drama\"\n\"2006\",\"8/15/1997\",\"Steel\",1.6e+07,1686429,1686429,\"Warner Bros.\",\"PG-13\",\"Action\"\n\"2007\",\"12/21/2005\",\"The White Countess\",1.6e+07,1669971,2814566,\"Sony Pictures Classics\",\"PG-13\",\"Drama\"\n\"2008\",\"10/1/2014\",\"Men, Women and Children\",1.6e+07,705908,1685403,\"Paramount Pictures\",\"R\",\"Comedy\"\n\"2009\",\"12/31/2008\",\"Good\",1.6e+07,31631,31631,\"ThinkFilm\",\"R\",\"Drama\"\n\"2010\",\"6/21/2002\",\"Juwanna Mann\",15600000,13571817,13771817,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"2011\",\"6/8/2007\",\"La Môme\",15500000,10299782,88611837,\"Picturehouse\",\"PG-13\",\"Drama\"\n\"2012\",\"11/15/2002\",\"Ararat\",15500000,1693000,1693000,\"Miramax\",\"R\",\"Drama\"\n\"2013\",\"4/22/2005\",\"Madison\",15500000,517262,517262,\"MGM\",\"PG\",\"Drama\"\n\"2014\",\"2/26/2010\",\"The Yellow Handkerchief\",15500000,318623,318623,\"Samuel Goldwyn Films\",\"PG-13\",\"Drama\"\n\"2015\",\"3/31/2006\",\"Slither\",15250000,7802450,12930343,\"Universal\",\"R\",\"Horror\"\n\"2016\",\"11/16/1990\",\"Home Alone\",1.5e+07,285761243,476684675,\"20th Century Fox\",\"PG\",\"Comedy\"\n\"2017\",\"12/5/1984\",\"Beverly Hills Cop\",1.5e+07,234760478,316300000,\"Paramount Pictures\",\"R\",\"Action\"\n\"2018\",\"5/16/1986\",\"Top Gun\",1.5e+07,179800601,356799634,\"Paramount Pictures\",\"PG\",\"Action\"\n\"2019\",\"12/17/1982\",\"Tootsie\",1.5e+07,177200000,177200000,\"Sony Pictures\",\"PG\",\"Comedy\"\n\"2020\",\"11/25/1987\",\"3 Men and a Baby\",1.5e+07,167780960,167780960,\"Walt Disney\",\"PG\",\"Comedy\"\n\"2021\",\"11/26/2010\",\"The King’s Speech\",1.5e+07,138797449,430821168,\"Weinstein Co.\",\"R\",\"Drama\"\n\"2022\",\"9/15/1999\",\"American Beauty\",1.5e+07,130058047,356258047,\"Dreamworks SKG\",\"R\",\"Drama\"\n\"2023\",\"12/8/2000\",\"Crouching Tiger, Hidden Dragon\",1.5e+07,128067808,213514672,\"Sony Pictures Classics\",\"PG-13\",\"Action\"\n\"2024\",\"12/9/1988\",\"Twins\",1.5e+07,111936388,216600000,\"Universal\",\"PG\",\"Comedy\"\n\"2025\",\"12/20/1996\",\"Scream\",1.5e+07,103046663,173046663,\"Miramax\",\"R\",\"Horror\"\n\"2026\",\"8/11/2017\",\"Annabelle: Creation\",1.5e+07,102092201,305385888,\"Warner Bros.\",\"R\",\"Horror\"\n\"2027\",\"10/25/2013\",\"Jackass Presents: Bad Grandpa\",1.5e+07,102003019,160903019,\"Paramount Pictures\",\"R\",\"Comedy\"\n\"2028\",\"6/28/1978\",\"Heaven Can Wait\",1.5e+07,98800000,98800000,\"Paramount Pictures\",\"PG\",\"Comedy\"\n\"2029\",\"12/18/1985\",\"The Color Purple\",1.5e+07,93589701,93589701,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"2030\",\"11/28/2014\",\"The Imitation Game\",1.5e+07,91125143,227773686,\"Weinstein Co.\",\"PG-13\",\"Drama\"\n\"2031\",\"3/30/1988\",\"Beetlejuice\",1.5e+07,73326666,73326666,\"Warner Bros.\",\"PG\",\"Comedy\"\n\"2032\",\"11/18/1959\",\"Ben-Hur\",1.5e+07,7.3e+07,7.3e+07,\"MGM\",\"G\",\"Adventure\"\n\"2033\",\"1/18/2013\",\"Mama\",1.5e+07,71628180,148095566,\"Universal\",\"PG-13\",\"Horror\"\n\"2034\",\"10/10/1980\",\"Private Benjamin\",1.5e+07,69847348,69847348,\"Warner Bros.\",\"R\",\"Comedy\"\n\"2035\",\"3/7/1980\",\"Coal Miner's Daughter\",1.5e+07,67182787,67182787,\"Universal\",\"PG\",\"Drama\"\n\"2036\",\"3/6/1987\",\"Lethal Weapon\",1.5e+07,65192350,120192350,\"Warner Bros.\",\"R\",\"Action\"\n\"2037\",\"3/19/2010\",\"Diary of a Wimpy Kid\",1.5e+07,64003625,76954311,\"20th Century Fox\",\"PG\",\"Adventure\"\n\"2038\",\"7/29/1983\",\"National Lampoon’s Vacation\",1.5e+07,61400000,61400000,\"Warner Bros.\",\"R\",\"Comedy\"\n\"2039\",\"9/30/2006\",\"The Queen\",1.5e+07,56441711,128885873,\"Miramax\",\"PG-13\",\"Drama\"\n\"2040\",\"12/21/1994\",\"Little Women\",1.5e+07,50003303,50003303,\"Sony Pictures\",\"PG\",\"Drama\"\n\"2041\",\"1/1/1979\",\"The Deer Hunter\",1.5e+07,5e+07,50009253,\"Universal\",\"R\",\"Drama\"\n\"2042\",\"2/3/2006\",\"When a Stranger Calls\",1.5e+07,47860214,67215435,\"Sony Pictures\",\"PG-13\",\"Horror\"\n\"2043\",\"2/8/2002\",\"Big Fat Liar\",1.5e+07,47811275,52461017,\"Universal\",\"PG\",\"Adventure\"\n\"2044\",\"8/15/1997\",\"Cop Land\",1.5e+07,44906632,63706632,\"Miramax\",\"R\",\"Drama\"\n\"2045\",\"12/25/1997\",\"Wag the Dog\",1.5e+07,43057470,64252038,\"New Line\",\"R\",\"Drama\"\n\"2046\",\"5/2/2003\",\"The Lizzie McGuire Movie\",1.5e+07,42734455,55534455,\"Walt Disney\",\"PG\",\"Adventure\"\n\"2047\",\"12/25/1998\",\"The Faculty\",1.5e+07,40283321,40283321,\"Miramax\",\"R\",\"Horror\"\n\"2048\",\"6/9/1993\",\"What's Love Got to Do With It\",1.5e+07,39100956,39100956,\"Walt Disney\",\"R\",\"Drama\"\n\"2049\",\"12/14/2001\",\"Not Another Teen Movie\",1.5e+07,37882551,62401343,\"Sony Pictures\",\"R\",\"Comedy\"\n\"2050\",\"12/3/2014\",\"Wild\",1.5e+07,37880356,52460543,\"Fox Searchlight\",\"R\",\"Drama\"\n\"2051\",\"12/16/1962\",\"Lawrence of Arabia\",1.5e+07,37495385,69995385,\"Sony Pictures\",\"PG\",\"Adventure\"\n\"2052\",\"11/7/2014\",\"The Theory of Everything\",1.5e+07,35893537,123327692,\"Focus Features\",\"PG-13\",\"Drama\"\n\"2053\",\"9/16/2011\",\"Drive\",1.5e+07,35060689,81357930,\"FilmDistrict\",\"R\",\"Action\"\n\"2054\",\"4/18/2003\",\"Malibu's Most Wanted\",1.5e+07,34308901,34499204,\"Warner Bros.\",\"PG-13\",\"Comedy\"\n\"2055\",\"4/28/2000\",\"Where the Heart Is\",1.5e+07,33771174,40862054,\"20th Century Fox\",\"PG-13\",\"Drama\"\n\"2056\",\"8/28/2009\",\"Halloween 2\",1.5e+07,33392973,38512850,\"Weinstein/Dimension\",\"R\",\"Horror\"\n\"2057\",\"3/13/2009\",\"The Last House on the 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Everything\",1e+07,34121140,61604439,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"2411\",\"9/17/2010\",\"Devil\",1e+07,33679655,63354114,\"Universal\",\"PG-13\",\"Horror\"\n\"2412\",\"11/22/2002\",\"Friday After Next\",1e+07,33253609,33526835,\"New Line\",\"R\",\"Comedy\"\n\"2413\",\"3/22/1985\",\"The Last Dragon\",1e+07,3.3e+07,3.3e+07,\"Sony Pictures\",NA,\"Action\"\n\"2414\",\"4/28/2017\",\"How to Be a Latin Lover\",1e+07,32149404,62556228,\"Lionsgate\",\"PG-13\",\"Comedy\"\n\"2415\",\"3/6/1992\",\"The Lawnmower Man\",1e+07,32100816,32100816,\"New Line\",\"R\",\"Action\"\n\"2416\",\"10/3/2008\",\"Nick and Norah's Infinite Playlist\",1e+07,31487293,33886017,\"Sony Pictures\",\"PG-13\",\"Drama\"\n\"2417\",\"12/19/2003\",\"Calendar Girls\",1e+07,31011616,93074616,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"2418\",\"11/12/1999\",\"Dogma\",1e+07,30651422,43948865,\"Lionsgate\",\"R\",\"Comedy\"\n\"2419\",\"9/20/2002\",\"The Banger Sisters\",1e+07,30306281,38067218,\"20th Century Fox\",\"R\",\"Comedy\"\n\"2420\",\"5/19/1989\",\"Road House\",1e+07,30050028,30050028,\"United Artists\",\"R\",\"Action\"\n\"2421\",\"7/27/2018\",\"Teen Titans Go! To The Movies\",1e+07,29562341,51411600,\"Warner Bros.\",\"PG\",\"Adventure\"\n\"2422\",\"6/24/1983\",\"Twilight Zone: The Movie\",1e+07,29500000,29500000,\"Warner Bros.\",\"PG\",\"Horror\"\n\"2423\",\"11/23/1994\",\"A Low Down Dirty Shame\",1e+07,29317886,29317886,\"Walt Disney\",\"R\",\"Action\"\n\"2424\",\"9/6/2002\",\"Swimfan\",1e+07,28564995,34084228,\"20th Century Fox\",\"PG-13\",\"Drama\"\n\"2425\",\"10/6/2006\",\"Employee of the Month\",1e+07,28444855,38364855,\"Lionsgate\",\"PG-13\",\"Comedy\"\n\"2426\",\"8/21/2015\",\"Sinister 2\",1e+07,27740955,54104225,\"Focus Features\",\"R\",\"Horror\"\n\"2427\",\"3/25/1983\",\"The Outsiders\",1e+07,25697647,25697647,\"Warner Bros.\",\"PG-13\",\"Drama\"\n\"2428\",\"6/12/1998\",\"Can't Hardly Wait\",1e+07,25358996,25358996,\"Sony Pictures\",\"PG-13\",\"Comedy\"\n\"2429\",\"4/26/2013\",\"Mud\",1e+07,21590086,31556959,\"Roadside Attractions\",\"PG-13\",\"Drama\"\n\"2430\",\"9/16/2016\",\"Blair Witch\",1e+07,20777061,37478274,\"Lionsgate\",\"R\",\"Horror\"\n\"2431\",\"10/21/1983\",\"The Dead Zone\",1e+07,20766000,20766000,\"Paramount Pictures\",NA,\"Horror\"\n\"2432\",\"2/2/2001\",\"Valentine\",1e+07,20384136,20384136,\"Warner Bros.\",\"R\",\"Horror\"\n\"2433\",\"6/9/2006\",\"A Prairie Home Companion\",1e+07,20342852,26716191,\"Picturehouse\",\"PG-13\",\"Comedy\"\n\"2434\",\"2/23/2007\",\"Reno 911!: Miami\",1e+07,20342161,21851362,\"20th Century Fox\",\"R\",\"Comedy\"\n\"2435\",\"7/24/1998\",\"Jane Austen's Mafia\",1e+07,19843795,30143795,\"Walt Disney\",\"PG-13\",\"Comedy\"\n\"2436\",\"2/25/1994\",\"Sugar Hill\",1e+07,18272447,18423914,\"20th Century Fox\",\"R\",\"Drama\"\n\"2437\",\"6/20/2008\",\"Kit Kittredge: An American Girl\",1e+07,17657973,17657973,\"Picturehouse\",\"G\",\"Drama\"\n\"2438\",\"9/27/1985\",\"Invasion U.S.A.\",1e+07,17536256,17536256,\"Cannon\",\"R\",\"Action\"\n\"2439\",\"9/23/2005\",\"Roll Bounce\",1e+07,17380866,17433072,\"Fox Searchlight\",\"PG-13\",\"Comedy\"\n\"2440\",\"1/19/1990\",\"Tremors\",1e+07,16667084,16667084,\"Universal\",\"PG-13\",\"Action\"\n\"2441\",\"8/3/1990\",\"Mo' Better Blues\",1e+07,16153000,16153000,\"Universal\",\"R\",\"Drama\"\n\"2442\",\"1/25/2002\",\"Kung Pow: Enter the Fist\",1e+07,16033556,17033556,\"20th Century Fox\",\"PG-13\",\"Comedy\"\n\"2443\",\"10/7/2016\",\"The Birth of a Nation\",1e+07,15861566,16891011,\"Fox Searchlight\",\"R\",\"Drama\"\n\"2444\",\"5/30/2003\",\"Wrong Turn\",1e+07,15417771,28649556,\"20th Century Fox\",\"R\",\"Horror\"\n\"2445\",\"5/16/1980\",\"The Long Riders\",1e+07,15198912,15198912,\"United Artists\",NA,\"Action\"\n\"2446\",\"3/12/1999\",\"The Corruptor\",1e+07,15164492,15164492,\"New Line\",\"R\",\"Action\"\n\"2447\",\"8/14/2009\",\"The Goods: Live Hard, Sell Hard\",1e+07,15122676,15297318,\"Paramount Vantage\",\"R\",\"Comedy\"\n\"2448\",\"11/23/2011\",\"My Week with Marilyn\",1e+07,14597405,34240572,\"Weinstein Co.\",\"R\",\"Drama\"\n\"2449\",\"12/25/2014\",\"Big Eyes\",1e+07,14482031,27317872,\"Weinstein Co.\",\"PG-13\",\"Drama\"\n\"2450\",\"6/28/2002\",\"Hey Arnold! The Movie\",1e+07,13684949,13684949,\"Paramount Pictures\",\"PG\",\"Adventure\"\n\"2451\",\"3/14/1997\",\"Love Jones\",1e+07,12554569,12554569,\"New Line\",\"R\",\"Drama\"\n\"2452\",\"1/20/2006\",\"End of the Spear\",1e+07,11748661,11924041,\"M Power Releasing\",\"PG-13\",\"Drama\"\n\"2453\",\"10/20/2000\",\"The Legend of Drunken Master\",1e+07,11546543,11546543,\"Miramax\",\"R\",\"Action\"\n\"2454\",\"7/23/1999\",\"Drop Dead Gorgeous\",1e+07,10571408,10571408,\"New Line\",\"PG-13\",\"Comedy\"\n\"2455\",\"4/3/1998\",\"The Spanish Prisoner\",1e+07,10162034,13835130,\"Sony Pictures Classics\",\"PG\",\"Drama\"\n\"2456\",\"6/11/1999\",\"Le Violon rouge\",1e+07,10019109,10019109,\"Lionsgate\",\"R\",\"Drama\"\n\"2457\",\"7/9/2004\",\"Sleepover\",1e+07,9408183,9408183,\"MGM\",\"PG\",\"Adventure\"\n\"2458\",\"1/25/2013\",\"Movie 43\",1e+07,8840453,31164747,\"Relativity\",\"R\",\"Comedy\"\n\"2459\",\"5/21/2010\",\"MacGruber\",1e+07,8525600,8629895,\"Universal\",\"R\",\"Comedy\"\n\"2460\",\"7/18/2003\",\"Dirty Pretty Things\",1e+07,8112414,14156753,\"Miramax\",\"R\",\"Drama\"\n\"2461\",\"3/14/2014\",\"Bad Words\",1e+07,7779614,7843145,\"Focus Features\",\"R\",\"Comedy\"\n\"2462\",\"3/27/2015\",\"While We're Young\",1e+07,7582065,14956484,\"A24\",\"R\",\"Comedy\"\n\"2463\",\"2/1/2008\",\"Over Her Dead Body\",1e+07,7570127,21596074,\"New Line\",\"PG-13\",\"Comedy\"\n\"2464\",\"10/24/2001\",\"Bones\",1e+07,7316658,8378853,\"New Line\",\"R\",\"Horror\"\n\"2465\",\"2/11/2011\",\"Cedar Rapids\",1e+07,6861102,7862131,\"Fox Searchlight\",\"R\",\"Comedy\"\n\"2466\",\"11/30/2012\",\"The Collection\",1e+07,6810754,8890094,\"LD Distribution\",\"R\",\"Horror\"\n\"2467\",\"10/30/1998\",\"American History X\",1e+07,6719864,6719864,\"New Line\",\"R\",\"Drama\"\n\"2468\",\"1/16/2004\",\"Teacher's Pet: The 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Me\",5e+06,0,0,\"Alchemy\",\"R\",\"Drama\"\n\"2894\",\"8/18/2014\",\"Henry & Me\",5e+06,0,0,\"Distrib Films\",\"PG\",\"Adventure\"\n\"2895\",\"1/23/2015\",\"Mommy\",4900000,3498695,17536004,\"Roadside Attractions\",\"R\",\"Drama\"\n\"2896\",\"11/20/1996\",\"Sling Blade\",4833610,24475416,34175000,\"Miramax\",\"R\",\"Drama\"\n\"2897\",\"1/6/2006\",\"Hostel\",4800000,47326473,82241110,\"Lionsgate\",\"R\",\"Horror\"\n\"2898\",\"9/30/2011\",\"Take Shelter\",4750000,1728953,4972016,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"2899\",\"8/22/1986\",\"The Texas Chainsaw Massacre 2\",4700000,8025872,8025872,\"Cannon\",NA,\"Horror\"\n\"2900\",\"4/22/1988\",\"Lady in White\",4700000,1705139,1705139,\"New Century Vista F…\",NA,\"Horror\"\n\"2901\",\"3/4/2005\",\"Dear Frankie\",4600000,1340891,3099369,\"Miramax\",\"PG-13\",\"Drama\"\n\"2902\",\"12/29/2004\",\"The Assassination of Richard Nixon\",4600000,708776,4880143,\"ThinkFilm\",\"R\",\"Drama\"\n\"2903\",\"6/24/2011\",\"Le nom des gens\",4600000,514237,9261711,\"Music Box Films\",\"R\",\"Comedy\"\n\"2904\",\"3/23/1984\",\"Police Academy\",4500000,81198894,81198894,\"Warner Bros.\",\"R\",\"Comedy\"\n\"2905\",\"6/20/1980\",\"The Blue Lagoon\",4500000,47923795,47923795,\"Universal\",\"R\",\"Drama\"\n\"2906\",\"8/13/1982\",\"Fast Times at Ridgemont High\",4500000,27092880,27092880,\"Universal\",NA,\"Comedy\"\n\"2907\",\"9/28/1996\",\"Secrets & Lies\",4500000,13417292,13417292,\"October Films\",\"R\",\"Drama\"\n\"2908\",\"12/19/2002\",\"25th Hour\",4500000,13084595,25344490,\"Walt Disney\",\"R\",\"Drama\"\n\"2909\",\"9/13/1985\",\"After Hours\",4500000,10609321,10609321,\"Warner Bros.\",NA,\"Comedy\"\n\"2910\",\"10/24/2008\",\"Låt den rätte komma in\",4500000,2122085,12247682,\"Magnolia Pictures\",\"R\",\"Horror\"\n\"2911\",\"2/12/1999\",\"Tango\",4500000,1687311,5428387,\"Sony Pictures Classics\",\"PG-13\",\"Drama\"\n\"2912\",\"4/23/1986\",\"Salvador\",4500000,1500000,1500000,\"Hemdale\",NA,\"Drama\"\n\"2913\",\"10/26/2001\",\"Donnie Darko\",4500000,1480006,7510877,\"Newmarket Films\",\"R\",\"Drama\"\n\"2914\",\"9/2/2011\",\"Salvando al Soldado Perez\",4500000,1400726,9330465,\"Lionsgate\",\"PG-13\",\"Action\"\n\"2915\",\"3/27/1998\",\"Karakter\",4500000,713413,713413,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"2916\",\"10/7/2011\",\"Blackthorn\",4500000,200558,1217307,\"Magnolia Pictures\",\"R\",\"Adventure\"\n\"2917\",\"5/8/2015\",\"Maggie\",4500000,187112,664346,\"Roadside Attractions\",\"PG-13\",\"Drama\"\n\"2918\",\"4/18/2003\",\"Lilja 4-ever\",4500000,181655,4556982,\"Newmarket Films\",\"R\",\"Drama\"\n\"2919\",\"4/9/2010\",\"After.Life\",4500000,108596,2481925,NA,\"R\",\"Horror\"\n\"2920\",\"3/1/2013\",\"The 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Darkness\",4e+06,10753574,10898293,\"High Top Releasing\",\"PG-13\",\"Horror\"\n\"2948\",\"11/14/2001\",\"The Wash\",4e+06,10097096,10097096,\"Lionsgate\",\"R\",\"Comedy\"\n\"2949\",\"3/1/2000\",\"3 Strikes\",4e+06,9821335,9821335,\"MGM\",\"R\",\"Comedy\"\n\"2950\",\"4/11/2008\",\"The Visitor\",4e+06,9427026,19174817,\"Overture Films\",\"PG-13\",\"Comedy\"\n\"2951\",\"11/26/2003\",\"The Cooler\",4e+06,8291572,11131455,\"Lionsgate\",\"R\",\"Drama\"\n\"2952\",\"8/4/2006\",\"The Night Listener\",4e+06,7836393,10770993,\"Miramax\",\"R\",\"Drama\"\n\"2953\",\"2/3/1995\",\"The Jerky Boys\",4e+06,7555256,7555256,\"Walt Disney\",\"R\",\"Comedy\"\n\"2954\",\"12/28/2007\",\"El orfanato\",4e+06,7159147,79250193,\"Picturehouse\",\"R\",\"Horror\"\n\"2955\",\"5/25/2007\",\"Bug\",4e+06,7006708,8302995,\"Lionsgate\",\"R\",\"Drama\"\n\"2956\",\"11/17/2006\",\"Let's Go to Prison\",4e+06,4630045,4630045,\"Universal\",\"R\",\"Comedy\"\n\"2957\",\"12/25/1995\",\"Four 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Guy\",1e+06,2057193,2315026,\"Fox Searchlight\",\"R\",\"Drama\"\n\"3280\",\"9/18/2009\",\"The Secrets of Jonathan Sperry\",1e+06,1355079,1355079,\"Five & Two Pictures\",\"PG\",\"Drama\"\n\"3281\",\"9/19/2003\",\"Bubba Ho-Tep\",1e+06,1239183,1492895,\"Vitagraph Films\",\"R\",\"Comedy\"\n\"3282\",\"12/7/2001\",\"No Man's Land\",1e+06,1067481,2684207,\"MGM\",\"R\",\"Drama\"\n\"3283\",\"10/9/1998\",\"Slam\",1e+06,1009819,1087521,\"Trimark\",\"R\",\"Drama\"\n\"3284\",\"8/18/2017\",\"Patti Cake$\",1e+06,800148,1471090,\"Fox Searchlight\",\"R\",\"Comedy\"\n\"3285\",\"12/1/2000\",\"Panic\",1e+06,779137,1425707,\"Roxie Releasing\",\"R\",\"Drama\"\n\"3286\",\"5/9/2014\",\"Palo Alto\",1e+06,767732,1156309,\"TriBeca Films\",\"R\",\"Drama\"\n\"3287\",\"7/29/2011\",\"The Future\",1e+06,568662,1239174,\"Roadside Attractions\",\"R\",\"Drama\"\n\"3288\",\"2/14/2003\",\"All the Real Girls\",1e+06,549666,703020,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3289\",\"10/24/2014\",\"23 Blast\",1e+06,549185,549185,\"Abramorama Films\",\"PG-13\",\"Drama\"\n\"3290\",\"6/20/1997\",\"Dream With The Fishes\",1e+06,542909,542909,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3291\",\"5/2/2003\",\"Blue Car\",1e+06,464126,475367,\"Miramax\",\"R\",\"Drama\"\n\"3292\",\"10/19/2007\",\"Wristcutters: A Love Story\",1e+06,446165,473769,\"Autonomous Films\",\"R\",\"Comedy\"\n\"3293\",\"5/5/2000\",\"Luminarias\",1e+06,428535,428535,NA,\"R\",\"Comedy\"\n\"3294\",\"7/18/2014\",\"I Origins\",1e+06,336472,852399,\"Fox Searchlight\",\"R\",\"Drama\"\n\"3295\",\"8/22/2003\",\"The Battle of Shaker Heights\",1e+06,280351,839145,\"Miramax\",\"PG-13\",\"Comedy\"\n\"3296\",\"12/30/2002\",\"Love Liza\",1e+06,213137,213137,NA,\"R\",\"Drama\"\n\"3297\",\"8/22/2001\",\"Lisa Picard is Famous\",1e+06,113433,113433,NA,\"PG-13\",\"Comedy\"\n\"3298\",\"10/30/2009\",\"The House of the Devil\",1e+06,101215,102812,\"Magnolia Pictures\",\"R\",\"Horror\"\n\"3299\",\"6/1/2012\",\"Hardflip\",1e+06,96734,96734,\"Rocky Mountain Pict…\",\"PG-13\",\"Drama\"\n\"3300\",\"3/11/2016\",\"Creative Control\",1e+06,63014,63014,\"Magnolia Pictures\",\"R\",\"Drama\"\n\"3301\",\"10/17/2014\",\"Camp X-Ray\",1e+06,9837,9837,\"IFC Films\",\"R\",\"Drama\"\n\"3302\",\"11/21/2008\",\"Special\",1e+06,7202,26822,\"Revolver Entertainment\",\"R\",\"Drama\"\n\"3303\",\"4/10/2015\",\"The Sisterhood of Night\",1e+06,6870,6870,\"Freestyle Releasing\",\"PG-13\",\"Drama\"\n\"3304\",\"3/18/2005\",\"The Helix…Loaded\",1e+06,3700,3700,\"Romar\",\"R\",\"Comedy\"\n\"3305\",\"5/15/2015\",\"Childless\",1e+06,1036,1036,\"Monterey Media\",\"R\",\"Drama\"\n\"3306\",\"4/21/2006\",\"In Her Line of Fire\",1e+06,884,884,\"Regent Releasing\",\"R\",\"Action\"\n\"3307\",\"9/15/2006\",\"Jimmy and Judy\",1e+06,0,0,\"Outrider Pictures\",\"R\",\"Action\"\n\"3308\",\"7/17/2009\",\"The Poker House\",1e+06,0,0,\"Phase 4 Films\",\"R\",\"Drama\"\n\"3309\",\"9/23/2005\",\"Proud\",1e+06,0,0,\"Castle Hill Product…\",\"PG\",\"Drama\"\n\"3310\",\"12/31/2008\",\"Steppin: The Movie\",1e+06,0,0,\"Weinstein Co.\",\"PG-13\",\"Comedy\"\n\"3311\",\"1/29/2010\",\"Zombies of Mass Destruction\",1e+06,0,0,\"After Dark\",\"R\",\"Comedy\"\n\"3312\",\"4/14/2006\",\"Hard Candy\",950000,1024640,8267066,\"Lionsgate\",\"R\",\"Horror\"\n\"3313\",\"9/27/2002\",\"Charly\",950000,814666,814666,\"Excel Entertainment\",\"PG\",\"Comedy\"\n\"3314\",\"4/13/2012\",\"L!fe Happens\",930000,30905,30905,\"PMK*BNC\",\"R\",\"Comedy\"\n\"3315\",\"5/12/2017\",\"Lowriders\",916000,6179955,6188421,\"BH Tilt\",\"PG-13\",\"Drama\"\n\"3316\",\"7/12/2013\",\"Fruitvale Station\",9e+05,16098998,17549645,\"Weinstein Co.\",\"R\",\"Drama\"\n\"3317\",\"4/1/2016\",\"Meet the Blacks\",9e+05,9097072,9097072,\"Freestyle Releasing\",\"R\",\"Comedy\"\n\"3318\",\"8/26/2011\",\"Circumstance\",9e+05,454121,958978,\"Roadside Attractions\",\"R\",\"Drama\"\n\"3319\",\"8/25/2006\",\"The Quiet\",9e+05,381420,381420,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3320\",\"8/13/1942\",\"Bambi\",858000,102797000,2.68e+08,\"RKO Radio Pictures\",\"G\",\"Drama\"\n\"3321\",\"8/31/2012\",\"For a Good Time, Call\",850000,1251749,1386088,\"Focus Features\",\"R\",\"Comedy\"\n\"3322\",\"1/30/2004\",\"Latter Days\",850000,833118,865708,\"TLA Releasing\",\"R\",\"Drama\"\n\"3323\",\"10/25/2002\",\"Time Changer\",825000,1500711,1500711,\"Five & Two Pictures\",\"PG\",\"Drama\"\n\"3324\",\"12/30/2011\",\"Jodaeiye Nader az Simin\",8e+05,7098492,24426169,\"Sony Pictures Classics\",\"PG-13\",\"Drama\"\n\"3325\",\"5/10/1996\",\"Welcome to the Dollhouse\",8e+05,4198137,5034794,\"Sony Pictures Classics\",\"R\",\"Comedy\"\n\"3326\",\"3/28/2003\",\"Raising Victor Vargas\",8e+05,2073984,2900578,\"Samuel Goldwyn Films\",\"R\",\"Drama\"\n\"3327\",\"10/1/1993\",\"Ruby in Paradise\",8e+05,1001437,1001437,NA,\"R\",\"Drama\"\n\"3328\",\"5/7/2004\",\"The Mudge Boy\",8e+05,62544,62544,\"Strand\",\"R\",\"Drama\"\n\"3329\",\"8/6/2004\",\"Saints and Soldiers\",780000,1310470,1310470,\"Excel Entertainment\",\"PG-13\",\"Drama\"\n\"3330\",\"8/11/1973\",\"American Graffiti\",777000,1.15e+08,1.4e+08,\"Universal\",\"PG\",\"Drama\"\n\"3331\",\"6/8/2012\",\"Safety Not Guaranteed\",750000,4010957,4422318,\"FilmDistrict\",\"R\",\"Comedy\"\n\"3332\",\"2/3/2012\",\"The Innkeepers\",750000,78396,1011535,\"Magnolia Pictures\",\"R\",\"Horror\"\n\"3333\",\"8/29/2014\",\"Il conformista\",750000,59656,89609,\"Kino Lorber\",\"R\",\"Drama\"\n\"3334\",\"7/1/2005\",\"Undead\",750000,41196,229250,\"Lionsgate\",\"R\",\"Horror\"\n\"3335\",\"10/11/2013\",\"All the Boys Love Mandy Lane\",750000,0,1960521,\"Radius\",\"R\",\"Horror\"\n\"3336\",\"6/25/1968\",\"La mariée était en noir\",747000,44566,44566,\"Film Forum\",NA,\"Drama\"\n\"3337\",\"8/11/2006\",\"Half Nelson\",7e+05,2697938,4911725,\"ThinkFilm\",\"R\",\"Drama\"\n\"3338\",\"6/19/1998\",\"Hav Plenty\",650000,2301777,2301777,\"Miramax\",\"R\",\"Comedy\"\n\"3339\",\"7/14/1999\",\"The Blair Witch Project\",6e+05,140539099,248300000,\"Artisan\",\"R\",\"Horror\"\n\"3340\",\"8/10/1977\",\"The Kentucky Fried Movie\",6e+05,1.5e+07,2e+07,\"United Film Distrib…\",NA,\"Comedy\"\n\"3341\",\"10/31/2000\",\"Mercy Streets\",6e+05,173599,173599,NA,\"PG-13\",\"Drama\"\n\"3342\",\"7/2/1999\",\"Broken Vessels\",6e+05,15030,85343,NA,\"R\",\"Drama\"\n\"3343\",\"5/22/2015\",\"Drunk Wedding\",6e+05,3301,3301,\"Paramount Pictures\",\"R\",\"Comedy\"\n\"3344\",\"8/11/1964\",\"A Hard Day's Night\",560000,1537860,1626784,\"Universal\",\"G\",\"Comedy\"\n\"3345\",\"5/9/1980\",\"Friday the 13th\",550000,39754601,59754601,\"Paramount Pictures\",NA,\"Horror\"\n\"3346\",\"9/26/2008\",\"Fireproof\",5e+05,33456317,33473297,\"Samuel Goldwyn Films\",\"PG\",\"Drama\"\n\"3347\",\"11/15/1974\",\"Benji\",5e+05,31559560,31559560,NA,\"G\",\"Adventure\"\n\"3348\",\"10/3/2003\",\"The Station Agent\",5e+05,5801558,9470209,\"Miramax\",\"R\",\"Drama\"\n\"3349\",\"1/22/2010\",\"To Save a Life\",5e+05,3777210,3824868,\"Samuel Goldwyn Films\",\"PG-13\",\"Drama\"\n\"3350\",\"2/1/2002\",\"The Singles Ward\",5e+05,1250798,1250798,\"Halestorm Entertain…\",\"PG\",\"Comedy\"\n\"3351\",\"1/30/2004\",\"Osama\",5e+05,1127331,1971479,\"MGM\",\"PG-13\",\"Drama\"\n\"3352\",\"6/9/2000\",\"Groove\",5e+05,1115313,1167524,\"Sony Pictures Classics\",\"R\",\"Comedy\"\n\"3353\",\"1/31/2003\",\"The R.M.\",5e+05,1111615,1111615,\"Halestone\",\"PG\",\"Comedy\"\n\"3354\",\"7/30/1999\",\"Twin Falls Idaho\",5e+05,985341,1027228,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3355\",\"8/20/2004\",\"Mean Creek\",5e+05,603951,1348750,\"Paramount Vantage\",\"R\",\"Drama\"\n\"3356\",\"8/23/2013\",\"Drinking Buddies\",5e+05,343706,407100,\"Magnolia Pictures\",\"R\",\"Drama\"\n\"3357\",\"2/13/1998\",\"Hurricane Streets\",5e+05,334041,367582,\"MGM\",NA,\"Drama\"\n\"3358\",\"8/29/2003\",\"Civil Brand\",5e+05,254293,254293,\"Lionsgate\",\"R\",\"Drama\"\n\"3359\",\"10/29/2010\",\"Monsters\",5e+05,237301,5639730,\"Magnet Pictures\",\"R\",\"Drama\"\n\"3360\",\"3/24/2006\",\"Lonesome Jim\",5e+05,154187,602789,\"IFC Films\",\"R\",\"Comedy\"\n\"3361\",\"12/11/2015\",\"O Menino e o Mundo\",5e+05,129479,271893,\"GKIDS\",\"PG\",\"Adventure\"\n\"3362\",\"1/1/1991\",\"Johnny Suede\",5e+05,55000,55000,\"Miramax\",\"R\",\"Drama\"\n\"3363\",\"10/21/2005\",\"The Californians\",5e+05,4134,4134,\"Fabrication Films\",\"PG\",\"Drama\"\n\"3364\",\"11/2/2001\",\"Everything Put Together\",5e+05,0,7890,NA,\"R\",\"Drama\"\n\"3365\",\"9/25/2009\",\"Paranormal Activity\",450000,107918810,194183034,\"Paramount Pictures\",\"R\",\"Horror\"\n\"3366\",\"3/31/2006\",\"Brick\",450000,2075743,4243996,\"Focus/Rogue Pictures\",\"R\",\"Drama\"\n\"3367\",\"8/22/1997\",\"Sunday\",450000,410919,450349,NA,NA,\"Drama\"\n\"3368\",\"8/11/2006\",\"Conversations with Other Women\",450000,379418,1297745,\"Fabrication Films\",\"R\",\"Comedy\"\n\"3369\",\"8/3/1990\",\"Metropolitan\",430000,2938000,2938000,NA,\"PG-13\",\"Comedy\"\n\"3370\",\"6/11/2004\",\"Napoleon Dynamite\",4e+05,44540956,46122713,\"Fox Searchlight\",\"PG\",\"Comedy\"\n\"3371\",\"5/10/1975\",\"Monty Python and the Holy Grail\",4e+05,3427696,5028948,NA,NA,\"Comedy\"\n\"3372\",\"8/2/2006\",\"Quinceanera\",4e+05,1692693,2797199,\"Sony Pictures Classics\",\"R\",\"Drama\"\n\"3373\",\"10/24/2008\",\"Heroes\",4e+05,655538,655538,\"Eros Entertainment\",\"R\",\"Adventure\"\n\"3374\",\"1/1/1983\",\"E tu vivrai nel terrore - L'aldilà\",4e+05,126387,126387,NA,NA,\"Horror\"\n\"3375\",\"7/27/2001\",\"Jackpot\",4e+05,44452,44452,NA,\"R\",\"Drama\"\n\"3376\",\"12/10/2004\",\"Fabled\",4e+05,31425,31425,\"Indican Pictures\",\"R\",\"Horror\"\n\"3377\",\"10/13/2005\",\"The Dark Hours\",4e+05,423,423,\"Freestyle Releasing\",\"R\",\"Horror\"\n\"3378\",\"4/1/1986\",\"My Beautiful Laundrette\",4e+05,0,0,\"Orion Classics\",NA,\"Drama\"\n\"3379\",\"1/1/1980\",\"Maniac\",350000,1e+07,1e+07,\"Analysis\",NA,\"Horror\"\n\"3380\",\"1/1/1987\",\"American Ninja 2: The Confrontation\",350000,4e+06,4e+06,NA,NA,\"Action\"\n\"3381\",\"4/13/1957\",\"12 Angry Men\",340000,0,0,\"United Artists\",NA,\"Drama\"\n\"3382\",\"10/17/1978\",\"Halloween\",325000,4.7e+07,7e+07,\"Compass International\",\"R\",\"Horror\"\n\"3383\",\"11/24/1999\",\"Tumbleweeds\",312000,1350248,1788168,\"Fine Line\",\"PG-13\",\"Drama\"\n\"3384\",\"3/10/2000\",\"God's Army\",3e+05,2637726,2652515,\"Excel Entertainment\",\"PG\",\"Drama\"\n\"3385\",\"10/17/2003\",\"Pieces of April\",3e+05,2528664,3571253,\"MGM\",\"PG-13\",\"Comedy\"\n\"3386\",\"9/20/1996\",\"When The Cat's Away\",3e+05,1652472,2525984,\"Sony Pictures Classics\",\"R\",\"Comedy\"\n\"3387\",\"12/10/2008\",\"Wendy and Lucy\",3e+05,865695,1416046,\"Oscilloscope Pictures\",\"R\",\"Drama\"\n\"3388\",\"9/11/1998\",\"Let's Talk About Sex\",3e+05,373615,373615,\"Fine Line\",NA,\"Comedy\"\n\"3389\",\"7/15/2005\",\"First Morning\",3e+05,87264,87264,\"Illuminare\",\"PG-13\",\"Drama\"\n\"3390\",\"3/11/2011\",\"3 Backyards\",3e+05,39475,39475,\"Screen Media Films\",\"R\",\"Drama\"\n\"3391\",\"8/7/1998\",\"First Love, Last Rites\",3e+05,10876,10876,\"Strand\",\"R\",\"Drama\"\n\"3392\",\"5/6/2005\",\"Fighting Tommy Riley\",3e+05,10514,10514,\"Freestyle Releasing\",\"R\",\"Drama\"\n\"3393\",\"8/17/2012\",\"Compliance\",270000,319285,830700,\"Magnolia Pictures\",\"R\",\"Drama\"\n\"3394\",\"6/28/2002\",\"Lovely and Amazing\",250000,4210379,4613482,\"Lionsgate\",\"R\",\"Drama\"\n\"3395\",\"4/28/2017\",\"Sleight\",250000,3930990,3934450,\"High Top Releasing\",\"R\",\"Action\"\n\"3396\",\"4/11/2003\",\"Better Luck Tomorrow\",250000,3802390,3809226,\"Paramount Pictures\",\"R\",\"Drama\"\n\"3397\",\"10/28/2011\",\"Like Crazy\",250000,3395391,3728400,\"Paramount Pictures\",\"PG-13\",\"Drama\"\n\"3398\",\"7/14/2000\",\"Chuck&Buck\",250000,1055671,1157672,\"Artisan\",\"R\",\"Drama\"\n\"3399\",\"3/28/1997\",\"Love and Other Catastrophes\",250000,212285,743216,\"Fox Searchlight\",\"R\",\"Comedy\"\n\"3400\",\"8/28/1998\",\"I Married a Strange Person\",250000,203134,203134,\"Lionsgate\",NA,\"Comedy\"\n\"3401\",\"7/22/2005\",\"November\",250000,191862,191862,\"Sony Pictures Classics\",\"R\",\"Drama\"\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/possum_classification_model_select/possum_classification_model_select.R",
    "content": "# load packages -----------------------------------------------------\n\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\ndata(possum)\n\n# recode data -------------------------------------------------------\n\nPop  <- ifelse(possum$pop == \"Vic\", 1, 0)\nSex  <- ifelse(possum$sex == \"m\", 1, 0)\n\n# model output ------------------------------------------------------\n\nxtable(glm(Pop ~ Sex + headL + skullW + totalL + tailL, binomial, possum))\nxtable(glm(Pop ~ Sex + skullW + totalL + tailL, binomial, possum))\n\n# plot of variables -------------------------------------------------\n\nmyPDF(\"possum_variables.pdf\", 8*0.9, 3.7*0.9, \n      mfrow=c(2, 3), mar=c(3.7, 3.5, 0.75, 0.75), mgp=c(2, 0.55, 0))\n\n#_____ sex _____#\npar(mar = c(3.7, 3.2, 0.75, 0.75))\nhistPlot(Sex, breaks = seq(-0.375, 1.375, 0.25), \n         col = COL[1], \n         axes = FALSE, xlab=\"\", ylab=\"Frequency\")\nmtext(\"sex_male\", 1, 2.5, cex = 0.7)\naxis(1, at = 0:1, labels = c(\"0\\n(Female)\", \"1\\n(Male)\"), mgp = c(2, 1.5, 0))\naxis(2, at = seq(0, 60, 20))\n\n#_____ head_length _____#\nhistPlot(possum$headL, breaks = 15, \n         col = COL[1], \n         xlab = \"head_length (in mm)\", ylab = \"Frequency\")\n\n#_____ skull_width _____#\nhistPlot(possum$skullW, breaks=15, \n         col = COL[1], \n         xlab = \"skull_width (in mm)\", ylab = \"Frequency\")\n\n#_____ total_length _____#\nhistPlot(possum$totalL, breaks = 18, \n         col = COL[1], \n         xlab = \"total_length (in cm)\", ylab = \"Frequency\", axes = FALSE)\naxis(1)\naxis(2, at = seq(0, 10, 5))\n\n#_____ tail_length _____#\nhistPlot(possum$tailL, breaks=18, \n         col = COL[1], \n         xlab = \"tail_length (in cm)\", ylab = \"Frequency\")\n\n#_____ population _____#\nhistPlot(Pop, breaks = seq(-0.375, 1.375, 0.25), \n         col = COL[1], \n         axes = FALSE, xlab = \"\", ylab = \"Frequency\")\naxis(1, at = 0:1, labels = c(\"0\\n(Not Victoria)\", \"1\\n(Victoria)\"), \n     mgp = c(2, 1.5, 0))\nmtext(\"population\", 1, 2.5, cex = 0.7)\naxis(2, at = seq(0, 60, 20))\n\ndev.off()\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/spam_filtering_model_sel/spam_filtering_model_sel.R",
    "content": "library(openintro)\nlibrary(xtable)\nd <- email\nnames(d)\n\ntable(d$sent_email, d$spam)\nSGlm <- function(form, data = d) {\n  m <- glm(\n      form,\n      data = d,\n      family = binomial)\n  summary(m)\n}\n\nvars <- c(\n    \"to_multiple\", \"cc\", \"attach\", \"dollar\",\n    \"winner\", \"inherit\", \"password\", \"format\",\n    \"re_subj\", \"exclaim_subj\", \"sent_email\")\nform <- spam ~ 1\nfor (v in vars) {\n  form <- update(form, paste(\". ~ . +\", v))\n}\nm <- glm(\n    form,\n    data = d,\n    family = binomial)\nsummary(m)\n\n# form <- update(form, . ~ . - exclaim_subj - cc)\n\naic <- c(\"Drop None\" = SGlm(form)$aic)\nvars. <- names(unlist(sapply(vars, grep, x = as.character(form)[3], fixed = TRUE)))\nfor (v in vars.) {\n  m. <- update(form, paste(\". ~ . -\", v))\n  aic[v] <- SGlm(m.)$aic\n}\n# aic <- unlist(aic)\nwhich.min(aic)\n# xtable(data.frame(cbind(aic, aic[1] - aic)))\nxtable(data.frame(aic))\n\n\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/eoce/spam_filtering_predict/spam_filtering_predict.R",
    "content": "library(openintro)\nlibrary(xtable)\nd <- email\nnames(d)\n\ntable(d$sent_email, d$spam)\nSGlm <- function(form, data = d) {\n  m <- glm(\n      form,\n      data = d,\n      family = binomial)\n  summary(m)\n}\n\nvars <- c(\n    \"to_multiple\", \"cc\", \"attach\", \"dollar\",\n    \"winner\", \"inherit\", \"password\", \"format\",\n    \"re_subj\", \"exclaim_subj\", \"sent_email\")\nform <- spam ~ 1\nfor (v in vars) {\n  form <- update(form, paste(\". ~ . +\", v))\n}\nform <- update(form, . ~ . - exclaim_subj - cc - inherit - password - sent_email - dollar - attach)\nm <- glm(\n    form,\n    data = d,\n    family = binomial)\nsummary(m)\nxtable(summary(m))\n\nwhich.max(predict(m))\nmax(predict(m, type = \"response\"))\n\n\n\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/loansDiagnostics/loans_analysis.R",
    "content": "library(xtable)\nlibrary(openintro)\nd <- loans_full_schema\nd$credit_util <- round(ifelse(d$total_credit_limit == 0, 0,\n    d$total_credit_utilized / d$total_credit_limit), 4)\nd$past_bankr <- (d$public_record_bankrupt > 0) + 0\nd$ver_income <- ifelse(d$verified_income == \"Verified\", \"verified\",\n    ifelse(d$verified_income == \"Not Verified\", \"not\", \"source_only\"))\nd$credit_checks <- d$inquiries_last_12m\nd$issued <- gsub(\"-\", \"\", d$issue_month, fixed = TRUE)\nthese <- d$annual_income %in% 0:1\nd$debt_to_income[these] <- d$total_credit_utilized[these] /\n    d$annual_income_joint[these]\nd$sqrt_debt_to_income <- sqrt(d$debt_to_income)\nd$debt_to_income_50 <-\n    ifelse(d$debt_to_income > 50, 50, d$debt_to_income)\nkeep <- c(\n    \"interest_rate\",\n    \"ver_income\",\n    \"debt_to_income\",\n    \"sqrt_debt_to_income\",\n    \"debt_to_income_50\",\n    \"credit_util\",\n    \"past_bankr\",\n    \"term\",\n    # \"issued\",\n    \"credit_checks\")\nd <- d[keep]\n\nF <- function(x, sub = 1:length(x)) {\n  as.formula(paste(\"interest_rate ~\", paste(x[sub], collapse = \"+\")))\n}\nsummary(fit <- lm(F(keep[-c(1, 4, 5)]), d))\nxtable(summary(fit))\n\ne <- fit$res\nf <- fit$fit\n\nint_rate_at <- seq(-30, 30, 5)\nIntRateAxis <- function(at) {\n  AxisInPercent(2, at)\n}\ngrid_lines_color <- COL[7, 3]\npt_col <- COL[1, 4]\n\nmyPDF(\"loansDiagNormalQuantilePlot.pdf\", 4.5, 3.7,\n    mgp = c(2.5,0.6,0))\nqqnorm(e,\n    ylab = \"Residuals\",\n    main = \"\",\n    col = COL[1,2],\n    pch = 19)\ndev.off()\n\nmyPDF(\"loansDiagNormalHistogram.pdf\", 6, 3.7,\n    mar = c(3.9, 4, 0.5, 0.5), mgp = c(2.5,0.6,0))\nhistPlot(e,\n    xlab = \"Residuals\",\n    ylab = \"\",\n    col = COL[1],\n    axes = FALSE)\nAxisInPercent(1, pretty(e))\naxis(2)\npar(las = 0)\nmtext(\"Frequency\", 2, 2.9)\ndev.off()\n\nmyPDF(\"ignore-loansDiagInOrder.pdf\", 5.65, 3.9,\n    mgp = c(2.5, 0.6, 0))\nplot(e,\n    xlab = \"Order of collection\",\n    ylab = \"Residuals\",\n    axes = FALSE,\n    type = \"n\")\naxis(1)\nIntRateAxis(int_rate_at)\nabline(h = int_rate_at, col = grid_lines_color, lwd = 1)\npoints(e, col = pt_col, pch = 19)\nbox()\ndev.off()\n\nmyPDF(\"loansDiagEvsF.pdf\", 5.65, 4.61,\n    mgp = c(2.5, 0.6, 0))\nplot(f, e,\n    xlab = \"Fitted values\",\n    ylab = \"Residuals\",\n    axes = FALSE)\naxis(1)\nIntRateAxis(int_rate_at)\nabline(h = int_rate_at, col = grid_lines_color, lwd = 1)\npoints(f, e, col = pt_col, pch = 19)\nbox()\ndev.off()\n\nmyPDF(\"loansDiagEvsAbsF.pdf\", 5.5, 3.7,\n    mgp = c(2.5, 0.6, 0))\nplot(f, abs(e),\n    xlab = \"Fitted Values\",\n    ylab = \"Absolute Value of Residuals\",\n    axes = FALSE,\n    type = \"n\")\naxis(1)\nIntRateAxis(int_rate_at)\nabline(h = int_rate_at, col = grid_lines_color, lwd = 1)\npoints(f, abs(e), col = pt_col, pch = 19)\nsmooth <- loess(abs(e) ~ f)\no <- order(smooth$x)\nlines(smooth$x[o], smooth$fitted[o],\n    lwd = 2, col = COL[7,3])\nlines(smooth$x[o], smooth$fitted[o],\n    lwd = 2, lty = 2, col = COL[2])\n\nbox()\ndev.off()\n\n\nPlotCatVar <- function(x, xlab, key) {\n  if (missing(key)) {\n    key <- unique(d[[x]])\n  }\n  boxPlot(e, d[[x]],\n      xlab = \"\",\n      ylab = \"Residuals\",\n      axes = FALSE,\n      lcol = \"#00000000\",\n      col = \"#00000000\",\n      key = key)\n  mtext(xlab, 1, line = 2)\n  n_levels <- length(unique(d[[x]]))\n  axis(1, at = 1:n_levels, key)\n  IntRateAxis(int_rate_at)\n  abline(h = int_rate_at, col = grid_lines_color, lwd = 1)\n  boxPlot(e, d[[x]], add = 1:n_levels, axes = FALSE,\n      lcol = COL[1], col = COL[1, 4])\n  box()\n}\n\nPlotNumVar <- function(x, xlab) {\n  plot(d[[x]], e,\n      xlab = \"\",\n      ylab = \"Residuals\",\n      axes = FALSE,\n      type = \"n\")\n  mtext(xlab, 1, line = 2)\n  axis(1)\n  IntRateAxis(int_rate_at)\n  abline(h = int_rate_at, v = pretty(d[[x]]),\n      col = grid_lines_color, lwd = 1)\n  points(d[[x]], e, col = pt_col, pch = 19)\n  smooth <- loess(e ~ d[[x]])\n  o <- order(smooth$x)\n  # polygon(smooth$one.delta\n  sx <- unique(smooth$x[o])\n  sy <- smooth$fitted[o][match(sx, smooth$x[o])]\n  lines(sx, sy, lwd = 2, col = COL[7,3])\n  lines(sx, sy, lwd = 2, lty = 2, col = COL[2])\n  box()\n}\n\n\nmgp <- c(2.5, 0.6, 0)\nmar_left <- c(4.1, 3.8, 0.9, 2)\nmar_right <- c(4.1, 5.6, 0.9, 0.4)\nw <- 7.5\nh <- 3.3\nmyPDF(\"loansDiagEvsVariables_1.pdf\", w, h,\n    mgp = mgp, mfrow = c(1, 2), mar = mar_left)\nPlotCatVar(\"ver_income\", \"Verified Income\")\npar(mar = mar_right)\nPlotNumVar(\"debt_to_income\", \"Debt to Income\")\ndev.off()\nmyPDF(\"loansDiagEvsVariables_2.pdf\", w, h,\n    mgp = mgp, mfrow = c(1, 2), mar = mar_left)\nPlotNumVar(\"credit_util\", \"Credit Utilization\")\npar(mar = mar_right)\nPlotCatVar(\"past_bankr\", \"Any Past Bankruptcy\")\ndev.off()\nmyPDF(\"loansDiagEvsVariables_3.pdf\", w, h,\n    mgp = mgp, mfrow = c(1, 2), mar = mar_left)\nPlotCatVar(\"term\", \"Loan Term, in Months\", c(36, 60))\npar(mar = mar_right)\nPlotNumVar(\"credit_checks\", \"Credit Checks in Last 12 Months\")\ndev.off()\n\n\nmyPDF(\"loansDebtToIncomeHist.pdf\", 5, 2.7,\n    mar = c(2.9, 4, 0.5, 0.5))\nhistPlot(d$debt_to_income, breaks = 30, col = COL[1],\n    xlab = \"\", ylab = \"Frequency\")\nmtext(\"Debt to Income\", 1, 1.8)\ndev.off()\n\n\n# Diagnostics when Debt to Income is Transformed\nmyPDF(\"loansDiagEvsTransformDebtToIncome.pdf\", w, h,\n    mar = c(2.9, 4, 0.5, 0.5),\n    mfrow = c(1, 2))\n\n# Checking square root transformation\nsummary(fit <- lm(F(keep[-c(1, 3, 5)]), d))\ne <- fit$res\nf <- fit$fit\nPlotNumVar(\"sqrt_debt_to_income\", \"SQRT(Debt to Income)\")\n\n# Checking truncation\nsummary(fit <- lm(F(keep[-c(1, 3, 4)]), d))\ne <- fit$res\nf <- fit$fit\nPlotNumVar(\"debt_to_income_50\", \"Debt to Income, Truncated at 50\")\n\ndev.off()\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/loansSingles/intRateVsPastBankrScatter.R",
    "content": "library(xtable)\nlibrary(openintro)\nd <- loans_full_schema\nd$past_bankr <- (d$public_record_bankrupt > 0) + 0\n\n\n\nmyPDF(\"intRateVsPastBankrScatter.pdf\", 4.2, 4,\n      mar = c(3.7, 3.7, 0, 0.5),\n      mgp = c(2.5,0.55,0))\nplot(d$past_bankr, d$interest_rate,\n     xlim = c(-0.15, 1.15),\n     axes = FALSE,\n     type = \"n\",\n     xlab = \"\",\n     ylab = \"Interest Rate\")\nat <- seq(0, 30, 5)\nabline(h = at, col = COL[7, 3])\npoints(d$past_bankr, # + runif(nrow(d), -0.05, 0.05),\n    d$interest_rate, # + rnorm(nrow(d), sd = 0.5),\n    col = COL[1, 4],\n    pch = 19,\n    cex = 0.7)\nAxisInPercent(2, at)\npar(mgp = c(2.5, 1.55, 0))\naxis(1, at = 0:1, labels = c(\"0\\n(no)\", \"1\\n(yes)\"))\npar(mgp = c(2.5, 0.55, 0))\nmtext(\"Any Past Bankruptcy\", 1, 2.6)\nm <- lm(interest_rate ~ past_bankr, data = d)\nabline(m, col = COL[5], lwd = 1.5)\ndev.off()\n\nsummary(m)\nxtable(m)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/loansSingles/intRateVsVerIncomeScatter.R",
    "content": "library(xtable)\nlibrary(openintro)\nd <- loans_full_schema\nd$ver_income <- ifelse(d$verified_income == \"Verified\", \"verified\",\n    ifelse(d$verified_income == \"Not Verified\", \"not\", \"source_only\"))\n\n\n# This isn't currently correct.\nmyPDF(\"intRateVsVerIncomeScatter.pdf\", 4.2, 4,\n      mar = c(3.7, 3.7, 0, 0.5),\n      mgp = c(2.5,0.55,0))\nplot(d$ver_income, d$interest_rate,\n     xlim = c(-0.15, 1.15),\n     axes = FALSE,\n     type = \"n\",\n     xlab = \"\",\n     ylab = \"Interest Rate\")\nat <- seq(0, 30, 5)\nabline(h = at, col = COL[7, 3])\npoints(d$ver_income, # + runif(nrow(d), -0.05, 0.05),\n    d$interest_rate, # + rnorm(nrow(d), sd = 0.5),\n    col = COL[1, 4],\n    pch = 19,\n    cex = 0.7)\nAxisInPercent(2, at)\npar(mgp = c(2.5, 1.55, 0))\naxis(1, at = 0:1, labels = c(\"0\\n(no)\", \"1\\n(yes)\"))\npar(mgp = c(2.5, 0.55, 0))\nmtext(\"Verified Income\", 1, 2.6)\nm <- lm(interest_rate ~ ver_income, data = d)\nabline(m, col = COL[5], lwd = 1.5)\ndev.off()\n\nsummary(m)\nxtable(m)\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/logisticModel/logisticModel.R",
    "content": "library(openintro)\nlibrary(splines)\nlibrary(dplyr)\na <- resume\nd <- data.frame(\n    callback = a$received_callback,\n    job_city = a$job_city,\n    college_degree = a$college_degree,\n    years_experience = a$years_experience,\n    honors = a$honors,\n    military = a$military,\n    email_address = a$has_email_address,\n    race = a$race,\n    gender = ifelse(a$gender == \"m\", \"male\", \"female\"))\n\n\nm <- glm(callback ~\n    job_city + college_degree + years_experience +\n    honors + military + email_address +\n    race + gender,\n    data = d, family = binomial)\nm <- glm(callback ~ job_city + years_experience + honors + race,\n    data = d, family = binomial)\nsummary(m)\np  <- predict(m, type = \"response\")\np. <- p\n\n\n\n\nset.seed(1)\nmyPDF(\"logisticModelPredict.pdf\", 8, 3,\n      mar = c(3.9, 6.5, 0.5, 0.5),\n      mgp = c(2.4, 0.55, 0))\nnoise <- rnorm(nrow(d), sd = 0.08)\nplot(p, d$callback + noise,\n     xlim = 0:1,\n     ylim = c(-0.5, 1.5),\n     axes = FALSE,\n     xlab = \"Predicted Probability\",\n     ylab = \"\",\n     col = fadeColor(COL[1], \"22\"),\n     pch = 20)\naxis(1)\naxis(2,\n     at = c(0,1),\n     labels = c(\"0 (No Callback)\", \"1 (Callback)\"))\ndev.off()\n\n\n\nns1 <- 4\nmyPDF(\"logisticModelSpline.pdf\", 7.7, 4.4,\n      mar = c(3.9, 7, 0.5, 0.2),\n      mgp = c(2.4, 0.55, 0))\nplot(p, d$callback + noise / 5,\n     type = \"n\",\n     xlim = 0:1,\n     ylim = c(-0.07, 1.07),\n     axes = FALSE,\n     xlab = \"Predicted Probability\",\n     ylab = \"\")\npar(las = 0)\nmtext(\"Truth\", 2, 5.5)\npar(las = 1)\nrect(0, 0, 1, 1,\n     border = COL[6],\n     col = \"#00000000\",\n     lwd = 1.5)\nlines(0:1, 0:1,\n      lty = 2,\n      col = COL[6],\n      lwd = 1.5)\npoints(p, d$callback + noise / 5,\n       col = fadeColor(COL[1], \"18\"),\n       pch = 20)\naxis(1)\nat <- seq(0, 1, length.out = 6)\nlabels <- c(\"0 (No Callback)\",\n            \"0.2  \",\n            \"0.4  \",\n            \"0.6  \",\n            \"0.8  \",\n            \"1 (Callback)\")\naxis(2, at, labels)\ng1 <- lm(d$callback ~ ns(p, ns1))\np  <- seq(min(p), max(p), length.out = 100)\nY  <- predict(g1,\n              data.frame(ns(p, ns1)),\n              se.fit = TRUE)\nyb <- Y$fit - 1.96 * Y$se.fit\nyt <- rev(Y$fit + 1.96 * Y$se.fit)\npolygon(c(p, rev(p)),\n        c(yb, yt),\n        col = COL[3, 3],\n        border = \"#00000000\")\nlines(p, Y$fit, lwd = 2.5)\narrows(0.15, 0.34,\n       0.15, 0.22,\n       length = 0.07)\ntext(0.15, 0.34,\n     \"Locally-estimated\\nprobabilities with\\nconfidence bounds\",\n     cex = 0.75, pos = 3)\narrows(0.4, 0.21,\n       max(p) + 0.02, max(p) - 0.08,\n       length = 0.07)\ntext(0.4, 0.19,\n     paste(\"The bounds become wide\\nbecause not much data\",\n           \"are found this far right\",\n           sep = \"\\n\"),\n     cex = 0.75, pos = 4)\n# arrows(0.83, 0.57,\n#        0.8, 0.785,\n#        length = 0.07)\ntext(0.42, 0.63,\n     \"The smoothed line\\nshould fall close to the\\ndashed line if the\\nlogistic model\\nis reasonable\",\n     cex = 0.75)\ndev.off()\n\n\n\n\np <- p.\nns1 <- 4\nmyPDF(\"logisticModelBucketDiag.pdf\", 7.7, 4.4,\n    mar = c(3.9, 7, 0.5, 0.2),\n    mgp = c(2.4, 0.55, 0))\nplot(p, d$callback + noise / 5,\n     type = \"n\",\n     xlim = 0:1,\n     ylim = c(-0.07, 1.07),\n     axes = FALSE,\n     xlab = \"Predicted Probability\",\n     ylab = \"\")\npar(las = 0)\nmtext(\"Truth\", 2, 5.5)\npar(las = 1)\nrect(0, 0, 1, 1,\n     border = COL[6],\n     col = \"#00000000\",\n     lwd = 1.5)\nlines(0:1, 0:1,\n      lty = 2,\n      col = COL[6],\n      lwd = 1.5)\npoints(p, d$callback + noise / 5,\n       col = fadeColor(COL[1], \"18\"),\n       pch = 20)\naxis(1)\nat <- seq(0, 1, length.out = 6)\nlabels <- c(\"0 (No Callback)\",\n            \"0.2  \",\n            \"0.4  \",\n            \"0.6  \",\n            \"0.8  \",\n            \"1 (Callback)\")\naxis(2, at, labels)\neps <- 1e-4\nbucket_breaks <- quantile(p, seq(0, 1, 0.01))\nbucket_breaks[1] <- bucket_breaks[1] - eps\nn_buckets <- length(bucket_breaks) - 1\nbucket_breaks[n_buckets] <- bucket_breaks[n_buckets] + 1e3 * eps\nbucket_breaks. <- bucket_breaks\nk <- 1\nfor (i in 1:n_buckets) {\n  if (abs(bucket_breaks.[i] - bucket_breaks[k]) >= 0.01) {\n    k <- k + 1\n    bucket_breaks[k] <- bucket_breaks.[i]\n  }\n}\nbucket_breaks <- bucket_breaks[1:k]\n\nn_buckets <- length(bucket_breaks)\nxp <- rep(NA, n_buckets)\nyp <- rep(NA, n_buckets)\nyp_lower <- rep(NA, n_buckets)\nyp_upper <- rep(NA, n_buckets)\nzs <- qnorm(0.975)\nfor (i in 1:n_buckets) {\n  these <- bucket_breaks[i] < p & p <= bucket_breaks[i + 1]\n  xp[i] <- mean(p[these])\n  y <- d$callback[these]\n  yp[i] <- mean(y)\n  yp_lower[i] <- yp[i] - zs * sqrt(yp[i] * (1 - yp[i]) / length(y))\n  yp_upper[i] <- yp[i] + zs * sqrt(yp[i] * (1 - yp[i]) / length(y))\n}\npoints(xp, yp, pch = 19, cex = 0.7)\nsegments(xp, yp_lower, xp, yp_upper)\narrows(0.3, 0.17,\n       0.24, 0.22,\n       length = 0.07)\ntext(0.3, 0.15,\n    paste(\"Observations are bucketed,\",\n        \"then we compute the observed probability in each bucket (y)\",\n        \"against the average predicted probability (x)\",\n        \"for each of the buckets with 95% confidence intervals.\",\n        sep = \"\\n\"),\n    cex = 0.85, pos = 4)\ndev.off()\n\n\n\n# This plot is still a bit of a mess\nns2 <- 10\nmyPDF(\"logisticModelResidual.pdf\", 8, 6,\n      mar = c(4.9, 6, 5.5, 0.5),\n      mgp = c(2.4, 0.55, 0),\n      mfrow = 2:1)\nnoise <- rnorm(nrow(d), sd = 0.08)\np <- p.\nres   <- d$callback - p\nplot(d$years_experience, res,\n     axes = FALSE,\n     main = \"THIS PLOT IS A BIT OF A MESS\",\n     xlab = \"Time email was sent\",\n     ylab = \"Residual\",\n     col = COL[1, 4],\n     pch = 20)\nTR  <- range(as.numeric(d$years_experience))\nDR  <- diff(TR)\nMo  <- TR[1] + c(0, DR * 31, DR * 59, DR * 90) / 90\naxis(1)\naxis(2)\nTime <- d$years_experience\ng2   <- lm(res ~ ns(Time, ns2))\nTime <- seq(TR[1], TR[2], length.out = 200)\nY    <- predict(g2, ns(Time, ns2), se.fit = TRUE)\nabline(h = 0, lty = 2, col = \"#00000088\")\nyb <- Y$fit - 1.96 * Y$se.fit\nyt <- rev(Y$fit + 1.96 * Y$se.fit)\npolygon(c(Time, rev(Time)),\n        c(yb, yt),\n        col = \"#E0E317B5\",\n        border = \"#00000000\")\nlines(Time, Y$fit, lwd = 1.75)\n\npar(mar = c(3.9, 6, 1.5, 0.5))\nnoise <- rnorm(nrow(d), sd = 0.08)\nres   <- d$callback - p\nTR  <- range(as.numeric(d$years_experience))\nplot(d$years_experience, res,\n     axes = FALSE,\n     xlab = \"January\",\n     ylab = \"Residual\",\n     col = \"#22558855\",\n     pch = 20,\n     xlim = c(TR[1], quantile(TR, 0.08)))\nDR  <- diff(TR)\naxis(1)\naxis(2)\ndev.off()\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/logitTransformationFigureHoriz/logitTransformationFigureHoriz.R",
    "content": "library(openintro)\ndata(COL)\np  <- seq(0.0001, 0.9999, 0.0002)\nlp <- log(p/(1-p))\n\npts  <- seq(0.01, 0.99, length.out = 25)\nR    <- c(-6,6)\nadj  <- 0.07\nadj1 <- 0.02\n\n\nmyPDF(\"logitTransformationFigureHoriz.pdf\", 7, 4,\n      mar = c(3.3, 3.4, 0.8, 0.8),\n      mgp = c(2.1, 0.55, 0))\n\n\nplot(lp, p,\n     xlab = expression(logit(p[i])),\n     ylab = \"\",\n     xlim = c(-5.8, 6.5),\n     ylim = c(-0.05, 1.1),\n     type = \"n\")\nlines(lp, p,\n      type = \"l\",\n      col = COL[5],\n      lwd = 1.5)\nmtext(expression(p[i]), 2, 2.4)\nabline(h = 0:1,\n       lty = 2,\n       col = COL[1],\n       lwd = 1.5)\nthis <- which.min(abs(p - 0.2))\nLP <- c(seq(6, -5, -1))\nP <- exp(LP) / (1 + exp(LP))\nPOS <- c(3, 1, 3, 1, 2, 2, 2, 2, 4, 3, 1, 3)\nxOFF <- c()\nRound <- c(3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 3)\nfor (i in 1:length(LP)) {\n  points(LP[i], P[i], col = COL[4], lwd = 2)\n  t1   <- format(round(c(LP, 0.9), Round[i]))[i]\n  t2   <- format(round(P, Round[i]))[i]\n  text(LP[i], P[i],\n       paste0(\"(\", t1, \", \", t2, \")\"),\n       cex = 0.6,\n       pos = POS[i],\n       col = COL[5])\n  \n}\ndev.off()\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/marioKartDiagnostics/marioKartAnalysis.R",
    "content": "library(xtable)\nlibrary(openintro)\ndata(COL)\ndata(marioKart)\ntoss <- which(marioKart$totalPr > 80)\nkeep <- c(\"totalPr\",\n          \"cond\",\n          \"stockPhoto\",\n          \"duration\",\n          \"wheels\",\n          \"shipSp\")\nd <- marioKart[-toss, keep]\nd$stockPhoto <- (d$stockPhoto == \"yes\") + 0\nd$cond <- (d$cond == \"new\") + 0\nthisOne <- names(d) == \"cond\"\nnames(d)[thisOne] <- \"condNew\"\nd$shipSp <- as.character(d$shipSp)\nthese <- d$shipSp %in%\n         c(\"firstClass\", \"priority\", \"parcel\", \"media\")\nd$shipSp[these] <- \"usps\"\nd$shipSp[grep(\"ups\", d$shipSp)] <- \"ups\"\nthese <- d$shipSp %in% c(\"other\", \"standard\")\nd$shipSp[these] <- \"unknown\"\nd$shipSp <- as.factor(d$shipSp)\nd <- d[,-which(colnames(d) == \"shipSp\")]\n\nsummary(lm(totalPr ~ ., d))\nsummary(lm(totalPr ~ condNew +\n                     stockPhoto +\n                     duration +\n                     wheels,\n           data = d))\nfit <- lm(totalPr ~ condNew + stockPhoto + wheels, data = d)\nxtable(summary(fit))\n\ne <- fit$res\nf <- fit$fit\n\nwidth <- 4.7\nheight <- 4\n\n\nmyPDF(\"mkDiagnosticNormalQuantilePlot.pdf\", width, height,\n      mgp = c(2.5,0.6,0))\nqqnorm(e,\n       ylab = \"Residuals\",\n       main = \"\",\n       col = COL[1,2],\n       pch = 19)\ndev.off()\n\nmyPDF(\"mkDiagResHist.pdf\", width, 0.7 * height)\nhistPlot(e,\n    breaks = 12,\n    xlab = \"Residuals\",\n    ylab = \"Frequency\",\n    col = COL[1],\n    axes = FALSE)\naxis(1, pretty(e))\naxis(2)\ndev.off()\n\n\nmyPDF(\"mkDiagnosticInOrder.pdf\", width, 0.8 * height,\n      mgp = c(2.5, 0.6, 0))\nplot(e,\n     xlab = \"Order of Collection\",\n     ylab = \"Residuals\",\n     axes = FALSE)\naxis(1)\nAxisInDollars(2, c(-10, 0, 10))\nrect(-10, -50, 200, 50,\n     col = COL[7,3])\nabline(h = seq(-50, 50, 10),\n       col = \"#FFFFFF\",\n       lwd = 3)\nabline(h = seq(-50, 50, 5),\n       col = \"#FFFFFF\",\n       lwd = 1)\npoints(e, col = COL[1, 2], pch = 19)\nbox()\ndev.off()\n\nmyPDF(\"mkDiagnosticEvsF.pdf\", 0.9 * width, 0.9 * height,\n      mgp = c(2.5, 0.6, 0))\nplot(f, e,\n     xlab = \"Fitted Values\",\n     ylab = \"Residuals\",\n     axes = FALSE)\nAxisInDollars(1, seq(35, 65, 5))\nAxisInDollars(2, seq(-10, 10, 10))\nrect(-10, -50, 100, 50,\n     col = COL[7, 3])\nabline(h = seq(-50, 50, 10),\n       col = \"#FFFFFF\",\n       lwd = 3)\nabline(h = seq(-50, 50, 5),\n       col = \"#FFFFFF\",\n       lwd = 1)\npoints(f, e,\n       col = COL[1, 2],\n       pch = 19)\nbox()\ndev.off()\n\nmyPDF(\"mkDiagnosticEvsAbsF.pdf\", width, 0.9 * height,\n      mgp = c(2.5, 0.6, 0))\nplot(f, abs(e),\n     xlab = \"Fitted Values\",\n     ylab = \"Absolute Value of Residuals\",\n     axes = FALSE)\nAxisInDollars(1, seq(35, 65, 5))\nAxisInDollars(2, seq(-10, 10, 5))\nrect(-10, -50, 100, 50,\n     col = COL[7,3])\nabline(h = seq(-50, 50, 10),\n       col = \"#FFFFFF\",\n       lwd = 3)\nabline(h = seq(-50, 50, 5),\n       col = \"#FFFFFF\",\n       lwd = 1)\npoints(f, abs(e),\n       col = COL[1, 2],\n       pch = 19)\nbox()\ndev.off()\n\nmyPDF(\"mkDiagnosticEvsVariables.pdf\", width, 1.5 * height,\n      mgp = c(2, 0.55, 0),\n      mfrow = c(3, 1),\n      mar = c(4.1, 3.1, 0.9, 0.5))\nboxPlot(e, d$condNew,\n        xlab = \"Condition\",\n        ylab = \"Residuals\",\n        axes = FALSE)\naxis(1, at = 1:2, c(\"Used\", \"New\"))\nAxisInDollars(2, seq(-10, 10, 10))\nrect(-10, -50, 100, 50,\n     col = COL[7, 3])\nabline(h = seq(-50, 50, 10),\n       col = \"#FFFFFF\",\n       lwd = 3)\nabline(h = seq(-50, 50, 5),\n       col = \"#FFFFFF\",\n       lwd = 1)\nboxPlot(e, d$condNew,\n        add = 1:2,\n        axes = FALSE)\ndotPlot(e[d$condNew == 0],\n        vertical = TRUE,\n        at = 1.05,\n        add = TRUE,\n        col = COL[1, 2],\n        pch = 19,\n        cex = 0.7)\ndotPlot(e[d$condNew == 1],\n        vertical = TRUE,\n        at = 2.05,\n        add = TRUE,\n        col = COL[1, 2],\n        pch = 19,\n        cex = 0.7)\nbox()\n\npar(mar = c(3.8, 3.1, 1.2, 0.5))\nboxPlot(e, d$stockPhoto,\n        xlab = \"Photo Type\",\n        ylab = \"Residuals\",\n        axes = FALSE)\naxis(1, at = 1:2, c(\"Unique Photo\", \"Stock Photo\"))\nAxisInDollars(2, seq(-10, 10, 10))\nrect(-10, -50, 100, 50,\n     col = COL[7, 3])\nabline(h = seq(-50, 50, 10),\n       col = \"#FFFFFF\",\n       lwd = 3)\nabline(h = seq(-50, 50, 5),\n       col = \"#FFFFFF\",\n       lwd = 1)\nboxPlot(e, d$stockPhoto,\n        add = 1:2,\n        axes = FALSE)\ndotPlot(e[d$stockPhoto == 0],\n        vertical = TRUE,\n        at = 1.05,\n        add = TRUE,\n        col = COL[1, 2],\n        pch = 19,\n        cex = 0.7)\ndotPlot(e[d$stockPhoto == 1],\n        vertical = TRUE,\n        at = 2.05,\n        add = TRUE,\n        col = COL[1, 2],\n        pch = 19,\n        cex = 0.7)\nbox()\n\npar(mar = c(3.1, 3.1, 1.2, 0.5))\nplot(d$wheels, e,\n     xlab = \"Number of Wheels\",\n     ylab = \"Residuals\",\n     axes = FALSE)\naxis(1)\nAxisInDollars(2, seq(-10, 10, 10))\nrect(-10, -50, 100, 50,\n     col = COL[7, 3])\nabline(h = seq(-50, 50, 10),\n       col = \"#FFFFFF\",\n       lwd = 3)\nabline(h = seq(-50, 50, 5),\n       col = \"#FFFFFF\",\n       lwd = 1)\npoints(d$wheels, e,\n       col = COL[1, 2],\n       pch = 19)\nbox()\ndev.off()\n\n\nfit <- lm(totalPr ~ condNew + wheels + I(wheels^2), d)\nplot(fit)\n\n\nfit1 <- lm(totalPr ~\n           duration + condNew + stockPhoto + wheels,\n           d)\nfit2 <- lm(totalPr ~\n           condNew + stockPhoto + wheels,\n           d)\nanova(fit1, fit2)\n\nfit1 <- lm(totalPr ~ condNew + stockPhoto, d)\nfit2 <- lm(totalPr ~ stockPhoto, d)\nanova(fit1, fit2)\n\n\n\nfit <- lm(totalPr ~\n          condNew + stockPhoto + duration + wheels,\n          d)\nxtable(fit)\nsummary(fit)\nfit <- lm(totalPr ~\n          condNew + stockPhoto + wheels,\n          d)\nxtable(fit)\nsummary(fit)\n\n# _____ Backward-Selection, Stage 1 _____ #\nfit <- lm(totalPr ~\n          stockPhoto + duration + wheels,\n          d)\nsummary(fit)\nfit <- lm(totalPr ~\n          condNew + duration + wheels,\n          d)\nsummary(fit)\nfit <- lm(totalPr ~\n          condNew + stockPhoto + wheels,\n          d)\nsummary(fit)\nfit <- lm(totalPr ~\n          condNew + stockPhoto + duration,\n          d)\nsummary(fit)\n\n# _____ Backward-Selection, Stage 2 _____ #\nfit <- lm(totalPr ~ stockPhoto + wheels, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ condNew + wheels, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ condNew + stockPhoto, d)\nsummary(fit)$adj.r.squared\n\n\n# _____ Forward-Selection, Stage 1 _____ #\nfit <- lm(totalPr ~ 1, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ condNew, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ stockPhoto, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ duration, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ wheels, d)\nsummary(fit)$adj.r.squared\n\n# _____ Forward-Selection, Stage 2 _____ #\nfit <- lm(totalPr ~ wheels, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ wheels + condNew, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ wheels + stockPhoto, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ wheels + duration, d)\nsummary(fit)$adj.r.squared\n\n# _____ Forward-Selection, Stage 3 _____ #\nfit <- lm(totalPr ~ wheels + condNew, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ wheels + condNew + stockPhoto, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ wheels + condNew + duration, d)\nsummary(fit)$adj.r.squared\n\n# _____ Forward-Selection, Stage 4 _____ #\nfit <- lm(totalPr ~ wheels + condNew + stockPhoto, d)\nsummary(fit)$adj.r.squared\nfit <- lm(totalPr ~ wheels + condNew + stockPhoto + duration, d)\nsummary(fit)$adj.r.squared\n\n"
  },
  {
    "path": "ch_regr_mult_and_log/figures/marioKartSingle/marioKartSingle.R",
    "content": "library(xtable)\nlibrary(openintro)\n\ntoss <- which(marioKart$totalPr > 80)\nkeep <- c(\"totalPr\",\n          \"cond\",\n          \"stockPhoto\",\n          \"duration\",\n          \"wheels\")\nd <- marioKart[-toss, keep]\nd$stockPhoto <- ifelse(d$stockPhoto == \"yes\", 1, 0)\nd$cond <- ifelse(d$cond == \"new\", 1, 0)\n\nmyPDF(\"marioKartSingle.pdf\", 4.5, 3.5,\n      mar = c(3.7, 3.7, 0, 0.5),\n      mgp = c(2.5,0.55,0))\nplot(d$cond, d$totalPr,\n     xlim = c(-0.15, 1.15),\n     axes = FALSE,\n     col = COL[1, 3],\n     pch = 19,\n     cex = 1.3,\n     xlab = \"\",\n     ylab = \"Price\")\nAxisInDollars(2, at = seq(30, 70, 10))\npar(mgp = c(2.5, 1.55, 0))\naxis(1, at = 0:1, labels = c(\"0\\n(used)\", \"1\\n(new)\"))\npar(mgp = c(2.5, 0.55, 0))\nmtext(\"Condition\", 1, 2.6)\ng <- lm(d$totalPr ~ d$cond)\nabline(g, col = COL[5], lwd = 1.5)\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/TeX/ch_regr_simple_linear.tex",
    "content": "\\begin{chapterpage}{Introduction to linear regression}\n  \\chaptertitle{Introduction to linear \\titlebreak{} regression}\n  \\label{linRegrForTwoVar}\n  \\label{ch_regr_simple_linear}\n  \\chaptersection{fitting_line_to_data_section}\n  \\chaptersection{fittingALineByLSR}\n  \\chaptersection{typesOfOutliersInLinearRegression}\n  \\chaptersection{inferenceForLinearRegression}\n\\end{chapterpage}\n\\renewcommand{\\chapterfolder}{ch_regr_simple_linear}\n\n\n\\index{regression|textbf}\n\\index{regression|(}\n\\index{linear regression|seealso{regression}}\n\n\\chapterintro{Linear regression is a very powerful\n  statistical technique.\n  Many people have some familiarity with regression just from\n  reading the news, where straight lines are overlaid\n  on scatterplots.\n  Linear models can be used for prediction or to\n  evaluate whether there is a linear relationship\n  between two numerical variables.}\n\n\n\n%__________\n\\section{Fitting a line, residuals, and correlation}\n% \\section{Using a line to model data}\n\\label{fitting_line_to_data_section}\n\nIt's helpful to think deeply about the line fitting process.\nIn this section, we define the form of a linear model,\nexplore criteria for what makes a good fit,\nand introduce a new statistic called\n\\emph{correlation}\\index{correlation}.\n\n\n\\subsection{Fitting a line to data}\n\nFigure~\\ref{perfLinearModel} shows two variables whose\nrelationship can be modeled perfectly with a straight line.\nThe equation for the line is\n\\begin{eqnarray*}\ny = 5 + 64.96 x\n\\end{eqnarray*}\nConsider what a perfect linear relationship means:\nwe know the exact value of $y$ just by knowing\nthe value of $x$.\nThis is unrealistic in almost any natural process.\nFor example, if we took family income ($x$),\nthis value would provide some useful information about\nhow much financial support a college may offer a prospective\nstudent~($y$).\nHowever, the prediction would be far from perfect,\nsince other factors play a role in financial support\nbeyond a family's finances.\n\n\\begin{figure}[h]\n   \\centering\n   \\Figure[A scatterplot with a straight line fit to the data are shown for the date December 28th, 2018. The horizontal axis is \"Number of Target Corporation Stocks to Purchase\" and the vertical axis is \"Total Cost of the Shares Purchase\". Twelve data points are shown that all fall exactly on a straight line with an equation of y equals 5 plus 64.96 times x. Because the cost is computed using a linear formula, this explains why the linear fit is perfect.]{0.6}{perfLinearModel}\n   \\caption{Requests from twelve separate buyers were\n       simultaneously placed with a trading company to purchase\n       Target Corporation stock\n       (ticker \\texttt{TGT}, December 28th, 2018),\n       and the total cost of the shares were reported.\n       Because the cost is computed using a linear formula,\n       the linear fit is perfect.}\n   \\label{perfLinearModel}\n\\end{figure}\n\nLinear regression is the statistical method for fitting\na line to data where the relationship between two variables,\n$x$ and $y$, can be modeled by a straight line with some error:\n\\begin{align*}\ny = \\beta_0 + \\beta_1x + \\varepsilon\n\\end{align*}\nThe values $\\beta_0$ and $\\beta_1$ represent the model's\nparameters\\index{parameter}\n($\\beta$ is the Greek letter\n  \\emph{beta}\\index{Greek!beta@beta ($\\beta$)}),\nand the error is represented by $\\varepsilon$\n(the Greek letter \\emph{epsilon}\\index{Greek!epsilon@epsilon ($\\varepsilon$)}).\nThe parameters are estimated using data,\nand we write their point estimates as $b_0$ and $b_1$.\nWhen we use $x$ to predict $y$,\nwe usually call $x$ the explanatory\\index{explanatory variable}\nor \\term{predictor} variable,\nand we call $y$ the response;\nwe also often drop the $\\epsilon$ term when writing down the\nmodel since our main focus is often on the prediction of\nthe average outcome.\n\nIt is rare for all of the data to fall perfectly on a straight line.\nInstead, it's more common for data to appear as\na \\emph{cloud of points}\\index{cloud of points},\nsuch as those examples shown in  Figure~\\ref{imperfLinearModel}.\nIn each case, the data fall around a straight line,\neven if none of the observations fall exactly on the line.\nThe first plot shows a relatively strong downward\nlinear trend,\nwhere the remaining variability in the data around the\nline is minor relative to the strength of the relationship\nbetween $x$ and $y$.\nThe second plot shows an upward trend that,\nwhile evident, is not as strong as the first.\nThe last plot shows a very weak downward trend in the data,\nso slight we can hardly notice it.\nIn each of these examples,\nwe will have some uncertainty regarding our estimates\nof the model parameters, $\\beta_0$ and $\\beta_1$.\nFor instance, we might wonder, should we move the line\nup or down a little, or should we tilt it more or less?\nAs we move forward in this chapter,\nwe will learn about criteria for line-fitting,\nand we will also learn about the uncertainty associated\nwith estimates of model parameters.\n\n\\begin{figure}\n   \\centering\n   \\Figure[Three scatterplots are shown. The first has data ranging from -50 to positive 50 on both the horizontal and vertical axes. The data start in the upper left corner of the plot and then move steadily down to the right corner. The second plot has the horizontal axis running from 500 to about 2,000 and the vertical axis from about 0 to 25,000. At the left side of the plot, the data are in the lower half of the plot, and the points generally are steadily higher as we move right, where most points near the right end of the plot are in the upper region of the plot. A upwards trending line has been fit to these points. The last plot runs from about -10 to positive 50 on the horizontal axis and about -200 to positive 400 on the vertical axis. The points are scattered broadly across the range, with only the slightest downward trend evident in the data. A trend line has been fit to this data, though it is nearly flat.]{}{imperfLinearModel}\n   \\caption{Three data sets where a linear model may be useful\n       even though the data do not all fall exactly on the line.}\n   \\label{imperfLinearModel}\n\\end{figure}\n\nThere are also cases where fitting a straight line to the data,\neven if there is a clear relationship between the variables,\nis not helpful.\nOne such case is shown in\nFigure~\\ref{notGoodAtAllForALinearModel}\nwhere there is a very clear relationship between the variables\neven though the trend is not linear.\nWe discuss \\index{nonlinear}nonlinear trends in this chapter\nand the next, but details of fitting nonlinear models\nare saved for a later course.\n\n\\begin{figure}\n   \\centering\n   \\Figure[A linear model is not useful in a nonlinear set of data shown in this plot. The data are from an introductory physics experiment, where a ball is shot at many angles of inclination between 0 degrees and 90 degrees (represented by the horizontal axis), and the measured horizontal distance traveled by the ball before it hits the ground is shown in meters. The first point, at an angle of inclination of 0 hits the ground at 0 meters traveled. As the angle is increased, the ball travels further before it hits the ground until reaching a peak at 45 degrees angle of inclination, at which point it decreases again until we reach an angle of 90 degrees, at which point the ball again does not travel any horizontal distance before it hits the ground. For the data shown, the best fitting straight line is shown and is flat. This is a good example of why a straight line fit to data where there is curvature is often not useful.]{0.8}{notGoodAtAllForALinearModel}\n   \\caption{A linear model is not useful in this nonlinear case.\n       These data are from an introductory physics experiment.}\n   \\label{notGoodAtAllForALinearModel}\n\\end{figure}\n\n\n\n\n\\subsection{Using linear regression to predict possum head lengths}\n\n\\index{data!possum|(}\n\nBrushtail possums are a marsupial that lives in Australia,\nand a photo of one is shown in\nFigure~\\ref{brushtail_possum}.\nResearchers captured 104 of these animals and took body\nmeasurements before releasing the animals back into the wild.\nWe consider two of these measurements:\nthe total length of each possum, from head to tail,\nand the length of each possum's head.\n\n\\captionsetup{width=0.83\\mycaptionwidth}\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A common brushtail possum of Australia is shown. It has a brown fur coat with some gray sprinkled in along with a face and ears that somewhat resemble a house cat. The possum also has a big bushy tail.]{0.5}{brushtail_possum}\n  \\caption{The common brushtail possum of Australia.\\vspace{-1mm} \\\\\n      -----------------------------\\vspace{-2mm}\\\\\n      {\\footnotesize Photo by Greg Schechter\n      (\\oiRedirect{textbook-flickr_com_schechter_brushtail_possum_5653697137}\n          {https://flic.kr/p/9BAFbR}).\n      \\oiRedirect{textbook-CC_BY_2}\n          {CC~BY~2.0~license}.}}\n  \\label{brushtail_possum}\n\\end{figure}\n\\captionsetup{width=\\mycaptionwidth}\n\n%Scatterplots were introduced in Chapter~\\ref{introductionToData}\n%as a graphical technique to present two numerical variables\n%simultaneously.\n%Such plots permit the relationship between the variables\n%to be examined with ease.\nFigure~\\ref{scattHeadLTotalL} shows a scatterplot for the head\nlength and total length of the possums.\nEach point represents a single possum from the data.\nThe head and total length variables are associated:\npossums with an above average total length also tend to have\nabove average head lengths.\nWhile the relationship is not perfectly linear, it could\nbe helpful to partially explain the connection between these\nvariables with a straight line.\n\n\\D{\\newpage}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A scatterplot showing head length against total length for 104 brushtail possums, where the horizontal axis for total length runs from 75 centimeters to about 97 centimeters (2.5 to 3.3 feet) and the vertical axis for head length runs from about 82 millimeters up to about 104 millimeters (3 to 4 inches). For possums with a total length between 75 to 80 centimeters, there are three points shown, each with head lengths of about 85 millimeters. For possums with total length from 80 to 85 centimeters, most head lengths range from about 85 millimeters to 95 millimeters. For possums with total lengths from 85 to 90 centimeters, head lengths mostly lie between 90 millimeters and 97 millimeters. For possums with total lengths larger than 90 centimeters, the head lengths are mostly between 93 millimeters and 100 millimeters. The trend is evidently upward and approximately linear. A point representing a possum with head length 94.1mm and total length 89cm is highlighted (although not relevant for any other purpose than giving an example or reminder for how a point is read in a scatterplot).]{0.75}{scattHeadLTotalL}\n  \\caption{A scatterplot showing head length against total length\n      for 104 brushtail possums.\n      A point representing a possum with head length 94.1mm\n      and total length 89cm is highlighted.}\n  \\label{scattHeadLTotalL}\n\\end{figure}\n\n%Straight lines should only be used when the data appear to have\n%a linear relationship, such as the case shown in the left panel\n%of Figure~\\ref{scattHeadLTotalLTube}.\n%The right panel of Figure~\\ref{scattHeadLTotalLTube} shows\n%a case where a curved line would be more useful in understanding\n%the relationship between the two variables.\n\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figure{0.95}{scattHeadLTotalLTube}\n%  \\caption{The figure on the left shows head length versus\n%      total length, and reveals that many of the points could\n%      be captured by a straight band.\n%      On the right, we see that a curved band is more appropriate\n%      in this scatterplot.}\n%  \\label{scattHeadLTotalLTube}\n%\\end{figure}\n\nWe want to describe the relationship between the head length and total length variables in the possum data set using a line. In this example, we will use the total length as the predictor variable, $x$, to predict a possum's head length, $y$. We could fit the linear relationship by eye, as in Figure~\\ref{scattHeadLTotalLLine}. The equation for this line is\n\\begin{align*}\n\\hat{y} = 41 + 0.59x\n\\end{align*}\nA ``hat'' on $y$ is used to signify that this is an estimate.\nWe can use this line to discuss properties of possums.\nFor instance, the equation predicts a possum with a total length\nof 80 cm will have a head length of\n\\begin{align*}\n\\hat{y} &= 41 + 0.59\\times 80 \\\\\n\t&= 88.2 % mm\n\\end{align*}\nThe estimate may be viewed as an average:\nthe equation predicts that possums with a total length of\n80~cm will have an average head length of 88.2~mm.\nAbsent further information about an 80~cm possum,\nthe prediction for head length that uses the average\nis a reasonable estimate.\n\n\\begin{figure}\n  \\centering\n  \\Figures[The same scatterplot showing head length against total length for 104 brushtail possums is shown. A linear trend line has been added with an equation of y-hat equals 41 plus 0.59 times x, which shows the clear upward trajectory of the data. Additionally, three points are highlighted. The first is labeled with an \"X\" and is at approximately (77, 85) and lies about 1 unit below the trend line. A second point labeled with a \"plus sign\" is at about (85, 98) and appears to be about 7 units above the trend line. The last point highlighted is a \"triangle\" and is located at about (95, 93) and is about 3 units below the trend line.]{0.7}{scattHeadLTotalLLine}\n      {scattHeadLTotalLLineResiduals}\n  \\caption{A reasonable linear model was fit to represent\n      the relationship between head length and total length.}\n  \\label{scattHeadLTotalLLine}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{What other variables might help us predict the\n    head length of a possum besides its length?}\n  Perhaps the relationship would be a little different for\n  male possums than female possums,\n  or perhaps it would differ for possums from one region\n  of Australia versus another region.\n  In Chapter~\\ref{ch_regr_mult_and_log},\n  we'll learn about how we can include more than one predictor.\n  Before we get there, we first need to better understand\n  how to best build a simple linear model with one predictor.\n\\end{nexample}\n\\end{examplewrap}\n\n\n\\subsection{Residuals}\n\n\\index{residual|(}\n\n\\noindent%\n\\termsub{Residuals}{residual} are the leftover variation in the data after accounting for the model fit:\n\\begin{align*}\n\\text{Data} = \\text{Fit} + \\text{Residual}\n\\end{align*}\nEach observation will have a residual, and three of the\nresiduals for the linear model we fit for the \\data{possum}\ndata is shown in\nFigure~\\ref{scattHeadLTotalLLine}.\nIf an observation is above the regression line, then its residual,\nthe vertical distance from the observation to the line, is positive.\nObservations below the line have negative residuals.\nOne goal in picking the right linear model is for these residuals\nto be as small as possible.\n\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figures{0.7}{scattHeadLTotalLLine}\n%      {scattHeadLTotalLLineResiduals}\n%  \\caption{The linear model from\n%      Figure~\\ref{scattHeadLTotalLLine}\n%      where 3 residuals are highlighted.}\n%  \\label{scattHeadLTotalLLineResiduals}\n%\\end{figure}\n\nLet's look closer at the three residuals featured in\nFigure~\\ref{scattHeadLTotalLLine}.\nThe observation marked by an ``$\\times$'' has a small,\nnegative residual of about -1;\nthe observation marked by ``$+$'' has a large residual of about +7;\nand the observation marked by ``$\\triangle$'' has a moderate\nresidual of about -4.\nThe size of a residual is usually discussed in terms of its\nabsolute value.\nFor example, the residual for ``$\\triangle$'' is larger than\nthat of ``$\\times$'' because $|-4|$ is larger than $|-1|$.\n\n\\begin{onebox}{Residual: difference between observed and expected}\nThe residual of the $i^{th}$ observation $(x_i, y_i)$ is the difference of the observed response ($y_i$) and the response we would predict based on the model fit ($\\hat{y}_i$):\n\\begin{eqnarray*}\ne_i = y_i - \\hat{y}_i\n\\end{eqnarray*}\nWe typically identify $\\hat{y}_i$ by plugging $x_i$ into the model.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{The linear fit shown in Figure~\\ref{scattHeadLTotalLLine} is given as $\\hat{y} = 41 + 0.59x$. Based on this line, formally compute the residual of the observation $(77.0, 85.3)$. This observation is denoted by ``$\\times$'' in Figure~\\ref{scattHeadLTotalLLine}.\nCheck it against the earlier visual estimate,~-1.}\nWe first compute the predicted value of point ``$\\times$'' based on the model:\n\\begin{eqnarray*}\n\\hat{y}_{\\times} = 41+0.59x_{\\times} = 41+0.59\\times 77.0 = 86.4\n\\end{eqnarray*}\nNext we compute the difference of the actual head length and the predicted head length:\n\\begin{eqnarray*}\ne_{\\times} = y_{\\times} - \\hat{y}_{\\times} = 85.3 -  86.4 = -1.1\n\\end{eqnarray*}\nThe model's error is $e_{\\times} = -1.1$mm,\nwhich is very close to the visual estimate of -1mm.\nThe negative residual indicates that the linear model\noverpredicted head length for this particular possum.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIf a model underestimates an observation, will the residual be positive or negative? What about if it overestimates the observation?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{If a model underestimates an observation, then the model estimate is below the actual. The residual, which is the actual observation value minus the model estimate, must then be positive. The opposite is true when the model overestimates the observation: the residual is negative.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nCompute the residuals for the ``$+$'' observation $(85.0, 98.6)$\nand the ``$\\triangle$'' observation $(95.5, 94.0)$ in the figure\nusing the linear relationship $\\hat{y} = 41 + 0.59x$.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{($+$) First compute the predicted value based on\n  the model:\n  \\begin{align*}\n  \\hat{y}_{+} = 41+0.59x_{+} = 41+0.59\\times 85.0 = 91.15\n  \\end{align*}\n  Then the residual is given by\n  \\begin{align*}\n  e_{+} = y_{+} - \\hat{y}_{+} = 98.6-91.15=7.45\n  \\end{align*}\n  This was close to the earlier estimate of 7.\n\n\\noindent%\n($\\triangle$) $\\hat{y}_{\\triangle} = 41+0.59x_{\\triangle} = 97.3$.\n$e_{\\triangle} = y_{\\triangle} - \\hat{y}_{\\triangle} = -3.3$,\nclose to the estimate of -4.}\n\nResiduals are helpful in evaluating how well a linear model\nfits a data set.\nWe often display them in a \\term{residual plot} such as the\none shown in Figure~\\ref{scattHeadLTotalLResidualPlot}\nfor the regression line in Figure~\\ref{scattHeadLTotalLLine}.\nThe residuals are plotted at their original horizontal locations\nbut with the vertical coordinate as the residual.\nFor instance, the point $(85.0,98.6)_{+}$ had a residual\nof 7.45, so in the residual plot it is placed at $(85.0, 7.45)$.\nCreating a residual plot is sort of like tipping the\nscatterplot over so the regression line is horizontal. \n\\index{data!possum|)}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A residual plot for the trend line fit to the brushtail possum data is shown. Here, the horizontal axis is the same -- representing \"total length\", it spans 75 to 97 -- while the vertical axis represents \"Residuals\" and spans from about -7 to positive 8. There is on evident trend in the residuals. Three points are specifically highlighted to reflect the three points discussed in the last figure. The first is labeled with an \"X\" with a total length of 77 and a residual of about -1. The second is labeled with a \"plus sign\" and has a total length of 85 and a residual of about 7. The last point highlighted is a \"triangle\" with a total length of about 95 and a residual of about -3. Note that the location of the residuals above and below the trend line reflects exactly with whether the residual is positive or negative, respectively.]{0.7}{scattHeadLTotalLResidualPlot}\n  \\caption{Residual plot for the model in\n      Figure~\\ref{scattHeadLTotalLLine}.}\n  \\label{scattHeadLTotalLResidualPlot}\n\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{One purpose of residual plots is to identify\n    characteristics or patterns still apparent in data after\n    fitting a model.\n    Figure~\\ref{sampleLinesAndResPlots} shows three scatterplots\n    with linear models in the first row and residual plots in the\n    second row.\n    Can you identify any patterns remaining in the residuals?}\n\n  In the first data set (first column), the residuals show\n  no obvious patterns.\n  The residuals appear to be scattered randomly around the\n  dashed line that represents 0.\n\n  The second data set shows a pattern in the residuals.\n  There is some curvature in the scatterplot, which is more\n  obvious in the residual plot.\n  We should not use a straight line to model these data.\n  Instead, a more advanced technique should be used.\n\n  The last plot shows very little upwards trend, and the\n  residuals also show no obvious patterns.\n  It is reasonable to try to fit a linear model to the data.\n  However, it is unclear whether there is statistically\n  significant evidence that the slope parameter is different\n  from zero.\n  The point estimate of the slope parameter, labeled $b_1$,\n  is not zero, but we might wonder if this could just be due\n  to chance.\n  We will address this sort of scenario in\n  Section~\\ref{inferenceForLinearRegression}.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}\n   \\centering\n   \\Figure[Sample data with their best fitting lines (top row of three plots) and their corresponding residual plots (bottom row of three plots). The upper left plot shows a scatterplot where the data trend downwards steadily with a straight line fit to the data, which appears to fit well everywhere. The bottom left plot is the residual plot of this first scatterplot, and it likewise shows no pattern in the residuals when looking left to right. The upper middle plot shows data with a downward trend, but the data's trend is more steep on the right side of the plot, so the overall shape of the data is that it trends downward and curves downward. A straight, downward-trending line has also been fit to this data, but it doesn't fit as well. The data are below this downward trending line initially, but it is above the line in the middle, and finally on the right it is once again below the linear trend line. The residual plot for this scatterplot is shown in the lower middle plot, and the curvature in the residuals is more evident than what was visible in the scatterplot: the residuals have negative values on the left and trend upwards until peaking with positive residuals in the middle, and then trending back down and having negative residual values again on the right. The last scatterplot in the upper right shows data with very little trend, but a slightly-upward trending straight line has been fit to the data. The corresponding residual plot, shown as the bottom right plot, also shows data with no evident trend or pattern, where observations appear relatively randomly scattered above and below 0 (in the vertical).]{0.9}{sampleLinesAndResPlots}\n   \\caption{Sample data with their best fitting lines (top row) and their corresponding residual plots (bottom row).}\n   \\label{sampleLinesAndResPlots}\n\\end{figure}\n\n\\index{residual|)}\n\n\n\\subsection{Describing linear relationships with correlation}\n\n\\index{correlation|(}\n\n\\noindent%\nWe've seen plots with strong linear relationships and\nothers with very weak linear relationships.\nIt would be useful if we could quantify the strength of these\nlinear relationships with a statistic.\n\n\\begin{onebox}{Correlation: strength of a linear relationship}\n  \\termsub{Correlation}{correlation}, which always takes values\n  between -1 and 1, describes the strength of the linear\n  relationship between two variables.\n  We denote the correlation by $R$.\n\\end{onebox}\n\nWe can compute the correlation using a formula, just as we did\nwith the sample mean and standard deviation.\nThis formula is rather complex,\\footnote{Formally,\n  we can compute the correlation for observations $(x_1, y_1)$,\n  $(x_2, y_2)$, ..., $(x_n, y_n)$ using the formula\n  \\begin{align*}\n  R = \\frac{1}{n-1}\n      \\sum_{i=1}^{n} \\frac{x_i-\\bar{x}}{s_x}\\frac{y_i-\\bar{y}}{s_y}\n  \\end{align*}\n  where $\\bar{x}$, $\\bar{y}$, $s_x$, and $s_y$ are the sample\n  means and standard deviations for each variable.}\nand like with other statistics, we generally perform the\ncalculations on a computer or calculator.\nFigure~\\ref{posNegCorPlots} shows eight plots and their\ncorresponding correlations.\nOnly when the relationship is perfectly linear is the\ncorrelation either -1 or~1.\nIf~the relationship is strong and positive, the correlation\nwill be near~+1.\nIf~it is strong and negative, it will be near~-1.\nIf~there is no apparent linear relationship between the\nvariables, then the correlation will be near zero.\n\n\\begin{figure}\n   \\centering\n   \\Figure[Eight scatterplots are shown, each with their correlation noted. Each scatterplot appears to represent about 50 points. The first has a correlation of R equals 0.33, and there is a slight upward trend evident in the data -- if a trend line were drawn for this data, much of the data would fall relatively far from the line. The second plot has a correlation of R equals 0.69, and a clearer upward trend is evident, but it is still pretty volatile with many points deviating far from where the trend line would be. The third plot has a correlation of 0.98, and the data show a very clear upward trend, where if a trend line were drawn, the data would be (relatively) quite close to this line. The fourth plot shows a correlation of R equals 1.00, and here the points appear exactly on a line with an upward trajectory. The fifth plot shows data with a correlation of R equals 0.08, where no trend is visually evident in the data. The sixth plot has a correlation of R equals -0.64, and a downward trend is evident in the data, but the individual observations would in many cases be pretty distant from any trend line fit to the data (on a relative basis). The seventh plot has a correlation of R equals -0.92 and shows data with a clear downward trend, where the data would deviate just a modest amount from a trend line fit to the data. The last plot shows a correlation of R equals -1, where the observations would fit exactly on a line trending downwards.]{0.9}{posNegCorPlots}\n   \\caption{Sample scatterplots and their correlations.\n       The first row shows variables with a positive\n       relationship, represented by the trend up and to\n       the right.\n       The second row shows one plot with an approximately neutral trend\n       and three plots with a negative trend.}\n   \\label{posNegCorPlots}\n\\end{figure}\n\nThe correlation is intended to quantify the strength of a linear trend. Nonlinear trends, even when strong, sometimes produce correlations that do not reflect the strength of the relationship; see three such examples in Figure~\\ref{corForNonLinearPlots}.\n\n\\begin{figure}[h]\n   \\centering\n   \\Figures[Three scatterplots are shown. In each case, there is a strong relationship between the variables. However, because the relationship is nonlinear, the correlation is relatively weak. The first plot shows data that trends upwards on the left before peaking and then trending downward on the right -- the correlation of the data in this plot is R equals -0.23. The second plot shows data with a sharp downward trend on the left before reaching a trough and rising then sharply upward before reaching a peak and then trending sharply downwards again -- the correlation of the data in this plot is R equals 0.31. The third plots shows data that without a trend on the far left, followed by a steep drop, a trough, and then a steep rise to a peak, and then another drop and then finally a slight increase at the end -- the correlation of the data in this plot is R equals 0.50.]{0.85}{posNegCorPlots}{corForNonLinearPlots}\n   \\caption{Sample scatterplots and their correlations.\n       In each case, there is a strong relationship between\n       the variables.\n       However, because the relationship is nonlinear,\n       the correlation is relatively weak.}\n   \\label{corForNonLinearPlots}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nNo straight line is a good fit for the data sets\nrepresented in Figure~\\ref{corForNonLinearPlots}.\nTry drawing nonlinear curves on each plot.\nOnce you create a curve for each, describe what is important\nin your~fit.\\footnotemark{}\n\\index{correlation|)}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We'll leave it to you to draw the lines. In general, the lines you draw should be close to most points and reflect overall trends in the data.}\n\n%\\begin{examplewrap}\n%\\begin{nexample}{What other variables might help us predict the\n%    head length of a possum besides its length?}\n%  Perhaps the relationship would be a little different for\n%  male possums than female possums,\n%  as shown in Figure~\\ref{scattHeadLTotalLSex},\n%  Or perhaps it would differ for possums from one region\n%  of Australia versus another region.\n%  In Chapter~\\ref{ch_regr_mult_and_log},\n%  we'll learn about how we can include more than one predictor.\n%  Before we get there, we first need to better understand\n%  how to best build a simple linear model with one predictor.\n%\\end{nexample}\n%\\end{examplewrap}\n%\n%\\begin{figure}\n%  \\centering\n%  \\Figure{0.6}{scattHeadLTotalLSex}\n%  \\caption{Possums where the possum's sex is represented\n%      by the plotting icon.}\n%  \\label{scattHeadLTotalLSex}\n%\\end{figure}\n\n\n{\\input{ch_regr_simple_linear/TeX/line_fitting_residuals_and_correlation.tex}}\n\n\n\n\n\n\n\n%__________________\n\\section{Least squares regression}\n\\label{fittingALineByLSR}\n\n\\index{least squares regression|(}\n\nFitting linear models by eye is open to criticism since\nit is based on an individual's preference.\nIn this section, we use \\emph{least squares regression}\nas a more rigorous approach.\n\n\n\\subsection{Gift aid for freshman at Elmhurst College}\n\nThis section considers family income and gift aid data from\na random sample of fifty students in the freshman class of\nElmhurst College in Illinois.\nGift aid is financial aid that does not need to be paid back,\nas opposed to a loan.\nA scatterplot of the data is shown in\nFigure~\\ref{elmhurstScatterW2Lines}\nalong with two linear fits.\nThe lines follow a negative trend in the data;\nstudents who have higher family incomes tended to have lower\ngift aid from the university.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[A scatterplot is shown for a random sample of 50 freshman students from Elmhurst College. The horizontal axis is for \"family income\" and has values ranging from \\$0 to about \\$300,000. The vertical axis is for \"gift aid\" and has values ranging from \\$0 to about \\$35,000. Two lines are fit to the data, which show a downward trend, representing a slight downward trend in the data. One of those lines is a solid line representing what is called the \"least squares line\". About 10 observations are shown where family income is between \\$0 and \\$50,000, and gift aid for these values is roughly between \\$17,000 and \\$28,000. About 20 observations are shown where family income is between \\$50,000 and \\$100,000, and gift aid for these values is roughly between \\$10,000 and \\$33,000. About 10 observations are shown where family income is between \\$100,000 and \\$150,000, and gift aid for these values is roughly between \\$9,000 and \\$25,000. Three observations are shown where family income is between \\$150,000 and \\$200,000, and gift aid for these values of \\$25,000, \\$12,000, and \\$13,000. Six more observations are shown where family income is larger than \\$200,000, and gift aid for these values range from about \\$7,000 to \\$22,000, \\$12,000, and \\$13,000. The data in this graph will be frequently discussed throughout this section and referred to as the \"Elmhurst data\".]{0.67}{elmhurstPlots}{elmhurstScatterW2Lines}\n  \\caption{Gift aid and family income for a random sample of\n      50~freshman students from Elmhurst College.\n      Two lines are fit to the data, the solid line being the\n      \\emph{least squares line}.}\n  \\label{elmhurstScatterW2Lines}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIs the correlation positive or negative in Figure~\\ref{elmhurstScatterW2Lines}?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Larger family incomes are associated with lower amounts of aid, so the correlation will be negative. Using a computer, the correlation can be computed: -0.499.}\n\n\n\\subsection{An objective measure for finding the best line}\n\nWe begin by thinking about what we mean by ``best''.\nMathematically, we want a line that has small residuals.\nThe first option that may come to mind is to minimize the\nsum of the residual magnitudes:\n\\begin{align*}\n|e_1| + |e_2| + \\dots + |e_n|\n\\end{align*}\nwhich we could accomplish with a computer program.\nThe resulting dashed line shown in\nFigure~\\ref{elmhurstScatterW2Lines}\ndemonstrates this fit can be quite reasonable.\nHowever, a more common practice is to choose the line that\nminimizes the sum of the squared residuals:\n\\begin{align*}\ne_{1}^2 + e_{2}^2 + \\dots + e_{n}^2\n\\end{align*}\n\n\nThe line that minimizes this \\term{least squares criterion}\nis represented as the solid line in\nFigure~\\ref{elmhurstScatterW2Lines}.\nThis is commonly called the \\term{least squares line}.\nThe following are three possible reasons to choose this option\ninstead of trying to minimize the sum of residual magnitudes\nwithout any squaring:\n\\begin{enumerate}\n\\item\n    It is the most commonly used method.\n\\item\n    Computing the least squares line is widely supported\n    in statistical software.\n\\item\n    In many applications, a residual twice as large\n    as another residual is more than twice as bad.\n    For example, being off by 4 is usually more than twice\n    as bad as being off by 2.\n    Squaring the residuals accounts for this discrepancy.\n\\end{enumerate}\nThe first two reasons are largely for tradition and convenience;\nthe last reason explains why the least squares criterion\nis typically most helpful.\\footnote{There\n  are applications where the sum of residual magnitudes\n  may be more useful, and there are plenty of other criteria\n  we might consider.\n  However, this book only applies the least squares criterion.}\n\n\n\\subsection{Conditions for the least squares line}\n\n\\noindent%\nWhen fitting a least squares line, we generally require\n\\begin{description}\n\\setlength{\\itemsep}{0mm}\n\\item[Linearity.]\n    The data should show a linear trend.\n    If there is a nonlinear trend (e.g. left panel of\n    Figure~\\ref{whatCanGoWrongWithLinearModel}),\n    an advanced regression method from another book\n    or later course should be applied.\n\\item[Nearly normal residuals.]\n    Generally, the residuals must be nearly normal.\n    When this condition is found to be unreasonable,\n    it is usually because of outliers or concerns\n    about influential points,\n%    The theoretical condition is that the residuals\n%    must be normally distributed.\n%    The importance of this condition depends on a few factors:\n%    \\begin{enumerate}[(1)]\n%    \\item\n%        Is there any interest in predicting the range of\n%        plausible values for individual observations?\n%        If yes, then normality is important.\n%    \\item\n%        Are there very few observations, such as fewer than~30?\n%        If yes, then normality is important.\n%    \\end{enumerate}\n%    If the answer is \\emph{no} to each of these questions,\n%    then\n%    However, this condition can be taken with a grain of salt\n%    when primarily focused on the trend of the data.\n%    When the data's trend is the focus,\n%    the number of observations can be modest in number,\n%    such as 30 or more, at which point this condition\n%    can be somewhat relaxed.\n%    Generally, it is important to look for outliers,\n    which we'll talk about more in\n    Sections~\\ref{typesOfOutliersInLinearRegression}.\n    An example of a residual that would be a potentially\n    concern is shown in\n    Figure~\\ref{whatCanGoWrongWithLinearModel},\n    where one observation is clearly much further from the\n    regression line than the others.\n\\item[Constant variability.]\n    The variability of points around the least squares line\n    remains roughly constant.\n    An example of non-constant variability is shown in the\n    third panel of Figure~\\ref{whatCanGoWrongWithLinearModel},\n    which represents the most common pattern observed\n    when this condition fails:\n    the variability of $y$ is larger when $x$ is larger.\n\\item[Independent observations.]\n    Be cautious about applying regression to \\term{time series}\n    data, which are sequential observations in time such as a\n    stock price each day.\n    Such data may have an underlying structure that should\n    be considered in a model and analysis.\n    An example of a data set where successive observations\n    are not independent is shown in the fourth panel of\n    Figure~\\ref{whatCanGoWrongWithLinearModel}.\n    There are also other instances where correlations within\n    the data are important, which is further discussed in\n    Chapter~\\ref{ch_regr_mult_and_log}.\n\\end{description}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[Four scatterplots are shown, each with their own residual plot. These four examples show when methods in this chapter are insufficient to apply to the data. In the first set, a scatterplot with arch-shaped data is shown with a straight line fit to the data, which poorly fits the curved nature of the data; this is meant to highlight an example where \"linearity\" fails. In the second set, a set of data with a line fit is shown, where the data tightly pack around the line, except one point in particular that is far from the line and represents the case where there are \"extreme outliers\" in the data. The third set shows a case where a straight line fits the data, but the variability around the line changes, where observations tend to be quite close to the line on the left, but when looking further right, the observations tend to be increasingly far from the line, indicating \"changing variability\" in the residuals over different regions of the plot. The fourth set provides another case of what is called \"time series\" data, which is a context where \"successive observations are correlated\".]{}{whatCanGoWrongWithLinearModel}\n  \\caption{Four examples showing when the methods in this\n      chapter are insufficient to apply to the data.\n      First panel: linearity fails.\n      Second panel: there are outliers, most especially\n      one point that is very far away from the line.\n      Third panel: the variability of the errors is related\n      to the value of $x$.\n      Fourth panel: a time series data set is shown,\n      where successive observations are highly correlated.}\n  \\label{whatCanGoWrongWithLinearModel}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nShould we have concerns about applying least squares regression to the Elmhurst data in Figure~\\ref{elmhurstScatterW2Lines}?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The trend appears to be linear, the data fall around the line with no obvious outliers, the variance is roughly constant. These are also not time series observations. Least squares regression can be applied to these data.}\n\n\n\\D{\\newpage}\n\n\\subsection{Finding the least squares line}\n\\label{findingTheLeastSquaresLineSection}\n\nFor the Elmhurst data, we could write the equation of the least squares regression line as\n\\begin{eqnarray*}\n\\widehat{aid} = \\beta_0 + \\beta_{1}\\times\n    \\textit{family\\us{}income}\n\\end{eqnarray*}\nHere the equation is set up to predict gift aid based on a student's family income, which would be useful to students considering Elmhurst. These two values, $\\beta_0$ and $\\beta_1$, are the parameters\\index{parameter} of the regression line.\n\nAs in\nChapters~\\ref{ch_foundations_for_inf},\n\\ref{ch_inference_for_props},\nand~\\ref{ch_inference_for_means},\nthe parameters are estimated using observed data. In practice, this estimation is done using a computer in the same way that other estimates, like a sample mean, can be estimated using a computer or calculator. However, we can also find the parameter estimates by applying two properties of the least squares line:\n\\begin{itemize}\n\\item\n    The slope of the least squares line can be estimated by\n    \\begin{align*}\n    b_1 = \\frac{s_y}{s_x} R\n    \\end{align*}\n    where $R$ is the correlation between the two variables,\n    and $s_x$ and $s_y$ are the sample standard deviations\n    of the explanatory variable and response, respectively.\n\\item\n    If $\\bar{x}$ is the sample mean of the explanatory variable\n    and $\\bar{y}$ is the sample mean of the vertical variable,\n    then the point $(\\bar{x}, \\bar{y})$ is on the least squares\n    line.\n\n    Figure~\\ref{summaryStatsElmhurstRegr} shows the sample means\n    for the family income and gift aid as \\$101,780 and \\$19,940,\n    respectively.\n    We could plot the point $(101.8, 19.94)$ on\n    Figure~\\vref{elmhurstScatterW2Lines}\n    to verify it falls on the least squares line (the solid line).\n%     and from the point-slope formula, we can identify $b_0$:\n%    \\begin{align*}\n%    \\hat{y} - \\bar{y} = b_1 (x - \\bar{x})\n%    \\qquad \\to \\qquad\n%    \\hat{y} = (\\bar{y} - b_1 \\bar{x}) + b_1 x\n%    \\end{align*}\n%    This is the point-slope form of a line,\n%    where $b_0 = \\bar{y} - b_1 \\bar{x}$.\n\\end{itemize}\nNext, we formally find the point estimates $b_0$ and $b_1$\nof the parameters $\\beta_0$ and $\\beta_1$.\n\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l rr}\n\\hline\n\\vspace{-4mm} & & \\\\\n\\vspace{0.4mm}\t&\t\\ \\ Family Income ($x$)\t&\n    \\ \\ Gift Aid ($y$) \\\\\n\\hline\n  \\vspace{-3.9mm} & & \\\\\nmean & $\\bar{x} = \\text{\\$101,780}$ &\n    $\\bar{y} = \\text{\\$19,940}$ \\\\\nsd & $s_x = \\text{\\$63,200}$ &\n    $s_y = \\text{\\$5,460}$ \\vspace{0.4mm} \\\\\n\\hline\n\\vspace{-4mm}\\ &\\\\\n\t& \\multicolumn{2}{r}{$R=-0.499$} \\\\\n\\hline\n\\end{tabular}\n\\caption{Summary statistics for family income and gift aid.}\n\\label{summaryStatsElmhurstRegr}\n\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{findingTheSlopeOfTheLSRLineForIncomeAndAid}\nUsing the summary statistics in Figure~\\ref{summaryStatsElmhurstRegr}, compute the slope for the regression line of gift aid against family income.\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Compute the slope using the summary statistics from Figure~\\ref{summaryStatsElmhurstRegr}:\n\\begin{eqnarray*}\nb_1\n  = \\frac{s_y}{s_x} R\n  = \\frac{\\text{5,460}}{\\text{63,200}}(-0.499)\n  = -0.0431\n\\end{eqnarray*}}\n\nYou might recall the \\term{point-slope} form of a line\nfrom math class, which we can use to find the model fit,\nincluding the estimate of $b_0$.\nGiven the slope of a line and a point on the line,\n$(x_0, y_0)$, the equation for the line can be written as\n\\begin{align*}\ny - y_0 = slope\\times (x - x_0)\n\\end{align*}\n%We could plug in $(\\bar{x}, \\bar{y})$ in for $(x_0, y_0$ and solve for $\\hat{y}$ to arrive at the model.\n%A common exercise to become more familiar with foundations of least squares regression is to use basic summary statistics and point-slope form to produce the least squares line. \n\n\\begin{onebox}{Identifying the least squares line from summary statistics}\nTo identify the least squares line from summary statistics:\\vspace{-1mm}\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item\n    Estimate the slope parameter, $b_1 = (s_y / s_x) R$.\n\\item\n    Noting that the point $(\\bar{x}, \\bar{y})$ is on the least\n    squares line, use $x_0 = \\bar{x}$ and $y_0 = \\bar{y}$ with\n    the point-slope equation: $y - \\bar{y} = b_1 (x - \\bar{x})$.\n\\item\n    Simplify the equation, which would reveal that\n    $b_0 = \\bar{y} - b_1 \\bar{x}$.\n\\end{itemize}\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{Using the point $(101780, 19940)$\n    from the sample means and the slope estimate\n    $b_1 = -0.0431$ from Guided\n    Practice~\\ref{findingTheSlopeOfTheLSRLineForIncomeAndAid},\n    find the least-squares line for predicting aid based\n    on family income.}\n  \\label{exampleToFindLSRLineOfElmhurstData}%\n  Apply the point-slope equation using $(101.78, 19.94)$\n  and the slope $b_1 = -0.0431$:\n  \\begin{align*}\n  y - y_0    &= b_1 (x - x_0) \\\\\n  y - \\text{19,940}  &= -0.0431(x - \\text{101,780})\n  \\end{align*}\n  Expanding the right side and then adding 19,940 to each side,\n  the equation simplifies:\n  \\begin{align*}\n  \\widehat{aid} = \\text{24,327} - 0.0431 \\times\n      \\textit{family\\us{}income}\n  \\end{align*}\n  Here we have replaced $y$ with $\\widehat{aid}$ and $x$ with\n  \\textit{family\\us{}income} to put the equation in context.\n  The final equation should always include a ``hat''\n  on the variable being predicted, whether it is a generic\n  ``$y$'' or a named variable like ``$aid$''.\n\\end{nexample}\n\\end{examplewrap}\n\nA computer is usually used to compute the least squares line,\nand a summary table generated using software for the Elmhurst\nregression line is shown in\nFigure~\\ref{rOutputForIncomeAidLSRLine}.\nThe first column of numbers provides estimates for ${b}_0$\nand ${b}_1$, respectively.\nThese results match those from\nExample~\\ref{exampleToFindLSRLineOfElmhurstData}\n(with some minor rounding error).\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l rrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  \\vspace{-3.6mm} & & & & \\\\\n(Intercept) & 24319.3 & 1291.5 & 18.83 & $<$0.0001 \\\\ \nfamily\\us{}income & -0.0431 & 0.0108 & -3.98 & 0.0002 \\\\ \n  \\hline\n\\end{tabular}\n\\caption{Summary of least squares fit for the Elmhurst data.\n    Compare the parameter estimates in the first column to\n    the results of\n    Example~\\ref{exampleToFindLSRLineOfElmhurstData}.}\n\\label{rOutputForIncomeAidLSRLine}\n\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{Examine the second, third, and fourth columns\n    in Figure~\\ref{rOutputForIncomeAidLSRLine}.\n    Can you guess what they represent?\n    (If you have not reviewed any inference chapter yet,\n    skip this example.)}\n  We'll describe the meaning of the columns using the\n  second row, which corresponds to~$\\beta_1$.\n  The first column provides the point estimate for $\\beta_1$,\n  as we calculated in an earlier example: $b_1 = -0.0431$.\n  The second column is a standard error for this point estimate:\n  $SE_{b_1} = 0.0108$.\n  The third column is a $t$-test statistic for the null\n  hypothesis that $\\beta_1 = 0$: $T = -3.98$.\n  The last column is the p-value for the $t$-test statistic\n  for the null hypothesis $\\beta_1 = 0$ and a two-sided\n  alternative hypothesis: 0.0002.\n  We will get into more of these details in\n  Section~\\ref{inferenceForLinearRegression}.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose a high school senior is considering\n    Elmhurst College.\n    Can she simply use the linear equation that we have estimated\n    to calculate her financial aid from the university?}\n  She may use it as an estimate, though some qualifiers on this\n  approach are important.\n  First, the data all come from one freshman class,\n  and the way aid is determined by the university may change\n  from year to year.\n  Second, the equation will provide an imperfect estimate.\n  While the linear equation is good at capturing the trend\n  in the data, no individual student's aid will be perfectly\n  predicted.\n\\end{nexample}\n\\end{examplewrap} \n\n\\index{least squares regression|)}\n\n\n\\subsection{Interpreting regression model parameter estimates}\n\n\\index{least squares regression!interpreting parameters|(}\n\n\\noindent%\nInterpreting parameters in a regression model is often one\nof the most important steps in the analysis.\n\n\\begin{examplewrap}\n\\begin{nexample}{The intercept and slope estimates for\n    the Elmhurst data are $b_0 = \\text{24,319}$\n    and $b_1 = -0.0431$.\n    What do these numbers really mean?}\n  Interpreting the slope parameter is helpful in almost any\n  application.\n  For each additional \\$1,000 of family income, we would expect\n  a student to receive a net difference of\n  $\\$\\text{1,000}\\times (-0.0431) = -\\$43.10$ in aid on average,\n  i.e. \\$43.10 \\emph{less}.\n  Note that a higher family income corresponds to less aid\n  because the coefficient of family income is negative in\n  the model.\n  We must be cautious in this interpretation:\n  while there is a real association, we cannot interpret\n  a causal connection between the variables because these\n  data are observational.\n  That is, increasing a student's family income may not\n  cause the student's aid to drop.\n  (It would be reasonable to contact the college and ask\n  if the relationship is causal,\n  i.e. if Elmhurst College's aid decisions are partially\n  based on students' family income.)\n\n  The estimated intercept $b_0 = \\text{24,319}$\n  describes the average aid if a student's family had no income.\n  The meaning of the intercept is relevant to this application\n  since the family income for some students at Elmhurst is~\\$0.\n  In other applications, the intercept may have little\n  or no practical value if there are no observations where\n  $x$ is near zero.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{Interpreting parameters estimated by least squares}\n  The slope describes the estimated difference in the\n  $y$ variable if the explanatory variable $x$ for a case\n  happened to be one unit larger.\n  The intercept describes the average outcome of $y$ if $x=0$\n  \\emph{and} the linear model is valid all the way to $x=0$,\n  which in many applications is not the case.\n\\end{onebox}\n\n\\index{least squares regression!interpreting parameters|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Extrapolation is treacherous}\n\n\\index{least squares regression!extrapolation|(}\n\n{\\em\\small When those blizzards hit the East Coast this winter,\nit proved to my satisfaction that global warming was a fraud.\nThat snow was freezing cold.\nBut in an alarming trend, temperatures this spring have risen.\nConsider this: On February $6^{th}$ it was 10 degrees.\nToday it hit almost 80. At this rate, by August it will be\n220 degrees.\nSo clearly folks the climate debate rages on.\\vspace{0.5mm}}\n\n\\noindent\\hspace{\\textwidth}\\hspace{-40mm}Stephen Colbert\n\n\\noindent\\hspace{\\textwidth}\\hspace{-40mm}April 6th,\n2010\\footnote{\\oiRedirect{textbook-colbert_extrapolation}\n    {www.cc.com/video-clips/l4nkoq}} \\\\\n\nLinear models can be used to approximate the relationship\nbetween two variables.\nHowever, these models have real limitations.\nLinear regression is simply a modeling framework.\nThe truth is almost always much more complex than our simple line.\nFor example, we do not know how the data outside of our limited\nwindow will behave.\n\n\\begin{examplewrap}\n\\begin{nexample}{Use the model\n    $\\widehat{aid}\n      = \\text{24,319} - 0.0431 \\times\n          \\textit{family\\us{}income}$\n    to estimate the aid of another freshman student whose\n    family had income of \\$1~million.}\n  We want to calculate the aid for\n  $\\textit{family\\us{}income} = \\text{1,000,000}$:\n  \\begin{align*}\n  \\text{24,319} - 0.0431\\times \\textit{family\\us{}income}\n    = \\text{24,319} - 0.0431\\times \\text{1,000,000}\n    = -\\text{18,781}\n  \\end{align*}\n  The model predicts this student will have -\\$18,781 in aid (!).\n  However, Elmhurst College does not offer \\emph{negative aid}\n  where they select some students to pay extra on top of tuition\n  to attend.\n\\end{nexample}\n\\end{examplewrap}\n\nApplying a model estimate to values outside of the realm of the\noriginal data is called \\term{extrapolation}.\nGenerally, a linear model is only an approximation of the real\nrelationship between two variables.\nIf we extrapolate, we are making an unreliable bet that the\napproximate linear relationship will be valid in places where\nit has not been analyzed.\n\n\\index{least squares regression!extrapolation|)}\n\n\n\\subsection{Using $R^2$ to describe the strength of a fit}\n\n\\index{least squares regression!R-squared ($R^2$)|(}\n\nWe evaluated the strength of the linear relationship between\ntwo variables earlier using the correlation, $R$.\nHowever, it is more common to explain the strength of a linear\nfit using $R^2$, called\n\\termsub{R-squared}{least squares regression!R-squared ($R^2$)}.\n\\index{R-squared ($R^2$)|textbf}\nIf provided with a linear model, we might like to describe how\nclosely the data cluster around the linear fit.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[A scatterplot of the Elmhurst data is shown for gift aid and family income with the least squares regression line overlaid against the data, which has a slight downward trend.]{0.7}{elmhurstPlots}{elmhurstScatterWLSROnly}\n  \\caption{Gift aid and family income for a random sample\n      of 50 freshman students from Elmhurst College, shown\n      with the least squares regression line.}\n  \\label{elmhurstScatterWLSROnly}\n\\end{figure}\n\n\\newcommand{\\mil}[0]{\\text{ million}}\nThe $R^2$ of a linear model describes the amount of variation\nin the response that is explained by the least squares line.\nFor example, consider the Elmhurst data,\nshown in Figure~\\ref{elmhurstScatterWLSROnly}.\nThe variance of the response variable, aid received,\nis about $s_{aid}^2 \\approx 29.8$ million.\nHowever, if we apply our least squares line, then this model\nreduces our uncertainty in predicting aid using a student's\nfamily income.\nThe variability in the residuals describes how much variation\nremains after using the model: $s_{_{RES}}^2 \\approx 22.4$ million.\nIn short, there was a reduction of\n\\begin{align*}\n\\frac{s_{aid}^2 - s_{_{RES}}^2}{s_{aid}^2}\n  = \\frac{\\text{29,800,000} - \\text{22,400,000}}\n      {\\text{29,800,000}}\n  = \\frac{\\text{7,500,000}}{\\text{29,800,000}}\n  = 0.25\n\\end{align*}\nor about  25\\% in the data's variation by using information\nabout family income for predicting aid using a linear model.\nThis corresponds exactly to the R-squared value:\n\\begin{align*}\nR &= -0.499 &R^2 &= 0.25\n\\end{align*}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIf a linear model has a very strong negative relationship with\na correlation of -0.97, how much of the variation in the response\nis explained by the explanatory variable?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{About $R^2 = (-0.97)^2 = 0.94$ or 94\\%\n  of the variation is explained by the linear model.}\n\n\\index{least squares regression!R-squared ($R^2$)|)}\n\n\n\\subsection{Categorical predictors with two levels}\n\\label{categoricalPredictorsWithTwoLevels}\n\nCategorical variables are also useful in predicting outcomes.\nHere we consider a categorical predictor with two levels\n(recall that a \\emph{level} is the same as a \\emph{category}).\nWe'll consider Ebay auctions for a video game, \\emph{Mario Kart}\nfor the Nintendo Wii, where both the total price of the auction\nand the condition of the game were recorded.\nHere we want to predict total price based on game condition,\nwhich takes values \\resp{used} and \\resp{new}.\nA plot of the auction data is shown in Figure~\\ref{marioKartNewUsed}.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A scatterplot is shown for total auction prices for the video game \"Mario Kart\", broken down by condition on the horizontal axis. The prices are divided into \"used\" and \"new\" condition groups. All used games are shown with an x-value of 0 on the left, and all new games are shown with an x-value of 1 on the right of the plot. The used games on the left show a lower average price of about \\$43, and new games on the right show a higher average price of about \\$54. The least squares regression line is also shown for this scatterplot, which shows an upward trend and has a formula of \"price equals 42.87 plus 10.90 times cond-subscript-new.]{0.6}{marioKartNewUsed}\n  \\caption{Total auction prices for the video game\n      \\emph{Mario Kart}, divided into used ($x=0$)\n      and new ($x=1$) condition games.\n      The least squares regression line is also shown.}\n  \\label{marioKartNewUsed}\n\\end{figure}\n\nTo incorporate the game condition variable into a regression\nequation, we must convert the categories into a numerical form.\nWe will do so using an \\term{indicator variable}\ncalled \\var{cond\\us{}new}, which takes value 1 when the game\nis new and 0 when the game is used.\nUsing this indicator variable, the linear model may be written as\n\\begin{align*}\n\\widehat{price} = \\beta_0 + \\beta_1 \\times \\text{\\var{cond\\us{}new}}\n\\end{align*}\nThe parameter estimates are given in\nFigure~\\ref{marioKartNewUsedRegrSummary},\nand the model equation can be summarized as\n\\begin{align*}\n\\widehat{price} = 42.87 + 10.90 \\times \\text{\\var{cond\\us{}new}}\n\\end{align*}\nFor categorical predictors with just two levels,\nthe linearity assumption will always be satisfied.\nHowever, we must evaluate whether the residuals in\neach group are approximately normal and have approximately\nequal variance.\nAs can be seen in Figure~\\ref{marioKartNewUsed},\nboth of these conditions are reasonably satisfied\nby the auction data.\n\n\\begin{figure}\n\\centering\n\\begin{tabular}{rrrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  \\vspace{-3.6mm} & & & & \\\\\n(Intercept) & 42.87 & 0.81 & 52.67 & $<$0.0001 \\\\ \n  cond\\us{}new & 10.90 & 1.26 & 8.66 & $<$0.0001 \\\\ \n   \\hline\n\\end{tabular}\n\\caption{Least squares regression summary for the final auction price against the condition of the game.}\n\\label{marioKartNewUsedRegrSummary}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Interpret the two parameters estimated in the\n    model for the price of \\emph{Mario Kart} in eBay auctions.}\n  The intercept is the estimated price when \\var{cond\\us{}new}\n  takes value 0, i.e. when the game is in used condition.\n  That is, the average selling price of a used version of\n  the game is \\$42.87.\n\n  The slope indicates that, on average, new games sell for\n  about \\$10.90 more than used games.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{Interpreting model estimates for\n    categorical predictors}\n  The estimated intercept is the value of the response variable\n  for the first category (i.e. the category corresponding to an\n  indicator value of~0).\n  The estimated slope is the average change in the response\n  variable between the two categories.\n\\end{onebox}\n\nWe'll elaborate further on this topic in\nChapter~\\ref{ch_regr_mult_and_log},\nwhere we examine the influence of many\npredictor variables simultaneously using\nmultiple regression.\n\n\n{\\input{ch_regr_simple_linear/TeX/fitting_a_line_by_least_squares_regression.tex}}\n\n\n\n\n\n\n\n\n%__________________\n\\section{Types of outliers in linear regression}\n\\label{typesOfOutliersInLinearRegression}\n\nIn this section, we identify criteria for determining which\noutliers are important and influential.\nOutliers in regression are observations that fall far from\nthe cloud of points.\nThese points are especially important because they can have\na strong influence on the least squares line. \n\n\\begin{examplewrap}\n\\begin{nexample}{There are six plots shown in\n    Figure~\\ref{outlierPlots} along with the least squares\n    line and residual plots.\n    For~each scatterplot and residual plot pair,\n    identify the outliers and note how they influence\n    the least squares line.\n    Recall that an outlier is any point that doesn't appear\n    to belong with the vast majority of the other points.}\n  \\label{outlierPlotsExample}%\n  \\begin{itemize}\n  %\\setlength{\\itemsep}{0mm}\n  \\item[(1)]\n      There is one outlier far from the other points,\n      though it only appears to slightly influence the~line.\n  \\item[(2)]\n      There is one outlier on the right, though it is quite\n      close to the least squares line, which suggests it\n      wasn't very influential.\n  \\item[(3)]\n      There is one point far away from the cloud, and this\n      outlier appears to pull the least squares line up on\n      the right;\n      examine how the line around the primary cloud doesn't\n      appear to fit very~well.\n  \\item[(4)]\n      There is a primary cloud and then a small secondary\n      cloud of four outliers.\n      The secondary cloud appears to be influencing the line\n      somewhat strongly, making the least square line fit\n      poorly almost everywhere.\n      There might be an interesting explanation for the dual\n      clouds, which is something that could be investigated.\n  \\item[(5)]\n      There is no obvious trend in the main cloud of points\n      and the outlier on the right appears to largely control\n      the slope of the least squares line.\n  \\item[(6)]\n      There is one outlier far from the cloud.\n      However, it falls quite close to the least squares line\n      and does not appear to be very influential.\n  \\end{itemize}\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}\n  \\centering\n  \\Figure[Six scatterplots, each with a least squares line and residual plot. All data sets have at least one outlier. (1) A clear positive upward trend is evident in the points with a regression line overlaying these points, but one point is shown deviating substantially from the line about one-third of the way from the left side of the plot and far below the other points. (2) A slight downward trend is evident in the points on the left half of the plot with a regression line overlaying these points and extending to a single point on the far right of the plot that is also very close to the regression line. (3) A positive upward trend is evident for points shown on the left two-thirds of the plot with a regression line overlaying these points, but a single point is shown on the far right and lying substantially above the line. This one point appears to be \"pulling\" the regression line up on the right, making the line fit the rest of the data less well. (4) Most of the data is shown in the left two-thirds of the plot with a clear downward, linear trend. A cluster of 4 points is shown on the far right but deviating notably above the trend of the other points. The regression line fit to the data shows it largely \"trying\" to fit the bulk of the data on the left but being \"pulled\" upward on the right towards the cluster of points deviating from the linear trend. (5) A large cluster of points is shown on the far bottom-left, and there is no apparent trend in this large cluster. A single point is shown on the far upper-right. A regression line is fit to the data with a line extending from the cluster on the bottom-left and trending upwards near the single point on the upper right. (6) A clear downward trend is evident in the points on the right two-thirds of the plot with a regression line overlaying these points and extending to a single point on the far left of the plot that is also very close to the regression line.]{}{outlierPlots}\n  \\caption{Six plots, each with a least squares line and\n      residual plot. All data sets have at least one outlier.}\n\\label{outlierPlots}\n\\end{figure}\n\nExamine the residual plots in Figure~\\ref{outlierPlots}.\nYou will probably find that there is some trend in the main\nclouds of~(3) and~(4).\nIn these cases, the outliers influenced the slope of the\nleast squares lines.\nIn~(5), data with no clear trend were assigned a line with\na large trend simply due to one outlier (!).\n \n\\begin{onebox}{Leverage}\n  Points that fall horizontally away from the center of the\n  cloud tend to pull harder on the line, so we call them points\n  with \\term{high leverage}.\\index{leverage}\n\\end{onebox}\n\nPoints that fall horizontally far from the line are points\nof high leverage;\nthese points can strongly influence the slope of the least\nsquares line.\nIf one of these high leverage points does appear to actually\ninvoke its influence on the slope of the line --\nas in cases~(3), (4), and (5) of Example~\\ref{outlierPlotsExample}\n-- then we call it an \\term{influential point}.\nUsually we can say a point is influential if, had we fitted\nthe line without it, the influential point would have been\nunusually far from the least squares line.\n\nIt is tempting to remove outliers.\nDon't do this without a very good reason.\nModels that ignore exceptional (and interesting) cases often\nperform poorly.\nFor instance, if a financial firm ignored the largest market\nswings -- the ``outliers'' --  they would soon go bankrupt\nby making poorly thought-out investments.\n\n\n{\\input{ch_regr_simple_linear/TeX/types_of_outliers_in_linear_regression.tex}}\n\n\n\n\n\n\n\n\n%__________________\n\\section{Inference for linear regression}\n\\label{inferenceForLinearRegression}\n\nIn this section, we discuss uncertainty in the estimates\nof the slope and y-intercept for a regression line.\nJust as we identified standard errors for point estimates\nin previous chapters, we first discuss standard errors for\nthese new estimates.\n\n\n\\subsection{Midterm elections and unemployment}\n\n\\index{data!midterm elections|(}\n\nElections for members of the United States House\nof Representatives occur every two years, coinciding\nevery four years with the U.S. Presidential election.\nThe set of House elections occurring during the middle\nof a Presidential term are called\n\\indexthis{midterm elections}{midterm election}.\nIn America's two-party system, one political theory\nsuggests the higher the unemployment rate, the worse\nthe President's party will do in the midterm elections.\n\nTo assess the validity of this claim, we can compile\nhistorical data and look for a connection.\nWe consider every midterm election from 1898 to 2018,\nwith the exception of those elections during the Great\nDepression.\nFigure~\\ref{unemploymentAndChangeInHouse} shows these data\nand the least-squares regression line: \\vspace{-2mm}\n\\begin{align*}\n&\\text{\\% change in House seats for President's party}  \\\\\n&\\qquad\\qquad= -7.36 - 0.89 \\times \\text{(unemployment rate)}\n\\end{align*}\nWe consider the percent change in the number of seats\nof the President's party (e.g. percent change in the number\nof seats for Republicans in 2018) against the unemployment\nrate.\n\nExamining the data, there are no clear deviations from\nlinearity, the constant variance condition,\nor substantial outliers.\nWhile the data are collected sequentially, a separate analysis\nwas used to check for any apparent correlation between successive\nobservations;\nno such correlation was found.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[A scatterplot is shown for the percent change in House seats for the President's party in each midterm election from 1898 to 2018 plotted against the unemployment rate. The two points for the Great Depression have been removed, and a least squares regression line has been fit to the data with a slightly downward trend. The horizontal axis is for \"Unemployment Rate\" with values ranging from about 3\\% to 12\\%. The vertical axis is for \"Percent Change in Seats of the President's Party in the House of Representatives\" with values ranging from about -30\\% to positive 10\\%. The bulk of the observations have Unemployment Rate between 3\\% and 8\\%, and these have the percent change in seats ranging from about -27\\% to positive 4\\% without any discernible trend. There are four observations with unemployment rate above 8\\%, and these have the percent change in seats ranging from -25\\% to -9\\%. Each point in the scatterplot is also labeled as \"Democrat\" in blue or \"Republican\" in red, though this doesn't reveal any additional pattern.]{}{unemploymentAndChangeInHouse}\n  \\caption{The percent change in House seats for the\n      President's party in each midterm election from 1898 to 2018\n      plotted against the unemployment rate.\n      The two points for the Great Depression have been\n      removed, and a least squares regression line has been\n      fit to the data.}\n  \\label{unemploymentAndChangeInHouse}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe data for the Great Depression (1934 and 1938) were removed\nbecause the unemployment rate was 21\\% and 18\\%, respectively.\nDo you agree that they should be removed for this investigation?\nWhy or why not?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We will provide two considerations.\n  Each of these points would have very high leverage on any\n  least-squares regression line, and years with such high\n  unemployment may not help us understand what would happen\n  in other years where the unemployment is only modestly high.\n  On the other hand, these are exceptional cases, and we would\n  be discarding important information if we exclude them from\n  a final analysis.}\n\nThere is a negative slope in the line shown in\nFigure~\\ref{unemploymentAndChangeInHouse}.\nHowever, this slope (and the y-intercept) are only estimates\nof the parameter values.\nWe might wonder, is this convincing evidence that the ``true''\nlinear model has a negative slope?\nThat is, do the data provide strong evidence that the political\ntheory is accurate, where the unemployment rate is a useful\npredictor of the midterm election?\nWe can frame this investigation into a statistical hypothesis\ntest:\n\\begin{itemize}\n\\item[$H_0$:]\n    $\\beta_1 = 0$.\n    The true linear model has slope zero.\n\\item[$H_A$:]\n    $\\beta_1 \\neq 0$.\n    The true linear model has a slope different than zero.\n    The unemployment is predictive of whether the President's\n    party wins or loses seats in the House of Representatives.\n\\end{itemize}\nWe would reject $H_0$ in favor of $H_A$ if the data provide\nstrong evidence that the true slope parameter is different\nthan zero.\nTo assess the hypotheses, we identify a standard error\nfor the estimate, compute an appropriate test statistic,\nand identify the p-value.\n\n\n\\subsection{Understanding regression output from software}\n\\label{testStatisticForTheSlope}\n\n\\newcommand{\\midtermshouseDF}{27}\n\nJust like other point estimates we have seen before,\nwe can compute a standard error and test statistic for $b_1$.\nWe will generally label the test statistic using a $T$,\nsince it follows the $t$-distribution.\n\nWe will rely on statistical software to compute the standard\nerror and leave the explanation of how this standard error\nis determined to a second or third statistics course.\nFigure~\\ref{midtermUnempRegTable} shows software output for\nthe least squares regression line in\nFigure~\\ref{unemploymentAndChangeInHouse}.\nThe row labeled \\emph{unemp} includes the point estimate\nand other hypothesis test information for the slope,\nwhich is the coefficient of the unemployment variable.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{rrrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n  & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  \\vspace{-3.6mm} & & & & \\\\\n  (Intercept) & -7.3644 & 5.1553 & -1.43 & 0.1646 \\\\ \n  unemp & -0.8897 & 0.8350 & -1.07 & 0.2961 \\\\ \n  \\hline\n  \\multicolumn{5}{r}{$df=\\midtermshouseDF{}$} \\\\\n\\end{tabular}\n\\caption{Output from statistical software for the regression\n    line modeling the midterm election losses for the\n    President's party as a response to unemployment.}\n\\label{midtermUnempRegTable}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{What do the first and second columns\n    of Figure~\\ref{midtermUnempRegTable} represent?}\n  The entries in the first column represent the least\n  squares estimates, $b_0$ and $b_1$, and the values in\n  the second column correspond to the standard errors\n  of each estimate.\n  Using the estimates, we could write the equation\n  for the least square regression line as\n  \\begin{align*}\n  \\hat{y} = -7.3644 - 0.8897 x\n  \\end{align*}\n  where $\\hat{y}$ in this case represents the predicted\n  change in the number of seats for the president's party,\n  and $x$ represents the unemployment rate.\n\\end{nexample}\n\\end{examplewrap}\n\n\\D{\\newpage}\n\nWe previously used a $t$-test statistic for hypothesis testing\nin the context of numerical data.\nRegression is very similar.\nIn the hypotheses we consider, the null value for the slope is~0,\nso we can compute the test statistic using the T (or Z) score\nformula:\n\\begin{align*}\nT\n  = \\frac{\\text{estimate} - \\text{null value}}{\\text{SE}}\n  = \\frac{-0.8897 - 0}{0.8350}\n  = -1.07\n\\end{align*}\nThis corresponds to the third column of\nFigure~\\ref{midtermUnempRegTable}.\n\n%\\begin{figure}[h]\n%  \\centering\n%  \\Figure{0.82}{pValueMidtermUnemp}\n%  \\caption{The distribution shown here is the sampling distribution for $b_1$, if the null hypothesis was true. The shaded tail represents the p-value for the hypothesis test evaluating whether there is convincing evidence that higher unemployment corresponds to a greater loss of House seats for the President's party during a midterm election.}\n%  \\label{pValueMidtermUnemp}\n%\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Use the table in\n    Figure~\\ref{midtermUnempRegTable}\n    to determine the p-value for the hypothesis test.}\n  The last column of the table gives the p-value for\n  the two-sided hypothesis test for the coefficient of\n  the unemployment rate: 0.2961.\n  That is, the data do not provide convincing evidence\n  that a higher unemployment rate has any correspondence\n  with smaller or larger losses for the President's party\n  in the House of Representatives in midterm elections.\n\\end{nexample}\n\\end{examplewrap}\n\n\\index{data!midterm elections|)}\n\n\\begin{onebox}{Inference for regression}\n  We usually rely on statistical software to identify point\n  estimates, standard errors, test statistics, and p-values\n  in practice.\n  However, be aware that software will not generally\n  check whether the method is appropriate, meaning we must\n  still verify conditions are met.\n\\end{onebox}\n\n\\begin{examplewrap}\n\\begin{nexample}{Examine Figure~\\vref{elmhurstScatterWLSROnly},\n    which relates the Elmhurst College aid and student family\n    income.\n    How sure are you that the slope is statistically\n    significantly different from zero?\n    That is, do you think a formal hypothesis test would reject\n    the claim that the true slope of the line should be zero?}\n  \\label{overallAidIncomeInfAssessOfRegrLineSlope}%\n  While the relationship between the variables is not perfect,\n  there is an evident decreasing trend in the data.\n  This suggests the hypothesis test will reject the null claim\n  that the slope is zero.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nFigure~\\ref{rOutputForIncomeAidLSRLineInInferenceSection}\nshows statistical software output from fitting the least\nsquares regression line shown in\nFigure~\\ref{elmhurstScatterWLSROnly}.\nUse this output to formally evaluate the following\nhypotheses.\\footnotemark{}\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[$H_0$:]\n    The true coefficient for family income is zero.\n\\item[$H_A$:]\n    The true coefficient for family income is not zero.\n\\end{itemize}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We look in the second row corresponding\n  to the family income variable.\n  We see the point estimate of the slope of the line is -0.0431,\n  the standard error of this estimate is 0.0108, and the $t$-test\n  statistic is $T = -3.98$.\n  The p-value corresponds exactly to the two-sided test we are\n  interested in: 0.0002.\n  The p-value is so small that we reject the null hypothesis\n  and conclude that family income and financial aid at Elmhurst\n  College for freshman entering in the year 2011 are negatively\n  correlated and the true slope parameter is indeed less than~0,\n  just as we believed in\n  Example~\\ref{overallAidIncomeInfAssessOfRegrLineSlope}.}\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{rrrrr}\n  \\hline\n  \\vspace{-3.7mm} & & & & \\\\\n & Estimate & Std. Error & t value & Pr($>$$|$t$|$) \\\\ \n  \\hline\n  \\vspace{-3.6mm} & & & & \\\\\n(Intercept) & 24319.3 & 1291.5 & 18.83 & $<$0.0001 \\\\ \nfamily\\us{}income & -0.0431 & 0.0108 & -3.98 & 0.0002 \\\\ \n   \\hline\n   \\multicolumn{5}{r}{$df=48$} \\\\\n\\end{tabular}\n\\caption{Summary of least squares fit for the Elmhurst\n    College data, where we are predicting the gift aid\n    by the university based on the family income of\n    students.}\n\\label{rOutputForIncomeAidLSRLineInInferenceSection}\n\\end{figure}\n\n\n\\newpage\n\n\\subsection{Confidence interval for a coefficient}\n\n\\index{confidence interval!regression|(}%\n\nSimilar to how we can conduct a hypothesis test for\na model coefficient using regression output, we can also\nconstruct a confidence interval for that coefficient.\n\n\\begin{examplewrap}\n\\begin{nexample}{\n    Compute the 95\\% confidence interval for the\n    \\var{family\\us{}income} coefficient using the\n    regression output from\n    Table~\\ref{rOutputForIncomeAidLSRLineInInferenceSection}.}\n  The point estimate is -0.0431 and the standard error is\n  $SE = 0.0108$.\n  When constructing a confidence interval for a model\n  coefficient, we generally use a $t$-distribution.\n  The degrees of freedom for the distribution are noted in\n  the regression output, $df = 48$, allowing us to identify\n  $t_{48}^{\\star} = 2.01$ for use in the confidence interval.\n\n  We can now construct the confidence interval in the usual way:\n  \\begin{align*}\n  \\text{point estimate} \\pm t_{48}^{\\star} \\times SE\n    \\qquad\\to\\qquad -0.0431 \\pm 2.01 \\times 0.0108\n    \\qquad\\to\\qquad (-0.0648, -0.0214)\n  \\end{align*}\n  We are 95\\% confident that with each dollar increase in\n  \\var{family\\us{}income}, the university's gift aid is\n  predicted to decrease on average by \\$0.0214 to \\$0.0648.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{onebox}{Confidence intervals for coefficients}\n  Confidence intervals for model coefficients can be computed\n  using the $t$-distribution:\n  \\begin{align*}\n  b_i \\ \\pm\\ t_{df}^{\\star} \\times SE_{b_{i}}\n  \\end{align*}\n  where $t_{df}^{\\star}$ is the appropriate $t$-value\n  corresponding to the confidence level with the\n  model's degrees of freedom.\n\\end{onebox}\n\nOn the topic of intervals in this book, we've focused exclusively\non confidence intervals for model parameters.\nHowever, there are other types of intervals that may be\nof interest, including\nprediction intervals\\index{prediction interval}\nfor a response value\nand also\nconfidence intervals for a\nmean response value\\index{mean response value}\nin the context of regression.\nThese two interval types are introduced in an online extra\nthat you may download at\n\\begin{center}\n\\oiRedirect{stat_extra_linear_regression_supp}\n    {www.openintro.org/d?file=stat\\_extra\\_linear\\_regression\\_supp}\n\\end{center}\n\n\\index{confidence interval!regression|)}%\n\\index{regression|)}\n\n\n{\\input{ch_regr_simple_linear/TeX/inference_for_linear_regression.tex}}\n"
  },
  {
    "path": "ch_regr_simple_linear/TeX/fitting_a_line_by_least_squares_regression.tex",
    "content": "\\exercisesheader{}\n\n% 17\n\n\\eoce{\\qt{Units of regression\\label{regression_units}} Consider a regression \npredicting weight (kg) from height (cm) for a sample of adult males. \nWhat are the units of the correlation coefficient, the intercept, \nand the slope?\n}{}\n\n% 18\n\n\\eoce{\\qtq{Which is higher\\label{which_higher_scatter}} Determine if I or II \nis higher or if they are equal. Explain your reasoning.\n\\noindent For a regression line, the uncertainty associated with the \nslope estimate, $b_1$, is higher when\n\\begin{enumerate}\n\\item[I.] there is a lot of scatter around the regression line or\n\\item[II.] there is very little scatter around the regression line\n\\end{enumerate}\n}{}\n\n% 19\n\n\\eoce{\\qt{Over-under, Part I\\label{residual_apple_weight}} Suppose we fit a \nregression line to predict the shelf life of an apple based on its weight. \nFor a particular apple, we predict the shelf life to be 4.6 days. The \napple's residual is -0.6 days. Did we over or under estimate the \nshelf-life of the apple? Explain your reasoning.\n}{}\n\n% 20\n\n\\eoce{\\qt{Over-under, Part II\\label{residual_sun_cancer}} Suppose we fit a \nregression line to predict the number of incidents of skin cancer per \n1,000 people from the number of sunny days in a year. For a particular \nyear, we predict the incidence of skin cancer to be 1.5 per 1,000 people, \nand the residual for this year is 0.5. Did we over or under estimate \nthe incidence of skin cancer? Explain your reasoning.\n}{}\n\n% 21\n\n\\eoce{\\qt{Tourism spending\\label{tourism_spending_reg_conds}} The Association of \nTurkish Travel Agencies reports the number of foreign tourists \nvisiting Turkey and tourist spending by year.\n\\footfullcite{data:turkeyTourism} Three plots are provided: \nscatterplot showing the relationship between these two variables \nalong with the least squares fit, residuals plot, and histogram of \nresiduals.\n\\begin{center}\n\\FigureFullPath[A scatterplot with a least squares regression line is fit based on about 50 points. The horizontal axis represents \"Number of tourists\" and has values ranging from about 0 to about 27 million. The vertical axis represents \"Spending, in US dollars\", with values ranging from about \\$0 to about \\$17 billion. There are many points shown with the number of tourists between 0 and 5 million, which has spending between about \\$0 and \\$3 billion, where even on this small scale a roughly linear trend is evident. The linear trend continues on across the plot and is quite strong -- where residuals generally do not deviate from the least square line by more than very roughly \\$1 billion. The data are also more sparse for larger values in the plot. There is one region in the center of the plot where about 10 points in a row lie above the regression line. Also consider the next two plots before answering any questions for this exercise.]{0.32}{ch_regr_simple_linear/figures/eoce/tourism_spending_reg_conds/tourism_spending_count}\n\\FigureFullPath[A residual plot is shown. The horizontal axis represents \"Number of tourists\" and has values ranging from about 0 to about 27 million. Residuals are shown on the vertical axis and have values ranging from about -\\$1.5 billion to about \\$1.2 billion. The points on the far left between 0 and 3 million tourists shows a \"v\" pattern. There are about 15 points with number of tourists between 3 million and 10 million, which shows an slight upward trend from about -\\$700 million to \\$1.2 billion. There about 10 points with number of tourists greater than 10 million up to about 27 million, and these show a slight downward trend from about \\$1 billion to -\\$1.5 billion.]{0.32}{ch_regr_simple_linear/figures/eoce/tourism_spending_reg_conds/tourism_spending_count_residuals}\n\\FigureFullPath[A histogram is shown for the residuals, which shows a roughly bell-shaped distribution centered at 0 and a standard deviation of about \\$500 million.]{0.32}{ch_regr_simple_linear/figures/eoce/tourism_spending_reg_conds/tourism_spending_count_residuals_hist}\n\\end{center}\n\\begin{parts}\n\\item Describe the relationship between number of tourists and spending.\n\\item What are the explanatory and response variables?\n\\item Why might we want to fit a regression line to these data?\n\\item Do the data meet the conditions required for fitting a least squares \nline? In addition to the scatterplot, use the residual plot and histogram \nto answer this question. \n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 22\n\n\\eoce{\\qt{Nutrition at Starbucks, Part I\\label{starbucks_cals_carbos}} \nThe scatterplot below shows the relationship between the number of \ncalories and amount of carbohydrates (in grams) Starbucks food menu \nitems contain.\\footfullcite{data:starbucksCals} Since Starbucks only \nlists the number of calories on the display items, we are interested \nin predicting the amount of carbs a menu item has based on its \ncalorie content.\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown with about 75 points and an overlaid regression line that trends upward. The horizontal axis represents \"Calories\" and has values ranging from about 100 to 500. The vertical axis represents \"Carbs, in grams\" and has values ranging from about 20 to 80. About 15 points are shown with fewer than 200 calories, and these have between about 18 and 25 grams of carbs. About 30 points are shown with 200 to 400 calories, and these mostly have between 30 and 60 grams of carbs. About 20 points are shown with more than 400 calories, and these mostly have between 35 and 80 grams of carbs.]{0.32}{ch_regr_simple_linear/figures/eoce/starbucks_cals_carbos/starbucks_cals_carbos}\n\\FigureFullPath[A residual plot is shown with about 75 points. The horizontal axis represents \"Calories\" and has values ranging from about 100 to 500. The vertical axis represents \"Residuals\" and has values ranging from about -30 to 30. About 15 points are shown with fewer than 200 calories, and these have residuals roughly between -7 and positive 2. About 30 points are shown with 200 to 400 calories, and these residuals largely range from about -15 to positive 15. About 20 points are shown with more than 400 calories, and the residuals for these points mostly range between -20 and positive 20.]{0.32}{ch_regr_simple_linear/figures/eoce/starbucks_cals_carbos/starbucks_cals_carbos_residuals}\n\\FigureFullPath[A histogram is shown for the residuals, which shows a roughly bell-shaped distribution centered at 0 and a standard deviation of about 10.]{0.32}{ch_regr_simple_linear/figures/eoce/starbucks_cals_carbos/starbucks_cals_carbos_residuals_hist}\n\\end{center}\n\\begin{parts}\n\\item Describe the relationship between number of calories and amount \nof carbohydrates (in grams) that Starbucks food menu items contain.\n\\item In this scenario, what are the explanatory and response \nvariables?\n\\item Why might we want to fit a regression line to these data?\n\\item Do these data meet the conditions required for fitting a least \nsquares line?\n\\end{parts}\n}{}\n\n% 23\n\n\\eoce{\\qt{The Coast Starlight, Part II\\label{coast_starlight_reg}}\nExercise~\\ref{coast_starlight_corr_units} introduces data on the Coast Starlight \nAmtrak train that runs from Seattle to Los Angeles. The mean travel \ntime from one stop to the next on the Coast Starlight is 129 mins, \nwith a standard deviation of 113 minutes. The mean distance traveled \nfrom one stop to the next is 108 miles with a standard deviation of \n99 miles. The correlation between travel time and distance is 0.636.\n\\begin{parts}\n\\item Write the equation of the regression line for predicting travel \ntime.\n\\item Interpret the slope and the intercept in this context.\n\\item Calculate $R^2$ of the regression line for predicting travel \ntime from distance traveled for the Coast Starlight, and interpret \n$R^2$ in the context of the application.\n\\item The distance between Santa Barbara and Los Angeles is 103 \nmiles. Use the model to estimate the time it takes for the Starlight \nto travel between these two cities.\n\\item It actually takes the Coast Starlight about 168 mins to travel \nfrom Santa Barbara to Los Angeles. Calculate the residual and explain \nthe meaning of this residual value.\n\\item Suppose Amtrak is considering adding a stop to the Coast \nStarlight 500 miles away from Los Angeles. Would it be appropriate to \nuse this linear model to predict the travel time from Los Angeles to \nthis point? \n\\end{parts}\n}{}\n\n% 24\n\n\\eoce{\\qt{Body measurements, Part III\\label{body_measurements_shoulder_height_reg}}\nExercise~\\ref{body_measurements_shoulder_height_corr_units} introduces \ndata on shoulder girth and height of a group of individuals. The \nmean shoulder girth is 107.20 cm with a standard deviation of \n10.37 cm. The mean height is 171.14 cm with a standard deviation \nof 9.41 cm. The correlation between height and shoulder girth is 0.67.\n\\begin{parts}\n\\item Write the equation of the regression line for predicting height.\n\\item Interpret the slope and the intercept in this context.\n\\item Calculate $R^2$ of the regression line for predicting height \nfrom shoulder girth, and interpret it in the context of the \napplication.\n\\item A randomly selected student from your class has a shoulder \ngirth of 100 cm. Predict the height of this student using the model.\n\\item The student from part~(d) is 160 cm tall. Calculate the \nresidual, and explain what this residual means.\n\\item A one year old has a shoulder girth of 56 cm. Would it be \nappropriate to use this linear model to predict the height of this \nchild?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 25\n\n\\eoce{\\qt{Murders and poverty, Part I\\label{murders_poverty_reg}} The following \nregression output is for predicting annual murders per million from \npercentage living in poverty in a random sample of 20 metropolitan \nareas.\\\\[2mm]\n\\begin{minipage}[c]{0.54\\textwidth}\n{\\footnotesize\n\\begin{tabular}{rrrrr}\n    \\hline\n            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n    \\hline\n(Intercept) & -29.901   & 7.789         & -3.839    & 0.001 \\\\ \npoverty\\%   & 2.559     & 0.390         & 6.562     & 0.000 \\\\ \n   \\hline\n\\end{tabular} \\\\\n$s = 5.512 \\hfill R^2 = 70.52\\% \\hfill R^2_{adj} = 68.89\\%$ \n}\n\\begin{parts}\n\\item Write out the linear model.\n\\item Interpret the intercept.\n\\item Interpret the slope.\n\\item Interpret $R^2$.\n\\item Calculate the correlation coefficient.\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.02\\textwidth}\n$\\:$\\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.41\\textwidth}\n\\FigureFullPath[A scatterplot is shown with 20 points. The horizontal axis is \"Percent in Poverty\" and has values ranging from 14\\% to 26\\%. The vertical axis is \"Annual Murders per Million\" with values ranging from about 5 to 40. There are 6 points with poverty below 18\\%, and the Murder Rate for these values ranges from 5 to 13, with one exception of a point at about 17\\% with a murder rate of about 25. There are 9 points with a poverty rate of 18\\% to 22\\%, and the murder rate for these points largely range from 14 to 25, with one exception of a point at about 21\\% poverty and a murder rate of 35. There are 5 points where poverty is larger than 22\\%, and these have murder rates ranging from 25 to 40.]{}{ch_regr_simple_linear/figures/eoce/murders_poverty_reg/murders_poverty.pdf}\n\\end{minipage}\n}{}\n\n% 26\n\n\\eoce{\\qt{Cats, Part I\\label{cat_body_heart_reg}} The following regression output is \nfor predicting the heart weight (in g) of cats from their body weight \n(in kg). The coefficients are estimated using a dataset of 144 \ndomestic cats.\\\\[2mm]\n\\begin{minipage}[c]{0.54\\textwidth}\n{\\footnotesize\n\\begin{tabular}{rrrrr}\n    \\hline\n            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n    \\hline\n(Intercept) & -0.357    & 0.692         & -0.515    & 0.607 \\\\ \nbody wt     & 4.034     & 0.250         & 16.119    & 0.000 \\\\ \n    \\hline\n\\end{tabular} \\\\\n$s = 1.452 \\hfill R^2 = 64.66\\% \\hfill R^2_{adj} = 64.41\\%$ \n}\n\\begin{parts}\n\\item Write out the linear model.\n\\item Interpret the intercept.\n\\item Interpret the slope.\n\\item Interpret $R^2$.\n\\item Calculate the correlation coefficient.\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.02\\textwidth}\n$\\:$\\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.41\\textwidth}\n\\FigureFullPath[A scatterplot is shown with about 150 points. The horizontal axis is \"Body weight, in kilograms\" and has values ranging from 2 to 4. The vertical axis is \"Heart weight, in grams\" with values ranging from about 5 to 20. About 25\\% of the data has a body weight below 2.5 kilograms, and these have heart weights mostly ranging from 7  to 11 grams. About 35\\% of the data has body weights between 2.5 and 3 kilograms, and the heart weight for these values mostly ranges from 8 to 12 grams. About 30\\% of the data has body weights between 3 and 3.5 kilograms, and the heart weight for these values mostly ranges from 11 to 15 grams. About 10\\% of the data has body weights above 3.5 kilograms, and the heart weight for these values mostly ranges from 12 to 17 grams.]{}{ch_regr_simple_linear/figures/eoce/cat_body_heart_reg/cat_body_heart.pdf}\n\\end{minipage}\n}{}\n"
  },
  {
    "path": "ch_regr_simple_linear/TeX/inference_for_linear_regression.tex",
    "content": "\\exercisesheader{}\n\n\\noindent%\nIn the following exercises, visually check the conditions\nfor fitting a least squares regression line.\nHowever, you do not need to report these conditions in\nyour solutions.\\\\[6mm]\n\n% 31\n\n\\eoce{\\qt{Body measurements, Part IV\\label{body_measurements_weight_height_inf}} \nThe scatterplot and least squares summary below show the relationship \nbetween weight measured in kilograms and height measured in centimeters \nof 507 physically active individuals.\n\n\\noindent\\begin{minipage}[c]{0.4\\textwidth}\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown with around 500 points. The horizontal axis is for \"Height, in centimeters\" and takes values between about 150 to 200 centimeters. The vertical axis is for \"Weight, in kilograms\" and takes values between about 40 to 120 centimeters. For heights smaller than about 160 centimeters, weights mostly range between 45 and 70 kilograms. For heights between 160 and 175 centimeters, weights mostly range between 55 and 80 kilograms. For heights between 175 and 185 centimeters, weights mostly range between 65 and 90 kilograms. For heights between 185 and 195 centimeters, where there are fewer points, weights mostly range between 80 and 95 kilograms. There are two points with heights at about than 196cm, and these have weights of about 85 and 95 kilograms.]{}{ch_regr_simple_linear/figures/eoce/body_measurements_weight_height_inf/body_measurements_weight_height}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.6\\textwidth}\n{\\scriptsize\n\\begin{center}\n\\begin{tabular}{rrrrr}\n    \\hline\n            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n    \\hline\n(Intercept) & -105.0113 & 7.5394        & -13.93    & 0.0000 \\\\ \nheight      & 1.0176    & 0.0440        & 23.13     & 0.0000 \\\\\n    \\hline\n\\end{tabular}\n\\end{center}\n}\n\\end{minipage}\n\\begin{parts}\n\\item Describe the relationship between height and weight.\n\\item Write the equation of the regression line. Interpret the slope \nand intercept in context.\n\\item Do the data provide strong evidence that an increase in height \nis associated with an increase in weight? State the null and alternative \nhypotheses, report the p-value, and state your conclusion.\n\\item The correlation coefficient for height and weight is 0.72. \nCalculate $R^2$ and interpret it in context.\n\\end{parts}\n}{}\n\n% 32\n\n\\eoce{\\qt{Beer and blood alcohol content\\label{beer_blood_alcohol_inf}} \nMany people believe that gender, \nweight, drinking habits, and many other factors are much more important \nin predicting blood alcohol content (BAC) than simply considering the \nnumber of drinks a person consumed. Here we examine data from sixteen \nstudent volunteers at Ohio State University who each drank a randomly \nassigned number of cans of beer. These students were evenly divided \nbetween men and women, and they differed in weight and drinking habits. \nThirty minutes later, a police officer measured their blood alcohol \ncontent (BAC) in grams of alcohol per deciliter of blood.\n\\footfullcite{Malkevitc+Lesser:2008} The scatterplot and regression \ntable summarize the findings.\n\n\\noindent\\begin{minipage}[c]{0.4\\textwidth}\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown with around 15 points. The horizontal axis is for \"Cans of beer\" and takes values between about 1 and 9. The vertical axis is for \"Blood Alcohol Concentration (BAC), in grams per deciliter\" and takes values between about 0.01 to 0.2 centimeters. The point at 1 can of beer is at 0.01 BAC, lower than any other values. For the four points at 2 and 3 cans of beer, BAC ranges from 0.02 to 0.07. For the six points at 4 and 5 cans of beer, BAC ranges from 0.05 to 0.10. Two points are at 7 cans of beer and have BAC of 0.09 and 0.10. There is a single point for 8 cans of beer, which has a BAC of 0.12, and one last point at 9 cans of beer, which has a BAC of about 0.19.]{}{ch_regr_simple_linear/figures/eoce/beer_blood_alcohol_inf/beer_blood_alcohol}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.6\\textwidth}\n{\\scriptsize\n\\begin{center}\n\\begin{tabular}{rrrrr}\n    \\hline\n            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n    \\hline\n(Intercept) & -0.0127   & 0.0126        & -1.00     & 0.3320 \\\\ \nbeers       & 0.0180    & 0.0024        & 7.48      & 0.0000 \\\\ \n    \\hline\n\\end{tabular}\n\\end{center}\n}\n\\end{minipage}\n\\begin{parts}\n\\item Describe the relationship between the number of cans of beer \nand BAC.\n\\item Write the equation of the regression line. Interpret the slope \nand intercept in context.\n\\item Do the data provide strong evidence that drinking more cans of \nbeer is associated with an increase in blood alcohol? State the null \nand alternative hypotheses, report the p-value, and state your \nconclusion.\n\\item The correlation coefficient for number of cans of beer and BAC \nis 0.89. Calculate $R^2$ and interpret it in context.\n\\item Suppose we visit a bar, ask people how many drinks they have had, \nand also take their BAC. Do you think the relationship between number \nof drinks and BAC would be as strong as the relationship found in the \nOhio State study?\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 33\n\n\\eoce{\\qt{Husbands and wives, Part II\\label{husbands_wives_height_inf}} The \nscatterplot below summarizes husbands' and wives' heights in a random \nsample of 170 married couples in Britain, where both partners' ages are \nbelow 65 years. Summary output of the least squares fit for predicting \nwife's height from husband's height is also provided in the table.\n\n\\noindent\\begin{minipage}[c]{0.4\\textwidth}\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown with around 200 points. The horizontal axis is for \"Husband's height, in inches\" and takes values between 60 and 75 inches. The vertical axis is for \"Wife's height, in inches\" and takes values between 55 and 70 inches. For the approximately fifteen husband heights smaller than 65 inches, wife heights are mostly between 59 and 65 inches. For the approximately 100 husband heights between 65 and 70 inches, wife heights are mostly between 59 and 66 inches. For the approximately 30 husband heights taller than 70 inches, wife heights are mostly between 62 and 67 inches.]{}{ch_regr_simple_linear/figures/eoce/husbands_wives_height_inf_2s/husbands_wives_height_inf_2s}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.6\\textwidth}\n{\\scriptsize\n\\begin{center}\n\\begin{tabular}{rrrrr}\n    \\hline\n                    & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n    \\hline\n(Intercept)         & 43.5755   & 4.6842        & 9.30      & 0.0000 \\\\ \nheight\\_\\hspace{0.3mm}husband   & 0.2863    & 0.0686        & 4.17      & 0.0000 \\\\ \n    \\hline\n\\end{tabular}\n\\end{center}\n}\n\\end{minipage}\n\\begin{parts}\n\\item Is there strong evidence that taller men marry taller women? \nState the hypotheses and include any information used to conduct the test.\n\\item Write the equation of the regression line for predicting wife's \nheight from husband's height.\n\\item Interpret the slope and intercept in the context of the application.\n\\item Given that $R^2 = 0.09$, what is the correlation of heights \nin this data set?\n\\item You meet a married man from Britain who is 5'9\" (69 inches). \nWhat would you predict his wife's height to be? How reliable is this \nprediction?\n\\item You meet another married man from Britain who is 6'7\" (79 inches). \nWould it be wise to use the same linear model to predict his wife's \nheight? Why or why not?\n\\end{parts}\n}{}\n\n% 34\n\n\\eoce{\\qt{Urban homeowners, Part II\\label{urban_homeowners_cond}}\nExercise~\\ref{urban_homeowners_outlier} gives a scatterplot displaying the \nrelationship between the percent of families that own their home and \nthe percent of the population living in urban areas. Below is a \nsimilar scatterplot, excluding District of Columbia, as well as the \nresiduals plot. There were 51 cases.\n\n\\noindent\\begin{minipage}[c]{0.45\\textwidth}\n{\\raggedright\\begin{parts}\n\\item For these data, $R^2=0.28$. What is the correlation? How can \nyou tell if it is positive or negative?\n\\item Examine the residual plot. What do you observe? Is a simple \nleast squares fit appropriate for these data?\n\\end{parts}\\vspace{15mm}}\n\\end{minipage}\n\\begin{minipage}[c]{0.1\\textwidth}\n$\\:$ \\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.43\\textwidth}\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown. The horizontal axis represents \"Husband's Age (in years)\" with values ranging from about 20 to 65. The vertical axis represents \"Wife's Age (in years)\" with values ranging from about 18 to 65. When husband age is between 20 and 30, wife age mostly ranges from 18 to about 30. When husband age is between 30 and 40, wife age mostly ranges from 23 to about 40. When husband age is between 40 and 50, wife age mostly ranges from 35 to about 50. When husband age is between 50 and 60, wife age mostly ranges from 45 to about 60. When husband age is larger than 60, wife age mostly ranges from 55 to about 65.]{}{ch_regr_simple_linear/figures/eoce/urban_homeowners_cond/urban_homeowners_cond}\n\\end{center}\n\\end{minipage}\n}{}\n\n\\D{\\newpage}\n\n% 35\n\n\\eoce{\\qt{Murders and poverty, Part II\\label{murders_poverty_inf}}\nExercise~\\ref{murders_poverty_reg} presents regression output from a model \nfor predicting annual murders per million from percentage living in \npoverty based on a random sample of 20 metropolitan areas. The model \noutput is also provided below.\n\\begin{center}\n\\begin{tabular}{rrrrr}\n    \\hline\n            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n    \\hline\n(Intercept) & -29.901   & 7.789         & -3.839    & 0.001 \\\\ \npoverty\\%   & 2.559     & 0.390         & 6.562     & 0.000 \\\\ \n    \\hline\n\\end{tabular}\n\\[ s = 5.512 \\qquad R^2 = 70.52\\% \\qquad R^2_{adj} = 68.89\\% \\]\n\\end{center}\n\\begin{parts}\n\\item What are the hypotheses for evaluating whether poverty percentage \nis a significant predictor of murder rate?\n\\item State the conclusion of the hypothesis test from part (a) in \ncontext of the data.\n\\item Calculate a 95\\% confidence interval for the slope of poverty \npercentage, and interpret it in context of the data.\n\\item Do your results from the hypothesis test and the confidence \ninterval agree? Explain.\n\\end{parts}\n}{}\n\n% 36\n\n\\eoce{\\qt{Babies\\label{babies_head_gestation_inf}} Is the gestational age \n(time between conception and birth) of a low birth-weight baby useful \nin predicting head circumference at birth? Twenty-five low birth-weight \nbabies were studied at a Harvard teaching hospital; the investigators \ncalculated the regression of head circumference (measured in centimeters) \nagainst gestational age (measured in weeks). The estimated regression \nline is\n\\[ \\widehat{head~circumference} = 3.91 + 0.78 \\times gestational~age \\]\n\\begin{parts}\n\\item What is the predicted head circumference for a baby whose \ngestational age is 28 weeks?\n\\item The standard error for the coefficient of gestational age is 0.\n35, which is associated with $df=23$. Does the model provide strong \nevidence that gestational age is significantly associated with head \ncircumference?\n\\end{parts} \n}{}\n"
  },
  {
    "path": "ch_regr_simple_linear/TeX/line_fitting_residuals_and_correlation.tex",
    "content": "\\exercisesheader{}\n\n% 1\n\n\\eoce{\\qt{Visualize the residuals\\label{visualize_residuals}} \nThe scatterplots shown below each have a \nsuperimposed regression line. If we were to construct a residual plot \n(residuals versus $x$) for each, describe what those plots would look \nlike.\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown, where the data have a steady upward trend throughout. The observations above and below the line appear random and have stable variability moving from left to right.]{0.42}{ch_regr_simple_linear/figures/eoce/visualize_residuals/visualize_residuals_linear}\n\\FigureFullPath[A scatterplot is shown, where the data have a steady upward trend throughout. The observations above and below the line appear random. If looking at the leftmost region of data, the observations are more broadly scattered around the line, while when moving further right the variability of the points around the line gets notably smaller by a factor of at least 5 (if using standard deviation).]{0.42}{ch_regr_simple_linear/figures/eoce/visualize_residuals/visualize_residuals_fan_back}\n\\end{center}\n}{}\n\n% 2\n\n\\eoce{\\qt{Trends in the residuals\\label{trends_in_residuals}} \nShown below are two plots of residuals \nremaining after fitting a linear model to two different sets of data. \nDescribe important features and determine if a linear model would be \nappropriate for these data. Explain your reasoning.\n\\begin{center}\n\\FigureFullPath[A scatterplot of the residuals is shown. When looking at any horizontal region of the plot, the observations are consistently scattered around the \"y equals 0\" line. On the left, the points tend to be very close to this horizontal 0 line. The further moving to the right, the more variability that is evident in the observations around \"y equals 0\".]{0.42}{ch_regr_simple_linear/figures/eoce/trends_in_residuals/trends_in_residuals_fan} \n\\FigureFullPath[A scatterplot of the residuals is shown. The points on the very left tend to be below the \"y equals 0\" line for the first 5\\% of the horizontal region, where the trend is sharply upwards to the \"y equals 0\" line. The points then tend to be stably clustered around \"y equals 0\", if not slightly above, with a slight downward trend evident in the observations on the right half of the plot. The vertical variability of observations is about stable throughout.]{0.42}{ch_regr_simple_linear/figures/eoce/trends_in_residuals/trends_in_residuals_log}\n\\end{center}\n}{}\n\n% 3\n\n\\eoce{\\qt{Identify relationships, Part I\\label{identify_relationships_1}} \nFor each of the six plots, \nidentify the strength of the relationship (e.g. weak, moderate, or \nstrong) in the data and whether fitting a linear model would be \nreasonable.\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown. The observations start in the upper left corner of the plot, trend sharply downwards before tapering off and stabilizing at about the middle of the plot, before steadily and then faster rising again to the upper right corner of the plot. The trend is approximately symmetric from left-to-right.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_1/identify_relationships_u}\n\\FigureFullPath[A scatterplot is shown. The start on the lower left corner, only spanning about 20\\% of the vertical region of the plot, and have a steady upwards trend to the upper right corner of the plot. The vertical variability of the points around the trend is relatively stable across the plot.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_1/identify_relationships_lin_pos_strong}\n\\FigureFullPath[A scatterplot is shown. On the left side of the plot, the points are appear randomly scattered across the full range of the plot, and this property holds across the entire plot. No trend is evident.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_1/identify_relationships_lin_pos_weak}\n%\n\\FigureFullPath[A scatterplot is shown. On the left side of the plot, the observations are in concentrated in the bottom half of the plot but rise steadily. The trend peaks near the center of the plot, where nearly all the points in the (horizontal) center region of the scatterplot are concentrated in the upper half of the scatterplot. On the right side of the plot, the points show a trend downwards, with points concentrated in the lower quarter of the scatterplot for the rightmost handful of points.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_1/identify_relationships_n}\n\\FigureFullPath[A scatterplot is shown. The start on the upper left corner, only spanning about 20\\% of the vertical region of the plot, and have a steady downwards trend to the bottom right corner of the plot. The vertical variability of the points around the trend is relatively stable across the plot.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_1/identify_relationships_lin_neg_strong}\n\\FigureFullPath[A scatterplot is shown. On the left side of the plot, the points are appear randomly scattered across the full range of the plot, and this property holds across the entire plot. No trend is evident or at least obvious.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_1/identify_relationships_none}\n\\end{center}\n}{}\n\n\\D{\\newpage}\n\n% 4\n\n\\eoce{\\qt{Identify relationships, Part II\\label{identify_relationships_2}} \nFor each of the six plots, \nidentify the strength of the relationship (e.g. weak, moderate, or \nstrong) in the data and whether fitting a linear model would be \nreasonable.\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown. On the left side of the plot, the observations are in concentrated in the upper corner of the plot, with a sharp trend downwards, before stabilizing, then rising slightly at halfway through the plot, reaching a peak, and then declining again, with a sharp decline on the right-most portion of the plot to the bottom-right corner of the plot.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_2/identify_relationships_s}\n\\FigureFullPath[A scatterplot is shown. On the left side of the plot, the observations are concentrated around a region about 30\\% of the way up from the bottom-left corner of the plot, there is a slight downward trend that reaches the bottom area of the plot for about the center half of the plot, then the points rise gradually and then sharply in the last 25-30\\% of the plot.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_2/identify_relationships_hockey_stick}\n\\FigureFullPath[A scatterplot is shown. Pointers in the leftmost region of the plot are concentrated in the lower-left corner, ranging from the bottom up to about 25\\% of the way up the plot. The points follow a steady upward trend to the top-right corner of the plot and show consistent vertical variability around the trend throughout.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_2/identify_relationships_pos_lin_strong}\n%\n\\FigureFullPath[A scatterplot is shown. The points appear randomly scattered across the left, middle, and right portion of the plot. There might be a very slight upward trend.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_2/identify_relationships_pos_weak}\n\\FigureFullPath[A scatterplot is shown. The points appear randomly scattered across the left, middle, and right portion of the plot. There is a very slight downward trend.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_2/identify_relationships_pos_weaker}\n\\FigureFullPath[A scatterplot is shown. The points on the leftmost side are concentrated in the upper half of the plot, and the data trend steadily downwards and with consistent variability to the bottom right portion of the plot.]{0.32}{ch_regr_simple_linear/figures/eoce/identify_relationships_2/identify_relationships_neg_lin_weak}\n\\end{center}\n}{}\n\n% 5\n\n\\eoce{\\qt{Exams and grades\\label{exams_grades_correlation}} \nThe two scatterplots below show the \nrelationship between final and mid-semester exam grades recorded \nduring several years for a Statistics course at a university.\n\\begin{parts}\n\\item Based on these graphs, which of the two exams has the strongest \ncorrelation with the final exam grade? Explain.\n\\item Can you think of a reason why the correlation between the exam \nyou chose in part (a) and the final exam is higher?\n\\end{parts}\n\\begin{center}\n\\FigureFullPath[A scatter plot with 100 points is shown with an upward trending line fit to the data. Exam 1 scores are on the horizontal axis and range from 40 to 100. Final Exam scores are on the vertical axis and also range from 40 to 100. Only about ten Exam 1 scores are below 60, and these have Final Exam scores between about 55 and 85. Exam 1 scores between 60 and 80 represent about 50\\% of the points shown and have Final Exam scores mostly between 50 and 85. For the points where Midterm 1 scores are larger than 80, Final Exam scores mostly lie between 65 and 90, where a slightly upward trend is evident.]{0.485}{ch_regr_simple_linear/figures/eoce/exams_grades_correlation/exam_grades_1}\n\\hspace{0.02\\textwidth}%\n\\FigureFullPath[A scatter plot with 100 points is shown with an upward trending line fit to the data. Exam 2 scores are on the horizontal axis and range from 40 to 100. Final Exam scores are on the vertical axis and also range from 40 to 100. Midterm 2 scores are roughly uniformly distributed across the full range. For Exam 2 scores below 60, these mostly have Final Exam scores between about 45 and 70. Exam 2 scores between 60 and 80 have Final Exam scores mostly between 55 and 80. For the points where Midterm 2 scores are larger than 80, Final Exam scores mostly lie between 70 and 90.]{0.485}{ch_regr_simple_linear/figures/eoce/exams_grades_correlation/exam_grades_2}\n\\end{center}\n}{}\n\n\\D{\\newpage}\n\n% 6\n\n\\eoce{\\qt{Husbands and wives, Part I\\label{husbands_wives_correlation}}\nThe Great Britain Office of Population Census and Surveys once \ncollected data on a random sample of 170 married couples in \nBritain, recording the age (in years) and heights (converted \nhere to inches) of the husbands and wives.\\footfullcite{Hand:1994} \nThe scatterplot on the left shows the wife's age plotted against her \nhusband's age, and the plot on the right shows wife's height \nplotted against husband's height.\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown. The horizontal axis represents \"Husband's Age (in years)\" with values ranging from about 20 to 65. The vertical axis represents \"Wife's Age (in years)\" with values ranging from about 18 to 65. When husband age is between 20 and 30, wife age mostly ranges from 18 to about 30. When husband age is between 30 and 40, wife age mostly ranges from 23 to about 40. When husband age is between 40 and 50, wife age mostly ranges from 35 to about 50. When husband age is between 50 and 60, wife age mostly ranges from 45 to about 60. When husband age is larger than 60, wife age mostly ranges from 55 to about 65.]{0.35}{ch_regr_simple_linear/figures/eoce/husbands_wives_correlation/husbands_wives_age} \n\\hspace{5mm}\n\\FigureFullPath[A scatterplot is shown. The horizontal axis represents \"Husband's Height (in inches)\" with values ranging from about 60 to 75. The vertical axis represents \"Wife's Height (in inches)\" with values ranging from about 55 to 70. When husband height is between 60 and 65, wife height mostly ranges from about 58 to 65 inches, though there are only about 10 points in this range, which is about 5\\% of the data. When husband height is between 65 and 70, wife height mostly ranges from 57 to 68 inches. When husband height is larger than 70 inches, wife height mostly ranges from 61 to about 74 inches.]{0.35}{ch_regr_simple_linear/figures/eoce/husbands_wives_correlation/husbands_wives_height}\n\\end{center}\n\\begin{parts}\n\\item Describe the relationship between husbands' and wives' ages.\n\\item Describe the relationship between husbands' and wives' heights.\n\\item Which plot shows a stronger correlation? Explain your reasoning.\n\\item Data on heights were originally collected in centimeters, and \nthen converted to inches. Does this conversion affect the correlation \nbetween husbands' and wives' heights?\n\\end{parts}\n}{}\n\n% 7\n\n\\eoce{\\qt{Match the correlation, Part I\\label{match_corr_1}} \nMatch each correlation to the corresponding scatterplot.\n\n\\noindent%\n\\begin{minipage}[c]{0.17\\textwidth}\n\\begin{parts}\n\\item $R = -0.7$\n\\item $R = 0.45$ \n\\item $R = 0.06$\n\\item $R = 0.92$\n\\end{parts}\\vspace{3mm}\n\\end{minipage}%\n\\begin{minipage}[c]{0.83\\textwidth}\n\\FigureFullPath[A scatterplot is shown. The observations start in the upper left corner of the plot, trend sharply downwards before tapering off and stabilizing at about the middle of the plot, before steadily and then faster rising again to the upper right corner of the plot. The trend is approximately symmetric from left-to-right.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_1/match_corr_1_u}\n\\FigureFullPath[A scatterplot is shown. The start on the lower left corner, only spanning about 20\\% of the vertical region of the plot, and have a steady upwards trend to the upper right corner of the plot. The vertical variability of the points around the trend is relatively stable across the plot.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_1/match_corr_2_strong_pos}\n\\FigureFullPath[A scatterplot is shown. The points appear randomly scattered across the left, middle, and right portion of the plot. There is a very slight upward trend.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_1/match_corr_3_weak_pos}\n\\FigureFullPath[A scatterplot is shown. The points appear randomly scattered across the left, middle, and right portion of the plot. There is a very slight downward trend.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_1/match_corr_4_weak_neg}\n\\end{minipage}\n}{}\n\n% 8\n\n\\eoce{\\qt{Match the correlation, Part II\\label{match_corr_2}} \nMatch each correlation to the corresponding scatterplot.\n\n\\noindent%\n\\begin{minipage}[c]{0.17\\textwidth}\n\\begin{parts}\n\\item $R = 0.49$\n\\item $R = -0.48$ \n\\item $R = -0.03$ \n\\item $R = -0.85$\n\\end{parts}\\vspace{3mm}\n\\end{minipage}%\n\\begin{minipage}[c]{0.83\\textwidth}\n\\FigureFullPath[A scatterplot is shown. For the left half of the plot, the points are scattered around the upper half of the plot. On the right portion of the plot, the data show a clear downward trend, and for the points on the far right, they are concentrated in the lower 25\\% of the plot.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_2/match_corr_1_strong_neg_curved}\n\\FigureFullPath[A scatterplot is shown. The points appear randomly scattered across the left, middle, and right portion of the plot. There is a very slight upward trend.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_2/match_corr_2_weak_pos}\n\\FigureFullPath[A scatterplot is shown. The observations start in the lower left corner of the plot, trend sharply upwards before tapering off and stabilizing at about the middle of the plot, before steadily and then faster dropping to the lower right corner of the plot. The trend is approximately symmetric from left-to-right.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_2/match_corr_3_n}\n\\FigureFullPath[A scatterplot is shown. The points appear randomly scattered across the left, middle, and right portion of the plot. There is a very slight downward trend.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_2/match_corr_4_weak_neg}\n\\end{minipage}\n}{}\n\n% 9\n\n\\eoce{\\qt{Speed and height\\label{speed_height_gender}} 1,302 UCLA students \nwere asked to fill out a survey where they were asked about their height, \nfastest speed they have ever driven, and gender. The scatterplot on the \nleft displays the relationship between height and fastest speed, and \nthe scatterplot on the right displays the breakdown by gender in \nthis relationship.\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown. The horizontal axis represents \"Height (in inches)\" with values ranging from about 50 to 80. The vertical axis represents \"Fastest Speed (in mph)\" and has values ranging from 0 to 150. First, it is worth noting that there several points along the bottom of the plot with a fastest speed of 0 mph. The remainder of the description will concentrate on the other points. A small portion of the points are shown with heights below 60 inches, and these have fastest speeds mostly ranging from about 70 to 110 mph. For points shown with heights between 60 and 70, fastest speeds mostly ranged from about 30 to 120 mph. For points shown with heights of 70 or more, fastest speeds mostly ranged from about 50 to 140 mph. There were no points corresponding to heights greater than 75 that had fastest speeds slower than about 75 mph, which left a sort of gap in the lower right portion of the scatterplot.]{0.4}{ch_regr_simple_linear/figures/eoce/speed_height_gender/speed_height}\n\\hspace{0.02\\textwidth}%\n\\FigureFullPath[A scatterplot is shown, where points are colored to differentiate between males and females. The horizontal axis represents \"Height (in inches)\" with values ranging from about 50 to 80. The vertical axis represents \"Fastest Speed (in mph)\" and has values ranging from 0 to 150. Female heights are largely 70 inches or smaller, while Male heights are largely 65 inches and taller. When focusing exclusively on Females, no upward trend is evident, with about 95\\% of observations having Fastest Speed between about 30 mph and 120 mph. When focusing exclusively on Males, no upward trend is evident there either, with about 95\\% of observations having Fastest Speed between about 50 mph and 140 mph. In contrast, if we ignore the male/female differentiation, there is a slight upward trend in the points.]{0.4}{ch_regr_simple_linear/figures/eoce/speed_height_gender/speed_height_gender.pdf}\n\\end{center}\n\\begin{parts}\n\\item Describe the relationship between height and fastest speed.\n\\item Why do you think these variables are positively associated?\n\\item What role does gender play in the relationship between height \nand fastest driving speed?\n\\end{parts}\n}{}\n\n% 10\n\n\\eoce{\\qt{Guess the correlation\\label{guess_correlation}} Eduardo and Rosie \nare both collecting data on number of rainy days in a year and the total \nrainfall for the year. Eduardo records rainfall in inches and Rosie in \ncentimeters. How will their correlation coefficients compare?\n}{}\n\n% 11\n\n\\eoce{\\qt{The Coast Starlight, Part I\\label{coast_starlight_corr_units}} \nThe Coast Starlight Amtrak train runs from Seattle to Los Angeles. \nThe scatterplot below displays the distance between each stop \n(in miles) and the amount of time it takes to travel from one stop \nto another (in minutes).\\vspace{2mm}\n\n\\noindent\\begin{minipage}[c]{0.4\\textwidth}\n{\\raggedright\\begin{parts}\n\\item Describe the relationship between distance and travel time.\n\\item How would the relationship change if travel time was instead measured \nin hours, and distance was instead measured in kilometers?\n\\item The correlation between travel time (in miles) and distance (in minutes) \nis $r = 0.636$.\nSuppose we had instead measured travel time in hours\nand measured distance in kilometers (km).\nWhat would be the correlation in these different units?\n\\end{parts}\\vspace{7mm}}\n\\end{minipage}\n\\begin{minipage}[c]{0.1\\textwidth}\n$\\:$\\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.485\\textwidth}\n\\FigureFullPath[A scatterplot is shown with about 15 points. The horizontal axis represents \"Distance (miles)\" with values ranging from just over 0 to about 350. The vertical axis represents \"Travel Time (in minutes)\" and has values ranging from about 20 to 380. The point with the smallest distance -- about 10 miles -- shows a travel time of about 40 minutes. Next, there is a cluster of 6 points with distances between 40 and 60 miles and travel times ranging from about 20 to 60 minutes. The remainder of the points are scattered pretty broadly but may show a slightly upward trend. A few points that highlight the widely varying nature of the data are located at the following approximate locations: (190 miles, 60 minutes), (240 miles, 250 minutes), (250 miles, 380 minutes), and (350 miles, 200 minutes).]{}{ch_regr_simple_linear/figures/eoce/coast_starlight_corr_units/coast_starlight}\n\\end{minipage}\n}{}\n\n% 12\n\n\\eoce{\\qt{Crawling babies, Part I\\label{crawling_babies_corr_units}}  \nA study conducted at the University of Denver investigated whether babies \ntake longer to learn to crawl in cold months, when they are often bundled \nin clothes that restrict their movement, than in warmer months.\n\\footfullcite{Benson:1993} Infants born during the study year were split \ninto twelve groups, one for each birth month. We consider the average \ncrawling age of babies in each group against the average temperature when \nthe babies are six months old (that's when babies often begin trying to \ncrawl). Temperature is measured in degrees Fahrenheit (\\degree F) and age \nis measured in weeks.\\vspace{2mm}\n\n\\noindent\\begin{minipage}[c]{0.4\\textwidth}\n{\\raggedright\\begin{parts}\n\\item Describe the relationship between temperature and crawling age.\n\\item How would the relationship change if temperature was measured in \ndegrees Celsius (\\degree C) and age was measured in months?\n\\item The correlation between temperature in \\degree F and age in weeks \nwas $r=-0.70$. If we converted the temperature to \\degree C and age to \nmonths, what would the correlation be?\n\\end{parts}\\vspace{3mm}}\n\\end{minipage}\n\\begin{minipage}[c]{0.1\\textwidth}\n$\\:$\\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.485\\textwidth}\n\\FigureFullPath[A scatterplot is shown with a dozen points. The horizontal axis is \"Temperature (F)\" with values ranging from 30 to 75. The vertical axis is \"Average Crawling Age (weeks)\" with values ranging from 28.5 to 34. For those points with temperatures from 30 to 40, average crawling ages range from 31.5 to 34. For the single point with temperatures between 40 to 50, average crawling age was about 33.5. For the two points with temperature between 50 and 60, average crawling age was 28.5 and 32.5. For the last 4 points with temperature above 60, average crawling ages were 32, 30, 30, and 30.5.]{}{ch_regr_simple_linear/figures/eoce/crawling_babies_corr_units/crawling_babies}\n\\end{minipage}\n}{}\n\n\\D{\\newpage}\n\n% 13\n\n\\eoce{\\qt{Body measurements, Part I\\label{body_measurements_shoulder_height_corr_units}} \nResearchers studying anthropometry collected body girth measurements and \nskeletal diameter measurements, as well as age, weight, height and gender \nfor 507 physically active individuals.\\footfullcite{Heinz:2003} The \nscatterplot below shows the relationship between height and shoulder \ngirth (over deltoid muscles), both measured in centimeters.\\vspace{3mm}\n\n\\noindent%\n\\begin{minipage}[c]{0.4\\textwidth}\n{\\raggedright\\begin{parts}\n\\item Describe the relationship between shoulder girth and height.\n\\item How would the relationship change if shoulder girth was measured \nin inches while the units of height remained in centimeters?\n\\end{parts}\\vspace{20mm}}\n\\end{minipage}\n\\begin{minipage}[c]{0.1\\textwidth}                  \n$\\:$\\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.485\\textwidth}\n\\FigureFullPath[A scatter plot with several hundred points is shown. The horizontal axis represents \"Shoulder Girth (cm)\" with values ranging from about 85 to 135. The vertical axis represents \"Height (cm)\" with values ranging from about 145 to 200. For points where Shoulder Girth is smaller than 100, 95\\% of points have heights between 152 and 170. For points where Shoulder Girth is between 100 and 110, 95\\% of points have heights between 155 and 180. For points where Shoulder Girth is between 110 and 120, 95\\% of points have heights between 162 and 190. For points where Shoulder Girth larger than 120, 95\\% of points have heights between 170 and 190.]{}{ch_regr_simple_linear/figures/eoce/body_measurements_shoulder_height_corr_units/body_measurements_height_shoulder_girth}\n\\end{minipage}\n}{}\n\n% 14\n\n\\eoce{\\qt{Body measurements, Part II\\label{body_measurements_hip_weight_corr_units}} \nThe scatterplot below shows the relationship between weight \nmeasured in kilograms and hip girth measured in centimeters \nfrom the data described in \nExercise~\\ref{body_measurements_shoulder_height_corr_units}.%\n\\vspace{3mm}\n\n\\noindent%\n\\begin{minipage}[c]{0.4\\textwidth}\n{\\raggedright\\begin{parts}\n\\item Describe the relationship between hip girth and weight.\n\\item How would the relationship change if weight was measured in pounds \nwhile the units for hip girth remained in centimeters?\n\\end{parts}\\vspace{20mm}}\n\\end{minipage}\n\\begin{minipage}[c]{0.1\\textwidth}\n$\\:$\\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.485\\textwidth}\n\\FigureFullPath[A scatter plot with several hundred points is shown. The horizontal axis represents \"Hip Girth (cm)\" with values ranging from about 80 to 115, with about 4 observations with larger hip girth up to about 130 cm. The vertical axis represents \"Weight (kg)\" with values ranging from about 40 to 105, with a few observations with larger weights up to 120. For points where Hip Girth is smaller than 90, 95\\% of points have weight between roughly 45 and 60. For points where Hip Girth is between 90 and 100, 95\\% of points have heights between roughly 50 and 80. For points where Hip Girth is between 100 and 110, 95\\% of points have heights between roughly 65 and 90. For points where Hip Girth is between 110 and 115, points have heights between roughly 70 and 105. There are four additional points located at about (115, 120), (115, 90), (118, 90), and (128, 105).]{}{ch_regr_simple_linear/figures/eoce/body_measurements_hip_weight_corr_units/body_measurements_weight_hip_girth.pdf}\n\\end{minipage}\n}{}\n\n% 15\n\n\\eoce{\\qt{Correlation, Part I\\label{corr_husband_wife_age}} What would be the \ncorrelation between the ages of husbands and wives if men always married \nwoman who were\n\\begin{parts}\n\\item 3 years younger than themselves? \n\\item 2 years older than themselves? \n\\item half as old as themselves?\n\\end{parts}\n}{}\n\n% 16\n\n\\eoce{\\qt{Correlation, Part II\\label{corr_men_women_salary}} What would be the \ncorrelation between the annual salaries of males and females at a company \nif for a certain type of position men always made\n\\begin{parts}\n\\item \\$5,000 more than women?\n\\item 25\\% more than women?\n\\item 15\\% less than women?\n\\end{parts}\n}{}\n"
  },
  {
    "path": "ch_regr_simple_linear/TeX/review_exercises.tex",
    "content": "\\reviewexercisesheader{}\n\n% 37\n\n\\eoce{\\qt{True / False\\label{tf_correlation}}\nDetermine if the following statements are true or false.\nIf false, explain why.\n\\begin{parts}\n\\item A correlation coefficient of -0.90 indicates a stronger \nlinear relationship than a correlation of 0.5.\n\\item Correlation is a measure of the association between any \ntwo variables.\n\\end{parts}\n}{}\n\n% 38\n\n\\eoce{\\qt{Trees\\label{trees_volume_height_diameter}} The scatterplots below \nshow the relationship between height, diameter, and volume of timber \nin 31 felled black cherry trees. The diameter of the tree is measured \n4.5 feet above the ground.\\footfullcite{data:trees}\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown with around 30 points. The horizontal axis is for \"Height, in feet\" and takes values between 60 and 90 feet. The vertical axis is for \"Volume, in cubic feet\" and takes values between 8 and 80 cubic feet. For the five points with heights smaller than 70 feet, volumes range from about 8 to 25 cubic feet. For the fifteen points with heights between 70 and 80 feet, volumes mostly range from about 15 to 50 cubic feet. For the ten points with heights larger than 80 feet, volumes mostly range from about 20 to 65 cubic feet, with one outlier with a height of about 88 feet and a volume of about 80 cubic feet.]{0.46}{ch_regr_simple_linear/figures/eoce/trees_volume_height_diameter/trees_volume_height}\n\\hspace{0.07\\textwidth}%\n\\FigureFullPath[A scatterplot is shown with around 30 points. The horizontal axis is for \"Diameter, in inches\" and takes values between 8 and 22 inches. The vertical axis is for \"Volume, in cubic feet\" and takes values between 8 and 80 cubic feet. About 15 points with circumferences smaller than 12 inches, volumes range from about 8 to 25 cubic feet. For the approximately ten points with circumferences between 12 and 16 feet, volumes range from 22 to 35 cubic feet. For the 6 points with circumferences larger than 16 inches, volumes range from 40 to 60 cubic feet, with one outlier with a circumference of 22 inches and a volume of about 80 cubic feet.]{0.46}{ch_regr_simple_linear/figures/eoce/trees_volume_height_diameter/trees_volume_diameter}\n\\end{center}\n\\begin{parts}\n\\item Describe the relationship between volume and height of these trees.\n\\item Describe the relationship between volume and diameter of these trees.\n\\item Suppose you have height and diameter measurements for another black \ncherry tree. Which of these variables would be preferable to use to predict \nthe volume of timber in this tree using a simple linear regression model? \nExplain your reasoning.\n\\end{parts}\n}{}\n\n% 39\n\n\\eoce{\\qt{Husbands and wives, Part III\\label{husbands_wives_age_inf}}\nExercise~\\ref{husbands_wives_height_inf} presents a scatterplot displaying the \nrelationship between husbands' and wives' ages in a random sample of \n170 married couples in Britain, where both partners' ages are below 65 \nyears. Given below is summary output of the least squares fit for \npredicting wife's age from husband's age.\n\n\\noindent\\begin{minipage}[c]{0.4\\textwidth}\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown with about 150 points. The horizontal axis is for \"Hus, in inches\" and takes values between 8 and 22 inches. The vertical axis is for \"Volume, in cubic feet\" and takes values between 8 and 80 cubic feet. About 15 points with circumferences smaller than 12 inches, volumes range from about 8 to 25 cubic feet. For the approximately ten points with circumferences between 12 and 16 feet, volumes range from 22 to 35 cubic feet. For the 6 points with circumferences larger than 16 inches, volumes range from 40 to 60 cubic feet, with one outlier with a circumference of 22 inches and a volume of about 80 cubic feet.]{}{ch_regr_simple_linear/figures/eoce/husbands_wives_age_inf/husbands_wives_age}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.6\\textwidth}\n{\\scriptsize\n\\begin{center}\n\\begin{tabular}{rrrrr}\n  \\hline\n                & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n  \\hline\n(Intercept)     & 1.5740    & 1.1501        & 1.37      & 0.1730 \\\\ \nage\\_\\hspace{0.3mm}husband  & 0.9112    & 0.0259        & 35.25     & 0.0000 \\\\ \n   \\hline\n\\multicolumn{5}{r}{$df = 168$} \\\\\n\\end{tabular}\n\\end{center}\n}\n\\end{minipage}\n\\begin{parts}\n\\item We might wonder, is the age difference between husbands and \nwives consistent across ages? If this were the case, then the slope \nparameter would be $\\beta_1 = 1$. Use the information above to evaluate \nif there is strong evidence that the difference in husband and wife ages \ndiffers for different ages.\n\\item Write the equation of the regression line for predicting wife's \nage from husband's age.\n\\item Interpret the slope and intercept in context.\n\\item Given that $R^2 = 0.88$, what is the correlation of ages  in \nthis data set?\n\\item You meet a married man from Britain who is 55 years old. What \nwould you predict his wife's age to be? How reliable is this prediction?\n\\item You meet another married man from Britain who is 85 years old. \nWould it be wise to use the same linear model to predict his wife's \nage? Explain.\n\\end{parts}\n}{}\n\n% 40\n\n\\eoce{\\qt{Cats, Part II\\label{cat_body_heart_inf}}\nExercise~\\ref{cat_body_heart_reg} \npresents regression output from a model for predicting the heart \nweight (in g) of cats from their body weight (in kg). The coefficients \nare estimated using a dataset of 144 domestic cat. The model output \nis also provided below.\n\\begin{center}\n\\begin{tabular}{rrrrr}\n    \\hline\n            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n    \\hline\n(Intercept) & -0.357    & 0.692         & -0.515    & 0.607 \\\\ \nbody wt     & 4.034     & 0.250         & 16.119    & 0.000 \\\\ \n    \\hline\n\\end{tabular}\n\\[ s = 1.452 \\qquad R^2 = 64.66\\% \\qquad R^2_{adj} = 64.41\\% \\]\n\\end{center}\n\\begin{parts}\n\\item We see that the point estimate for the slope is positive.\n    What are the hypotheses for evaluating whether body weight is\n    positively associated with heart weight in cats?\n\\item State the conclusion of the hypothesis test from part (a) in \ncontext of the data.\n\\item Calculate a 95\\% confidence interval for the slope of body \nweight, and interpret it in context of the data.\n\\item Do your results from the hypothesis test and the confidence \ninterval agree? Explain.\n\\end{parts}\n}{}\n\n% 41\n\n\\eoce{\\qt{Nutrition at Starbucks, Part II\\label{starbucks_cals_protein}} \nExercise~\\ref{starbucks_cals_carbos} introduced a data set on nutrition \ninformation on Starbucks food menu items. Based on the scatterplot \nand the residual plot provided, describe the relationship between the \nprotein content and calories of these menu items, and determine if a \nsimple linear model is appropriate to predict amount of protein from \nthe number of calories.\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown with about 75 points and an overlaid regression line that trends upward along with a residual plot. The horizontal axis represents \"Calories\" and has values ranging from about 100 to 500. The vertical axis represents \"Protein, in grams\" and has values ranging from 0 to about 30. Scatterplot: About 15 points are shown with fewer than 200 calories, and these have between about 0 and 5 grams of protein. About 30 points are shown with 200 to 400 calories, and these mostly have between 5 and 30 grams of protein. About 20 points are shown with more than 400 calories, and these mostly have between 5 and 30 grams of carbs. Residual plot: About 15 points are shown with fewer than 200 calories, and these have residuals roughly between -5 and positive 2. About 30 points are shown with 200 to 400 calories, and these residuals largely range from about -10 to positive 20. About 20 points are shown with more than 400 calories, and the residuals for these points mostly range between -10 and positive 8.]{0.35}{ch_regr_simple_linear/figures/eoce/starbucks_cals_protein/starbucks_cals_protein}\n\\end{center}\n}{}\n\n% 42\n\n\\eoce{\\qt{Helmets and lunches\\label{helmet_lunch}}\nThe scatterplot shows the \nrelationship between socioeconomic status measured as the percentage of \nchildren in a neighborhood receiving reduced-fee lunches at school \n({\\tt lunch}) and the percentage of bike riders in the neighborhood \nwearing helmets ({\\tt helmet}). The average percentage of children \nreceiving reduced-fee lunches is 30.8\\% with a standard deviation \nof 26.7\\% and the average percentage of bike riders wearing helmets \nis 38.8\\% with a standard deviation of 16.9\\%.\n\n\\noindent\\begin{minipage}[c]{0.5\\textwidth}\n{\\raggedright\\begin{parts}\n\\item If the $R^2$ for the least-squares regression line for these \ndata is $72\\%$, what is the correlation between {\\tt lunch} \nand {\\tt helmet}?\n\\item Calculate the slope and intercept for the least-squares regression \nline for these data.\n\\item Interpret the intercept of the least-squares regression line in \nthe context of the application.\n\\item Interpret the slope of the least-squares regression line in the \ncontext of the application.\n\\item What would the value of the residual be for a neighborhood where \n40\\% of the children receive reduced-fee lunches and 40\\% of the bike \nriders wear helmets? Interpret the meaning of this residual in the context \nof the application.\n\\end{parts}}\n\\end{minipage}\n\\begin{minipage}[c]{0.05\\textwidth}\n$\\:$ \\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.42\\textwidth}\n\\begin{center}\n\\FigureFullPath[A scatterplot is shown with 12 points. The horizontal axis is for \"Rate of Receiving a Reduced-Fee Lunch\" and takes values between 0\\% and 82\\%. The vertical axis is for \"Rate of Wearing a Helmet\" and takes values between about 3\\% and 58\\%. Eight points have a reduced-fee lunch rate smaller than 25\\%, and these points have helmet wearing rates between about 20\\% and 58\\%. Two points have a reduced-fee lunch rate of about 50\\%, and these points have helmet wearing rates about 21\\% and 22\\%. Two points have a reduced-fee lunch rate of 75\\% and 82\\%, and these points have helmet wearing rates of 5\\% and 3\\%, respectively.]{}{ch_regr_simple_linear/figures/eoce/helmet_lunch/helmet_lunch} \\\\\n\\end{center}\n\\end{minipage}\n}{}\n\n% 43\n\n\\eoce{\\qt{Match the correlation, Part III\\label{match_corr_3}} \nMatch each correlation to the corresponding scatterplot.\n\n\\noindent%\n\\begin{minipage}[c]{0.17\\textwidth}\n\\begin{parts}\n\\item $r = -0.72$\n\\item $r = 0.07$ \n\\item $r = 0.86$ \n\\item $r = 0.99$\n\\end{parts}\\vspace{3mm}\n\\end{minipage}%\n\\begin{minipage}[c]{0.83\\textwidth}\n\\FigureFullPath[A scatterplot is shown. The left third of the data has values that range in the bottom half of the range in the vertical direction. The middle third of the data has values that mostly range in the middle 50\\% of the vertical direction. The right third of the data has values that range in the upper half of the range in the vertical direction.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_3/scatter_1}\n\\FigureFullPath[A scatterplot is shown. The pattern resembles an arch, where the left third of the arch has been cut off. The peak of this \"arch\" of data is about a third of the way into the horizontal range.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_3/scatter_2}\n\\FigureFullPath[A scatterplot is shown, with what appears to be a stable upward trend in the data. If we were to imagine a line drawn against the data, the residuals would generally have a standard deviation equal to only about 5\\% of the vertical range of the data. That is, the data would be very \"tightly packed\" around the regression line.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_3/scatter_3}\n\\FigureFullPath[A scatterplot is shown. There is no clear pattern in the data when looking from left to right.]{0.245}{ch_regr_simple_linear/figures/eoce/match_corr_3/scatter_4}\n\\end{minipage}\n}{}\n\n% 44\n\n\\eoce{\\qt{Rate my professor\\label{rate_my_prof}}\nMany college courses conclude by giving students\nthe opportunity to evaluate the course and the\ninstructor anonymously.\nHowever, the use of these student evaluations\nas an indicator of course quality and teaching\neffectiveness is often criticized because these\nmeasures may reflect the influence of\nnon-teaching related characteristics,\nsuch as the physical appearance of the instructor.\nResearchers at University of Texas, Austin\ncollected data on teaching evaluation score\n(higher score means better) and standardized\nbeauty score (a score of 0 means average, negative\nscore means below average, and a positive score\nmeans above average) for a sample of 463\nprofessors.\\footfullcite{Hamermesh:2005}\nThe scatterplot below shows the relationship\nbetween these variables, and regression output\nis provided for predicting teaching evaluation\nscore from beauty score.\n\\begin{center}\n\\begin{tabular}{rrrrr}\n    \\hline\n            & Estimate  & Std. Error    & t value   & Pr($>$$|$t$|$) \\\\ \n  \\hline\n(Intercept) & 4.010     & 0.0255        & \t157.21  & 0.0000 \\\\ \nbeauty      &  \\fbox{\\textcolor{white}{{\\footnotesize Cell 1}}}  \n                        & 0.0322        & 4.13      & 0.0000\\vspace{0.8mm} \\\\ \n   \\hline\n\\end{tabular}\n\\end{center}\n\\noindent\\begin{minipage}[c]{0.45\\textwidth}\n{\\raggedright\\begin{parts}\n\\item\n    Given that the average standardized beauty score\n    is -0.0883 and average teaching evaluation score\n    is 3.9983, calculate the slope.\n    Alternatively, the slope may be computed using just\n    the information provided in the model summary table.\n\\item\n    Do these data provide convincing evidence that the\n    slope of the relationship between teaching evaluation\n    and beauty is positive?\n    Explain your reasoning.\n\\item\n    List the conditions required for linear regression\n    and check if each one is satisfied for this model\n    based on the following diagnostic plots.\n\\end{parts}}\n\\end{minipage}\n\\begin{minipage}[c]{0.07\\textwidth}\n$\\:$ \\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.45\\textwidth}\n\\FigureFullPath[A scatterplot is shown for several hundred points. The horizontal axis is for a \"Beauty\" score and takes values between -1.8 and positive 2. The vertical axis is for \"Teaching evaluation\" and takes values between 2 and 5. For beauty scores smaller than 0, the Teaching Evaluation scores range mostly between 2.5 and 4.8, with no obvious trend in this region of the data. For beauty scores between 0 and 1, the Teaching Evaluation scores range mostly between 3 and 4.7. For beauty scores between 1 and 2, the Teaching Evaluation scores range mostly between 3.2 and 4.8.]{}{ch_regr_simple_linear/figures/eoce/rate_my_prof/rate_my_prof_eval_beauty} \\\\\n\\end{minipage}\n\\begin{center}\n\\FigureFullPath[A residual plot is shown for several hundred points. The horizontal axis is for a \"Beauty\" score and takes values between -1.8 and positive 2. The vertical axis is for \"Residuals\" and takes values between -1.5 and positive 1. For beauty scores smaller than 0, the residuals range mostly between -1.2 and positive 1. For beauty scores between 0 and 1, the residuals range mostly between -1.2 and positive 0.8. For beauty scores between 1 and 2, which has somewhat fewer points, the residuals range mostly between -1.0 and positive 0.5.]{0.32}{ch_regr_simple_linear/figures/eoce/rate_my_prof/rate_my_prof_residuals}\n\\FigureFullPath[A histogram is shown for residuals, where bins range between -2 and 1.5. The distribution is centered at zero and very slightly skewed to the left.]{0.32}{ch_regr_simple_linear/figures/eoce/rate_my_prof/rate_my_prof_residuals_hist}\n\\FigureFullPath[A scatterplot is shown. The horizontal axis is for \"Order of data collection\" and takes values between 1 and about 450. The vertical axis is for \"Residuals\" and takes values between about -1.5 and positive 1. The residuals mostly lie between -1.2 and 0.9 across the range with no discernible pattern.]{0.32}{ch_regr_simple_linear/figures/eoce/rate_my_prof/rate_my_prof_residuals_order}\n\\end{center}\n}{}\n"
  },
  {
    "path": "ch_regr_simple_linear/TeX/types_of_outliers_in_linear_regression.tex",
    "content": "\\exercisesheader{}\n\n% 27\n\n\\eoce{\\qt{Outliers, Part I\\label{outliers_1}} Identify the outliers in the \nscatterplots shown below, and determine what type of outliers they are. \nExplain your reasoning.\n\\begin{center}\n\\FigureFullPath[Most of the data is shown in the left third of the plot with a clear downward, linear trend extending from from the upper-left corner of the plot and to the bottom of the plot only a third of the way from the left side of the plot. A single point is shown on the bottom-right of the plot. A regression line is fit to the data, but it does not fit the bulk of the data well: On the furthest left portion, the line is below the points, crosses over the trend of the bulk of the data, then lies above the remainder of the bulk of the data. If it were shown fully, it would extend well below the single point on the bottom-right.]{0.32}{ch_regr_simple_linear/figures/eoce/outliers_1/outliers_1_influential}\n\\FigureFullPath[A clear downward trend is evident in the points on the left third of the plot with a regression line overlaying these points and extending to a single point on the far bottom right of the plot that is also almost exactly on the regression line.]{0.32}{ch_regr_simple_linear/figures/eoce/outliers_1/outliers_2_leverage}\n\\FigureFullPath[A downward trend is evident in the bulk of the points with an overlaid regression line. A single point is shown far above the regression line at the center-top of the plot.]{0.32}{ch_regr_simple_linear/figures/eoce/outliers_1/outliers_3_outlier}\n\\end{center}\n}{}\n\n% 28\n\n\\eoce{\\qt{Outliers, Part II\\label{outliers_2}} Identify the outliers in the scatterplots \nshown below and determine what type of outliers they are. Explain \nyour reasoning.\n\\begin{center}\n\\FigureFullPath[Most of the data is shown in the right half of the plot with a clear upward, linear trend extending from from the bottom-center and extending to the upper-right corner of the plot. A single point is shown on the upper-left of the plot. A regression line is fit to the data, but it does not fit the bulk of the data well: Focusing first on the bulk of points at the bottom center of the plot, the regression line is well above these points, crosses over the trend of the bulk of the data about 25\\% from the right of the plot, then lies below the remainder of the bulk of the data in the upper-right. If it were shown fully, the regression line would extend well below the single point on the upper-left.]{0.32}{ch_regr_simple_linear/figures/eoce/outliers_2/outliers_1_influential}\n\\FigureFullPath[A clear upward trend is evident in the points on the right half of the plot with a regression line approximately overlaying these points and extending towards a single point on the far bottom left of the plot, but the regression line is notably higher than this single point, which would have by far the largest residual (in absolute value) of all other points shown in the plot. Close inspection of the regression line fit over the bulk of points, it appears to be partially misfitting that data, \"pulled\" down on the left side.]{0.32}{ch_regr_simple_linear/figures/eoce/outliers_2/outliers_2_influential}\n\\FigureFullPath[An upper trend is evident in the bulk of the points with an overlaid regression line. A single point is shown far above the regression line at the center-top of the plot.]{0.32}{ch_regr_simple_linear/figures/eoce/outliers_2/outliers_3_outlier}\n\\end{center}\n}{}\n\n% 29\n\n\\eoce{\\qt{Urban homeowners, Part I\\label{urban_homeowners_outlier}} The \nscatterplot below shows the percent of families who own their \nhome vs. the percent of the population living in urban areas.\n\\footfullcite{data:urbanOwner} There are 52 observations, each \ncorresponding to a state in the US. Puerto Rico and District of \nColumbia are also included.\n\n\\noindent\\begin{minipage}[c]{0.5\\textwidth}\n\\begin{parts}\n\\item Describe the relationship between the percent of families who \nown their home and the percent of the population living in urban areas.\n\\item The outlier at the bottom right corner is District of Columbia, \nwhere 100\\% of the population is considered urban. What type of an outlier \nis this observation?\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.05\\textwidth}\n$\\:$\\\\\n\\end{minipage}\n\\begin{minipage}[c]{0.4\\textwidth}\n\\FigureFullPath[A scatterplot is shown with about 50 points. The horizontal axis is for \"Percent Urban Population\" and has values ranging from 40\\% to 100\\%. The vertical axis is for \"Percent Own Their Home\" with values ranging from about 40\\% to about 75\\%. About 10 points have Urban Population with values smaller than 60\\%, and these have Homeownership rates between 65\\% and 75\\%, with most of those points above 70\\%. About 20 points have Urban Population with values between 60\\% and 70\\%, and these have Homeownership rates between 62\\% and 75\\%. About 20 points have Urban Population with values greater than 70\\%, and these have Homeownership rates between 55\\% and 73\\%, with one exception of a point with 100\\% urban population that has a homeownership rate of about 43\\%.]{0.95}{ch_regr_simple_linear/figures/eoce/urban_homeowners_outlier/urban_homeowners_outlier} \\vspace{-3mm}\n\\end{minipage}\n}{}\n\n% 30\n\n\\eoce{\\qt{Crawling babies, Part II\\label{crawling_babies_outlier}} \nExercise~\\ref{crawling_babies_corr_units} introduces \ndata on the average monthly temperature during the month babies first \ntry to crawl (about 6 months after birth) and the average first \ncrawling age for babies born in a given month. A scatterplot of these \ntwo variables reveals a potential outlying month when the average \ntemperature is about 53\\degree F and average crawling age is about \n28.5 weeks. Does this point have high leverage? Is it an influential \npoint?\n}{}\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/brushtail_possum/ReadMe.txt",
    "content": "\nhttps://www.flickr.com/photos/gregthebusker/5653697137/\n\nPhoto by Greg Schechter\nCreative Commons Attribution 2.0 license\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/elmhurstPlots/elmhurstScatterW2Lines.R",
    "content": "library(openintro)\nd <- elmhurst\nd$gift_aid <- d$gift_aid * 1000\nd$family_income <- d$family_income * 1000\n\ng <- lm(d$gift_aid ~ d$family_income)\nsummary(g)\n\nloss <- function(a, b, d) {\n  p <- a + b * d$family_income\n  sum(abs(d$gift_aid - p))\n}\na      <- round(g$coef[1], 2) + seq(-500, 500, 1)\nb      <- round(g$coef[2], 3) + seq(-0.01, 0.01, 0.0001)\nmins   <- c(a[1], b[1])\ntheMin <- loss(a[1], b[1], d)\npb     <- txtProgressBar(1, length(a), style=3)\nfor (i in 1:length(a)) {\n  for (j in 1:length(b)) {\n    hold <- loss(a[i], b[j], d)\n    if (hold < theMin) {\n      mins <- c(a[i],b[j])\n      theMin <- hold\n    }\n  }\n  setTxtProgressBar(pb, i)\n}\n\nBuildElmhurtPlot <- function() {\n  plot(d$family_income, d$gift_aid,\n      col = COL[1, 2],\n      pch = 19,\n      xlab = 'Family Income',\n      ylab = '', axes=FALSE,\n      xlim = c(0, 280e3), \n      ylim = c(0, 35e3))\n  AxisInDollars(1, at = (0:8) * 50e3)\n  AxisInDollars(2, at = (0:3) * 10e3)\n  box()\n  par(las = 0)\n  mtext(\"Gift Aid From University\", 2, line = 3)\n}\n\nmyPDF('elmhurstScatterW2Lines.pdf', 5.5, 3.3,\n      mar = c(3.1, 4.1, 0.5, 0.5),\n      mgp = c(2, 0.6, 0))\nBuildElmhurtPlot()\nabline(mins[1], mins[2], lty=2, lwd=2)\nabline(g, lwd = 2)\ndev.off()\n\n\nmyPDF('elmhurstScatterWLSROnly.pdf', 5.5, 3.3,\n      mar = c(3.1, 4.1, 0.5, 0.5),\n      mgp = c(2, 0.6, 0))\nBuildElmhurtPlot()\nabline(g, lwd = 2)\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/beer_blood_alcohol_inf/beer_blood_alcohol.txt",
    "content": "student\tbeers\tBAC\r1\t5\t0.1\r2\t2\t0.03\r3\t9\t0.19\r4\t8\t0.12\r5\t3\t0.04\r6\t7\t0.095\r7\t3\t0.07\r8\t5\t0.06\r9\t3\t0.02\r10\t5\t0.05\r11\t4\t0.07\r12\t6\t0.1\r13\t5\t0.085\r14\t7\t0.09\r15\t1\t0.01\r16\t4\t0.05\r"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/beer_blood_alcohol_inf/beer_blood_alcohol_inf.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nbeer_data <- read.table(\"beer_blood_alcohol.txt\", h = T, sep = \"\\t\")\n\n# scatterplot of BAC vs. beers --------------------------------------  \n\npdf(\"beer_blood_alcohol.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(beer_data$BAC ~ beer_data$beers, \n     xlab = \"Cans of beer\", ylab = \"BAC (grams / deciliter)\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# model summary -----------------------------------------------------\n\nm_bac <- lm(beer_data$BAC ~ beer_data$beers)\n\nxtable(summary(m_bac))"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/body_measurements_hip_weight_corr_units/body_measurements_hip_weight.R",
    "content": "library(openintro)\n\nmyPDF(\"body_measurements_weight_hip_girth.pdf\", 5.7, 4.3,\n    mar = c(3.8, 3.8, 0.5, 1),\n    mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.25,\n    cex.axis = 1.25)\n\nplot(bdims$wgt ~ bdims$hip_gi, \n    xlab = \"Hip girth (cm)\", ylab = \"Weight (kg)\", \n    pch = 19, col = COL[1,2])\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/body_measurements_shoulder_height_corr_units/body_measurements_shoulder_height.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\ndata(bdims)\n\n# correlation -------------------------------------------------------\n\nround(cor(crawling_babies$avg_crawling_age, crawling_babies$temperature), 2)\n\n# plot height vs. shoulder girth ------------------------------------\n\npdf(\"body_measurements_height_shoulder_girth.pdf\", 5.5, 4.3)\n\npar(mar = c(3.8, 3.8, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\n\nplot(bdims$hgt ~ bdims$sho.gi, \n     xlab = \"Shoulder girth (cm)\", ylab = \"Height (cm)\", \n     pch = 19, col = COL[1,2])\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/body_measurements_weight_height_inf/body_measurements_weight_height_inf.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\ndata(bdims)\n\n# correlation -------------------------------------------------------\n\nround(cor(bdims$hgt, bdims$wgt), 2)\n\n# model -------------------------------------------------------------\n\nm_weight_height <- lm(bdims$wgt ~ bdims$hgt)\n\nxtable(summary(m_weight_height))\n\n# plot weight vs. height --------------------------------------------\n\npdf(\"body_measurements_weight_height.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(bdims$wgt ~ bdims$hgt, \n     ylab = \"Weight (kg)\", xlab = \"Height (cm)\", \n     pch = 19, col = COL[1,2],\n     axes = FALSE, xlim = c(147,199))\naxis(1, at = seq(150, 200, 25))\naxis(2, at = seq(50, 110, 20))\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/cat_body_heart_reg/cat_body_heart_reg.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\nlibrary(MASS)\n\n# load data ---------------------------------------------------------\n\ndata(cats)\n\n# model heart weight vs. weight -------------------------------------\n\nm_cats_hwt_bwt <- lm(cats$Hwt ~ cats$Bwt)\n\nxtable(summary(m_cats_hwt_bwt), digits = 3)\n\nround(summary(m_cats_hwt_bwt)$r.squared, 4)\nround(summary(m_cats_hwt_bwt)$adj.r.squared, 4)\n  \n# plot heart weight vs. weight --------------------------------------\n\npdf(\"cat_body_heart.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(cats$Hwt ~ cats$Bwt, \n     xlab = \"Body weight (kg)\", ylab = \"Heart weight (g)\", \n     pch = 19, col = COL[1,2],\n     xlim = c(2,4), ylim = c(5, 20.5), axes = FALSE)\naxis(1, at = seq(2, 4, 0.5))\naxis(2, at = seq(5, 20, 5))\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/coast_starlight_corr_units/coast_starlight.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\ncoast_starlight <- read.table(\"coast_starlight.txt\", h = T, sep = \"\\t\")\n\n# plot trave time vs. distance --------------------------------------\n\npdf(\"coast_starlight.pdf\", 5.5, 4.3)\n\npar(mar = c(3.8, 3.8, 0.5, 0.5), las = 1, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\n\nplot(coast_starlight$travel_time ~ coast_starlight$dist, \n     xlab = \"Distance (miles)\", ylab = \"Travel Time (minutes)\", \n     pch = 20, col = COL[1], axes = FALSE)\naxis(1, at = seq(0, 400, 100))\naxis(2, at = seq(0, 360, 60))\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/coast_starlight_corr_units/coast_starlight.txt",
    "content": "station\tdistance\thour\tminute\ttravel_time\tdist\tz_time\tz_dist\ttravel_time_hrs\tdist_km\rTacoma\t40\t10\t57\t57\t40\t-0.634081\t-0.679118\t0.0158333 h\t103.6\rOlympia\t72\t11\t43\t46\t32\t-0.731123\t-0.759681\t0.0127778 h\t82.8796\rCentralia\t94\t12\t6\t23\t43\t-0.934029\t-0.648907\t0.00638889 h\t111.369\rKelso\t137\t12\t52\t46\t39\t-0.731123\t-0.689189\t0.0127778 h\t101.01\rVancouver\t176\t13\t35\t43\t10\t-0.757589\t-0.981228\t0.0119444 h\t25.8999\rPortland\t186\t13\t55\t20\t53\t-0.960495\t-0.548204\t0.00555556 h\t137.269\rSalem\t239\t15\t45\t110\t28\t-0.166515\t-0.799962\t0.0305556 h\t72.5197\rAlbany\t267\t16\t17\t32\t43\t-0.854631\t-0.648907\t0.00888889 h\t111.369\rEugene\t310\t17\t7\t49\t195\t-0.704657\t0.881784\t0.0136111 h\t505.048\rSacramento\t837\t6\t30\t177\t84\t0.424558\t-0.236024\t0.0491667 h\t217.559\rEmeryville\t921\t8\t30\t120\t113\t-0.0782952\t0.0560163\t0.0333333 h\t292.669\rSalinas\t1034\t12\t1\t211\t352\t0.724506\t2.46283\t0.0586111 h\t911.676\rSantaBarbara\t1286\t18\t17\t376\t252\t2.18014\t1.45579\t0.104444 h\t652.677\rLosAngeles\t1389\t21\t5\t168\t103\t0.345161\t-0.0446871\t0.0466667 h\t266.769\rChico\t742\t3\t33\t326\t95\t1.73904\t-0.12525\t0.0905556 h\t246.049\rKlamathFalls\t505\t22\t7\t258\t237\t1.13914\t1.30474\t0.0716667 h\t613.827\r"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/crawling_babies_corr_units/crawling_babies.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\ncrawling_babies <- read.csv(\"crawling_babies.csv\")\n\n# correlation -------------------------------------------------------\n\nround(cor(crawling_babies$avg_crawling_age, crawling_babies$temperature), 2)\n\n# plot trave time vs. distance --------------------------------------\n\npdf(\"crawling_babies.pdf\", 5.5, 4.3)\n\npar(mar = c(3.5, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\n\nplot(crawling_babies$avg_crawling_age ~ crawling_babies$temperature, \n     xlab = \"Temperature (F)\", ylab = \"Avg. crawling age (weeks)\",\n     pch = 19, col = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/crawling_babies_corr_units/crawling_babies.csv",
    "content": "birth_month,avg_crawling_age,sd,n,temperature\r\nJanuary,29.84,7.08,32,66\r\nFebruary,30.52,6.96,36,73\r\nMarch,29.7,8.33,23,72\r\nApril,31.84,6.21,26,63\r\nMay,28.58,8.07,27,52\r\nJune,31.44,8.1,29,39\r\nJuly,33.64,6.91,21,33\r\nAugust,32.82,7.61,45,30\r\nSeptember,33.83,6.93,38,33\r\nOctober,33.35,7.29,44,37\r\nNovember,33.38,7.42,49,48\r\nDecember,32.32,5.71,44,57"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/exams_grades_correlation/exam_grades.txt",
    "content": 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  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/exams_grades_correlation/exams_grades_correlation.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nexam_data <- read.table(\"exam_grades.txt\", h = T, sep = \"\\t\")\n\n# plot course grade vs. exam 1 --------------------------------------\n\npdf(\"exam_grades_1.pdf\", 5.5, 4.3)\n\npar(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(exam_data$course_grade ~ exam_data$exam1, \n     pch = 19, col = COL[1,2], \n     xlab = \"Exam 1\", ylab = \"Final Exam\", \n     xlim = c(40,100), ylim = c(40,100), axes=FALSE)\naxis(1, at = seq(40,100,20))\naxis(2, at = seq(40,100,20))\nbox()\n\nm_course_grade_exam1 = lm(exam_data$course_grade ~ exam_data$exam1)\nabline(m_course_grade_exam1, col = COL[2], lwd = 2)\n\ndev.off()\n\n# plot course grade vs. exam 2 --------------------------------------\n\npdf(\"exam_grades_2.pdf\", 5.5, 4.3)\n\npar(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(exam_data$course_grade ~ exam_data$exam2, \n     pch = 19, col = COL[1,2], \n     xlab = \"Exam 2\", ylab = \"Final Exam\", \n     xlim = c(40,100), ylim = c(40,100), axes=FALSE)\naxis(1, at = seq(40,100,20))\naxis(2, at = seq(40,100,20))\nbox()\n\nm_course_grade_exam2 = lm(exam_data$course_grade ~ exam_data$exam2)\nabline(m_course_grade_exam2, col = COL[2], lwd = 2)\n\ndev.off()"
  },
  {
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    "content": 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  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/full_lin_regr_1/rate_my_prof.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nprof_evals_beauty <- read.csv(\"prof_evals_beauty.csv\")\n\n# rename variables for convenience ----------------------------------\n\nbeauty <- prof_evals_beauty$btystdave\neval <- prof_evals_beauty$courseevaluation\n\n# model evaluation scores vs. beauty --------------------------------\n\nm_eval_beauty = lm(eval ~ beauty)\n\nxtable(summary(m_eval_beauty))\n\n# scatterplot of evaluation scores vs. beauty -----------------------\n\npdf(\"rate_my_prof_eval_beauty.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5, las = 1)\n\nplot(eval ~ beauty, \n     xlab = \"Beauty\", ylab = \"Teaching evaluation\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE)\naxis(1, at = seq(-1, 2, 1))\naxis(2, at = seq(2, 5, 1))\nbox()\n\ndev.off()\n\n# residuals plot ----------------------------------------------------\n\npdf(\"rate_my_prof_residuals.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5, las = 1)\n\nplot(m_eval_beauty$residuals ~ beauty, \n     xlab = \"Beauty\", ylab = \"Residuals\", \n     pch = 19, col = COL[1,2], \n     ylim = c(-1.82, 1.82), axes = FALSE)\naxis(1, at = seq(-1, 2, 1))\naxis(2, at = seq(-1, 1, 1))\nbox()\nabline(h = 0, lty = 3)\n\ndev.off()\n\n# residuals histogram -----------------------------------------------\n\npdf(\"rate_my_prof_residuals_hist.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3, 0, 0), cex.lab = 1.5, cex.axis = 1.5)\n\nhist(m_eval_beauty$residuals, \n     xlab = \"Residuals\", ylab = \"\", main = \"\",\n     col = COL[1],\n     xlim = c(-2,2))\n\ndev.off()\n\n# normal probability plot of residuals ------------------------------\n\npdf(\"rate_my_prof_residuals_qq.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nqqnorm(m_eval_beauty$residuals, \n       pch = 19, col = COL[1,2],\n       main = \"\", las = 0)\nqqline(m_eval_beauty$residuals, col = COL[1])\n\ndev.off()\n\n# order of residuals ---------------------------------------------===\n\npdf(\"rate_my_prof_residuals_order.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_eval_beauty$residuals, \n     xlab = \"Order of data collection\", ylab = \"Residuals\", main = \"\",\n     pch = 19, col = COL[1,2],\n     ylim = c(-1.82, 1.82), axes = FALSE)\naxis(1)\naxis(2, at = seq(-1, 1, 1))\nbox()\n\nabline(h = 0, lty = 3)\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/full_lin_regr_2/prof_evals_beauty.csv",
    "content": 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,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.3,52,0,0,1,0,0,0,0,60.46511,4.5,86,1,0,3.107088,1.14304,0\r\n0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-0.6050149,1.360087,-0.5273642,0.2750198,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.3,52,1,0,1,0,0,1,0,77.61194,4.4,67,1,0,3.018447,0.332051,0\r\n0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-0.6050149,1.360087,-0.5273642,0.2750198,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.2,54,1,0,1,0,0,1,0,81.81818,4.4,66,1,0,3.018447,0.332051,0\r\n0,94,1,42,7,3,8,4,4,6,0.3320507,0.7665288,-0.6050149,1.360087,-0.5273642,0.2750198,0.723047,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4.1,28,1,0,1,1,0,1,1,80,4.1,35,1,0,3.018447,0.332051,0\r\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/full_lin_regr_2/rate_my_prof.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nprof_evals_beauty <- read.csv(\"prof_evals_beauty.csv\")\n\n# rename variables for convenience ----------------------------------\n\nbeauty <- prof_evals_beauty$btystdave\neval <- prof_evals_beauty$courseevaluation\n\n# model evaluation scores vs. beauty --------------------------------\n\nm_eval_beauty = lm(eval ~ beauty)\n\nxtable(summary(m_eval_beauty))\n\n# scatterplot of evaluation scores vs. beauty -----------------------\n\npdf(\"rate_my_prof_eval_beauty.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5, las = 1)\n\nplot(eval ~ beauty, \n     xlab = \"Beauty\", ylab = \"Teaching evaluation\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE)\naxis(1, at = seq(-1, 2, 1))\naxis(2, at = seq(2, 5, 1))\nbox()\n\ndev.off()\n\n# residuals plot ----------------------------------------------------\n\npdf(\"rate_my_prof_residuals.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5, las = 1)\n\nplot(m_eval_beauty$residuals ~ beauty, \n     xlab = \"Beauty\", ylab = \"Residuals\", \n     pch = 19, col = COL[1,2], \n     ylim = c(-1.82, 1.82), axes = FALSE)\naxis(1, at = seq(-1, 2, 1))\naxis(2, at = seq(-1, 1, 1))\nbox()\nabline(h = 0, lty = 3)\n\ndev.off()\n\n# residuals histogram -----------------------------------------------\n\npdf(\"rate_my_prof_residuals_hist.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3, 0, 0), cex.lab = 1.5, cex.axis = 1.5)\n\nhist(m_eval_beauty$residuals, \n     xlab = \"Residuals\", ylab = \"\", main = \"\",\n     col = COL[1],\n     xlim = c(-2,2))\n\ndev.off()\n\n# normal probability plot of residuals ------------------------------\n\npdf(\"rate_my_prof_residuals_qq.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nqqnorm(m_eval_beauty$residuals, \n       pch = 19, col = COL[1,2],\n       main = \"\", las = 0)\nqqline(m_eval_beauty$residuals, col = COL[1])\n\ndev.off()\n\n# order of residuals ---------------------------------------------===\n\npdf(\"rate_my_prof_residuals_order.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_eval_beauty$residuals, \n     xlab = \"Order of data collection\", ylab = \"Residuals\", main = \"\",\n     pch = 19, col = COL[1,2],\n     ylim = c(-1.82, 1.82), axes = FALSE)\naxis(1)\naxis(2, at = seq(-1, 1, 1))\nbox()\n\nabline(h = 0, lty = 3)\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/helmet_lunch/helmet_lunch.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\n\nlunch <- c(50, 11, 2, 19, 26, 73, 81, 51, 11, 2, 19, 25)\nhelmet <- c(22.1, 35.9, 57.9, 22.2, 42.4, 5.8, \n            3.6, 21.4, 55.2, 33.3, 32.4, 38.4)\n\n# summary stats -----------------------------------------------------\n\nround(mean(lunch), 1)\nround(mean(helmet), 1)\n\nround(sd(lunch), 1)\nround(sd(helmet), 1)\n\ncor(lunch, helmet)\n\n# model helmet vs. lunch --------------------------------------------\n\nm_helmet_lunch <- lm(helmet ~ lunch)\n\nsummary(m_helmet_lunch)\n\nround(summary(m_helmet_lunch)$r.squared, 2)\n  \n# plot helmet vs. lunch ---------------------------------------------\n\nmyPDF(\"helmet_lunch.pdf\", 5.5, 4.3,\n    mar = c(3.7, 5, 0.5, 0.5),\n    mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.3,\n    cex.axis = 1.5)\n\nplot(helmet ~ lunch, \n    xlab = \"Rate of Receiving a Reduced-Fee Lunch\",\n    ylab = \"\",\n    pch = 19, col = COL[1],\n    ylim = c(0, 60), axes = FALSE)\nAxisInPercent(1, at = seq(0, 80, 20))\nAxisInPercent(2, at = seq(0, 60, 20))\npar(las = 0)\nmtext(\"Rate of Wearing a Helmet\", 2, 3.8, cex = 1.5)\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/husbands_wives_age_inf/husbands_wives.txt",
    "content": "age_husband\tht_husband\tage_wife\tht_wife\tage_husb_at_marriage\tyears_married\tage_wife_at_marriage\tduration\r49\t1809\t43\t1590\t25\t24\t19\t>20\r25\t1841\t28\t1560\t19\t6\t22\t<= 20\r40\t1659\t30\t1620\t38\t2\t28\t<= 20\r52\t1779\t57\t1540\t26\t26\t31\t>20\r58\t1616\t52\t1420\t30\t28\t24\t>20\r32\t1695\t27\t1660\t23\t9\t18\t<= 20\r43\t1730\t52\t1610\t33\t10\t42\t<= 20\r42\t1753\t\t1635\t30\t12\t\t<= 20\r47\t1740\t43\t1580\t26\t21\t22\t>20\r31\t1685\t23\t1610\t26\t5\t18\t<= 20\r26\t1735\t25\t1590\t23\t3\t22\t<= 20\r40\t1713\t39\t1610\t23\t17\t22\t<= 20\r35\t1736\t32\t1700\t31\t4\t28\t<= 20\r45\t1715\t\t1522\t41\t4\t\t<= 20\r35\t1799\t35\t1680\t19\t16\t19\t<= 20\r35\t1785\t33\t1680\t24\t11\t22\t<= 20\r47\t1758\t43\t1630\t24\t23\t20\t>20\r38\t1729\t35\t1570\t27\t11\t24\t<= 20\r33\t1720\t32\t1720\t28\t5\t27\t<= 20\r32\t1810\t30\t1740\t22\t10\t20\t<= 20\r38\t1725\t40\t1600\t31\t7\t33\t<= 20\r45\t1764\t\t1689\t24\t21\t\t>20\r29\t1683\t29\t1600\t25\t4\t25\t<= 20\r59\t1585\t55\t1550\t23\t36\t19\t>20\r26\t1684\t25\t1540\t18\t8\t17\t<= 20\r50\t1674\t45\t1640\t25\t25\t20\t>20\r49\t1724\t44\t1640\t27\t22\t22\t>20\r42\t1630\t40\t1630\t28\t14\t26\t<= 20\r33\t1855\t31\t1560\t22\t11\t20\t<= 20\r31\t1796\t\t1652\t25\t6\t\t<= 20\r27\t1700\t25\t1580\t21\t6\t19\t<= 20\r57\t1765\t51\t1570\t32\t25\t26\t>20\r34\t1700\t31\t1590\t28\t6\t25\t<= 20\r28\t1721\t25\t1650\t23\t5\t20\t<= 20\r46\t1823\t\t1591\t\t\t\t>20\r37\t1829\t35\t1670\t22\t15\t20\t<= 20\r56\t1710\t55\t1600\t44\t12\t43\t<= 20\r27\t1745\t23\t1610\t25\t2\t21\t<= 20\r36\t1698\t35\t1610\t22\t14\t21\t<= 20\r31\t1853\t28\t1670\t20\t11\t17\t<= 20\r57\t1610\t52\t1510\t25\t32\t20\t>20\r55\t1680\t53\t1520\t21\t34\t19\t>20\r47\t1809\t43\t1620\t25\t22\t21\t>20\r64\t1580\t61\t1530\t21\t43\t18\t>20\r60\t1600\t\t1451\t26\t34\t\t>20\r31\t1585\t23\t1570\t28\t3\t20\t<= 20\r35\t1705\t35\t1580\t25\t10\t25\t<= 20\r36\t1675\t35\t1590\t22\t14\t21\t<= 20\r40\t1735\t39\t1670\t23\t17\t22\t<= 20\r30\t1686\t24\t1630\t27\t3\t21\t<= 20\r32\t1768\t29\t1510\t21\t11\t18\t<= 20\r27\t1721\t\t1560\t26\t1\t\t<= 20\r20\t1754\t21\t1660\t19\t1\t20\t<= 20\r45\t1739\t39\t1610\t25\t20\t19\t<= 20\r59\t1699\t52\t1440\t27\t32\t20\t>20\r43\t1825\t52\t1570\t25\t18\t34\t<= 20\r29\t1740\t26\t1670\t24\t5\t21\t<= 20\r48\t1704\t\t1635\t27\t21\t\t>20\r39\t1719\t\t1670\t25\t14\t\t<= 20\r47\t1731\t48\t1730\t21\t26\t22\t>20\r54\t1679\t53\t1560\t\t\t\t>20\r43\t1755\t42\t1590\t20\t23\t19\t>20\r54\t1713\t50\t1600\t23\t31\t19\t>20\r61\t1723\t64\t1490\t26\t35\t29\t>20\r27\t1783\t26\t1660\t20\t7\t19\t<= 20\r51\t1585\t\t1504\t50\t1\t\t<= 20\r27\t1749\t32\t1580\t24\t3\t29\t<= 20\r32\t1710\t31\t1500\t31\t1\t30\t<= 20\r54\t1724\t53\t1640\t20\t34\t19\t>20\r37\t1620\t39\t1650\t21\t16\t23\t<= 20\r55\t1764\t45\t1620\t29\t26\t19\t>20\r36\t1791\t33\t1550\t30\t6\t27\t<= 20\r32\t1795\t32\t1640\t25\t7\t25\t<= 20\r57\t1738\t55\t1560\t24\t33\t22\t>20\r51\t1639\t\t1552\t25\t26\t\t>20\r62\t1734\t\t1600\t33\t29\t\t>20\r57\t1695\t\t1545\t22\t35\t\t>20\r51\t1666\t52\t1570\t24\t27\t25\t>20\r50\t1745\t50\t1550\t22\t28\t22\t>20\r32\t1775\t32\t1600\t20\t12\t20\t<= 20\r54\t1669\t54\t1660\t20\t34\t20\t>20\r34\t1700\t32\t1640\t22\t12\t20\t<= 20\r45\t1804\t41\t1670\t27\t18\t23\t<= 20\r64\t1700\t61\t1560\t24\t40\t21\t>20\r55\t1664\t43\t1760\t31\t24\t19\t>20\r27\t1753\t28\t1640\t23\t4\t24\t<= 20\r55\t1788\t51\t1600\t26\t29\t22\t>20\r27\t1765\t\t1571\t\t\t\t>20\r41\t1680\t41\t1550\t22\t19\t22\t<= 20\r44\t1715\t41\t1570\t24\t20\t21\t<= 20\r22\t1755\t21\t1590\t21\t1\t20\t<= 20\r30\t1764\t28\t1650\t29\t1\t27\t<= 20\r53\t1793\t47\t1690\t31\t22\t25\t>20\r42\t1731\t37\t1580\t23\t19\t18\t<= 20\r31\t1713\t28\t1590\t28\t3\t25\t<= 20\r36\t1725\t35\t1510\t26\t10\t25\t<= 20\r56\t1828\t55\t1600\t30\t26\t29\t>20\r46\t1735\t45\t1660\t22\t24\t21\t>20\r34\t1760\t34\t1700\t23\t11\t23\t<= 20\r55\t1685\t51\t1530\t34\t21\t30\t>20\r44\t1685\t39\t1490\t27\t17\t22\t<= 20\r45\t1559\t35\t1580\t34\t11\t24\t<= 20\r48\t1705\t45\t1500\t28\t20\t25\t<= 20\r44\t1723\t44\t1600\t41\t3\t41\t<= 20\r59\t1700\t47\t1570\t39\t20\t27\t<= 20\r64\t1660\t57\t1620\t32\t32\t25\t>20\r34\t1681\t33\t1410\t22\t12\t21\t<= 20\r37\t1803\t38\t1560\t23\t14\t24\t<= 20\r54\t1866\t59\t1590\t49\t5\t54\t<= 20\r49\t1884\t46\t1710\t25\t24\t22\t>20\r63\t1705\t60\t1580\t27\t36\t24\t>20\r48\t1780\t47\t1690\t22\t26\t21\t>20\r64\t1801\t55\t1610\t37\t27\t28\t>20\r33\t1795\t45\t1660\t17\t16\t29\t<= 20\r52\t1669\t47\t1610\t23\t29\t18\t>20\r27\t1708\t24\t1590\t26\t1\t23\t<= 20\r33\t1691\t32\t1530\t21\t12\t20\t<= 20\r46\t1825\t47\t1690\t23\t23\t24\t>20\r54\t1760\t57\t1600\t23\t31\t26\t>20\r27\t1949\t\t1693\t25\t2\t\t<= 20\r50\t1685\t\t1580\t21\t29\t\t>20\r42\t1806\t\t1636\t22\t20\t\t<= 20\r54\t1905\t46\t1670\t32\t22\t24\t>20\r49\t1739\t42\t1600\t28\t21\t21\t>20\r62\t1736\t63\t1570\t22\t40\t23\t>20\r34\t1845\t32\t1700\t24\t10\t22\t<= 20\r23\t1868\t24\t1740\t19\t4\t20\t<= 20\r36\t1765\t32\t1540\t27\t9\t23\t<= 20\r53\t1736\t\t1555\t30\t23\t\t>20\r32\t1741\t\t1614\t22\t10\t\t<= 20\r59\t1720\t56\t1530\t24\t35\t21\t>20\r53\t1871\t50\t1690\t25\t28\t22\t>20\r55\t1720\t55\t1590\t21\t34\t21\t>20\r62\t1629\t58\t1610\t23\t39\t19\t>20\r42\t1624\t38\t1670\t22\t20\t18\t<= 20\r50\t1653\t44\t1690\t35\t15\t29\t<= 20\r37\t1786\t35\t1550\t21\t16\t19\t<= 20\r51\t1620\t44\t1650\t30\t21\t23\t>20\r25\t1695\t25\t1540\t19\t6\t19\t<= 20\r54\t1674\t43\t1660\t35\t19\t24\t<= 20\r34\t1864\t31\t1620\t23\t11\t20\t<= 20\r43\t1643\t35\t1630\t29\t14\t21\t<= 20\r43\t1705\t41\t1610\t22\t21\t20\t>20\r58\t1736\t50\t1540\t32\t26\t24\t>20\r28\t1691\t23\t1610\t23\t5\t18\t<= 20\r45\t1753\t43\t1630\t21\t24\t19\t>20\r47\t1680\t49\t1530\t20\t27\t22\t>20\r57\t1724\t59\t1520\t24\t33\t26\t>20\r27\t1710\t\t1544\t20\t7\t\t<= 20\r34\t1638\t38\t1570\t33\t1\t37\t<= 20\r57\t1725\t42\t1580\t52\t5\t37\t<= 20\r27\t1725\t21\t1550\t24\t3\t18\t<= 20\r54\t1630\t\t1570\t34\t20\t\t<= 20\r24\t1810\t\t1521\t16\t8\t\t<= 20\r48\t1774\t42\t1580\t30\t18\t24\t<= 20\r37\t1771\t35\t1630\t28\t9\t26\t<= 20\r25\t1815\t26\t1650\t20\t5\t21\t<= 20\r57\t1575\t57\t1640\t20\t37\t20\t>20\r40\t1729\t34\t1650\t26\t14\t20\t<= 20\r61\t1749\t63\t1520\t21\t40\t23\t>20\r25\t1705\t23\t1620\t24\t1\t22\t<= 20\r32\t1875\t\t1744\t22\t10\t\t<= 20\r37\t1784\t\t1647\t22\t15\t\t<= 20\r45\t1584\t\t1615\t29\t16\t\t<= 20\r24\t1774\t23\t1680\t22\t2\t21\t<= 20\r47\t1658\t46\t1670\t24\t23\t23\t>20\r44\t1790\t40\t1620\t24\t20\t20\t<= 20\r52\t1798\t53\t1570\t25\t27\t26\t>20\r45\t1824\t40\t1660\t23\t22\t18\t>20\r20\t1796\t22\t1550\t19\t1\t21\t<= 20\r60\t1725\t60\t1590\t21\t39\t21\t>20\r36\t1685\t32\t1620\t25\t11\t21\t<= 20\r25\t1769\t24\t1560\t18\t7\t17\t<= 20\r25\t1749\t28\t1670\t21\t4\t24\t<= 20\r35\t1716\t40\t1650\t17\t18\t22\t<= 20\r35\t1664\t\t1539\t22\t13\t\t<= 20\r49\t1773\t48\t1470\t21\t28\t20\t>20\r33\t1760\t33\t1580\t20\t13\t20\t<= 20\r50\t1725\t49\t1670\t23\t27\t22\t>20\r63\t1645\t64\t1520\t28\t35\t29\t>20\r57\t1694\t55\t1620\t24\t33\t22\t>20\r41\t1851\t41\t1710\t23\t18\t23\t<= 20\r38\t1691\t38\t1530\t20\t18\t20\t<= 20\r30\t1880\t31\t1630\t22\t8\t23\t<= 20\r52\t1835\t52\t1720\t30\t22\t30\t>20\r51\t1730\t43\t1570\t22\t29\t14\t>20\r46\t1644\t51\t1560\t27\t19\t32\t<= 20\r50\t1723\t47\t1650\t25\t25\t22\t>20\r32\t1758\t\t1635\t24\t8\t\t<= 20\r52\t1718\t32\t1590\t25\t27\t5\t>20\r30\t1723\t33\t1590\t22\t8\t25\t<= 20\r33\t1708\t\t1566\t21\t12\t\t<= 20\r20\t1786\t18\t1590\t19\t1\t17\t<= 20\r32\t1764\t\t1662\t\t\t\t>20\r51\t1675\t45\t1550\t25\t26\t19\t>20\r64\t1641\t64\t1570\t30\t34\t30\t>20\r44\t1743\t43\t1560\t25\t19\t24\t<= 20\r40\t1823\t39\t1630\t23\t17\t22\t<= 20\r59\t1720\t56\t1530\t24\t35\t21\t>20\r"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/husbands_wives_age_inf/husbands_wives_age_inf.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nhw <- read.table(\"husbands_wives.txt\", h = T, sep = \"\\t\")\n\n# converts heights to inches ----------------------------------------\n\nhw$ht_husband_in <- hw$ht_husband / 25.4\nhw$ht_wife_in <- hw$ht_wife / 25.4\n\n# remove cases where wife's age is missing --------------------------\n\nhw <- hw[!is.na(hw$age_wife),]\n\n# model summary -----------------------------------------------------\n\nm_h_w_age <- lm(hw$age_wife ~ hw$age_husband)\n\nxtable(summary(m_h_w_age))\n\n# plot wife vs. husband age -----------------------------------------\n\npdf(\"husbands_wives_age.pdf\", 5.5, 4.3)\n\npar(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(hw$age_wife ~ hw$age_husband, \n     xlab = \"Husband's age (in years)\", \n     ylab = \"Wife's age (in years)\", \n     pch = 19, col = COL[1,2], \n     xlim = c(18, 66), ylim = c(16, 66), axes = FALSE)\naxis(1, at = seq(20,60,20))\naxis(2, at = seq(20,60,20))\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/husbands_wives_correlation/husbands_wives.txt",
    "content": "age_husband\tht_husband\tage_wife\tht_wife\tage_husb_at_marriage\tyears_married\tage_wife_at_marriage\tduration\r49\t1809\t43\t1590\t25\t24\t19\t>20\r25\t1841\t28\t1560\t19\t6\t22\t<= 20\r40\t1659\t30\t1620\t38\t2\t28\t<= 20\r52\t1779\t57\t1540\t26\t26\t31\t>20\r58\t1616\t52\t1420\t30\t28\t24\t>20\r32\t1695\t27\t1660\t23\t9\t18\t<= 20\r43\t1730\t52\t1610\t33\t10\t42\t<= 20\r42\t1753\t\t1635\t30\t12\t\t<= 20\r47\t1740\t43\t1580\t26\t21\t22\t>20\r31\t1685\t23\t1610\t26\t5\t18\t<= 20\r26\t1735\t25\t1590\t23\t3\t22\t<= 20\r40\t1713\t39\t1610\t23\t17\t22\t<= 20\r35\t1736\t32\t1700\t31\t4\t28\t<= 20\r45\t1715\t\t1522\t41\t4\t\t<= 20\r35\t1799\t35\t1680\t19\t16\t19\t<= 20\r35\t1785\t33\t1680\t24\t11\t22\t<= 20\r47\t1758\t43\t1630\t24\t23\t20\t>20\r38\t1729\t35\t1570\t27\t11\t24\t<= 20\r33\t1720\t32\t1720\t28\t5\t27\t<= 20\r32\t1810\t30\t1740\t22\t10\t20\t<= 20\r38\t1725\t40\t1600\t31\t7\t33\t<= 20\r45\t1764\t\t1689\t24\t21\t\t>20\r29\t1683\t29\t1600\t25\t4\t25\t<= 20\r59\t1585\t55\t1550\t23\t36\t19\t>20\r26\t1684\t25\t1540\t18\t8\t17\t<= 20\r50\t1674\t45\t1640\t25\t25\t20\t>20\r49\t1724\t44\t1640\t27\t22\t22\t>20\r42\t1630\t40\t1630\t28\t14\t26\t<= 20\r33\t1855\t31\t1560\t22\t11\t20\t<= 20\r31\t1796\t\t1652\t25\t6\t\t<= 20\r27\t1700\t25\t1580\t21\t6\t19\t<= 20\r57\t1765\t51\t1570\t32\t25\t26\t>20\r34\t1700\t31\t1590\t28\t6\t25\t<= 20\r28\t1721\t25\t1650\t23\t5\t20\t<= 20\r46\t1823\t\t1591\t\t\t\t>20\r37\t1829\t35\t1670\t22\t15\t20\t<= 20\r56\t1710\t55\t1600\t44\t12\t43\t<= 20\r27\t1745\t23\t1610\t25\t2\t21\t<= 20\r36\t1698\t35\t1610\t22\t14\t21\t<= 20\r31\t1853\t28\t1670\t20\t11\t17\t<= 20\r57\t1610\t52\t1510\t25\t32\t20\t>20\r55\t1680\t53\t1520\t21\t34\t19\t>20\r47\t1809\t43\t1620\t25\t22\t21\t>20\r64\t1580\t61\t1530\t21\t43\t18\t>20\r60\t1600\t\t1451\t26\t34\t\t>20\r31\t1585\t23\t1570\t28\t3\t20\t<= 20\r35\t1705\t35\t1580\t25\t10\t25\t<= 20\r36\t1675\t35\t1590\t22\t14\t21\t<= 20\r40\t1735\t39\t1670\t23\t17\t22\t<= 20\r30\t1686\t24\t1630\t27\t3\t21\t<= 20\r32\t1768\t29\t1510\t21\t11\t18\t<= 20\r27\t1721\t\t1560\t26\t1\t\t<= 20\r20\t1754\t21\t1660\t19\t1\t20\t<= 20\r45\t1739\t39\t1610\t25\t20\t19\t<= 20\r59\t1699\t52\t1440\t27\t32\t20\t>20\r43\t1825\t52\t1570\t25\t18\t34\t<= 20\r29\t1740\t26\t1670\t24\t5\t21\t<= 20\r48\t1704\t\t1635\t27\t21\t\t>20\r39\t1719\t\t1670\t25\t14\t\t<= 20\r47\t1731\t48\t1730\t21\t26\t22\t>20\r54\t1679\t53\t1560\t\t\t\t>20\r43\t1755\t42\t1590\t20\t23\t19\t>20\r54\t1713\t50\t1600\t23\t31\t19\t>20\r61\t1723\t64\t1490\t26\t35\t29\t>20\r27\t1783\t26\t1660\t20\t7\t19\t<= 20\r51\t1585\t\t1504\t50\t1\t\t<= 20\r27\t1749\t32\t1580\t24\t3\t29\t<= 20\r32\t1710\t31\t1500\t31\t1\t30\t<= 20\r54\t1724\t53\t1640\t20\t34\t19\t>20\r37\t1620\t39\t1650\t21\t16\t23\t<= 20\r55\t1764\t45\t1620\t29\t26\t19\t>20\r36\t1791\t33\t1550\t30\t6\t27\t<= 20\r32\t1795\t32\t1640\t25\t7\t25\t<= 20\r57\t1738\t55\t1560\t24\t33\t22\t>20\r51\t1639\t\t1552\t25\t26\t\t>20\r62\t1734\t\t1600\t33\t29\t\t>20\r57\t1695\t\t1545\t22\t35\t\t>20\r51\t1666\t52\t1570\t24\t27\t25\t>20\r50\t1745\t50\t1550\t22\t28\t22\t>20\r32\t1775\t32\t1600\t20\t12\t20\t<= 20\r54\t1669\t54\t1660\t20\t34\t20\t>20\r34\t1700\t32\t1640\t22\t12\t20\t<= 20\r45\t1804\t41\t1670\t27\t18\t23\t<= 20\r64\t1700\t61\t1560\t24\t40\t21\t>20\r55\t1664\t43\t1760\t31\t24\t19\t>20\r27\t1753\t28\t1640\t23\t4\t24\t<= 20\r55\t1788\t51\t1600\t26\t29\t22\t>20\r27\t1765\t\t1571\t\t\t\t>20\r41\t1680\t41\t1550\t22\t19\t22\t<= 20\r44\t1715\t41\t1570\t24\t20\t21\t<= 20\r22\t1755\t21\t1590\t21\t1\t20\t<= 20\r30\t1764\t28\t1650\t29\t1\t27\t<= 20\r53\t1793\t47\t1690\t31\t22\t25\t>20\r42\t1731\t37\t1580\t23\t19\t18\t<= 20\r31\t1713\t28\t1590\t28\t3\t25\t<= 20\r36\t1725\t35\t1510\t26\t10\t25\t<= 20\r56\t1828\t55\t1600\t30\t26\t29\t>20\r46\t1735\t45\t1660\t22\t24\t21\t>20\r34\t1760\t34\t1700\t23\t11\t23\t<= 20\r55\t1685\t51\t1530\t34\t21\t30\t>20\r44\t1685\t39\t1490\t27\t17\t22\t<= 20\r45\t1559\t35\t1580\t34\t11\t24\t<= 20\r48\t1705\t45\t1500\t28\t20\t25\t<= 20\r44\t1723\t44\t1600\t41\t3\t41\t<= 20\r59\t1700\t47\t1570\t39\t20\t27\t<= 20\r64\t1660\t57\t1620\t32\t32\t25\t>20\r34\t1681\t33\t1410\t22\t12\t21\t<= 20\r37\t1803\t38\t1560\t23\t14\t24\t<= 20\r54\t1866\t59\t1590\t49\t5\t54\t<= 20\r49\t1884\t46\t1710\t25\t24\t22\t>20\r63\t1705\t60\t1580\t27\t36\t24\t>20\r48\t1780\t47\t1690\t22\t26\t21\t>20\r64\t1801\t55\t1610\t37\t27\t28\t>20\r33\t1795\t45\t1660\t17\t16\t29\t<= 20\r52\t1669\t47\t1610\t23\t29\t18\t>20\r27\t1708\t24\t1590\t26\t1\t23\t<= 20\r33\t1691\t32\t1530\t21\t12\t20\t<= 20\r46\t1825\t47\t1690\t23\t23\t24\t>20\r54\t1760\t57\t1600\t23\t31\t26\t>20\r27\t1949\t\t1693\t25\t2\t\t<= 20\r50\t1685\t\t1580\t21\t29\t\t>20\r42\t1806\t\t1636\t22\t20\t\t<= 20\r54\t1905\t46\t1670\t32\t22\t24\t>20\r49\t1739\t42\t1600\t28\t21\t21\t>20\r62\t1736\t63\t1570\t22\t40\t23\t>20\r34\t1845\t32\t1700\t24\t10\t22\t<= 20\r23\t1868\t24\t1740\t19\t4\t20\t<= 20\r36\t1765\t32\t1540\t27\t9\t23\t<= 20\r53\t1736\t\t1555\t30\t23\t\t>20\r32\t1741\t\t1614\t22\t10\t\t<= 20\r59\t1720\t56\t1530\t24\t35\t21\t>20\r53\t1871\t50\t1690\t25\t28\t22\t>20\r55\t1720\t55\t1590\t21\t34\t21\t>20\r62\t1629\t58\t1610\t23\t39\t19\t>20\r42\t1624\t38\t1670\t22\t20\t18\t<= 20\r50\t1653\t44\t1690\t35\t15\t29\t<= 20\r37\t1786\t35\t1550\t21\t16\t19\t<= 20\r51\t1620\t44\t1650\t30\t21\t23\t>20\r25\t1695\t25\t1540\t19\t6\t19\t<= 20\r54\t1674\t43\t1660\t35\t19\t24\t<= 20\r34\t1864\t31\t1620\t23\t11\t20\t<= 20\r43\t1643\t35\t1630\t29\t14\t21\t<= 20\r43\t1705\t41\t1610\t22\t21\t20\t>20\r58\t1736\t50\t1540\t32\t26\t24\t>20\r28\t1691\t23\t1610\t23\t5\t18\t<= 20\r45\t1753\t43\t1630\t21\t24\t19\t>20\r47\t1680\t49\t1530\t20\t27\t22\t>20\r57\t1724\t59\t1520\t24\t33\t26\t>20\r27\t1710\t\t1544\t20\t7\t\t<= 20\r34\t1638\t38\t1570\t33\t1\t37\t<= 20\r57\t1725\t42\t1580\t52\t5\t37\t<= 20\r27\t1725\t21\t1550\t24\t3\t18\t<= 20\r54\t1630\t\t1570\t34\t20\t\t<= 20\r24\t1810\t\t1521\t16\t8\t\t<= 20\r48\t1774\t42\t1580\t30\t18\t24\t<= 20\r37\t1771\t35\t1630\t28\t9\t26\t<= 20\r25\t1815\t26\t1650\t20\t5\t21\t<= 20\r57\t1575\t57\t1640\t20\t37\t20\t>20\r40\t1729\t34\t1650\t26\t14\t20\t<= 20\r61\t1749\t63\t1520\t21\t40\t23\t>20\r25\t1705\t23\t1620\t24\t1\t22\t<= 20\r32\t1875\t\t1744\t22\t10\t\t<= 20\r37\t1784\t\t1647\t22\t15\t\t<= 20\r45\t1584\t\t1615\t29\t16\t\t<= 20\r24\t1774\t23\t1680\t22\t2\t21\t<= 20\r47\t1658\t46\t1670\t24\t23\t23\t>20\r44\t1790\t40\t1620\t24\t20\t20\t<= 20\r52\t1798\t53\t1570\t25\t27\t26\t>20\r45\t1824\t40\t1660\t23\t22\t18\t>20\r20\t1796\t22\t1550\t19\t1\t21\t<= 20\r60\t1725\t60\t1590\t21\t39\t21\t>20\r36\t1685\t32\t1620\t25\t11\t21\t<= 20\r25\t1769\t24\t1560\t18\t7\t17\t<= 20\r25\t1749\t28\t1670\t21\t4\t24\t<= 20\r35\t1716\t40\t1650\t17\t18\t22\t<= 20\r35\t1664\t\t1539\t22\t13\t\t<= 20\r49\t1773\t48\t1470\t21\t28\t20\t>20\r33\t1760\t33\t1580\t20\t13\t20\t<= 20\r50\t1725\t49\t1670\t23\t27\t22\t>20\r63\t1645\t64\t1520\t28\t35\t29\t>20\r57\t1694\t55\t1620\t24\t33\t22\t>20\r41\t1851\t41\t1710\t23\t18\t23\t<= 20\r38\t1691\t38\t1530\t20\t18\t20\t<= 20\r30\t1880\t31\t1630\t22\t8\t23\t<= 20\r52\t1835\t52\t1720\t30\t22\t30\t>20\r51\t1730\t43\t1570\t22\t29\t14\t>20\r46\t1644\t51\t1560\t27\t19\t32\t<= 20\r50\t1723\t47\t1650\t25\t25\t22\t>20\r32\t1758\t\t1635\t24\t8\t\t<= 20\r52\t1718\t32\t1590\t25\t27\t5\t>20\r30\t1723\t33\t1590\t22\t8\t25\t<= 20\r33\t1708\t\t1566\t21\t12\t\t<= 20\r20\t1786\t18\t1590\t19\t1\t17\t<= 20\r32\t1764\t\t1662\t\t\t\t>20\r51\t1675\t45\t1550\t25\t26\t19\t>20\r64\t1641\t64\t1570\t30\t34\t30\t>20\r44\t1743\t43\t1560\t25\t19\t24\t<= 20\r40\t1823\t39\t1630\t23\t17\t22\t<= 20\r59\t1720\t56\t1530\t24\t35\t21\t>20\r"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/husbands_wives_correlation/husbands_wives_correlation.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nhw <- read.table(\"husbands_wives.txt\", h = T, sep = \"\\t\")\n\n# converts heights to inches ----------------------------------------\n\nhw$ht_husband_in <- hw$ht_husband / 25.4\nhw$ht_wife_in <- hw$ht_wife / 25.4\n\n# remove cases where wife's age is missing --------------------------\n\nhw <- hw[!is.na(hw$age_wife),]\n\n# plot wife vs. husband age -----------------------------------------\n\npdf(\"husbands_wives_age.pdf\", 5.5, 4.3)\n\npar(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(hw$age_wife ~ hw$age_husband, \n     xlab = \"Husband's age (in years)\", \n     ylab = \"Wife's age (in years)\", \n     pch = 19, col = COL[1,2], \n     xlim = c(18, 66), ylim = c(16, 66), axes = FALSE)\naxis(1, at = seq(20,60,20))\naxis(2, at = seq(20,60,20))\nbox()\n\ndev.off()\n\n# plot wife vs. husband height --------------------------------------\n\npdf(\"husbands_wives_height.pdf\", 5.5, 4.3)\n\npar(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(hw$ht_wife_in ~ hw$ht_husband_in, \n     xlab = \"Husband's height (in inches)\", \n     ylab = \"Wife's height (in inches)\", \n     pch = 19, col = COL[1,2], \n     xlim = c(60, 77), ylim = c(55, 70), axes = FALSE)\naxis(1, at = seq(60, 75, 5))\naxis(2, at = seq(55, 70, 5))\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/husbands_wives_height_inf/husbands_wives.txt",
    "content": "age_husband\tht_husband\tage_wife\tht_wife\tage_husb_at_marriage\tyears_married\tage_wife_at_marriage\tduration\r49\t1809\t43\t1590\t25\t24\t19\t>20\r25\t1841\t28\t1560\t19\t6\t22\t<= 20\r40\t1659\t30\t1620\t38\t2\t28\t<= 20\r52\t1779\t57\t1540\t26\t26\t31\t>20\r58\t1616\t52\t1420\t30\t28\t24\t>20\r32\t1695\t27\t1660\t23\t9\t18\t<= 20\r43\t1730\t52\t1610\t33\t10\t42\t<= 20\r42\t1753\t\t1635\t30\t12\t\t<= 20\r47\t1740\t43\t1580\t26\t21\t22\t>20\r31\t1685\t23\t1610\t26\t5\t18\t<= 20\r26\t1735\t25\t1590\t23\t3\t22\t<= 20\r40\t1713\t39\t1610\t23\t17\t22\t<= 20\r35\t1736\t32\t1700\t31\t4\t28\t<= 20\r45\t1715\t\t1522\t41\t4\t\t<= 20\r35\t1799\t35\t1680\t19\t16\t19\t<= 20\r35\t1785\t33\t1680\t24\t11\t22\t<= 20\r47\t1758\t43\t1630\t24\t23\t20\t>20\r38\t1729\t35\t1570\t27\t11\t24\t<= 20\r33\t1720\t32\t1720\t28\t5\t27\t<= 20\r32\t1810\t30\t1740\t22\t10\t20\t<= 20\r38\t1725\t40\t1600\t31\t7\t33\t<= 20\r45\t1764\t\t1689\t24\t21\t\t>20\r29\t1683\t29\t1600\t25\t4\t25\t<= 20\r59\t1585\t55\t1550\t23\t36\t19\t>20\r26\t1684\t25\t1540\t18\t8\t17\t<= 20\r50\t1674\t45\t1640\t25\t25\t20\t>20\r49\t1724\t44\t1640\t27\t22\t22\t>20\r42\t1630\t40\t1630\t28\t14\t26\t<= 20\r33\t1855\t31\t1560\t22\t11\t20\t<= 20\r31\t1796\t\t1652\t25\t6\t\t<= 20\r27\t1700\t25\t1580\t21\t6\t19\t<= 20\r57\t1765\t51\t1570\t32\t25\t26\t>20\r34\t1700\t31\t1590\t28\t6\t25\t<= 20\r28\t1721\t25\t1650\t23\t5\t20\t<= 20\r46\t1823\t\t1591\t\t\t\t>20\r37\t1829\t35\t1670\t22\t15\t20\t<= 20\r56\t1710\t55\t1600\t44\t12\t43\t<= 20\r27\t1745\t23\t1610\t25\t2\t21\t<= 20\r36\t1698\t35\t1610\t22\t14\t21\t<= 20\r31\t1853\t28\t1670\t20\t11\t17\t<= 20\r57\t1610\t52\t1510\t25\t32\t20\t>20\r55\t1680\t53\t1520\t21\t34\t19\t>20\r47\t1809\t43\t1620\t25\t22\t21\t>20\r64\t1580\t61\t1530\t21\t43\t18\t>20\r60\t1600\t\t1451\t26\t34\t\t>20\r31\t1585\t23\t1570\t28\t3\t20\t<= 20\r35\t1705\t35\t1580\t25\t10\t25\t<= 20\r36\t1675\t35\t1590\t22\t14\t21\t<= 20\r40\t1735\t39\t1670\t23\t17\t22\t<= 20\r30\t1686\t24\t1630\t27\t3\t21\t<= 20\r32\t1768\t29\t1510\t21\t11\t18\t<= 20\r27\t1721\t\t1560\t26\t1\t\t<= 20\r20\t1754\t21\t1660\t19\t1\t20\t<= 20\r45\t1739\t39\t1610\t25\t20\t19\t<= 20\r59\t1699\t52\t1440\t27\t32\t20\t>20\r43\t1825\t52\t1570\t25\t18\t34\t<= 20\r29\t1740\t26\t1670\t24\t5\t21\t<= 20\r48\t1704\t\t1635\t27\t21\t\t>20\r39\t1719\t\t1670\t25\t14\t\t<= 20\r47\t1731\t48\t1730\t21\t26\t22\t>20\r54\t1679\t53\t1560\t\t\t\t>20\r43\t1755\t42\t1590\t20\t23\t19\t>20\r54\t1713\t50\t1600\t23\t31\t19\t>20\r61\t1723\t64\t1490\t26\t35\t29\t>20\r27\t1783\t26\t1660\t20\t7\t19\t<= 20\r51\t1585\t\t1504\t50\t1\t\t<= 20\r27\t1749\t32\t1580\t24\t3\t29\t<= 20\r32\t1710\t31\t1500\t31\t1\t30\t<= 20\r54\t1724\t53\t1640\t20\t34\t19\t>20\r37\t1620\t39\t1650\t21\t16\t23\t<= 20\r55\t1764\t45\t1620\t29\t26\t19\t>20\r36\t1791\t33\t1550\t30\t6\t27\t<= 20\r32\t1795\t32\t1640\t25\t7\t25\t<= 20\r57\t1738\t55\t1560\t24\t33\t22\t>20\r51\t1639\t\t1552\t25\t26\t\t>20\r62\t1734\t\t1600\t33\t29\t\t>20\r57\t1695\t\t1545\t22\t35\t\t>20\r51\t1666\t52\t1570\t24\t27\t25\t>20\r50\t1745\t50\t1550\t22\t28\t22\t>20\r32\t1775\t32\t1600\t20\t12\t20\t<= 20\r54\t1669\t54\t1660\t20\t34\t20\t>20\r34\t1700\t32\t1640\t22\t12\t20\t<= 20\r45\t1804\t41\t1670\t27\t18\t23\t<= 20\r64\t1700\t61\t1560\t24\t40\t21\t>20\r55\t1664\t43\t1760\t31\t24\t19\t>20\r27\t1753\t28\t1640\t23\t4\t24\t<= 20\r55\t1788\t51\t1600\t26\t29\t22\t>20\r27\t1765\t\t1571\t\t\t\t>20\r41\t1680\t41\t1550\t22\t19\t22\t<= 20\r44\t1715\t41\t1570\t24\t20\t21\t<= 20\r22\t1755\t21\t1590\t21\t1\t20\t<= 20\r30\t1764\t28\t1650\t29\t1\t27\t<= 20\r53\t1793\t47\t1690\t31\t22\t25\t>20\r42\t1731\t37\t1580\t23\t19\t18\t<= 20\r31\t1713\t28\t1590\t28\t3\t25\t<= 20\r36\t1725\t35\t1510\t26\t10\t25\t<= 20\r56\t1828\t55\t1600\t30\t26\t29\t>20\r46\t1735\t45\t1660\t22\t24\t21\t>20\r34\t1760\t34\t1700\t23\t11\t23\t<= 20\r55\t1685\t51\t1530\t34\t21\t30\t>20\r44\t1685\t39\t1490\t27\t17\t22\t<= 20\r45\t1559\t35\t1580\t34\t11\t24\t<= 20\r48\t1705\t45\t1500\t28\t20\t25\t<= 20\r44\t1723\t44\t1600\t41\t3\t41\t<= 20\r59\t1700\t47\t1570\t39\t20\t27\t<= 20\r64\t1660\t57\t1620\t32\t32\t25\t>20\r34\t1681\t33\t1410\t22\t12\t21\t<= 20\r37\t1803\t38\t1560\t23\t14\t24\t<= 20\r54\t1866\t59\t1590\t49\t5\t54\t<= 20\r49\t1884\t46\t1710\t25\t24\t22\t>20\r63\t1705\t60\t1580\t27\t36\t24\t>20\r48\t1780\t47\t1690\t22\t26\t21\t>20\r64\t1801\t55\t1610\t37\t27\t28\t>20\r33\t1795\t45\t1660\t17\t16\t29\t<= 20\r52\t1669\t47\t1610\t23\t29\t18\t>20\r27\t1708\t24\t1590\t26\t1\t23\t<= 20\r33\t1691\t32\t1530\t21\t12\t20\t<= 20\r46\t1825\t47\t1690\t23\t23\t24\t>20\r54\t1760\t57\t1600\t23\t31\t26\t>20\r27\t1949\t\t1693\t25\t2\t\t<= 20\r50\t1685\t\t1580\t21\t29\t\t>20\r42\t1806\t\t1636\t22\t20\t\t<= 20\r54\t1905\t46\t1670\t32\t22\t24\t>20\r49\t1739\t42\t1600\t28\t21\t21\t>20\r62\t1736\t63\t1570\t22\t40\t23\t>20\r34\t1845\t32\t1700\t24\t10\t22\t<= 20\r23\t1868\t24\t1740\t19\t4\t20\t<= 20\r36\t1765\t32\t1540\t27\t9\t23\t<= 20\r53\t1736\t\t1555\t30\t23\t\t>20\r32\t1741\t\t1614\t22\t10\t\t<= 20\r59\t1720\t56\t1530\t24\t35\t21\t>20\r53\t1871\t50\t1690\t25\t28\t22\t>20\r55\t1720\t55\t1590\t21\t34\t21\t>20\r62\t1629\t58\t1610\t23\t39\t19\t>20\r42\t1624\t38\t1670\t22\t20\t18\t<= 20\r50\t1653\t44\t1690\t35\t15\t29\t<= 20\r37\t1786\t35\t1550\t21\t16\t19\t<= 20\r51\t1620\t44\t1650\t30\t21\t23\t>20\r25\t1695\t25\t1540\t19\t6\t19\t<= 20\r54\t1674\t43\t1660\t35\t19\t24\t<= 20\r34\t1864\t31\t1620\t23\t11\t20\t<= 20\r43\t1643\t35\t1630\t29\t14\t21\t<= 20\r43\t1705\t41\t1610\t22\t21\t20\t>20\r58\t1736\t50\t1540\t32\t26\t24\t>20\r28\t1691\t23\t1610\t23\t5\t18\t<= 20\r45\t1753\t43\t1630\t21\t24\t19\t>20\r47\t1680\t49\t1530\t20\t27\t22\t>20\r57\t1724\t59\t1520\t24\t33\t26\t>20\r27\t1710\t\t1544\t20\t7\t\t<= 20\r34\t1638\t38\t1570\t33\t1\t37\t<= 20\r57\t1725\t42\t1580\t52\t5\t37\t<= 20\r27\t1725\t21\t1550\t24\t3\t18\t<= 20\r54\t1630\t\t1570\t34\t20\t\t<= 20\r24\t1810\t\t1521\t16\t8\t\t<= 20\r48\t1774\t42\t1580\t30\t18\t24\t<= 20\r37\t1771\t35\t1630\t28\t9\t26\t<= 20\r25\t1815\t26\t1650\t20\t5\t21\t<= 20\r57\t1575\t57\t1640\t20\t37\t20\t>20\r40\t1729\t34\t1650\t26\t14\t20\t<= 20\r61\t1749\t63\t1520\t21\t40\t23\t>20\r25\t1705\t23\t1620\t24\t1\t22\t<= 20\r32\t1875\t\t1744\t22\t10\t\t<= 20\r37\t1784\t\t1647\t22\t15\t\t<= 20\r45\t1584\t\t1615\t29\t16\t\t<= 20\r24\t1774\t23\t1680\t22\t2\t21\t<= 20\r47\t1658\t46\t1670\t24\t23\t23\t>20\r44\t1790\t40\t1620\t24\t20\t20\t<= 20\r52\t1798\t53\t1570\t25\t27\t26\t>20\r45\t1824\t40\t1660\t23\t22\t18\t>20\r20\t1796\t22\t1550\t19\t1\t21\t<= 20\r60\t1725\t60\t1590\t21\t39\t21\t>20\r36\t1685\t32\t1620\t25\t11\t21\t<= 20\r25\t1769\t24\t1560\t18\t7\t17\t<= 20\r25\t1749\t28\t1670\t21\t4\t24\t<= 20\r35\t1716\t40\t1650\t17\t18\t22\t<= 20\r35\t1664\t\t1539\t22\t13\t\t<= 20\r49\t1773\t48\t1470\t21\t28\t20\t>20\r33\t1760\t33\t1580\t20\t13\t20\t<= 20\r50\t1725\t49\t1670\t23\t27\t22\t>20\r63\t1645\t64\t1520\t28\t35\t29\t>20\r57\t1694\t55\t1620\t24\t33\t22\t>20\r41\t1851\t41\t1710\t23\t18\t23\t<= 20\r38\t1691\t38\t1530\t20\t18\t20\t<= 20\r30\t1880\t31\t1630\t22\t8\t23\t<= 20\r52\t1835\t52\t1720\t30\t22\t30\t>20\r51\t1730\t43\t1570\t22\t29\t14\t>20\r46\t1644\t51\t1560\t27\t19\t32\t<= 20\r50\t1723\t47\t1650\t25\t25\t22\t>20\r32\t1758\t\t1635\t24\t8\t\t<= 20\r52\t1718\t32\t1590\t25\t27\t5\t>20\r30\t1723\t33\t1590\t22\t8\t25\t<= 20\r33\t1708\t\t1566\t21\t12\t\t<= 20\r20\t1786\t18\t1590\t19\t1\t17\t<= 20\r32\t1764\t\t1662\t\t\t\t>20\r51\t1675\t45\t1550\t25\t26\t19\t>20\r64\t1641\t64\t1570\t30\t34\t30\t>20\r44\t1743\t43\t1560\t25\t19\t24\t<= 20\r40\t1823\t39\t1630\t23\t17\t22\t<= 20\r59\t1720\t56\t1530\t24\t35\t21\t>20\r"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/husbands_wives_height_inf/husbands_wives_height_inf.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nhw <- read.table(\"husbands_wives.txt\", h = T, sep = \"\\t\")\n\n# converts heights to inches ----------------------------------------\n\nhw$ht_husband_in <- hw$ht_husband / 25.4\nhw$ht_wife_in <- hw$ht_wife / 25.4\n\n# remove cases where wife's age is missing --------------------------\n\nhw <- hw[!is.na(hw$age_wife),]\n\n# model summary -----------------------------------------------------\n\nm_h_w_height <- lm(hw$ht_wife_in ~ hw$ht_husband_in)\n\nxtable(summary(m_h_w_height))\n\n# plot wife vs. husband height --------------------------------------\n\npdf(\"husbands_wives_height.pdf\", 5.5, 4.3)\n\npar(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(hw$ht_wife_in ~ hw$ht_husband_in, \n     xlab = \"Husband's height (in inches)\", \n     ylab = \"Wife's height (in inches)\", \n     pch = 19, col = COL[1,2], \n     xlim = c(60, 77), ylim = c(55, 70), axes = FALSE)\naxis(1, at = seq(60, 75, 5))\naxis(2, at = seq(55, 70, 5))\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/husbands_wives_height_inf_2s/husbands_wives.txt",
    "content": "age_husband\tht_husband\tage_wife\tht_wife\tage_husb_at_marriage\tyears_married\tage_wife_at_marriage\tduration\r49\t1809\t43\t1590\t25\t24\t19\t>20\r25\t1841\t28\t1560\t19\t6\t22\t<= 20\r40\t1659\t30\t1620\t38\t2\t28\t<= 20\r52\t1779\t57\t1540\t26\t26\t31\t>20\r58\t1616\t52\t1420\t30\t28\t24\t>20\r32\t1695\t27\t1660\t23\t9\t18\t<= 20\r43\t1730\t52\t1610\t33\t10\t42\t<= 20\r42\t1753\t\t1635\t30\t12\t\t<= 20\r47\t1740\t43\t1580\t26\t21\t22\t>20\r31\t1685\t23\t1610\t26\t5\t18\t<= 20\r26\t1735\t25\t1590\t23\t3\t22\t<= 20\r40\t1713\t39\t1610\t23\t17\t22\t<= 20\r35\t1736\t32\t1700\t31\t4\t28\t<= 20\r45\t1715\t\t1522\t41\t4\t\t<= 20\r35\t1799\t35\t1680\t19\t16\t19\t<= 20\r35\t1785\t33\t1680\t24\t11\t22\t<= 20\r47\t1758\t43\t1630\t24\t23\t20\t>20\r38\t1729\t35\t1570\t27\t11\t24\t<= 20\r33\t1720\t32\t1720\t28\t5\t27\t<= 20\r32\t1810\t30\t1740\t22\t10\t20\t<= 20\r38\t1725\t40\t1600\t31\t7\t33\t<= 20\r45\t1764\t\t1689\t24\t21\t\t>20\r29\t1683\t29\t1600\t25\t4\t25\t<= 20\r59\t1585\t55\t1550\t23\t36\t19\t>20\r26\t1684\t25\t1540\t18\t8\t17\t<= 20\r50\t1674\t45\t1640\t25\t25\t20\t>20\r49\t1724\t44\t1640\t27\t22\t22\t>20\r42\t1630\t40\t1630\t28\t14\t26\t<= 20\r33\t1855\t31\t1560\t22\t11\t20\t<= 20\r31\t1796\t\t1652\t25\t6\t\t<= 20\r27\t1700\t25\t1580\t21\t6\t19\t<= 20\r57\t1765\t51\t1570\t32\t25\t26\t>20\r34\t1700\t31\t1590\t28\t6\t25\t<= 20\r28\t1721\t25\t1650\t23\t5\t20\t<= 20\r46\t1823\t\t1591\t\t\t\t>20\r37\t1829\t35\t1670\t22\t15\t20\t<= 20\r56\t1710\t55\t1600\t44\t12\t43\t<= 20\r27\t1745\t23\t1610\t25\t2\t21\t<= 20\r36\t1698\t35\t1610\t22\t14\t21\t<= 20\r31\t1853\t28\t1670\t20\t11\t17\t<= 20\r57\t1610\t52\t1510\t25\t32\t20\t>20\r55\t1680\t53\t1520\t21\t34\t19\t>20\r47\t1809\t43\t1620\t25\t22\t21\t>20\r64\t1580\t61\t1530\t21\t43\t18\t>20\r60\t1600\t\t1451\t26\t34\t\t>20\r31\t1585\t23\t1570\t28\t3\t20\t<= 20\r35\t1705\t35\t1580\t25\t10\t25\t<= 20\r36\t1675\t35\t1590\t22\t14\t21\t<= 20\r40\t1735\t39\t1670\t23\t17\t22\t<= 20\r30\t1686\t24\t1630\t27\t3\t21\t<= 20\r32\t1768\t29\t1510\t21\t11\t18\t<= 20\r27\t1721\t\t1560\t26\t1\t\t<= 20\r20\t1754\t21\t1660\t19\t1\t20\t<= 20\r45\t1739\t39\t1610\t25\t20\t19\t<= 20\r59\t1699\t52\t1440\t27\t32\t20\t>20\r43\t1825\t52\t1570\t25\t18\t34\t<= 20\r29\t1740\t26\t1670\t24\t5\t21\t<= 20\r48\t1704\t\t1635\t27\t21\t\t>20\r39\t1719\t\t1670\t25\t14\t\t<= 20\r47\t1731\t48\t1730\t21\t26\t22\t>20\r54\t1679\t53\t1560\t\t\t\t>20\r43\t1755\t42\t1590\t20\t23\t19\t>20\r54\t1713\t50\t1600\t23\t31\t19\t>20\r61\t1723\t64\t1490\t26\t35\t29\t>20\r27\t1783\t26\t1660\t20\t7\t19\t<= 20\r51\t1585\t\t1504\t50\t1\t\t<= 20\r27\t1749\t32\t1580\t24\t3\t29\t<= 20\r32\t1710\t31\t1500\t31\t1\t30\t<= 20\r54\t1724\t53\t1640\t20\t34\t19\t>20\r37\t1620\t39\t1650\t21\t16\t23\t<= 20\r55\t1764\t45\t1620\t29\t26\t19\t>20\r36\t1791\t33\t1550\t30\t6\t27\t<= 20\r32\t1795\t32\t1640\t25\t7\t25\t<= 20\r57\t1738\t55\t1560\t24\t33\t22\t>20\r51\t1639\t\t1552\t25\t26\t\t>20\r62\t1734\t\t1600\t33\t29\t\t>20\r57\t1695\t\t1545\t22\t35\t\t>20\r51\t1666\t52\t1570\t24\t27\t25\t>20\r50\t1745\t50\t1550\t22\t28\t22\t>20\r32\t1775\t32\t1600\t20\t12\t20\t<= 20\r54\t1669\t54\t1660\t20\t34\t20\t>20\r34\t1700\t32\t1640\t22\t12\t20\t<= 20\r45\t1804\t41\t1670\t27\t18\t23\t<= 20\r64\t1700\t61\t1560\t24\t40\t21\t>20\r55\t1664\t43\t1760\t31\t24\t19\t>20\r27\t1753\t28\t1640\t23\t4\t24\t<= 20\r55\t1788\t51\t1600\t26\t29\t22\t>20\r27\t1765\t\t1571\t\t\t\t>20\r41\t1680\t41\t1550\t22\t19\t22\t<= 20\r44\t1715\t41\t1570\t24\t20\t21\t<= 20\r22\t1755\t21\t1590\t21\t1\t20\t<= 20\r30\t1764\t28\t1650\t29\t1\t27\t<= 20\r53\t1793\t47\t1690\t31\t22\t25\t>20\r42\t1731\t37\t1580\t23\t19\t18\t<= 20\r31\t1713\t28\t1590\t28\t3\t25\t<= 20\r36\t1725\t35\t1510\t26\t10\t25\t<= 20\r56\t1828\t55\t1600\t30\t26\t29\t>20\r46\t1735\t45\t1660\t22\t24\t21\t>20\r34\t1760\t34\t1700\t23\t11\t23\t<= 20\r55\t1685\t51\t1530\t34\t21\t30\t>20\r44\t1685\t39\t1490\t27\t17\t22\t<= 20\r45\t1559\t35\t1580\t34\t11\t24\t<= 20\r48\t1705\t45\t1500\t28\t20\t25\t<= 20\r44\t1723\t44\t1600\t41\t3\t41\t<= 20\r59\t1700\t47\t1570\t39\t20\t27\t<= 20\r64\t1660\t57\t1620\t32\t32\t25\t>20\r34\t1681\t33\t1410\t22\t12\t21\t<= 20\r37\t1803\t38\t1560\t23\t14\t24\t<= 20\r54\t1866\t59\t1590\t49\t5\t54\t<= 20\r49\t1884\t46\t1710\t25\t24\t22\t>20\r63\t1705\t60\t1580\t27\t36\t24\t>20\r48\t1780\t47\t1690\t22\t26\t21\t>20\r64\t1801\t55\t1610\t37\t27\t28\t>20\r33\t1795\t45\t1660\t17\t16\t29\t<= 20\r52\t1669\t47\t1610\t23\t29\t18\t>20\r27\t1708\t24\t1590\t26\t1\t23\t<= 20\r33\t1691\t32\t1530\t21\t12\t20\t<= 20\r46\t1825\t47\t1690\t23\t23\t24\t>20\r54\t1760\t57\t1600\t23\t31\t26\t>20\r27\t1949\t\t1693\t25\t2\t\t<= 20\r50\t1685\t\t1580\t21\t29\t\t>20\r42\t1806\t\t1636\t22\t20\t\t<= 20\r54\t1905\t46\t1670\t32\t22\t24\t>20\r49\t1739\t42\t1600\t28\t21\t21\t>20\r62\t1736\t63\t1570\t22\t40\t23\t>20\r34\t1845\t32\t1700\t24\t10\t22\t<= 20\r23\t1868\t24\t1740\t19\t4\t20\t<= 20\r36\t1765\t32\t1540\t27\t9\t23\t<= 20\r53\t1736\t\t1555\t30\t23\t\t>20\r32\t1741\t\t1614\t22\t10\t\t<= 20\r59\t1720\t56\t1530\t24\t35\t21\t>20\r53\t1871\t50\t1690\t25\t28\t22\t>20\r55\t1720\t55\t1590\t21\t34\t21\t>20\r62\t1629\t58\t1610\t23\t39\t19\t>20\r42\t1624\t38\t1670\t22\t20\t18\t<= 20\r50\t1653\t44\t1690\t35\t15\t29\t<= 20\r37\t1786\t35\t1550\t21\t16\t19\t<= 20\r51\t1620\t44\t1650\t30\t21\t23\t>20\r25\t1695\t25\t1540\t19\t6\t19\t<= 20\r54\t1674\t43\t1660\t35\t19\t24\t<= 20\r34\t1864\t31\t1620\t23\t11\t20\t<= 20\r43\t1643\t35\t1630\t29\t14\t21\t<= 20\r43\t1705\t41\t1610\t22\t21\t20\t>20\r58\t1736\t50\t1540\t32\t26\t24\t>20\r28\t1691\t23\t1610\t23\t5\t18\t<= 20\r45\t1753\t43\t1630\t21\t24\t19\t>20\r47\t1680\t49\t1530\t20\t27\t22\t>20\r57\t1724\t59\t1520\t24\t33\t26\t>20\r27\t1710\t\t1544\t20\t7\t\t<= 20\r34\t1638\t38\t1570\t33\t1\t37\t<= 20\r57\t1725\t42\t1580\t52\t5\t37\t<= 20\r27\t1725\t21\t1550\t24\t3\t18\t<= 20\r54\t1630\t\t1570\t34\t20\t\t<= 20\r24\t1810\t\t1521\t16\t8\t\t<= 20\r48\t1774\t42\t1580\t30\t18\t24\t<= 20\r37\t1771\t35\t1630\t28\t9\t26\t<= 20\r25\t1815\t26\t1650\t20\t5\t21\t<= 20\r57\t1575\t57\t1640\t20\t37\t20\t>20\r40\t1729\t34\t1650\t26\t14\t20\t<= 20\r61\t1749\t63\t1520\t21\t40\t23\t>20\r25\t1705\t23\t1620\t24\t1\t22\t<= 20\r32\t1875\t\t1744\t22\t10\t\t<= 20\r37\t1784\t\t1647\t22\t15\t\t<= 20\r45\t1584\t\t1615\t29\t16\t\t<= 20\r24\t1774\t23\t1680\t22\t2\t21\t<= 20\r47\t1658\t46\t1670\t24\t23\t23\t>20\r44\t1790\t40\t1620\t24\t20\t20\t<= 20\r52\t1798\t53\t1570\t25\t27\t26\t>20\r45\t1824\t40\t1660\t23\t22\t18\t>20\r20\t1796\t22\t1550\t19\t1\t21\t<= 20\r60\t1725\t60\t1590\t21\t39\t21\t>20\r36\t1685\t32\t1620\t25\t11\t21\t<= 20\r25\t1769\t24\t1560\t18\t7\t17\t<= 20\r25\t1749\t28\t1670\t21\t4\t24\t<= 20\r35\t1716\t40\t1650\t17\t18\t22\t<= 20\r35\t1664\t\t1539\t22\t13\t\t<= 20\r49\t1773\t48\t1470\t21\t28\t20\t>20\r33\t1760\t33\t1580\t20\t13\t20\t<= 20\r50\t1725\t49\t1670\t23\t27\t22\t>20\r63\t1645\t64\t1520\t28\t35\t29\t>20\r57\t1694\t55\t1620\t24\t33\t22\t>20\r41\t1851\t41\t1710\t23\t18\t23\t<= 20\r38\t1691\t38\t1530\t20\t18\t20\t<= 20\r30\t1880\t31\t1630\t22\t8\t23\t<= 20\r52\t1835\t52\t1720\t30\t22\t30\t>20\r51\t1730\t43\t1570\t22\t29\t14\t>20\r46\t1644\t51\t1560\t27\t19\t32\t<= 20\r50\t1723\t47\t1650\t25\t25\t22\t>20\r32\t1758\t\t1635\t24\t8\t\t<= 20\r52\t1718\t32\t1590\t25\t27\t5\t>20\r30\t1723\t33\t1590\t22\t8\t25\t<= 20\r33\t1708\t\t1566\t21\t12\t\t<= 20\r20\t1786\t18\t1590\t19\t1\t17\t<= 20\r32\t1764\t\t1662\t\t\t\t>20\r51\t1675\t45\t1550\t25\t26\t19\t>20\r64\t1641\t64\t1570\t30\t34\t30\t>20\r44\t1743\t43\t1560\t25\t19\t24\t<= 20\r40\t1823\t39\t1630\t23\t17\t22\t<= 20\r59\t1720\t56\t1530\t24\t35\t21\t>20\r"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/husbands_wives_height_inf_2s/husbands_wives_height_inf_2s.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nhw <- read.table(\"husbands_wives.txt\", h = T, sep = \"\\t\")\n\n# converts heights to inches ----------------------------------------\n\nhw$ht_husband_in <- hw$ht_husband / 25.4\nhw$ht_wife_in <- hw$ht_wife / 25.4\n\n# remove cases where wife's age is missing --------------------------\n\nhw <- hw[!is.na(hw$age_wife),]\n\n# model summary -----------------------------------------------------\n\nm_h_w_height <- lm(hw$ht_wife_in ~ hw$ht_husband_in)\n\nxtable(summary(m_h_w_height))\n\n# plot wife vs. husband height --------------------------------------\n\npdf(\"husbands_wives_height_inf_2s.pdf\", 5.5, 4.3)\n\npar(mar = c(3.75, 3.75, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(hw$ht_wife_in ~ hw$ht_husband_in, \n     xlab = \"Husband's height (in inches)\", \n     ylab = \"Wife's height (in inches)\", \n     pch = 19, col = COL[1,2], \n     xlim = c(60, 77), ylim = c(55, 70), axes = FALSE)\naxis(1, at = seq(60, 75, 5))\naxis(2, at = seq(55, 70, 5))\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/identify_relationships_1/identify_relationships_1.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# simulate data -----------------------------------------------------\n\nset.seed(9274)\n\nx1 <- seq(0, 6, by = 0.05)\n\ny_u <- (x1-3)^2 - 4 + rnorm(length(x1), mean = 0, sd = 1)\ny_lin_pos_strong <- 3*x1 + 10 + rnorm(length(x1), mean = 0, sd = 2)\ny_lin_pos_weak <- 3*x1 + 10 + rnorm(length(x1), mean = 0, sd = 20)\n\n\nx2 <- seq(-8, -2, by = 0.05)\n\ny_n <- -1 * (x2 + 5)^2 + 1 + rnorm(length(x2), mean = 0, sd = 2)\ny_lin_neg_strong <- -5 * x2 + 3 + rnorm(length(x2), mean = 0, sd = 2)\ny_none <- rnorm(length(x2), mean = 0, sd = 1)\n\n# plot u-shaped -----------------------------------------------------\n\npdf(\"identify_relationships_u.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_u ~ x1, xlab = \"(a)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot linear positive strong ---------------------------------------\n\npdf(\"identify_relationships_lin_pos_strong.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_lin_pos_strong ~ x1, xlab = \"(b)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot linear positive weak -----------------------------------------\n\npdf(\"identify_relationships_lin_pos_weak.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_lin_pos_weak ~ x1, xlab = \"(c)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot n-shaped -----------------------------------------------------\n\npdf(\"identify_relationships_n.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_n ~ x2, xlab = \"(d)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot n-shaped -----------------------------------------------------\n\npdf(\"identify_relationships_lin_neg_strong.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_lin_neg_strong ~ x2, xlab = \"(e)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot no relationship ----------------------------------------------\n\npdf(\"identify_relationships_none.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_none ~ x2, xlab = \"(f)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/identify_relationships_2/identify_relationships_2.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# simulate data -----------------------------------------------------\n\nset.seed(9274)\n\nx <- seq(-3, 4, 0.05)\n\ny_s <-  -0.5 * x^3 + x^2 + x + rnorm(length(x), mean = 0, sd = 2)\ny_hockey_stick <-  2 * x^4 + -0.5 * x^3 + x^2 + x + rnorm(length(x), mean = 0, sd = 30)\ny_pos_lin_strong <- 3 * x + rnorm(length(x), mean = 0, sd = 2)\ny_pos_weak <- 3 * x + rnorm(length(x), mean = 0, sd = 20)\ny_pos_weaker <- -3 * x + rnorm(length(x), mean = 0, sd = 10) \ny_neg_lin_weak <- -3 * x + rnorm(length(x), mean = 0, sd = 5) \n\n# plot s-shaped -----------------------------------------------------\n\npdf(\"identify_relationships_s.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_s ~ x, xlab = \"(a)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot hockey stick -------------------------------------------------\n\npdf(\"identify_relationships_hockey_stick.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_hockey_stick ~ x, xlab = \"(b)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot linear positive strong ---------------------------------------\n\npdf(\"identify_relationships_pos_lin_strong.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_pos_lin_strong ~ x, xlab = \"(c)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot weak positive ------------------------------------------------\n\npdf(\"identify_relationships_pos_weak.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_pos_weak ~ x, xlab = \"(d)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot weaker positive ----------------------------------------------\n\npdf(\"identify_relationships_pos_weaker.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_pos_weaker ~ x, xlab = \"(e)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()\n\n# plot negative linear ----------------------------------------------\n\npdf(\"identify_relationships_neg_lin_weak.pdf\", 5.5, 4.3)\n\npar(mar= c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_neg_lin_weak ~ x, xlab = \"(f)\", ylab = \"\",\n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/match_corr_1/match_corr_1.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# simulate data -----------------------------------------------------\n\nset.seed(1234)\n\nx <- seq(0, 6, by = 0.05)\n\ny_1_u <- (x-3)^2 - 4 + rnorm(length(x), mean = 0, sd = 1)\ny_2_strong_pos <- 3*x + 10 + rnorm(length(x), mean = 0, sd = 2)\ny_3_weak_pos <- 3*x + 10 + rnorm(length(x), mean = 0, sd = 10)\ny_4_weak_neg <- -3 * x + rnorm(length(x), mean = 0, sd = 5)\n\n# calculate correlations --------------------------------------------\n\nround(cor(x, y_1_u), 2)\nround(cor(x, y_2_strong_pos), 2)\nround(cor(x, y_3_weak_pos), 2)\nround(cor(x, y_4_weak_neg), 2)\n\n# plot -----------------------------------------------------\nwidth <- 4.5\nheight <- 3.7\ncex.lab <- 2\nmgp <- c(1.2,0.7,0)\nmar <- c(2.6,1,0.5,1)\npch <- 19\ncex <- 1.5\ncol <- COL[1, 2]\n\nMyPlot <- function(fn, x, y, i) {\n  myPDF(fn, width, height,\n      mar = mar, mgp = mgp, cex.lab = cex.lab)\n  plot(x, y,\n      xlab = paste0(\"(\", i, \")\"), ylab = \"\", \n      yaxt = \"n\", xaxt = \"n\", \n      pch = pch, col = col, cex = cex)\n  dev.off()\n}\nMyPlot(\"match_corr_1_u.pdf\", x, y_1_u, 1)\nMyPlot(\"match_corr_2_strong_pos.pdf\", x, y_2_strong_pos, 2)\nMyPlot(\"match_corr_3_weak_pos.pdf\", x, y_3_weak_pos, 3)\nMyPlot(\"match_corr_4_weak_neg.pdf\", x, y_4_weak_neg, 4)\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/match_corr_2/match_corr_2.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# simulate data -----------------------------------------------------\n\nset.seed(1234)\n\nx <- seq(0, 6, by = 0.05)\n\ny_1_strong_neg_curved <-  -0.5 * x^2 + x + rnorm(length(x), mean = 0, sd = 2)\ny_2_weak_pos <-  x + rnorm(length(x), mean = 0, sd = 3)\ny_3_n <- -(x-3)^2 - 4 + rnorm(length(x), mean = 0, sd = 0.98)\ny_4_weak_neg <- -3 * x + rnorm(length(x), mean = 0, sd = 10)\n\n# calculate correlations --------------------------------------------\n# note that these correlations are slightly off from\n# those in the textbook due to not having set a seed \n# when the figures were produced (to be fixed for 4th edition)\n\nround(cor(x, y_1_strong_neg_curved), 2)\nround(cor(x, y_2_weak_pos), 2)\nround(cor(x, y_3_n), 2)\nround(cor(x, y_4_weak_neg), 2)\n\n# plot -----------------------------------------------------\nwidth <- 4.5\nheight <- 3.7\ncex.lab <- 2\nmgp <- c(1.2,0.7,0)\nmar <- c(2.6,1,0.5,1)\npch <- 19\ncex <- 1.5\ncol <- COL[1, 2]\n\nMyPlot <- function(fn, x, y, i) {\n  myPDF(fn, width, height,\n      mar = mar, mgp = mgp, cex.lab = cex.lab)\n  plot(x, y,\n      xlab = paste0(\"(\", i, \")\"), ylab = \"\", \n      yaxt = \"n\", xaxt = \"n\", \n      pch = pch, col = col, cex = cex)\n  dev.off()\n}\nMyPlot(\"match_corr_1_strong_neg_curved.pdf\", x, y_1_strong_neg_curved, 1)\nMyPlot(\"match_corr_2_weak_pos.pdf\", x, y_2_weak_pos, 2)\nMyPlot(\"match_corr_3_n.pdf\", x, y_3_n, 3)\nMyPlot(\"match_corr_4_weak_neg.pdf\", x, y_4_weak_neg, 4)\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/match_corr_3/match_corr_2.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# simulate data -----------------------------------------------------\n\nset.seed(1234)\n\nx <- seq(0, 6, by = 0.05)\n\ny_1_strong_neg_curved <-  -0.5 * x^2 + x + rnorm(length(x), mean = 0, sd = 2)\ny_2_weak_pos <-  x + rnorm(length(x), mean = 0, sd = 3)\ny_3_n <- -(x-3)^2 - 4 + rnorm(length(x), mean = 0, sd = 0.98)\ny_4_weak_neg <- -3 * x + rnorm(length(x), mean = 0, sd = 10)\n\n# calculate correlations --------------------------------------------\n# note that these correlations are slightly off from\n# those in the textbook due to not having set a seed \n# when the figures were produced (to be fixed for 4th edition)\n\nround(cor(x, y_1_strong_neg_curved), 2)\nround(cor(x, y_2_weak_pos), 2)\nround(cor(x, y_3_n), 2)\nround(cor(x, y_4_weak_neg), 2)\n\n# plot strong negative curved ---------------------------------------\n\npdf(\"match_corr_1_strong_neg_curved.pdf\", 5.5, 4.3)\npar(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), cex.lab = 1.75)\nplot(y_1_strong_neg_curved ~ x, xlab = \"(1)\", ylab = \"\", \n     yaxt = \"n\", xaxt = \"n\", \n     pch=19, col=COL[1])\ndev.off()\n\n# plot weak positive ------------------------------------------------\n\npdf(\"match_corr_2_weak_pos.pdf\", 5.5, 4.3)\npar(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), cex.lab = 1.75)\nplot(y_2_weak_pos ~ x, xlab = \"(2)\", ylab = \"\", \n     yaxt = \"n\", xaxt = \"n\", \n     pch=19, col=COL[1])\ndev.off()\n\n# plot n-shaped -----------------------------------------------------\n\npdf(\"match_corr_3_n.pdf\", 5.5, 4.3)\npar(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), cex.lab = 1.75)\nplot(y_3_n ~ x, xlab = \"(3)\", ylab = \"\", \n     yaxt = \"n\", xaxt = \"n\", \n     pch=19, col=COL[1])\ndev.off()\n\n# plot weak negative ------------------------------------------------\n\npdf(\"match_corr_4_weak_neg.pdf\", 5.5, 4.3)\npar(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), cex.lab = 1.75)\nplot(y_4_weak_neg ~ x, xlab = \"(4)\", ylab = \"\", \n     yaxt = \"n\", xaxt = \"n\", \n     pch=19, col=COL[1])\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/match_corr_3/match_corr_3.R",
    "content": "library(openintro)\n\nset.seed(1)\nn.plots <- 16\n\nn <- 2 * round(runif(n.plots, 25, 75))\nb0 <- runif(n.plots, -50, 50)\nb1 <- runif(n.plots, -5, 5)\nb2 <- runif(n.plots, -0.5, 0.5)\nb3 <- runif(n.plots, -0.1, 0.1)\nx <- lapply(1:n.plots, function(i) {\n  c(runif(n[i] / 2, 0, 10), rnorm(n[i] / 2, 7, 2))\n})\ns <- runif(n.plots, 0.5, 20)\npow <- 2 * round(runif(n.plots, 0.5, 3) / 2, 1)\ny <- lapply(1:n.plots, function(i) {\n  noise <- rnorm(n[i], s[i])^pow[i]\n  if (any(is.nan(noise))) {\n    noise <- rnorm(n[i], s[i])\n  }\n  b0[i] + b1[i] * x[[i]] + b2[i] * x[[i]]^2 + b3[i] * x[[i]]^3 + noise\n})\nsapply(x, length)\nsapply(y, length)\n# par(mfrow = rep(sqrt(n.plots), 2))\ntmp <- sapply(1:n.plots, function(i) {\n  # plot(x[[i]], y[[i]])\n  cor(x[[i]], y[[i]])\n})\n\n\n\n\nthese <- c(3, 9, 11, 15)\ntmp[these]\n\nfor (j in 1:length(these)) {\n  i <- these[j]\n  myPDF(paste0(\"scatter_\", j, \".pdf\"),\n      4.5, 3.7,\n      mar = c(2.6, 1, 0.5, 1),\n      mgp = c(1.2, 0.7, 0),\n      cex.lab = 2)\n  plot(y[[i]] ~ x[[i]],\n      xlab = paste0(\"(\", j, \")\"), ylab = \"\", \n      yaxt = \"n\", xaxt = \"n\", \n      pch = 19, col = COL[1, 2], cex = 1.5)\n  dev.off()\n}\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/murders_poverty_reg/murders.csv",
    "content": "population,perc_pov,perc_unemp,annual_murders_per_mil\r587000,16.5,6.2,11.2\r643000,20.5,6.4,13.4\r635000,26.3,9.3,40.7\r692000,16.5,5.3,5.3\r1248000,19.2,7.3,24.8\r643000,16.5,5.9,12.7\r1964000,20.2,6.4,20.9\r1531000,21.3,7.6,35.7\r713000,17.2,4.9,8.7\r749000,14.3,6.4,9.6\r7895000,18.1,6,14.5\r762000,23.1,7.4,26.9\r2793000,19.1,5.8,15.7\r741000,24.7,8.6,36.2\r625000,18.6,6.5,18.1\r854000,24.9,8.3,28.9\r716000,17.9,6.7,14.9\r921000,22.4,8.6,25.8\r595000,20.2,8.4,21.7\r3353000,16.9,6.7,25.7"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/murders_poverty_reg/murders_poverty.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nmurders <- read.csv(\"murders.csv\")\n\n# model murders vs. poverty -----------------------------------------\n\nm_murders_poverty <- lm(murders$annual_murders_per_mil ~ murders$perc_pov)\n\nxtable(summary(m_murders_poverty), digits = 3)\n\nround(summary(m_murders_poverty)$r.squared, 4)\nround(summary(m_murders_poverty)$adj.r.squared, 4)\n  \n# plot murders vs. poverty ------------------------------------------\n\npdf(\"murders_poverty.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(murders$annual_murders_per_mil ~ murders$perc_pov, \n     xlab = \"Percent in Poverty\",\n     ylab = \"Annual Murders per Million\", \n     pch = 19, col = COL[1],\n     xlim = c(14, 27), ylim = c(5, 40), axes = FALSE)\nAxisInPercent(1, at = seq(14, 26, 4))\naxis(2, at = seq(10, 40, 10))\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/outliers_1/outliers_1.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# simulate data -----------------------------------------------------\n\nset.seed(83629)\n\nx <- seq(1,50,1)\n\ny <- -2 * x + 20 + rnorm(length(x), mean = 0, sd = 10)\n\nx_influential <- c(x[1:49], 200)\n\ny_leverage <- c(y[1:49], -370)\n\ny_outlier <- c(y[1:25], y[26]+100, y[27:50])\n\n# plot influential -------------------------------------------------\n\npdf(\"outliers_1_influential.pdf\", width = 4, height = 3)\n\npar(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)\n\nplot(y ~ x_influential, \n     pch = 19, col = COL[1,2], \n     xlab = \"(a)\", ylab = \"\", \n     xaxt = \"n\", yaxt = \"n\")\n\nm_influential = lm(y ~ x_influential)\nabline(m_influential, col = COL[2])\n\ndev.off()\n\n# plot leverage ----------------------------------------------------\n\npdf(\"outliers_2_leverage.pdf\", width = 4, height = 3)\n\npar(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)\n\nplot(y_leverage ~ x_influential, \n     pch = 19, col = COL[1,2], \n     xlab = \"(b)\", ylab = \"\", \n     xaxt = \"n\", yaxt = \"n\")\n\nm_leverage = lm(y_leverage ~ x_influential)\nabline(m_leverage, col = COL[2])\n\ndev.off()\n\n# plot outlier -----------------------------------------------------\n\npdf(\"outliers_3_outlier.pdf\", width = 4, height = 3)\n\npar(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)\n\nplot(y_outlier ~ x, \n     pch = 19, col = COL[1,2], \n     xlab = \"(c)\", ylab = \"\", \n     xaxt = \"n\", yaxt = \"n\")\n\nm_outlier = lm(y_outlier ~ x)\nabline(m_outlier, col = COL[2])\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/outliers_2/outliers_2.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# simulate data -----------------------------------------------------\n\nset.seed(83629)\n\nx <- seq(1,50,1)\n\ny <- 3 * x + 3 + rnorm(length(x), mean = 0, sd = 10)\n\nx_influential <- c(x[1:49], -50)\n\ny_influential <- c(y[1:49], -300)\n\ny_outlier <- c(y[1:25], y[26]+100, y[27:50])\n\n# plot influential -------------------------------------------------\n\npdf(\"outliers_1_influential.pdf\", width = 4, height = 3)\n\npar(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)\n\nplot(y ~ x_influential, \n     pch = 19, col = COL[1,2], \n     xlab = \"(a)\", ylab = \"\", \n     xaxt = \"n\", yaxt = \"n\")\n\nm_influential = lm(y ~ x_influential)\nabline(m_influential, col = COL[2])\n\ndev.off()\n\n# plot another influential ------------------------------------------\n\npdf(\"outliers_2_influential.pdf\", width = 4, height = 3)\n\npar(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)\n\nplot(y_influential ~ x_influential, \n     pch = 19, col = COL[1,2], \n     xlab = \"(b)\", ylab = \"\", \n     xaxt = \"n\", yaxt = \"n\")\n\nm_influential = lm(y_influential ~ x_influential)\nabline(m_influential, col = COL[2])\n\ndev.off()\n\n# plot outlier -----------------------------------------------------\n\npdf(\"outliers_3_outlier.pdf\", width = 4, height = 3)\n\npar(mar = c(2.4, 0.5, 0.2, 0.5), las = 1, mgp = c(1, 0.7, 0), cex.lab = 1.5)\n\nplot(y_outlier ~ x, \n     pch = 19, col = COL[1,2], \n     xlab = \"(c)\", ylab = \"\", \n     xaxt = \"n\", yaxt = \"n\")\n\nm_outlier = lm(y_outlier ~ x)\nabline(m_outlier, col = COL[2])\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/rate_my_prof/prof_evals_beauty.csv",
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  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/rate_my_prof/rate_my_prof.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(xtable)\n\n# load data ---------------------------------------------------------\n\nprof_evals_beauty <- read.csv(\"prof_evals_beauty.csv\")\n\n# rename variables for convenience ----------------------------------\n\nbeauty <- prof_evals_beauty$btystdave\neval <- prof_evals_beauty$courseevaluation\n\n# model evaluation scores vs. beauty --------------------------------\n\nm_eval_beauty = lm(eval ~ beauty)\n\nxtable(summary(m_eval_beauty))\n\n# scatterplot of evaluation scores vs. beauty -----------------------\n\npdf(\"rate_my_prof_eval_beauty.pdf\", 5.5, 4.3)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5, las = 1)\n\nplot(eval ~ beauty, \n     xlab = \"Beauty\", ylab = \"Teaching evaluation\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE)\naxis(1, at = seq(-1, 2, 1))\naxis(2, at = seq(2, 5, 1))\nbox()\n\ndev.off()\n\n# residuals plot ----------------------------------------------------\n\npdf(\"rate_my_prof_residuals.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), las = 0, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5, las = 1)\n\nplot(m_eval_beauty$residuals ~ beauty, \n     xlab = \"Beauty\", ylab = \"Residuals\", \n     pch = 19, col = COL[1,2], \n     ylim = c(-1.82, 1.82), axes = FALSE)\naxis(1, at = seq(-1, 2, 1))\naxis(2, at = seq(-1, 1, 1))\nbox()\nabline(h = 0, lty = 3)\n\ndev.off()\n\n# residuals histogram -----------------------------------------------\n\npdf(\"rate_my_prof_residuals_hist.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3, 0, 0), cex.lab = 1.5, cex.axis = 1.5)\n\nhist(m_eval_beauty$residuals, \n     xlab = \"Residuals\", ylab = \"\", main = \"\",\n     col = COL[1],\n     xlim = c(-2,2))\n\ndev.off()\n\n# normal probability plot of residuals ------------------------------\n\npdf(\"rate_my_prof_residuals_qq.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nqqnorm(m_eval_beauty$residuals, \n       pch = 19, col = COL[1,2],\n       main = \"\", las = 0)\nqqline(m_eval_beauty$residuals, col = COL[1])\n\ndev.off()\n\n# order of residuals ---------------------------------------------===\n\npdf(\"rate_my_prof_residuals_order.pdf\", height = 5, width = 5)\n\npar(mar = c(3.9, 3.9, 0.5, 0.5), mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_eval_beauty$residuals, \n     xlab = \"Order of data collection\", ylab = \"Residuals\", main = \"\",\n     pch = 19, col = COL[1,2],\n     ylim = c(-1.82, 1.82), axes = FALSE)\naxis(1)\naxis(2, at = seq(-1, 1, 1))\nbox()\n\nabline(h = 0, lty = 3)\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/speed_height_gender/speed_height_gender.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nspeed_survey <- read.csv(\"speed_survey.csv\")\n\n# assign colors and plotting characters to gender -------------------\n\nspeed_survey$col[speed_survey$gender == \"female\"] <- COL[4]\nspeed_survey$col[speed_survey$gender == \"male\"] <- COL[2]\n\nspeed_survey$pch[speed_survey$gender == \"female\"] <- 4\nspeed_survey$pch[speed_survey$gender == \"male\"] <- 19\n\n# plot speed vs. height ---------------------------------------------\n\npdf(\"speed_height.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\n\nplot(speed_survey$speed ~ speed_survey$height, \n     xlab = \"Height (in inches)\", ylab = \"Fastest speed (in mph)\", \n     pch = 19, col = COL[1,2], \n     axes = FALSE, ylim = c(0,150))\naxis(1, at = seq(50, 80, 10))\naxis(2, at = seq(0, 150, 50))\nbox()\n\ndev.off()\n\n# plot speed vs. height vs. gender ----------------------------------\n\npdf(\"speed_height_gender.pdf\", 5.5, 4.3)\n\npar(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\n\nplot(speed_survey$speed ~ speed_survey$height, \n     xlab = \"Height (in inches)\", ylab = \"Fastest speed (in mph)\", \n     pch = speed_survey$pch, col = speed_survey$col, \n     axes = FALSE, ylim = c(0,150))\naxis(1, at = seq(50, 80, 10))\naxis(2, at = seq(0, 150, 50))\nbox()\n\nlegend(\"bottomright\", inset = 0.05, \n       col = c(COL[4],COL[2]), \n       pch = c(4,19), legend = c(\"female\", \"male\"))\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/speed_height_gender/speed_survey.csv",
    "content": 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  {
    "path": "ch_regr_simple_linear/figures/eoce/starbucks_cals_carbos/starbucks.csv",
    "content": "item,calories,fat,carb,fiber,protein,type\r8-Grain Roll,350,8,67,5,10,bakery\rApple Bran Muffin,350,9,64,7,6,bakery\rApple Fritter,420,20,59,0,5,bakery\rBanana Nut Loaf,490,19,75,4,7,bakery\rBirthday Cake Mini Doughnut,130,6,17,0,0,bakery\rBlueberry Oat Bar,370,14,47,5,6,bakery\rBlueberry Scone,460,22,61,2,7,bakery\rBountiful Blueberry Muffin,370,14,55,0,6,bakery\rButter Croissant ,310,18,32,0,5,bakery\rCheese Danish,420,25,39,0,7,bakery\rChocolate Chunk Cookie,380,17,51,2,4,bakery\rChocolate Cinnamon Bread,320,12,53,3,6,bakery\rChocolate Croissant,300,17,34,2,5,bakery\rChocolate Old-Fashioned Doughnut,420,21,57,2,5,bakery\rChonga Bagel,310,5,52,3,12,bakery\rCinnamon Chip Scone,480,18,70,3,7,bakery\rCranberry Orange Scone,490,18,73,2,8,bakery\rDouble Chocolate Brownie,410,24,46,3,6,bakery\rDouble Fudge Mini Doughnut,130,7,16,0,0,bakery\rEverything with Cheese Bagel,280,2,56,2,10,bakery\rGinger Molasses Cookie,360,12,58,0,3,bakery\rIced Lemon Pound Cake,490,23,67,0,5,bakery\rMallorca Sweet Bread,420,25,42,0,7,bakery\rMaple Oat Pecan Scone ,440,18,59,3,8,bakery\rMarble Pound Cake,350,13,54,0,6,bakery\rMarshmallow Dream Bar,210,4,43,0,0,bakery\rMorning Bun,350,16,45,2,6,bakery\rMultigrain Bagel,300,3,60,6,15,bakery\rOld-Fashioned Glazed Doughnut,420,21,57,0,4,bakery\rOutrageous Oatmeal Cookie,370,14,56,3,5,bakery\rPetite Vanilla Bean Scone,140,5,21,0,0,bakery\rPlain Bagel,280,1,59,2,9,bakery\rPumpkin Bread,390,14,61,2,6,bakery\rPumpkin Scone ,480,17,78,2,6,bakery\rRaspberry Scone,480,25,59,3,8,bakery\rRaspberry Swirl Pound Cake,430,16,69,0,4,bakery\rReduced-Fat Banana Chocolate Chip Coffee Cake,400,8,80,4,5,bakery\rReduced-Fat Cinnamon Swirl Coffee Cake,340,9,62,2,4,bakery\rReduced-Fat Very Berry Coffee Cake ,350,10,59,4,7,bakery\rStarbucks Classic Coffee Cake,440,19,63,0,6,bakery\rZucchini Walnut Muffin ,490,28,52,2,7,bakery\rCheese & Fruit,480,28,39,6,18,bistro box\rChicken & Hummus,270,8,29,6,16,bistro box\rChicken Lettuce Wraps,360,19,32,4,17,bistro box\rChipotle Chicken Wraps,380,15,35,6,26,bistro box\rProtein,380,19,37,5,13,bistro box\rSalumi & Cheese,420,26,22,3,25,bistro box\rSesame Noodles,350,11,50,6,15,bistro box\rTuna Salad,380,21,25,5,23,bistro box\rApple Pie,180,7,27,0,2,petite\rBirthday Cake Pop,170,9,22,0,0,petite\rBrown Sugar Walnut Tart,190,12,24,0,2,petite\rCherry Pie,170,7,24,0,2,petite\rChocolate Crme Whoopie Pie,190,11,23,0,0,petite\rChocolate Hazelnut Tart,180,10,23,0,2,petite\rRaspberry Truffle Cake Pop,160,8,24,0,2,petite\rRed Velvet Whoopie Pie,190,11,21,0,0,petite\rTiramisu Cake Pop,170,9,22,0,0,petite\rBacon & Gouda Artisan Breakfast Sandwich,350,18,30,0,17,hot breakfast\rChicken Sausage Breakfast Wrap,300,10,33,5,14,hot breakfast\rHam & Cheddar Artisan Breakfast Sandwich,350,16,31,0,20,hot breakfast\rSausage & Cheddar Classic Breakfast Sandwich,500,28,41,0,19,hot breakfast\rSpinach & Feta Breakfast Wrap,290,10,33,6,19,hot breakfast\rStarbucks Perfect Oatmeal,140,2.5,25,4,5,hot breakfast\rTurkey Bacon & White Cheddar Classic Breakfast Sandwich,320,7,43,3,18,hot breakfast\rVeggie & Monterey Jack Artisan Breakfast Sandwich,350,18,30,0,17,hot breakfast\rDeluxe Fruit Blend,80,0,20,2,0,salad\rChicken Santa Fe Panini,400,11,47,2,26,sandwich\rEgg Salad Sandwich ,460,27,37,5,22,sandwich\rHam & Swiss Panini,360,9,43,2,28,sandwich\rRoasted Tomato & Mozzarella Panini,390,18,44,3,15,sandwich\rRoasted Vegetable Panini,350,12,48,4,13,sandwich\rTarragon Chicken Salad Sandwich,420,13,46,6,32,sandwich\rTurkey & Swiss Sandwich,390,13,36,2,34,sandwich\rGreek Yogurt Honey Parfait,300,12,44,0,8,parfait\rPeach Raspberry Yogurt Parfait,300,4,57,3,10,parfait\rStrawberry & Blueberry Yogurt Parfait,300,3.5,60,3,7,parfait"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/starbucks_cals_carbos/starbucks_cals_carbos.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nstarbucks <- read.csv(\"starbucks.csv\")\n\n# model calories vs. carbos -----------------------------------------\n\nm_carb_cals <- lm(carb ~ calories, data = starbucks)\n\n# plot calories vs. carbos ------------------------------------------\n\npdf(\"starbucks_cals_carbos.pdf\", 5.5, 4.3)\n\npar(mar = c(3.5, 4, 1, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(carb ~ calories, data = starbucks, \n     pch = 19, col = COL[1,2], \n     xlab = \"Calories\", ylab = \"Carbs (grams)\", axes = FALSE)\naxis(1)\naxis(2, at = seq(20, 80, 20))\nbox()\n\nabline(m_carb_cals, col = COL[2], lwd = 2)\n\ndev.off()\n\n# plot residuals ----------------------------------------------------\n\npdf(\"starbucks_cals_carbos_residuals.pdf\", 5.5, 4.3)\n\npar(mar = c(3.5, 4, 1, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(m_carb_cals$residuals ~ starbucks$calories,\n     xlab = \"Calories\", ylab = \"Residuals\", \n     col = COL[1,2], pch = 19,\n     ylim = c(-30, 30), axes = FALSE)\naxis(1)\naxis(2, at = seq(-20, 20, 20))\nbox()\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# histogram of residuals --------------------------------------------\n\npdf(\"starbucks_cals_carbos_residuals_hist.pdf\", 5.5, 4.3)\n\npar(mar = c(3.5, 2.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nhist(m_carb_cals$residuals,\n     col = COL[1], \n     xlab = \"Residuals\", ylab = \"\", main = \"\", \n     axes = FALSE, xlim = c(-40,40))\naxis(1, at = seq(-40, 40, 20))\naxis(2)\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/starbucks_cals_protein/starbucks.csv",
    "content": "item,calories,fat,carb,fiber,protein,type\r8-Grain Roll,350,8,67,5,10,bakery\rApple Bran Muffin,350,9,64,7,6,bakery\rApple Fritter,420,20,59,0,5,bakery\rBanana Nut Loaf,490,19,75,4,7,bakery\rBirthday Cake Mini Doughnut,130,6,17,0,0,bakery\rBlueberry Oat Bar,370,14,47,5,6,bakery\rBlueberry Scone,460,22,61,2,7,bakery\rBountiful Blueberry Muffin,370,14,55,0,6,bakery\rButter Croissant ,310,18,32,0,5,bakery\rCheese Danish,420,25,39,0,7,bakery\rChocolate Chunk Cookie,380,17,51,2,4,bakery\rChocolate Cinnamon Bread,320,12,53,3,6,bakery\rChocolate Croissant,300,17,34,2,5,bakery\rChocolate Old-Fashioned Doughnut,420,21,57,2,5,bakery\rChonga Bagel,310,5,52,3,12,bakery\rCinnamon Chip Scone,480,18,70,3,7,bakery\rCranberry Orange Scone,490,18,73,2,8,bakery\rDouble Chocolate Brownie,410,24,46,3,6,bakery\rDouble Fudge Mini Doughnut,130,7,16,0,0,bakery\rEverything with Cheese Bagel,280,2,56,2,10,bakery\rGinger Molasses Cookie,360,12,58,0,3,bakery\rIced Lemon Pound Cake,490,23,67,0,5,bakery\rMallorca Sweet Bread,420,25,42,0,7,bakery\rMaple Oat Pecan Scone ,440,18,59,3,8,bakery\rMarble Pound Cake,350,13,54,0,6,bakery\rMarshmallow Dream Bar,210,4,43,0,0,bakery\rMorning Bun,350,16,45,2,6,bakery\rMultigrain Bagel,300,3,60,6,15,bakery\rOld-Fashioned Glazed Doughnut,420,21,57,0,4,bakery\rOutrageous Oatmeal Cookie,370,14,56,3,5,bakery\rPetite Vanilla Bean Scone,140,5,21,0,0,bakery\rPlain Bagel,280,1,59,2,9,bakery\rPumpkin Bread,390,14,61,2,6,bakery\rPumpkin Scone ,480,17,78,2,6,bakery\rRaspberry Scone,480,25,59,3,8,bakery\rRaspberry Swirl Pound Cake,430,16,69,0,4,bakery\rReduced-Fat Banana Chocolate Chip Coffee Cake,400,8,80,4,5,bakery\rReduced-Fat Cinnamon Swirl Coffee Cake,340,9,62,2,4,bakery\rReduced-Fat Very Berry Coffee Cake ,350,10,59,4,7,bakery\rStarbucks Classic Coffee Cake,440,19,63,0,6,bakery\rZucchini Walnut Muffin ,490,28,52,2,7,bakery\rCheese & Fruit,480,28,39,6,18,bistro box\rChicken & Hummus,270,8,29,6,16,bistro box\rChicken Lettuce Wraps,360,19,32,4,17,bistro box\rChipotle Chicken Wraps,380,15,35,6,26,bistro box\rProtein,380,19,37,5,13,bistro box\rSalumi & Cheese,420,26,22,3,25,bistro box\rSesame Noodles,350,11,50,6,15,bistro box\rTuna Salad,380,21,25,5,23,bistro box\rApple Pie,180,7,27,0,2,petite\rBirthday Cake Pop,170,9,22,0,0,petite\rBrown Sugar Walnut Tart,190,12,24,0,2,petite\rCherry Pie,170,7,24,0,2,petite\rChocolate Crme Whoopie Pie,190,11,23,0,0,petite\rChocolate Hazelnut Tart,180,10,23,0,2,petite\rRaspberry Truffle Cake Pop,160,8,24,0,2,petite\rRed Velvet Whoopie Pie,190,11,21,0,0,petite\rTiramisu Cake Pop,170,9,22,0,0,petite\rBacon & Gouda Artisan Breakfast Sandwich,350,18,30,0,17,hot breakfast\rChicken Sausage Breakfast Wrap,300,10,33,5,14,hot breakfast\rHam & Cheddar Artisan Breakfast Sandwich,350,16,31,0,20,hot breakfast\rSausage & Cheddar Classic Breakfast Sandwich,500,28,41,0,19,hot breakfast\rSpinach & Feta Breakfast Wrap,290,10,33,6,19,hot breakfast\rStarbucks Perfect Oatmeal,140,2.5,25,4,5,hot breakfast\rTurkey Bacon & White Cheddar Classic Breakfast Sandwich,320,7,43,3,18,hot breakfast\rVeggie & Monterey Jack Artisan Breakfast Sandwich,350,18,30,0,17,hot breakfast\rDeluxe Fruit Blend,80,0,20,2,0,salad\rChicken Santa Fe Panini,400,11,47,2,26,sandwich\rEgg Salad Sandwich ,460,27,37,5,22,sandwich\rHam & Swiss Panini,360,9,43,2,28,sandwich\rRoasted Tomato & Mozzarella Panini,390,18,44,3,15,sandwich\rRoasted Vegetable Panini,350,12,48,4,13,sandwich\rTarragon Chicken Salad Sandwich,420,13,46,6,32,sandwich\rTurkey & Swiss Sandwich,390,13,36,2,34,sandwich\rGreek Yogurt Honey Parfait,300,12,44,0,8,parfait\rPeach Raspberry Yogurt Parfait,300,4,57,3,10,parfait\rStrawberry & Blueberry Yogurt Parfait,300,3.5,60,3,7,parfait"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/starbucks_cals_protein/starbucks_cals_protein.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\nstarbucks <- read.csv(\"starbucks.csv\")\n\n# lmPlot protein vs. calories ---------------------------------------\n\nmyPDF(\"starbucks_cals_protein.pdf\", 5, 4.55)\n\nlmPlot(starbucks$calories, starbucks$protein,\n       col = COL[1,2], \n       xlab = \"Calories\", ylab = \"Protein (grams)\", \n       lCol = COL[2], lwd = 2, \n       resSymm = TRUE, resAxis = 3, \n       xAxis = 6,\n       cex.lab = 1.25, cex.axis = 1.25, mgp = c(2.1, 0.7, 0))\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/tourism_spending_reg_conds/tourism_spending.csv",
    "content": "year,visitor_count_thousand,tourist_spending\r1963,198,7\r1964,229,8\r1965,361,13\r1966,449,12\r1967,574,13\r1968,602,24\r1969,694,36\r1970,724,51\r1971,926,62\r1972,1034,103\r1973,1341,171\r1974,1110,193\r1975,1540,200\r1976,1675,180\r1977,1661,204\r1978,1644,230\r1979,1523,280\r1980,1288,326\r1981,1405,381\r1982,1391,370\r1983,1625,411\r1984,2117,840\r1985,2614,1482\r1986,2391,1215\r1987,2855,1721\r1988,4172,2355\r1989,4459,2556\r1990,5389,2705\r1991,5517,2654\r1992,7076,3639\r1993,6500,3959\r1994,6670,4321\r1995,7726,4957\r1996,8614,5650\r1997,9689,7008\r1998,9752,7177\r1999,7464,5193\r2000,10412,7636\r2001,11569,8090\r2002,13247,8481\r2003,14030,9677\r2004,17517,12125\r2005,21124,13929\r2006,19820,12554\r2007,23341,13990\r2008,26337,16761\r2009,27077,15853"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/tourism_spending_reg_conds/tourism_spending_reg_cond.R",
    "content": "rm(list = ls())\nlibrary(openintro)\n\ntourism$visitor_count <- 1e3 * tourism$visitor_count_tho\ntourism$tourist_spending <- 1e6 * tourism$tourist_spending\n\nm_spending_count <- lm(tourist_spending ~ visitor_count,\n                       data = tourism)\n\n\n# plot spending vs. count -------------------------------------------\n\nmyPDF(\n  \"tourism_spending_count.pdf\",\n  5.5, 4.3,\n  mar = c(3.5, 5.5, 1, 0.5),\n  mgp = c(2.5, 0.7, 0), \n  cex.lab = 1.5,\n  cex.axis = 1.5\n)\n\nplot(tourist_spending ~ visitor_count,\n     data = tourism, \n     col = COL[1,2], \n     xlab = \"Number of Tourists\",\n     ylab = \"\",\n     pch = 19,\n     axes = FALSE)\nat <- seq(0, 25e6, 5e6)\naxis(1, at = at, labels = paste0(at / 1e6, \"m\"))\nAxisInDollars(2, at = seq(0, 15e9, 5e9))\npar(las = 0)\nmtext(\"Spending\", 2, 4.2, cex = 1.5)\n\nabline(m_spending_count, col = COL[2], lwd = 2)\n\ndev.off()\n\n# plot residuals ----------------------------------------------------\n\nmyPDF(\n  \"tourism_spending_count_residuals.pdf\",\n  5.5, 4.3,\n  mar = c(3.5, 5.5, 1, 0.5),\n  mgp = c(2.5, 0.7, 0), \n  cex.lab = 1.5,\n  cex.axis = 1.5\n)\n\nplot(\n  tourism$visitor_count,\n  m_spending_count$residuals,\n  xlab = \"Number of Tourists\",\n  ylab = \"Residuals\", \n  col = COL[1,2], pch = 19,\n  ylim = c(-1600e6, 1600e6), axes = FALSE\n)\nat <- seq(0, 25e6, 5e6)\naxis(1, at = at, labels = paste0(at / 1e6, \"m\"))\naxis(2, at = seq(-1e9, 1e9, 1e9), labels = c(\"-$1b\", \"$0\", \"$1b\"))\n\nabline(h = 0, lty = 2)\n\ndev.off()\n\n# histogram of residuals --------------------------------------------\n\nmyPDF(\n  \"tourism_spending_count_residuals_hist.pdf\",\n  5.5, 4.3,\n  mar = c(3.7, 4, 1, 0.5),\n  mgp = c(2.5, 0.7, 0), \n  cex.lab = 1.5,\n  cex.axis = 1.5\n)\n\nhist(m_spending_count$residuals,\n     col = COL[1], \n     xlab = \"Residuals\", ylab = \"Count\", main = \"\", \n     axes = FALSE, ylim = c(0,20))\naxis(1, at = seq(-2e9, 2e9, 1e9), labels = c(\"-$2b\", \"-$1b\", \"$0\", \"$1b\", \"$2b\"))\naxis(2, c(0, 10, 20))\nabline(h = 0)\n\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/trees_volume_height_diameter/trees_volume_height_diameter.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\n\ndata(trees)\n\n# plot volume vs. height ---------------------------------------------\n\npdf(\"trees_volume_height.pdf\", 5, 4)\n\npar(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\n\nplot(trees$Volume ~ trees$Height, \n     xlab = \"Height (feet)\", ylab = \"Volume (cubic feet)\", \n     pch = 19, col = COL[1], axes = FALSE,\n     xlim = c(60, 90),\n     ylim = 1.1 * range(0, trees$Volume))\naxis(1, at = seq(60, 90, 10))\naxis(2)\nbox()\n\ndev.off()\n\n# plot volume vs. diameter ---------------------------------------------\n\npdf(\"trees_volume_diameter.pdf\", 5, 4)\n\npar(mar = c(3.7, 3.7, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.25, cex.axis = 1.25)\n\nplot(trees$Volume ~ trees$Girth, \n     xlab = \"Diameter (inches)\", ylab = \"Volume (cubic feet)\", \n     pch = 19, col = COL[1], axes = FALSE,\n     xlim = c(7, 21),\n     ylim = 1.1 * range(0, trees$Volume))\naxis(1, at = seq(8,20,4))\naxis(2)\nbox()\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/trends_in_residuals/trends_in_residuals.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# simulate data -----------------------------------------------------\n\nset.seed(8313)\n\nx = seq(1:300)\ny_fan = rep(NA,300)\nfor(i in 1:300){\n\ty_fan[i] = x[i]+rnorm(1)*x[i]\n}\ny_log = log(x) + rnorm(300, mean = 0, s = 0.5)\n\n# fit models --------------------------------------------------------\n\nm_fan = lm(y_fan ~ x)\nm_log = lm(y_log ~ x)\n\n# plot fan residuals ------------------------------------------------\n\npdf(\"trends_in_residuals_fan.pdf\", 5.5, 2)\n\npar(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(m_fan$res ~ x, xlab = \"(a)\", ylab = \"\", \n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\nabline(h = 0, lty = 2, lwd = 2)\n\ndev.off()\n\n# plot log residuals ------------------------------------------------\n\npdf(\"trends_in_residuals_log.pdf\", 5.5, 2)\n\npar(mar = c(2,1,1,1), las = 1, mgp = c(0.9,0.7,0), \n    cex.lab = 1.75, cex.axis = 1.75)\n\nplot(m_log$res ~ x, xlab = \"(b)\", ylab = \"\", \n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\nabline(h = 0, lty = 2, lwd = 2)\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/urban_homeowners_cond/urban_homeowners_cond.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load packages -----------------------------------------------------\nurban_state_data <- read.csv(\"urban_state_data.csv\")\n\n# drop outlier DC ---------------------------------------------------\n\nurban_state_data_noDC <- urban_state_data[urban_state_data$state != \"District of Columbia\",]\n\n# lmPlot of % urban vs. % owner without DC --------------------------\n\npdf(\"urban_homeowners_cond.pdf\", 5.5, 6)\n\nlmPlot(urban_state_data_noDC$poppct_urban, \n       urban_state_data_noDC$pct_owner_occupied, \n       col = COL[1,2], \n       xlab = \"% Urban population\", ylab = \"% Who own home\", \n       lCol = COL[2], lwd = 2, \n       resSymm = TRUE, \n       resAxis = 3, xAxis = 5, yAxis = 5, \n       cex.lab = 1.5, cex.axis = 1.5)\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/urban_homeowners_cond/urban_state_data.csv",
    "content": "state,total_housing_units_2000,total_housing_units_2010,pct_vacant,occupied,pct_owner_occupied,pop_st,area_st,pop_urban,poppct_urban,area_urban,areapct_urban,popden_urban,pop_ua,poppct_urban,area_ua,areapct_ua,popden_ua,pop_uc,poppct_uc,area_uc,areapct_uc,popden_uc,pop_rural,poppct_rural,area_rural,areapct_rural,popden_rural\rAlabama,\"1,963,711\",\"2,171,853\",13.3,\"1,883,791\",69.7,4779736,1.31171E+11,2821804,59.04,5716365701,4.36,1278.5,2325304,48.65,4415733067,3.37,1363.9,496500,10.39,1300632634,0.99,988.7,1957932,40.96,1.25454E+11,95.64,40.4\rAlaska,\"260,978\",\"306,967\",15.9,\"258,058\",63.1,710231,1.47795E+12,468893,66.02,673703920,0.05,1802.6,315756,44.46,362866427,0.02,2253.7,153137,21.56,310837493,0.02,1276,241338,33.98,1.47728E+12,99.95,0.4\rArizona,\"2,189,189\",\"2,844,526\",16.3,\"2,380,990\",66,6392017,2.94207E+11,5740659,89.81,5663221936,1.92,2625.4,5117783,80.07,4696616836,1.6,2822.2,622876,9.74,966605100,0.33,1669,651358,10.19,2.88544E+11,98.08,5.8\rArkansas,\"1,173,043\",\"1,316,299\",12.9,\"1,147,084\",67,2915918,1.34771E+11,1637589,56.16,2841198188,2.11,1492.8,1152837,39.54,1881196989,1.4,1587.2,484752,16.62,960001199,0.71,1307.8,1278329,43.84,1.3193E+11,97.89,25.1\rCalifornia,\"12,214,549\",\"13,680,081\",8.1,\"12,577,498\",55.9,37253956,4.03466E+11,35373606,94.95,21287926350,5.28,4303.7,33427689,89.73,18915178185,4.69,4577.1,1945917,5.22,2372748165,0.59,2124.1,1880350,5.05,3.82178E+11,94.72,12.7\rColorado,\"1,808,037\",\"2,212,898\",10.8,\"1,972,868\",65.5,5029196,2.68431E+11,4332761,86.15,3956737225,1.47,2836.1,3865471,76.86,3258048896,1.21,3072.9,467290,9.29,698688329,0.26,1732.2,696435,13.85,2.64475E+11,98.53,6.8\rConnecticut,\"1,385,975\",\"1,487,891\",7.9,\"1,371,087\",67.5,3574097,12541641427,3144942,87.99,4730500209,37.72,1721.9,3031980,84.83,4500564038,35.88,1744.8,112962,3.16,229936171,1.83,1272.4,429155,12.01,7811141218,62.28,142.3\rDelaware,\"343,072\",\"405,885\",15.7,\"342,297\",72.1,897934,5046703785,747949,83.3,1053792304,20.88,1838.3,616982,68.71,740579379,14.67,2157.7,130967,14.59,313212925,6.21,1083,149985,16.7,3992911481,79.12,97.3\rDistrict of Columbia,\"274,845\",\"296,719\",10.1,\"266,707\",42,601723,158114680,601723,100,158114680,100,9856.5,601723,100,158114680,100,9856.5,0,0,0,0,,0,0,0,0,\rFlorida,\"7,302,947\",\"8,989,580\",17.5,\"7,420,802\",67.4,18801310,1.38887E+11,17139844,91.16,19173902265,13.81,2315.2,16439936,87.44,17700464722,12.74,2405.5,699908,3.72,1473437543,1.06,1230.3,1661466,8.84,1.19714E+11,86.19,35.9\rGeorgia,\"3,281,737\",\"4,088,801\",12.3,\"3,585,584\",65.7,9687653,1.48959E+11,7272151,75.07,12423724190,8.34,1516,6334271,65.38,10239668028,6.87,1602.2,937880,9.68,2184056162,1.47,1112.2,2415502,24.93,1.36536E+11,91.66,45.8\rHawaii,\"460,542\",\"519,508\",12.4,\"455,338\",57.7,1360301,16634529975,1250489,91.93,1018212915,6.12,3180.8,972075,71.46,585035739,3.52,4303.4,278414,20.47,433177176,2.6,1664.7,109812,8.07,15616317060,93.88,18.2\rIdaho,\"527,824\",\"667,796\",13.2,\"579,408\",69.9,1567582,2.14045E+11,1106370,70.58,1292606730,0.6,2216.8,791843,50.51,886257981,0.41,2314.1,314527,20.06,406348749,0.19,2004.7,461212,29.42,2.12752E+11,99.4,5.6\rIllinois,\"4,885,615\",\"5,296,715\",8.7,\"4,836,972\",67.5,12830632,1.43793E+11,11353553,88.49,10218955838,7.11,2877.6,10260671,79.97,8610185823,5.99,3086.5,1092882,8.52,1608770015,1.12,1759.5,1477079,11.51,1.33574E+11,92.89,28.6\rIndiana,\"2,532,319\",\"2,795,541\",10.5,\"2,502,154\",69.9,6483802,92789193658,4697100,72.44,6540696730,7.05,1860,3836584,59.17,5187412076,5.59,1915.5,860516,13.27,1353284654,1.46,1646.9,1786702,27.56,86248496928,92.95,53.7\rIowa,\"1,232,511\",\"1,336,417\",8.6,\"1,221,576\",72.1,3046355,1.44669E+11,1950256,64.02,2468980575,1.71,2045.8,1268964,41.66,1507132351,1.04,2180.7,681292,22.36,961848224,0.66,1834.5,1096099,35.98,1.422E+11,98.29,20\rKansas,\"1,131,200\",\"1,233,215\",9.8,\"1,112,096\",67.8,2853118,2.11754E+11,2116961,74.2,2519183616,1.19,2176.5,1431424,50.17,1623806507,0.77,2283.1,685537,24.03,895377109,0.42,1983,736157,25.8,2.09235E+11,98.81,9.1\rKentucky,\"1,750,927\",\"1,927,164\",10.8,\"1,719,965\",68.7,4339367,1.02269E+11,2533343,58.38,3653655859,3.57,1795.8,1778528,40.99,2207361288,2.16,2086.8,754815,17.39,1446294571,1.41,1351.7,1806024,41.62,98615485782,96.43,47.4\rLouisiana,\"1,847,181\",\"1,964,981\",12,\"1,728,360\",67.2,4533372,1.11898E+11,3317805,73.19,5097451640,4.56,1685.8,2780406,61.33,4010132099,3.58,1795.8,537399,11.85,1087319541,0.97,1280.1,1215567,26.81,1.068E+11,95.44,29.5\rMaine,\"651,901\",\"721,830\",22.8,\"557,219\",71.3,1328361,79882800680,513542,38.66,931423305,1.17,1428,348137,26.21,616415489,0.77,1462.8,165405,12.45,315007816,0.39,1360,814819,61.34,78951377375,98.83,26.7\rMaryland,\"2,145,283\",\"2,378,814\",9.3,\"2,156,411\",67.5,5773552,25141638381,5034331,87.2,5191942757,20.65,2511.4,4822869,83.53,4767985793,18.96,2619.8,211462,3.66,423956964,1.69,1291.8,739221,12.8,19949695624,79.35,96\rMassachusetts,\"2,621,989\",\"2,808,254\",9.3,\"2,547,075\",62.3,6547629,20202057805,6021989,91.97,7735338848,38.29,2016.3,5912700,90.3,7498364724,37.12,2042.3,109289,1.67,236974124,1.17,1194.5,525640,8.03,12466718957,61.71,109.2\rMichigan,\"4,234,279\",\"4,532,233\",14.6,\"3,872,508\",72.1,9883640,1.46435E+11,7369957,74.57,9384151623,6.41,2034.1,6560163,66.37,7875668905,5.38,2157.4,809794,8.19,1508482718,1.03,1390.4,2513683,25.43,1.37051E+11,93.59,47.5\rMinnesota,\"2,065,946\",\"2,347,201\",11.1,\"2,087,227\",73,5303925,2.06232E+11,3886311,73.27,4416575848,2.14,2279,3076032,58,3182448693,1.54,2503.4,810279,15.28,1234127155,0.6,1700.5,1417614,26.73,2.01816E+11,97.86,18.2\rMississippi,\"1,161,953\",\"1,274,719\",12.5,\"1,115,768\",69.6,2967297,1.21531E+11,1464224,49.35,2864191371,2.36,1324,819522,27.62,1581129734,1.3,1342.4,644702,21.73,1283061637,1.06,1301.4,1503073,50.65,1.18667E+11,97.64,32.8\rMissouri,\"2,442,017\",\"2,712,729\",12.4,\"2,375,611\",68.8,5988927,1.7804E+11,4218371,70.44,5320506862,2.99,2053.5,3390061,56.61,3899820503,2.19,2251.4,828310,13.83,1420686359,0.8,1510.1,1770556,29.56,1.72719E+11,97.01,26.6\rMontana,\"412,633\",\"482,825\",15.2,\"409,607\",68,989415,3.76962E+11,553014,55.89,769702271,0.2,1860.8,262137,26.49,334839591,0.09,2027.6,290877,29.4,434862680,0.12,1732.4,436401,44.11,3.76192E+11,99.8,3\rNebraska,\"722,668\",\"796,793\",9.5,\"721,130\",67.2,1826341,1.98974E+11,1335686,73.13,1357102386,0.68,2549.1,982197,53.78,944821650,0.47,2692.4,353489,19.36,412280736,0.21,2220.7,490655,26.87,1.97617E+11,99.32,6.4\rNevada,\"827,457\",\"1,173,814\",14.3,\"1,006,250\",58.8,2700551,2.84332E+11,2543797,94.2,1987575459,0.7,3314.8,2336222,86.51,1565145978,0.55,3866,207575,7.69,422429481,0.15,1272.7,156754,5.8,2.82344E+11,99.3,1.4\rNew Hampshire,\"547,024\",\"614,754\",15.6,\"518,973\",71,1316470,23187259277,793872,60.3,1668054122,7.19,1232.6,623168,47.34,1344142228,5.8,1200.8,170704,12.97,323911894,1.4,1364.9,522598,39.7,21519205155,92.81,62.9\rNew Jersey,\"3,310,275\",\"3,553,562\",9.5,\"3,214,360\",65.4,8791894,19047341691,8324126,94.68,7561624746,39.7,2851.2,8109908,92.24,7178066812,37.69,2926.2,214218,2.44,383557934,2.01,1446.5,467768,5.32,11485716945,60.3,105.5\rNew Mexico,\"780,579\",\"901,388\",12.2,\"791,395\",68.5,2059179,3.14161E+11,1594361,77.43,2141181968,0.68,1928.5,1106721,53.75,1267853668,0.4,2260.8,487640,23.68,873328300,0.28,1446.2,464818,22.57,3.1202E+11,99.32,3.9\rNew York,\"7,679,307\",\"8,108,103\",9.7,\"7,317,755\",53.3,19378102,1.22057E+11,17028105,87.87,10597911232,8.68,4161.4,16018144,82.66,9059207000,7.42,4579.5,1009961,5.21,1538704232,1.26,1700,2349997,12.13,1.11459E+11,91.32,54.6\rNortch Carolina,\"3,523,944\",\"4,327,528\",13.5,\"3,745,155\",66.7,9535483,1.2592E+11,6301756,66.09,11937724456,9.48,1367.2,5232799,54.88,9285141220,7.37,1459.6,1068957,11.21,2652583236,2.11,1043.7,3233727,33.91,1.13982E+11,90.52,73.5\rNorth Dakota,\"289,677\",\"317,498\",11.4,\"281,192\",65.4,672591,1.78711E+11,402872,59.9,475973352,0.27,2192.2,269056,40,290454982,0.16,2399.2,133816,19.9,185518370,0.1,1868.2,269719,40.1,1.78235E+11,99.73,3.9\rOhio,\"4,783,051\",\"5,127,508\",10.2,\"4,603,435\",67.6,11536504,1.05829E+11,8989694,77.92,11448575862,10.82,2033.7,7534686,65.31,9282948899,8.77,2102.2,1455008,12.61,2165626963,2.05,1740.1,2546810,22.08,94380130830,89.18,69.9\rOklahoma,\"1,514,400\",\"1,664,378\",12.3,\"1,460,450\",67.2,3751351,1.7766E+11,2485029,66.24,3384365635,1.9,1901.7,1717572,45.79,2169231644,1.22,2050.7,767457,20.46,1215133991,0.68,1635.8,1266322,33.76,1.74276E+11,98.1,18.8\rOregon,\"1,452,709\",\"1,675,562\",9.3,\"1,518,938\",62.2,3831074,2.48608E+11,3104382,81.03,2866510400,1.15,2804.9,2393393,62.47,1933314021,0.78,3206.3,710989,18.56,933196379,0.38,1973.3,726692,18.97,2.45741E+11,98.85,7.7\rPennsylvania,\"5,249,750\",\"5,567,315\",9.9,\"5,018,904\",69.6,12702379,1.15883E+11,9991287,78.66,12186542023,10.52,2123.4,8977537,70.68,10468869338,9.03,2221,1013750,7.98,1717672685,1.48,1528.6,2711092,21.34,1.03697E+11,89.48,67.7\rRhode Island,\"439,837\",\"463,388\",10.7,\"413,600\",60.7,1052567,2677566454,955043,90.73,1037649938,38.75,2383.8,952101,90.46,1026796770,38.35,2401.6,2942,0.28,10853168,0.41,702.1,97524,9.27,1639916516,61.25,154\rSouth Carolina,\"1,753,670\",\"2,137,683\",15.7,\"1,801,181\",69.3,4625364,77856841944,3067809,66.33,6168413106,7.92,1288.1,2580045,55.78,5037540904,6.47,1326.5,487764,10.55,1130872202,1.45,1117.1,1557555,33.67,71688428838,92.08,56.3\rSouth Dakota,\"323,208\",\"363,438\",11.3,\"322,282\",68.1,814180,1.9635E+11,461247,56.65,586090288,0.3,2038.3,243587,29.92,290234955,0.15,2173.7,217660,26.73,295855333,0.15,1905.4,352933,43.35,1.95763E+11,99.7,4.7\rTennessee,\"2,439,443\",\"2,812,133\",11.3,\"2,493,552\",68.2,6346105,1.06798E+11,4213245,66.39,7524311791,7.05,1450.3,3450715,54.38,5689184718,5.33,1570.9,762530,12.02,1835127073,1.72,1076.2,2132860,33.61,99273574201,92.95,55.6\rTexas,\"8,157,575\",\"9,977,436\",10.6,\"8,922,933\",63.7,25145561,6.76587E+11,21298039,84.7,22651009601,3.35,2435.3,18947957,75.35,18698378243,2.76,2624.6,2350082,9.35,3952631358,0.58,1539.9,3847522,15.3,6.53936E+11,96.65,15.2\rUtah,\"768,594\",\"979,709\",10.4,\"877,692\",70.4,2763885,2.12818E+11,2503595,90.58,2369045186,1.11,2737.1,2243441,81.17,1950862546,0.92,2978.4,260154,9.41,418182640,0.2,1611.2,260290,9.42,2.10449E+11,98.89,3.2\rVermont,\"294,382\",\"322,539\",20.5,\"256,442\",70.7,625741,23871030489,243385,38.9,404380140,1.69,1558.8,108740,17.38,159947183,0.67,1760.8,134645,21.52,244432957,1.02,1426.7,382356,61.1,23466650349,98.31,42.2\rVirginia,\"2,904,192\",\"3,364,939\",9.2,\"3,056,058\",67.2,8001024,1.02279E+11,6037094,75.45,6902790588,6.75,2265.2,5584039,69.79,5907724619,5.78,2448.1,453055,5.66,995065969,0.97,1179.2,1963930,24.55,95376058721,93.25,53.3\rWashington,\"2,451,075\",\"2,885,677\",9.2,\"2,620,076\",63.9,6724540,1.72119E+11,5651869,84.05,6150546552,3.57,2380,5041475,74.97,5088055314,2.96,2566.3,610394,9.08,1062491238,0.62,1487.9,1072671,15.95,1.65968E+11,96.43,16.7\rWest Virginia,\"844,623\",\"881,917\",13.4,\"763,831\",73.4,1852994,62258675601,902810,48.72,1658489502,2.66,1409.9,615254,33.2,1097015856,1.76,1452.6,287556,15.52,561473646,0.9,1326.4,950184,51.28,60600186099,97.34,40.6\rWisconsin,\"2,321,144\",\"2,624,358\",13.1,\"2,279,768\",68.1,5686986,1.40268E+11,3989638,70.15,4866498071,3.47,2123.3,3173382,55.8,3601725983,2.57,2282,816256,14.35,1264772088,0.9,1671.5,1697348,29.85,1.35402E+11,96.53,32.5\rWyoming,\"223,854\",\"261,868\",13.4,\"226,879\",69.2,563626,2.5147E+11,364993,64.76,503865599,0.2,1876.2,138136,24.51,169577798,0.07,2109.8,226857,40.25,334287801,0.13,1757.6,198633,35.24,2.50966E+11,99.8,2\rPuerto Rico,\"1,418,476\",\"1,636,946\",15.9,\"1,376,531\",71.6,3725789,8867536532,3493256,93.76,4340823295,48.95,2084.3,3379977,90.72,4183015867,47.17,2092.8,113279,3.04,157807428,1.78,1859.2,232533,6.24,4526713237,51.05,133"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/urban_homeowners_outlier/urban_homeowners_outlier.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# load packages -----------------------------------------------------\nurban_state_data <- read.csv(\"urban_state_data.csv\")\n\n# plot with outlier DC ----------------------------------------------\n\npdf(\"urban_homeowners_outlier.pdf\", 5.5, 4.3)\n\npar(mar = c(4.5, 5, 1.5, 1), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nplot(urban_state_data$pct_owner_occupied ~ urban_state_data$poppct_urban,\n     xlab = 'Percent Urban Population',\n     ylab = '', \n     pch = 19, col = COL[1,2], \n     ylim = c(41, 75), axes = FALSE)\nAxisInPercent(1, at = seq(40, 100, 20))\nAxisInPercent(2, at = seq(45, 75, 10))\nbox()\npar(las = 0)\nmtext(\"Percent Own Their Home\", 2, 3.8, cex = 1.5)\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/urban_homeowners_outlier/urban_state_data.csv",
    "content": "state,total_housing_units_2000,total_housing_units_2010,pct_vacant,occupied,pct_owner_occupied,pop_st,area_st,pop_urban,poppct_urban,area_urban,areapct_urban,popden_urban,pop_ua,poppct_urban,area_ua,areapct_ua,popden_ua,pop_uc,poppct_uc,area_uc,areapct_uc,popden_uc,pop_rural,poppct_rural,area_rural,areapct_rural,popden_rural\rAlabama,\"1,963,711\",\"2,171,853\",13.3,\"1,883,791\",69.7,4779736,1.31171E+11,2821804,59.04,5716365701,4.36,1278.5,2325304,48.65,4415733067,3.37,1363.9,496500,10.39,1300632634,0.99,988.7,1957932,40.96,1.25454E+11,95.64,40.4\rAlaska,\"260,978\",\"306,967\",15.9,\"258,058\",63.1,710231,1.47795E+12,468893,66.02,673703920,0.05,1802.6,315756,44.46,362866427,0.02,2253.7,153137,21.56,310837493,0.02,1276,241338,33.98,1.47728E+12,99.95,0.4\rArizona,\"2,189,189\",\"2,844,526\",16.3,\"2,380,990\",66,6392017,2.94207E+11,5740659,89.81,5663221936,1.92,2625.4,5117783,80.07,4696616836,1.6,2822.2,622876,9.74,966605100,0.33,1669,651358,10.19,2.88544E+11,98.08,5.8\rArkansas,\"1,173,043\",\"1,316,299\",12.9,\"1,147,084\",67,2915918,1.34771E+11,1637589,56.16,2841198188,2.11,1492.8,1152837,39.54,1881196989,1.4,1587.2,484752,16.62,960001199,0.71,1307.8,1278329,43.84,1.3193E+11,97.89,25.1\rCalifornia,\"12,214,549\",\"13,680,081\",8.1,\"12,577,498\",55.9,37253956,4.03466E+11,35373606,94.95,21287926350,5.28,4303.7,33427689,89.73,18915178185,4.69,4577.1,1945917,5.22,2372748165,0.59,2124.1,1880350,5.05,3.82178E+11,94.72,12.7\rColorado,\"1,808,037\",\"2,212,898\",10.8,\"1,972,868\",65.5,5029196,2.68431E+11,4332761,86.15,3956737225,1.47,2836.1,3865471,76.86,3258048896,1.21,3072.9,467290,9.29,698688329,0.26,1732.2,696435,13.85,2.64475E+11,98.53,6.8\rConnecticut,\"1,385,975\",\"1,487,891\",7.9,\"1,371,087\",67.5,3574097,12541641427,3144942,87.99,4730500209,37.72,1721.9,3031980,84.83,4500564038,35.88,1744.8,112962,3.16,229936171,1.83,1272.4,429155,12.01,7811141218,62.28,142.3\rDelaware,\"343,072\",\"405,885\",15.7,\"342,297\",72.1,897934,5046703785,747949,83.3,1053792304,20.88,1838.3,616982,68.71,740579379,14.67,2157.7,130967,14.59,313212925,6.21,1083,149985,16.7,3992911481,79.12,97.3\rDistrict of Columbia,\"274,845\",\"296,719\",10.1,\"266,707\",42,601723,158114680,601723,100,158114680,100,9856.5,601723,100,158114680,100,9856.5,0,0,0,0,,0,0,0,0,\rFlorida,\"7,302,947\",\"8,989,580\",17.5,\"7,420,802\",67.4,18801310,1.38887E+11,17139844,91.16,19173902265,13.81,2315.2,16439936,87.44,17700464722,12.74,2405.5,699908,3.72,1473437543,1.06,1230.3,1661466,8.84,1.19714E+11,86.19,35.9\rGeorgia,\"3,281,737\",\"4,088,801\",12.3,\"3,585,584\",65.7,9687653,1.48959E+11,7272151,75.07,12423724190,8.34,1516,6334271,65.38,10239668028,6.87,1602.2,937880,9.68,2184056162,1.47,1112.2,2415502,24.93,1.36536E+11,91.66,45.8\rHawaii,\"460,542\",\"519,508\",12.4,\"455,338\",57.7,1360301,16634529975,1250489,91.93,1018212915,6.12,3180.8,972075,71.46,585035739,3.52,4303.4,278414,20.47,433177176,2.6,1664.7,109812,8.07,15616317060,93.88,18.2\rIdaho,\"527,824\",\"667,796\",13.2,\"579,408\",69.9,1567582,2.14045E+11,1106370,70.58,1292606730,0.6,2216.8,791843,50.51,886257981,0.41,2314.1,314527,20.06,406348749,0.19,2004.7,461212,29.42,2.12752E+11,99.4,5.6\rIllinois,\"4,885,615\",\"5,296,715\",8.7,\"4,836,972\",67.5,12830632,1.43793E+11,11353553,88.49,10218955838,7.11,2877.6,10260671,79.97,8610185823,5.99,3086.5,1092882,8.52,1608770015,1.12,1759.5,1477079,11.51,1.33574E+11,92.89,28.6\rIndiana,\"2,532,319\",\"2,795,541\",10.5,\"2,502,154\",69.9,6483802,92789193658,4697100,72.44,6540696730,7.05,1860,3836584,59.17,5187412076,5.59,1915.5,860516,13.27,1353284654,1.46,1646.9,1786702,27.56,86248496928,92.95,53.7\rIowa,\"1,232,511\",\"1,336,417\",8.6,\"1,221,576\",72.1,3046355,1.44669E+11,1950256,64.02,2468980575,1.71,2045.8,1268964,41.66,1507132351,1.04,2180.7,681292,22.36,961848224,0.66,1834.5,1096099,35.98,1.422E+11,98.29,20\rKansas,\"1,131,200\",\"1,233,215\",9.8,\"1,112,096\",67.8,2853118,2.11754E+11,2116961,74.2,2519183616,1.19,2176.5,1431424,50.17,1623806507,0.77,2283.1,685537,24.03,895377109,0.42,1983,736157,25.8,2.09235E+11,98.81,9.1\rKentucky,\"1,750,927\",\"1,927,164\",10.8,\"1,719,965\",68.7,4339367,1.02269E+11,2533343,58.38,3653655859,3.57,1795.8,1778528,40.99,2207361288,2.16,2086.8,754815,17.39,1446294571,1.41,1351.7,1806024,41.62,98615485782,96.43,47.4\rLouisiana,\"1,847,181\",\"1,964,981\",12,\"1,728,360\",67.2,4533372,1.11898E+11,3317805,73.19,5097451640,4.56,1685.8,2780406,61.33,4010132099,3.58,1795.8,537399,11.85,1087319541,0.97,1280.1,1215567,26.81,1.068E+11,95.44,29.5\rMaine,\"651,901\",\"721,830\",22.8,\"557,219\",71.3,1328361,79882800680,513542,38.66,931423305,1.17,1428,348137,26.21,616415489,0.77,1462.8,165405,12.45,315007816,0.39,1360,814819,61.34,78951377375,98.83,26.7\rMaryland,\"2,145,283\",\"2,378,814\",9.3,\"2,156,411\",67.5,5773552,25141638381,5034331,87.2,5191942757,20.65,2511.4,4822869,83.53,4767985793,18.96,2619.8,211462,3.66,423956964,1.69,1291.8,739221,12.8,19949695624,79.35,96\rMassachusetts,\"2,621,989\",\"2,808,254\",9.3,\"2,547,075\",62.3,6547629,20202057805,6021989,91.97,7735338848,38.29,2016.3,5912700,90.3,7498364724,37.12,2042.3,109289,1.67,236974124,1.17,1194.5,525640,8.03,12466718957,61.71,109.2\rMichigan,\"4,234,279\",\"4,532,233\",14.6,\"3,872,508\",72.1,9883640,1.46435E+11,7369957,74.57,9384151623,6.41,2034.1,6560163,66.37,7875668905,5.38,2157.4,809794,8.19,1508482718,1.03,1390.4,2513683,25.43,1.37051E+11,93.59,47.5\rMinnesota,\"2,065,946\",\"2,347,201\",11.1,\"2,087,227\",73,5303925,2.06232E+11,3886311,73.27,4416575848,2.14,2279,3076032,58,3182448693,1.54,2503.4,810279,15.28,1234127155,0.6,1700.5,1417614,26.73,2.01816E+11,97.86,18.2\rMississippi,\"1,161,953\",\"1,274,719\",12.5,\"1,115,768\",69.6,2967297,1.21531E+11,1464224,49.35,2864191371,2.36,1324,819522,27.62,1581129734,1.3,1342.4,644702,21.73,1283061637,1.06,1301.4,1503073,50.65,1.18667E+11,97.64,32.8\rMissouri,\"2,442,017\",\"2,712,729\",12.4,\"2,375,611\",68.8,5988927,1.7804E+11,4218371,70.44,5320506862,2.99,2053.5,3390061,56.61,3899820503,2.19,2251.4,828310,13.83,1420686359,0.8,1510.1,1770556,29.56,1.72719E+11,97.01,26.6\rMontana,\"412,633\",\"482,825\",15.2,\"409,607\",68,989415,3.76962E+11,553014,55.89,769702271,0.2,1860.8,262137,26.49,334839591,0.09,2027.6,290877,29.4,434862680,0.12,1732.4,436401,44.11,3.76192E+11,99.8,3\rNebraska,\"722,668\",\"796,793\",9.5,\"721,130\",67.2,1826341,1.98974E+11,1335686,73.13,1357102386,0.68,2549.1,982197,53.78,944821650,0.47,2692.4,353489,19.36,412280736,0.21,2220.7,490655,26.87,1.97617E+11,99.32,6.4\rNevada,\"827,457\",\"1,173,814\",14.3,\"1,006,250\",58.8,2700551,2.84332E+11,2543797,94.2,1987575459,0.7,3314.8,2336222,86.51,1565145978,0.55,3866,207575,7.69,422429481,0.15,1272.7,156754,5.8,2.82344E+11,99.3,1.4\rNew Hampshire,\"547,024\",\"614,754\",15.6,\"518,973\",71,1316470,23187259277,793872,60.3,1668054122,7.19,1232.6,623168,47.34,1344142228,5.8,1200.8,170704,12.97,323911894,1.4,1364.9,522598,39.7,21519205155,92.81,62.9\rNew Jersey,\"3,310,275\",\"3,553,562\",9.5,\"3,214,360\",65.4,8791894,19047341691,8324126,94.68,7561624746,39.7,2851.2,8109908,92.24,7178066812,37.69,2926.2,214218,2.44,383557934,2.01,1446.5,467768,5.32,11485716945,60.3,105.5\rNew Mexico,\"780,579\",\"901,388\",12.2,\"791,395\",68.5,2059179,3.14161E+11,1594361,77.43,2141181968,0.68,1928.5,1106721,53.75,1267853668,0.4,2260.8,487640,23.68,873328300,0.28,1446.2,464818,22.57,3.1202E+11,99.32,3.9\rNew York,\"7,679,307\",\"8,108,103\",9.7,\"7,317,755\",53.3,19378102,1.22057E+11,17028105,87.87,10597911232,8.68,4161.4,16018144,82.66,9059207000,7.42,4579.5,1009961,5.21,1538704232,1.26,1700,2349997,12.13,1.11459E+11,91.32,54.6\rNortch Carolina,\"3,523,944\",\"4,327,528\",13.5,\"3,745,155\",66.7,9535483,1.2592E+11,6301756,66.09,11937724456,9.48,1367.2,5232799,54.88,9285141220,7.37,1459.6,1068957,11.21,2652583236,2.11,1043.7,3233727,33.91,1.13982E+11,90.52,73.5\rNorth Dakota,\"289,677\",\"317,498\",11.4,\"281,192\",65.4,672591,1.78711E+11,402872,59.9,475973352,0.27,2192.2,269056,40,290454982,0.16,2399.2,133816,19.9,185518370,0.1,1868.2,269719,40.1,1.78235E+11,99.73,3.9\rOhio,\"4,783,051\",\"5,127,508\",10.2,\"4,603,435\",67.6,11536504,1.05829E+11,8989694,77.92,11448575862,10.82,2033.7,7534686,65.31,9282948899,8.77,2102.2,1455008,12.61,2165626963,2.05,1740.1,2546810,22.08,94380130830,89.18,69.9\rOklahoma,\"1,514,400\",\"1,664,378\",12.3,\"1,460,450\",67.2,3751351,1.7766E+11,2485029,66.24,3384365635,1.9,1901.7,1717572,45.79,2169231644,1.22,2050.7,767457,20.46,1215133991,0.68,1635.8,1266322,33.76,1.74276E+11,98.1,18.8\rOregon,\"1,452,709\",\"1,675,562\",9.3,\"1,518,938\",62.2,3831074,2.48608E+11,3104382,81.03,2866510400,1.15,2804.9,2393393,62.47,1933314021,0.78,3206.3,710989,18.56,933196379,0.38,1973.3,726692,18.97,2.45741E+11,98.85,7.7\rPennsylvania,\"5,249,750\",\"5,567,315\",9.9,\"5,018,904\",69.6,12702379,1.15883E+11,9991287,78.66,12186542023,10.52,2123.4,8977537,70.68,10468869338,9.03,2221,1013750,7.98,1717672685,1.48,1528.6,2711092,21.34,1.03697E+11,89.48,67.7\rRhode Island,\"439,837\",\"463,388\",10.7,\"413,600\",60.7,1052567,2677566454,955043,90.73,1037649938,38.75,2383.8,952101,90.46,1026796770,38.35,2401.6,2942,0.28,10853168,0.41,702.1,97524,9.27,1639916516,61.25,154\rSouth Carolina,\"1,753,670\",\"2,137,683\",15.7,\"1,801,181\",69.3,4625364,77856841944,3067809,66.33,6168413106,7.92,1288.1,2580045,55.78,5037540904,6.47,1326.5,487764,10.55,1130872202,1.45,1117.1,1557555,33.67,71688428838,92.08,56.3\rSouth Dakota,\"323,208\",\"363,438\",11.3,\"322,282\",68.1,814180,1.9635E+11,461247,56.65,586090288,0.3,2038.3,243587,29.92,290234955,0.15,2173.7,217660,26.73,295855333,0.15,1905.4,352933,43.35,1.95763E+11,99.7,4.7\rTennessee,\"2,439,443\",\"2,812,133\",11.3,\"2,493,552\",68.2,6346105,1.06798E+11,4213245,66.39,7524311791,7.05,1450.3,3450715,54.38,5689184718,5.33,1570.9,762530,12.02,1835127073,1.72,1076.2,2132860,33.61,99273574201,92.95,55.6\rTexas,\"8,157,575\",\"9,977,436\",10.6,\"8,922,933\",63.7,25145561,6.76587E+11,21298039,84.7,22651009601,3.35,2435.3,18947957,75.35,18698378243,2.76,2624.6,2350082,9.35,3952631358,0.58,1539.9,3847522,15.3,6.53936E+11,96.65,15.2\rUtah,\"768,594\",\"979,709\",10.4,\"877,692\",70.4,2763885,2.12818E+11,2503595,90.58,2369045186,1.11,2737.1,2243441,81.17,1950862546,0.92,2978.4,260154,9.41,418182640,0.2,1611.2,260290,9.42,2.10449E+11,98.89,3.2\rVermont,\"294,382\",\"322,539\",20.5,\"256,442\",70.7,625741,23871030489,243385,38.9,404380140,1.69,1558.8,108740,17.38,159947183,0.67,1760.8,134645,21.52,244432957,1.02,1426.7,382356,61.1,23466650349,98.31,42.2\rVirginia,\"2,904,192\",\"3,364,939\",9.2,\"3,056,058\",67.2,8001024,1.02279E+11,6037094,75.45,6902790588,6.75,2265.2,5584039,69.79,5907724619,5.78,2448.1,453055,5.66,995065969,0.97,1179.2,1963930,24.55,95376058721,93.25,53.3\rWashington,\"2,451,075\",\"2,885,677\",9.2,\"2,620,076\",63.9,6724540,1.72119E+11,5651869,84.05,6150546552,3.57,2380,5041475,74.97,5088055314,2.96,2566.3,610394,9.08,1062491238,0.62,1487.9,1072671,15.95,1.65968E+11,96.43,16.7\rWest Virginia,\"844,623\",\"881,917\",13.4,\"763,831\",73.4,1852994,62258675601,902810,48.72,1658489502,2.66,1409.9,615254,33.2,1097015856,1.76,1452.6,287556,15.52,561473646,0.9,1326.4,950184,51.28,60600186099,97.34,40.6\rWisconsin,\"2,321,144\",\"2,624,358\",13.1,\"2,279,768\",68.1,5686986,1.40268E+11,3989638,70.15,4866498071,3.47,2123.3,3173382,55.8,3601725983,2.57,2282,816256,14.35,1264772088,0.9,1671.5,1697348,29.85,1.35402E+11,96.53,32.5\rWyoming,\"223,854\",\"261,868\",13.4,\"226,879\",69.2,563626,2.5147E+11,364993,64.76,503865599,0.2,1876.2,138136,24.51,169577798,0.07,2109.8,226857,40.25,334287801,0.13,1757.6,198633,35.24,2.50966E+11,99.8,2\rPuerto Rico,\"1,418,476\",\"1,636,946\",15.9,\"1,376,531\",71.6,3725789,8867536532,3493256,93.76,4340823295,48.95,2084.3,3379977,90.72,4183015867,47.17,2092.8,113279,3.04,157807428,1.78,1859.2,232533,6.24,4526713237,51.05,133"
  },
  {
    "path": "ch_regr_simple_linear/figures/eoce/visualize_residuals/visualize_residuals.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# simulate data -----------------------------------------------------\n\nx <- seq(1,100,1)\n\nset.seed(84628)\n\ny_linear <- 3 * x + 5 + rnorm(length(x), mean = 0, sd = 20)\ny_fan_back <- 4*x + 5 + rnorm(length(x), mean = 0, sd = sort(x, decreasing = TRUE))\n\n# fit models --------------------------------------------------------\n\nm_linear = lm(y_linear ~ x)\nm_fan_back = lm(y_fan_back ~ x)\n\n# plot linear -------------------------------------------------------\n\npdf(\"visualize_residuals_linear.pdf\", 5.5, 4.3)\n\npar(mar=c(2,1,1,1), las=1, mgp=c(0.9,0.7,0), cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_linear ~ x, \n     xlab = \"(a)\", ylab = \"\", \n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\nabline(m_linear, col = COL[2], lwd = 2)\n\ndev.off()\n\n# plot backwards fan shaped -----------------------------------------\n\npdf(\"visualize_residuals_fan_back.pdf\", 5.5, 4.3)\n\npar(mar=c(2,1,1,1), las=1, mgp=c(0.9,0.7,0), cex.lab = 1.75, cex.axis = 1.75)\n\nplot(y_fan_back ~ x, \n     xlab = \"(b)\", ylab = \"\", \n     yaxt = \"n\", xaxt = \"n\", \n     pch = 19, col = COL[1])\n\nabline(m_fan_back, col = COL[2], lwd = 2)\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/identifyingInfluentialPoints/identifyingInfluentialPoints.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('identifyingInfluentialPoints.pdf', 7, 2.73,\n      mar = c(0.35, 0.654, 0.35, 0.654))\nmyMat <- rbind(matrix(1:6, 2))\nmyW <- rep(1, 3)\nmyH <- c(1, 0.45)\nlayout(myMat, myW, myH)\nset.seed(1)\n\nn <- c(95, 50, 78)\nm <- c(-4, 12, 7)\nxr <- list(2.16, -0.4, 1.42)\nyr <- list(xr[[1]] * m[1], 1, 5.5)\nss <- list(1:(n[1] - 1), 1:(n[2] - 1), 1:(n[3] - 3))\nfor (i in 1:3) {\n  x <- runif(n[i])\n  y <- m[i] * x + rnorm(n[i])\n  x <- c(x, xr[[i]])\n  y <- c(y, yr[[i]])\n  linResPlot(x, y,\n             col = COL[1, 2],\n             subset = ss[[i]],\n             yR = ifelse(i == 1, 0.12, 0.44))\n}\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/imperfLinearModel/imperfLinearModel.R",
    "content": "library(openintro)\n\ncol <- COL[1, 3]\n\nmyPDF('imperfLinearModel.pdf', 5.814, 1.875,\n      mfrow = c(1, 3),\n      mar = c(2, 2.5, 1, 2),\n      mgp = c(1.9, 0.6, 0),\n      las = 0)\npar(mar = c(2, 2.25, 0.5, 0.8))\nthese <- simulated_scatter$group == 1\nPlotWLine(simulated_scatter$x[these], simulated_scatter$y[these], col = col)\npar(mar = c(2, 2.9, 0.5, 0.4))\nthese <- simulated_scatter$group == 2\nPlotWLine(simulated_scatter$x[these], simulated_scatter$y[these], col = col)\npar(mar = c(2, 3.3, 0.5, 0))\nthese <- simulated_scatter$group == 3\nPlotWLine(simulated_scatter$x[these], simulated_scatter$y[these], col = col)\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/marioKartNewUsed/marioKartNewUsed.R",
    "content": "library(openintro)\ndata(COL)\nmk      <- mariokart[mariokart$total_pr < 100, ]\nmk$cond <- relevel(mk$cond, \"used\")\ncond <- as.numeric(ifelse(mk$cond == \"new\", 1, 0))\n\nmyPDF('marioKartNewUsed.pdf', 4.5, 3.2,\n      mar = c(3, 3.5, 0, 0.5),\n      mgp = c(1.9, 1.5 ,0))\ndotPlot(mk$total_pr, cond,\n        vertical = TRUE,\n        at = 0:1,\n        key = 0:1,\n        xlab = \"\",\n        ylab = \"\",\n        axes = FALSE,\n        col = COL[1, 3],\n        pch = 19,\n        cex = 1.3)\nat <- -1:2\nlabels <- c(\"\", \"0\\n(used)\", \"1\\n(new)\", \"\")\naxis(1, at, labels)\npar(mgp = c(1.9, 0.6, 0))\nAxisInDollars(2, at = seq(30, 70, 10))\npar(las = 0)\nmtext(\"Total Price\", 2, line = 2.5)\ng <- lm(mk$total_pr ~ cond)\nabline(g, lwd = 1.5, col = COL[5])\nrect(-10, -1000, -0.125, 1000,\n     border  =  rgb(1, 1, 1),\n     col  =  rgb(1, 1, 1))\nrect(10, -1000, 1.125, 1000,\n     border  =  rgb(1, 1, 1),\n     col  =  rgb(1, 1, 1))\ntext(0.48, 41.8,\n     expression(widehat(price) *\" = 42.87 + 10.90     cond_new\"),\n     cex = 0.8)\npoints(0.605, 41.5, pch = 4, cex = 0.9)\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/notGoodAtAllForALinearModel/notGoodAtAllForALinearModel.R",
    "content": "library(openintro)\ndata(COL)\n\nd <- subset(simulated_scatter, group == 5)\n\nmyPDF('notGoodAtAllForALinearModel.pdf', 6.4, 2.743,\n      mar = c(3, 4, 1, 2))\nPlotWLine(d$x, d$y,\n          xlab = 'Angle of Incline (Degrees)',\n          ylab = 'Distance Traveled (m)',\n          axes = FALSE, col = COL[1])\naxis(1, at = seq(0, 90, length.out = 7), rep(\"\", 7), tcl = -0.1)\naxis(1, at = seq(0, 90, length.out = 4))\naxis(2, at = seq(0, 15, 5))\nabline(h = 0)\ntext(mean(d$x), mean(d$y),\n     'Best fitting straight line is flat (!)',\n     pos = 1,\n     col = COL[4])\nabline(h = 0)\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/outlierPlots/outlierPlots.R",
    "content": "library(openintro)\n\npdf('outlierPlots.pdf', 7, 7)\nmyMat <- rbind(matrix(1:6, 2),\n               matrix(7:12, 2))\nmyW <- rep(1, 3)\nmyH <- c(0.95, 0.5, 1, 0.45)\nlayout(myMat, myW, myH)\n\nfor(i in 1:6){\n  par(mar = c(0.25, 0.5, 1.75, 0.5))\n  these <- simulated_scatter$group == 23 + i\n  x <- simulated_scatter$x[these]\n  y <- simulated_scatter$y[these]\n  yR <- c(rep(0.13, 3), 0.5, 0.1, 0.1)\n  linResPlot(x, y,\n             col = COL[1, 2],\n             marRes = c(ifelse(i < 4, 4, 1), 2, 1, 2) / 4,\n             yR = yR[i],\n             main = paste0(\"(\", i, \")\"))\n}\ndev.off()\n\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/pValueMidtermUnemp/pValueMidtermUnemp.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"pValueMidtermUnemp.pdf\", 6.325, 2.7,\n      mar = c(1.8, 0.5, 0.2, 0.5))\nnormTail(0, 0.8350,\n         L = -0.8897,\n         U = 0.8897,\n         df = 27,\n         col = COL[1])\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/perfLinearModel/perfLinearModel.R",
    "content": "library(openintro)\ndata(COL)\n\nthese <- simulated_scatter$group == 4\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\n\nmyPDF('perfLinearModel.pdf', 4.5, 3.1,\n      mar = c(3, 4, 1, 1),\n      mgp = c(1.9, 0.55, 0))\nplot(x, y,\n     ylim = c(0, max(y)),\n     axes = FALSE,\n     xlab = 'Number of Target Corporation Stocks to Purchase',\n     ylab = '',\n     pch = 20,\n     cex = 1.7,\n     col = COL[1])\nbuildAxis(1, x, 4, nMax = 4)\nAxisInDollars(2, c(-1000, pretty(y, 2)))\nabline(5, 64.96, col = COL[5])\npar(las = 0)\nmtext('Total Cost of the Share Purchase', 2, 2.8)\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/posNegCorPlots/CorrelationPlot.R",
    "content": "CorrelationPlot <- function(x, y, ...) {\n  plot(x, y,\n       axes = FALSE,\n       pch = 20,\n       col = COL[1, 2],\n       cex = 1.351,\n       xlab = '',\n       ...)\n  box()\n  mtext(paste('R =', format(c(round(cor(x,y), 2), 0.01))[1]),\n        side = 1,\n        line = 1,\n        cex = 1.1)\n}\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/posNegCorPlots/corForNonLinearPlots.R",
    "content": "library(openintro)\ndata(COL)\nset.seed(1)\nsource(\"CorrelationPlot.R\")\n\nn <- 50\n\nmyPDF('corForNonLinearPlots.pdf', 6, 2,\n      mfrow = c(1, 3),\n      mar = c(2.7, rep(0.5, 3)),\n      mgp = c(1, 0, 0))\nthese <- simulated_scatter$group == 17\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y)\n\nthese <- simulated_scatter$group == 18\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y)\n\nthese <- simulated_scatter$group == 19\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nyR <- range(y) + c(-1, 1) * 0.1 * diff(range(y))\nCorrelationPlot(x, y, ylim = yR)\n\ndev.off()"
  },
  {
    "path": "ch_regr_simple_linear/figures/posNegCorPlots/posNegCorPlots.R",
    "content": "library(openintro)\ndata(COL)\ndata(possum)\nsource(\"CorrelationPlot.R\")\nset.seed(1)\n\nn <- 50\n\nmyPDF('posNegCorPlots.pdf', 6, 3.6,\n      mfrow = c(2, 4),\n      mar = c(2.7, rep(0.5, 3)),\n      mgp = c(1, 0, 0))\n\n\n# _____ Line 1 _____ #\nthese <- simulated_scatter$group == 9\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y, xlim = c(-0.2, 4.2), ylim = c(-9, 17))\n\nthese <- simulated_scatter$group == 10\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y)\n\nthese <- simulated_scatter$group == 11\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y, xlim = c(-0.2, 4.2), ylim = c(-2, 9.6))\n\nthese <- simulated_scatter$group == 12\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y, xlim = c(-0.03, 1.03), ylim = c(-.1, 1.1))\n\n\n# _____ Line 2 _____ #\npar(mar = c(2.1,0.5,1.1,0.5))\n\nthese <- simulated_scatter$group == 13\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y, xlim = c(-0.2, 4.2), ylim = c(-17, 14))\n\nthese <- simulated_scatter$group == 14\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y, xlim = c(-5.2, 5.2), ylim = c(-12, 10))\n\nthese <- simulated_scatter$group == 15\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y, xlim = c(-0.03, 1.03), ylim = c(-10, 2))\n\nthese <- simulated_scatter$group == 16\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nCorrelationPlot(x, y, xlim = c(-0.03, 1.03), ylim = c(-1.2, .2))\n\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/sampleLinesAndResPlots/sampleLinesAndResPlots.R",
    "content": "library(openintro)\n\nGenerateLmPlot <- function(x, y, xlim, ylim1, ylim2.mult) {\n  plot(x, y,\n       axes = FALSE,\n       pch = 20,\n       col = COL[1, 2],\n       cex = 1.202,\n       xlim = xlim,\n       ylim = ylim1)\n  box()\n  g <- lm(y ~ x)\n  abline(g, col = COL[5])\n  plot(x, g$residuals,\n       pch = 20,\n       col = COL[1, 2],\n       cex = 1.202,\n       xlim = xlim,\n       axes = FALSE,\n       ylim = ylim2.mult * c(-1, 1) * max(abs(g$residuals)))\n  box()\n  abline(h = 0, col = COL[5], lty = 2)\n}\n\nmyPDF('sampleLinesAndResPlots.pdf', 5, 2.5,\n      mfrow = 2:3,\n      mar = rep(0.5, 4))\n\nMyLayOut <- matrix(1:6, 2)\nlayout(mat = MyLayOut,\n       widths = rep(2, 3),\n       heights = c(2, 1),\n       respect = TRUE)\n\nthese <- simulated_scatter$group == 6\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nGenerateLmPlot(x, y,\n               xlim = c(-0.03, 1.03),\n               ylim1 = c(-10, 1),\n               ylim2.mult = 2.5)\n\nthese <- simulated_scatter$group == 7\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nGenerateLmPlot(x, y,\n               xlim = c(-0.2, 4.2),\n               ylim1 = c(-35, 2),\n               ylim2.mult = 1.8)\n\nthese <- simulated_scatter$group == 8\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nGenerateLmPlot(x, y,\n               xlim = c(-0.03, 1.03),\n               ylim1 = c(-2, 2),\n               ylim2.mult = 1.2)\n\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/scattHeadLTotalL/scattHeadLTotalL.R",
    "content": "library(openintro)\ndata(COL)\ndata(possum)\n\nmyPDF('scattHeadLTotalL.pdf', 6, 4,\n      mar = c(3.7, 3.7, 0.5, 0.5),\n      mgp = c(2.6, 0.55, 0))\nplot(possum$totalL, possum$headL,\n     pch = 19,\n     col = COL[1, 2],\n     cex = 1.2,\n     xlab = 'Total Length (cm)',\n     ylab = 'Head Length (mm)')\npoints(89, 94.1, col = COL[4], cex = 1.7)\nlines(rep(89, 2), c(0, 93.8), lty = 2, col = COL[4])\nlines(c(0, 88.7), rep(94.1, 2), lty = 2, col = COL[4])\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/scattHeadLTotalLLine/scattHeadLTotalLLine.R",
    "content": "require(openintro)\ndata(COL)\ndata(possum)\nset.seed(1)\n\nmyPDF('scattHeadLTotalLLine.pdf', 5.5, 3.2,\n      mar = c(3, 3.2, 0.1, 1),\n      mgp = c(1.9, 0.45, 0))\nplot(possum$totalL, possum$headL,\n     pch = 20,\n     col = COL[1, 2],\n     cex = 1.7,\n     xlab = 'Total Length (cm)',\n     ylab = 'Head Length (mm)')\nabline(41, 0.59, col = COL[5])\ndev.off()\n\n\nmyPDF('scattHeadLTotalLLineResiduals.pdf', 5.5, 3.2,\n      mar = c(3, 3.2, 0.1, 1),\n      mgp = c(1.9, 0.45, 0))\nthese <- c(48, 42, 3)\nplot(possum$totalL[-these], possum$headL[-these],\n     pch = 20,\n     col = COL[1, 2],\n     cex = 1.7,\n     xlab = 'Total Length (cm)',\n     ylab = 'Head Length (mm)')\npoints(possum$totalL[these] + rnorm(3,0,0.02),\n       possum$headL[these] + rnorm(3,0,0.02),\n       pch = c(3, 4, 2),\n       col = COL[4],\n       cex = 1.5,\n       lwd = 2.5)\nabline(41, 0.59, col = COL[5])\nfor(i in 1:3){\n  y2 <- 41 + 0.59 * possum$totalL[these[i]]\n  lines(rep(possum$totalL[these[i]], 2),\n        c(possum$headL[these[i]], y2),\n        lty = 2,\n        lwd = 1,\n        col = COL[4])\n}\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/scattHeadLTotalLResidualPlot/scattHeadLTotalLResidualPlot.R",
    "content": "require(openintro)\ndata(COL)\ndata(possum)\n\nmyPDF('scattHeadLTotalLResidualPlot.pdf', 5.5, 2.7,\n      mar = c(3, 3, 0.5, 1),\n      mgp = c(1.8, 0.6, 0))\nthese <- c(48, 42, 3)\nplot(possum$totalL[-these],\n     possum$headL[-these] - (41 + 0.59 * possum$totalL[-these]),\n     pch = 19,\n     col = COL[1, 2],\n     xlab = 'Total Length (cm)',\n     ylab = 'Residuals',\n     ylim = c(-7, 9))\ny.extra <- 0.59 * possum$totalL[these] + rnorm(1,0,0.01)\npoints(possum$totalL[these] + rnorm(1, 0, 0.01),\n       possum$headL[these] - (41 + y.extra),\n       pch = c(3, 4, 2),\n       col = COL[4],\n       cex = 1.3,\n       lwd = 2.5)\nabline(h = 0, lty = 2)\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/scattHeadLTotalLSex/scattHeadLTotalLSex.R",
    "content": "library(openintro)\n\nmyPDF('scattHeadLTotalLSex.pdf', 5, 3,\n    mar = c(3.5, 3.5, 0.5, 0.5),\n    mgp = c(2.4, 0.55, 0))\nplot(possum$totalL, possum$headL,\n    pch = ifelse(possum$sex == \"m\", 1, 3),\n    col = ifelse(possum$sex == \"m\", COL[1, 1], COL[4, 1]),\n    lwd = ifelse(possum$sex == \"m\", 2, 3),\n    cex = ifelse(possum$sex == \"m\", 1.2, 0.7),\n    xlab = 'Total Length (cm)',\n    ylab = 'Head Length (mm)')\nlegend(\"topleft\", pch = c(1, 3), col = COL[c(1, 4)], cex = 0.9,\n    legend = c(\"Male\", \"Female\"))\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/scattHeadLTotalLTube/scattHeadLTotalLTube.R",
    "content": "library(openintro)\ndata(COL)\ndata(possum)\ndata(cars)\nmyPDF('scattHeadLTotalLTube.pdf', 7.3, 3,\n      mar = c(3.2, 3.8, 1, 2),\n      mgp = c(2.4, 0.55, 0),\n      mfrow = 1:2)\nplot(possum$totalL, possum$headL,\n     pch = 20,\n     col = COL[1, 2],\n     cex = 1.7,\n     xlab = '',\n     ylab = 'Head length (mm)',\n     type = \"n\")\nmtext(\"Total length (cm)\", 1, line = 2.1)\ng <- lm(headL ~ totalL, possum)\nx <- c(0, 200, 200, 0, 0)\ny <- 42.71 + c(-5, 0.5729 * 200 - 5, 0.5729 * 200 + 5, 5, -5)\npolygon(x, y,\n        col = COL[7],\n        border = '#00000000')\npoints(possum$totalL, possum$headL,\n       pch = 20,\n       col = COL[1, 2],\n       cex = 1.7)\n\n\nset.seed(5)\npar(mar = c(3.2, 4.8, 1, 1))\nn <- 50\nx <- sample(150:420, n, prob = (150:420)^2)\ny <- 87 - 0.35 * x + 5.4e-4 * x^2 + rnorm(n, sd = 2)\nsimulated_scatter <- rbind.data.frame(simulated_scatter,\n    data.frame(group = 30, x, y))\n\nplot(x, y,\n     pch = 20,\n     col = COL[1, 2],\n     cex = 1.7,\n     xlab = '',\n     ylab = 'y',\n     type = \"n\")\nmtext(\"x\", 1, line = 2.1)\ng <- lm(y ~ x + I(x^2), cars)\nx1 <- seq(100, 500, 10)\nx2 <- c(x1, rev(x1), 100)\nnx1 <- length(x1)\ny2 <- g$coef[1] + g$coef[2] * x2 + g$coef[3] * x2^2 +\n     2 * sd(g$residuals) * c(rep(-1, nx1), rep(1, nx1), -1)\npolygon(x2, y2,\n        col = COL[7],\n        border = '#00000000')\npoints(x, y,\n       pch = 20,\n       col = COL[1, 2],\n       cex = 1.7)\ndev.off()\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/unemploymentAndChangeInHouse/unemploymentAndChangeInHouse.R",
    "content": "rm(list=ls())\nlibrary(openintro)\n\nd <- midterms_house\n\nmyPDF(\"unemploymentAndChangeInHouse.pdf\", 7.2, 4.2,\n      mar = c(3.2, 5.3, 0.5, 0.5),\n      mgp = c(3.2, 0.55, 0))\nth <- !d$year %in% c(1935, 1939)\nplot(d$unemp[th], d$house_change[th],\n     # col = COL[ifelse(d$party[th] == \"Republican\", 4, 1)],\n     pch = 19,\n     xlim = c(3, 12),\n     ylim = c(-30, 13),\n     axes = FALSE,\n     type = 'n',\n     xlab = '',\n     ylab = paste0(\"Percent Change in Seats of\\n\",\n                  \"President's Party in House of Rep.\"))\nmtext('Unemployment Rate', 1, 2)\nabline(h = seq(-100, 100, 10), col = COL[7, 3], lwd = 2)\nabline(h = seq(-105, 100, 10), col = COL[7, 3], lwd = 0.7)\nabline(v = seq(-100, 100, 4), col = COL[7, 3], lwd = 2)\nabline(v = seq(-102, 100, 4), col = COL[7, 3], lwd = 0.7)\nrepub <- (d$party[th] == \"Republican\")\npoints(d$unemp[th], d$house_change[th],\n       col = COL[ifelse(repub, 4, 1)],\n       pch = ifelse(repub, 17, 19))\nAxisInPercent(1, at = seq(0, 20, 4))\nAxisInPercent(2, at = seq(-100, 100, 10))\nbox()\ncases <- c(1, 22, 25, 27, 29, 31)\nfor (i in 1:length(cases)) {\n  potus  <- as.character(d$potus[cases[i]])\n  potus  <- tail(strsplit(potus, \" \")[[1]], 1)\n  year   <- d$year[cases[i]]-1\n  potus  <- paste0(potus, \"\\n\", year)\n  unem   <- d$unemp[cases[i]]\n  change <- d$house_change[cases[i]]\n  text(unem, change, potus, pos = 3, cex = 0.6)\n}\n\nsummary(lm(house_change ~ unemp, d))\n\ng <- lm(house_change ~ unemp, d[th,])\nsummary(g)\nabline(g, col = COL[5])\nlegend('topright',\n       bg = \"#FFFFFF\",\n       pch = c(19, 17),\n       col = COL[c(1, 4)],\n       legend = c(\"Democrat\", \"Republican\"))\ndev.off()\n\n# library(xtable)\n# xtable(g)\n# acf(g$residual)\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/whatCanGoWrongWithLinearModel/makeTubeAdv.R",
    "content": "makeTubeAdv <- function(x, y, Z=2, R=1, col='#00000022', border='#00000000', type=c('lin', 'quad', 'robust'), variance=c('constant', 'linear', 'other'), length.out=99, bw='default', plotTube=TRUE, ...){\n\tn <- length(x)\n\tr <- range(x)\n\tR <- abs(R)\n\tR <- r + c(-R,R)*diff(r)\n\tX <- seq(R[1], R[2], length.out=length.out)\n\ttype <- type[1]\n\tif(type %in% c('l', 'L', 'lin', 'Lin', 'linear', 'Linear')){\n\t\tg <- lm(y ~ x)\n\t\thold <- data.frame(x=X)\n\t\tY <- predict(g, hold)\n\t\tS <- sd(g$residuals)\n\t} else if(type %in% c('q', 'quad', 'Q', 'Quad')){\n\t\tx2 <- x^2\n\t\tg <- lm(y ~ x + x2)\n\t\thold <- data.frame(x=X, x2=X^2)\n\t\tY <- predict(g, hold)\n\t\tS <- sd(g$residuals)\n\t} else if(type %in% c('r', 'R', 'robust', 'Robust')){\n\t\tif(bw[1] == 'default'){\n\t\t\tbw <- bw.nrd0(x)\n\t\t}\n\t\tY <- rep(NA, length(X))\n\t\tfor(i in 1:length(X)){\n\t\t\tif(min(x - X[i]) < 2*bw){\n\t\t\t\ttemp <- dnorm(x-X[i], sd=bw)\n\t\t\t\tY[i] <- sum(y*temp)/sum(temp)\n\t\t\t}\n\t\t}\n\t\thold <- c()\n\t\tfor(i in 1:length(y)){\n\t\t\thold[i] <- Y[which.min(abs(X-x[i]))[1]]\n\t\t}\n\t\tS <- rep(sd(hold-y), length(Y))\n\t} else {\n\t\tstop('Argument \"type\" not recognized.\\n')\n\t}\n\tvariance <- variance[1]\n\tif(variance %in% c('o', 'O', 'other', 'Other')){\n\t\tif(bw[1] == 'default'){\n\t\t\tbw <- bw.nrd0(x)\n\t\t}\n\t\tS <- rep(NA, length(X))\n\t\tfor(i in 1:length(X)){\n\t\t\tif(min(x - X[i]) < 2*bw){\n\t\t\t\ttemp <- dnorm(x-X[i], sd=bw)\n\t\t\t\tif(sum(temp) > 2){\n\t\t\t\t\twtdV <- sum(temp*(y-Y[i])^2)/(sum(temp)-1)\n\t\t\t\t\tS[i] <- sqrt(wtdV)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tthese <- !is.na(Y) & !is.na(S)\n\t\tX <- X[these]\n\t\tY <- Y[these]\n\t\tS <- S[these]\n\t} else if(variance %in% c('L', 'l', 'linear', 'Linear')){\n\t\tif(bw[1] == 'default'){\n\t\t\tbw <- bw.nrd0(x)\n\t\t}\n\t\tS <- rep(NA, length(X))\n\t\tfor(i in 1:length(X)){\n\t\t\tif(min(x - X[i]) < 2*bw){\n\t\t\t\ttemp <- dnorm(x-X[i], sd=bw)\n\t\t\t\tif(sum(temp) > 2){\n\t\t\t\t\twtdV <- sum(temp*(y-Y[i])^2)/(sum(temp)-1)\n\t\t\t\t\tS[i] <- sqrt(wtdV)\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tg <- lm(S ~ X)\n\t\tS <- predict(g, list(X=X))\n\t\tthese <- !is.na(Y) & !is.na(S) & (S > 0)\n\t\tX <- X[these]\n\t\tY <- Y[these]\n\t\tS <- S[these]\n\t} else if(!(variance %in% c('c', 'C', 'constant', 'Constant'))){\n\t\tstop('Did not recognize form of the \"variance\" argument.\\n')\n\t}\n\tx <- c(X, rev(X))\n\ty <- c(Y-Z*S, rev(Y+Z*S))\n\tif(plotTube){\n\t\tpolygon(x, y, border=border, col=col, ...)\n\t}\n\tinvisible(list(x=x, y=y))\n}\n\n\n\n"
  },
  {
    "path": "ch_regr_simple_linear/figures/whatCanGoWrongWithLinearModel/whatCanGoWrongWithLinearModel.R",
    "content": "library(openintro)\nsource(\"makeTubeAdv.R\")\ndata(COL)\n\n# load the makeTube function (ch7 folder)\npch <- 20\ncex <- 1.75\ncol <- COL[1, 3]\n\nmyPDF('whatCanGoWrongWithLinearModel.pdf', 10, 2.8,\n      mar = rep(0.5, 4))\nlayout(matrix(1:8, 2),\n       rep(1, 4),\n       c(2, 1))\n\nthese <- simulated_scatter$group == 20\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nplot(x, y,\n     axes = FALSE,\n     pch = pch,\n     cex = cex,\n     col = \"#00000000\")\nbox()\nmakeTube(x, y,\n         type = 'quad',\n         addLine = FALSE,\n         col = COL[7, 3])\npoints(x, y,\n       pch = pch,\n       cex = cex,\n       col = COL[1, 2])\ng <- lm(y ~ x)\nabline(g)\nyR <- range(g$residuals)\nyR <- yR + c(-1, 1) * diff(yR) / 10\nplot(x, g$residuals,\n     axes = FALSE,\n     pch = pch,\n     cex = cex,\n     col = COL[1, 2],\n     ylim = yR)\nabline(h = 0, lty = 2)\nbox()\n\nthese <- simulated_scatter$group == 21\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nplot(x, y,\n     axes = FALSE,\n     pch = pch,\n     cex = cex,\n     col = \"#00000000\")\nbox()\nmakeTube(x, y,\n         addLine = FALSE,\n         col = COL[7, 3])\npoints(x, y,\n       pch = pch,\n       cex = cex,\n       col = COL[1,2])\ng <- lm(y ~ x)\nabline(g)\nyR <- range(g$residuals)\nyR <- yR + c(-1, 1) * diff(yR) / 10\nplot(x, g$residuals,\n     axes = FALSE,\n     pch = pch,\n     cex = cex,\n     col = COL[1, 2],\n     ylim = yR)\nabline(h = 0, lty = 2)\nbox()\n\nthese <- simulated_scatter$group == 22\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nplot(x, y,\n     axes = FALSE,\n     pch = pch,\n     cex = cex,\n     col = \"#00000000\")\nbox()\nmakeTubeAdv(x, y,\n            type = 'l',\n            variance = 'l',\n            bw = 0.03,\n            Z = 1.7,\n            col = COL[7, 3])\npoints(x, y,\n       pch = pch,\n       cex = cex,\n       col = COL[1, 2])\ng <- lm(y ~ x)\nabline(g)\nyR <- range(g$residuals)\nyR <- yR + c(-1, 1) * diff(yR) / 10\nplot(x, g$residuals,\n     axes = FALSE,\n     pch = pch,\n     cex = cex,\n     col = COL[1, 2],\n     ylim = yR)\nabline(h = 0, lty = 2)\nbox()\n\nthese <- simulated_scatter$group == 23\nx <- simulated_scatter$x[these]\ny <- simulated_scatter$y[these]\nplot(x, y,\n     axes = FALSE,\n     pch = pch,\n     cex = cex,\n     col = \"#00000000\")\nbox()\nmakeTube(x, y,\n         addLine = FALSE,\n         col = COL[7, 3])\npoints(x, y,\n       pch = pch,\n       cex = cex,\n       col = COL[1, 2])\ng <- lm(y ~ x)\nabline(g)\nyR <- range(g$residuals)\nyR <- yR + c(-1, 1) * diff(yR) / 10\nplot(x, g$residuals,\n     axes = FALSE,\n     pch = pch,\n     cex = cex,\n     col = COL[1, 2],\n     ylim = yR)\nabline(h = 0, lty = 2)\nbox()\n\nmakeTubeAdv(x,y, col = COL[7,3])\n\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/TeX/case_study_malaria_vaccine.tex",
    "content": "\\exercisesheader{}\n\n% 25\n\n\\eoce{\\qt{Side effects of Avandia\\label{randomization_avandia}} Rosiglitazone is the \nactive ingredient in the controversial type~2 diabetes medicine Avandia and has \nbeen linked to an increased risk of serious cardiovascular problems such as \nstroke, heart failure, and death. A common alternative treatment is pioglitazone, \nthe active ingredient in a diabetes medicine called Actos. In a nationwide \nretrospective observational study of 227,571 Medicare beneficiaries aged  \n65 years or older, it was found that 2,593 of the 67,593 patients using \nrosiglitazone and 5,386 of the 159,978 using pioglitazone had serious \ncardiovascular problems. These data are summarized in the contingency \ntable below. \\footfullcite{Graham:2010}\n\\begin{center}\n\\begin{tabular}{ll  cc c} \n                                &   & \\multicolumn{2}{c}{\\textit{Cardiovascular problems}} \\\\\n\\cline{3-4} \n                                    &               & Yes   & No        & Total \\\\\n\\cline{2-5}\n\\multirow{2}{*}{\\textit{Treatment}} & Rosiglitazone & 2,593 & 65,000    & 67,593 \\\\\n                                    & Pioglitazone  & 5,386 & 154,592   & 159,978 \\\\\n\\cline{2-5}\n                                    & Total         & 7,979 & 219,592   & 227,571\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item Determine if each of the following statements is true or false. If false, explain why. \\textit{Be careful:} The reasoning may be wrong even if the statement's conclusion is correct. In such cases, the statement should be considered false.\n\\begin{subparts}\n\\item Since more patients on pioglitazone had cardiovascular problems (5,386 vs. 2,593), we can conclude that the rate of cardiovascular problems for those on a pioglitazone treatment is higher.\n\\item The data suggest that diabetic patients who are taking rosiglitazone are more likely to have cardiovascular problems since the rate of incidence was (2,593 / 67,593 = 0.038) 3.8\\% for patients on this treatment, while it was only (5,386 / 159,978 = 0.034) 3.4\\% for patients on pioglitazone.\n\\item The fact that the rate of incidence is higher for the rosiglitazone group proves that rosiglitazone causes serious cardiovascular problems.\n\\item Based on the information provided so far, we cannot tell if the difference between the rates of incidences is due to a relationship between the two variables or due to chance.\n\\end{subparts}\n\\item What proportion of all patients had cardiovascular problems?\n\\item If the type of treatment and having cardiovascular problems were independent, about how many patients in the rosiglitazone group would we expect to have had cardiovascular problems?\n\\item We can investigate the relationship between outcome and treatment in this study using a randomization technique.  While in reality we would carry out the simulations required for randomization using statistical software, suppose we actually simulate using index cards. In order to simulate from the independence model, which states that the outcomes were independent of the treatment, we write whether or not each patient had a cardiovascular problem on cards, shuffled all the cards together, then deal them into two groups of size 67,593 and 159,978. We repeat this simulation 1,000 times and each time record the number of people in the rosiglitazone group who had cardiovascular problems. Use the relative frequency histogram of these counts to answer (i)-(iii).\n\\end{parts}\n\\begin{minipage}[c]{0.5\\textwidth}\n\\begin{subparts}\n\\item What are the claims being tested?\n\\item Compared to the number calculated in part~(c), which would provide more support for the alternative hypothesis,  \\textit{more} or \\textit{fewer} patients with cardiovascular problems in the rosiglitazone group?\n\\item What do the simulation results suggest about the relationship between taking rosiglitazone and having cardiovascular problems in diabetic patients?\n\\end{subparts}\n\\end{minipage}\n\\begin{minipage}[c]{0.5\\textwidth}\n\\Figures[A histogram is shown for \"Simulated rosiglitazone cardiovascular events\", where values range between 2250 to 2450. The histogram, starting from the left, starts with bins that have low values until about 2280, at which point the bins rises gradually until rising steeply starting at 2320 to a peak at about 2360. The bins decline sharply at about 2380 to about half of the height of the peak, and then gradually decline out to 2460 before being zero after that point.]{}{eoce/randomization_avandia}{randomization_avandia} \\\\\n\\end{minipage}\n}{}\n\n\\D{\\newpage}\n\n% 26\n\n\\eoce{\\qt{Heart transplants\\label{randomization_heart_transplants}} The Stanford \nUniversity Heart Transplant Study was conducted to determine whether an \nexperimental heart transplant program increased lifespan. Each patient \nentering the program was designated an official heart transplant candidate, \nmeaning that he was gravely ill and would most likely benefit from a new heart. \nSome patients got a transplant and some did not. The variable \\texttt{transplant} \nindicates which group the patients were in; patients in the treatment group got a \ntransplant and those in the control group did not. Of the 34 patients in the \ncontrol group, 30 died. Of the 69 people in the treatment group, 45 died. Another \nvariable called \\texttt{survived} was used to indicate whether or not the patient \nwas alive at the end of the study. \\footfullcite{Turnbull+Brown+Hu:1974}\n\\begin{center}\n\\Figures[A mosaic plot for variables \"experiment group\" (primary split) and \"survived\". The first tall rectangle for the \"control\" experiment group is about half the width of the second tall rectangle for \"treatment\". When looking at the secondary split for the control group, the \"alive\" outcome represents about 10\\% of the height and \"dead\" represents about 90\\% of the height. When looking at the secondary split for the treatment group, the \"alive\" outcome represents about 35\\% of the height and \"dead\" represents about 65\\% of the height.]{0.48}{eoce/randomization_heart_transplants}{randomization_heart_transplants_mosaic}\n\\Figures[A side-by-side box plot is shown for the variable \"Survival Time (days)\" for two box plots labeled \"control\" and \"survived\". The axis for survival time spans 0 to about 1800. The box for the control group spans about 0 to 50 with the median line at about 20, and the whiskers extend down to 0 and up to about 125. There are five observations shown beyond the upper whisker at locations of about 150, 250, 300, 400, and 1400. The box for the treatment spans about 100 to 650 with the median line about 250, and the whiskers extend down to 0 and up to about 1400. There are a few points beyond the upper whiskers at about 1550, 1575, and 1800.]{0.48}{eoce/randomization_heart_transplants}{randomization_heart_transplants_box}\n\\end{center}\n\\begin{parts}\n\\item Based on the mosaic plot, is survival independent of whether or not the \npatient got a transplant? Explain your reasoning.\n\\item What do the box plots below suggest about the efficacy (effectiveness) of the heart transplant treatment.\n\\item What proportion of patients in the treatment group and what proportion of \npatients in the control group died?\n\\item One approach for investigating whether or not the treatment is effective \nis to use a randomization technique.\n\\begin{subparts}\n\\item What are the claims being tested?\n\\item The paragraph below describes the set up for such approach, if we were \nto do it without using statistical software. Fill in the blanks with a number \nor phrase, whichever is appropriate.\n\\begin{adjustwidth}{2em}{2em}\nWe write \\textit{alive} on \\rule{2cm}{0.5pt} cards representing patients who were \nalive at the end of the study, and \\textit{dead} on \\rule{2cm}{0.5pt} cards \nrepresenting patients who were not. Then, we shuffle these cards and split them \ninto two groups: one group of size \\rule{2cm}{0.5pt} representing treatment, and \nanother group of size \\rule{2cm}{0.5pt} representing control. We calculate the \ndifference between the proportion of \\textit{dead} cards in the treatment and \ncontrol groups (treatment - control) and record this value. We repeat this 100 \ntimes to build a distribution centered at \\rule{2cm}{0.5pt}. Lastly, we calculate \nthe fraction of simulations where the simulated differences in proportions are \n\\rule{2cm}{0.5pt}. If this fraction is low, we conclude that it is unlikely to \nhave observed such an outcome by chance and that the null hypothesis should \nbe rejected in favor of the alternative.\n\\end{adjustwidth}\n\\item What do the simulation results shown below suggest about the effectiveness \nof the transplant program?\n\\end{subparts}\n\\end{parts}\n\\begin{center}\n\\Figures[A stacked dot plot is shown for what appears to be about 100 points on the variable \"Simulated Differences in Proportions\", which spans values of -0.25 to 0.25. There are 11 stacks of points, which are located at the following locations and in the following approximate quantities: 2 points at -0.23, 1 point at -0.19, 8 at -0.14, 15 points at -0.10, 18 points at -0.05, 20 points at -0.01, 12 points at 0.04, 10 points at 0.08, 6 points at 0.12, 4 points at 0.17, and 3 points at 0.21.]{0.6}{eoce/randomization_heart_transplants}{randomization_heart_transplants_rando}\n\\end{center}\n}{}\n"
  },
  {
    "path": "ch_summarizing_data/TeX/ch_summarizing_data.tex",
    "content": "\\begin{chapterpage}{Summarizing data}\n  \\chaptertitle{Summarizing data}\n  \\label{summarizingData}\n  \\label{ch_summarizing_data}\n  \\chaptersection{numericalData}\n  \\chaptersection{categoricalData}\n  \\chaptersection{caseStudyMalariaVaccine}\n\\end{chapterpage}\n\\renewcommand{\\chapterfolder}{ch_summarizing_data}\n\n\\chapterintro{This chapter focuses on the mechanics\n  and construction of summary statistics and graphs.\n  We use statistical software for generating\n  the summaries and graphs presented in this chapter\n  and book.\n  However, since this might be your first exposure to these\n  concepts, we take our time in this chapter to detail\n  how to create them.\n  Mastery of the content presented in this chapter\n  will be crucial for understanding the methods and\n  techniques introduced in rest of the book.}\n\n\n\n%%%%%\n\\section{Examining numerical data}\n\\label{numericalData}\n\n% library(openintro); ind <- c(1:5, 50); d <- loan50$interest_rate; (m <- round(mean(d), 2)); d[ind]; (dev <- d - m)[ind]; (dev2 <- dev^2)[ind]; (s2 <- sum(dev2) / 49); (s <- sqrt(s2)); var(d); sd(d); median(d); IQR(d); quantile(d, c(0.25, 0.75))\n\\newcommand{\\loanA}{10.90}\n\\newcommand{\\loanB}{9.92}\n\\newcommand{\\loanC}{26.30}\n\\newcommand{\\loanD}{9.92}\n\\newcommand{\\loanY}{9.43}\n\\newcommand{\\loanZ}{6.08}\n\\newcommand{\\loanAvg}{11.57}\n\\newcommand{\\loanVar}{25.52}\n\\newcommand{\\loanSD}{5.05}\n\\newcommand{\\loanN}{50}\n\\newcommand{\\loanMedianBelow}{9.93\\%}\n\\newcommand{\\loanMedianAbove}{9.93\\%}\n\\newcommand{\\loanMedian}{9.93\\%}\n\\newcommand{\\loanQA}{7.96}\n\\newcommand{\\loanQC}{13.72}\n\\newcommand{\\loanIQR}{5.76}\n\\newcommand{\\loanAdev}{-0.67}\n\\newcommand{\\loanBdev}{-1.65}\n\\newcommand{\\loanCdev}{14.73}\n\\newcommand{\\loanDdev}{-1.65}\n\\newcommand{\\loanYdev}{-2.14}\n\\newcommand{\\loanZdev}{-5.49}\n\\newcommand{\\loanSmallestValue}{5.31}\n\\newcommand{\\loanLargestValue}{26.30}\n\n\nIn this section we will explore techniques for\nsummarizing numerical variables.\nFor example, consider the \\var{loan\\us{}amount} variable\nfrom the \\data{loan50} data set, which represents the loan\nsize for all 50 loans in the data set.\nThis variable is numerical since we can sensibly discuss\nthe numerical difference of the size of two loans.\nOn the other hand, area codes and zip codes are not numerical,\nbut rather they are categorical variables.\n\nThroughout this section and the next, we will apply these\nmethods using the \\data{loan50} and \\data{county} data sets,\nwhich were introduced in Section~\\ref{dataBasics}.\nIf you'd like to review the variables from either data set,\nsee Figures~\\ref{loan50DF} and~\\ref{countyDF}.\n\n\n\\subsection{Scatterplots for paired data}\n\\label{scatterPlots}\n\n\\index{data!loan50|(}\n\nA \\term{scatterplot} provides a case-by-case view of data\nfor two numerical variables.\nIn Figure~\\ref{multiunitsVsOwnership} on\npage~\\pageref{multiunitsVsOwnership}, a scatterplot\nwas used to examine the homeownership rate against\nthe fraction of housing units that were part of\nmulti-unit properties\n(e.g. apartments) in the \\data{county} data set.\nAnother scatterplot is shown in Figure~\\ref{loan50_amt_vs_income},\ncomparing the total income of a borrower\n(\\var{total\\us{}income}) and the amount they borrowed\n(\\var{loan\\us{}amount}) for the \\data{loan50} data set.\nIn any scatterplot, each point represents a single case.\nSince there are \\loanN{} cases in \\data{loan50},\nthere are \\loanN{} points in Figure~\\ref{loan50_amt_vs_income}.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure\n    [A scatterplot is shown with \"Total Income\" along the horizontal axis (range from \\$0 to \\$325,000) and \"Loan Amount\" along the vertical axis (range from \\$0 to \\$40,000). The points lie in a range from \\$2,000 to \\$33,000 in loan amount when total income is smaller than \\$150,000 (representing most of the points). The range of loan amounts is higher when total income is greater than \\$175,000, with the range of observations being about \\$15,000 to \\$40,000.]\n    {0.8}{loan50_amt_vs_income}\n  \\caption{A scatterplot of \\var{total\\us{}income}\n      versus \\var{loan\\us{}amount} for the\n      \\data{loan50} data set.}\n  \\label{loan50_amt_vs_income}\n\\end{figure}\n\nLooking at Figure~\\ref{loan50_amt_vs_income},\nwe see that there are many borrowers with an income below\n\\$100,000 on the left side of the graph,\nwhile there are a handful of borrowers with income above~\\$250,000.\n\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{medianHHIncomePoverty}\n    shows a plot of median household income\n    against the poverty rate for 3,142 counties.\n    What can be said about the relationship between\n    these variables?}\n  The relationship is evidently \\term{nonlinear},\n  as highlighted by the dashed line.\n  This is different from previous scatterplots we've seen,\n  which show relationships that do not show much, if any,\n  curvature in the trend.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure\n    [A scatterplot of a few thousand points is shown with \"Poverty Rate\" along the horizontal axis (range from 0\\% to 55\\%) and \"Median Household Income\" along the vertical axis (range from \\$0 to \\$130,000). A curved trend line is overlaid on the points starting higher on the left and decreasing as it moves right, but it starts flattening the further right it goes. Below 10\\% poverty rate, points range from about \\$40,000 to \\$130,000. Between 10\\% to 20\\%, the range is lower at about \\$25,000 to close to \\$100,000. For 20\\% to 30\\%, the points ranges from about \\$22,000 to just over \\$60,000. For 30\\% to 50\\%, the trend is mostly flat with values ranging from about \\$20,000 to \\$50,000.]\n    {0.8}{medianHHIncomePoverty}\n  \\caption{A scatterplot of the median household income\n      against the poverty rate for the\n      \\data{county} data set.\n      A statistical model has also been fit to the data\n      and is shown as a dashed line.}\n  \\label{medianHHIncomePoverty}\n\\end{figure}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat do scatterplots reveal about the data,\nand how are they useful?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Answers may vary.\n  Scatterplots are helpful in quickly spotting associations\n  relating variables,\n  whether those associations come in the form of simple\n  trends or whether those relationships are more complex.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nDescribe two variables that would have a horseshoe-shaped\nassociation in a scatterplot ($\\cap$ or $\\frown$).\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Consider the case\n  where your vertical axis represents something ``good'' and\n  your horizontal axis represents something that is only good\n  in moderation.\n  Health and water consumption fit this description: we require\n  some water to survive, but consume too much and it becomes\n  toxic and can kill a person.}\n\n\n\n\\subsection{Dot plots and the mean}\n\\label{dotPlot}\n\nSometimes two variables are one too many:\nonly one variable may be of interest.\nIn these cases, a dot plot provides the most basic of displays.\nA~\\term{dot plot} is a one-variable scatterplot;\nan example using the interest rate of \\loanN{} loans\nis shown in Figure~\\ref{loan_int_rate_dot_plot}.\nA stacked version of this dot plot is shown in\nFigure~\\ref{loan_int_rate_dot_plot_stacked}.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure\n    [A dot plot is shown for the variable \"Interest Rate\". There is a horizontal axis ranging from about 5\\% to a bit over 25\\%, and then several points are shown horizontally above the axis, scattered over the range. There is a higher density of points between 5\\% to 11\\%, with a moderate density of points from 12\\% to about 20\\%, and then a few more observations at about 22\\%, 25\\%, and 26\\%. A red triangle is also shown at approximately 12\\%.]\n    {0.76}{loan_int_rate_dot_plot}\n  \\caption{A dot plot of \\var{interest\\us{}rate}\n      for the \\data{loan50} data set.\n      The distribution's mean is shown as a red triangle.}\n  \\label{loan_int_rate_dot_plot}\n\\end{figure}\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures\n    [A stacked dot plot is shown for the variable \"Interest Rate\". There is a horizontal axis ranging from about 5\\% to a bit over 25\\%, and then several stacks of points are shown at values 5\\%, 6\\%, 7\\%, and so on. There are 3 points stacked at 5\\%, 3 points stacked at 6\\%, 5 at 7\\%, 4 at 8\\%, 5 at 9\\%, 8 at 10\\%, 5 at 11\\%, 1 at 11\\%, 3 at 12\\%, then 1 each at 14\\%, 15\\%, and 16\\%, 3 at 17\\%, 2 at 18\\%, and then 1 each at 19\\%, 20\\%, 21\\%, 25\\%, and 26\\%. A red triangle is also shown at approximately 12\\%.]\n    {0.76}\n    {loan_int_rate_dot_plot}\n    {loan_int_rate_dot_plot_stacked}\n  \\caption{A stacked dot plot of\n      \\var{interest\\us{}rate}\n      for the \\data{loan50} data set.\n      The~rates have been rounded to the nearest\n      percent in this plot, and the\n      distribution's mean is shown as a red triangle.}\n  \\label{loan_int_rate_dot_plot_stacked}\n\\end{figure}\n\n\\D{\\newpage}\n\nThe \\term{mean}, often called the\n\\term{average}\\index{mean!average}, is a common way\nto measure the center of a \\mbox{\\term{distribution}} of data.\nTo compute the mean interest rate, we add up all the interest\nrates and divide by the number of observations:\n\\begin{align*}\n\\bar{x}\n    = \\frac{\\text{\\loanA\\%} + \\text{\\loanB\\%} + \\text{\\loanC\\%} +\n        \\cdots + \\text{\\loanZ\\%}}{\\loanN{}}\n    = \\loanAvg{}\\%\n% library(openintro); loan50$interest_rate[c(1:3, 50)]; mean(loan50$interest_rate)\n\\end{align*}\nThe sample mean is often labeled $\\bar{x}$.\nThe letter $x$ is being used as a generic placeholder\nfor the variable of interest, \\var{interest\\us{}rate},\nand the bar over the $x$ communicates we're looking at the\naverage interest rate, which for these 50 loans was \\loanAvg{}\\%.\nIt is useful to think of the mean as the balancing point\nof the distribution, and it's shown as a triangle in Figures~\\ref{loan_int_rate_dot_plot}\nand~\\ref{loan_int_rate_dot_plot_stacked}.\n\n\\begin{onebox}{Mean}%\nThe sample mean can be computed as the sum of the\nobserved values divided by the number of observations:\n\\begin{align*}\n\\bar{x} = \\frac{x_1 + x_2 + \\cdots + x_n}{n}\n\\end{align*}\nwhere $x_1$, $x_2$, $\\dots$, $x_n$ represent\nthe $n$ observed values.\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nExamine the equation for the mean.\nWhat does $x_1$ correspond to? And $x_2$?\nCan you infer a general meaning to what $x_i$\nmight represent?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$x_1$ corresponds to the\n  interest rate for the first loan in the sample (\\loanA\\%),\n  $x_2$ to the second loan's interest rate (\\loanB\\%),\n  and $x_i$ corresponds to the interest rate for the\n  $i^{th}$ loan in the data set.\n  For example, if $i = 4$, then we're examining $x_4$,\n  which refers to the fourth observation in the data set.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat was $n$ in this sample of\nloans?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The sample size was $n = 50$.}\n\nThe \\data{loan50} data set represents a sample from\na larger population of loans made through Lending Club.\nWe could compute a mean for this population in the same way\nas the sample mean.\nHowever, the population mean has a special label: $\\mu$.\n\\index{Greek!mu@mu ($\\mu$)}\nThe symbol $\\mu$ is the Greek letter \\emph{mu} and represents\nthe average of all observations in the population.\nSometimes a subscript, such as $_x$,\nis used to represent which variable the population mean\nrefers to, e.g. $\\mu_x$.\nOften times it is too expensive to measure the\npopulation mean precisely, so we often estimate\n$\\mu$ using the sample mean, $\\bar{x}$.\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{The average interest rate across all loans\n    in the population can be estimated using the sample data.\n    Based on the sample of 50 loans,\n    what would be a reasonable estimate of $\\mu_x$,\n    the mean interest rate for all loans in the\n    full data set?}\n  The sample mean, \\loanAvg{}\\%, provides a rough estimate\n  of $\\mu_x$.\n  While it's not perfect, this is our single best guess\n  %\\emph{point estimate}\\index{point estimate}\n  of the average interest rate of all the loans in the\n  population under study.\n\n  In Chapter~\\ref{foundationsForInference} and beyond,\n  we will develop tools to characterize the accuracy\n  of \\emph{point estimates}\\index{point estimate}\n  like the sample mean.\n  As you might have guessed,\n  point estimates based on larger samples tend to be\n  more accurate than those based on smaller samples.\n\\end{nexample}\n\\end{examplewrap}\n\n\n\\begin{examplewrap}\n\\begin{nexample}{The mean is useful because it allows us to\n    rescale or standardize a metric into something more easily\n    interpretable and comparable.\n    Provide 2 examples where the mean\n    is useful for making comparisons.}\n\n  1. We would like to understand if a new drug is more\n  effective at treating asthma attacks than the standard drug.\n  A trial of 1500 adults is set up, where 500 receive the new\n  drug, and 1000 receive a standard drug in the control\n  group:\\vspace{-2mm}\n  \\begin{center}\n  \\begin{tabular}{l cc}\n  %\\hline\n  &           New drug & Standard drug \\\\\n  \\hline\n  Number of patients   & 500 & 1000 \\\\\n  Total asthma attacks & 200 & 300 \\\\\n  \\hline\n  %average attacks\n  %per patient    & 0.4 & 0.2 \\\\\n  %\\hline\n  \\end{tabular}\n  \\end{center}\n  Comparing the raw counts of 200 to 300 asthma attacks\n  would make it appear that the new drug is better,\n  but this is an artifact of the imbalanced group sizes.\n  Instead, we should look at the average number of asthma\n  attacks per patient in each group:\n  \\begin{align*}\n  & \\text{New drug: } 200 / 500 = 0.4 % \\\\\n      %\\frac{200}{500} = 0.4 % \\\\\n  && \\text{Standard drug: } 300 / 1000 = 0.3\n      %\\frac{300}{1000} = 0.3\n  % &     &&  %\\\\\n  % & = 0.3\\text{ asthma attacks per patient}\n  %     && = 0.4\\text{ asthma attacks per patient}\n  \\end{align*}\n  The standard drug has a lower average number of asthma\n  attacks per patient than the average in the treatment group.\n\n  2. Emilio opened a food truck last year where he sells burritos,\n  and his business has stabilized over the last 3 months.\n  Over that 3 month period, he has made \\$11,000 while\n  working 625 hours.\n  Emilio's average hourly earnings provides\n  a useful statistic for evaluating whether his venture is,\n  at~least from a financial perspective, worth it:\n  \\begin{align*}\n  \\frac{\\$11000}{625\\text{ hours}} = \\$17.60\\text{ per hour}\n  \\end{align*}\n  By knowing his average hourly wage,\n  Emilio now has put his earnings into a standard unit that\n  is easier to compare with many other jobs that he might\n  consider.\n\\end{nexample}\n\\end{examplewrap}\n\n%{What are some contexts that highlight\n%    the value of the mean?}\n%  Here are a few scenarios highlighting why the mean can be\n%  particularly useful.\n%  \\begin{itemize}\n%  \\item If a waitress makes an average of \\$3.20 per table,\n%      then she can get a reasonable estimate of how much\n%      money she will make if she knows she'll turn over\n%      about 15 tables in a night:\n%      \\begin{align*}\n%      total &= average \\times count\n%          = \\$3.20 \\times 15\n%          = \\$48.00\n%      \\begin{align*}\n%      The estimate won't be perfect, but it will still\n%      be a useful reference of what she can expect.\n%  \\item For every \\$1 played on roulette,\n%      a gambler will lose, on average, 2.7 cents.\n%      If she plays 1000 games and bets \\$1 each time,\n%      her expected loss is\n%      \\begin{align*}\n%      total = average \\times count\n%          = 2.7 \\cents \\times 1000\n%          = \\$27\n%      \\begin{align*}\n%  \\end{itemize}\n%  The average provides us a sensible value to think\n%  about scaling gains and losses.\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose we want to compute the average income\n    per person in the US.\n    To do so, we might first think to take\n    the mean of the per capita incomes across the 3,142 counties\n    in the \\data{county} data set.\n    What would be a better approach?}\n    \\label{wtdMeanOfIncome}\n  The \\data{county} data set is special in that each county\n  actually represents many individual people.\n  If we were to simply average across the \\var{income}\n  variable, we would be treating counties with 5,000 and\n  5,000,000 residents equally in the calculations.\n  Instead, we should compute the total income for each county,\n  add up all the counties' totals, and then divide by the number\n  of people in all the counties.\n  If we completed these steps with the \\data{county} data,\n  we would find that the per capita income for the US is\n  \\$30,861.\n  Had we computed the \\emph{simple} mean of per capita income\n  across counties, the result would have been just \\$26,093!\n\n  This example used what is called a\n  \\term{weighted mean}\\index{mean!weighted mean}.\n  For more information\n  on this topic, check out the following\n  online supplement regarding weighted means\n  \\oiRedirect{stat_wtd_mean}\n      {openintro.org/d?file=stat\\_wtd\\_mean}.\n\\end{nexample}\n\\end{examplewrap}\n% library(openintro); all_income <- sum(county$pop2017 * county$per_capita_income, na.rm = TRUE); all_pop <- sum(county$pop2017, na.rm = TRUE); all_income / all_pop; mean(county$per_capita_income, na.rm = TRUE)\n\n%Example~\\ref{wtdMeanOfIncome} used what is called\n%a \\term{weighted mean}\\index{mean!weighted mean},\n%which will not be a key topic in this textbook.\n%However, we have provided an online supplement on\n%weighted means for interested readers under\n%\\oiRedirect{stat_wtd_mean}\n%    {www.openintro.org/d?file=stat\\_wtd\\_mean}.\n\n\n\n\\subsection{Histograms and shape}\n\\label{histogramsAndShape}\n\nDot plots show the exact value for each observation.\nThis is useful for small data sets, but they can become\nhard to read with larger samples. Rather than showing the\nvalue of each observation, we prefer to think of the value\nas belonging to a \\emph{bin}.\nFor example, in the \\data{loan50} data set, we created\na table of counts for the number of loans with interest\nrates between 5.0\\% and 7.5\\%, then the number of loans\nwith rates between 7.5\\% and 10.0\\%, and so on.\nObservations that fall on the boundary of a bin\n(e.g. 10.00\\%) are allocated to the lower bin.\nThis tabulation is shown in Figure~\\ref{binnedIntRateAmountTable}.\nThese binned counts are plotted as bars in\nFigure~\\ref{loan50IntRateHist} into what is called\na \\term{histogram}, which resembles a more heavily binned\nversion of the stacked dot plot shown in\nFigure~\\ref{loan_int_rate_dot_plot_stacked}.\n\n\\begin{figure}[ht]\n\\centering\\small\n\\begin{tabular}{l ccc ccc ccc}\n  \\hline\n  Interest Rate &\n      5.0\\% - 7.5\\% &\n      7.5\\% - 10.0\\% &\n      10.0\\% - 12.5\\% &\n      12.5\\% - 15.0\\% &\n      $\\cdots$ &\n      25.0\\% - 27.5\\% \\\\\n  \\hline\n  Count & 11 & 15 & 8 & 4 & $\\cdots$ & 1 \\\\\n  \\hline\n\\end{tabular}\n\\caption{Counts for the binned\n    \\var{interest\\us{}rate} data.}\n\\label{binnedIntRateAmountTable}\n\\end{figure}\n% library(openintro); library(xtable); d <- loan50$interest_rate; max(d); t1 <- table(cut(d, seq(5, 27.5, 2.5), right = TRUE)); t1; xtable(rbind(t1))\n\n\\begin{figure}[bth]\n  \\centering\n  \\Figure\n    [A histogram with a horizontal axis of \"Interest Rate\" and a vertical axis showing the frequency of occurrence of different bins of interest rate. The first bin is from 5\\%-7.5\\% with a frequency (count) of 11 observations, 7.5\\%-10\\% has a frequency of 15, 10\\%-12.5\\% has 8, 12.5\\%-15\\% has 4, 15\\%-17.5\\% has 5, 17.5\\%-20\\% has 4, and then the 20\\%-22.5\\%, 22.5\\%-25\\%, and 25\\%-27.5\\% bins each have a frequency of 1.]\n    {0.76}{loan50IntRateHist}\n  \\caption{A histogram of \\var{interest\\us{}rate}.\n      This distribution is strongly skewed to the right.\n      \\index{skew!strong}}\n  \\label{loan50IntRateHist}\n\\end{figure}\n\nHistograms provide a view of the \\term{data density}.\nHigher bars represent where the data are relatively more common.\nFor instance, there are many more loans with rates between\n5\\%~and~10\\% than loans with rates between 20\\% and~25\\%\nin the data set.\nThe bars make it easy to see how the density of the data\nchanges relative to the interest rate.\n\nHistograms are especially convenient for understanding the\nshape of the data distribution\\label{shapeFirstDiscussed}.\nFigure~\\ref{loan50IntRateHist} suggests that most loans\nhave rates under 15\\%, while only a handful\nof loans have rates above 20\\%.\nWhen data trail off to the right in this way\nand has a longer right \\hiddenterm{tail}\\index{skew!tail},\nthe shape is said to be\n\\termsub{right skewed}{skew!right skewed}.\\footnote{Other\n  ways to describe data that are right skewed:\n  \\termni{skewed to the right},\n  \\termni{skewed to the high end},\n  or \\termni{skewed to the positive end}.}\n\nData sets with the reverse characteristic --\na long, thinner tail to the left --\nare said to be \\termsub{left skewed}{skew!left skewed}.\nWe also say that such a distribution has a long left tail.\nData sets that show roughly equal trailing off in both\ndirections are called \\term{symmetric}.\\index{skew!symmetric}\n\n\\begin{onebox}{Long tails to identify skew}\n  When data trail off in one direction, the distribution\n  has a \\term{long tail}. \\index{skew!long tail|textbf}\n  If a distribution has a long left tail, it is left skewed.\n  If a distribution has a long right tail, it is right skewed.\n\\end{onebox}\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nTake a look at the dot plots in\nFigures~\\ref{loan_int_rate_dot_plot}\nand~\\ref{loan_int_rate_dot_plot_stacked}.\nCan you see the skew in the data? Is it easier to see the\nskew in this histogram or the dot plots?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The skew\n  is visible in all three plots, though the flat dot plot\n  is the least useful.\n  The stacked dot plot and histogram are helpful\n  visualizations for identifying skew.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nBesides the mean (since it was labeled), what can you see\nin the dot plots that you cannot see in the\nhistogram?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The interest rates for individual loans.}\n\nIn addition to looking at whether a distribution is skewed\nor symmetric, histograms can be used to identify modes.\nA \\term{mode} is represented by a prominent peak in the\ndistribution.\nThere is only one prominent peak in the histogram of\n\\var{loan\\us{}amount}.\n\nA definition of \\emph{mode} sometimes\ntaught in math classes is the value with the\nmost occurrences in the data set.\nHowever, for many real-world data sets, it is common to have\n\\emph{no} observations with the same value in a data set,\nmaking this definition impractical in data analysis.\n\nFigure~\\ref{singleBiMultiModalPlots} shows histograms that\nhave one, two, or three prominent peaks.\nSuch distributions are called\n\\term{unimodal},\n\\term{bimodal}, and\n\\term{multimodal}, respectively.\nAny distribution with more than 2~prominent peaks is\ncalled multimodal.\nNotice that there was one prominent peak in the unimodal\ndistribution with a second less prominent peak that was\nnot counted since it only differs from its neighboring\nbins by a few observations.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure\n    [Three histograms are shown. The first histogram shows bins of width 2 between 0 to 18 (this is along the horizontal axis), and the frequencies are 3, 16, 16, 7, 11, 6, 4, 1, and 1. The second histogram, representing a different data set, shows bins of width 2 with values ranging from 0 to 20, where the bin counts in order are 2, 9, 5, 2, 2, 2, 2, 10, 19, and 9. The third histogram, representing yet another data set, shows bins of width 2 with values ranging from 0 to 22, where the bin counts in order are 10, 8, 4, 3, 1, 20, 15, 3, 15, 18, and 5.]\n    {0.9}{singleBiMultiModalPlots}\n  \\caption{Counting only prominent peaks, the\n      distributions are (left to right) unimodal,\n      bimodal, and multimodal.\n      Note that we've said the left plot is unimodal\n      intentionally.\n      This is because we are counting \\emph{prominent}\n      peaks, not just any peak.}\n  \\label{singleBiMultiModalPlots}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{loan50IntRateHist}\n    reveals only one prominent mode in the interest rate.\n    Is the distribution unimodal, bimodal, or multimodal?}\n  Unimodal.\n  Remember that \\emph{uni} stands for 1 (think \\emph{uni}cycles).\n  Similarly, \\emph{bi} stands for~2 (think \\emph{bi}cycles).\n  We're hoping a \\emph{multicycle} will be invented to complete\n  this analogy.\n\\end{nexample}\n\\end{examplewrap}\n\n%{Looking back the stacked dot plot in\n%    Figure~\\ref{loan_int_rate_dot_plot_stacked},\n%    it would be reasonable to wonder if the distribution\n%    of loan amounts is actually bimodal or even multimodal.\n%    In fact, we wondered the same thing -- so we investigated!}\n%  What we found is that the bumps evident in the dot plot\n%  tend to happen at \\$5,000 increments.\n%  That is, people made loan requests in round amounts.\n%  While that is interesting, we often are more interested\n%  in understanding the general shape of a data set rather\n%  than characterizing some special property like this,\n%  and for this reason, we think the data set is better\n%  described as unimodal.\n%  However, this example highlights that there isn't\n%  always one ``correct'' answer for the number of modes.\n%\n%  There's a broader lesson to take away\n%  from this example:\n%  when we plot data in multiple ways,\n%  we learn about different properties of the data\n%  that no one plot would reveal all on its own.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nHeight measurements of young students and adult teachers\nat a K-3 elementary school were taken.\nHow many modes would you expect in this height\ndata set?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{There might be two height groups visible\n  in the data set: one of the students and one of the adults.\n  That is, the data are probably bimodal.}\n\nLooking for modes isn't about finding a clear and correct\nanswer about the number of modes in a distribution,\nwhich is why \\emph{prominent}\\index{prominent} is not\nrigorously defined in this book.\nThe most important part of this examination is to better\nunderstand your data.\n\n\n\n\\D{\\newpage}\n\n\\subsection{Variance and standard deviation}\n\\label{variability}\n\nThe mean was introduced as a method to describe the center of\na data set, and \\indexthis{variability}{variability} in the\ndata is also important.\nHere, we introduce two measures of variability:\nthe variance and the standard deviation.\nBoth of these are very useful in data analysis,\neven though their formulas are a bit tedious to calculate\nby hand.\nThe standard deviation is the easier of the two to comprehend,\nand it roughly describes how far away the typical observation\nis from the mean.\n\nWe call the distance of an observation from its mean its \\term{deviation}. Below are the deviations for the $1^{st}_{}$, $2^{nd}_{}$, $3^{rd}$, and $50^{th}_{}$ observations in the \\var{interest\\us{}rate} variable:\n\\begin{align*}\nx_1^{}-\\bar{x} &= \\loanA - \\loanAvg{} = \\loanAdev \\hspace{5mm}\\text{ } \\\\\nx_2^{}-\\bar{x} &= \\loanB - \\loanAvg{} = \\loanBdev \\\\\nx_3^{}-\\bar{x} &= \\loanC - \\loanAvg{} = \\loanCdev \\\\\n\t\t\t&\\ \\vdots \\\\\nx_{50}^{}-\\bar{x} &= \\loanZ - \\loanAvg{} = \\loanZdev\n\\end{align*}\nIf we square these deviations and then take an average,\nthe result is equal to the sample\n\\term{variance}\\label{varianceIsDefined},\ndenoted by $s_{}^2$:\n\\begin{align*}\ns_{}^2 &= \\frac{(\\loanAdev)_{}^2 + (\\loanBdev)_{}^2 + (\\loanCdev)_{}^2 + \\cdots + (\\loanZdev)_{}^2}{\\loanN{}-1} \\\\\n\t&= \\frac{0.45 + 2.72 + 216.97 + \\cdots + 30.14}{49} \\\\\n\t&= \\loanVar{}\n\\end{align*}\nWe divide by $n - 1$, rather than dividing by $n$,\nwhen computing a sample's variance;\nthere's some mathematical nuance here, but the end result is that\ndoing this makes this statistic slightly more reliable and useful.\n\nNotice that squaring the deviations does two things.\nFirst, it makes large values relatively much larger,\nseen by comparing $(\\loanAdev)^2$, $(\\loanBdev)^2$, $(\\loanCdev)^2$,\nand $(\\loanZdev)^2$.\nSecond, it gets rid of any negative signs.\n\nThe \\term{standard deviation} is defined as the square root of the variance:\n\\begin{align*}\ns = \\sqrt{\\loanVar{}} = \\loanSD{}\n\\end{align*}\nWhile often omitted, a subscript of $_x$ may be added\nto the variance and standard deviation,\ni.e. $s_x^2$ and $s_x^{}$, if it is useful as a reminder\nthat these are the variance and standard deviation of the\nobservations represented by $x_1^{}$, $x_2^{}$, ..., $x_n^{}$.\n\n\\begin{onebox}{Variance and standard deviation}\n  The variance is the average squared distance from the mean.\n  The standard deviation is the square root of the variance.\n  The standard deviation is useful when considering how far\n  the data are distributed from the mean.\\vspace{3mm}\n\n  The standard deviation represents the typical deviation\n  of observations from the mean.\n  Usually about 70\\% of the data will be within one standard\n  deviation of the mean and about 95\\% will be within two\n  standard deviations.\n  However, as seen in Figures~\\ref{sdRuleForIntRate}\n  and~\\ref{severalDiffDistWithSdOf1}, these percentages are\n  not strict rules.\n\\end{onebox}\n\nLike the mean, the population values for variance\nand standard deviation have special symbols:\n$\\sigma_{}^2$ for the variance and $\\sigma$ for the\nstandard deviation.\nThe symbol $\\sigma$\\index{Greek!sigma@sigma ($\\sigma$)}\nis the Greek letter \\emph{sigma}.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure\n    [A dot plot of 50 observations is shown with values ranging from about 5\\% to 26\\%. The data set is the same as that shown in the dot plot in Figure~\\ref{loan_int_rate_dot_plot}, where the data is more dense from 5\\% to about 11\\%, has medium density from about 12\\% to 20\\%, and then there are a few more values scattered in the 20\\% to 27\\% range. Shading is shown to represent the regions within 1, 2, and 3 standard deviations. The region within 1 standard deviation is from 6.5\\% to 16.7\\%, representing 34 of the 50 data points. The region within 2 standard deviation runs left off of the chart (but would be from about 1.4\\%) to 21.8\\% and contains 48 of the 50 data points. The third standard deviation is shown to extend out to 26.9\\%, and all 50 observations are contained within the 3 standard deviations.]\n    {0.73}{sdRuleForIntRate}\n  \\caption{For the \\var{interest\\us{}rate} variable,\n      34 of the 50 loans (68\\%) had interest rates within\n      1~standard deviation of the mean,\n      and 48 of the 50 loans (96\\%) had rates within\n      2~standard deviations.\n      Usually about 70\\% of the data are within 1~standard\n      deviation of the mean and 95\\% within 2~standard\n      deviations, though this is far from a hard rule.}\n  \\label{sdRuleForIntRate}\n\\end{figure}\n\n%\\begin{onebox}{How to think about the standard deviation}\n%  The standard deviation represents the typical deviation\n%  of observations from the mean.\n%  Usually about 70\\% of the data will be within one standard\n%  deviation of the mean and about 95\\% will be within two\n%  standard deviations.\n%  However, as seen in Figures~\\ref{sdRuleForIntRate}\n%  and~\\ref{severalDiffDistWithSdOf1}, these percentages are\n%  not strict rules.\n%\\end{onebox}\n\n\\begin{figure}\n  \\centering\n  \\Figure\n    [Three histograms are shown (upper, middle, lower). Each distribution also shows shading -- dark gray between -1 to 1, lighter gray between -2 and 2, and light gray between -3 and 3, and then very light gray further out. The upper plot shows only two bins with non-zero values and of equal height at -1 and 1. middle plot shows a bell-shaped curve, where most of the higher bin values are between -1 and 1, middling heights are between -2 to -1 and 1 to 2, and the data trails off in each direction with ever-smaller values further out. The lower histogram shows no data below about -1.6, a quick increase to a peak at about -0.7 and then a slow decline of values to about half the max height at 1 and further trails off to ever smaller values to a horizontal location of 3 and beyond.]\n    {0.6}{severalDiffDistWithSdOf1}\n  \\caption{Three very different population distributions\n      with the same mean $\\mu=0$ and standard deviation\n      $\\sigma=1$.}\n  \\label{severalDiffDistWithSdOf1}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nOn page~\\pageref{shapeFirstDiscussed}, the concept of\nshape of a distribution was introduced.\nA good description of the shape of a distribution should\ninclude modality and whether the distribution is symmetric\nor skewed to one side.\nUsing Figure~\\ref{severalDiffDistWithSdOf1} as an example,\nexplain why such a description is\nimportant.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Figure~\\ref{severalDiffDistWithSdOf1}\n  shows three distributions that look quite different,\n  but all have the same mean, variance,\n  and standard deviation.\n  Using modality, we can distinguish between the\n  first plot (bimodal) and the last two (unimodal).\n  Using skewness, we can distinguish between the\n  last plot (right skewed) and the first two.\n  While a picture, like a histogram, tells a more\n  complete story, we can use modality and shape\n  (symmetry/skew) to characterize basic information\n  about a~distribution.}\n\n\\begin{examplewrap}\n\\begin{nexample}{Describe the distribution of the\n    \\var{interest\\us{}rate} variable using\n    the histogram in Figure~\\ref{loan50IntRateHist}.\n    The description should incorporate the center,\n    variability, and shape of the distribution,\n    and it should also be placed in context.\n    Also note any especially unusual cases.}\n  The distribution of interest rates is unimodal\n  and skewed to the high end.\n  Many of the rates fall near the mean at 11.57\\%,\n  and most fall within one standard deviation (5.05\\%)\n  of the mean.\n  There are a few exceptionally large interest rates\n  in the sample that are above 20\\%.\n\\end{nexample}\n\\end{examplewrap}\n\nIn practice, the variance and standard deviation are sometimes\nused as a means to an end, where the ``end'' is being able to\naccurately estimate the uncertainty associated with a sample\nstatistic.\nFor example, in Chapter~\\ref{foundationsForInference}\nthe standard deviation is used in calculations that help us\nunderstand how much a sample mean varies from one sample\nto the next.\n\n\n\\D{\\newpage}\n\n\\subsection{Box plots, quartiles, and the median}\n\nA \\term{box plot} summarizes a data set using five\nstatistics while also plotting unusual observations.\nFigure~\\ref{loan_int_rate_box_plot_layout} provides\na vertical dot plot alongside a box plot of the\n\\var{interest\\us{}rate} variable from\nthe \\data{loan50} data set.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure\n    [What is shown in a a dot plot adjacent to what is called a \"box plot\". The data values are the same ones used in past dot plots, where the data shows greatest density from 5\\% to 11\\%, moderate density from 12\\% to 20\\%, and then a few more values at about 22\\%, 25\\%, and 26\\%. The box plot adjacent to the data shows a box that would encapsulate the middle 50\\% of the data, from about 8\\% to 13\\%. The median is also annotated with a line through the center of the box. From here, the data extend out with \"whiskers\" up to a distance up to $1.5 \\times IQR$ below and above the box to capture as much data as possible. There are two observations that extend beyond this range at 25\\% and 26\\%.]\n    {0.86}{loan_int_rate_box_plot_layout}\n  \\caption{A vertical dot plot, where points have been\n      horizontally stacked, next to a labeled box plot\n      for the interest rates of the \\loanN{} loans.}\n  \\label{loan_int_rate_box_plot_layout}\n\\end{figure}\n\nThe first step in building a box plot is drawing a dark line\ndenoting the \\term{median}, which splits the data in half.\nFigure~\\ref{loan_int_rate_box_plot_layout} shows 50\\% of the\ndata falling below the median and other 50\\% falling above\nthe median.\nThere are \\loanN{} loans in the data set\n(an even number) so the data are perfectly split into two\ngroups of~25.\nWe take the median in this case to be the average of the\ntwo observations closest to the $50^{th}$ percentile,\nwhich happen to be the same value in this data set:\n$(\\text{\\loanMedianAbove{}} + \\text{\\loanMedianBelow{}}) / 2\n  = \\text{\\loanMedian{}}$.\nWhen there are an odd number of observations,\nthere will be exactly one observation that splits the data\ninto two halves, and in such a case that observation\nis the median (no average needed).\n\n\\begin{onebox}{Median: the number in the middle}\n  If the data are ordered from smallest to largest,\n  the \\term{median} is the observation right in the middle.\n  If there are an even number of observations,\n  there will be two values in the middle,\n  and the median is taken as their average.\n\\end{onebox}\n\nThe second step in building a box plot is drawing\na rectangle to represent the middle 50\\% of the data.\nThe total length of the box, shown vertically in\nFigure~\\ref{loan_int_rate_box_plot_layout},\nis called the \\term{interquartile range} (\\hiddenterm{IQR},\nfor short).\nIt, like the standard deviation, is a measure\nof \\indexthis{variability}{variability} in data.\nThe more variable the data, the larger the standard\ndeviation and~IQR tend to be.\nThe two boundaries of the box are called the\n\\term{first quartile} \\index{quartile!first quartile}\n(the $25^{th}$ \\hiddenterm{percentile},\ni.e. 25\\% of the data fall below this value)\nand the \\term{third quartile} \\index{quartile!third quartile}\n(the $75^{th}$ percentile), and these are often labeled $Q_1$\n\\index{quartile!Q1@Q$_1$} and $Q_3$\\index{quartile!Q3@Q$_3$},\nrespectively.\n\n\\begin{onebox}{Interquartile range (IQR)}\n  The IQR\\index{interquartile range} is the length\n  of the box in a box plot.\n  It is computed as\n  \\begin{eqnarray*}\n  IQR = Q_3 - Q_1\n  \\end{eqnarray*}\n  where $Q_1$ and $Q_3$ are the $25^{th}$ and $75^{th}$\n  percentiles.\n\\end{onebox}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat percent of the data fall between $Q_1$ and the median?\nWhat percent is between the median and $Q_3$?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Since\n  $Q_1$ and $Q_3$ capture the middle 50\\% of the data and\n  the median splits the data in the middle, 25\\% of the data\n  fall between $Q_1$ and the median, and another 25\\% falls\n  between the median and $Q_3$.}\n\nExtending out from the box, the \\term{whiskers} attempt\nto capture the data outside of the box.\nHowever, their reach is never allowed to be more than\n$1.5\\times IQR$.\nThey capture everything within this reach.\nIn Figure~\\ref{loan_int_rate_box_plot_layout},\nthe upper whisker does not extend to the last two points,\nwhich is beyond $Q_3 + 1.5\\times IQR$,\nand so it extends only to the last point below this limit.\nThe lower whisker stops at the lowest value,\n\\loanSmallestValue{}\\%,\nsince there is no additional data to reach;\nthe lower whisker's limit is not shown in the figure because\nthe plot does not extend down to $Q_1 - 1.5\\times IQR$.\nIn a sense, the box is like the body of the box plot\nand the whiskers are like its arms trying to reach the\nrest of the data.\n\nAny observation lying beyond the whiskers is labeled with a dot.\nThe purpose of labeling these points --\ninstead of extending the whiskers to the minimum\nand maximum observed values --\nis to help identify any observations that appear to be\nunusually distant from the rest of the data.\nUnusually distant observations are called\n\\termsub{outliers}{outlier}.\nIn this case, it would be reasonable to classify the\ninterest rates of 24.85\\% and \\loanLargestValue{}\\%\nas outliers since they are numerically distant from\nmost of the data.\n\n\\begin{onebox}{Outliers are extreme}\n  An \\term{outlier} is an observation that appears\n  extreme relative to the rest of the data. \\vspace{3mm}\n  \n  Examining data for outliers serves\n  many useful purposes, including\\vspace{-1mm}\n  \\begin{enumerate}\n  \\setlength{\\itemsep}{0mm}\n  \\item Identifying\n      \\indexthis{strong skew}{skew!strong}\n      in the distribution.\n  \\item Identifying possible data collection or\n      data entry errors.\n  \\item Providing insight into interesting properties\n      of the data.\\vspace{-1mm}\n  \\end{enumerate}\n\\end{onebox}\n\n%The observation \\loanLargestValue{}\\%, a suspected outlier,\n%was found to be an accurate observation.\n%What would such an observation suggest about the nature\n%of interest rates through Lending Club?\\footnote{That\n%  occasionally there may be very long emails.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nUsing Figure~\\ref{loan_int_rate_box_plot_layout},\nestimate the following values for\n\\var{interest\\us{}rate} in the \\data{loan50} data set: \\\\\n(a) $Q_1$,\n(b) $Q_3$, and\n(c) IQR.\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{These\n  visual estimates will vary a little from one person\n  to the next:\n  $Q_1=$ 8\\%,\n  $Q_3=$ 14\\%,\n  $\\text{IQR} = Q_3 - Q_1 = 6\\%$.\n  (The true values: $Q_1= \\loanQA{}\\%$, $Q_3 = \\loanQC{}\\%$,\n  $\\text{IQR} = \\loanIQR{}\\%$.)}\n\n\\CalculatorVideos{how to create statistical summaries and box plots}\n\n\n\\D{\\newpage}\n\n\\subsection{Robust statistics}\n\nHow are the \\indexthis{sample statistics}{sample statistic}\nof the \\data{interest\\us{}rate} data set affected\nby the observation, 26.3\\%?\nWhat would have happened if this loan had instead\nbeen only 15\\%?\nWhat would happen to these\n\\indexthis{summary statistics}{summary statistic}\nif the observation at 26.3\\% had been even larger,\nsay 35\\%? These scenarios are plotted alongside the\noriginal data in Figure~\\ref{loan_int_rate_robust_ex},\nand sample statistics are computed under each scenario in\nFigure~\\ref{robustOrNotTable}.\n\n\\begin{figure}[ht]\n  \\centering\n  \\Figure\n    [Three dot plots are shown in the same plot. The largest observation from the original data set (discussed in previous dot plots) at about 26\\% is moved to 15\\% in the second dot plot and instead to 35\\% in the third dot plot.]\n    {1}{loan_int_rate_robust_ex}\n  \\caption{Dot plots of the original interest rate data\n      and two modified data sets.}\n  \\label{loan_int_rate_robust_ex}\n\\end{figure}\n\n% See `loan_int_rate_robust_ex` figure code for calculations.\n\\captionsetup{width=135mm}\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l c cc c cc}\n% \\cline{3-4} \\cline{6-7}\n& \\hspace{0mm} & \\multicolumn{2}{c}{\\bf robust} &\n    \\hspace{2mm} & \\multicolumn{2}{c}{\\bf not robust} \\\\\n\\hline\nscenario && median & IQR && $\\bar{x}$ & $s$ \\\\ \n\\hline\n%   & & \\multicolumn{2}{c|} & & \\multicolumn{2}{c|} \\\\\noriginal \\var{interest\\us{}rate} data\n    && 9.93\\% & 5.76\\% && 11.57\\% & 5.05\\% \\\\\nmove 26.3\\% $\\to$ 15\\%\n    && 9.93\\% & 5.76\\% && 11.34\\% & 4.61\\% \\\\\nmove 26.3\\% $\\to$ 35\\%\n    && 9.93\\% & 5.76\\% && 11.74\\% & 5.68\\% \\\\\n   \\hline\n\\end{tabular}\n\\caption{A comparison of how the median, IQR,\n  mean ($\\bar{x}$), and standard deviation ($s$) change\n  had an extreme observations from the \\var{interest\\us{}rate}\n  variable been different.}\n\\label{robustOrNotTable}\n\\end{figure}\n\\captionsetup{width=\\mycaptionwidth}\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{interestRateWhichIsMoreRobust}\n(a)~Which is more affected by extreme observations,\nthe mean or median?\nFigure~\\ref{robustOrNotTable} may be helpful.\n(b)~Is the standard deviation or IQR more affected by\nextreme observations?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~Mean is affected more.\n  (b)~Standard deviation is affected more.\n  Complete explanations are provided in the material\n  following Guided Practice~\\ref{interestRateWhichIsMoreRobust}.}\n\nThe median and IQR are called \\term{robust statistics} because\nextreme observations have little effect on their values:\nmoving the most extreme value generally has little influence\non these statistics.\nOn the other hand, the mean and standard deviation\nare more heavily influenced by changes in extreme observations,\nwhich can be important in some situations.\n\n\\begin{examplewrap}\n\\begin{nexample}{The median and IQR did not change under the\n    three scenarios in Figure~\\ref{robustOrNotTable}.\n    Why might this be the case?}\n  The median and IQR are only sensitive to numbers\n  near $Q_1$, the median, and $Q_3$.\n  Since values in these regions are stable in the three\n  data sets, the median and IQR estimates are also stable.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe distribution of loan amounts in the \\data{loan50} data set\nis right skewed, with a few large loans lingering out into the\nright tail.\nIf you were wanting to understand the typical loan size,\nshould you be more interested in the mean\nor median?\\footnotemark\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Answers will vary!\n  If we're looking to simply understand what a typical individual\n  loan looks like, the median is probably more useful.\n  However, if the goal is to understand something that\n  scales well, such as the total amount of money we might\n  need to have on hand if we were to offer 1,000 loans,\n  then the mean would be more useful.}\n\n\\index{data!loan50|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Transforming data (special topic)}\n\\label{transformingDataSubsection}\n\n\\noindent%\nWhen data are very strongly skewed, we sometimes transform\nthem so they are easier to model.\n\n\\begin{figure}[ht]\n  \\centering\n  \\subfigure[]{\n    \\Figures[A histogram with a horizontal axis of Population with possible data ranging from 0 to about 10 million. The first bar representing 0 to 400,000 shows a frequency (bar height) of about 3000, the second bar for 400,000 to 800,000 shows about frequency of about 100. All other bars are sufficiently small that they are virtually indistinguishable from 0.]\n        {0.46}\n        {county_pop_transformed}\n        {county_pop_transformed_i}\n    \\label{county_pop_transformed_i}\n  }\n  \\subfigure[]{\n    \\Figures[A histogram is shown where the horizontal axis represents log-base-10 of the population. The horizontal axis runs from about 2 to 7, and frequency (bin/box height) peaks at a little over 1000. The data show an approximate bell shape, peaking in the middle between 4 to 4.5, then showing lower frequencies the further out from 4-4.5 with frequencies being close to zero outside of 2.5 to 6.5.]\n        {0.46}\n        {county_pop_transformed}\n        {county_pop_transformed_log}\n  \\label{county_pop_transformed_log}\n  }\n  \\caption{\\subref{county_pop_transformed_i} A histogram of\n      the populations of all US counties.\n      \\subref{county_pop_transformed_log} A histogram of\n      log$_{10}$-transformed county populations.\n      For this plot, the x-value corresponds to the power\n      of 10, e.g. ``4'' on the x-axis corresponds to\n      $10^4 =$ 10,000.}\n    \\label{county_pop_transformed}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Consider the histogram of county populations\n    shown in Figure~\\ref{county_pop_transformed_i},\n    which shows extreme skew\\index{skew!extreme}.\n    What isn't useful about this plot?}\n  Nearly all of the data fall into the left-most bin,\n  and the extreme skew obscures many of the potentially\n  interesting details in the data.\n\\end{nexample}\n\\end{examplewrap}\n\nThere are some standard transformations that may be\nuseful for strongly right skewed data where much of the\ndata is positive but clustered near zero.\nA \\term{transformation} is a rescaling of the data\nusing a function.\nFor instance, a plot of the logarithm (base 10) of\ncounty populations results in the new histogram in\nFigure~\\ref{county_pop_transformed_log}.\nThis data is symmetric, and any potential outliers\nappear much less extreme than in the original data set.\nBy reigning in the outliers and extreme skew,\ntransformations like this often make it easier to build\nstatistical models against the data.\n\nTransformations can also be applied to one or both\nvariables in a scatterplot.\nA scatterplot of the population change from 2010 to 2017\nagainst the population in 2010 is shown in Figure~\\ref{county_pop_change_v_pop_transform_i}.\nIn this first scatterplot, it's hard to decipher any\ninteresting patterns because the population variable\nis so strongly skewed.\nHowever, if we apply a log$_{10}$ transformation to\nthe population variable, as shown in\nFigure~\\ref{county_pop_change_v_pop_transform_log},\na positive association between the variables is revealed.\nIn fact, we may be interested in fitting a trend line to\nthe data when we explore methods around fitting regression\nlines in Chapter~\\ref{linRegrForTwoVar}.\n\n\\begin{figure}\n  \\centering\n  \\subfigure[]{\n    \\Figures[A scatterplot of the population on the horizontal axis (ranging from 0 to 10 million) and population change as a percent on the vertical axis (ranging from -35\\% to positive 40\\%). The data is particularly concentrated on the left of the graph below 1 million, where the data with populations below 1 million have populations changes that are mostly clustered between about -10\\% and positive 15\\%. There are a relatively small number of observations with population greater than 1 million, and these all have population changes between roughly -3\\% and positive 10\\%. There is no discernible trend in the data.]\n        {0.47}\n        {county_pop_change_v_pop_transform}\n        {county_pop_change_v_pop_transform_i}\n    \\label{county_pop_change_v_pop_transform_i}\n  }\n  \\subfigure[]{\n    \\Figures[A scatterplot of the log-base-10 of the population on the horizontal axis (ranging from 2 to 7) and population change as a percent on the vertical axis (-35\\% to positive 40\\%). The data well distributed between about 3 and 6 on the horizontal axis and shows a cloud of points with a slight upward trend. Between 3 and 4 on the horizontal, nearly all points take values between -10\\% and positive 10\\%. Between 4 and 5 on the horizontal, nearly all points take vertical values between -8\\% and positive 15\\%. Between 5 and 6 on the horizontal, nearly all points take vertical values between -5\\% and positive 18\\%.]\n        {0.47}\n        {county_pop_change_v_pop_transform}\n        {county_pop_change_v_pop_transform_log}\n    \\label{county_pop_change_v_pop_transform_log}\n  }\n  \\caption{\\subref{county_pop_change_v_pop_transform_i}\n      Scatterplot of population change\n      against the population before the change.\n      \\subref{county_pop_change_v_pop_transform_log}~A~scatterplot\n      of the same data but where the population\n      size has been log-transformed.}\n  \\label{county_pop_change_v_pop_transform_main}\n\\end{figure}\n\nTransformations other than the logarithm can be useful, too.\nFor instance, the square root\n($\\sqrt{\\text{original observation}}$) and inverse\n($\\frac{1}{\\text{original observation}}$) are commonly used\nby data scientists.\nCommon goals in transforming data are to see the data\nstructure differently, reduce skew, assist in modeling,\nor straighten a nonlinear relationship in a scatterplot.\n\n\\index{data!county|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Mapping data (special topic)}\n\n\\index{data!county|(}\n%\\index{intensity map|(}\n\nThe \\data{county} data set offers many numerical variables\nthat we could plot using dot plots, scatterplots,\nor box plots, but these miss the true nature of the data.\nRather, when we encounter geographic data, we should create\nan \\term{intensity map}, where colors are used\nto show higher and lower values of a variable.\nFigures~\\ref{countyIntensityMaps1}\nand~\\ref{countyIntensityMaps2} shows intensity maps for\npoverty rate in percent (\\var{poverty}),\nunemployment rate (\\var{unemployment\\us{}rate}),\nhomeownership rate in percent (\\var{homeownership}),\nand median household income\n(\\var{median\\us{}hh\\us{}income}).\nThe color key indicates which colors correspond to which values.\nThe intensity maps are not generally very helpful\nfor getting precise values in any given county,\nbut they are very helpful for seeing geographic trends\nand generating interesting research questions or hypotheses.\n\n\\begin{examplewrap}\n\\begin{nexample}{What interesting features are evident in the\n    \\var{poverty} and \\var{unemployment\\us{}rate}\n    intensity maps?}\\label{map_example_poverty_and_unemployment}\n  Poverty rates are evidently higher in a few locations.\n  Notably, the deep south shows higher poverty rates,\n  as does much of Arizona and New Mexico.\n  High poverty rates are evident in the Mississippi\n  flood plains a little north of New Orleans and\n  also in a large section of Kentucky.\n\n  The unemployment rate follows similar trends,\n  and we can see correspondence between the two\n  variables. In fact, it makes sense for higher rates\n  of unemployment to be closely related to poverty rates.\n  One observation that stand out when comparing the two maps:\n  the poverty rate is much higher than the unemployment\n  rate, meaning while many people may be working,\n  they are not making enough to break out of poverty.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat interesting features are evident in the\n\\var{median\\us{}hh\\us{}income} intensity map in\nFigure~\\ref{countyMedIncomeMap}?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Note: answers will vary.\n  There is some correspondence between high earning\n  and metropolitan areas, where we can see darker spots\n  (higher median household income),\n  though there are several exceptions.\n  You might look for large cities you are familiar with and\n  try to spot them on the map as dark spots.}\n\n\\begin{figure}\n  \\centering\n  \\subfigure[]{\n    \\Figures[An intensity map of the United States is shown for poverty, where each county is colored a shade representing a value between 2\\% and over 25\\% for the poverty rate. See Example~\\ref{map_example_poverty_and_unemployment} for an additional summary of this graph.]\n        {1.00}\n        {countyIntensityMaps}\n        {countyPovertyMap}\n    \\label{countyPovertyMap}\n  }\n  \\subfigure[]{\n    \\Figures[An intensity map of the United States is shown for unemployment rate, where each county is colored a shade representing a value between 2\\% and over 7\\% for the unemployment rate. See Example~\\ref{map_example_poverty_and_unemployment} for an additional summary of this graph.]\n        {1.00}\n        {countyIntensityMaps}\n        {countyUnemploymentRateMap}\n    \\label{countyUnemploymentRateMap}\n  }\n  \\caption{\\subref{countyPovertyMap} Intensity map of\n      poverty rate (percent).\n      \\subref{countyUnemploymentRateMap}~Map of the\n      unemployment rate (percent).}\n  \\label{countyIntensityMaps1}\n\\end{figure}\n\n\\begin{figure}\n  \\centering\n  \\subfigure[]{\n    \\Figures[An intensity map of the United States is shown for homeownership rate, where each county is colored a shade representing a value between below 55\\% and over 91\\% for the homeownership rate. The data look mostly random but may be slightly lower in the west, especially along the California coast, and shading representing slightly higher values in the upper midwest as well as in Florida.]\n        {1.00}\n        {countyIntensityMaps}\n        {countyHomeownershipMap}\n    \\label{countyHomeownershipMap}\n  }\n  \\subfigure[]{\n    \\Figures[An intensity map of the United States is shown for median household income, where each county is colored a shade representing a value between below \\$19,000 and over \\$75,000. The shading appears quite random in any particular region. However, some metro areas in California and near New York City and Boston appear to have higher median household incomes. The Mississippi Delta leading down into Louisiana and Mississippi show evidently lower median household income values.]\n        {1.00}\n        {countyIntensityMaps}\n        {countyMedIncomeMap}\n    \\label{countyMedIncomeMap}\n  }\n  \\caption{\\subref{countyHomeownershipMap} Intensity map\n      of homeownership rate (percent).\n      \\subref{countyMedIncomeMap}~Intensity map of median\n      household income (\\$1000s).}\n\\label{countyIntensityMaps2}\n\\end{figure}\n\n%\\index{intensity map|)}\n\\index{data!county|)}\n\n\n{\\input{ch_summarizing_data/TeX/examining_numerical_data.tex}}\n\n\n\n\n\\section{Considering categorical data}\n\\label{categoricalData}\n\n\\index{data!loans|(}\n\nIn this section, we will introduce tables and other basic tools\nfor categorical data that are used throughout this book.\nThe \\data{loan50} data set represents a sample from a larger\nloan data set called \\data{loans}.\nThis larger data set contains information on 10,000 loans made\nthrough Lending Club.\nWe~will examine the relationship between\n\\var{homeownership}, which for the \\data{loans} data can take\na value of \\resp{rent}, \\resp{mortgage}\n(owns but has a mortgage), or \\resp{own},\nand \\var{app\\us{}type},\nwhich indicates whether the loan application was made\nwith a partner or whether it was an individual application.\n% library(openintro); dim(loans_full_schema)\n\n\n\\subsection{Contingency tables and bar plots}\n\n\\newcommand{\\loanapphomeAA}{3496}\n\\newcommand{\\loanapphomeAB}{3839}\n\\newcommand{\\loanapphomeAC}{1170}\n\\newcommand{\\loanapphomeAD}{8505}\n\\newcommand{\\loanapphomeBA}{362}\n\\newcommand{\\loanapphomeBB}{950}\n\\newcommand{\\loanapphomeBC}{183}\n\\newcommand{\\loanapphomeBD}{1495}\n\\newcommand{\\loanapphomeDA}{3858}\n\\newcommand{\\loanapphomeDAPt}{0.3858} % Overall frequency\n\\newcommand{\\loanapphomeDB}{4789}\n\\newcommand{\\loanapphomeDC}{1353}\n\\newcommand{\\loanapphomeDD}{10000}\n\\newcommand{\\loanapphomeN}{\\loanapphomeDD{}}\n\nFigure~\\ref{loan_home_app_type_totals} summarizes two variables:\n\\var{app\\us{}type}\n%\\footnote{For those readers already familiar\n%  with \\emph{joint probabilities}, \\resp{joint} in the table\n%  refers to a level of the \\var{app\\us{}type} variable\n%  for a joint application.\n%  The does not refer to a joint probability!}\nand \\var{homeownership}.\nA table that summarizes data for two categorical variables in\nthis way is called a \\term{contingency table}.\nEach value in the table represents the number of times\na particular combination of variable outcomes occurred.\nFor example, the value \\loanapphomeAA{} corresponds to the number of\nloans in the data set where the borrower rents their home\nand the application type was by an individual.\nRow and column totals are also included.\nThe \\term{row totals} \\index{contingency table!row totals}\nprovide the total counts across each row\n(e.g. $\\loanapphomeAA{} + \\loanapphomeAB{} +\n  \\loanapphomeAC{} = \\loanapphomeAD{}$),\nand \\term{column totals} \\index{contingency table!column totals}\nare total counts down each column.\nWe can also create a table that shows only the overall \npercentages or proportions for each combination of categories,\nor we can create a table for a single variable,\nsuch as the one shown in Figure~\\ref{loan_homeownership_totals}\nfor the \\var{homeownership} variable.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{ll  ccc  rr}\n  & & \\multicolumn{3}{c}{\\bf \\var{homeownership}} & \\\\\n  \\cline{3-5}\n  & & rent & mortgage & own & Total & \\hspace{2mm}\\  \\\\ \n  \\cline{2-6}\n  & individual &\n      \\loanapphomeAA{} &\n      \\loanapphomeAB{} &\n      \\loanapphomeAC{} &\n      \\loanapphomeAD{} \\\\\n  \\raisebox{1.5ex}[0pt]{\\var{app\\us{}type}} &\n  joint &\n      \\loanapphomeBA{} &\n      \\loanapphomeBB{} &\n\t  \\loanapphomeBC{} &\n\t  \\loanapphomeBD{} \\\\\n  \\cline{2-6}\n  & Total &\n      \\loanapphomeDA{} &\n      \\loanapphomeDB{} &\n      \\loanapphomeDC{} &\n      \\loanapphomeDD{} \\\\\n  \\cline{2-6}\n\\end{tabular}\n\\caption{A contingency table for\n    \\var{app\\us{}type} and \\var{homeownership}.}\n\\label{loan_home_app_type_totals}\n%library(openintro); library(xtable); tab <- table(loans_full_schema[,c(\"application_type\", \"homeownership\")])[, c(\"RENT\", \"MORTGAGE\", \"OWN\")]; xtable(tab); rowSums(tab); colSums(tab); sum(tab)\n\\end{figure}\n\n\\begin{figure}[htb]\n\\centering\n\\begin{tabular}{lc}\n  \\hline\n  \\var{homeownership} & Count \\\\\n  \\hline\n  rent & \\loanapphomeDA{} \\\\\n  mortgage & \\loanapphomeDB{} \\\\\n  own & \\loanapphomeDC{} \\\\\n  \\hline\n  Total & \\loanapphomeDD{} \\\\ \n  \\hline\n\\end{tabular}\n\\caption{A table summarizing the frequencies of each\n    value for the \\var{homeownership} variable.}\n\\label{loan_homeownership_totals}\n\\end{figure}\n\nA bar plot is a common way to display a single\ncategorical variable.\nThe left panel of Figure~\\ref{loan_homeownership_bar_plot}\nshows a \\term{bar plot} for the \\var{homeownership} variable.\nIn the right panel, the counts are converted into proportions,\nshowing the proportion of observations that are in each level\n(e.g. $\\loanapphomeDA{} / \\loanapphomeDD{} = 0.3858$ for\n  \\resp{rent}).\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[Two bar plots, which are described as the left bar plot and the right bar plot. The left bar plot has Homeownership on the horizontal axis and Frequency (count) on the Vertical axis. Each level of homeownership has its own \"bar\" (which looks like a tall rectangle resting on the horizontal axis) with a height corresponding the frequency of that bar in the data set. For example, the \"Rent\" bar extends from the horizontal axis up to a frequency of about 3900. The \"Mortgage\" bar extends from the horizontal axis up to about 4700, and the bar for \"Own\" extends up to at about 1300. Moving to the next plot, the right bar plot, it looks very similar to the left bar plot except that it reports the proportion of cases on the vertical axes instead of the frequency (count). The values in this bar plot are: about 0.39 for Rent, about 0.47 for Mortgage, and about 0.13 for Own.]\n    {0.9}{loan_homeownership_bar_plot}\n  \\caption{Two bar plots of \\var{number}.\n      The left panel shows the counts, and the right panel\n      shows the proportions in each group.}\n  \\label{loan_homeownership_bar_plot}\n\\end{figure}\n\n\n\\D{\\newpage}\n\n\\subsection{Row and column proportions}\n\nSometimes it is useful to understand the fractional breakdown\nof one variable in another,\nand we can modify our contingency table to provide such a view.\nFigure~\\ref{rowPropAppTypeHomeownership}\nshows the\n\\termsub{row proportions}{contingency table!row proportions}\nfor Figure~\\ref{loan_home_app_type_totals},\nwhich are computed as the counts divided by their row totals.\nThe value \\loanapphomeAA{} at the intersection of\n\\resp{individual} and \\resp{rent} is replaced by\n$\\loanapphomeAA{}/\\loanapphomeAD{} = 0.411$,\ni.e. \\loanapphomeAA{} divided by its row total,\n\\loanapphomeAD{}.\nSo what does 0.411 represent?\nIt corresponds to the proportion of individual\napplicants who rent.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{l rrr r}\n  \\hline\n  & rent & mortgage & own & Total \\\\\n  \\hline\n  individual & \n%      $\\loanapphomeAA{}/\\loanapphomeAD{} = 0.411$ &\n%      $\\loanapphomeAB{}/\\loanapphomeAD{} = 0.451$ &\n%      $\\loanapphomeAC{}/\\loanapphomeAD{} = 0.138$ &\n      0.411 &\n      0.451 &\n      0.138 &\n      1.000 \\\\\n  joint &\n%      $\\loanapphomeBA{}/\\loanapphomeBD{} = 0.242$ &\n%      $\\loanapphomeBB{}/\\loanapphomeBD{} = 0.635$ &\n%      $\\loanapphomeBC{}/\\loanapphomeBD{} = 0.122$ &\n      0.242 &\n      0.635 &\n      0.122 &\n      1.000 \\\\\n  \\hline\n  Total &\n%      $\\loanapphomeDA{}/\\loanapphomeDD{} = 0.386$ &\n%      $\\loanapphomeDB{}/\\loanapphomeDD{} = 0.479$ &\n%      $\\loanapphomeDC{}/\\loanapphomeDD{} = 0.135$ &\n      0.386 &\n      0.479 &\n      0.135 &\n      1.000 \\\\\n  \\hline\n\\end{tabular}\n\\caption{A contingency table with row proportions\n    for the \\var{app\\us{}type} and\n    \\var{homeownership} variables.\n    The row total is off by 0.001 for the\n    \\resp{joint} row due to a rounding error.}\n\\label{rowPropAppTypeHomeownership}\n\\end{figure}\n\nA contingency table of the column proportions is computed in\na similar way, where each\n\\termsub{column proportion}{contingency table!column proportion}\nis computed as the count divided by the corresponding column total.\nFigure~\\ref{colPropAppTypeHomeownership} shows such a table,\nand here the value 0.906 indicates that 90.6\\% of renters applied\nas individuals for the loan.\nThis rate is higher compared to loans from people with\nmortgages (80.2\\%) or who own their home (86.5\\%).\nBecause these rates vary between the three levels of\n\\var{homeownership} (\\resp{rent}, \\resp{mortgage}, \\resp{own}),\nthis provides evidence that the \\var{app\\us{}type} and\n\\var{homeownership} variables are associated.\n\n\\begin{figure}[h]\n\\centering%\\small\n\\begin{tabular}{l rrr r}\n  \\hline\n  & rent & mortgage & own & Total \\\\\n  \\hline\n  individual &\n%      $\\loanapphomeAA{}/\\loanapphomeDA{} = 0.906$ &\n%      $\\loanapphomeAB{}/\\loanapphomeDB{} = 0.802$ &\n%      $\\loanapphomeAC{}/\\loanapphomeDC{} = 0.865$ &\n%      $\\loanapphomeAD{}/\\loanapphomeDD{} = 0.851$ \\\\\n      0.906 &\n      0.802 &\n      0.865 &\n      0.851 \\\\\n  joint &\n%      $\\loanapphomeBA{}/\\loanapphomeDA{} = 0.094$ &\n%      $\\loanapphomeBB{}/\\loanapphomeDB{} = 0.198$ &\n%      $\\loanapphomeBC{}/\\loanapphomeDC{} = 0.135$ &\n%      $\\loanapphomeBD{}/\\loanapphomeDD{} = 0.150$ \\\\\n      0.094 &\n      0.198 &\n      0.135 &\n      0.150 \\\\\n  \\hline\n  Total & 1.000 & 1.000 & 1.000 & 1.000 \\\\\n  \\hline\n\\end{tabular}\n\\caption{A contingency table with column proportions for the\n    \\var{app\\us{}type} and \\var{homeownership}\n    variables.\n    The total for the last column is off by 0.001 due\n    to a rounding error.}\n\\label{colPropAppTypeHomeownership}\n\\end{figure}\n\nWe could also have checked for an association between \\var{app\\us{}type} and \\var{homeownership} in Figure~\\ref{rowPropAppTypeHomeownership} using row proportions. When comparing these row proportions, we would look down columns to see if the fraction of loans where the borrower rents, has a mortgage, or owns varied across the \\resp{individual} to \\resp{joint} application types.\n\n\\D{\\newpage}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n(a)~What does 0.451 represent in\nFigure~\\ref{rowPropAppTypeHomeownership}?\n\n(b)~What does 0.802 represent in\nFigure~\\ref{colPropAppTypeHomeownership}?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~0.451 represents the proportion of individual\n  applicants who have a mortgage.\n  (b)~0.802 represents the fraction\n  of applicants with mortgages who applied as individuals.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n(a)~What does 0.122 at the intersection of \\resp{joint} and\n\\resp{own} represent in\nFigure~\\ref{rowPropAppTypeHomeownership}?\n\n(b)~What does 0.135 represent in the\nFigure~\\ref{colPropAppTypeHomeownership}?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{(a)~0.122 represents the fraction of joint borrowers\n  who own their home.\n  (b)~0.135 represents the home-owning borrowers\n  who had a joint application for the loan.}\n\n\\begin{examplewrap}\n\\begin{nexample}{\n    Data scientists use statistics to filter spam from incoming\n    email messages.\n    By noting specific characteristics of an email,\n    a data scientist may be able to classify some emails as spam\n    or not spam with high accuracy.\n    One such characteristic is whether the email\n    contains no numbers, small numbers, or big numbers.\n    Another characteristic is the email format, which\n    indicates whether or not an email has any HTML content,\n    such as bolded text.\n    We'll focus on email format and spam status using the\n    \\data{email} data set, and these variables are summarized\n    in a contingency table in\n    Figure~\\ref{emailSpamHTMLTableTotals}.\n    Which would be more helpful to someone hoping to classify\n    email as spam or regular email for this table:\n    row or column proportions?}\n  \\label{weighingRowColumnProportions}\n  A data scientist would be interested in how the proportion\n  of spam changes within each email format.\n  This corresponds to column proportions:\n  the proportion of spam in plain text emails\n  and the proportion of spam in HTML emails.\n\n  If we generate the column proportions, we can see\n  that a higher fraction of plain text emails are\n  spam ($209/1195 = 17.5\\%$)\n  than compared to HTML emails ($158/2726 = 5.8\\%$).\n  This information on its own is insufficient to classify\n  an email as spam or not spam, as over 80\\% of plain text\n  emails are not spam.\n  Yet, when we carefully combine this information with many\n  other characteristics,\n  we stand a reasonable chance of being able to classify\n  some emails as spam or not spam with confidence.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l cc r}\n  \\hline\n  & text & HTML & Total \\\\ \n  \\hline\n  spam & 209 & 158 & 367 \\\\ \n  not spam & 986 & 2568 & 3554 \\\\ \n  \\hline\n  Total & 1195 & 2726 & 3921 \\\\\n  \\hline\n\\end{tabular}\n\\caption{A contingency table for \\var{spam} and \\var{format}.}\n\\label{emailSpamHTMLTableTotals}\n%library(openintro); library(xtable); data(email); tab <- table(email[,c(\"spam\", \"format\")])[2:1,]; tab; colSums(tab); rowSums(tab)\n\\end{figure}\n\nExample~\\ref{weighingRowColumnProportions} points out\nthat row and column proportions are not equivalent.\nBefore settling on one form for a table,\nit is important to consider each to ensure that the\nmost useful table is constructed.\nHowever, sometimes it simply isn't clear which, if either,\nis more useful.\n\n\\begin{examplewrap}\n\\begin{nexample}{Look back to\n    Tables~\\ref{rowPropAppTypeHomeownership}\n    and~\\ref{colPropAppTypeHomeownership}.\n    Are there any obvious scenarios where one might be more\n    useful than the other?}\n  None that we thought were obvious!\n  What is distinct about \\var{app\\us{}type}\n  and \\var{homeownership} vs the email example is that\n  these two variables don't have a clear explanatory-response\n  variable relationship that we might hypothesize\n  (see Section~\\ref{explanatoryAndResponse} for these terms).\n  Usually it is most useful to ``condition'' on the\n  explanatory variable.\n  For instance, in the email example, the email format\n  was seen as a possible explanatory variable of whether\n  the message was spam, so we would find it more interesting\n  to compute the relative frequencies (proportions)\n  for each email format.\n\\end{nexample}\n\\end{examplewrap}\n\n%\\Comment{Any risk with the above example that students\n%  would think they need not know how to describe (what\n%  are effectively) conditional probabilities based\n%  on row or column proportions?\n%  If so, we could add in an exercise that calls this\n%  out and requires them to create such a description.}\n\n\n\\D{\\newpage}\n\n\\subsection{Using a bar plot with two variables}\n\\label{bar_plots_subsection}\n\nContingency tables using row or column proportions\nare especially useful for examining how two categorical\nvariables are related.\nStacked bar plots provide a way to visualize\nthe information in these tables.\n\nA \\termsub{stacked bar plot}{bar plot!stacked bar plot}\n\\index{bar plot!segmented bar plot}\nis a graphical display of contingency table information.\nFor example, a~stacked bar plot representing\nFigure~\\ref{colPropAppTypeHomeownership}\nis shown in Figure~\\ref{loan_app_type_home_seg_bar},\nwhere we have first created a bar plot using the\n\\var{homeownership} variable and then divided each group\nby the levels of \\mbox{\\var{app\\us{}type}}.\n\nOne related visualization to the stacked bar plot is the\n\\termsub{side-by-side bar plot}{bar plot!side-by-side},\nwhere an example is shown in\nFigure~\\ref{loan_app_type_home_sbs_bar}.\n\nFor the last type of bar plot we introduce,\nthe column proportions for the\n\\var{app\\us{}type} and \\var{homeownership} contingency table\nhave been translated into a standardized stacked bar plot\nin Figure~\\ref{loan_app_type_home_seg_bar_standardized}.\nThis type of visualization is helpful in understanding\nthe fraction of individual or joint loan applications\nfor borrowers in each level of \\var{homeownership}.\nAdditionally, since the proportions of \\resp{joint}\nand \\resp{individual} vary across the groups,\nwe can conclude that the two variables are associated.\n\n\\newcommand{\\loanapptypehomesegbarplotwidth}{0.48\\textwidth}\n\\begin{figure}[h]\n  \\centering\n  \\subfigure[]{\n    \\Figuress[A stacked bar plot with Homeownership on the horizontal axis and Frequency (count) on the Vertical axis, where \"app\\_type\" is used to break each bar into two categories: \"joint\" application type and \"individual\" application type. The first bar is for \"Rent\" and extends up to about 3900 total for the two application types together. This \"Rent\" bar is also broken into two categories, blue for \"individual\" and yellow for \"joint\". The bottom portion of the bar, running up to about 3500, is blue to represent the \"joint\" applications where the application had a \"rent\" value for homeownership, and the rest (about vertical height representing about 400) of the bar is yellow to represent the \"individual\" applications. The second bar is for \"Mortgage\" at about 4700 total, the bottom 3900 of which are shown as blue for individual applications and the top of which is yellow for \"joint\" applications and appears to have a height of about 800. The third bar is for \"Own\" at about 1300, of which about 1100 is for the individual (blue) application type and about 200 of which is joint (yellow) application type. Again, each homeownership bar is broken into a lower (blue) and upper portion (yellow) portion to express the breakdown of a homeownership level into the application types, allowing us to express a breakdown along two categorical variables in a single plot.]\n        {\\loanapptypehomesegbarplotwidth}\n        {loan_app_type_home_seg_bar}\n        {loan_app_type_home_seg_bar}\n    \\label{loan_app_type_home_seg_bar}\n  }\n  \\subfigure[]{\n    \\Figuress[A side-by-side bar plot is shown. In this side-by-side plot, instead of having the blue and yellow portions of a single bar for a homeownership level, such as rent, the bar has been slimmed down and the blue and yellow portions are now side-by-side, each resting on the horizontal axis. Reading across, we see a blue and yellow bar side-by-side and touching. These are shown over a homeownership category of \"rent\". The first of these two bars is blue for \"individual\" application type (having a height of about 3500) and the second is yellow for the \"joint\" application type (having a height of about 400). After this first group of two bars, there is a small horizontal gap before the next pair of bars that represent the mortgage homeownership category. Here again, there is first a blue bar for individual application type, where this blue bar stretches up to a value of about 3900, and next to it is a yellow bar for the joint application type, which stretches up to about 800. After this second pair of bars, there is a little more space as we move right along the plot before we reach the \"own\" homeownership category, which shows another pair of bars: blue (with a bar reaching a frequency or count of about 1100) and yellow (with a bar reaching a value of about 200).]\n        {\\loanapptypehomesegbarplotwidth}\n        {loan_app_type_home_seg_bar}\n        {loan_app_type_home_sbs_bar}\n    \\label{loan_app_type_home_sbs_bar}\n  }\n  \\subfigure[]{\n    \\Figuress[The last plot is a standardized version of the stacked bar plot, where each bar has been standardized to add up to 1. This bar plot shows the homeownership variable and its three levels -- from left to right: rent, mortgage, and own -- as their own bars, where each bar runs from the horizontal axis at 0 up to a value of 1. This standardization where all total bars span the same vertical distance allows for an easier comparison of the proportional breakdown of the coloring in each stacked bar. The coloring breakdown of each bar represents the application type: individual (blue) and joint (yellow). For the first bar, rent, the blue runs up to about 0.9 on the vertical, and the yellow portion of the bar runs from 0.9 to 1.0. In the second bar, mortgage, the blue runs from horizontal axis up to about 0.8, and the yellow portion of the bar runs from 0.8 to 1.0. The third bar, own, has its blue portion run from the horizontal axis up to about 0.87, and the yellow portion runs from 0.87 to 1.0.]\n        {\\loanapptypehomesegbarplotwidth}\n        {loan_app_type_home_seg_bar}\n        {loan_app_type_home_seg_bar_standardized}\n    \\label{loan_app_type_home_seg_bar_standardized}\n  }\n%  \\subtable{\n%    \\footnotesize\n%    \\begin{tabular}{l  ccc  r}\n%      \\multicolumn{5}{l}{Contingency table summarizing}\\\\\n%      \\multicolumn{5}{l}{application type and homeownership:} \\\\\n%      \\\\\n%      & \\multicolumn{3}{c}{\\bf \\var{homeownership}} & \\\\\n%      \\cline{2-4}\n%      \\var{app\\us{}type} &\n%          rent & mortgage & own & Total \\\\ \n%      \\hline\n%      individual &\n%          \\loanapphomeAA{} &\n%          \\loanapphomeAB{} &\n%          \\loanapphomeAC{} &\n%          \\loanapphomeAD{} \\\\\n%      joint &\n%          \\loanapphomeBA{} &\n%          \\loanapphomeBB{} &\n%    \t  \\loanapphomeBC{} &\n%    \t  \\loanapphomeBD{} \\\\\n%      \\hline\n%      Total &\n%          \\loanapphomeDA{} &\n%          \\loanapphomeDB{} &\n%          \\loanapphomeDC{} &\n%          \\loanapphomeDD{} \\\\\n%      \\hline\n%      \\ \\\\\n%      \\ \\\\\n%      \\multicolumn{5}{l}{Version of the table}\\\\\n%      \\multicolumn{5}{l}{with column proportions:} \\\\\n%      \\\\\n%      & \\multicolumn{3}{c}{\\bf \\var{homeownership}} & \\\\\n%      \\cline{2-4}\n%      \\var{app\\us{}type} &\n%          rent & mortgage & own & Total \\\\ \n%      \\hline\n%      individual &\n%          0.906 &\n%          0.802 &\n%          0.865 &\n%          0.851 \\\\\n%      joint &\n%          0.094 &\n%          0.198 &\n%          0.135 &\n%          0.150 \\\\\n%      \\hline\n%      Total & 1.000 & 1.000 & 1.000 & 1.000 \\\\\n%      \\hline\n%      \\ \\\\\n%    \\end{tabular}\n%    \\label{loan_app_type_home_copied_table}\n%  }\n  \\caption{\\subref{loan_app_type_home_seg_bar} Stacked\n      bar plot for \\var{homeownership},\n      where the counts have been further broken down\n      by \\var{app\\us{}type}.\n      \\subref{loan_app_type_home_sbs_bar}~Side-by-side\n      bar plot for \\var{homeownership}\n      and \\var{app\\us{}type}.\n      \\subref{loan_app_type_home_seg_bar_standardized}~Standardized\n      version of the stacked bar plot.}\n  \\label{loan_app_type_home_seg_bar_plot}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Examine the three bar plots in\n    Figure~\\ref{loan_app_type_home_seg_bar_plot}.\n    When is the stacked, side-by-side, or standardized\n    stacked bar plot the most useful?}\n  The stacked bar plot is most useful when it's reasonable\n  to assign one variable as the explanatory variable and\n  the other variable as the response, since we are effectively\n  grouping by one variable first and then breaking it down by\n  the others.\n\n  Side-by-side bar plots are more agnostic in their display\n  about which variable, if any, represents the explanatory\n  and which the response variable.\n  It is also easy to discern the number of cases\n  in the six different group combinations.\n  However, one downside\n  is that it tends to require more horizontal space;\n  the narrowness of Figure~\\ref{loan_app_type_home_sbs_bar}\n  makes the plot feel a bit cramped.\n  Additionally, when two groups are of very different sizes,\n  as we see in the \\resp{own} group relative to either of the\n  other two groups,\n  it is difficult to discern if there is an association\n  between the variables.\n\n  The standardized stacked bar plot is helpful if the primary\n  variable in the stacked bar plot is relatively imbalanced,\n  e.g. the \\resp{own} category has only a third of the\n  observations in the \\resp{mortgage} category,\n  making the simple stacked bar plot less useful for\n  checking for an association.\n  The major downside of the standardized version\n  is that we lose all sense of how many cases each of the\n  bars represents.\n\\end{nexample}\n\\end{examplewrap}\n\n%Before settling on a particular bar plot, consider each\n%carefully.\n%It can also be useful to make a couple of the versions,\n%which will offer different views and insights into the data\n%than if only one bar plot variant is reviewed.\n\n\\subsection{Mosaic plots}\n\\label{mosaic_plots_subsection}\n\nA \\term{mosaic plot} is a visualization technique\nsuitable for contingency tables that resembles\na standardized stacked bar plot with the benefit\nthat we still see the relative group sizes of the\nprimary variable as well.\n\nTo get started in creating our first mosaic plot,\nwe'll break a square into columns for each category\nof the \\var{homeownership} variable,\nwith the result shown in Figure~\\ref{loan_home_mosaic}.\nEach column represents a level of \\var{homeownership},\nand the column widths correspond to the proportion of\nloans in each of those categories.\nFor~instance, there are fewer loans where the borrower\nis an owner than where the borrower has a mortgage.\nIn general, mosaic plots use box \\emph{areas}\nto represent the number of cases in each category.\n\n\\begin{figure}[h]\n  \\centering\n  \\subfigure[]{\n    \\Figures[A one-variable mosaic plot is shown for the homeownership variable, which has levels rent, mortgage, and own. A one-variable mosaic plot can first be pictured as a square that has partitions running vertically, breaking that square up into three pieces, one piece per level. The portion of the square assigned to each piece is proportional to the number of cases for each level. In this particular mosaic plot, we see a \"rent\" piece on the left portion of the square that has been colored green -- this tall rectangle represents about 40\\% of the square. Now considering the middle tall rectangle, which is blue and has been labeled as \"mortgage\", its width is close to half of the total width of the square. The rightmost tall rectangle is red and is labeled \"own\", and it appears to represent a little more than 10\\% of the total width of the rectangle.]\n        {0.36}\n        {loan_app_type_home_mosaic_plot}\n        {loan_home_mosaic}\n    \\label{loan_home_mosaic}\n  }\n  \\subfigure[]{\n    \\Figures[A two-variable mosaic plot is shown, partitioned with vertical slices first for the homeownership variable in the same way as a one-variable mosaic plot, and then each of the tall rectangle from that one-variable mosaic plot has been sliced horizontally to represent the application types individual (shown as the upper portion of each tall rectangle) and joint (shown as the lower portion of each tall rectangle). Taking the first tall rectangle on the left of the mosaic plot, which is green and labeled as \"rent\", it is divided into a small \"joint\" rectangle at the bottom of the \"rent\" rectangle and a much larger upper portion that represents the \"individual\" application types of the rent homeownership cases. This same partitioning is repeated for the tall middle rectangle representing the blue mortgage homeownership cases, where a small portion of those applications are broken off into a smaller rectangle on the bottom for \"joint\" and a larger rectangle for the cases that are \"individual\". Similarly, the rightmost tall rectangle that is red and represents \"own\" has been divided into a lower rectangle for \"joint\" and an upper portion for \"individual\" application types. The benefit of this plot is that we can now get a sense of the proportional makeup of each homeownership category by looking at the relative widths of the three different colored tall rectangles, and we can also look at where each of these tall rectangles is broken into joint and individual applications. In this case, the tall rectangle for rent is broken lower than the mortgage and own levels, indicating it has fewer of the \"joint\" application types (which if you recall, was the lower sub-divided rectangles). The \"own\" category also has its horizontal break a bit lower than the \"mortgage\" rectangle's break, implying the mortgage category has the highest proportion of joint applications of the rent, mortgage, and own homeownership categories.]\n        {0.44}\n        {loan_app_type_home_mosaic_plot}\n        {loan_app_type_home_mosaic}\n    \\label{loan_app_type_home_mosaic}\n  }\n  \\caption{\\subref{loan_home_mosaic}~The one-variable mosaic\n      plot for \\var{homeownership}.\n      \\subref{loan_app_type_home_mosaic}~Two-variable mosaic\n      plot for both \\var{homeownership}\n      and \\var{app\\us{}type}.}\n  \\label{loan_app_type_home_mosaic_plot}\n\\end{figure}\n\nTo create a completed mosaic plot, the single-variable\nmosaic plot is further divided into pieces in\nFigure~\\ref{loan_app_type_home_mosaic} using the\n\\var{app\\us{}type} variable.\nEach column is split proportional to the\nnumber of loans from individual and joint\nborrowers.\nFor example, the second column represents loans\nwhere the borrower has a mortgage,\nand it was divided into individual loans (upper)\nand joint loans (lower).\nAs another example, the bottom segment of the third column\nrepresents loans where the borrower owns their home\nand applied jointly, while the upper segment\nof this column represents\nborrowers who are homeowners and filed individually.\nWe can again use this plot to see that\nthe \\var{homeownership} and \\var{app\\us{}type}\nvariables are associated, since some columns are divided\nin different vertical locations than others,\nwhich was the same technique used for checking an\nassociation in the standardized stacked bar plot.\n\nIn Figure~\\ref{loan_app_type_home_mosaic_plot},\nwe chose to first split by the homeowner status\nof the borrower.\nHowever, we could have instead first split by\nthe application type, as in\nFigure~\\ref{loan_app_type_home_mosaic_rev}.\nLike with the bar plots, it's common to use\nthe explanatory variable to represent the\nfirst split in a mosaic plot,\nand then for the response to break\nup each level of the explanatory variable,\nif these labels are reasonable to attach to\nthe variables under consideration.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figures[A two-variable mosaic plot that has been first divided vertically using the mortgage application type (individual on the left and joint on the right), and then each of those rectangles subdivided horizontally (\"own\" in red on the bottom, \"mortgage\" in blue in the middle, and \"rent\" in green on the top). The \"individual\" category as the left main rectangle spans about 85\\% of the square, while the right main rectangle for \"joint\" spans about 15\\% of the square. The homeownership breakdown within each of the main rectangles shows \"own\" represents roughly the same proportion in each, running up about 10\\% of the way up from the bottom. The next subdivided portion of each rectangle is \"mortgage\", and here we see that the left \"individual\" rectangle has only about 45\\% of its rectangle as \"mortgage\" while it represents about 60\\%  in the right \"joint\" rectangle. The \"rent\" subdivided portions at the top of each rectangle represents about 40\\% of the left \"individual\" rectangle and about 25\\% of the \"joint\" rectangle.]\n      {0.37}\n      {loan_app_type_home_mosaic_plot}\n      {loan_app_type_home_mosaic_rev}\n  \\caption{Mosaic plot where loans are grouped by\n      the \\var{homeownership} variable after they've\n      been divided into the \\resp{individual} and\n      \\resp{joint} application types.}\n  \\label{loan_app_type_home_mosaic_rev}\n\\end{figure}\n\n%In a similar way, a mosaic plot representing row proportions of Figure~\\ref{loan_home_app_type_totals} could be constructed, as shown in Figure~\\ref{loan_app_type_home_mosaic_rev}. However, because it is more insightful for this application to consider the fraction of spam in each category of the \\var{number} variable, we prefer Figure~\\ref{loan_app_type_home_mosaic}.\n\n\n\\subsection{The only pie chart you will see in this book}\n\nA \\term{pie chart} is shown in\nFigure~\\ref{loan_homeownership_pie_chart} alongside\na bar plot representing the same information.\nPie charts can be useful for giving a high-level overview\nto show how a set of cases break down.\nHowever, it is also difficult to decipher details\nin a pie chart.\nFor example, it takes a couple seconds longer to recognize\nthat there are more loans where the borrower has\na mortgage than rent when looking at the pie chart,\nwhile this detail is very obvious in the bar plot.\nWhile pie charts can be useful, we prefer bar plots\nfor their ease in comparing groups.\n%One benefit of pie charts is that they to make it easier\n%to see when a series of groups make up at least 50\\%,\n%e.g. \\Comment{would need to show a pie chart with\n%  a large number of categories for this point to make sense}.\n\n\\begin{figure}[h]\n  \\centering\n  \\Figure[There are two plots, each providing a visualization of the homeownership variable. The left plot is a pie chart, which is a circle that has three lines drawn from the center of the circle to its edge, dividing the circle into \"slices\". The lower left slice is large, representing close to 50\\% of the total circle, it is colored blue, and it is labeled \"mortgage\". The upper slice is also quite large, representing almost 40\\% of the circle, is colored green, and it is labeled \"rent\". The lower right slice is much smaller, representing about 15\\% of the circle, it is colored red, and it is labeled \"own\". Next, moving to the right plot, is shown a bar plot. This bar plot has homeownership categories along the horizontal axis and frequency along the vertical axis. The leftmost bar is green, is labeled \"rent\", and has a frequency of about 3900. The middle bar is blue, is labeled \"mortgage\", and has a frequency of about 4700. The rightmost bar is red, is labeled \"own\", and has a frequency of about 1300.]\n      {}{loan_homeownership_pie_chart}\n  \\caption{A pie chart and bar plot of \\var{homeownership}.}\n  \\label{loan_homeownership_pie_chart}\n\\end{figure}\n\n\\index{data!loans|)}\n\n\n\\D{\\newpage}\n\n\\subsection{Comparing numerical data across groups}\n\\label{comparingAcrossGroups}\n\n\\index{data!county|(}\n\nSome of the more interesting investigations can be considered\nby examining numerical data across groups.\nThe methods required here aren't really new:\nall that's required is to make a numerical plot for each group\nin the same graph.\nHere two convenient methods are introduced:\nside-by-side box plots and hollow histograms.\n\nWe will take a look again at the \\data{county} data set\nand compare the median household income for counties that\ngained population from 2010 to 2017 versus counties that\nhad no gain.\nWhile we might like to make a causal connection here,\nremember that these are observational data and so such\nan interpretation would be, at best, half-baked.\n\n\\newcommand{\\numcountieswithgains}{1454}\n\\newcommand{\\numcountieswithgainsC}{1,454}\n\\newcommand{\\numcountieswithoutgains}{1672}\n\\newcommand{\\numcountieswithoutgainsC}{1,672}\n\nThere were \\numcountieswithgainsC{} counties where\nthe population increased from 2010 to 2017, and there\nwere \\numcountieswithoutgainsC{} counties with no gain\n(all but one were a loss).\nA~random sample of 100 counties from the first group and\n50 from the second group are shown in\nFigure~\\ref{countyIncomeSplitByPopGainTable}\nto give a better sense of some of the raw median\nincome data.\n\n\\newcommand{\\npgpad}[1]{\\hspace{2mm}#1\\hspace{1.5mm}\\ }\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{ ccc ccc c ccc }\n\\multicolumn{10}{c}{\\bf Median Income for 150 Counties,\n    in \\$1000s} \\\\\n\\hline\n\\vspace{-2mm} \\\\\n\\multicolumn{6}{c}{\\bf Population Gain} &\\hspace{5mm}\\ &\n    \\multicolumn{3}{c}{\\bf No Population Gain} \\\\ \n  \\cline{1-6} \\cline{8-10}\n38.2 & 43.6 & 42.2 & 61.5 & 51.1 & 45.7 &&\n    \\npgpad{48.3} & \\npgpad{60.3} & \\npgpad{50.7} \\\\\n44.6 & 51.8 & 40.7 & 48.1 & 56.4 & 41.9 && 39.3 & 40.4 & 40.3 \\\\\n40.6 & 63.3 & 52.1 & 60.3 & 49.8 & 51.7 && 57 & 47.2 & 45.9 \\\\\n51.1 & 34.1 & 45.5 & 52.8 & 49.1 & 51 && 42.3 & 41.5 & 46.1 \\\\\n80.8 & 46.3 & 82.2 & 43.6 & 39.7 & 49.4 && 44.9 & 51.7 & 46.4 \\\\\n75.2 & 40.6 & 46.3 & 62.4 & 44.1 & 51.3 && 29.1 & 51.8 & 50.5 \\\\\n51.9 & 34.7 & 54 & 42.9 & 52.2 & 45.1 && 27 & 30.9 & 34.9 \\\\\n61 & 51.4 & 56.5 & 62 & 46 & 46.4 && 40.7 & 51.8 & 61.1 \\\\\n53.8 & 57.6 & 69.2 & 48.4 & 40.5 & 48.6 && 43.4 & 34.7 & 45.7 \\\\\n53.1 & 54.6 & 55 & 46.4 & 39.9 & 56.7 && 33.1 & 21 & 37 \\\\\n63 & 49.1 & 57.2 & 44.1 & 50 & 38.9 && 52 & 31.9 & 45.7 \\\\\n46.6 & 46.5 & 38.9 & 50.9 & 56 & 34.6 && 56.3 & 38.7 & 45.7 \\\\\n74.2 & 63 & 49.6 & 53.7 & 77.5 & 60 && 56.2 & 43 & 21.7 \\\\\n63.2 & 47.6 & 55.9 & 39.1 & 57.8 & 42.6 && 44.5 & 34.5 & 48.9 \\\\\n50.4 & 49 & 45.6 & 39 & 38.8 & 37.1 && 50.9 & 42.1 & 43.2 \\\\\n57.2 & 44.7 & 71.7 & 35.3 & 100.2 &  && 35.4 & 41.3 & 33.6 \\\\\n42.6 & 55.5 & 38.6 & 52.7 & 63 &  && 43.4 & 56.5 &  \\\\\n\\cline{1-6} \\cline{8-10}\n\\end{tabular}\n\\caption{In this table, median household income (in \\$1000s)\n    from a random sample of 100 counties that had population\n    gains are shown on the left.\n    Median incomes from a random sample of 50 counties that\n    had no population gain are shown on the right.}\n\\label{countyIncomeSplitByPopGainTable}\n\\end{figure}\n\n\\D{\\newpage}\n\nThe \\term{side-by-side box plot}\n\\index{box plot!side-by-side box plot}\nis a traditional tool for comparing across groups.\nAn example is shown in the left panel of\nFigure~\\ref{countyIncomeSplitByPopGain},\nwhere there are two box plots, one for each group,\nplaced into one plotting window and drawn on the same scale.\n\n\\begin{figure}\n  \\centering\n  \\Figure[There are two figures shown: a side-by-side box plot on the left, and a two overlaid hollow histograms on the right. These two plots describe the same data for the \"county\" data set: a numerical variable for median household income and a categorical variable with levels of \"gain\" and \"no gain\" for the population change in the county. First, the side-by-side box plots shown as the left plot are described. This plot shows two box plots side-by-side, enclosed in the same general plot so they are close and so easier to compare. The left box plot represents \"gain\", and the right plot represents \"no gain\". The vertical axis runs from about \\$20,000 to about \\$130,000. Starting at the lower levels, the \"no gain\" lower whisker is at about \\$20,000, while the \"gain\" lower whisker starts at about \\$25,000. Each whisker runs upwards to the box, where the \"no gain\" box is reached first at about \\$40,000 and the \"gain\" box at about \\$47,000. The median line in each box is shown, where the \"no gain\" median is shown to at about \\$45,000, even lower than the start of the \"gain\" box\". The \"gain\" box's median is at about \\$53,000 and is above the top of the \"no gain box\" at about \\$52,000. The left \"gain\" box finally ends at about \\$62,000. Above each box is the upper whisker. The upper whisker in the \"gain\" box plot extends far above that of the \"no gain\" box, reaching about \\$87,000 vs \\$70,000. Each box plot has many individual observations shown above the upper whisker. The largest outlier for \"gain\" is about \\$130,000, and the largest outlier for \"no gain\" is about \\$112,000. Next, moving onto the right plot of the two hollow histograms for the \"gain\" (in blue) and \"no gain\" (in red) categories. The hollow histograms are overlaid, making it easier to compare their shapes more directly. The histograms share a horizontal axis that runs from about \\$20,000 up to about \\$130,000. In each case, the histograms do not show the bins explicitly and instead only show the top portion of each histogram (hence the term \"hollow histogram\"), meaning each hollow histogram is described by a line outlining the top of each bin in each histogram. It is these lines that will be described. Starting at the left of the histograms, the \"no gain\" histogram line rises up slightly at \\$20,000 before the \"gain\" histogram line starts rising starting at about \\$25,000. The \"no gain\" line then ascends rapidly starting at about \\$30,000, followed by the \"gain\" line ascending rapidly at about \\$40,000, which is also about where the \"no gain\" category reaches a peak and holds steady until about \\$50,000, which is also where the \"gain\" line has now peaked. It is at this \\$50,000 point that the \"no gain\" line falls rapidly from what had been a relatively steady peak between about \\$35,000 to \\$50,000, with the \"gain\" group also much more slowly starting to descend at about \\$50,000. At close to \\$70,000, the \"no gain\" group is nearly touching the horizontal axis, while the \"gain\" group has only descended about 70\\% of the way. The \"no gain\" group hovers close to horizontal axis until appearing indistinguishable from the horizontal axis a bit above \\$90,000. On the other hand, the \"gain\" group shows a slow but steady decline from about 30\\% of its peak at \\$70,000 down to close to the horizontal axis at \\$100,000. The \"gain\" category bumps up just a tiny amount between \\$100,000 and \\$130,000 before becoming indistinguishable from the horizontal axis.]\n      {1.00}{countyIncomeSplitByPopGain}\n  \\caption{Side-by-side box plot (left panel)\n      and hollow histograms (right panel) for\n      \\var{med\\us{}hh\\us{}income},\n      where the counties are split by whether there was\n      a population gain or there was no gain.}\n  \\label{countyIncomeSplitByPopGain}\n\\end{figure}\n\nAnother useful plotting method uses \\termsub{hollow histograms}{hollow histogram} to compare numerical data across groups. These are just the outlines of histograms of each group put on the same plot, as shown in the right panel of Figure~\\ref{countyIncomeSplitByPopGain}.\n\n\\begin{exercisewrap}\n\\begin{nexercise} \\label{comparingPriceByTypeExercise}\nUse the plots in Figure~\\ref{countyIncomeSplitByPopGain}\nto compare the incomes for counties across the two groups.\nWhat do you notice about the approximate center of each group?\nWhat do you notice about the variability between groups?\nIs the shape relatively consistent between groups?\nHow many \\emph{prominent} modes are there for each\ngroup?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Answers may vary a little.\n  The counties with population gains tend to have higher\n  income (median of about \\$45,000) versus counties without\n  a gain (median of about \\$40,000).\n  The variability is also slightly larger for the population\n  gain group.\n  This is evident in the IQR, which is about 50\\% bigger\n  in the \\emph{gain} group.\n  Both distributions show slight to moderate right skew\n  and are unimodal.\n  The box plots indicate there are many observations\n  far above the median in each group, though we should\n  anticipate that many observations will fall beyond\n  the whiskers when examining any data set that\n  contain more than a couple hundred data points.}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat components of each plot in\nFigure~\\ref{countyIncomeSplitByPopGain}\ndo you find most useful?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Answers will vary.\n  The side-by-side box plots are especially useful for comparing\n  centers and spreads, while the hollow histograms are more useful\n  for seeing distribution shape, skew, and potential anomalies.}\n\\index{data!county|)}\n\n\n\n%%___________________________________________\n%\\section{Exploratory data analysis}\n%\\label{eda_section}\n%\n%Over the last two sections, we've learned fundamental\n%methods for graphing data.\n%In this section, we leverage what we've learned to expand\n%into more advanced techniques.\n%We'll learn more graphical methods, and importantly,\n%examine more complex relationships.\n%\n%\n%\\subsection{}\n\n\n{\\input{ch_summarizing_data/TeX/considering_categorical_data.tex}}\n\n\n\n\n\n%___________________________________________\n\\section{Case study: malaria vaccine}\n\\label{caseStudyMalariaVaccine}\n\n\\begin{examplewrap}\n\\begin{nexample}{Suppose your professor splits the students in class into two groups: students on the left and students on the right. If $\\hat{p}_{_L}$ and $\\hat{p}_{_R}$ represent the proportion of students who own an Apple product on the left and right, respectively, would you be surprised if $\\hat{p}_{_L}$ did not {exactly} equal $\\hat{p}_{_R}$?}\\label{classRightLeftSideApple}\nWhile the proportions would probably be close to each other, it would be unusual for them to be exactly the same. We would probably observe a small difference due to {chance}.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIf we don't think the side of the room a person sits on\nin class is related to whether the person owns an Apple product,\nwhat assumption are we making about the relationship between\nthese two variables?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We would be assuming that these two variables\n  are independent.}\n\n\n\\subsection{Variability within data}\n\\label{variabilityWithinData}\n\n\\index{data!malaria vaccine|(}\n\nWe consider a study on a new malaria vaccine\ncalled PfSPZ.\nIn this study, volunteer patients were randomized\ninto one of two experiment groups:\n14 patients received an experimental vaccine\nand 6 patients received a placebo vaccine.\nNineteen weeks later, all 20 patients were exposed\nto a drug-sensitive malaria parasite strain;\nthe motivation of using a drug-sensitive strain\nof parasite here is for ethical considerations,\nallowing any infections to be treated effectively.\nThe results are summarized in\nFigure~\\ref{malaria_vaccine_20_exp_summary},\nwhere 9 of the 14 treatment patients remained free\nof signs of infection while all of the~6 patients\nin the control group patients showed some baseline\nsigns of infection.\n\n\\newcommand{\\malariaAA}{5}\n\\newcommand{\\malariaAB}{9}\n\\newcommand{\\malariaAD}{14}\n\\newcommand{\\malariaBA}{6}\n\\newcommand{\\malariaBB}{0}\n\\newcommand{\\malariaBD}{6}\n\\newcommand{\\malariaDA}{11}\n\\newcommand{\\malariaDB}{9}\n\\newcommand{\\malariaDD}{20}\n\\newcommand{\\malariaVIR}{0.357}\n\\newcommand{\\malariaVIRPerc}{35.7\\%}\n\\newcommand{\\malariaPIR}{1.000}\n\\newcommand{\\malariaPIRPerc}{100\\%}\n\\newcommand{\\malariaIRDiff}{0.643}\n\\newcommand{\\malariaIRDiffPerc}{64.3\\%}\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l l cc rr}\n  & & \\multicolumn{2}{c}{\\var{outcome}} \\\\\n  \\cline{3-4}\n  &  &  {infection} & {no infection} & Total & \\hspace{3mm}  \\\\ \n  \\cline{2-5}\n  & {vaccine} &\n      \\malariaAA{} &\n      \\malariaAB{} &\n      \\malariaAD{} \\\\ \n  \\raisebox{1.5ex}[0pt]{\\var{treatment}}\n  & {placebo} & \n      \\malariaBA{} &\n      \\malariaBB{} &\n      \\malariaBD{} \\\\ \n  \\cline{2-5}\n  & Total & \n      \\malariaDA{} &\n      \\malariaDB{} &\n      \\malariaDD{} \\\\ \n  \\cline{2-5}\n\\end{tabular}\n\\caption{Summary results for the malaria vaccine experiment.}\n\\label{malaria_vaccine_20_exp_summary}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nIs this an observational study or an experiment?\nWhat implications does the study type have on what can\nbe inferred from the results?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{The\n  study is an experiment, as patients were randomly\n  assigned an experiment group.\n  Since this is an experiment, the results can be used\n  to evaluate a causal relationship between the malaria\n  vaccine and whether patients showed signs\n  of an infection.}\n\nIn this study, a smaller proportion of patients\nwho received the vaccine showed signs of an infection\n(\\malariaVIRPerc{} versus \\malariaPIRPerc{}).\nHowever, the sample is very small,\nand it is unclear whether the difference provides\n\\emph{convincing evidence} that the vaccine is\neffective.\n\n\\D{\\newpage}\n\n\\begin{examplewrap}\n\\begin{nexample}{Data scientists are sometimes called\n    upon to evaluate the strength of evidence.\n    When looking at the rates of infection for patients\n    in the two groups in this study,\n    what comes to mind as we try to determine whether\n    the data show convincing evidence of a real difference?}\n  \\label{malaria_vaccine_20_what_is_convincing}\n  The observed infection rates\n  (\\malariaVIRPerc{} for the treatment group versus\n  \\malariaPIRPerc{} for the control group)\n  suggest the vaccine may be effective.\n  However, we cannot be sure if the observed difference\n  represents the vaccine's efficacy or is just from\n  random chance.\n  Generally there is a little bit of fluctuation\n  in sample data, and we wouldn't expect the sample\n  proportions to be \\emph{exactly} equal,\n  even if the truth was that the infection rates\n  were independent of getting the vaccine.\n  Additionally, with such small samples,\n  perhaps it's common to observe such large differences\n  when we randomly split a group due to chance alone!\n\\end{nexample}\n\\end{examplewrap}\n\nExample~\\ref{malaria_vaccine_20_what_is_convincing}\nis a reminder that the observed outcomes in the data\nsample may not perfectly reflect the true relationships\nbetween variables since there is \\term{random noise}.\nWhile the observed difference in rates of infection\nis large, the sample size for the study is small,\nmaking it unclear if this observed difference represents\nefficacy of the vaccine or whether it is simply due to\nchance.\nWe label these two competing claims, $H_0$ and $H_A$,\nwhich are spoken as ``H-nought'' and ``H-A'':\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[$H_0$:] \\textbf{Independence model.}\n    The variables \\var{treatment} and \\var{outcome}\n    are independent.\n    They have no relationship, and the observed difference\n    between the proportion of patients who developed\n    an infection in the two groups, \\malariaIRDiffPerc{},\n    was due to chance.\n\\item[$H_A$:] \\textbf{Alternative model.}\n    The variables are \\emph{not} independent.\n    The difference in infection rates of\n    \\malariaIRDiffPerc{}\n    was not due to chance,\n    and vaccine affected the rate of infection.\n\\end{itemize}\n\nWhat would it mean if the independence model,\nwhich says the vaccine had no influence on the\nrate of infection, is true?\nIt would mean 11~patients were going to\ndevelop an infection \\emph{no matter which group\nthey were randomized into},\nand 9~patients would not develop an infection\n\\emph{no matter which group they were randomized\ninto}.\nThat~is, if the vaccine did not affect the rate\nof infection, the difference in the infection rates\nwas due to chance alone in how the patients were\nrandomized.\n\nNow consider the alternative model:\ninfection rates were influenced by whether a patient\nreceived the vaccine or not.\nIf this was true, and especially if this influence\nwas substantial, we would expect to see some difference\nin the infection rates of patients in the groups.\n\nWe choose between these two competing claims\nby assessing if the data conflict so much with\n$H_0$ that the independence model cannot be deemed\nreasonable.\nIf this is the case, and the data support $H_A$,\nthen we will reject the notion of independence\nand conclude the vaccine was effective.\n\n\n\\subsection{Simulating the study}\n\\label{simulatingTheStudy}\n\nWe're going to implement\n\\termsub{simulations}{simulation},\nwhere we will pretend we know that the malaria\nvaccine being tested does \\emph{not} work.\nUltimately, we want to understand if the large\ndifference we observed is common in these\nsimulations.\nIf it is common, then maybe the difference\nwe observed was purely due to chance.\nIf it is very uncommon, then the possibility\nthat the vaccine was helpful seems more plausible.\n\nFigure~\\ref{malaria_vaccine_20_exp_summary}\nshows that 11 patients developed infections and 9 did not.\nFor our simulation, we will suppose the infections\nwere independent of the vaccine and we were able to\n\\emph{rewind} back to when the researchers randomized\nthe patients in the study.\nIf we happened to randomize the patients differently,\nwe may get a different result in this hypothetical\nworld where the vaccine doesn't influence the infection.\nLet's complete another \\term{randomization} using\na simulation.\n\n\\D{\\newpage}\n\nIn this \\term{simulation}, we take 20 notecards to\nrepresent the 20 patients, where we write down ``infection''\non 11 cards and ``no infection'' on 9 cards.\nIn this hypothetical world, we believe each patient\nthat got an infection was going to get it regardless\nof which group they were in, so let's see what happens\nif we randomly assign the patients to the treatment\nand control groups again.\nWe thoroughly shuffle the notecards and deal 14 into\na \\resp{vaccine} pile and 6 into a \\resp{placebo} pile.\nFinally, we tabulate the results, which are shown in\nFigure~\\ref{malaria_vaccine_20_exp_summary_rand_1}.\n\n\\begin{figure}[ht]\n\\centering\n\\begin{tabular}{l l cc rr}\n  & & \\multicolumn{2}{c}{\\var{outcome}} \\\\\n  \\cline{3-4}\n  &  &  {infection} & {no infection} & Total & \\hspace{3mm}  \\\\ \n  \\cline{2-5}\n  treatment & {vaccine} & 7 & 7 & 14 \\\\ \n  (simulated) & {placebo} & 4 & 2 & 6 \\\\ \n  \\cline{2-5}\n  & Total & 11 & 9 & 20 \\\\\n  \\cline{2-5}\n\\end{tabular}\n\\caption{Simulation results, where any difference\n    in infection rates is purely due to chance.}\n\\label{malaria_vaccine_20_exp_summary_rand_1}\n\\end{figure}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\n\\label{malaria_vaccine_20_exp_summary_rand_1_diff}\nWhat is the difference in infection rates between\nthe two simulated groups in\nFigure~\\ref{malaria_vaccine_20_exp_summary_rand_1}?\nHow does this compare to the observed\n\\malariaIRDiffPerc{} difference\nin the actual data?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{$4 / 6 - 7 / 14 = 0.167$\n  or about 16.7\\% in favor of the vaccine.\n  This difference due to chance is much smaller than the\n  difference observed in the actual groups.}\n\n\n\\subsection{Checking for independence}\n\nWe computed one possible difference under the\nindependence model in Guided\nPractice~\\ref{malaria_vaccine_20_exp_summary_rand_1_diff},\nwhich represents one difference due to chance.\nWhile in this first simulation, we physically dealt\nout notecards to represent the patients,\nit is more efficient to perform this simulation\nusing a computer.\nRepeating the simulation on a computer, we get another\ndifference due to chance:\n\\begin{align*}\n\\frac{2}{\\malariaBD{}} - \\frac{9}{\\malariaAD{}} = -0.310\n\\end{align*}\nAnd another:\n\\begin{align*}\n\\frac{3}{\\malariaBD{}} - \\frac{8}{\\malariaAD{}} = -0.071\n\\end{align*}\nAnd so on until we repeat the simulation enough times\nthat we have a good idea of what represents the\n\\emph{distribution of differences from chance alone}.\nFigure~\\ref{malaria_rand_dot_plot} shows a stacked plot\nof the differences found from 100 simulations,\nwhere each dot represents a simulated difference between\nthe infection rates (control rate minus treatment rate).\n\n\\begin{figure}[ht]\n  \\centering\n  \\Figure[A stacked dot plot is shown. The horizontal axis represents \"difference in infection rates\" and has a range of -0.6 to 0.8. There are six stacks of dots shown in the plot, with 3 points shown at -0.55, 20-25 points shown at -0.32, 30-35 points shown at -0.08, 25-30 points shown at 0.18, 10-12 points shown at 0.41, and 2 points shown at 0.64.]\n      {0.85}{malaria_rand_dot_plot}\n  \\caption{A stacked dot plot of differences from\n      100 simulations produced under the independence model,\n      $H_0$, where in these simulations infections are\n      unaffected by the vaccine.\n      Two of the 100 simulations had a difference of\n      at least \\malariaIRDiffPerc{}, the difference observed\n      in the study.}\n  \\label{malaria_rand_dot_plot}\n\\end{figure}\n\nNote that the distribution of these simulated differences\nis centered around 0.\nWe simulated these differences assuming that the independence\nmodel was true, and under this condition,\nwe expect the difference to be near zero with some random\nfluctuation, where \\emph{near} is pretty generous in this\ncase since the sample sizes are so small in this study.\n\n\\begin{examplewrap}\n\\begin{nexample}{How often would you observe a difference\n    of at least \\malariaIRDiffPerc{} (\\malariaIRDiff{})\n    according to Figure~\\ref{malaria_rand_dot_plot}?\n    Often, sometimes, rarely, or never?}\n  It appears that a difference of at least\n  \\malariaIRDiffPerc{} due to chance alone would only\n  happen about 2\\% of the time according to\n  Figure~\\ref{malaria_rand_dot_plot}.\n  Such a low probability indicates a rare event.\n\\end{nexample}\n\\end{examplewrap}\n\n\\D{\\newpage}\n\nThe difference of \\malariaIRDiffPerc{} being\na rare event suggests two possible interpretations\nof the results of the study:\n\\begin{itemize}\n  \\setlength{\\itemsep}{0mm}\n  \\item[$H_0$] \\textbf{Independence model.}\n      The vaccine has no effect on infection rate,\n      and we just happened to observe a difference\n      that would only occur on a rare occasion.\n  \\item[$H_A$] \\textbf{Alternative model.}\n      The vaccine has an effect on infection rate,\n      and the difference we observed was actually due to\n      the vaccine being effective at combatting malaria,\n      which explains the large difference\n      of~\\malariaIRDiffPerc{}.\n\\end{itemize}\nBased on the simulations, we have two options.\n(1)~We conclude that the study results do not provide\nstrong evidence against the independence model.\nThat is, we do not have sufficiently strong evidence\nto conclude the vaccine had an effect in this\nclinical setting.\n(2)~We conclude the evidence is sufficiently strong\nto reject $H_0$ and assert that the vaccine was useful.\nWhen we conduct formal studies, usually we reject the\nnotion that we just happened to observe a rare\nevent.\\footnote{This reasoning does not generally extend\n    to anecdotal observations.\n    Each of us observes incredibly rare events every day,\n    events we could not possibly hope to predict.\n    However, in the non-rigorous setting of anecdotal\n    evidence, almost anything may appear to be a rare event,\n    so the idea of looking for rare events in day-to-day\n    activities is treacherous.\n    For example, we might look at the lottery:\n    there was only a 1 in 292 million chance that the\n    Powerball numbers for the largest jackpot in history\n    (January 13th, 2016) would be (04, 08, 19, 27, 34)\n    with a Powerball of (10),\n    but nonetheless those numbers came up!\n    However, no matter what numbers had turned up,\n    they would have had the same incredibly rare odds.\n    That is, \\emph{any set of numbers we could have\n    observed would ultimately be incredibly rare}.\n    This type of situation is typical of our daily lives:\n    each possible event in itself seems incredibly rare,\n    but if we consider every alternative, those outcomes\n    are also incredibly rare.\n    We should be cautious not to misinterpret such\n    anecdotal evidence.}\nSo in this case, we reject the independence model in favor\nof the alternative.\nThat is, we are concluding the data provide strong evidence\nthat the vaccine provides some protection against malaria\nin this clinical setting.\n\n\\index{data!malaria vaccine|)}\n\nOne field of statistics, statistical inference, is built\non evaluating whether such differences are due to chance.\nIn statistical inference, data scientists evaluate which\nmodel is most reasonable given the data.\nErrors do occur, just like rare events, and we might choose\nthe wrong model.\nWhile we do not always choose correctly, statistical\ninference gives us tools to control and evaluate how\noften these errors occur.\nIn Chapter~\\ref{foundationsForInference},\nwe give a formal introduction to the problem of model\nselection.\nWe spend the next two chapters building a foundation\nof probability and theory necessary to make that\ndiscussion rigorous.\n\n\n\n{\\input{ch_summarizing_data/TeX/case_study_malaria_vaccine.tex}}\n"
  },
  {
    "path": "ch_summarizing_data/TeX/considering_categorical_data.tex",
    "content": "\\exercisesheader{}\n\n% 21\n\n\\eoce{\\qt{Antibiotic use in children\\label{antibiotic_use_children}} The bar plot \nand the pie chart below show the distribution of pre-existing medical \nconditions of children involved in a study on the optimal duration of \nantibiotic use in treatment of tracheitis, which is an upper respiratory \ninfection.\n\\begin{center}\n\\Figures[A bar plot is shown, where values on the axis range of relative frequency from 0 to just over 0.35. The values, in decreasing order and their approximate values, are Prematurity at 0.36, Cardiovascular at 0.17, Respiratory at 0.14, Trauma at 0.11, and Neuromuscular at 0.11, Genetic/metabolic at 0.07, Immunocompromised at 0.02, and Gastrointestinal at 0.02.]{0.45}{eoce/antibiotic_use_children}{antibiotic_use_children_bar}\n\\Figures[A pie chart is shown of the same data from a previous chart, which was a bar chart. The Prematurity category appears to represent about a third of the pie chart (though this and other proportions are difficult to estimate accurately). The Cardiovascular group is roughly one-sixth of the total pie. About a quarter of the pie consists of an even split between Respiratory and Trauma. The remaining categories represent just under a quarter of the pie: Neuromascular (about an eighth of the pie), Genetic/metabolic (about one-fifteenth of the pie), and the remainder evenly distributed between Immunocompromised and Gastrointestinal.]{0.45}{eoce/antibiotic_use_children}{antibiotic_use_children_pie}\n\\end{center}\n\\begin{parts}\n\\item What features are apparent in the bar plot but not in the pie chart?\n\\item What features are apparent in the pie chart but not in the bar plot?\n\\item Which graph would you prefer to use for displaying these categorical data?\n\\end{parts}\n}{}\n\n% 22\n\n\\eoce{\\qt{Views on immigration\\label{immigration}} 910 randomly sampled registered \nvoters from Tampa, FL were asked if they thought workers who have illegally \nentered the US should be (i) allowed to keep their jobs and apply for \nUS citizenship, (ii) allowed to keep their jobs as temporary guest workers \nbut not allowed to apply for US citizenship, or (iii) lose their jobs and \nhave to leave the country. The results of the survey by political ideology \nare shown below.\\footfullcite{survey:immigFL:2012}\n\\begin{center}\n\\begin{tabular}{l l c c c c}\n                        &                           & \\multicolumn{3}{c}{\\textit{Political ideology}} \\\\\n\\cline{3-5}\n                        &                           & Conservative  & Moderate  & Liberal   & Total \\\\\n\\cline{2-6}\n                        & (i) Apply for citizenship & 57            & 120       & 101       & 278 \\\\\n                        & (ii) Guest worker         & 121           & 113       & 28        & 262 \\\\\n\\raisebox{1.5ex}[0pt]{\\emph{Response}} & (iii) Leave the country    & 179       & 126       & 45        & 350 \\\\ \n                        & (iv) Not sure             & 15            & 4         & 1         & 20\\\\\n\\cline{2-6}\n                        & Total                     & 372           & 363       & 175       & 910\n\\end{tabular}\n\\end{center}\n\\begin{parts}\n\\item What percent of these Tampa, FL voters identify themselves as conservatives?\n\\item What percent of these Tampa, FL voters are in favor of the citizenship option?\n\\item What percent of these Tampa, FL voters identify themselves as conservatives \nand are in favor of the citizenship option?\n\\item What percent of these Tampa, FL voters who identify themselves as \nconservatives are also in favor of the citizenship option? What percent of \nmoderates share this view? What percent of liberals share this view?\n\\item Do political ideology and views on immigration appear to be independent? \nExplain your reasoning.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 23\n\n\\eoce{\\qt{Views on the DREAM Act\\label{dream_act_mosaic}} A random sample of registered \nvoters from Tampa, FL were asked if they support the DREAM Act, a proposed law which would provide a path to citizenship for people brought illegally to the US as children.\nThe survey also collected information on the political ideology of the respondents. \nBased on the mosaic plot shown below, do views on the DREAM Act and  \npolitical ideology appear to be independent? Explain your reasoning.\n\\footfullcite{survey:immigFL:2012}\n\\begin{center}\n\\Figures[A mosaic plot is shown. The square (or, more accurately, a rectangle in this case), is divided into three main categories as tall rectangles: Conservative (about 40\\% of the data), Moderate (about 40\\% of the data), and Liberal (about 20\\%). The tall rectangles are each divided into \"Support\", \"Not Support\", and \"Not Sure\".  The \"Support\" category is about 45-50\\% for the Conservative and Moderate political groups and about 60-65\\% for Liberal. The \"Not Support\" category is about 40-45\\% for the Conservative and Moderate groups, while it is about 30\\% for the Liberal group. In all three of the main groupings, \"Not sure\" is about the same, representing about 5-10\\% of each political categories.]{0.8}{eoce/dream_act_mosaic}{dream_act_mosaic}\n\\end{center}\n}{}\n\n% 24\n\n\\eoce{\\qt{Raise taxes\\label{raise_taxes_mosaic}} A random sample of registered \nvoters nationally were asked whether they think it's better to raise taxes \non the rich or raise taxes on the poor. The survey also collected information \non the political party affiliation of the respondents. Based on the mosaic \nplot shown below, do views on raising taxes and  \npolitical affiliation appear to be independent? Explain your reasoning.\n\\footfullcite{survey:raiseTaxes:2015}\n\\begin{center}\n\\Figures[A mosaic plot is shown for variables of political affiliation (main variable split) and opinion on whether to raise taxes on the rich, poor, or not sure. The political split, representing the main vertical splits in the mosaic plot, is roughly evenly split between Democrat, Republican, and Independent/Other, with perhaps a little more respondents in the Democrat group. The very large portion of the Democrat group -- about 85\\% -- overwhelmingly supports raising taxes on the rich, with only about 5\\% of this group supports raising taxes on the poor, and 5\\% are unsure. About 45-50\\% of the Republican and Independent/Other groups each support raising taxes on the rich, about 10\\% of these groups support raising taxes on the poor, and about 40-45\\% of each of these groups are not sure.]{0.75}{eoce/raise_taxes_mosaic}{raise_taxes_mosaic}\n\\end{center}\n}{}\n"
  },
  {
    "path": "ch_summarizing_data/TeX/examining_numerical_data.tex",
    "content": "\\exercisesheader{}\n\n% 1\n\n\\eoce{\\qt{Mammal life spans\\label{mammal_life_spans}} Data were collected on life spans (in \nyears) and gestation lengths (in days) for 62 mammals. A scatterplot of life span versus \nlength of gestation is shown below. \\footfullcite{Allison+Cicchetti:1975}\n\n\\noindent\\begin{minipage}[c]{0.44\\textwidth}\n\\begin{parts}\n\\item What type of an association is apparent between life span and length of gestation?\n\\item What type of an association would you expect to see if the axes of the plot were reversed, i.e. if we plotted length of gestation versus life span?\n\\item Are life span and length of gestation independent? Explain your reasoning.\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.55\\textwidth}\n\\begin{center}\n\\Figures[A scatterplot of 62 points is shown. The variable \"Gestation\" is shown along the horizontal axis with a range of 0 days to about 650 days. The variable \"Life Span\" is shown along the vertical axis with a range of 0 years to 100 years. The a large cluster of points is shown between 0 to 250 gestational days and 0 to 30 years. Outside of this cluster, there is one point at approximately (10, 50). There is another cluster of points between 250 and 450 gestational days and 25 and 50 years. Beyond the points so far described are three points located at (250 days, 100 years), (640 days, 70 years), and (650 days, 45 years).]{0.86}{eoce/mammal_life_spans}{mammal_life_spans_scatterplot}\n\\end{center}\n\\end{minipage}\n}{}\n\n% 2\n\n\\eoce{\\qt{Associations\\label{association_plots}}\nIndicate which of the plots show\n(a)~a positive association,\n(b)~a negative association, or\n(c)~no~association.\nAlso determine if the positive and negative associations\nare linear or nonlinear.\nEach part may refer to more than one plot.\n\\begin{center}\n\\Figures[Four scatterplots are shown and are labeled 1, 2, 3, and 4. There are no label axes on these plots, as only the patterns of the points in the plots are important for this exercise. In plot 1, the points are moderately clustered in the lower left corner of the plot and remain clustered looking further right in the plot, where the points follow steadily upwards to the top-right corner. In plot 2, the points appear to be scattered almost randomly all around the rectangular plotting region. Plot 3 shows points clustered tightly in the lower left corner and the data points remain clustered even as moving right, with the data trending upwards gradually and then more steeply as it reaches the right side of the plot. Plot 4, when looking on the left portion, shows data moderately clustered in the upper-left corner, which then steadily trends downward to the lower-right corner of the plot.]{0.95}{eoce/association_plots}{association_plots}\n\\end{center}\n}{}\n\n% 3\n\n\\eoce{\\qt{Reproducing bacteria\\label{reproducing_bacteria}} Suppose that there is only \nsufficient space and nutrients to support one million bacterial cells in a petri dish. \nYou place a few bacterial cells in this petri dish, allow them to reproduce freely, and \nrecord the number of bacterial cells in the dish over time. Sketch a plot representing \nthe relationship between number of bacterial cells and time.\n% first exponential\n}{}\n\n% 4\n\n\\eoce{\\qt{Office productivity\\label{office_productivity}} Office productivity is relatively low \nwhen the employees feel no stress about their work or job security. However, high levels \nof stress can also lead to reduced employee productivity. Sketch a plot to represent the \nrelationship between stress and productivity.\n}{}\n\n% 5\n\n\\eoce{\\qt{Parameters and statistics\\label{parameters_stats}} Identify which value represents \nthe sample mean and which value represents the claimed population mean.\n\\begin{parts}\n\\item American households spent an average of about \\$52 in 2007 on Halloween \nmerchandise such as costumes, decorations and candy. To see if this number had changed, \nresearchers conducted a new survey in 2008 before industry numbers were reported. The \nsurvey included 1,500 households and found that average Halloween spending was \\$58 per \nhousehold.\n\\item The average GPA of students in 2001 at a private university was 3.37. A survey \non a sample of 203 students from this university yielded an average GPA of 3.59 \na decade later.\n\\end{parts}\n}{}\n\n% 6\n\n\\eoce{\\qt{Sleeping in college\\label{college_sleeping}} A recent article in a college newspaper \nstated that college students get an average of 5.5 hrs of sleep each night. A student who \nwas skeptical about this value decided to conduct a survey by randomly sampling 25 \nstudents. On average, the sampled students slept 6.25 hours per night. Identify which \nvalue represents the sample mean and which value represents the claimed population mean.\n}{}\n\n\\D{\\newpage}\n\n% 7\n\n\\eoce{\\qt{Days off at a mining plant\\label{days_off_mining}} Workers at a particular mining \nsite receive an average of 35 days paid vacation, which is lower than the national \naverage. The manager of this plant is under pressure from a local union to increase the \namount of paid time off. However, he does not want to give more days off to the workers \nbecause that would be costly. Instead he decides he should fire 10 employees in such a \nway as to raise the average number of days off that are reported by his employees. In \norder to achieve this goal, should he fire employees who have the most number of days \noff, least number of days off, or those who have about the average number of days off?\n}{}\n\n% 8\n\n\\eoce{\\qt{Medians and IQRs} For each part, compare distributions (1) and (2) based on their medians and IQRs. You do not need to calculate these statistics; simply state how the medians and IQRs compare. Make sure to explain your reasoning. \n\\begin{multicols}{2}\n\\begin{parts}\n\\item (1) 3, 5, 6, 7, 9 \\\\\n(2) 3, 5, 6, 7, 20\n\\item (1) 3, 5, 6, 7, 9 \\\\\n(2) 3, 5, 7, 8, 9\n\\item (1) 1, 2, 3, 4, 5 \\\\\n(2) 6, 7, 8, 9, 10\n\\item (1) 0, 10, 50, 60, 100 \\\\\n(2) 0, 100, 500, 600, 1000\n\\end{parts}\n\\end{multicols}\n}{}\n\n% 9\n\n\\eoce{\\qt{Means and SDs} For each part, compare distributions (1) and (2) based on their means and standard deviations. You do not need to calculate these statistics; simply state how the means and the standard deviations compare. Make sure to explain your reasoning. \\textit{Hint:} It may be useful to sketch dot plots of the distributions.\n\\begin{multicols}{2}\n\\begin{parts}\n\\item (1) 3, 5, 5, 5, 8, 11, 11, 11, 13 \\\\\n(2) 3, 5, 5, 5, 8, 11, 11, 11, 20 \\\\\n\\item (1) -20, 0, 0, 0, 15, 25, 30, 30 \\\\\n(2) -40, 0, 0, 0, 15, 25, 30, 30\n\\item (1) 0, 2, 4, 6, 8, 10 \\\\\n(2) 20, 22, 24, 26, 28, 30\n\\item (1) 100, 200, 300, 400, 500 \\\\\n(2) 0, 50, 300, 550, 600\n\\end{parts}\n\\end{multicols}\n}{}\n\n% 10\n\n\\eoce{\\qt{Mix-and-match} Describe the distribution in the histograms below and match them to the box plots. \\\\\n\\begin{center}\n\\Figures[Six plots are shown, three histograms labeled a, b, and c, and 3 box plots labeled 1, 2, and 3.\n\nPlot (a) shows a histogram with horizontal range for the data of 50 to 70. The data are bell-shaped and centered in the plot, with only a little data reaching close to the lower end of 50 and the upper end of 70.\n\nPlot (b) shows another histogram, where the horizontal axis extends from 0 to 100, and the histogram bins are relatively steady in their height in the first bin near zero across the plot to the last bin near 100.\n\nPlot (c) is a histogram with a horizontal axis running from 0 to about 7. The first few bins rise quickly to a peak at the horizontal location of 1 and then fall until reaching 2 and then decline much more gradually until about 4, where the bins are near zero and stay near zero for larger values.\n\nPlot (1) is a box plot. The vertical axis for the box plot spans from 0 to about 7. The lower whisker is at 0, the box spans about 1 to 2, with the center line for the box plot at about 1.4. The upper whisker extends up to about 3.5, and then there are several points marked individually extending further upwards to about 7.\n\nPlot (2) is a box plot with a vertical axis spanning about 50 to 70. The box for the plot is centered at 60 and runs from about 58 to 62. The whiskers span about 52 to 68. There are 2 individually points shown below 52 and about 4 points shown above 68.\n\nPlot (3) is a box plot spanning from 0 to 100. The box is centered at about 50, and the box spans about 25 to 75. The whiskers extend down to 0 and up to 100.]{}{eoce/hist_box_match}{hist_box_match}\n\\end{center}\n}{}\n\n\\D{\\newpage}\n\n% 11\n\n\\eoce{\\qt{Air quality\\label{air_quality_durham}} Daily air quality is measured by the air \nquality index (AQI) reported by the Environmental Protection Agency. This index reports \nthe pollution level and what associated health effects might be a concern. The index is \ncalculated for five major air pollutants regulated by the Clean Air Act and takes values \nfrom 0 to 300, where a higher value indicates lower air quality. AQI was reported for a \nsample of 91 days in 2011 in Durham, NC. The relative frequency histogram below shows \nthe distribution of the AQI values on these days. \\footfullcite{data:durhamAQI:2011} \\\\\n\\begin{minipage}[c]{0.55\\textwidth}\n\\begin{parts}\n\\item Estimate the median AQI value of this sample.\n\\item Would you expect the mean AQI value of this sample to be higher or lower than the \nmedian? Explain your reasoning.\n\\item Estimate Q1, Q3, and IQR for the distribution.\n\\item Would any of the days in this sample be considered to have an unusually low or \nhigh AQI? Explain your reasoning.\n\\end{parts}\n\\end{minipage}\n\\begin{minipage}[c]{0.45\\textwidth}\n\\begin{center}\n\\Figures[A histogram of \"Daily AQI\", where the horizontal axis for the data runs from about 5 to 65. The bin width is 5, there are 12 bins from 5 to 60, and the vertical axis shows proportions. The heights of the 12 bins, in order from left to right, are about 0.02 (for the bin 5 to 10), 0.06, 0.20, 0.06, 0.20, 0.15, 0.07, 0.04, 0.07, 0.08, 0.03, and 0.02 for the last bin for 60 to 65.]{}{eoce/air_quality_durham}{air_quality_durham_rel_freq_hist}\n\\end{center}\n\\end{minipage}\n}{}\n\n% 12\n\n\\eoce{\\qt{Median vs. mean\\label{estimate_mean_median_simple}} Estimate the median for the \n400 observations shown in the histogram, and note whether you expect the mean \nto be higher or lower than the median.\n\\begin{center}\n\\Figures[A histogram is shown, with the horizontal axis for the data runs from 40 to 100, with a bin size width of 5. The frequencies for the bins are as follows, where counts are approximate: 2 (for bin 40 to 45), 4, 2, 10, 20, 25, 50, 75, 70, 85, 45, and 10 for the last bin from 95 to 100.\n]{0.6}{eoce/estimate_mean_median_simple}{estimate_mean_median_simple}\n\\end{center}\n}{}\n\n% 13\n\n\\eoce{\\qt{Histograms vs. box plots\\label{hist_vs_box}} Compare the two plots below. What \ncharacteristics of the distribution are apparent in the histogram and not in the box \nplot? What characteristics are apparent in the box plot but not in the histogram?\n\\begin{center}\n\\Figures[Two plots are shown, first a histogram and second a box plot. The data for each plot runs from about 0 to 30.\n\nThe histogram has bins of width 2. The bins, starting at the lower values, shows an initial peak at about the horizontal location of 5, then declines to near the horizontal axis at 10, before rising again between 10 and 14, and then lower values again for bins between 15 to 30.\n\nThe box plot has its box centered at 10 and runs from about 5 to 12. The whiskers reach out to about 2 and up to about 22. There are a few points above the upper whisker.\n]{0.6}{eoce/hist_vs_box}{hist_vs_box}\n\\end{center}\n}{}\n\n% 14\n\n\\eoce{\\qt{Facebook friends\\label{dist_shape_fb_friends}} Facebook data indicate that \n50\\% of Facebook users have 100 or more friends, and that the average friend \ncount of users is 190. What do these findings suggest about the shape of the \ndistribution of number of friends of Facebook users? \\footfullcite{Backstrom:2011}\n}{}\n\n% 15\n\n\\eoce{\\qt{Distributions and appropriate statistics, Part I\\label{dist_shape_pets_dist_height}} \nFor each of the following, state whether you expect the distribution to be \nsymmetric, right skewed, or left skewed. Also specify whether the mean or \nmedian would best represent a typical observation in the data, and whether \nthe variability of observations would be best represented using the \nstandard deviation or IQR. Explain your reasoning.\n\\begin{parts}\n\\item Number of pets per household. \n\\item Distance to work, i.e. number of miles between work and home.\n\\item Heights of adult males.\n\\end{parts}\n}{}\n\n\\D{\\newpage}\n\n% 16\n\n\\eoce{\\qt{Distributions and appropriate statistics, Part II\\label{dist_shape_housing_alcohol_salary}} \nFor each of the following, state whether you expect the distribution to be symmetric, \nright skewed, or left skewed. Also specify whether the mean or median would best \nrepresent a typical observation in the data, and whether the variability of observations \nwould be best represented using the standard deviation or IQR. Explain your reasoning.\n\\begin{parts}\n\\item Housing prices in a country where 25\\% of the houses cost below \\$350,000, \n50\\% of the houses cost below \\$450,000, 75\\% of the houses cost below \\$1,000,000 \nand there are a meaningful number of houses that cost more than \\$6,000,000.\n\\item Housing prices in a country where 25\\% of the houses cost below \\$300,000, \n50\\% of the houses cost below \\$600,000, 75\\% of the houses cost below \\$900,000 \nand very few houses that cost more than \\$1,200,000.\n\\item Number of alcoholic drinks consumed by college students in a given week. \nAssume that most of these students don't drink since they are under 21 years old, \nand only a few drink excessively.\n\\item Annual salaries of the employees at a Fortune 500 company where only a few \nhigh level executives earn much higher salaries than all the other employees.\n\\end{parts}\n}{}\n\n% 17\n\n\\eoce{\\qt{Income at the coffee shop\\label{income_coffee_shop}} The first histogram \nbelow shows the distribution of the yearly incomes of 40 patrons at a college \ncoffee shop. Suppose two new people walk into the coffee shop: one making \n\\$225,000 and the other \\$250,000. The second histogram shows the new income \ndistribution. Summary statistics are also provided. \\\\\n\\begin{minipage}[c]{0.57\\textwidth}\n\\Figures[Two histograms are shown and are labeled 1 and 2. Plot 1 has a horizontal axis from \\$60,000 to \\$70,000. The bins, from left to right, generally rise steadily from frequencies of 2 to 3 at \\$60,000 to \\$62,000 and up to a peak of about 7 to 8 between \\$64,000 to \\$66,000. From here, the bin counts steadily decline down to about 2 for the last bin, \\$69,000 to \\$70,000. Plot (2) shows a histogram, with the horizontal axis running from about \\$60,000 to \\$260,000. The width of the bins are \\$1,000, like in the first plot, and the first 10 bins reflect those described in Plot (1). Two additional bins are shown at about \\$225,000 and \\$250,000, each with a bin height of 1.]{}{eoce/income_coffee_shop}{income_coffee_shop}\n\\end{minipage}\n\\begin{minipage}[c]{0.4\\textwidth}\n\\begin{center}\n\\begin{tabular}{rrr}\n\\hline\n        & (1)       & (2) \\\\ \n\\hline\nn       & 40        & 42 \\\\ \nMin.    & 60,680    & 60,680 \\\\ \n1st Qu. & 63,620    & 63,710 \\\\ \nMedian  & 65,240    & 65,350 \\\\ \nMean    & 65,090    & 73,300 \\\\ \n3rd Qu. & 66,160    & 66,540 \\\\ \nMax.    & 69,890    & 250,000 \\\\ \nSD      & 2,122     & 37,321 \\\\ \n\\hline\n\\end{tabular}\n\\end{center}\n\\end{minipage}\n\\begin{parts}\n\\item Would the mean or the median best represent what we might think of as a \ntypical income for the 42 patrons at this coffee shop? What does this say about \nthe robustness of the two measures?\n\\item Would the standard deviation or the IQR best represent the amount of \nvariability in the incomes of the 42 patrons at this coffee shop? What does \nthis say about the robustness of the two measures?\n\\end{parts}\n}{}\n\n% 18\n\n\\eoce{\\qt{Midrange\\label{midrange}} The \\textit{midrange} of a distribution is defined as \nthe average of the maximum and the minimum of that distribution. Is this statistic \nrobust to outliers and extreme skew? Explain your reasoning\n}{}\n\n\\D{\\newpage}\n\n% 19\n\n\\eoce{\\qt{Commute times\\label{county_commute_times}} The US census collects data on \ntime it takes Americans to commute to work, among many other variables. The \nhistogram below shows the distribution of average commute times in 3,142 US \ncounties in 2010. Also shown below is a spatial intensity map of the same data.\n\\begin{center}\n\\Figures[A histogram is shown, where the horizontal axis is for the variable \"Mean work travel in minutes\" spans approximately 0 to 50, with the vertical axis representing frequency with a peak value of about 200. The bins start with small bin heights on the left side, and the bin heights start increasing at about 10 and then rapidly ascend by 15 before leveling off and reaching a peak at about 22. The bins begin declining again about 24 gradually and then more rapidly around 26 to 29. At 30, the bins continue declining, but at a slower pace, before they level off near a height of 0 at about 35.]{0.48}{eoce/county_commute_times}{county_commute_times_hist}\n\\Figures[A spatial intensity map is shown of the United States. The legend for the shading runs from values of 4 to about 33. The shading for the eastern half of the country suggests slightly higher values, while the western portion of the upper midwest (North Dakota, South Dakota, and Nebraska) shows lower values. Other specific regions that show patterns of higher values than surrounding areas are in lower Florida and northern California.]{0.48}{eoce/county_commute_times}{county_commute_times_map}\n\\end{center}\n\\begin{parts}\n\\item Describe the numerical distribution and comment on whether or not a log \ntransformation may be advisable for these data.\n\\item Describe the spatial distribution of commuting times using the map above.\n\\end{parts} \n}{}\n\n% 20\n\n\\eoce{\\qt{Hispanic population\\label{county_hispanic_pop}} The US census collects \ndata on race and ethnicity of Americans, among many other variables. The \nhistogram below shows the distribution of the percentage of the population \nthat is Hispanic in 3,142 counties in the US in 2010. Also shown is a \nhistogram of logs of these values.\n\\begin{center}\n\\Figures[A histogram is shown for the variable \"Percent Hispanic\", where the horizontal axis runs from 0 to 100. The first bin, from 0 to 5, is dramatically higher than all other bins at about 2000. From here, the bins descend rapidly: about 500 between 5 and 10, 200 between 10 and 15, 100 between 15 and 20, then then trail off with the bins being nearly indistinguishable from a height of 0 for bins about 50\\%.]{0.48}{eoce/county_hispanic_pop}{county_hispanic_pop_hist}\n\\Figures[A histogram is shown for the transformed variable, \"log-base-e of Percent Hispanic\", where the horizontal axis runs from about -2.5 to 4.5. The bins are very close to 0 in frequency until -1, then the rise slightly to about -0.5, before sharply rising to a peak at about 0.5. From here, the bins steadily decline towards a frequency of 0 at the horizontal location of 4.5.]{0.48}{eoce/county_hispanic_pop}{county_hispanic_pop_log_hist}\n\\Figures[A spatial intensity map is shown of the United States. The legend for the shading runs from values of 0\\% to a peak of \"greater than 40\\%\". A large portion of the eastern and central portion of the country -- east of Texas, east of Colorado, east of Utah, and east of Idaho -- is shaded mostly with values below 10\\%. Florida is an exception to this rule, where a handful of counties show higher values. Higher values are particularly prominent in Texas, New Mexico, Arizona, and California, which mostly shows shading for values of at least 20\\%. Nevada, Idaho, Oregon, and Washington shows values averaging around 10-20\\%.]{0.48}{eoce/county_hispanic_pop}{county_hispanic_pop_map}\n\\end{center}\n\\begin{parts}\n\\item Describe the numerical distribution and comment on why we might want \nto use log-transformed values in analyzing or modeling these data.\n\\item What features of the distribution of the Hispanic population in US \ncounties are apparent in the map but not in the histogram? What features are \napparent in the histogram but not the map?\n\\item Is one visualization more appropriate or helpful than the other? Explain \nyour reasoning.\n\\end{parts} \n}{}\n"
  },
  {
    "path": "ch_summarizing_data/TeX/review_exercises.tex",
    "content": "\\reviewexercisesheader{}\n\n% 27\n\n\\eoce{\\qt{Make-up exam\\label{makeup_exam}} In a class of 25 students, 24 of them took an exam \nin class and 1 student took a make-up exam the following day. The professor graded the \nfirst batch of 24 exams and found an average score of 74 points with a standard \ndeviation of 8.9 points. The student who took the make-up the following day scored 64 \npoints on the exam.\n\\begin{parts}\n\\item Does the new student's score increase or decrease the average score?\n\\item What is the new average?\n\\item Does the new student's score increase or decrease the standard deviation of the \nscores?\n\\end{parts}\n}{}\n\n% 28\n\n\\eoce{\\qt{Infant mortality\\label{infant_mortality}} The infant mortality rate is defined as \nthe number of infant deaths per 1,000 live births. This rate is often used as an \nindicator of the level of health in a country. The relative frequency histogram below \nshows the distribution of estimated infant death rates for 224 countries for which such \ndata were available in 2014. \n\\footfullcite{data:ciaFactbook}\n\n\\noindent\\begin{minipage}[c]{0.43\\textwidth}\n\\begin{parts}\n\\item Estimate Q1, the median, and Q3 from the histogram.\n\\item Would you expect the mean of this data set to be smaller or larger than the \nmedian? Explain your reasoning.\n\\end{parts} \\vfill \\\n\\end{minipage}\n\\begin{minipage}[c]{0.52\\textwidth}\n\\hfill%\n\\Figures[A histogram is shown for the variable \"Infant Mortality (per 1000 live births)\" with axis range of 0 to 120. The histogram vertical axis is for \"Fraction of Countries\" and runs from 0 to 0.4. The bins are as follows: the 0 to 10 bin has a height of 0.38, 10 to 20 has a height of 0.22, 20 to 30 a height of 0.11, 30 to 40 a height of 0.06, 40 to 50 a height of 0.07, 50 to 60 a height of 0.08, 60 to 70 a height of 0.04, 70 to 80 a height of 0.03, 80 to 90 a height of 0.01, 90 to 100 a height of 0.02, and 100 to 110 a height of 0.01.]{0.85}{eoce/infant_mortality_rel_freq}{infant_mortality_rel_freq_hist}\n\\end{minipage}\n}{}\n\n% 29\n\n\\eoce{\\qt{TV watchers\\label{dist_shape_TV_watchers}} Students in an AP Statistics class \nwere asked how many hours of television they watch per week (including online \nstreaming). This sample yielded an average of 4.71 hours, with a standard \ndeviation of 4.18 hours. Is the distribution of number of hours students watch \ntelevision weekly symmetric? If not, what shape would you expect this distribution \nto have? Explain your reasoning.\n}{}\n\n% 30\n\n\\eoce{\\qt{A new statistic\\label{new_stat}} The statistic $\\frac{\\bar{x}}{median}$ can \nbe used as a measure of skewness. Suppose we have a distribution where all \nobservations are greater than 0, $x_i > 0$. What is the expected shape of \nthe distribution under the following conditions? Explain your reasoning.\n\\begin{parts}\n\\item $\\frac{\\bar{x}}{median} = 1$\n\\item $\\frac{\\bar{x}}{median} < 1$\n\\item $\\frac{\\bar{x}}{median} > 1$\n\\end{parts}\n}{}\n\n% 31\n\n\\eoce{\\qt{Oscar winners\\label{oscar_winners}} The first Oscar awards for best actor \nand best actress were given out in 1929. The histograms below show the age \ndistribution for all of the best actor and best actress winners from 1929 to \n2018. Summary statistics for these distributions are also provided. Compare the \ndistributions of ages of best actor and actress winners.\\footfullcite{data:oscars} \\\\\n\\begin{minipage}[c]{0.72\\textwidth}\n\\begin{center}\n\\Figures[Two histograms are shown, one for \"Best Actress\" and a second for \"Best Actor\", where values for the histogram range from 15 to 85. The heights of the bins or the Best Actress histogram are as follows: the bin of 15 to 25 has a height of 9, the 25 to 35 bin has a height of 50, 35 to 45 a height of 19, 45 to 55 a height of 6, 55 to 65 a height of 8, 65 to 75 a height of 1, and 75 to 85 a height of 1. The heights of the bins or the Best Actress histogram are as follows: the bin of 15 to 25 has a height of 0, the 25 to 35 bin has a height of 14, 35 to 45 a height of 45, 45 to 55 a height of 23, 55 to 65 a height of 11, 65 to 75 a height of 0, and 75 to 85 a height of 1.]{0.95}{eoce/oscar_winners}{oscars_winners_hist}\n\\end{center}\n\\end{minipage}\n\\begin{minipage}[c]{0.27\\textwidth}\n{\\small\n\\begin{tabular}{l c}\n\\hline\n        & Best Actress  \\\\\n\\hline\nMean    & 36.2      \\\\\nSD      & 11.9      \\\\\nn       & 92        \\\\  \n        & \\\\\n        & \\\\\n        & \\\\\n        & \\\\\n        & \\\\\n\\hline\n        & Best Actor \\\\\n\\hline\nMean    & 43.8 \\\\\nSD      & 8.83 \\\\\nn       & 92\n\\end{tabular}\n}\n\\end{minipage}\n}{}\n\n% 32\n\n\\eoce{\\qt{Exam scores\\label{dist_shape_exam_scores}} The average on a history exam \n(scored out of 100 points) was 85, with a standard deviation of 15. Is the \ndistribution of the scores on this exam symmetric? If not, what shape would \nyou expect this distribution to have? Explain your reasoning.\n}{}\n\n% 33\n\n\\eoce{\\qt{Stats scores\\label{stats_scores_box}} Below are the final exam scores of twenty \nintroductory statistics students.\n\\begin{center}\n57, 66, 69, 71, 72, 73, 74, 77, 78, 78, 79, 79, 81, 81, 82, 83, 83, 88, 89, 94\n\\end{center}\nCreate a box plot of the distribution of these scores. The five number summary provided below may be useful.\n\\begin{center}\n\\renewcommand\\arraystretch{1.5}\n\\begin{tabular}{ccccc}\nMin & Q1    & Q2 (Median)   & Q3    & Max \\\\\n\\hline\n57  & 72.5  & 78.5          & 82.5  & 94 \\\\\n\\end{tabular}\n\\end{center}\n}{}\n\n% 34\n\n\\eoce{\\qt{Marathon winners\\label{marathon_winners}} The histogram and box plots below show the distribution of finishing times in hours for male and female winners of the New York Marathon between 1970 and 1999.\n\\begin{center}\n\\Figures[Two plots are shown, one that is a histogram and one that is a box plot, where the range of data for each is from 2.0 to 3.2. The bins for the histogram are as follows: the 2.0 to 2.2 bin has a height of 21, bin 2.2 to 2.4 a height of 6, 2.4 to 2.6 a height of 25, 2.6 to 2.8 a height of 3, 2.8 to 3.0 a height of 2, and 3.0 to 3.2 a height of 2. The box plot shows the box spanning 2.2 to 2.5, with the median line centered at 2.4. The whiskers extend from about 2.15 to 2.75. There are four points marked beyond the upper whisker at 2.9, 3.0, 3.10, and 3.15.]{0.56}{eoce/marathon_winners}{marathon_winners_hist_box}\n\\end{center}\n\\begin{parts}\n\\item What features of the distribution are apparent in the histogram and not the box plot? What features are apparent in the box plot but not in the histogram?\n\\item What may be the reason for the bimodal distribution? Explain.\n\\item Compare the distribution of marathon times for men and women based on the box plot shown below.\n\\begin{center}\n\\Figures[A side-by-side box plot is shown for marathon run times, one box plot for men and one for women. The axis for the run times spans from 2.0 to 3.2. All values described as follows are estimates. For the men box plot, the box spans 2.16 to 2.22 with the median line at 2.19. The whiskers span to 2.12 up to 2.27. There are 6 points above the upper whisker at 2.32, 2.36, 2.38, 2.44, 2.46, and 2.50. For the women box plot, the box spans from 2.44 to 2.52, with a median value of 2.46. The whiskers span from 2.41 to 2.57. There are 6 points above the upper whisker: 2.72, 2.78, 2.9, 2.92, 3.12, and 3.15.]{0.56}{eoce/marathon_winners}{marathon_winners_gender_box}\n\\end{center}\n\\item The time series plot shown below is another way to look at these data. Describe what is visible in this plot but not in the others.\n\\end{parts}\n\\begin{center}\n\\Figures[A time series plot is shown, which in this case gives the appearance of a scatterplot. The horizontal variable is for year, which runs from 1970 to 2000, and the vertical variable is \"Marathon times\", which runs from 2.0 to 3.2 hours. There are two colors of points, one for men and one for women, and there is one point for men and one for women for each year. The points start at about 2.5 for men in 1970 and 2.9 for women in 1971. The points bounce around for a few years and then decline in 1975 or 1976 to 2.2 for men and 2.7 for women. The values for women decreases for a few more years to about 2.5. For the remainder of the years, the values fluctuate up or down 0.1 hours from year to year but are stable until 1999, which is the last data points provided.]{0.6}{eoce/marathon_winners}{marathon_winners_time_series} \\\\\n\\end{center}\n}{}\n"
  },
  {
    "path": "ch_summarizing_data/figures/boxPlotLayoutNumVar/boxPlotLayoutNumVar.R",
    "content": "require(openintro)\ndata(email50)\ndata(COL)\nd   <- email50$num_char\n\nmyPDF(\"boxPlotLayoutNumVar.pdf\", 5.5, 3.8,\n      mar = c(0, 4, 0, 1),\n      mgp = c(2.8, 0.7, 0))\nboxPlot(d,\n        ylab = 'Number of Characters (in thousands)',\n        xlim = c(0.3, 3),\n        axes = FALSE,\n        ylim = range(d))\naxis(2)\n\narrows(2,0, 1.35, min(d) - 0.5, length = 0.08)\ntext(2, 0, 'lower whisker', pos = 4)\n\narrows(2, quantile(d, 0.25) + sd(d) / 7,\n       1.35, quantile(d, 0.25),\n       length = 0.08)\ntext(2, quantile(d, 0.25) + sd(d)/6.5,\n     expression(Q[1]~~'(first quartile)'), pos = 4)\n\nm <- median(d)\narrows(2, m + sd(d) / 5, 1.35, m, length = 0.08)\ntext(2,m + sd(d) / 4.7, 'median', pos = 4)\n\nq <- quantile(d, 0.75)\narrows(2, q + sd(d) / 4, 1.35, q, length = 0.08)\ntext(2, q + sd(d) / 3.8,\n     expression(Q[3]~~'(third quartile)'), pos = 4)\n\narrows(2, rev(sort(d))[4] - sd(d) / 7,\n       1.35, rev(sort(d))[4], length = 0.08)\ntext(2, rev(sort(d))[4] - sd(d) / 6.8,\n     'upper whisker', pos = 4)\n\ny <- quantile(d, 0.75) + 1.5 * IQR(d)\narrows(2, y - 0.1 * sd(d),\n       1.35, y, length = 0.08)\nlines(c(0.72, 1.28), rep(y, 2),\n      lty = 3, col = '#00000066')\ntext(2, y - 0.1 * sd(d),\n     'max whisker reach', pos = 4)\n\nm <- rev(tail(sort(d), 5))\ns <- m[1] - 0.3 * sd(m)\narrows(2, s, 1.1, m[1] - 0.2, length = 0.08)\narrows(2, s, 1.1, m[2] + 0.3, length = 0.08)\narrows(2, s, 1.1, m[3] + 0.35, length = 0.08)\ntext(2, s, 'suspected outliers', pos = 4)\n\nset.seed(5)\npt.jitter <- 0.08\npoints(rep(0.4, 50) + runif(50, -pt.jitter, pt.jitter),\n       d,\n       col = rep(COL[1, 3], 25),\n       pch = 19)\n# points(rep(0.4, 25) + runif(25, -pt.jitter, pt.jitter),\n#        rev(sort(d))[1:25],\n#        col = rep(COL[1, 3], 25),\n#        cex = 0.8)\n# points(rep(0.4, 25) + runif(25, -pt.jitter, pt.jitter),\n#        sort(d)[1:25],\n#        col = rep(COL[4,3], 25),\n#        pch = 19,\n#        cex = 0.8)\ndev.off()\n\nsort(d)[25:26]\nquantile(d, c(0.25, 0.5, 0.75))\ntail(sort(d), 4)\n\n\nmyPDF(\"boxPlotNumVarSmall.pdf\", 1.5, 2.5,\n      mar = c(0, 4.1, 0, 0),\n      mgp = c(2.3, 0.45, 0),\n      tcl = -0.2)\nboxPlot(d,\n        ylab = '',\n        axes = FALSE,\n        xlim = c(0.5, 1.45),\n        ylim = range(d) + sd(d) * c(-1,1) * 0.2)\naxis(2, cex = 1.1)\npar(las = 0)\nmtext(\"Number of Characters\\n(in thousands)\", 2,\n      line = 2,\n      cex = 1.1)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/carsPriceVsWeight/carsPriceVsWeight.R",
    "content": "library(openintro)\ndata(cars)\ndata(COL)\n\nmyPDF(\"carsPriceVsWeight.pdf\", 6, 3.7,\n      mar = c(3.6, 3.6, 1, 1),\n      mgp = c(2.5, 0.7, 0))\nplot(cars$weight, cars$price, \n     xlab = 'Weight (Pounds)', ylab = 'Price ($1000s)', \n     pch = 19, cex = 1.3, col = COL[1, 2], \n     ylim = c(0, max(cars$price)))\ng <- lm(price ~ weight + I(weight^2),\n        cars,\n        weights = 1/weight^2)\nw <- seq(1000, 5000, 100)\nlines(w,\n      predict(g, data.frame(weight = w)),\n      lty = 2,\n      col = COL[5, 3])\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/countyIncomeSplitByPopGain/countyIncomeSplitByPopGain.R",
    "content": "library(openintro)\ndata(countyComplete)\ndata(COL)\n\ncc  <- county\npop <- sign(cc$pop2017 - cc$pop2010 - 0.5)\nsum(is.na(pop))\npov <- cc$median_hh_income\n\nset.seed(1)\nthese <- sample(sum(pop == -1, na.rm = TRUE), 50)\nsampL <- round(pov[pop == -1][these] / 1000, 1)\nthese <- sample(sum(pop == 1, na.rm = TRUE), 100)\nsampG <- round(pov[pop == 1][these] / 1000, 1)\nM  <- matrix(c(sampG, rep(\"\", 2), sampL, rep(\"\", 1)), 17)\nDB <- 6\nfor(i in 1:nrow(M)){\n  for(j in 1:ncol(M)){\n    cat(M[i,j])\n    if (j == DB) {\n      cat(\" && \")\n    } else if (j == ncol(M)) {\n      cat(\" \\\\\\\\\")\n    } else {\n      cat(\" & \")\n    }\n  }\n  cat(\"\\n\")\n}\npop[pop == 1] <- \"Gain\"\npop[pop == -1] <- \"No Gain\"\n\n\nmyPDF(\"countyIncomeSplitByPopGain.pdf\", 7.5, 4,\n      mar = c(3.6, 4.6, 1, 0.5),\n      mgp = c(2.4, 0.7, 0),\n      mfrow = 1:2)\nboxPlot(pov, pop,\n        axes = FALSE,\n        xlim = c(0.5, 2.5),\n        xlab = 'Change in Population',\n        ylab = '',\n        lcol = \"#00000000\",\n        col = \"#00000000\")\naxis(1, at = 1:2, c(\"Gain\", \"No Gain\"))\nAxisInDollars(2, at = pretty(pov))\npar(las = 0)\nmtext(\"Median Household Income\", 2, 3.6)\npar(las = 1)\nboxPlot(pov[pop == \"Gain\"],\n        lcol = COL[1],\n        col = COL[1,3],\n        add = 1)\nboxPlot(pov[pop == \"No Gain\"],\n        lcol = COL[4],\n        col = COL[4,3],\n        add = 2)\n\npar(mar = c(3.6, 0.5, 1, 1))\n\nxlim <- range(pov[pop == 'No Gain'], na.rm = TRUE)\nhistPlot(pov[pop == 'No Gain'],\n         breaks = 50,\n         col = '#ffffff00',\n         border = COL[4],\n         probability = TRUE,\n         xlim = xlim,\n         xlab = 'Median Household Income',\n         ylab = '',\n         hollow = TRUE,\n         axes = FALSE,\n         lty = 3,\n         lwd = 4)\nhistPlot(pov[pop == 'No Gain'],\n         breaks = 50,\n         col = '#ffffff00',\n         border = COL[4],\n         probability = TRUE,\n         add = TRUE,\n         hollow = TRUE,\n         lty = 3,\n         lwd = 2)\nhistPlot(pov[pop == 'No Gain'],\n         breaks = 50,\n         col = '#ffffff00',\n         border = COL[4],\n         probability = TRUE,\n         add = TRUE,\n         hollow = TRUE,\n         lty = 3,\n         lwd = 1)\nhistPlot(pov[pop == 'Gain'],\n         breaks = 50,\n         col = '#ffffff00',\n         border = COL[1],\n         probability = TRUE,\n         add = TRUE,\n         hollow = TRUE,\n         lty = 1,\n         lwd = 2)\nAxisInDollars(1, at = pretty(xlim))\nlegend('topright',\n       col = COL[c(1,4)],\n       lty = c(1,3),\n       lwd = c(2,2.8),\n       legend = c('Gain', 'No Gain'))\nlegend('topright',\n       col = c(rgb(0,0,0,0), COL[4]),\n       lty = c(1, 3),\n       lwd = c(2,1.4),\n       legend = c('Gain', 'No Gain'),\n       bg = rgb(0,0,0,0),\n       box.col = rgb(0,0,0,0),\n       text.col = rgb(0,0,0,0))\nlegend('topright',\n       col = c(rgb(0,0,0,0), COL[4]),\n       lty = c(1, 3),\n       lwd = c(2,0.7),\n       legend = c('Gain', 'No Gain'),\n       bg = rgb(0,0,0,0),\n       box.col = rgb(0,0,0,0),\n       text.col = rgb(0,0,0,0))\ndev.off()\n\n"
  },
  {
    "path": "ch_summarizing_data/figures/countyIntensityMaps/countyIntensityMaps.R",
    "content": "library(openintro)\nsource(\"countyMap.R\")\n\nmyPDF(\"countyPovertyMap.pdf\", 7.8, 4.5)\nval <- county$poverty\nval[val > 25] <- 25\ncountyMap(val, county_complete$FIPS, \"red\", gtlt=\">\",\n    label = \"Poverty\")\ndev.off()\n\nmyPDF(\"countyPopChangeMap.pdf\", 7.8, 4.5)\nval <- county$pop_change\nval[val > 18] <- 18\ncountyMap(val, county_complete$FIPS, \"ye\", gtlt=\">\",\n    label = \"Population Change\")\ndev.off()\n\nmyPDF(\"countyUnemploymentRateMap.pdf\", 7.8, 4.5)\nval <- county$unemployment_rate\nval[val > 7] <- 7\ncountyMap(val, county_complete$FIPS, \"ye\", gtlt=\">\",\n    label = \"Unemployment Rate\")\ndev.off()\n\nmyPDF(\"countyHomeownershipMap.pdf\", 7.8, 4.5)\nval <- county$homeownership\nval[val < 55] <- 55\ncountyMap(val, county_complete$FIPS, \"bg\", gtlt=\"<\",\n    label = \"Homeownership Rate\")\ndev.off()\n\nmyPDF(\"countyMedIncomeMap.pdf\", 7.8, 4.5)\nval <- county$median_hh_income / 1000\nval[val > 75] <- 75\ncountyMap(val, county_complete$FIPS, \"green\", gtlt=\">\",\n    label = \"Median Household Income\", unit = \"dollars\")\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/countyIntensityMaps/countyMap.R",
    "content": "library(maps)\ncountyMap <- function(values, FIPS,\n                      col = c(\"red\", \"green\", \"blue\"),\n                      varTrans = I,\n                      gtlt = \"\",\n                      label = \"\",\n                      units = c(\"percent\", \"dollars\"),\n                      ...){\n  if(missing(FIPS)){\n    stop(\"Must provide the county FIPS\")\n  }\n  \n  # _____ Drop NAs _____ #\n  values[is.na(values)] <- median(values, na.rm = TRUE)\n  \n  # _____ Scale Values _____ #\n  MI  <- min(values)\n  MA  <- max(values)\n  Leg <- seq(MI, MA, length.out = 50)\n  if(identical(varTrans, log)){\n    VAL    <- varTrans(values+0.1)\n    valCol <- varTrans(values+0.1)\n    LegCol <- varTrans(Leg+0.1)\n  } else {\n    VAL    <- varTrans(values)\n    valCol <- varTrans(values)\n    LegCol <- varTrans(Leg)\n  }\n  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01\n  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01\n  \n  val.000 <- 0.500*(1-valCol)\n  val.114 <- 0.557*(1-valCol)\n  val.200 <- 0.600*(1-valCol)\n  val.298 <- 0.649*(1-valCol)\n  val.318 <- 0.659*(1-valCol)\n  val.337 <- 0.669*(1-valCol)\n  val.447 <- 0.724*(1-valCol)\n  val.608 <- 0.804*(1-valCol)\n  val.741 <- 0.871*(1-valCol)\n  val.863 <- 0.932*(1-valCol)\n  val.941 <- 0.971*(1-valCol)\n  val.957 <- 0.979*(1-valCol)\n  \n  Leg.000 <- 0.500*(1-LegCol)\n  Leg.114 <- 0.557*(1-LegCol)\n  Leg.200 <- 0.600*(1-LegCol)\n  Leg.298 <- 0.649*(1-LegCol)\n  Leg.318 <- 0.659*(1-LegCol)\n  Leg.337 <- 0.669*(1-LegCol)\n  Leg.447 <- 0.724*(1-LegCol)\n  Leg.608 <- 0.804*(1-LegCol)\n  Leg.741 <- 0.871*(1-LegCol)\n  Leg.863 <- 0.932*(1-LegCol)\n  Leg.941 <- 0.971*(1-LegCol)\n  Leg.957 <- 0.979*(1-LegCol)\n  \n  if(col[1] == \"red\"){\n    col <- rgb(val.941, val.318, val.200)\n    COL <- rgb(Leg.941, Leg.318, Leg.200)\n  } else if(col[1] == \"green\"){\n    col <- rgb(val.298, val.941, val.114)\n    COL <- rgb(Leg.298, Leg.941, Leg.114)\n    # col <- rgb(val.298, val.447, val.114)\n    # COL <- rgb(Leg.298, Leg.447, Leg.114)\n  } else if(col[1] == \"bg\"){\n    col <- rgb(val.337, val.741, val.957)\n    COL <- rgb(Leg.337, Leg.741, Leg.957)\n  } else if(col[1] == \"ye\"){\n    col <- rgb(val.957, val.863, val.000)\n    COL <- rgb(Leg.957, Leg.863, Leg.000)\n  } else {\n    col <- rgb(val.06, val.06, val.10)\n    COL <- rgb(Leg.06, Leg.06, Leg.10)\n  }\n\n  # _____ Remove These _____ #\n  data(county.fips)\n  col <- col[match(county.fips$fips, FIPS)]\n  plot(0,0,type = \"n\", axes = FALSE, xlab = \"\", ylab = \"\")\n  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))\n  map(\"county\", col = col, fill = TRUE, resolution = 0,\n    lty = 0, projection = \"polyconic\", mar = rep(0.1,4), add = TRUE, ...)\n  \n  x1 <- 0.305\n  x2 <- 0.335\n  for(i in 1:50){\n    y1 <- i/50 * 0.25 + 0.48\n    y2 <- (i-1)/50 * 0.25 + 0.48\n    rect(x1, y1, x2, y2, border = \"#00000000\", col = COL[i])\n  }\n  \n  \n  VR    <- range(VAL)\n  VR[3] <- VR[2]\n  VR[2] <- mean(VR[c(1,3)])\n  \n  VR1    <- c()\n  VR1[1] <- values[which.min(abs(VAL - VR[1]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[2]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[3]))]\n  \n  VR  <- round(VR)\n  units <- match.arg(units)\n  if (units == \"percent\") {\n    VR <- paste0(VR, \"%\")\n  } else if (units == \"dollars\") {\n    VR <- paste0(\"$\", VR)\n  }\n  if(gtlt %in% c(\">\", \"><\")){\n    VR[3] <- paste0(\">\", VR[3])\n  }\n  if(gtlt %in% c(\"<\", \"><\")){\n    VR[1] <- paste0(\"<\", VR[1])\n  }\n  text(0.335, 0.49, VR[1], pos = 4, cex = 0.9)\n  text(0.335, 0.605, VR[2], pos = 4, cex = 0.9)\n  text(0.335, 0.72, VR[3], pos = 4, cex = 0.9)\n  par(srt = 90)\n  text(0.395, 0.615, label, pos = 1)\n}\n\n\n\n\n\n"
  },
  {
    "path": "ch_summarizing_data/figures/county_pop_change_v_pop_transform/county_pop_change_v_pop_transform.R",
    "content": "library(openintro)\ndata(COL)\n\nx <- county$pop2010\ny <- county$pop_change\ncex <- 0.5\ncol <- COL[1, 4]\ncol.shell <- COL[1, 2]\n\nmyPDF(\"county_pop_change_v_pop_transform_i.pdf\",\n      4.5,\n      3.3,\n      mar = c(3, 3.9, 0.5, 1.2),\n      mgp = c(2.8, 0.5, 0))\nplot(x, y, type = \"n\",\n     xlab = \"\",\n     ylab = \"Population Change\",\n     axes = FALSE)\nabline(h = pretty(y), v = pretty(x), col = COL[7, 3])\npoints(x, y, pch = 19, cex = cex, col = col)\nAxisInPercent(2, at = pretty(y))\nat <- pretty(x)\naxis(1, at, paste0(at / 1e6, \"m\"))\nbox()\npoints(x, y, cex = cex, col = col.shell)\nmtext(\"Population Before Change (m = millions)\", 1, 1.9)\ndev.off()\n\n\nmyPDF(\"county_pop_change_v_pop_transform_log.pdf\",\n      4.5,\n      3.3,\n      mar = c(3, 4, 0.5, 1.2),\n      mgp = c(1.8, 0.5, 0))\nx. <- log(x, 10)\nplot(x., y, type = \"n\",\n     xlab = expression(log[10] * \"(Population Before Change)\"),\n     ylab = \"\",\n     axes = FALSE)\nabline(h = pretty(y), v = pretty(x.), col = COL[7, 3])\npoints(x., y, pch = 19, cex = cex, col = col)\npoints(x., y, cex = cex, col = col.shell)\naxis(1)\nAxisInPercent(2, at = pretty(y))\npar(las = 0)\nmtext(\"Population Change\", 2, 2.9)\nbox()\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/county_pop_transformed/county_pop_transformed.R",
    "content": "library(openintro)\ndata(COL)\n\nd <- county$pop2017\nmean(d, na.rm = TRUE)\nmedian(d, na.rm = TRUE)\n\nmyPDF(\"county_pop_transformed_i.pdf\",\n      4,\n      3,\n      mar = c(3.4, 4, 0.5, 0.5),\n      mgp = c(2.1, 0.5, 0))\nhist(d,\n     breaks = 25,\n     main = \"\",\n     xlab = \"Population (m = millions)\",\n     ylab = \"\",\n     axes = FALSE,\n     col = COL[1])\naxis(1, at = pretty(d), paste0(pretty(d / 1e6), \"m\"))\naxis(2, seq(0, 3000, 500))\npar(las = 0)\nmtext(\"Frequency\", 2, 2.9)\ndev.off()\n\nmyPDF(\"county_pop_transformed_log.pdf\",\n      4,\n      3,\n      mar = c(3.4, 3.7, 0.5, 0.5),\n      mgp = c(2.2, 0.5, 0))\nexpr <- expression(log[10]*\"(Population)\")\nhist(log(d, 10),\n     main = \"\",\n     breaks = 15,\n     xlab = expr,\n     axes = FALSE,\n     ylab = \"\",\n     col = COL[1])\naxis(1)\naxis(2, seq(0, 1000, 500))\npar(las = 0)\nmtext(\"Frequency\", 2, 2.6)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/discRandDotPlot/discRandDotPlot.R",
    "content": "library(openintro)\ndata(COL)\n\nset.seed(8535)\n\ngender  <- c(rep('male', 24), rep('female', 24))\noutcome <- c(rep(c('promoted', 'not promoted'), c(21, 3)),\n             rep(c('promoted', 'not promoted'), c(14, 10)))\n\nnsim    <- 100\nn       <- length(gender)\ngroup   <- gender\nvar1    <- outcome\nsuccess <- \"promoted\"\nsim     <- matrix(NA, nrow = n, ncol = nsim)\nn1      <- 24\nn2      <- 24\n\nstatistic <- function(var1, group) {\n  t1 <- var1 == success & group == levels(as.factor(group))[1]\n  t2 <- var1 == success & group == levels(as.factor(group))[2]\n  return(sum(t1) / n1 - sum(t2) / n2)\n}\n\nfor (i in 1:nsim) {\n  sim[,i] <- sample(group, replace = FALSE)\n}\n\n\nsim_dist <- apply(sim, 2, statistic, var1 = outcome)\ndiffs    <- sim_dist\npval     <- sum(diffs >= 0.29) / nsim\nvalues   <- table(sim_dist)\n\n\nX <- c()\nY <- c()\nfor (i in 1:length(diffs)) {\n  x   <- diffs[i]\n  rec <- sum(sim_dist == x)\n  X   <- append(X, rep(x, rec))\n  Y   <- append(Y, 1:rec)\n}\n\n\nmyPDF('discRandDotPlot.pdf', 6, 3.5,\n      mar = c(3.4, 0.5, 0.5, 0.5),\n      mgp = c(2.35, 0.6, 0))\nplot(X, Y,\n     xlim = range(diffs) + c(-1, 1) * sd(diffs) / 4,\n     xlab = \"Difference in promotion rates\",\n     axes = FALSE,\n     ylim = c(0, max(Y)),\n     col = COL[1],\n     pch = 20)\nat <- seq(-0.4, 0.4, 0.1)\nlabels <- c(-0.4, \"\", -0.2, \"\", 0, \"\", 0.2, \"\", 0.4)\naxis(1, at, labels)\nabline(h = 0)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/email50LinesCharacters/email50LinesCharacters.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\n\nmyPDF(\"email50LinesCharacters.pdf\",\n      6,\n      3.3,\n      mar = c(3, 3.9, 0.5, 1.2),\n      mgp = c(2.8, 0.5, 0))\nplot(email50$num_char,\n     email50$line_breaks,\n     pch = 19,\n     cex = 1.3,\n     col = COL[1, 4],\n     xlab = \"\",\n     ylab = \"Number of Lines\")\npoints(email50$num_char,\n       email50$line_breaks,\n       cex = 1.3,\n       col = COL[1])\nmtext(\"Number of Characters (in thousands)\", 1, 1.9)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/email50LinesCharactersMod/email50LinesCharactersMod.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\n\nmyPDF(\"email50LinesCharactersMod.pdf\",\n      4.5,\n      3.3,\n      mar = c(3, 3.9, 0.5, 1.2),\n      mgp = c(2.8, 0.5, 0))\nplot(email50$num_char,\n     email50$line_breaks,\n     pch = 19,\n     cex = 1.3,\n     col = COL[1,4],\n     xlab = \"\",\n     ylab = \"line_breaks\",\n     axes = FALSE)\naxis(2)\nat <- seq(0, 60, 10)\nlabels <- seq(0, 60, 10)\naxis(1, at, labels)\nbox()\npoints(email50$num_char,\n       email50$line_breaks,\n       cex = 1.3,\n       col = COL[1])\nmtext(\"num_char\", 1, 1.9)\ndev.off()\n\n\nmyPDF(\"email50LinesCharactersModLog.pdf\",\n      4.5,\n      3.3,\n      mar = c(3, 2.9, 0.5, 1.2),\n      mgp = c(1.8, 0.5, 0))\nplot(log(email50$num_char),\n     log(email50$line_breaks),\n     pch = 19,\n     cex = 1.3,\n     col = COL[1,4],\n     xlab = \"\",\n     ylab = expression(log[e](line_breaks)))\npoints(log(email50$num_char),\n       log(email50$line_breaks),\n       cex = 1.3,\n       col = COL[1])\nmtext(expression(log[e](num_char)), 1, 1.9)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/email50NumCharDotPlotRobustEx/email50NumCharDotPlotRobustEx.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\n\np1 <- email50$num_char\np2 <- p1[-which.max(p1)]\np3 <- p1\np3[which.max(p1)] <- 150\n\nmyPDF(\"email50NumCharDotPlotRobustEx.pdf\",\n      7.04,\n      1.43,\n      mar = c(2.6, 0.1, 0.3, 0),\n      mgp = c(1.45, 0.25, 0),\n      cex.lab = 0.85)\n\n\ndotPlot(p1,\n        at = 3,\n        xlab = 'Number of Characters (in thousands)',\n        ylab = '',\n        pch = 20,\n        col = COL[1,3],\n        cex = 1,\n        ylim = c(0.5, 3.5),\n        xlim = c(-35, 151),\n        axes = FALSE)\nat <- seq(0, 150, 50)\naxis(1, at, cex.axis = 0.8)\ntext(0, 3, 'Original', pos = 2, cex = 0.8)\n\ndotPlot(p2,\n        at = 2,\n        add = TRUE,\n        pch = 20,\n        col = COL[1, 3],\n        cex = 1)\ntext(0, 2,\n     'Drop 64,401',\n     pos = 2,\n     cex = 0.8)\n\ndotPlot(p3,\n        at = 1,\n        add = TRUE,\n        pch = 20,\n        col = COL[1, 3],\n        cex = 1)\ntext(0, 1,\n     '64,401 to 150,000',\n     pos = 2,\n     cex = 0.8)\n\ndev.off()\n\n\n# _____ Summary Statistics _____ #\nGetSummaries <- function(p) {\n  temp <- round(quantile(p, c(0.25, 0.5, 0.75)), 3)\n  hold <- temp[3] - temp[1]\n  names(hold) <- NULL\n  return(c(temp,\n           IQR = hold,\n           mean = mean(p),\n           sd = sd(p)))\n}\nGetSummaries(p1)\nGetSummaries(p2)\nGetSummaries(p3)\n"
  },
  {
    "path": "ch_summarizing_data/figures/email50NumCharHist/email50NumCharHist.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\n\nH <- hist(email50$num_char,\n          breaks = 12,\n          plot = FALSE)\ncounts <- rbind(H$counts)\nfrom   <- head(H$breaks, -1)\nto     <- tail(H$breaks, -1)\ncolnames(counts) <- paste(from, 'to', to)\nrequire(xtable)\nxtable(counts)\n\nmyPDF(\"email50NumCharHist.pdf\",\n      6.05, 3.1,\n      mar = c(3.5, 3.5, 0.5, 1),\n      mgp = c(2.4, 0.7, 0))\nhistPlot(email50$num_char,\n         breaks = 12,\n         xlab = 'Number of Characters (in thousands)',\n         ylab = \"Frequency\",\n         ylim = c(0, 20),\n         col = COL[1],\n         border = COL[5])\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/emailCharactersDotPlot/emailCharactersDotPlot.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\n\n\n\nmyPDF(\"emailCharactersDotPlot.pdf\",\n      7.5,\n      1.25,\n      mar = c(3.6, 1, 0, 1),\n      mgp = c(2.5, 0.7, 0),\n      tcl = -0.4)\nd <- email50$num_char\ndotPlot(d,\n        xlab = 'Number of Characters (in thousands)',\n        ylab = '',\n        pch = 20,\n        col = COL[1, 2],\n        cex = 1.5,\n        ylim = c(0.95, 1.05),\n        axes = FALSE)\naxis(1, at = seq(0, 70, 10))\nM <- mean(d)\npolygon(M + c(-2, 2, 0) * 1.5,\n        c(0.95, 0.95, 0.98),\n        border = COL[4],\n        col = COL[4])\ndev.off()\n\n\n\nset.seed(10)\nmyPDF(\"emailCharactersDotPlotStacked.pdf\",\n      5,\n      2,\n      mar = c(3.6, 1, 0.5, 1),\n      mgp = c(2.5, 0.7, 0))\nround.to <- 2\nbinned <- round.to * round(d / round.to)\ntab <- table(binned)\ncex    <- 1\nplot(0,\n     type = \"n\",\n     xlab = paste(\"Number of Characters\",\n                  \"(in thousands, with rounding)\"),\n     ylab = \"\",\n     axes = FALSE,\n     xlim = c(0, 75),\n     ylim = c(-1, max(tab)))\nfor (i in 1:length(binned)) {\n  points(rep(as.numeric(names(tab[i])), tab[i]),\n         1:tab[i] - 0.4,\n         pch = 19,\n         col = COL[1],\n         cex = cex)\n}\nabline(h = 0)\nat <- seq(0, 70, 10)\naxis(1, at)\npolygon(M + c(-1.7, 1.7, 0) * 2.5,\n        c(-1.7, -1.7, -0.1),\n        border = COL[4],\n        col = COL[4])\ndev.off()\n\nM\nsd(d)\n"
  },
  {
    "path": "ch_summarizing_data/figures/emailNumberBarPlot/emailNumberBarPlot.R",
    "content": "require(openintro)\ndata(email)\ndata(COL)\n\nmyPDF('emailNumberBarPlot.pdf',\n      7,\n      3,\n      mar = c(3.6, 4.5, 1, 1.5),\n      mgp = c(3.4, 0.7, 0),\n      mfrow = 1:2)\nt <- table(email$number)\nbarplot(t,\n        axes = TRUE,\n        xlab = '',\n        ylab = 'count',\n        main = '',\n        ylim = c(0,2700),\n        col = COL[1])\nabline(h = 0)\nmtext(\"number\", 1, 2.4)\n\npar(mar = c(3.6, 4.7, 1, 1))\nbarplot(t / sum(t),\n        axes = FALSE,\n        xlab = 'number',\n        ylab = '',\n        main = '',\n        ylim = c(0, 2700) / sum(t),\n        col = COL[1])\nat <- seq(0, 0.6, 0.2)\naxis(2, at)\npar(las = 0)\nmtext('proportion', side = 2, line = 2.7)\nmtext(\"number\", 1, 2.4)\nabline(h = 0)\ndev.off()\n\ntable(email$number, email$spam)\n"
  },
  {
    "path": "ch_summarizing_data/figures/emailNumberPieChart/emailNumberPieChart.R",
    "content": "library(openintro)\ndata(email)\ndata(COL)\n\nmyPDF(\"emailNumberPieChart.pdf\",\n      7.5,\n      4,\n      mar = c(0, 2, 0, 0.5),\n      mgp = c(2.4, 0.5, 0))\nlayout(matrix(1:2, 1), c(1, 1.1))\ntab <- table(email$number)\npie(tab, col = COL[c(3, 1, 2)], radius = 0.75)\n\npar(mar = c(3.6, 5.2, 1, 1))\nbarplot(tab,\n        axes = FALSE,\n        xlab = 'number',\n        ylab = '',\n        main = '',\n        col = COL[c(3, 1, 2)])\naxis(2)\nabline(h = 0)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/emailSpamNumberMosaicPlot/emailSpamNumberMosaicPlot.R",
    "content": "library(openintro)\ndata(email)\nemail$spam <- ifelse(email$spam == 0, \"not spam\", \"spam\")\ntab <- table(email[,c('spam', 'number')])\ntab  <- t(tab)\n\nrp <- prop.table(tab, 1)\ncp <- prop.table(tab, 2)\n\nmyPDF(\"emailNumberMosaic.pdf\",\n      2.625,\n      2.25,\n      mar = rep(1, 4) / 4)\nmosaicplot(rowSums(tab),\n           main = '',\n           xlab = '',\n           ylab = '',\n           off = 4,\n           col = COL[c(2,1,4)])\ndev.off()\ncolnames(tab)[1] <- \"not\\nspam\"\n\nemail$spam[email$spam == \"not spam\"] <- \"not    \\nspam\"\nmyPDF(\"emailSpamNumberMosaic.pdf\",\n      3,\n      2.25,\n      mar = c(0.25, 2, 1, 1))\nMosaicPlot(number ~ spam, email,\n           col = COL[c(2, 1, 4)],\n           off = 0.02)\ndev.off()\n\nmyPDF(\"emailSpamNumberMosaicRev.pdf\",\n      3,\n      2.25,\n      mar = rep(1, 4) / 4)\ncolnames(tab)[1] <- \"not spam\"\nmosaicplot(t(tab),\n           main = '',\n           xlab = '',\n           ylab = '',\n           col = COL[c(2, 1, 4)])\ndev.off()\n\n"
  },
  {
    "path": "ch_summarizing_data/figures/emailSpamNumberSegBar/emailSpamNumberSegBar.R",
    "content": "library(openintro)\ndata(email)\ndata(COL)\n\ntab <- table(email[,c('spam', 'number')])[2:1, ]\nrow.names(tab) <- c(\"spam\", \"not spam\")\ntab <- t(tab)\n\nrp <- prop.table(tab, 1)\ncp <- prop.table(tab, 2)\n\nmyPDF(\"emailSpamNumberSegBar.pdf\",\n      4.5,\n      3.5,\n      mar = c(2, 3, 0.5, 0.5),\n      mgp = c(2.2, 0.6, 0))\nbarplot(apply(tab, 1, sum),\n        col = COL[3])\ntabTemp <- tab[,1]\nnames(tabTemp) <- NULL\nbarplot(tabTemp,\n        col = COL[1],\n        add = TRUE,\n        axes = FALSE)\nabline(h = 0)\nlegend(\"topright\",\n       fill = COL[c(3,1)],\n       legend = c(\"not spam\", \"spam\"))\ndev.off()\n\nmyPDF(\"emailSpamNumberSegBarSta.pdf\",\n      4.5,\n      3.5,\n      mar = c(2, 2.5, 0.5, 0.5),\n      mgp = c(2.2, 0.6, 0))\nbarplot(apply(tab, 1, sum) / apply(tab, 1, sum), col = COL[3])\ntabTemp <- rp[, 1]\nnames(tabTemp) <- NULL\nbarplot(tabTemp,\n        col = COL[1],\n        add = TRUE,\n        axes = FALSE)\nabline(h = 0)\ndev.off()\n\n"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/air_quality_durham/air_quality_durham.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\npm25_durham = read.csv(\"pm25_2011_durham.csv\", \n                       na.strings = \".\", stringsAsFactors = FALSE)\n\n# calculate sample size ---------------------------------------------\nn = pm25_durham %>%\n  filter(!is.na(DAILY_AQI_VALUE)) %>%\n  nrow() # n = 91\n\n# histogram parameters ----------------------------------------------\nhisto = hist(pm25_durham$DAILY_AQI_VALUE, plot = FALSE)\nbreaks = histo$breaks\nwidth = breaks[2] - breaks[1]\ncounts = histo$counts\nrel_freqs = round(counts / n, 2)\n\nfive_perc = n * 0.05\n\n# relative frequency histogram --------------------------------------\npdf(\"air_quality_durham_rel_freq_hist.pdf\", 5.5, 4.3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nhist(pm25_durham$DAILY_AQI_VALUE, \n     main = \"\", xlab = \"Daily AQI\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0,five_perc*4))\naxis(1)\naxis(2, at = seq(0, five_perc*4, five_perc), label = round(seq(0, 0.20, 0.05),2))\nabline(h = seq(0, five_perc*4, five_perc), lty = 2, col = COL[6])\nhist(pm25_durham$DAILY_AQI_VALUE, \n     main = \"\", xlab = \"Daily AQI\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0,five_perc*4), add = TRUE)\ndev.off()\n\n# relative frequency histogram - solution ---------------------------\npdf(\"air_quality_durham_rel_freq_hist_soln.pdf\", 5.5, 4.3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nhist(pm25_durham$DAILY_AQI_VALUE, \n     main = \"\", xlab = \"Daily AQI\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0, five_perc*4 + 1))\naxis(1)\naxis(2, at = seq(0, five_perc*4, five_perc), label = round(seq(0, 0.20, 0.05),2))\nabline(h = seq(0, five_perc*4, five_perc), lty = 2, col = COL[6])\nhist(pm25_durham$DAILY_AQI_VALUE, \n     main = \"\", xlab = \"Daily AQI\", ylab = \"\",\n     col = COL[1], axes = FALSE, ylim = c(0, five_perc*4), add = TRUE)\ntext(x = breaks[-1] - width/2, y = counts + 1, \n     labels = paste(rel_freqs),\n     col = COL[4], cex = 1)\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/air_quality_durham/pm25_2011_durham.csv",
    "content": "Date,AQS_SITE_ID,POC,Daily Mean PM2.5 Concentration,UNITS,DAILY_AQI_VALUE,DAILY_OBS_COUNT,PERCENT_COMPLETE,AQS_PARAMETER_CODE,AQS_PARAMETER_DESC,CSA_CODE,CSA_NAME,CBSA_CODE,CBSA_NAME,STATE_CODE,STATE,COUNTY_CODE,COUNTY,SITE_LATITUDE,SITE_LONGITUDE\r1/3/11,37-063-0015,1,5.9,ug/m3 LC,19,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/6/11,37-063-0015,1,10.4,ug/m3 LC,34,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/9/11,37-063-0015,1,5.6,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/10/11,37-063-0015,1,6.2,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/18/11,37-063-0015,1,9.4,ug/m3 LC,31,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/21/11,37-063-0015,1,5,ug/m3 LC,16,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/24/11,37-063-0015,1,11.5,ug/m3 LC,37,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/27/11,37-063-0015,1,9.8,ug/m3 LC,32,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/30/11,37-063-0015,1,12.5,ug/m3 LC,41,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/2/11,37-063-0015,1,5.5,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/5/11,37-063-0015,1,5.3,ug/m3 LC,17,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/8/11,37-063-0015,1,5,ug/m3 LC,16,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/11/11,37-063-0015,1,11.3,ug/m3 LC,37,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/14/11,37-063-0015,1,5.9,ug/m3 LC,19,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/17/11,37-063-0015,1,17.2,ug/m3 LC,54,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/20/11,37-063-0015,1,5.3,ug/m3 LC,17,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/23/11,37-063-0015,1,7.5,ug/m3 LC,24,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/26/11,37-063-0015,1,7.6,ug/m3 LC,25,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/1/11,37-063-0015,1,3.7,ug/m3 LC,12,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/4/11,37-063-0015,1,8.9,ug/m3 LC,29,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/7/11,37-063-0015,1,4.5,ug/m3 LC,15,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/10/11,37-063-0015,1,2.7,ug/m3 LC,9,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/13/11,37-063-0015,1,10.5,ug/m3 LC,34,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/16/11,37-063-0015,1,6.1,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/19/11,37-063-0015,1,8.3,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/22/11,37-063-0015,1,13.8,ug/m3 LC,45,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/25/11,37-063-0015,1,9.1,ug/m3 LC,30,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/28/11,37-063-0015,1,10.6,ug/m3 LC,34,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/31/11,37-063-0015,1,4.8,ug/m3 LC,16,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/3/11,37-063-0015,1,6.1,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/6/11,37-063-0015,1,5.6,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/9/11,37-063-0015,1,9.1,ug/m3 LC,30,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/12/11,37-063-0015,1,7.2,ug/m3 LC,23,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/16/11,37-063-0015,1,6.6,ug/m3 LC,21,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/18/11,37-063-0015,1,8.6,ug/m3 LC,28,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/21/11,37-063-0015,1,8.6,ug/m3 LC,28,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/24/11,37-063-0015,1,11,ug/m3 LC,36,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/27/11,37-063-0015,1,5.6,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/30/11,37-063-0015,1,6.2,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/3/11,37-063-0015,1,8.5,ug/m3 LC,28,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/6/11,37-063-0015,1,9.3,ug/m3 LC,30,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/9/11,37-063-0015,1,8.8,ug/m3 LC,29,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/11/11,37-063-0015,1,18.6,ug/m3 LC,57,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/12/11,37-063-0015,1,20,ug/m3 LC,60,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/15/11,37-063-0015,1,8,ug/m3 LC,26,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/18/11,37-063-0015,1,6.3,ug/m3 LC,20,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/21/11,37-063-0015,1,10.8,ug/m3 LC,35,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/27/11,37-063-0015,1,6.8,ug/m3 LC,22,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/30/11,37-063-0015,1,14.9,ug/m3 LC,48,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/31/11,37-063-0015,1,22.5,ug/m3 LC,65,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/2/11,37-063-0015,1,16.9,ug/m3 LC,54,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/5/11,37-063-0015,1,16.8,ug/m3 LC,54,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/8/11,37-063-0015,1,21.3,ug/m3 LC,62,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/11/11,37-063-0015,1,14.2,ug/m3 LC,46,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/14/11,37-063-0015,1,10.8,ug/m3 LC,35,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/17/11,37-063-0015,1,12,ug/m3 LC,39,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/20/11,37-063-0015,1,8.4,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/23/11,37-063-0015,1,4.3,ug/m3 LC,14,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/26/11,37-063-0015,1,14.1,ug/m3 LC,46,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/29/11,37-063-0015,1,8.4,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/2/11,37-063-0015,1,17.3,ug/m3 LC,55,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/5/11,37-063-0015,1,10,ug/m3 LC,32,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/8/11,37-063-0015,1,12.4,ug/m3 LC,40,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/14/11,37-063-0015,1,9.2,ug/m3 LC,30,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/17/11,37-063-0015,1,7.9,ug/m3 LC,26,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/18/11,37-063-0015,1,9.6,ug/m3 LC,31,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/21/11,37-063-0015,1,18,ug/m3 LC,56,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/23/11,37-063-0015,1,17.2,ug/m3 LC,54,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/26/11,37-063-0015,1,10,ug/m3 LC,32,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/29/11,37-063-0015,1,14.3,ug/m3 LC,46,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/1/11,37-063-0015,1,10.7,ug/m3 LC,35,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/4/11,37-063-0015,1,16.2,ug/m3 LC,52,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/7/11,37-063-0015,1,10.1,ug/m3 LC,33,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/10/11,37-063-0015,1,8.8,ug/m3 LC,29,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/13/11,37-063-0015,1,15.2,ug/m3 LC,49,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/16/11,37-063-0015,1,10.1,ug/m3 LC,33,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/19/11,37-063-0015,1,13.7,ug/m3 LC,44,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/22/11,37-063-0015,1,8.4,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/25/11,37-063-0015,1,6.6,ug/m3 LC,21,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/28/11,37-063-0015,1,15.2,ug/m3 LC,49,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/31/11,37-063-0015,1,8.7,ug/m3 LC,28,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/3/11,37-063-0015,1,15.8,ug/m3 LC,52,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/6/11,37-063-0015,1,3.8,ug/m3 LC,12,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/9/11,37-063-0015,1,10.7,ug/m3 LC,35,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/12/11,37-063-0015,1,11.7,ug/m3 LC,38,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/15/11,37-063-0015,1,13.2,ug/m3 LC,43,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/18/11,37-063-0015,1,2.9,ug/m3 LC,9,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/21/11,37-063-0015,1,4.6,ug/m3 LC,15,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/24/11,37-063-0015,1,5.6,ug/m3 LC,18,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/27/11,37-063-0015,1,8.2,ug/m3 LC,27,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/30/11,37-063-0015,1,5.7,ug/m3 LC,19,1,100,88101,PM2.5 - Local Conditions,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/1/11,37-063-0015,3,16.7125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/2/11,37-063-0015,3,3.754166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/3/11,37-063-0015,3,4.855555556,ug/m3 LC,.,18,75,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/4/11,37-063-0015,3,8.6875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/5/11,37-063-0015,3,10.18333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/6/11,37-063-0015,3,8.495833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/7/11,37-063-0015,3,5.991666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/8/11,37-063-0015,3,5.320833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/9/11,37-063-0015,3,6.9125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/10/11,37-063-0015,3,6.604166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/11/11,37-063-0015,3,5.804166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/12/11,37-063-0015,3,7.808333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/13/11,37-063-0015,3,9.095833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/14/11,37-063-0015,3,10.45416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/15/11,37-063-0015,3,11.92916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/16/11,37-063-0015,3,14.01666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/17/11,37-063-0015,3,12.98333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/18/11,37-063-0015,3,8.579166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/19/11,37-063-0015,3,7.195833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/20/11,37-063-0015,3,6.9375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/21/11,37-063-0015,3,4.9125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/22/11,37-063-0015,3,7.183333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/23/11,37-063-0015,3,14.22916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/24/11,37-063-0015,3,10.61904762,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/25/11,37-063-0015,3,13.15833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/26/11,37-063-0015,3,3.95,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/27/11,37-063-0015,3,10.58333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/28/11,37-063-0015,3,12.18333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/29/11,37-063-0015,3,9.420833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/30/11,37-063-0015,3,14.25833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r1/31/11,37-063-0015,3,13.80833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/1/11,37-063-0015,3,10.25238095,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/2/11,37-063-0015,3,6.129166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/3/11,37-063-0015,3,6.7875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/4/11,37-063-0015,3,7.604166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/5/11,37-063-0015,3,4.320833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/6/11,37-063-0015,3,8.225,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/7/11,37-063-0015,3,10.31666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/8/11,37-063-0015,3,6.833333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/9/11,37-063-0015,3,5.6125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/10/11,37-063-0015,3,7.25,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/11/11,37-063-0015,3,11.30833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/12/11,37-063-0015,3,8.595833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/13/11,37-063-0015,3,5.2625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/14/11,37-063-0015,3,7.25,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/15/11,37-063-0015,3,7.070833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/16/11,37-063-0015,3,11.10416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/17/11,37-063-0015,3,21.9125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/18/11,37-063-0015,3,17.39166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/19/11,37-063-0015,3,2.683333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/20/11,37-063-0015,3,5.8875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/21/11,37-063-0015,3,7.485714286,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/22/11,37-063-0015,3,8.186363636,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/23/11,37-063-0015,3,7.770833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/24/11,37-063-0015,3,10.55833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/25/11,37-063-0015,3,7.416666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/26/11,37-063-0015,3,8.770833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/27/11,37-063-0015,3,15.825,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r2/28/11,37-063-0015,3,10.32380952,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/1/11,37-063-0015,3,3.5125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/2/11,37-063-0015,3,8.079166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/3/11,37-063-0015,3,4.595833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/4/11,37-063-0015,3,7.416666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/5/11,37-063-0015,3,5.041666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/6/11,37-063-0015,3,1.870833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/7/11,37-063-0015,3,4.6875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/8/11,37-063-0015,3,4.470833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/9/11,37-063-0015,3,5.904166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/10/11,37-063-0015,3,2.3875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/11/11,37-063-0015,3,4.395833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/12/11,37-063-0015,3,8.408333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/13/11,37-063-0015,3,11.71666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/14/11,37-063-0015,3,8.875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/15/11,37-063-0015,3,8.416666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/16/11,37-063-0015,3,6.279166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/17/11,37-063-0015,3,5.491666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/18/11,37-063-0015,3,12.34166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/19/11,37-063-0015,3,7.575,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/20/11,37-063-0015,3,6.166666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/21/11,37-063-0015,3,9.225,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/22/11,37-063-0015,3,11.16363636,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/23/11,37-063-0015,3,9.745454545,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/24/11,37-063-0015,3,3.9625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/25/11,37-063-0015,3,7.483333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/26/11,37-063-0015,3,6.354166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/27/11,37-063-0015,3,6.320833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/28/11,37-063-0015,3,9.5625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/29/11,37-063-0015,3,11.50416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/30/11,37-063-0015,3,5.7,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r3/31/11,37-063-0015,3,2.891666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/1/11,37-063-0015,3,9.195833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/2/11,37-063-0015,3,7.733333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/3/11,37-063-0015,3,5.570833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/4/11,37-063-0015,3,7.454166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/5/11,37-063-0015,3,3.566666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/6/11,37-063-0015,3,5.520833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/7/11,37-063-0015,3,7.783333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/8/11,37-063-0015,3,16.52083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/9/11,37-063-0015,3,7.883333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/10/11,37-063-0015,3,5.645833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/11/11,37-063-0015,3,12.15833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/12/11,37-063-0015,3,6.129166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/13/11,37-063-0015,3,4.266666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/14/11,37-063-0015,3,9.8625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/15/11,37-063-0015,3,8.891666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/16/11,37-063-0015,3,4.9875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/17/11,37-063-0015,3,4.983333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/18/11,37-063-0015,3,9.775,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/19/11,37-063-0015,3,15.72916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/20/11,37-063-0015,3,11,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/21/11,37-063-0015,3,8.641666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/22/11,37-063-0015,3,5.8625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/23/11,37-063-0015,3,8.85,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/24/11,37-063-0015,3,12.40833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/25/11,37-063-0015,3,10.1125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/26/11,37-063-0015,3,4.220833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/27/11,37-063-0015,3,5.514285714,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/28/11,37-063-0015,3,6.6375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/29/11,37-063-0015,3,5.904166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r4/30/11,37-063-0015,3,7.429166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/1/11,37-063-0015,3,9.325,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/2/11,37-063-0015,3,9.129166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/3/11,37-063-0015,3,8.104166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/4/11,37-063-0015,3,3.45,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/5/11,37-063-0015,3,5.541666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/6/11,37-063-0015,3,9.116666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/7/11,37-063-0015,3,8.679166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/8/11,37-063-0015,3,7.570833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/9/11,37-063-0015,3,8.645833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/10/11,37-063-0015,3,11.79166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/11/11,37-063-0015,3,16.47916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/12/11,37-063-0015,3,16.37083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/13/11,37-063-0015,3,11.47083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/14/11,37-063-0015,3,9.3875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/15/11,37-063-0015,3,5.691666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/16/11,37-063-0015,3,4.429166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/17/11,37-063-0015,3,5.366666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/18/11,37-063-0015,3,5.170833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/21/11,37-063-0015,3,9.9375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/22/11,37-063-0015,3,13.2625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/23/11,37-063-0015,3,14.3875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/24/11,37-063-0015,3,10.94166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/25/11,37-063-0015,3,8.961904762,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/26/11,37-063-0015,3,16.26666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/27/11,37-063-0015,3,3.995238095,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/28/11,37-063-0015,3,6.579166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/29/11,37-063-0015,3,11.26666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/30/11,37-063-0015,3,13.23333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r5/31/11,37-063-0015,3,19.67916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/1/11,37-063-0015,3,28.65,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/2/11,37-063-0015,3,15.675,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/3/11,37-063-0015,3,7.979166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/4/11,37-063-0015,3,14.50833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/7/11,37-063-0015,3,19.48333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/8/11,37-063-0015,3,23.2625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/9/11,37-063-0015,3,23.37083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/10/11,37-063-0015,3,20.39166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/11/11,37-063-0015,3,14.49583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/12/11,37-063-0015,3,15.61666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/15/11,37-063-0015,3,10.1625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/16/11,37-063-0015,3,14.79166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/17/11,37-063-0015,3,12.15,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/18/11,37-063-0015,3,12.39583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/19/11,37-063-0015,3,4.454545455,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/20/11,37-063-0015,3,9.2125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/21/11,37-063-0015,3,42.44583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/22/11,37-063-0015,3,8.245833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/23/11,37-063-0015,3,4.825,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/24/11,37-063-0015,3,9.716666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/25/11,37-063-0015,3,11.20416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/26/11,37-063-0015,3,15.7125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/27/11,37-063-0015,3,15.2,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/28/11,37-063-0015,3,9.85,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/29/11,37-063-0015,3,8.379166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r6/30/11,37-063-0015,3,12.5125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/1/11,37-063-0015,3,16.475,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/2/11,37-063-0015,3,18.1875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/3/11,37-063-0015,3,23.37916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/4/11,37-063-0015,3,19.64583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/5/11,37-063-0015,3,12.95833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/6/11,37-063-0015,3,19.87727273,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/7/11,37-063-0015,3,11.35833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/8/11,37-063-0015,3,11.95416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/9/11,37-063-0015,3,8.570833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/10/11,37-063-0015,3,17.77916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/11/11,37-063-0015,3,20.425,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/12/11,37-063-0015,3,18.9625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/13/11,37-063-0015,3,18.22083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/14/11,37-063-0015,3,9.9,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/15/11,37-063-0015,3,5.266666667,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/16/11,37-063-0015,3,6.266666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/17/11,37-063-0015,3,7.05,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/18/11,37-063-0015,3,10.12916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/19/11,37-063-0015,3,21.9,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/20/11,37-063-0015,3,19.525,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/21/11,37-063-0015,3,18.91666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/22/11,37-063-0015,3,21.9375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/23/11,37-063-0015,3,17.40416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/24/11,37-063-0015,3,13.30416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/25/11,37-063-0015,3,9.558333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/26/11,37-063-0015,3,10.93181818,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/27/11,37-063-0015,3,14.6,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/28/11,37-063-0015,3,18.75416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/29/11,37-063-0015,3,14.9,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/30/11,37-063-0015,3,19.44166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r7/31/11,37-063-0015,3,7.1375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/1/11,37-063-0015,3,9.475,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/2/11,37-063-0015,3,15.2875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/3/11,37-063-0015,3,19.225,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/4/11,37-063-0015,3,17.52083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/5/11,37-063-0015,3,15.625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/6/11,37-063-0015,3,8.879166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/7/11,37-063-0015,3,13.99166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/8/11,37-063-0015,3,12.27727273,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/9/11,37-063-0015,3,9.370833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/10/11,37-063-0015,3,9.38,ug/m3 LC,.,20,83,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/11/11,37-063-0015,3,12.19583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/12/11,37-063-0015,3,19.375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/13/11,37-063-0015,3,15.075,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/14/11,37-063-0015,3,6.225,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/15/11,37-063-0015,3,7.8625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/16/11,37-063-0015,3,12.025,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/17/11,37-063-0015,3,12.8,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/18/11,37-063-0015,3,13.99583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/19/11,37-063-0015,3,14.75454545,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/20/11,37-063-0015,3,12.20416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/21/11,37-063-0015,3,12.07083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/22/11,37-063-0015,3,8.283333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/23/11,37-063-0015,3,8.716666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/24/11,37-063-0015,3,9.663636364,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/25/11,37-063-0015,3,9.220833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/26/11,37-063-0015,3,8.695833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/27/11,37-063-0015,3,4.4375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/28/11,37-063-0015,3,15.875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/29/11,37-063-0015,3,13.91666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/30/11,37-063-0015,3,9.9625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r8/31/11,37-063-0015,3,8.920833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/1/11,37-063-0015,3,11.32083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/2/11,37-063-0015,3,17.72083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/3/11,37-063-0015,3,16.54166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/4/11,37-063-0015,3,13.375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/5/11,37-063-0015,3,11.91666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/6/11,37-063-0015,3,6.391666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/7/11,37-063-0015,3,5.941666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/8/11,37-063-0015,3,14.42916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/9/11,37-063-0015,3,14.83809524,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/10/11,37-063-0015,3,11.44166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/11/11,37-063-0015,3,9.333333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/12/11,37-063-0015,3,12.28333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/13/11,37-063-0015,3,14.55416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/14/11,37-063-0015,3,14.39166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/15/11,37-063-0015,3,13.4125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/17/11,37-063-0015,3,5.391666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/18/11,37-063-0015,3,3.333333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/19/11,37-063-0015,3,5.35,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/20/11,37-063-0015,3,7.620833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/21/11,37-063-0015,3,4.880952381,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/22/11,37-063-0015,3,6.152380952,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/23/11,37-063-0015,3,5.1,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/24/11,37-063-0015,3,7.070833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/25/11,37-063-0015,3,3.683333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/26/11,37-063-0015,3,5.120833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/27/11,37-063-0015,3,9.870833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/28/11,37-063-0015,3,7.375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/29/11,37-063-0015,3,8.533333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r9/30/11,37-063-0015,3,7.195833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/1/11,37-063-0015,3,2.145833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/2/11,37-063-0015,3,4.8875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/3/11,37-063-0015,3,5.329166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/4/11,37-063-0015,3,6.033333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/5/11,37-063-0015,3,8.304166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/6/11,37-063-0015,3,9.7875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/7/11,37-063-0015,3,7.325,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/8/11,37-063-0015,3,7.35,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/9/11,37-063-0015,3,5.775,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/10/11,37-063-0015,3,9.020833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/11/11,37-063-0015,3,10.58636364,ug/m3 LC,.,22,92,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/12/11,37-063-0015,3,7.208333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/13/11,37-063-0015,3,6.2,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/14/11,37-063-0015,3,7.366666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/15/11,37-063-0015,3,7.15,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/16/11,37-063-0015,3,5.820833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/17/11,37-063-0015,3,11.775,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/18/11,37-063-0015,3,11.45238095,ug/m3 LC,.,21,88,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/19/11,37-063-0015,3,1.5625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/20/11,37-063-0015,3,4.6875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/21/11,37-063-0015,3,6.641666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/22/11,37-063-0015,3,7.166666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/23/11,37-063-0015,3,9.904166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/24/11,37-063-0015,3,12.24583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/25/11,37-063-0015,3,10.27083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/26/11,37-063-0015,3,12.9625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/27/11,37-063-0015,3,12.44166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/28/11,37-063-0015,3,1.645833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/29/11,37-063-0015,3,2.108333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/30/11,37-063-0015,3,9.079166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r10/31/11,37-063-0015,3,6.483333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/1/11,37-063-0015,3,7.7625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/2/11,37-063-0015,3,9.508333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/3/11,37-063-0015,3,11.55416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/4/11,37-063-0015,3,8.425,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/5/11,37-063-0015,3,5.1625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/6/11,37-063-0015,3,5.983333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/7/11,37-063-0015,3,6.841666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/8/11,37-063-0015,3,9.458333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/9/11,37-063-0015,3,8.616666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/10/11,37-063-0015,3,7.115,ug/m3 LC,.,20,83,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/11/11,37-063-0015,3,6.475,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/12/11,37-063-0015,3,8.9125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/13/11,37-063-0015,3,9.204166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/14/11,37-063-0015,3,9.370833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/15/11,37-063-0015,3,8.975,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/16/11,37-063-0015,3,10.07916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/17/11,37-063-0015,3,3.408333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/18/11,37-063-0015,3,5.879166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/19/11,37-063-0015,3,11.85,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/20/11,37-063-0015,3,13.17083333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/21/11,37-063-0015,3,8.421052632,ug/m3 LC,.,19,79,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/22/11,37-063-0015,3,14.00416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/23/11,37-063-0015,3,2.25,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/24/11,37-063-0015,3,6.575,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/25/11,37-063-0015,3,8.775,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/26/11,37-063-0015,3,9.8375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/27/11,37-063-0015,3,6.395833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/28/11,37-063-0015,3,3.883333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/29/11,37-063-0015,3,2.175,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r11/30/11,37-063-0015,3,4.208333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/1/11,37-063-0015,3,6.15,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/2/11,37-063-0015,3,10.625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/3/11,37-063-0015,3,9.533333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/4/11,37-063-0015,3,10.95416667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/5/11,37-063-0015,3,9.2,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/6/11,37-063-0015,3,4.25,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/7/11,37-063-0015,3,1.9375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/8/11,37-063-0015,3,5.558333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/9/11,37-063-0015,3,10.625,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/10/11,37-063-0015,3,9.554166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/11/11,37-063-0015,3,7.245833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/12/11,37-063-0015,3,8.633333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/13/11,37-063-0015,3,11.54583333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/14/11,37-063-0015,3,10.37368421,ug/m3 LC,.,19,79,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/15/11,37-063-0015,3,10.6125,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/16/11,37-063-0015,3,7.466666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/17/11,37-063-0015,3,7.541666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/18/11,37-063-0015,3,10.8375,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/19/11,37-063-0015,3,12.025,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/20/11,37-063-0015,3,15.22916667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/21/11,37-063-0015,3,8.275,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/22/11,37-063-0015,3,7.366666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/23/11,37-063-0015,3,3.15,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/24/11,37-063-0015,3,7.929166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/25/11,37-063-0015,3,10.7875,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/26/11,37-063-0015,3,7.329166667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/27/11,37-063-0015,3,4.120833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/28/11,37-063-0015,3,4.283333333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/29/11,37-063-0015,3,8.4,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/30/11,37-063-0015,3,10.15833333,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417\r12/31/11,37-063-0015,3,8.616666667,ug/m3 LC,.,24,100,88502,Acceptable PM2.5 AQI & Speciation Mass,450,\"Raleigh-Durham-Cary, NC\",20500,\"Durham, NC\",37,North Carolina,63,Durham,36.032944,-78.905417"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/antibiotic_use_children/antibiotic_use_children.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\nconditions = c(rep(\"Prematurity\", 33),\n               rep(\"Neuromuscular\", 10),\n               rep(\"Cardiovascular\", 16),\n               rep(\"Genetic/metabolic\", 6),\n               rep(\"Respiratory\", 13),\n               rep(\"Trauma\", 10),\n               rep(\"Gastrointestinal\", 2),\n               rep(\"Immunocompromised\", 2)\n               )\n\n# summary table -----------------------------------------------------\nsummary_table = sort(table(conditions))/sum(table(conditions))\n\n# barplot -----------------------------------------------------------\npdf(\"antibiotic_use_children_bar.pdf\", height = 3, width = 6)\npar(mar = c(3.7, 11.3, 0, 0.5), las = 1, mgp = c(2.5, 1, 0),\n    cex.lab = 1.25, cex.axis = 1.25)\nbarplot(summary_table, ylab = \"\", xlab = \"Relative frequency\", \n        col = COL[1], horiz = TRUE)\ndev.off()\n\n# pie chart ---------------------------------------------------------\npdf(\"antibiotic_use_children_pie.pdf\", height = 3, width = 6)\npar(mar=c(0, 2.8, 0, 6), las = 1)\npie(summary_table, \n    col = c(COL[1,1], COL[1,4], COL[2,1], COL[2,4], \n            COL[3,1], COL[3,4], COL[4,1], COL[4,4]), \n    cex = 1, clockwise = FALSE,\n    labels = names(summary_table))\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/association_plots/association_plots.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# set seed ----------------------------------------------------------\nset.seed = 2306\n\n# create x ----------------------------------------------------------\nx = seq(0, 10, 0.1)\n\n# create y_poslin: positive linear with x ---------------------------\ny_poslin = x * runif(1, min = 0, max = 4) + \n  rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4)) - \n  runif(1, min = 0, max = 3)\n\n# create y_neglin: negative linear with x ---------------------------\ny_neglin = x * -runif(1, min = 0, max = 4) + \n  rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4)) - \n  runif(1, min = 0, max = 5)\n\n# create y_poscur: curved positive with x ---------------------------\ny_poscur = x^2 + rnorm(length(x), mean = 0, sd = runif(1, min = 3, max = 4))\n\n# create y_none: no association with x ------------------------------\ny_none = x + rnorm(length(x), mean = 0, sd = runif(1, min = 30, max = 40))\n\n# plot the associations --------------------------------------------- \nPlot <- function(x, y, i) {\n  plot(y ~ x,\n      xlab = paste0(\"(\", i, \")\"),\n      ylab = \"\",\n      col = COL[1, 2],\n      cex = 1.5)\n}\n\npdf(\"association_plots.pdf\", 10, 2.5)\npar(mar = c(2.4, 0.5, 0.5, 0.5), las = 1, mgp = c(0.9, 0.5, 0), \n    cex.lab = 1.75, pch = 19, mfrow = c(1, 4), \n    yaxt = \"n\", xaxt = \"n\")\nPlot(x, y_poslin, 1)\nPlot(x, y_none, 2)\nPlot(x, y_poscur, 3)\nPlot(x, y_neglin, 4)\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/cleveland_sacramento/cleveland_sacramento.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# take a sample -----------------------------------------------------\ncle_sac = cle_sac[!is.na(cle_sac$personal_income),]\n\nset.seed(8957)\nsac = sample(cle_sac$personal_income[cle_sac$city == \"Sacramento\"], 17)\ncle = sample(cle_sac$personal_income[cle_sac$city == \"Cleveland\"], 21)\n\n# plot of total personal income in Cle and Sac ----------------------\npdf(\"cleveland_sacramento_hist.pdf\", height = 5, width = 7)\n\npar(mar = c(3.7, 2, 1,1), las = 1, mgp = c(2.5, 0.7, 0), \n    mfrow = c(2,1), cex.lab = 1.25)\n\nhistPlot(cle, xlim = c(0, 180000), ylim = c(0,10),\n         ylab = \"\", xlab = \"\", col = COL[1], breaks = 8, axes = FALSE)\naxis(1, at = seq(0,180000,45000))\naxis(2, at = seq(0,10,5))\ntext(x = 120000, y = 8, labels = \"Cleveland, OH\", pos = 4, cex = 1.25)\n\nhistPlot(sac, xlim = c(0,180000), ylim = c(0,10), \n         ylab = \"\", xlab = \"Total personal income\", col = COL[1], breaks = 8,\n         axes = FALSE)\naxis(1, at = seq(0,180000,45000))\naxis(2, at = seq(0,10,5))\ntext(x = 120000, y = 8, labels = \"Sacramento, CA\", pos = 4, cex = 1.25)\n\ndev.off()\n\n# summary stats -----------------------------------------------------\nmean(cle, na.rm = TRUE)\nsd(cle, na.rm = TRUE)\nlength(cle)\n\nmean(sac, na.rm = TRUE)\nsd(sac, na.rm = TRUE)\nlength(sac)\n"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/county_commute_times/countyMap.R",
    "content": "library(maps)\ncountyMap <- function(values, FIPS,\n                      col = c(\"red\", \"green\", \"blue\"),\n                      varTrans = I,\n                      gtlt = \"\",\n                      ...){\n  if(missing(FIPS)){\n    stop(\"Must provide the county FIPS\")\n  }\n  \n  # _____ Drop NAs _____ #\n  FIPS   <- FIPS[!is.na(values)]\n  values <- values[!is.na(values)]\n  \n  # _____ Scale Values _____ #\n  MI  <- min(values)\n  MA  <- max(values)\n  Leg <- seq(MI, MA, length.out = 50)\n  if(identical(varTrans, log)){\n    VAL    <- varTrans(values+0.1)\n    valCol <- varTrans(values+0.1)\n    LegCol <- varTrans(Leg+0.1)\n  } else {\n    VAL    <- varTrans(values)\n    valCol <- varTrans(values)\n    LegCol <- varTrans(Leg)\n  }\n  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01\n  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01\n  \n  val.000 <- 0.500*(1-valCol)\n  val.114 <- 0.557*(1-valCol)\n  val.200 <- 0.600*(1-valCol)\n  val.298 <- 0.649*(1-valCol)\n  val.318 <- 0.659*(1-valCol)\n  val.337 <- 0.669*(1-valCol)\n  val.447 <- 0.724*(1-valCol)\n  val.608 <- 0.804*(1-valCol)\n  val.741 <- 0.871*(1-valCol)\n  val.863 <- 0.932*(1-valCol)\n  val.941 <- 0.971*(1-valCol)\n  val.957 <- 0.979*(1-valCol)\n  \n  Leg.000 <- 0.500*(1-LegCol)\n  Leg.114 <- 0.557*(1-LegCol)\n  Leg.200 <- 0.600*(1-LegCol)\n  Leg.298 <- 0.649*(1-LegCol)\n  Leg.318 <- 0.659*(1-LegCol)\n  Leg.337 <- 0.669*(1-LegCol)\n  Leg.447 <- 0.724*(1-LegCol)\n  Leg.608 <- 0.804*(1-LegCol)\n  Leg.741 <- 0.871*(1-LegCol)\n  Leg.863 <- 0.932*(1-LegCol)\n  Leg.941 <- 0.971*(1-LegCol)\n  Leg.957 <- 0.979*(1-LegCol)\n  \n  if(col[1] == \"red\"){\n    col <- rgb(val.941, val.318, val.200)\n    COL <- rgb(Leg.941, Leg.318, Leg.200)\n  } else if(col[1] == \"green\"){\n    col <- rgb(val.298, val.447, val.114)\n    COL <- rgb(Leg.298, Leg.447, Leg.114)\n  } else if(col[1] == \"bg\"){\n    col <- rgb(val.337, val.608, val.741)\n    COL <- rgb(Leg.337, Leg.608, Leg.741)\n  } else if(col[1] == \"ye\"){\n    col <- rgb(val.957, val.863, val.000)\n    COL <- rgb(Leg.957, Leg.863, Leg.000)\n  } else {\n    col <- rgb(val.06, val.06, val.10)\n    COL <- rgb(Leg.06, Leg.06, Leg.10)\n  }\n\n  # _____ Remove These _____ #\n  data(county.fips)\n  col <- col[match(county.fips$fips, FIPS)]\n  plot(0,0,type = \"n\", axes = FALSE, xlab = \"\", ylab = \"\")\n  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))\n  map(\"county\", col = col, fill = TRUE, resolution = 0,\n    lty = 0, projection = \"polyconic\", mar = rep(0.1,4), add = TRUE, ...)\n  \n  x1 <- 0.335\n  x2 <- 0.365\n  for(i in 1:50){\n    y1 <- i/50 * 0.25 + 0.5\n    y2 <- (i-1)/50 * 0.25 + 0.5\n    rect(x1, y1, x2, y2, border = \"#00000000\", col = COL[i])\n  }\n  \n  VR    <- range(VAL)\n  VR[3] <- VR[2]\n  VR[2] <- mean(VR[c(1,3)])\n  \n  VR1    <- c()\n  VR1[1] <- values[which.min(abs(VAL - VR[1]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[2]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[3]))]\n  \n  VR    <- round(VR)\n  if(gtlt %in% c(\">\", \"><\")){\n    VR[3] <- paste(\">\", VR[3], sep = \"\")\n  }\n  if(gtlt %in% c(\"<\", \"><\")){\n    VR[1] <- paste(\"<\", VR[1], sep = \"\")\n  }\n  text(0.365, 0.51, VR[1], pos = 4)\n  text(0.365, 0.625, VR[2], pos = 4)\n  text(0.365, 0.74, VR[3], pos = 4)\n}\n\n\n\n\n\n"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/county_commute_times/county_commute_times.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load mapproj package for map functions ----------------------------\nlibrary(mapproj)\n\n# load data ---------------------------------------------------------\ndata(countyComplete)\n\n# histogram of travel to work time ----------------------------------\npdf(\"county_commute_times_hist.pdf\", 7.5, 4)\n\npar(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(county_complete$mean_work_travel_2010, breaks = 40, \n         xlab = \"Mean work travel (in min)\", ylab = \"\", \n         col = COL[1], axes = FALSE)\naxis(1)\naxis(2, at = seq(0, 200, 100))\n\ndev.off()\n\n# source custom code for county maps --------------------------------\nsource(\"countyMap.R\")\n\n# map of travel to work time ----------------------------------------\n\npdf(\"county_commute_times_map.pdf\", 7.5, 4)\n\nval <- county_complete$mean_work_travel_2010\nval[val >= 33] <- 33\ncountyMap(val, county_complete$FIPS, \"green\", gtlt = \">\")\n\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/county_hispanic_pop/countyMap.R",
    "content": "library(maps)\ncountyMap <- function(values, FIPS,\n                      col = c(\"red\", \"green\", \"blue\"),\n                      varTrans = I,\n                      gtlt = \"\",\n                      ...){\n  if(missing(FIPS)){\n    stop(\"Must provide the county FIPS\")\n  }\n  \n  # _____ Drop NAs _____ #\n  FIPS   <- FIPS[!is.na(values)]\n  values <- values[!is.na(values)]\n  \n  # _____ Scale Values _____ #\n  MI  <- min(values)\n  MA  <- max(values)\n  Leg <- seq(MI, MA, length.out = 50)\n  if(identical(varTrans, log)){\n    VAL    <- varTrans(values+0.1)\n    valCol <- varTrans(values+0.1)\n    LegCol <- varTrans(Leg+0.1)\n  } else {\n    VAL    <- varTrans(values)\n    valCol <- varTrans(values)\n    LegCol <- varTrans(Leg)\n  }\n  valCol <- 0.98*(valCol - MI)/(MA - MI) + 0.01\n  LegCol <- 0.98*(LegCol - MI)/(MA - MI) + 0.01\n  \n  val.000 <- 0.500*(1-valCol)\n  val.114 <- 0.557*(1-valCol)\n  val.200 <- 0.600*(1-valCol)\n  val.298 <- 0.649*(1-valCol)\n  val.318 <- 0.659*(1-valCol)\n  val.337 <- 0.669*(1-valCol)\n  val.447 <- 0.724*(1-valCol)\n  val.608 <- 0.804*(1-valCol)\n  val.741 <- 0.871*(1-valCol)\n  val.863 <- 0.932*(1-valCol)\n  val.941 <- 0.971*(1-valCol)\n  val.957 <- 0.979*(1-valCol)\n  \n  Leg.000 <- 0.500*(1-LegCol)\n  Leg.114 <- 0.557*(1-LegCol)\n  Leg.200 <- 0.600*(1-LegCol)\n  Leg.298 <- 0.649*(1-LegCol)\n  Leg.318 <- 0.659*(1-LegCol)\n  Leg.337 <- 0.669*(1-LegCol)\n  Leg.447 <- 0.724*(1-LegCol)\n  Leg.608 <- 0.804*(1-LegCol)\n  Leg.741 <- 0.871*(1-LegCol)\n  Leg.863 <- 0.932*(1-LegCol)\n  Leg.941 <- 0.971*(1-LegCol)\n  Leg.957 <- 0.979*(1-LegCol)\n  \n  if(col[1] == \"red\"){\n    col <- rgb(val.941, val.318, val.200)\n    COL <- rgb(Leg.941, Leg.318, Leg.200)\n  } else if(col[1] == \"green\"){\n    col <- rgb(val.298, val.447, val.114)\n    COL <- rgb(Leg.298, Leg.447, Leg.114)\n  } else if(col[1] == \"bg\"){\n    col <- rgb(val.337, val.608, val.741)\n    COL <- rgb(Leg.337, Leg.608, Leg.741)\n  } else if(col[1] == \"ye\"){\n    col <- rgb(val.957, val.863, val.000)\n    COL <- rgb(Leg.957, Leg.863, Leg.000)\n  } else {\n    col <- rgb(val.06, val.06, val.10)\n    COL <- rgb(Leg.06, Leg.06, Leg.10)\n  }\n\n  # _____ Remove These _____ #\n  data(county.fips)\n  col <- col[match(county.fips$fips, FIPS)]\n  plot(0,0,type = \"n\", axes = FALSE, xlab = \"\", ylab = \"\")\n  par(mar = rep(0.1,4), usr = c(-0.385,0.41,0.44,0.91))\n  map(\"county\", col = col, fill = TRUE, resolution = 0,\n    lty = 0, projection = \"polyconic\", mar = rep(0.1,4), add = TRUE, ...)\n  \n  x1 <- 0.335\n  x2 <- 0.365\n  for(i in 1:50){\n    y1 <- i/50 * 0.25 + 0.5\n    y2 <- (i-1)/50 * 0.25 + 0.5\n    rect(x1, y1, x2, y2, border = \"#00000000\", col = COL[i])\n  }\n  \n  VR    <- range(VAL)\n  VR[3] <- VR[2]\n  VR[2] <- mean(VR[c(1,3)])\n  \n  VR1    <- c()\n  VR1[1] <- values[which.min(abs(VAL - VR[1]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[2]))]\n  VR1[2] <- values[which.min(abs(VAL - VR[3]))]\n  \n  VR    <- round(VR)\n  if(gtlt %in% c(\">\", \"><\")){\n    VR[3] <- paste(\">\", VR[3], sep = \"\")\n  }\n  if(gtlt %in% c(\"<\", \"><\")){\n    VR[1] <- paste(\"<\", VR[1], sep = \"\")\n  }\n  text(0.365, 0.51, VR[1], pos = 4)\n  text(0.365, 0.625, VR[2], pos = 4)\n  text(0.365, 0.74, VR[3], pos = 4)\n}\n\n\n\n\n\n"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/county_hispanic_pop/county_hispanic_pop.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load mapproj package for map functions ----------------------------\nlibrary(mapproj)\n\n# load data ---------------------------------------------------------\ndata(county_complete)\n\n# histogram of hispanic % -------------------------------------------\npdf(\"county_hispanic_pop_hist.pdf\", 7.5, 4)\n\npar(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(county_complete$hispanic_2010, breaks = 25, \n         xlab = \"Percent Hispanic\", ylab = \"\", \n         col = COL[1], axes = FALSE)\nAxisInPercent(1, at = seq(0, 100, 20))\naxis(2)\n\ndev.off()\n\n# log of histogram of hispanic % ------------------------------------\npdf(\"county_hispanic_pop_log_hist.pdf\", 7.5, 4)\n\npar(mar = c(3.8, 3.5, 0.5, 0.5), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(log(county_complete$hispanic_2010), breaks = 25, \n         xlab = \"log(Percent Hispanic)\", ylab = \"\", \n         col = COL[1])\n\ndev.off()\n\n# source custom code for county maps --------------------------------\nsource(\"countyMap.R\")\n\n# map of travel to work time ----------------------------------------\n\npdf(\"county_hispanic_pop_map.pdf\", 7.5, 4)\n\nval <- county_complete$hispanic_2010\nval[val >= 40] <- 40\ncountyMap(val, county_complete$FIPS, \"bg\", gtlt=\">\")\n\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/dream_act_mosaic/dream_act_mosaic.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\n\nideology = c(rep(\"Conservative\", 372), rep(\"Moderate\", 363), rep(\"Liberal\", 175))\nideology = factor(ideology, levels = c(\"Conservative\", \"Moderate\", \"Liberal\"))\ndream = c(rep(\"Support\", 186), rep(\"Not support\", 151), rep(\"Not sure\", 35), \n          rep(\"Support\", 174), rep(\"Not support\", 161), rep(\"Not sure\", 28),\n          rep(\"Support\", 114), rep(\"Not support\", 52), rep(\"Not sure\", 9)\n)\ndream = factor(dream, levels = c(\"Support\", \"Not support\", \"Not sure\"))\n\n\n# mosaicplot --------------------------------------------------------\n\npdf(\"dream_act_mosaic.pdf\", 7, 3)\npar(mar=c(0.5,0,0.25,0.5), las=1, mgp=c(4,1,0))\n\nmosaicplot(ideology ~ dream, main = \"\", cex.axis = 1.1, \n           xlab = \"\", ylab = \"\", color = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/estimate_mean_median_simple/estimate_mean_median_simple.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\nset.seed(9823)\nx <- 100 * rbeta(400, 12, 3)\n\n# plot --------------------------------------------------------------\nmyPDF(\"estimate_mean_median_simple.pdf\", 6, 2,\n      mar = c(1.7, 2.2, 0.2, 0.4), cex = 1.1)\nh <- hist(\n    x,\n    col = COL[1],\n    xlab = \"\",\n    ylab = \"\",\n    main = \"\",\n    axes = FALSE)\naxis(1)\nat <- pretty(par(\"yaxp\")[1:2])\naxis(2)\nabline(h = at, col = COL[6, 2], lty = 2)\nhist(x, col = COL[1, 2], add = TRUE)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/hist_box_match/hist_box_match.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# generate data -----------------------------------------------------\nset.seed(7365)\n\nsym = rnorm(1000, mean = 60, sd = 3)\nuni = runif(1000, min = 0, max = 100)\nrs = rgamma(1000, shape = 3, rate = 2)\n\n# histograms and box plots ------------------------------------------\npdf(\"hist_box_match.pdf\", width = 10, height = 3)\npar(mar=c(4, 3.6, 0, 0), las = 1, mgp = c(2.7, 0.7, 0), \n    mfrow = c(1,6), \n    cex.lab = 1.5, cex.axis = 1.5)\n\nhistPlot(sym, xlab = \"(a)\", ylab = \"\", col = COL[1], axes = FALSE)\naxis(1, seq(50,70,10))\n\nhistPlot(uni, xlab = \"(b)\", ylab = \"\", col = COL[1], axes = FALSE)\naxis(1, seq(0,100,50))\n\nhistPlot(rs, xlab = \"(c)\", ylab = \"\", col = COL[1], axes = FALSE)\naxis(1, seq(0,6,2))\n\nboxPlot(rs, xlab = \"(1)\", ylab = \"\", col = COL[1,3])\nboxPlot(sym, xlab = \"(2)\", ylab = \"\", col = COL[1,3])\nboxPlot(uni, xlab = \"(3)\", ylab = \"\", col = COL[1,3])\n\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/hist_vs_box/hist_vs_box.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# generate data -----------------------------------------------------\nset.seed(12345)\nbimod = c(rnorm(300, mean = 5, sd = 1), \n          rnorm(300, mean = 12, sd = 1), \n          runif(25, min = 13, max = 28))\n\n# histogram and box plot --------------------------------------------\npdf(\"hist_vs_box.pdf\", height = 2.2, width = 8)\npar(mar = c(2, 2.8, 0.2, 0.5), las = 1, mgp = c(2.9, 0.7, 0),\n    cex.axis = 1.5, cex.lab = 1.5)\nlayout(matrix(1:2, 1), 2:1)\nhistPlot(bimod, xlab = \"\", ylab = \"\", yaxt = \"n\", col = COL[1])\npar(mar = c(2, 2.8, 0.2, 0))\nboxPlot(bimod, col = COL[1,2], xlim = c(0.4, 1.6))\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/income_coffee_shop/income_coffee_shop.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# generate data -----------------------------------------------------\nset.seed(956)\n\nsal_symmetric = rnorm(40, mean = 65000, sd = 2000)\nsal_skewed = c(sal_symmetric, 225000, 250000)\n\noptions(scipen=2)\n\n# histograms --------------------------------------------------------\npdf(\"income_coffee_shop.pdf\", 5.5, 4.3)\npar(mar = c(3.6, 2, 0.5, 1), las = 1, mgp = c(2.5, 0.7, 0), \n    mfrow = c(2,1), cex.lab = 1.5, cex.axis = 1)\n\nhistPlot(sal_symmetric, xlim = c(60000, 70000), \n         xlab = \"(1)\", ylim = c(0,12), col = COL[1], \n         axes = FALSE, ylab = \"\")\nAxisInDollars(1, at = seq(0, 1000000, 2500))\naxis(2, at = seq(0,12,4))\n\nhistPlot(sal_skewed, xlab = \"(2)\", ylim = c(0,12), \n         breaks = seq(0, 260000, by = 1000), col = COL[1], \n         axes = FALSE, xlim = c(60000,260000), ylab = \"\")\nAxisInDollars(1, at = seq(60000, 260000, 50000))\naxis(2, at = seq(0,12,4))\n\ndev.off()\n\n# summary stats -----------------------------------------------------\nlibrary(xtable)\n\nsummary_table = as.data.frame(cbind(summary(sal_symmetric), summary(sal_skewed)))\nnames(summary_table) = c(\"(1)\",\"(2)\")\nsummary_table = rbind(c(length(sal_symmetric), length(sal_skewed)), \n                      summary_table, c(sd(sal_symmetric), sd(sal_skewed)))\nrownames(summary_table)[1] = \"n\"\nrownames(summary_table)[dim(summary_table)[1]] = \"SD\"\n\nxtable(summary_table, digits = 0)"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/infant_mortality_rel_freq/infant_mortality.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(dplyr)\n\n# load data ---------------------------------------------------------\nload(\"factbook.rda\")\n# this dataset will also be available in the cia_factbook package\n# with the same name\n\n# calculate # of countries with life exp. & internet data -----------\ncia_factbook %>%\n  subset(!is.na(infant_mortality_rate)) %>%\n  nrow() # n = 224\n\n# histogram parameters ----------------------------------------------\nhisto = hist(cia_factbook$infant_mortality_rate, plot = FALSE)\nbreaks = histo$breaks\nwidth = breaks[2] - breaks[1]\ncounts = histo$counts\nn = sum(counts)\nrel_freqs = round(counts / n, 2)\n\nfive_perc = n * 0.05\n\n# rel. freq. histogram of infant mortality --------------------------\npdf(\"infant_mortality_rel_freq_hist.pdf\", 5.5, 3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nhist(cia_factbook$infant_mortality_rate, \n     main = \"\",\n     xlab = \"Infant Mortality (per 1000 Live Births)\",\n     ylab = \"Fraction of Countries\",\n     col = COL[1], axes = FALSE, ylim = c(0,five_perc*8))\naxis(1)\naxis(2, at = seq(0, 8 * five_perc, 2 * five_perc),\n     labels = seq(0, 0.4, 0.1))\naxis(2, at = seq(five_perc, 7 * five_perc, 2 * five_perc),\n     labels = rep(\"\", 4), tcl = -0.25)\nabline(h = seq(0, five_perc*8, five_perc), lty = 2, col = COL[6])\nhist(cia_factbook$infant_mortality_rate, \n     main = \"\", xlab = \"\", ylab = \"\",\n     col = COL[1], axes = FALSE, add = TRUE)\ndev.off()\n\n# rel. freq. histogram of infant mortality  - solution --------------\nsummary(cia_factbook$infant_mortality_rate)\n\npdf(\"infant_mortality_rel_freq_hist_soln.pdf\", 6, 3.2)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nhist(cia_factbook$infant_mortality_rate, \n     main = \"\",\n     xlab = \"Infant Mortality (per 1000 Live Births)\",\n     ylab = \"Fraction of Countries\",\n     col = COL[1], axes = FALSE, ylim = c(0,five_perc*8))\naxis(1)\naxis(2, at = seq(0, five_perc*8, five_perc), label = c(0, NA, 0.1, NA, 0.2, NA, 0.3, NA, 0.4))\nabline(h = seq(0, five_perc*8, five_perc), lty = 2, col = COL[6])\nhist(cia_factbook$infant_mortality_rate, \n     main = \"\", xlab = \"\", ylab = \"\",\n     col = COL[1], axes = FALSE, add = TRUE)\ntext(x = breaks[-1] - width/2, y = counts + 5, \n     labels = paste(rel_freqs),\n     col = COL[4], cex = 1)\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/mammal_life_spans/mammal_life_spans.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(mammals)\n\n# calculate # of countries with life exp. & internet data -----------\nnrow(mammals) # n = 62\n\n# scatterplot of gpa vs. study hours --------------------------------\npdf(\"mammal_life_spans_scatterplot.pdf\", 5.5, 4.3)\npar(mar = c(4, 4.1, 1, 1), las = 1, mgp = c(2.9, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nplot(mammals$LifeSpan ~ mammals$Gestation, \n     xlab = \"Gestation (days)\", ylab = \"Life Span (years)\", \n     pch = 20, col = COL[1], axes = FALSE)\naxis(1, at = seq(0, 600, 100), labels = c(0, NA, 200, NA, 400, NA, 600))\naxis(2, at = seq(0, 100, 25))\nbox()\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/marathon_winners/marathon_winners.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# load data ---------------------------------------------------------\ndata(marathon)\n\n# histogram and box plot of marathon finishing times of winners -----\npdf(\"marathon_winners_hist_box.pdf\", height = 2.2, width = 7)\n\npar(mar = c(2, 2.8, 0.5, 5), las = 1, mgp = c(2.9, 0.7, 0),\n    cex.axis = 1.5, cex.lab = 1.5)\nlayout(matrix(1:2, 1), 2:1)\nhistPlot(marathon$Time, col = COL[1], \n         xlab = \"Marathon times\", ylab = \"\", yaxt = \"n\", \n         axes = FALSE)\naxis(1, at = seq(2, 3.2, 0.4))\naxis(2, at = seq(0, 20, 10))\n\npar(mar = c(2, 2.8, 0.5, 0))\nboxPlot(marathon$Time, col = COL[1,2], ylim = c(2, 3.2),\n        ylab = \"Marathon times\",\n        axes = FALSE)\naxis(2, at = seq(2, 3.2, 0.4))\n\ndev.off()\n\n# finishing times vs. gender ----------------------------------------\npdf(\"marathon_winners_gender_box.pdf\", height = 1.5, width = 7)\n\npar(mar = c(2, 5.1, 0, 1), las = 1, mgp = c(2.5, 0.7, 0), \n    mfrow = c(1,1), cex.lab = 1.5, cex.axis = 1.5)\nboxPlot(marathon$Time, horiz = TRUE, fact = marathon$Gender, \n        xlim = c(2,3.2), ylim = c(0.5, 2.5),\n        axes = FALSE, col = COL[1,2])\naxis(1, at = seq(2,3.2,0.4))\naxis(2, at = c(1,2), labels = c(\"Women\", \"Men\"))\n\ndev.off()\n\n# times series by gender --------------------------------------------\npdf(\"marathon_winners_time_series.pdf\", height = 3, width = 9)\n\npar(mar = c(2, 4, 0.5, 1.3), las = 1, mgp = c(2.7, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nplot(marathon$Time[marathon$Gender == \"m\"] ~ marathon$Year[marathon$Gender == \"m\"],\n     xlab = \"Year\", ylab = \"Marathon times\", \n     pch = 19, col = COL[1], ylim = c(2, 3.2), axes = FALSE)\n\npoints(marathon$Time[marathon$Gender == \"f\"] ~ marathon$Year[marathon$Gender == \"f\"],\n       xlab = \"Year\", pch = 4, lwd = 1.7, col = COL[2])\naxis(1)\naxis(2, at = seq(2, 3.2, 0.4))\nlegend(\"topright\", inset = 0, pch = c(4, 19), col = c(COL[2], COL[1]), \n       legend = c(\"Women\", \"Men\"))\n\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/office_productivity/office_productivity.R",
    "content": "# set seed ------------------------------------------------\nset.seed(2406)\n\n# sketch --------------------------------------------------\npdf(\"office_productivity_sketch.pdf\", 5.5, 3)\npar(mar = c(1.5, 1.5, 0.5, 0.5), mgp = c(0.3, 0.7, 0), \n    mfrow = c(1,1), cex.lab = 1.5)\ncurve(rev(dgamma(x, 2.5,1/2)), 0, 14, \n      xlab = \"stress\", ylab = \"productivity\", lwd = 2, axes = FALSE)\nbox()\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/oscar_winners/oscar_winners.R",
    "content": "# load packages -----------------------------------------------------\nlibrary(openintro)\nlibrary(forcats)\n\n# load data ---------------------------------------------------------\ndata(oscars)\n\n# plot of oscar winner women and men ages ---------------------------\nmyPDF(\"oscars_winners_hist.pdf\", 5, 3.15)\noscars %>%\n  ggplot(aes(x = age)) +\n    geom_histogram(binwidth = 10, fill = COL[1,1], color = COL[5,1], size = 0.3) +\n    facet_wrap(~fct_rev(award), ncol = 1) +\n    theme_minimal() +\n    theme(strip.text = element_text(hjust = 0)) +\n    labs(x = \"Age (in years)\", y = \"\")\ndev.off()\n\n# summary stats -----------------------------------------------------\noscars %>%\n  group_by(award) %>%\n  summarise(\n    mean = mean(age),\n    sd = sd(age),\n    n = n()\n  )\n"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/raise_taxes_mosaic/raise_taxes_mosaic.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# create data -------------------------------------------------------\n# based on http://www.publicpolicypolling.com/pdf/2015/PPP_Release_National_30215.pdf\n\nn = 691\n\nn_dem = round(n * 0.40)\nn_rep = round(n * 0.34)\nn_indep = 691 - (n_dem + n_rep)\n\nparty =  c(rep(\"Democrat\", n_dem), rep(\"Republican\", n_rep), rep(\"Indep / Other\", n_indep))\nparty = factor(party, levels = c(\"Democrat\", \"Republican\", \"Indep / Other\"))\ntaxes = c(rep(\"Raise taxes on the rich\", round(n_dem * 0.91)), \n          rep(\"Raise taxes on the poor\", round(n_dem * 0.04)), \n          rep(\"Not sure\", round(n_dem * 0.05)),\n          rep(\"Raise taxes on the rich\", round(n_rep * 0.47)), \n          rep(\"Raise taxes on the poor\", round(n_rep * 0.10)), \n          rep(\"Not sure\", round(n_rep * 0.43)),\n          rep(\"Raise taxes on the rich\", round(n_indep * 0.49)), \n          rep(\"Raise taxes on the poor\", round(n_indep * 0.11)), \n          rep(\"Not sure\", round(n_indep * 0.40)) \n)\ntaxes = factor(taxes, levels = c(\"Raise taxes on the rich\", \"Raise taxes on the poor\", \"Not sure\"))\n\n\n# mosaicplot --------------------------------------------------------\n\npdf(\"raise_taxes_mosaic.pdf\", 7, 3)\npar(mar=c(0.5,0,0.2,0.5), las=1, mgp=c(4,1,0))\n\nmosaicplot(party ~ taxes, main = \"\", cex.axis = 1.1, \n           xlab = \"\", ylab = \"\", color = COL[1])\n\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/randomization_avandia/randomization_avandia.R",
    "content": "# load openintro package for colors -----------------------\nlibrary(openintro)\n\n# create data ---------------------------------------------\ngr <- c(rep(\"rosig\", 67593), rep(\"piog\",159978))\nout <- c(rep(c(\"y\", \"n\"), c(2593, 67593-2593)), \n         rep(c(\"y\", \"n\"), c(5386, 159978-5386)))\n\nset.seed(13)\nN <- 10^2\nrand_dist <- rep(NA, N)\nfor(i in 1:N){\n  rand_group <- sample(gr)\n  rand_dist[i] <- sum(out[rand_group == \"rosig\"] == \"y\")\n}\n\n# plot randomization distribution -----------------------------------\npdf(\"randomization_avandia.pdf\", 6, 4)\npar(mar = c(4,2.7,0,0), las = 1 , mgp = c(2.7, 0.9, 0), \n    cex.lab = 1.5, cex.axis = 1.5)\nhistPlot(rand_dist, main=\"\", \n         xlab = \"Simulated rosiglitazone cardiovascular events\", ylab=\"\", \n         col = COL[1], axes = FALSE)\naxis(1, at = seq(2250, 2550, 100))\naxis(2, at = (0:4)*N/20, labels = c(0, NA, 2, NA, 4)/20)\nabline(h = 0)\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/randomization_heart_transplants/randomization_heart_transplants.R",
    "content": "library(openintro)\nheartTr <- heart_transplant\n\n# mosaic plot -------------------------------------------------------\npdf(\"randomization_heart_transplants_mosaic.pdf\", 5.5, 4.3)\npar(mar = c(0, 0, 0, 0), las = 1, mgp = c(2.7, 0.9, 0))\nmosaicplot(transplant ~ survived, data = heartTr, \n           main = \"\", xlab = \"\", ylab = \"\", color = COL[1],\n           cex.axis = 1.25)\ndev.off()\n\n# box plot ----------------------------------------------------------\npdf(\"randomization_heart_transplants_box.pdf\", 5.5, 4.3)\npar(mar = c(2, 4.8, 0, 0), las = 1, mgp = c(3.5, 0.7, 0), \n    cex.lab = 1.5, cex.axis = 1.25)\nboxPlot(heartTr$survtime, fact = heartTr$transplant, \n        ylab = \"Survival Time (days)\", col = COL[1,2])\ndev.off()\n\n# randomization -----------------------------------------------------\nload(\"inference.RData\")\n\ndiffs = inference(heartTr$survived, heartTr$transplant, \n                  success = \"dead\", order = c(\"treatment\",\"control\"), \n                  est = \"proportion\", type = \"ht\", method = \"simulation\", \n                  nsim = 100, null = 0, alternative = \"twosided\", simdist = TRUE,\n                  seed = 95632)\n\n# plot randomization distribution -----------------------------------\npdf(\"randomization_heart_transplants_rando.pdf\", height = 3, width = 7)\n\npar(mar = c(3.6, 2.2, 1, 1), las = 1, mgp = c(2.5, 0.7, 0), \n    cex.axis = 1.25, cex.lab = 1.5)\n\nvalues  <- table(diffs)\nplot(diffs, type = \"n\", xlim = c(-0.25, 0.25), \n     xlab = \"simulated differences in proportions\", \n     ylab = \"\", axes = FALSE, ylim = c(0, max(values)))\naxis(1, at = seq(-0.25, 0.25, 0.05), \n     labels = c(-0.25, NA,-0.15, NA,-0.05, NA, 0.05, NA, 0.15, NA, 0.25))\nfor(i in 1:length(diffs)){\n  x   <- diffs[i]\n  rec <- sum(diffs == x)\n  points(rep(x, rec), 1:rec, pch = 20, cex = 0.8, col = COL[1])\n}\n\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/reproducing_bacteria/reproducing_bacteria.R",
    "content": "# set seed ------------------------------------------------\nset.seed(2406)\n\n# sketch --------------------------------------------------\npdf(\"reproducing_bacteria_sketch.pdf\", 5.5, 3)\npar(mar = c(1.5, 1.5, 0.5, 0.5), mgp = c(0.3, 0.7, 0), \n    mfrow = c(1,1), cex.lab = 1.5)\ncurve(-1*dexp(x, rate = 4), lwd = 2,\n      xlab = \"time\", ylab = \"number of bacteria cells\", axes = FALSE)\nbox()\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/eoce/stats_scores_box/stats_scores_box.R",
    "content": "# load openintro package for colors ---------------------------------\nlibrary(openintro)\n\n# data --------------------------------------------------------------\nstats_scores = c(79, 83, 57, 82, 94, 83, 72, 74, 73, 71, 66, 89, 78, \n                 81, 78, 81, 88, 69, 77, 79)\n\n# summary -----------------------------------------------------------\nsummary(stats_scores)\n\n# scatterplot of gpa vs. study hours --------------------------------\npdf(\"stats_scores_boxplot.pdf\", 5.5, 2)\npar(mar = c(3, 0.5, 0.5, 0.5), las = 1, mgp = c(1.75, 0.7, 0), \n    cex.axis = 1.5, cex.lab = 1.5)\nboxplot(stats_scores, horizontal = TRUE, col = COL[1], xlab = \"Scores\")\ndev.off()"
  },
  {
    "path": "ch_summarizing_data/figures/histMLBSalaries/histMLBSalaries.R",
    "content": "library(openintro)\ndata(MLB)\ndata(COL)\n\nmyPDF(\"histMLBSalariesReg.pdf\",\n      4,\n      3,\n      mar = c(3.4, 2.4, 0.5, 0.5),\n      mgp = c(2.1, 0.5, 0))\nhist(MLB$salary/1000,\n     breaks = 15,\n     main = \"\",\n     xlab = \"Salary (millions of dollars)\",\n     ylab = \"\",\n     axes = FALSE,\n     col = COL[1])\naxis(1, seq(0, 40, 10))\naxis(2, c(0, 500))\naxis(2,\n     seq(100, 400, 100),\n     rep(\"\", 4),\n     tcl = -0.2)\ndev.off()\n\nmyPDF(\"histMLBSalariesLog.pdf\",\n      4,\n      3,\n      mar = c(3.4, 2.4, 0.5, 0.5),\n      mgp = c(2.2, 0.5, 0))\nexpr <- expression(log[e]*\"(Salary), where Salary is in millions USD\")\nhist(log(MLB$salary/1000),\n     main = \"\",\n     breaks = 15,\n     xlab = expr,\n     axes = FALSE,\n     ylab = \"\",\n     col = COL[1])\naxis(1)\naxis(2, seq(0, 300, 100))\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan50IncomeHist/loan50IncomeHist.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\n\nx <- loan50$total_income\nH <- hist(x,\n          breaks = 12,\n          plot = FALSE)\ncounts <- rbind(H$counts)\nfrom   <- head(H$breaks, -1)\nto     <- tail(H$breaks, -1)\ncolnames(counts) <- paste(from, 'to', to)\nrequire(xtable)\nxtable(counts)\n\nmyPDF(\"loan50IncomeHist.pdf\",\n      6.05, 3.1,\n      mar = c(3.5, 3.5, 0.5, 1),\n      mgp = c(2.4, 0.7, 0))\nhistPlot(x,\n         breaks = seq(0, 350e3, 25e3),\n         # breaks = seq(0, 40000, 5000),\n         xlab = 'Total Income',\n         ylab = \"Frequency\",\n         # ylim = c(0, 20),\n         col = COL[1],\n         border = COL[5],\n         axes = FALSE)\nbin <- table(round(x / 2000) * 2000)\nfor (i in 1:length(bin)) {\n  # points(rep(as.numeric(names(bin)[i]), bin[i]), 1:(bin[i]), cex = 2)\n}\naxis(2)\nAxisInDollars(1, pretty(x))\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan50IntRateHist/loan50IntRateHist.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\n\nx <- loan50$interest_rate\nH <- hist(x,\n          breaks = 12,\n          plot = FALSE)\ncounts <- rbind(H$counts)\nfrom   <- head(H$breaks, -1)\nto     <- tail(H$breaks, -1)\ncolnames(counts) <- paste(from, 'to', to)\nrequire(xtable)\nxtable(counts)\n\nmyPDF(\"loan50IntRateHist.pdf\",\n      6.05, 3.1,\n      mar = c(3.5, 3.5, 0.5, 1),\n      mgp = c(2.4, 0.7, 0))\nhistPlot(x,\n         breaks = seq(5, 27.5, 2.5),\n         # breaks = seq(0, 350e3, 25e3),\n         # breaks = seq(0, 350e3, 25e3),\n         # breaks = seq(0, 40000, 5000),\n         xlab = 'Interest Rate',\n         ylab = \"Frequency\",\n         # ylim = c(0, 20),\n         col = COL[1],\n         border = COL[5],\n         axes = FALSE)\nbin <- table(round(x / 2000) * 2000)\nfor (i in 1:length(bin)) {\n  # points(rep(as.numeric(names(bin)[i]), bin[i]), 1:(bin[i]), cex = 2)\n}\naxis(2)\nAxisInPercent(1, pretty(x))\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan50LoanAmountHist/loan50LoanAmountHist.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\n\nx <- loan50$loan_amount\nH <- hist(x,\n          breaks = 12,\n          plot = FALSE)\ncounts <- rbind(H$counts)\nfrom   <- head(H$breaks, -1)\nto     <- tail(H$breaks, -1)\ncolnames(counts) <- paste(from, 'to', to)\nrequire(xtable)\nxtable(counts)\n\nmyPDF(\"loan50LoanAmountHist.pdf\",\n      6.05, 3.1,\n      mar = c(3.5, 3.5, 0.5, 1),\n      mgp = c(2.4, 0.7, 0))\nhistPlot(x,\n         breaks = seq(0, 40000, 5000),\n         xlab = 'Loan Amount',\n         ylab = \"Frequency\",\n         # ylim = c(0, 20),\n         col = COL[1],\n         border = COL[5],\n         axes = FALSE)\nbin <- table(round(x / 2000) * 2000)\nfor (i in 1:length(bin)) {\n  # points(rep(as.numeric(names(bin)[i]), bin[i]), 1:(bin[i]), cex = 2)\n}\naxis(2)\nAxisInDollars(1, pretty(x))\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan50_amt_vs_income/loan50_amt_vs_income.R",
    "content": "library(openintro)\ndata(loan50)\ndata(COL)\n\nd <- loan50\n\nmyPDF(\"loan50_amt_vs_income.pdf\",\n      6, 3.5,\n      mar = c(3.4, 4.1, 0.5, 0.5),\n      mgp = c(2.1, 0.5, 0),\n      xaxs = \"i\", yaxs = \"i\")\nx <- d$total_income\ny <- d$loan_amount\nplot(x, y, type = \"n\",\n     xlim = c(0, 1.05 * max(x)),\n     ylim = c(0, 1.05 * max(y)),\n     xlab = \"Total Income\",\n     ylab = \"\",\n     axes = FALSE)\nabline(h = pretty(c(0, y)), v = pretty(c(0, x)), col = COL[7, 3])\npoints(x, y, pch = 19, col = COL[1, 2])\nAxisInDollars(1, pretty(c(0, x)))\nAxisInDollars(2, pretty(c(0, y)))\nmtext(\"Loan Amount\", 2, 3, las = 0)\n\nx. <- seq(min(x), max(x), length.out = 300)\nm <- lm(y ~ log(x))\ny. <- predict(m, newdata = data.frame(x = x.))\n# lines(x., y., lty = 2, col = COL[5, 3])\ndev.off()\n\n# library(ggplot2); qplot(x, y, geom = c(\"point\", \"smooth\"))\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan50_amt_vs_interest/loan50_amt_vs_interest.R",
    "content": "library(openintro)\ndata(loan50)\ndata(COL)\n\nd <- loan50\n\nmyPDF(\"loan50_amt_vs_interest.pdf\",\n      6, 3.5,\n      mar = c(3.4, 4.1, 0.5, 0.5),\n      mgp = c(2.1, 0.5, 0),\n      xaxs = \"i\", yaxs = \"i\")\nx <- d$loan_amount\ny <- d$interest_rate\nplot(x, y,\n     xlim = c(0, 1.05 * max(x)),\n     ylim = c(0, 1.05 * max(y)),\n     xlab = \"Loan Amount\",\n     ylab = \"\",\n     axes = FALSE,\n     pch = 19,\n     col = COL[1, 2])\nAxisInDollars(1, pretty(c(0, x)))\nAxisInPercent(2, pretty(c(0, y)))\nmtext(\"Interest Rate\", 2, 3, las = 0)\ndev.off()\n\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan_amount_dot_plot/loan_amount_dot_plot.R",
    "content": "library(openintro)\n\nmyPDF(\"loan_amount_dot_plot.pdf\",\n      5.5,\n      1.25,\n      mar = c(3.6, 1, 0, 1),\n      mgp = c(2.5, 0.7, 0),\n      tcl = -0.4)\nd <- loan50$loan_amount\ndotPlot(d,\n        at = 1.007,\n        xlab = 'Loan Amount',\n        ylab = '',\n        pch = 20,\n        col = COL[1, 3],\n        cex = 3, # 1.5,\n        xlim = c(0, 1.05 * max(d)),\n        ylim = c(0.95, 1.05),\n        axes = FALSE)\nabline(h = 0.983)\nAxisInDollars(1, pretty(c(0, d)))\nM <- mean(d)\npolygon(M + c(-1, 1, 0) * 1500,\n        c(0.95, 0.95, 0.98),\n        border = COL[4],\n        col = COL[4])\ndev.off()\n\n\n\nset.seed(10)\nmyPDF(\"loan_amount_dot_plot_stacked.pdf\",\n      5.5,\n      2.5,\n      mar = c(3.6, 1, 0.5, 1),\n      mgp = c(2.5, 0.7, 0))\nround.to <- 2000\nbinned <- round.to * round(d / round.to)\ntab <- table(binned)\ncex    <- 1\nplot(0,\n     type = \"n\",\n     xlab = \"Loan Amount, Rounded to Nearest $1000\",\n     ylab = \"\",\n     axes = FALSE,\n     xlim = c(0, 1.05 * max(d)),\n     ylim = c(-1, 1.5 * max(tab)))\nfor (i in 1:length(tab)) {\n  points(rep(as.numeric(names(tab[i])), tab[i]),\n         1.5 * (1:tab[i]) - 0.4,\n         pch = 19,\n         col = COL[1],\n         cex = 2 * cex)\n}\nabline(h = 0)\nAxisInDollars(1, pretty(c(0, d)))\npolygon(M + c(-1, 1, 0) * 1500,\n        c(-1.2, -1.2, -0.1),\n        border = COL[4],\n        col = COL[4])\ndev.off()\n\nM\nsd(d)\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan_app_type_home_mosaic_plot/loan_app_type_home_mosaic_plot.R",
    "content": "if (\"loans_full_schema\" %in% ls()) {\n  rm(loans_full_schema)\n}\nlibrary(openintro)\n\n# There are some levels for the factor variables below that don't\n# have any observations, so they create zeros and break the visuals.\n# The next lines address that while ensuring a consistent order of\n# the levels for the plots.\napplication_type_order <- c(\"individual\", \"joint\")\nloans_full_schema$application_type <- factor(\n  as.character(loans_full_schema$application_type),\n  levels = application_type_order\n)\nhomeownership_order <- c(\"rent\", \"mortgage\", \"own\")\nloans_full_schema$homeownership <- factor(\n  tolower(as.character(loans_full_schema$homeownership)),\n  levels = homeownership_order\n)\n\ntab <- table(loans_full_schema[,c('application_type', 'homeownership')])\ntab  <- t(tab)\n\nrp <- prop.table(tab, 1)\ncp <- prop.table(tab, 2)\n\nmyPDF(\"loan_home_mosaic.pdf\",\n      2.625,\n      2.25,\n      mar = rep(1, 4) / 4)\nmosaicplot(rowSums(tab),\n           main = '',\n           xlab = '',\n           ylab = '',\n           off = 4,\n           col = COL[c(2,1,4)])\ndev.off()\n# colnames(tab)[1] <- \"not\\nspam\"\n\nmyPDF(\"loan_app_type_home_mosaic.pdf\",\n      3,\n      2.25,\n      mar = c(0.25, 2, 1, 1))\nlevels(loans_full_schema$application_type)[1] <- \"indiv.\"\nMosaicPlot(homeownership ~ application_type, loans_full_schema,\n           col = COL[c(2, 1, 4)],\n           off = 0.02)\ndev.off()\n\nmyPDF(\"loan_app_type_home_mosaic_rev.pdf\",\n      3 / 1.2,\n      2.25 / 1.5,\n      mar = rep(1, 4) / 4)\n# colnames(tab)[1] <- \"not spam\"\nmosaicplot(t(tab),\n           main = '',\n           xlab = '',\n           ylab = '',\n           col = COL[c(2, 1, 4)])\ndev.off()\n\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan_app_type_home_seg_bar/loan_app_type_home_seg_bar.R",
    "content": "library(openintro)\n\ntab <- table(loans_full_schema[, c(\"application_type\", \"homeownership\")])\ntab <- tab[\n    c(\"individual\", \"joint\"),\n    c(\"RENT\", \"MORTGAGE\", \"OWN\")]\ntab <- t(tab)\nrownames(tab) <- tolower(rownames(tab))\n\nrp <- prop.table(tab, 1)\ncp <- prop.table(tab, 2)\n\nmyPDF(\"loan_app_type_home_seg_bar.pdf\",\n    4.5, 3.5,\n    mar = c(2, 4, 0.5, 0.5),\n    mgp = c(2.2, 0.6, 0))\nylim <- c(0, max(apply(tab, 1, sum)))\nbarplot(apply(tab, 1, sum),\n    col = COL[3],\n    ylim = ylim)\ntabTemp <- tab[,1]\nnames(tabTemp) <- NULL\nbarplot(tabTemp,\n    col = COL[1],\n    add = TRUE,\n    axes = FALSE)\nabline(h = 0)\nlegend(\"topright\",\n    fill = COL[c(3,1)],\n    legend = c(\"joint\", \"individual\"))\npar(las = 0)\nmtext(\"Frequency\", 2, 2.9)\ndev.off()\n\nmyPDF(\"loan_app_type_home_sbs_bar.pdf\",\n    4.5, 3.5,\n    mar = c(2, 4, 0.5, 0.5),\n    mgp = c(2.2, 0.6, 0))\nbarplot(t(tab),\n    ylim = ylim,\n    col = COL[c(1, 3)], beside = TRUE)\nabline(h = 0)\nlegend(\"topright\",\n    fill = COL[c(3,1)],\n    legend = c(\"joint\", \"individual\"))\npar(las = 0)\nmtext(\"Frequency\", 2, 2.9)\ndev.off()\n\nmyPDF(\"loan_app_type_home_seg_bar_standardized.pdf\",\n    5, 3.5,\n    mar = c(2, 4, 0.5, 0.5),\n    mgp = c(2.2, 0.6, 0))\nbarplot(apply(tab, 1, sum) / apply(tab, 1, sum), col = COL[3])\ntabTemp <- rp[, 1]\nnames(tabTemp) <- NULL\nbarplot(tabTemp,\n    col = COL[1],\n    add = TRUE,\n    axes = FALSE)\nlegend(2.65, 0.3,\n    fill = COL[c(3,1)],\n    legend = c(\"joint\", \"individual\"),\n    bg = \"white\")\nabline(h = 0)\npar(las = 0)\nmtext(\"Proportion\", 2, 2.9)\ndev.off()\n\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan_homeownership_bar_plot/loan_homeownership_bar_plot.R",
    "content": "require(openintro)\n\nx <- loans_full_schema$homeownership\n\nmyPDF('loan_homeownership_bar_plot.pdf',\n      7,\n      3,\n      mar = c(3.6, 4.2, 1, 1.5),\n      mgp = c(3.2, 0.55, 0),\n      mfrow = 1:2)\nt <- table(x)\nnames(t) <- tolower(names(t))\nbarplot(t,\n        axes = TRUE,\n        xlab = '',\n        ylab = 'Frequency',\n        main = '',\n        # ylim = c(0,2700),\n        col = COL[1])\nabline(h = 0)\nmtext(\"Homeownership\", 1, 2.4)\n\npar(mar = c(3.6, 4.7, 1, 1))\nbarplot(t / sum(t),\n        axes = FALSE,\n        xlab = '',\n        ylab = '',\n        main = '',\n        # ylim = c(0, 2700) / sum(t),\n        col = COL[1])\n# at <- seq(0, 0.6, 0.2)\naxis(2)\n# AxisInPercent(2, at = seq(0, 40, 10))\npar(las = 0)\nmtext('Proportion', side = 2, line = 2.7)\nmtext(\"Homeownership\", 1, 2.4)\nabline(h = 0)\ndev.off()\n\ntable(x)\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan_homeownership_pie_chart/loan_homeownership_pie_chart.R",
    "content": "library(openintro)\ndata(email)\ndata(COL)\n\ntab <- table(loans_full_schema$homeownership)\n\nmyPDF(\"loan_homeownership_pie_chart.pdf\",\n      7.5,\n      4,\n      mar = c(0, 2, 0, 0.5),\n      mgp = c(2.4, 0.5, 0))\nlayout(matrix(1:2, 1), c(1, 1.1))\npie(tab, col = COL[c(2, 1, 4)], radius = 0.75)\n\npar(mar = c(3.6, 5.2, 1, 1))\nbarplot(tab,\n        axes = FALSE,\n        xlab = 'Homeownership',\n        ylab = '',\n        main = '',\n        col = COL[c(2, 1, 4)])\naxis(2) #, at = seq(0, 4000, 1000), labels = c(0, paste0(1:4, \"k\")))\nabline(h = 0)\npar(las = 0)\nmtext(\"Frequency\", 2, line = 2.9)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan_int_rate_box_plot_layout/loan_int_rate_box_plot_layout.R",
    "content": "require(openintro)\ndata(COL)\nd <- loan50$interest_rate\nthe.seed <- 2\n\nmyPDF(\"loan_int_rate_box_plot_layout.pdf\", 5.5, 3.8,\n      mar = c(0, 4, 0, 1),\n      mgp = c(2.8, 0.55, 0))\nboxPlot(d,\n        ylab = 'Interest Rate',\n        xlim = c(0.3, 3),\n        axes = FALSE,\n        ylim = range(d) + sd(d) * c(-1,1) * 0.2)\nAxisInPercent(2, c(0, pretty(d)))\n\narrows(2, min(d) + 1, 1.35, min(d), length = 0.08)\ntext(2, min(d) + 1, 'lower whisker', pos = 4)\n\narrows(2, quantile(d, 0.25) + sd(d) / 7,\n       1.35, quantile(d, 0.25),\n       length = 0.08)\ntext(2, quantile(d, 0.25) + sd(d)/6.5,\n     expression(Q[1]~~'(first quartile)'), pos = 4)\n\nm <- median(d)\narrows(2, m + sd(d) / 5, 1.35, m, length = 0.08)\ntext(2,m + sd(d) / 4.7, 'median', pos = 4)\n\nq <- quantile(d, 0.75)\narrows(2, q + sd(d) / 4, 1.35, q, length = 0.08)\ntext(2, q + sd(d) / 3.8,\n     expression(Q[3]~~'(third quartile)'), pos = 4)\n\narrows(2, rev(sort(d))[3] - sd(d) / 4,\n       1.35, rev(sort(d))[3], length = 0.08)\ntext(2, rev(sort(d))[3] - sd(d) / 3.8,\n     'upper whisker', pos = 4)\n\ny <- quantile(d, 0.75) + 1.5 * IQR(d)\narrows(2, y - 0.1 * sd(d),\n       1.35, y, length = 0.08)\nlines(c(0.72, 1.28), rep(y, 2),\n      lty = 3, col = '#00000066')\ntext(2, y - 0.1 * sd(d),\n     'max whisker reach', pos = 4)\n\nm <- rev(tail(sort(d), 5))\ns <- m[1] - 0.3 * sd(m)\narrows(2, s, 1.1, m[1] - 0.2, length = 0.08)\narrows(2, s, 1.1, m[2] + 0.3, length = 0.08)\ntext(2, s, 'suspected outliers', pos = 4)\n\nset.seed(the.seed)\npt.jitter <- 0.05\nnco <- 50\ncutoffs <- seq(0.9 * min(d), max(d), length.out = nco)\nfor (i in 2:nco) {\n  these <- which(cutoffs[i - 1] < d & d <= cutoffs[i])\n  lt <- length(these)\n  if (lt == 0) {\n    next\n  }\n  x <- pt.jitter * (1:lt)\n  x <- x - mean(x)\n  points(rep(0.4, lt) + x, d[these],\n      col = rep(COL[1, 3], 25), pch = 19)\n}\n\ndev.off()\n\nsort(d)[25:26]\nquantile(d, c(0.25, 0.5, 0.75))\ntail(sort(d), 4)\n\n\nmyPDF(\"loan_int_rate_box_plot_layout_small.pdf\", 1.5, 2.5,\n      mar = c(0, 4.1, 0, 0),\n      mgp = c(2.3, 0.45, 0),\n      tcl = -0.2)\nboxPlot(d,\n        ylab = '',\n        axes = FALSE,\n        xlim = c(0.5, 1.45),\n        ylim = range(d) + sd(d) * c(-1,1) * 0.2)\nAxisInPercent(2, c(0, pretty(d)), cex = 1.1)\npar(las = 0)\nmtext(\"Interest Rate\", 2,\n      line = 2.5,\n      cex = 1.1)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan_int_rate_dot_plot/loan_int_rate_dot_plot.R",
    "content": "library(openintro)\n\nd <- loan50$interest_rate\nxlim <- c(0.9 * min(d), 1.05 * max(d))\n\nmyPDF(\"loan_int_rate_dot_plot.pdf\",\n      5.5,\n      1.25,\n      mar = c(3.6, 1, 0, 1),\n      mgp = c(2.5, 0.7, 0),\n      tcl = -0.4)\ndotPlot(d,\n        at = 1.007,\n        xlab = 'Interest Rate',\n        ylab = '',\n        pch = 20,\n        col = COL[1, 3],\n        cex = 3, # 1.5,\n        xlim = xlim,\n        ylim = c(0.95, 1.05),\n        axes = FALSE)\nabline(h = 0.983)\nAxisInPercent(1, pretty(c(0, d)))\nM <- mean(d)\npolygon(M + c(-1, 1, 0) * 1,\n        c(0.95, 0.95, 0.98),\n        border = COL[4],\n        col = COL[4])\ndev.off()\n\n\n\nset.seed(10)\nmyPDF(\"loan_int_rate_dot_plot_stacked.pdf\",\n      5.5,\n      2.5,\n      mar = c(3.6, 1, 0.5, 1),\n      mgp = c(2.5, 0.7, 0))\nround.to <- 1\nbinned <- round.to * round(d / round.to)\ntab <- table(binned)\ncex    <- 1\nplot(0,\n     type = \"n\",\n     xlab = \"Interest Rate, Rounded to Nearest Percent\",\n     ylab = \"\",\n     axes = FALSE,\n     xlim = xlim,\n     ylim = c(-1, 1.5 * max(tab)))\nfor (i in 1:length(tab)) {\n  points(rep(as.numeric(names(tab[i])), tab[i]),\n         1.5 * (1:tab[i]) - 0.4,\n         pch = 19,\n         col = COL[1],\n         cex = 2 * cex)\n}\nabline(h = 0)\nAxisInPercent(1, pretty(c(0, d)))\npolygon(M + c(-1, 1, 0) * 1,\n        c(-1.2, -1.2, -0.1),\n        border = COL[4],\n        col = COL[4])\ndev.off()\n\nM\nsd(d)\n"
  },
  {
    "path": "ch_summarizing_data/figures/loan_int_rate_robust_ex/loan_int_rate_robust_ex.R",
    "content": "library(openintro)\ndata(COL)\n\nset.seed(16)\n\nRetrieveOffsets <- function(d, jitter = 0.1, num_buckets = 70) {\n  cutoffs <- seq(0.9 * min(d), max(d), length.out = num_buckets)\n  x <- rep(NA, length(d))\n  for (i in 2:num_buckets) {\n    these <- which(cutoffs[i - 1] < d & d <= cutoffs[i])\n    lt <- length(these)\n    if (lt == 0) {\n      next\n    }\n    x[these] <- jitter * ((1:lt) - (lt + 1) / 2)\n  }\n  return(x)\n}\n\n\np1 <- loan50$interest_rate\ny1 <- 3 + RetrieveOffsets(p1)\np2 <- p1\np2[which.max(p2)] <- 15\ny2 <- 2 + RetrieveOffsets(p2, num_buckets = 50)\np3 <- p1\np3[which.max(p1)] <- 35\ny3 <- 1 + RetrieveOffsets(p3)\nn1 <- length(p1)\n\nmyPDF(\"loan_int_rate_robust_ex.pdf\",\n      7.04,\n      1.7,\n      mar = c(2.45, 0, 0, 0),\n      mgp = c(1.35, 0.25, 0),\n      cex.lab = 0.85)\n\nplot(p1, y1,\n    xlab = 'Interest Rate',\n    ylab = '',\n    pch = 20,\n    col = COL[1,3],\n    xlim = c(1, max(p1, p2, p3)),\n    ylim = c(0.6, 3.4),\n    axes = FALSE)\npoints(max(p1), y1[which.max(p1)],\n    col = COL[4])\n\nat <- seq(5, 100, 5)\nAxisInPercent(1, at, cex.axis = 0.8)\ntext(5, 3, 'Original', pos = 2, cex = 0.8)\n\n# y2 <- rep(2, n1) + rnorm(n1, sd = jitter)\ny2[p2 == 15] <- 2.15\npoints(p2, y2,\n    pch = 20, col = COL[1, 3])\npoints(15, y2[p2 == 15],\n    col = COL[4])\ntext(5, 2,\n     '26.3% to 15%',\n     pos = 2,\n     cex = 0.8)\n\n# y3 <- rep(1, n1) + rnorm(n1, sd = jitter)\npoints(p3, y3,\n    pch = 20, col = COL[1, 3])\npoints(35, y3[p3 == 35],\n    col = COL[4])\ntext(5, 1,\n     '26.3% to 35%',\n     pos = 2,\n     cex = 0.8)\n\ndev.off()\n\n\n# _____ Summary Statistics _____ #\nGetSummaries <- function(p) {\n  temp <- round(quantile(p, c(0.25, 0.5, 0.75)), 3)\n  hold <- temp[3] - temp[1]\n  names(hold) <- NULL\n  return(c(temp,\n           IQR = hold,\n           mean = mean(p),\n           sd = sd(p)))\n}\nGetSummaries(p1)\nGetSummaries(p2)\nGetSummaries(p3)\n"
  },
  {
    "path": "ch_summarizing_data/figures/malaria_rand_dot_plot/malaria_rand_dot_plot.R",
    "content": "library(openintro)\nlibrary(dplyr)\n\nset.seed(3)\n\nexp_gp  <- rep(c(\"vaccine\", \"placebo\"), c(14, 6))\noutcome <- c(rep(c('infection', 'no infection'), c(5, 9)),\n             rep(c('infection', 'no infection'), c(6, 0)))\n\nnsim    <- 100\nn       <- length(exp_gp)\nsuccess <- \"infection\"\n\nSimulateTable <- function(exp_gp, outcome, ...) {\n  table(sample(exp_gp), outcome)\n}\n# SimulateTable(exp_gp, outcome)\n\nsim_tables <-\n    lapply(1:nsim, SimulateTable,\n        exp_gp = exp_gp,\n        outcome = outcome)\nresult <- sim_tables %>%\n    lapply(function(x) {\n    \t  x[1, 1] / sum(x[1, ]) - x[2, 1] / sum(x[2, ])\n    \t}) %>%\n    \tunlist()\nsim_tables[1:5]\nresult[1:5]\n\npval <- sum(result >= 0.64) / nsim\nvalues <- table(result)\ndiffs <- unique(result)\n\nX <- c()\nY <- c()\nfor (i in 1:length(diffs)) {\n  x   <- diffs[i]\n  rec <- sum(result == x)\n  X   <- append(X, rep(x, rec))\n  Y   <- append(Y, 1:rec)\n}\n\n\nmyPDF('malaria_rand_dot_plot.pdf', 6, 3.5,\n      mar = c(3.4, 0.5, 0.5, 0.5),\n      mgp = c(2.35, 0.6, 0))\nplot(X, Y,\n     xlim = range(diffs) + c(-1, 1) * sd(diffs) / 4,\n     xlab = \"Difference in Infection Rates\",\n     axes = FALSE,\n     ylim = c(0, max(Y)),\n     col = COL[1],\n     pch = 20)\n# at <- seq(-0.4, 0.4, 0.1)\n# labels <- c(-0.4, \"\", -0.2, \"\", 0, \"\", 0.2, \"\", 0.4)\naxis(1) #, at, labels)\nabline(h = 0)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/medianHHIncomePoverty/medianHHIncomePoverty.R",
    "content": "library(openintro)\nlibrary(splines)\n\nind <- 1088\n\nmyPDF(\"medianHHIncomePoverty.pdf\", 6, 3.5,\n      mar = c(3, 4.7, 0.5, 1),\n      mgp = c(2.4, 0.5, 0))\nx <- county$poverty\ny <- county$median_hh_income\nplot(x, y, type = \"n\",\n     xlim = c(0, max(x, na.rm = TRUE)),\n     ylim = c(0, max(y, na.rm = TRUE)),\n     xlab = \"\",\n     ylab = \"\",\n     axes = FALSE)\nabline(h = pretty(c(0, y)), v = pretty(c(0, x)), col = COL[7, 3])\npoints(x, y, pch = 20, cex = 0.7, col = COL[1, 3])\nAxisInPercent(1, pretty(c(0, x)))\nAxisInDollars(2, pretty(c(0, y)))\nbox()\npoints(x, y, pch = \".\", col = COL[5, 4])\nmtext(\"Poverty Rate (Percent)\", 1, 1.9)\npar(las = 0)\nmtext(\"Median Household Income\", 2, 3.5)\nt1 <- x[ind]\nt2 <- y[ind]\n# lines(c(t1, t1), c(-1e5, t2), lty = 2, col = COL[4])\n# lines(c(-1e5, t1), c(t2, t2), lty = 2, col = COL[4])\n# points(t1, t2, col = COL[4])\nmy_exp <- 1.2\n(m <- lm(y ~ I(1 / x^my_exp) + I(x^0.3)))\n(m <- lm(y ~ x + I(x^2) + I(x^3)))\nx. <- seq(0.1, 100, 0.1)\ny. <- m$coef[1] + m$coef[2] / x.^my_exp + m$coef[3] * x.^0.3\ny. <- m$coef[1] + m$coef[2] * x. + m$coef[3] * x.^2 + m$coef[4] * x.^3\ni <- 350\nm. <- (y.[i] - y.[i-1]) / 0.1\nb. <- y.[i] - m. * i / 10\ny.[i:1000] <- m. * x.[i:1000] + b.\ny. <- y.[x. > 1.8]\nx. <- x.[x. > 1.8]\nlines(x., y., lwd = 1.5, col = COL[7, 1])\nlines(x., y., lty = 2, lwd = 1.5, col = COL[5])\n\ndev.off()\n\ncounty[ind, ]\n"
  },
  {
    "path": "ch_summarizing_data/figures/sdAsRuleForEmailNumChar/sdAsRuleForEmailNumChar.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\nd <- email50$num_char\nmean(d)\nsd(d)\n\nmyPDF(\"sdAsRuleForEmailNumChar.pdf\", 6, 1.5,\n      mar = c(3.5, 0, 0, 0),\n      mgp = c(2.2, 0.7, 0))\nexpr <- expression(paste(\"Number of Characters (in thousands), \",\n                         bar(x),\n                         \" = 11,600, \",\n                         s[x],\n                         \" = 13,130\"))\ndotPlot(d,\n        col = COL[1,2],\n        pch = 20,\n        cex = 2,\n        xlim = range(d) + sd(d) / 2 * c(-1, 1),\n        axes = FALSE,\n        xlab = expr,\n        type = 'n')\nm <- round(mean(d), 1)\ns <- round(sd(d), 1)\nabline(v = m, col = COL[7])\ncol <- '#0000000D'\nborder <- '#00000000'\nrect(m - s, -5, m + s, 5,\n     col = col, border = border)\nrect(m - 2 * s, -5, m + 2 * s, 5,\n     col = col, border = border)\nrect(m - 3 * s, -5, m + 3 * s, 5,\n     col = col, border = border)\nrect(m - 4 * s, -5, m + 4 * s, 5,\n     col = col, border = border)\ndotPlot(d,\n        col = COL[1, 2],\n        pch = 20,\n        cex = 2,\n        add = TRUE,\n        axes = FALSE)\ndotPlot(d,\n        col = 1,\n        pch = \".\",\n        add = TRUE,\n        axes = FALSE)\naxis(1,\n     at = m + s * (-3:7),\n     labels = format(m + s * (-3:7)))\ndev.off()\n\nsum(d > m - s & d < m + s) / length(d)\nsum(d > m - 2 * s & d < m + 2 * s) / length(d)\n"
  },
  {
    "path": "ch_summarizing_data/figures/sdRuleForIncome/sdRuleForIncome.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\nd <- loan50$total_income\nmean(d)\nsd(d)\n\nmyPDF(\"sdRuleForIncome.pdf\", 6.3, 1.5,\n      mar = c(3.5, 1.3, 0, 1.3),\n      mgp = c(2.2, 0.7, 0))\nexpr <- expression(paste(\"Loan Amount, \",\n                         bar(x),\n                         \" = $105,221, \",\n                         s[x],\n                         \" = $68,142\"))\ndotPlot(d,\n        col = COL[1,2],\n        pch = 20,\n        cex = 2,\n        xlim = range(d) + sd(d) / 2 * c(-1, 1),\n        axes = FALSE,\n        xlab = expr,\n        type = 'n')\nm <- round(mean(d), -3)\ns <- round(sd(d), -3)\nabline(v = m, col = COL[7])\ncol <- '#0000000D'\nborder <- '#00000000'\nrect(m - s, -5, m + s, 5,\n     col = col, border = border)\nrect(m - 2 * s, -5, m + 2 * s, 5,\n     col = col, border = border)\nrect(m - 3 * s, -5, m + 3 * s, 5,\n     col = col, border = border)\nrect(m - 4 * s, -5, m + 4 * s, 5,\n     col = col, border = border)\ndotPlot(d,\n        col = COL[1, 2],\n        pch = 20,\n        cex = 3,\n        add = TRUE,\n        axes = FALSE)\ndotPlot(d,\n        col = 1,\n        pch = \".\",\n        add = TRUE,\n        axes = FALSE)\nAxisInDollars(1, m + s * (-7:7))\ndev.off()\n\nsum(d > m - s & d < m + s) / length(d)\nsum(d > m - 2 * s & d < m + 2 * s) / length(d)\n"
  },
  {
    "path": "ch_summarizing_data/figures/sdRuleForIntRate/sdRuleForIntRate.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\nd <- loan50$interest_rate\nmean(d)\nsd(d)\n\nmyPDF(\"sdRuleForIntRate.pdf\", 6.3, 1.5,\n      mar = c(3.5, 1.3, 0, 1.3),\n      mgp = c(2.2, 0.7, 0))\nexpr <- expression(paste(\"Interest Rate, \",\n                         bar(x),\n                         \" = 11.57%, \",\n                         s[x],\n                         \" = 5.05%\"))\ndotPlot(d,\n        col = COL[1,2],\n        pch = 20,\n        cex = 2,\n        xlim = range(d) + sd(d) / 2 * c(-1, 1),\n        axes = FALSE,\n        xlab = expr,\n        type = 'n')\nm <- round(mean(d), 1)\ns <- round(sd(d), 1)\nabline(v = m, col = COL[7])\ncol <- '#0000000D'\nborder <- '#00000000'\nrect(m - s, -5, m + s, 5,\n     col = col, border = border)\nrect(m - 2 * s, -5, m + 2 * s, 5,\n     col = col, border = border)\nrect(m - 3 * s, -5, m + 3 * s, 5,\n     col = col, border = border)\nrect(m - 4 * s, -5, m + 4 * s, 5,\n     col = col, border = border)\ndotPlot(d,\n        col = COL[1, 2],\n        pch = 20,\n        cex = 3,\n        add = TRUE,\n        axes = FALSE)\ndotPlot(d,\n        col = 1,\n        pch = \".\",\n        add = TRUE,\n        axes = FALSE)\nAxisInPercent(1, m + s * (-7:7))\ndev.off()\n\nsum(d > m - s & d < m + s) / length(d)\nsum(d > m - 2 * s & d < m + 2 * s) / length(d)\n"
  },
  {
    "path": "ch_summarizing_data/figures/sdRuleForLoanAmount/sdRuleForLoanAmount.R",
    "content": "library(openintro)\ndata(email50)\ndata(COL)\nd <- loan50$loan_amount\nmean(d)\nsd(d)\n\nmyPDF(\"sdRuleForLoanAmount.pdf\", 6.3, 1.5,\n      mar = c(3.5, 1.3, 0, 1.3),\n      mgp = c(2.2, 0.7, 0))\nexpr <- expression(paste(\"Loan Amount, \",\n                         bar(x),\n                         \" = $17,083, \",\n                         s[x],\n                         \" = $10,455\"))\ndotPlot(d,\n        col = COL[1,2],\n        pch = 20,\n        cex = 2,\n        xlim = range(d) + sd(d) / 2 * c(-1, 1),\n        axes = FALSE,\n        xlab = expr,\n        type = 'n')\nm <- round(mean(d), -2)\ns <- round(sd(d), -2)\nabline(v = m, col = COL[7])\ncol <- '#0000000D'\nborder <- '#00000000'\nrect(m - s, -5, m + s, 5,\n     col = col, border = border)\nrect(m - 2 * s, -5, m + 2 * s, 5,\n     col = col, border = border)\nrect(m - 3 * s, -5, m + 3 * s, 5,\n     col = col, border = border)\nrect(m - 4 * s, -5, m + 4 * s, 5,\n     col = col, border = border)\ndotPlot(d,\n        col = COL[1, 2],\n        pch = 20,\n        cex = 2,\n        add = TRUE,\n        axes = FALSE)\ndotPlot(d,\n        col = 1,\n        pch = \".\",\n        add = TRUE,\n        axes = FALSE)\nAxisInDollars(1, m + s * (-7:7))\ndev.off()\n\nsum(d > m - s & d < m + s) / length(d)\nsum(d > m - 2 * s & d < m + 2 * s) / length(d)\n"
  },
  {
    "path": "ch_summarizing_data/figures/severalDiffDistWithSdOf1/severalDiffDistWithSdOf1.R",
    "content": "library(openintro)\ndata(COL)\npdf(\"severalDiffDistWithSdOf1.pdf\", 5.2, 3.8)\n\n\nx1 <- rep(0:1, c(10,10))\nx1 <- (x1-mean(x1))/sd(x1)\nx2 <- qnorm(seq(0.0025,0.9975, 0.00049))\nx2 <- (x2-mean(x2))/sd(x2)\nx3 <- qchisq(seq(0.01,0.98, 0.0005), 4)\nx3 <- (x3-mean(x3))/sd(x3)\n\ndrawSDs <- function(m = 0, s = 1) {\n  abline(v = m, col = '#00000033')\n  rect(m - s, -5, m + s, 500,\n       col = '#00000025',\n       border = '#00000000')\n  rect(m + s, -5, m + 2 * s, 500,\n       col = '#00000015', border = '#00000000')\n  rect(m - s, -5, m - 2 * s, 500,\n       col = '#00000015', border = '#00000000')\n  rect(m + 2 * s, -5, m + 3 * s, 500,\n       col = '#0000000B', border = '#00000000')\n  rect(m - 2 * s, -5, m - 3 * s, 500,\n       col = '#0000000B', border = '#00000000')\n  rect(m + 4 * s, -5, m + 3 * s, 500,\n       col = '#00000008', border = '#00000000')\n  rect(m - 4 * s, -5, m - 3 * s, 500,\n       col = '#00000008', border = '#00000000')\n}\n\nxR <- c(-1, 1) * max(c(x1, x2, x3))\npar(mfrow = c(3, 1),\n    mar = c(3, 1, 0, 1),\n    mgp = c(2.7, 1, 0))\nhistPlot(x1,\n         breaks = c(-1.05, -0.95, 0.95, 1.05),\n         xlim = xR,\n         ylim = c(0, 5.6),\n         axes = FALSE,\n         xlab = '',\n         border = TRUE,\n         lty = 1,\n         probability = TRUE)\ndrawSDs()\nhistPlot(x1,\n         breaks = c(-1.05, -0.95, 0.95, 1.05),\n         add = TRUE,\n         probability = TRUE,\n         col = COL[1],\n         ylim = c(0, 0.75))\naxis(1, at = -4:4, cex.axis = 1.5)\npar(mar = c(3,1,0,1),\n    mgp = c(2.7,1,0))\nhistPlot(x2,\n         breaks = 25,\n         xlim = xR,\n         axes = FALSE,\n         xlab = '',\n         border = TRUE,\n         lty = 1,\n         probability = TRUE,\n         ylim = c(0, 0.43))\ndrawSDs()\nhistPlot(x2,\n         breaks = 25,\n         add = TRUE,\n         probability = TRUE,\n         col = COL[1])\naxis(1, at = -4:4, cex.axis = 1.5)\npar(mar = c(2.1,1,0,1),\n    mgp = c(2.7,1,0))\nhistPlot(x3,\n         breaks = 25,\n         xlim = xR,\n         axes = FALSE,\n         xlab = '',\n         border = TRUE,\n         lty = 1,\n         probability = TRUE,\n         ylim = c(0, 0.5))\ndrawSDs()\nhistPlot(x3,\n         breaks = 25,\n         add = TRUE,\n         probability = TRUE,\n         col = COL[1])\naxis(1, at = -4:4, cex.axis = 1.5)\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/singleBiMultiModalPlots/singleBiMultiModalPlots.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"singleBiMultiModalPlots.pdf\", 6.5, 2)\n\nset.seed(51)\nx1 <- rchisq(65, 6)\nx2 <- c(rchisq(22, 5.8),\n        rnorm(40, 16.5, 2))\nx3 <- c(rchisq(25, 3),\n        rnorm(35, 11.7),\n        rnorm(42, 18, 1.5))\n\npar(mfrow=c(1, 3),\n    mar=c(1.9, 2, 1, 2),\n    mgp=c(2.4, 0.7, 0))\n\nHistPlot1 <- function(x, COL = COL) {\n  histPlot(x, axes=FALSE, xlab='', ylab='', col=COL[1],\n      ylim = c(0, 20))\n  abline(h = 0)\n  axis(1, at = seq(-20, 50, 5))\n}\n\nHistPlot1(x1, COL)\naxis(2)\n\nHistPlot1(x2, COL)\naxis(2)\n\nHistPlot1(x3, COL)\naxis(2)\n\ndev.off()\n"
  },
  {
    "path": "ch_summarizing_data/figures/total_income_dot_plot/total_income_dot_plot.R",
    "content": "library(openintro)\n\nd <- loan50$total_income\n\n\nmyPDF(\"total_income_dot_plot.pdf\",\n      5.5,\n      1.25,\n      mar = c(3.6, 1, 0, 1),\n      mgp = c(2.5, 0.7, 0),\n      tcl = -0.4)\ndotPlot(d,\n        at = 1.007,\n        xlab = 'Loan Amount',\n        ylab = '',\n        pch = 20,\n        col = COL[1, 3],\n        cex = 2.25, # 1.5,\n        xlim = c(0, 1.05 * max(d)),\n        ylim = c(0.95, 1.05),\n        axes = FALSE)\nabline(h = 0.983)\nAxisInDollars(1, pretty(c(0, d)))\nM <- mean(d)\npolygon(M + c(-1, 1, 0) * 15000,\n        c(0.95, 0.95, 0.98),\n        border = COL[4],\n        col = COL[4])\ndev.off()\n\n\n\nset.seed(10)\nmyPDF(\"total_income_dot_plot_stacked.pdf\",\n      5.5,\n      2.25,\n      mar = c(3.6, 1, 0.5, 1),\n      mgp = c(2.5, 0.7, 0))\nround.to <- 10000\nbinned <- round.to * round(d / round.to)\ntab <- table(binned)\ncex    <- 1\nplot(0,\n     type = \"n\",\n     xlab = \"Loan Amount, Rounded to Nearest $1000\",\n     ylab = \"\",\n     axes = FALSE,\n     xlim = c(0, 1.05 * max(d)),\n     ylim = c(-1, 1.5 * max(tab)))\nfor (i in 1:length(tab)) {\n  points(rep(as.numeric(names(tab[i])), tab[i]),\n         1.5 * (1:tab[i]) - 0.4,\n         pch = 19,\n         col = COL[1],\n         cex = 1.5 * cex)\n}\nabline(h = 0)\nAxisInDollars(1, pretty(c(0, d)))\npolygon(M + c(-1, 1, 0) * 15000,\n        c(-1.2, -1.2, -0.1),\n        border = COL[4],\n        col = COL[4])\ndev.off()\n\nM\nsd(d)\n"
  },
  {
    "path": "eoce.bib",
    "content": "% Chp 1 - Data Collection\n\n% migraine_and_acupuncture\n@article{Allais:2011,\n  title={\\oiRedirect{textbook-acupuncture_migraine_2011}{Ear acupuncture in the treatment of migraine attacks: a randomized trial on the efficacy of appropriate versus inappropriate acupoints}},\n  author={Allais, G. and Romoli, M. and Rolando, S. and Airola, G. and Castagnoli Gabellari, I. and Allais, R. and Benedetto, C.},\n  journal={Neurological Sci.},\n  volume={32},\n  number={1},\n  pages={173--175},\n  year={2011},\n  publisher={Springer},\n}\n\n% sinusitis_and_antibiotics\n@article{Garbutt:2012,\n  title={\\oiRedirect{textbook-amoxicillin_acute_rhinosinusitis_2012}{Amoxicillin for Acute Rhinosinusitis: A Randomized Controlled Trial}},\n  author={Garbutt, J.M. and Banister, C. and Spitznagel, E. and Piccirillo, J.F.},\n  journal={JAMA: The Journal of the American Medical Association},\n  volume={307},\n  number={7},\n  pages={685--692},\n  year={2012},\n  publisher={American Medical Association}\n}\n\n% study_components_airpoll\n@article{Ritz+Yu+Chapa+Fruin:2000,\n  title={\\oiRedirect{textbook-air_pollution_preterm_birth_2000}{Effect of air pollution on preterm birth among children born in Southern California between 1989 and 1993}},\n  author={Ritz, B. and Yu, F. and Chapa, G. and Fruin, S.},\n  journal={Epidemiology},\n  volume={11},\n  number={5},\n  pages={502--511},\n  year={2000},\n}\n\n% study_components_buteyko\n@article{McDowan:2003,\n  title={{Health Education: Does the Buteyko Institute Method make a difference?}},\n  author={McGowan, J.},\n  journal={Thorax},\n  volume={58},\n  year={2003}\n}\n\n% study_components_cheaters\n@article{Bucciol:2011,\n  title={\\oiRedirect{textbook-luck-cheating}{Luck or cheating? A field experiment on honesty with children}},\n  author={Bucciol, Alessandro and Piovesan, Marco},\n  journal={Journal of Economic Psychology},\n  volume={32},\n  number={1},\n  pages={73--78},\n  year={2011},\n  publisher={Elsevier}\n}\n\n% study_components_stealers\n@article{Piff:2012,\n  title={Higher social class predicts increased unethical behavior},\n  author={Piff, P.K. and Stancato, D.M. and C{\\^o}t{\\'e}, S. and Mendoza-Denton, R. and Keltner, D.},\n  journal={Proceedings of the National Academy of Sciences},\n  year={2012},\n  publisher={National Acad Sciences}\n}\n\n% fisher_irises\n@article{Fisher:1936,\n  title={\\oiRedirect{textbook-taxonomy_multiple_measurements_1936}{The Use of Multiple Measurements in Taxonomic Problems}},\n  author={Fisher, R.A},\n  journal={Annals of Eugenics},\n  volume={7},\n  pages={179-188},\n  year={1936}\n}\n\n@misc{irisPic,\n  note={Photo by rtclauss on Flickr, \\oiRedirect{textbook-iris_picture}{Iris}.}\n}\n\n% smoking_habits_UK_datamatrix\n@misc{data:smoking,\n  note = {National STEM Centre, \\oiRedirect{textbook-Stats4Schools_smoking}{Large Datasets from stats4schools}.}\n}\n\n% airports\n@misc{data:usairports,\n  note = {Federal Aviation Administration, \\oiRedirect{textbook-FAA_airports}{www.faa.gov/airports/airport\\_safety/airportdata\\_5010}.}\n}\n\n@Manual{data:unvotes,\n  title = {unvotes: United Nations General Assembly Voting Data},\n  author = {David Robinson},\n  year = {2017},\n  note = {R package version 0.2.0}, url = {https://CRAN.R-project.org/package=unvotes}\n}\n\n% eat_well_feel_better\n@article{conner2017let,\n  title={Let them eat fruit! The effect of fruit and vegetable consumption on psychological well-being in young adults: A randomized controlled trial},\n  author={Conner, Tamlin S and Brookie, Kate L and Carr, Anitra C and Mainvil, Louise A and Vissers, Margreet CM},\n  journal={PloS one},\n  volume={12},\n  number={2},\n  pages={e0171206},\n  year={2017},\n  publisher={Public Library of Science}\n}\n\n% screen time\n@article{orben2018screens,\n  title={\\oiRedirect{textbook-screens_orben_2018}{Screens, Teens and Psychological Well-Being: Evidence from three time-use diary studies}},\n  author={Orben, Amy and Baukney-Przybylski, AK},\n  journal={Psychological Science},\n  year={2018},\n  publisher={SAGE Publications}\n}\n\n% gender pay gap medicine\n@article{LoSassoMedicineGenderPayGap,\n  title={\\oiRedirect{textbook-LoSassoMedicineGenderPayGap}{The \\$16,819 Pay Gap For Newly Trained Physicians: The Unexplained Trend Of Men Earning More Than Women}},\n  author={Lo Sasso AT and Richards MR and Chou CF and Gerber SE},\n  journal={Health Affairs},\n  year={2011},\n  volume={30},\n  number={2}\n}\n\n% stanford open policing\n@article{pierson2017large,\n  title={\\oiRedirect{textbook-police_pierson_2017}{A large-scale analysis of racial disparities in police stops across the United States}},\n  author={Pierson, Emma and Simoiu, Camelia and Overgoor, Jan and Corbett-Davies, Sam and Ramachandran, Vignesh and Phillips, Cheryl and Goel, Sharad},\n  journal={arXiv preprint arXiv:1706.05678},\n  year={2017}\n}\n\n% space launches\n@misc{data:spacelaunches,\n  note = {JSR Launch Vehicle Database, \\oiRedirect{textbook-space-launches-data}{A comprehensive list of suborbital space launches, 2019 Feb 10 Edition}.}\n}\n\n% Torque on a rusty bolt\n@misc{youtube:torque_on_rusty_bolt,\n    note = {Project Farm on YouTube, \\oiRedirect{textbook-torque_on_rusty_bolt}{youtu.be/xUEob2oAKVs}, April 16, 2018.}\n}\n\n% vegetarianism\n@misc{webpage:vegetarianism,\n    note = {Gallup Poll, \\oiRedirect{textbook-gallup-vegetarianism-2018}{Snapshot: Few Americans Vegetarian or Vegan}, August 1, 2018.}\n}\n\n% NOAA 1948 and 2018 data\n@misc{webpage:noaa_1948_2018,\n    note = {NOAA, \\oiRedirect{textbook-noaa_1948_2018}{www.ncdc.noaa.gov/cdo-web/datasets}, April 24, 2019.}\n}\nRetrieved on 2019-04-24.\n  \\url{https://www.ncdc.noaa.gov/cdo-web/datasets}\n\n% Raising the minimum wage\n@misc{webpage:rasmussen-2019-raise-minimum-wage,\n    note = {Rasmussen Reports survey, \\oiRedirect{rasmussen-2019-raise-minimum-wage}{Most Favor Minimum Wage of \\$10.50 Or Higher}, April 16, 2019.}\n}\n\n% gss data\n@misc{data:gss,\n  note = {National Opinion Research Center, \\oiRedirect{textbook-gss-data}{General Social Survey, 2018}.}\n}\n\n\n\n@misc{data:ciaFactbook,\n  note = {CIA Factbook, \\oiRedirect{textbook-cia_factbook}{Country Comparisons, 2014}.}\n}\n\n@misc{data:ITU:2012,\n  note = {ITU World Telecommunication/ICT Indicators database, \\oiRedirect{textbook-telecommunication_ICT_2012}{World Telecommunication/ICT Indicators Database, 2012}}\n}\n\n@article{Hepler:2013,\n  title={\\oiRedirect{textbook-dispositional-attitude}{Attitudes without objects - Evidence for a dispositional attitude, its measurement, and its consequences}},\n  author={Hepler, Justin and Albarrac{\\'\\i}n, Dolores},\n  journal={Journal of personality and social psychology},\n  volume={104},\n  number={6},\n  pages={1060},\n  year={2013},\n  publisher={American Psychological Association}\n}\n\n@article{news:smokingDementia,\n  author={Rabin, R.C.}, \n  title = {\\oiRedirect{textbook-nytimes_smoking_dementia}{Risks: Smokers Found More Prone to Dementia}},\n  journal={New York Times},\n   MONTH = {October 29},\n   YEAR    = {2010}\n}\n\n@article{news:bullySleep,\n  author={Parker-Pope, T.},\n  title = {\\oiRedirect{textbook-school_bully_sleepy_2011}{The School Bully Is Sleepy}},\n  journal={New York Times},\n   MONTH = {June 2},\n   YEAR    = {2011}\n}\n\n@article{Orr:2009,\n  title={\\oiRedirect{textbook-shyness_FB_usage_2009}{The influence of shyness on the use of Facebook in an undergraduate sample}},\n  author={Orr, E.S. and Sisic, M. and Ross, C. and Simmering, M.G. and Arseneault, J.M. and Orr, R.R.},\n  journal={CyberPsychology \\& Behavior},\n  volume={12},\n  number={3},\n  pages={337--340},\n  year={2009},\n  publisher={Mary Ann Liebert, Inc.}\n}\n\n@article{Audera:2001,\n  title={\\oiRedirect{textbook-vitamin_C_cold_treatment_2001}{Mega-dose vitamin C in treatment of the common cold: a randomised controlled trial}},\n  author={Audera, C. and Patulny, R.V. and Sander, B.H. and Douglas, R.M. and others},\n  journal={Medical Journal of Australia},\n  volume={175},\n  number={7},\n  pages={359--362},\n  year={2001},\n  publisher={AUSTRALASIAN MEDICAL PUBLISHING COMPANY LTD}\n}\n\n@article{Nieman:2009,\n  title={\\oiRedirect{textbook-chia_seeds_2009}{Chia seed does not promote weight loss or alter disease risk factors in overweight adults}},\n  author={Nieman, D.C. and Cayea, E.J. and Austin, M.D. and Henson, D.A. and McAnulty, S.R. and Jin, F.},\n  journal={Nutrition Research},\n  volume={29},\n  number={6},\n  pages={414--418},\n  year={2009},\n  publisher={Elsevier}\n}\n\n@article{Suldo:2014,\n  title={\\oiRedirect{textbook-middle-school-satisfaction}{Increasing middle school students' life satisfaction: Efficacy of a positive psychology group intervention}},\n  author={Suldo, Shannon M and Savage, Jessica A and Mercer, Sterett H},\n  journal={Journal of happiness studies},\n  volume={15},\n  number={1},\n  pages={19--42},\n  year={2014},\n  publisher={Springer}\n}\n\n% Chp 2 - Summarizing data\n\n@misc{data:acs:2012,\n  note = {United States Census Bureau. Summary File. {\\oiRedirect{textbook-ACS_2012}{2012 American Community Survey}}. U.S. Census Bureau’s American Community Survey Office, 2013. Web.}\n}\n\n@misc{data:MLB:2014,\n  note = {\\oiRedirect{textbook-mlb2014-espn}{ESPN: MLB Team Stats - 2014}}\n}\n\n@article{Harris:2012,\n  title={\\oiRedirect{textbook-cereal-facts-2012}{Cereal FACTS 2012: Limited progress in the nutrition quality and marketing of children's cereals}},\n  author={Harris, JL and Schwartz, MB and Brownell, KD and Sarda, V and Dembek, C and Munsell, C and Shin, C and Ustjanauskas, A and Weinberg, M},\n  journal={Rudd Center for Food Policy \\& Obesity.},\n  volume={12},\n  year={2012}\n}\n\n@article{Allison+Cicchetti:1975,\n  title={\\oiRedirect{textbook-mammal_sleep_1975}{Sleep in mammals: ecological and constitutional correlates}},\n  author={Allison, T. and Cicchetti, D.V.},\n  journal={Arch. Hydrobiol},\n  volume={75},\n  pages={442},\n  year={1975}\n}\n\n@misc{data:ciaFactBookInfMort:2012,\n  note = {CIA Factbook, \\oiRedirect{textbook-cia_infant_mortality_2012}{Country Comparison: Infant Mortality Rate, 2012}}\n}\n\n@misc{data:durhamAQI:2011,\n  note = {US Environmental Protection Agency, \\oiRedirect{textbook-airdata_2011}{AirData, 2011.}}\n}\n\n@article{Backstrom:2011,\n  title={\\oiRedirect{textbook-anatomy-of-facebook}{Anatomy of Facebook}},\n  author={Backstrom, Lars},\n  journal={Facebook Data Team’s Notes},\n  year={2011}\n}\n\n@misc{survey:immigFL:2012,\n  note = {SurveyUSA, \\oiRedirect{textbook-SurveyUSA_18927}{News Poll \\#18927}, data collected Jan 27-29, 2012}\n}\n\n@misc{survey:raiseTaxes:2015,\n  note = {Public Policy Polling, \\oiRedirect{textbook-PPP_30215}{Americans on College Degrees, Classic Literature, the Seasons, and More}, data collected Feb 20-22, 2015}\n}\n\n@article{Graham:2010,\n  title={Risk of acute myocardial infarction, stroke, heart failure, and death in elderly Medicare patients treated with rosiglitazone or pioglitazone},\n  author={Graham, D.J. and Ouellet-Hellstrom, R. and MaCurdy, T.E. and Ali, F. and Sholley, C. and Worrall, C. and Kelman, J.A.},\n  journal = {JAMA},\n  volume={304},\n  number={4},\n  pages={411},\n  issn={0098-7484},\n  year={2010},\n  publisher={Am Med Assoc}\n}\n\n@article{Turnbull+Brown+Hu:1974,\n  title={\\oiRedirect{textbook-heart_transplant_1974}{Survivorship of Heart Transplant Data}},\n  author={Turnbull, B. and Brown, B. and Hu, M.},\n  journal={Journal of the American Statistical Association},\n  volume={69},\n  pages={74-80},\n  year={1974}\n}\n\n% Chp 3 - Probability\n\n@misc{data:BRFSS2010,\n  note={Office of Surveillance, Epidemiology, and Laboratory Services Behavioral Risk Factor Surveillance System, {\\oiRedirect{textbook-BRFSS_2010}{BRFSS 2010 Survey Data}}.}\n}\n\n@misc{rouletteWheelPic,\n  note={Photo by H\\r{a}kan Dahlstr\\\"{o}m on Flickr, \\oiRedirect{textbook-flickr_roulette_wheel}{Roulette wheel}.}\n}\n\n@misc{indepSwing,\n  note={Pew Research Center, \\oiRedirect{textbook-obama_economy_pew_2012}{With Voters Focused on Economy, Obama Lead Narrows}, data collected between April 4-15, 2012.}\n}\n\n@misc{pew_cyber_bully_2018,\n  note={Pew Research Center, \\oiRedirect{pew_cyber_bully_2018}{A Majority of Teens Have Experienced Some Form of Cyberbullying}. September 27, 2018.}\n}\n\n@misc{poorLang,\n  note={U.S. Census Bureau, 2010 American Community Survey 1-Year Estimates, \\oiRedirect{textbook-acs_language_2010}{Characteristics of People by Language Spoken at Home}.}\n}\n\n@misc{eduSex,\n  note={U.S. Census Bureau, 2010 American Community Survey 1-Year Estimates, \\oiRedirect{textbook-acs_educational_2010}{Educational Attainment}.}\n}\n\n@article{Mizan:2011,\n  title={\\oiRedirect{textbook-tardiness_asthma_2011}{Absence, Extended Absence, and Repeat Tardiness Related to Asthma Status among Elementary School Children}},\n  author={Mizan, S.S. and Shendell, D.G. and Rhoads, G.G.},\n  journal={Journal of Asthma},\n  volume={48},\n  number={3},\n  pages={228-234},\n  year={2011},\n  publisher={Informa Healthcare}\n}\n\n@misc{globalWarming,\n  note={Pew Research Center, \\oiRedirect{textbook-republicans_global_warming_2010}{Majority of Republicans No Longer See Evidence of Global Warming}, data collected on October 27, 2010.}\n}\n\n@misc{burgers,\n  note={SurveyUSA, \\oiRedirect{textbook-SurveyUSA_17718}{Results of SurveyUSA News Poll \\#17718},  data collected on December 2, 2010.}\n}\n\n@article{Laeng:2007,\n  title={\\oiRedirect{textbook-eye_color_pref_2010}{Why do blue-eyed men prefer women with the same eye color?}},\n  author={Laeng, B. and Mathisen, R. and Johnsen, J.A.},\n  journal={Behavioral Ecology and Sociobiology},\n  volume={61},\n  number={3},\n  pages={371--384},\n  year={2007},\n  publisher={Springer}\n}\n\n@misc{ciaFactBookHIV:2012,\n  note = {Source: CIA Factbook, \\oiRedirect{textbook-cia_hiv_2012}{Country Comparison: HIV/AIDS - Adult Prevalence Rate}.}\n}\n\n@misc{data:scott,\n  note = {New York Times, \\oiRedirect{textbook-nytimes_wi_exit_polls_2012}{Wisconsin recall exit polls}}\n}\n\n@misc{webpage:alcohol,\n    note = {SAMHSA, Office of Applied Studies, \\oiRedirect{textbook-SAMHSA_2007_8}{National Survey on Drug Use and Health, 2007 and 2008}.}\n}\n\n@Book{cats,\n    title = {Modern Applied Statistics with S},\n    author = {W. N. Venables and B. D. Ripley},\n    publisher = {Springer},\n    edition = {Fourth Edition},\n    address = {New York},\n    year = {2002},\n    note = {\\oiRedirect{textbook-modern_applied_stat_with_s}{www.stats.ox.ac.uk/pub/MASS4}},\n}\n\n@misc{acsIncome2005-2009,\n  note={U.S. Census Bureau, \\oiRedirect{textbook-acd2005_9}{2005-2009 American Community Survey}}\n}\n\n$ Chp 4 - Distributions\n\n@conference{Johnson+Murray:2010,\n  title={\\oiRedirect{textbook-rural_auto_speeds_2010}{Empirical Analysis of Truck and Automobile Speeds on Rural Interstates: Impact of Posted Speed Limits}},\n  author={Johnson, S. and Murray, D.},\n  booktitle={Transportation Research Board 89th Annual Meeting},\n  year={2010}\n}\n\n@misc{marWomenACS,\n  note={U.S. Census Bureau, 2010 American Community Survey, \\oiRedirect{textbook-acs_marriage_2010}{Marital Status}.}\n}\n\n@misc{surveysPew,\nnote={Pew Research Center, \\oiRedirect{textbook-pew_Representativeness_Surveys_2012}{Assessing the Representativeness of Public Opinion Surveys}, May 15, 2012.}\n}\n\n@misc{dreidelPic,\n  note={\\oiRedirect{textbook-flickr_dreidelPic}{Photo by Staccabees on Flickr}.}\n}\n\n@misc{webpage:spiders,\n    note = {Gallup Poll, \\oiRedirect{textbook-frightens_youth_2005}{What Frightens America's Youth?}, March 29, 2005.}\n}\n\n@misc{data:nsfg:2010,\n    note = {Centers for Disease Control and Prevention, \\oiRedirect{textbook-ntnl_survey_family_growth_2010}{National Survey of Family Growth, 2010.}\n    }\n}\n\n@misc{data:povertycps:2013,\n  note = {United States Census Bureau. {\\oiRedirect{textbook-CPS_2013_poverty}{2013 Current Population Survey}}.Historical Poverty Tables - People. Web.}\n}\n\n@misc{data:hispaniccps:2012,\n  note = {United States Census Bureau.{\\oiRedirect{textbook-CPS_2012_hispanic}{2012 Current Population Survey}}.The Hispanic Population in the United States: 2012. Web.}\n}\n\n@misc{data:pewsocialnetwork:2014,\n  note = {Pew Research Center, Washington, D.C. {\\oiRedirect{textbook-pew_socialnetwork}{Social Networking Fact Sheet}}, accessed on May 9, 2015.}\n}\n\n% Chp 5 - Foundations for inference\n\n@article{Heinz:2003,\n  title={\\oiRedirect{textbook-body_dim_2003}{Exploring relationships in body dimensions}},\n  author={Heinz, G. and Peterson, L.J. and Johnson, R.W. and Kerk, C.J.},\n  journal={Journal of Statistics Education},\n  volume={11},\n  number={2},\n  year={2003}\n}\n\n@misc{data:pewdiagnosis:2013,\n  note = {Pew Research Center, Washington, D.C. {\\oiRedirect{textbook-The_Diagnosis_Difference}{The Diagnosis Difference}}, November 26, 2013.}\n}\n\n@misc{data:pewtwitternews:2013,\n  note = {Pew Research Center, Washington, D.C. {\\oiRedirect{textbook-twitter_news_consumers_2013}{Twitter News Consumers: Young, Mobile and Educated}}, November 4, 2013.}\n}\n\n@misc{data:gss:2010,\n  note = {National Opinion Research Center, \\oiRedirect{textbook-gss_2010}{General Social Survey, 2010}.}\n}\n\n@misc{data:pewwomenleaders:2014,\n  note = {Pew Research Center, Washington, D.C. {\\oiRedirect{textbook-pew-womenleaders}{Women and Leadership: Public Says Women are Equally Qualified, but Barriers Persist}}, January 14, 2015.}\n}\n\n@misc{data:yawn,\n  note = {MythBusters, \\oiRedirect{textbook-mythbusters_s3e28}{Season 3, Episode 28.}}\n}\n\n@misc{data:egypt,\n  note={Gallup Politics, \\oiRedirect{textbook-americans_views_of_egypt_2011}{Americans' Views of Egypt Sharply More Negative}, data collected February 2-5, 2011.}\n}\n\n@misc{web:art,\n  title={\\oiRedirect{textbook-2008_Assisted_Reproductive_Technology_Report}{2008 Assisted Reproductive Technology Report}},\n  author ={CDC},\n}\n\n@misc{webpage:spam,\n  note = {Rockbridge, \\oiRedirect{textbook-spam_report_2009}{2009 National Technology Readiness Survey SPAM Report}.}\n}\n\n@misc{webpage:horrormovies,\n  note = {FiveThirtyEight, \\oiRedirect{textbook-fivethirtyeight-scary-movies}{Scary Movies Are The Best Investment In Hollywood}.}\n}\n\n% Chp 6 - Inference for proportions\n\n\n\n@misc{data:govt_shuthown,\n  note={Survey USA, \\oiRedirect{textbook-SurveyUSA_24568}{News Poll \\#24568}, data collected on April 21, 2019.}\n}\n\n@article{news:youngAmericans1,\n  author={Vaughn, A.}, \n  title = {\\oiRedirect{textbook-young_americans_2011}{Poll finds young adults optimistic, but not about money}},\n  journal={Los Angeles Times},\n   MONTH = {November 3},\n   YEAR    = {2011}\n}\n\n@article{news:youngAmericans2,\n  author={Demos.org}, \n  title = {\\oiRedirect{textbook-young_americans_2011_extra}{The State of Young America: The Poll}},\n     MONTH = {November 2},\n   YEAR    = {2011}\n}\n\n\n\n@misc{data:healthcare2010,\n  note = {Gallup, \\oiRedirect{textbook-healthcare_split_2012}{Americans Issue Split Decision on Healthcare Ruling}, data collected June 28, 2012.}\n}\n\n@misc{data:july4,\n  note={Survey USA, \\oiRedirect{textbook-SurveyUSA_19333}{News Poll \\#19333}, data collected on June 27, 2012.}\n}\n\n@misc{data:elderlyDriving,\n  note={Marist Poll, \\oiRedirect{textbook-drivers_at_65_2011}{Road Rules: Re-Testing Drivers at Age 65?}, March 4, 2011}\n}\n\n@misc{data:suffering,\n  note={Gallup World, \\oiRedirect{textbook-1_in_10_suffering_2011}{More Than One in 10 ``Suffering\" Worldwide}, data collected throughout 2011.}\n}\n\n@misc{data:studyAbroad,\n  note={studentPOLL, \\oiRedirect{textbook-Interests_in_Study_Abroad_2008}{College-Bound Students' Interests in Study Abroad and Other International Learning Activities}, January 2008}\n}\n\n@article{news:publicOption,\n  author={Balz, D. and Cohen, J.}, \n  title = {\\oiRedirect{textbook-healthcare_public_option_2009}{Most support public option for health insurance, poll finds}},\n  journal={The Washington Post},\n   MONTH = {October 20},\n   YEAR    = {2009}\n}\n\n@misc{data:KFF2019_nat_health_plan,\n  note={Kaiser Family Foundation, \\oiRedirect{textbook-kff_nat_health_plan_2019}{The Public On Next Steps For The ACA And Proposals To Expand Coverage}, data collected between Jan 9-14, 2019.}\n}\n\n@misc{data:civilWar,\n  note={Pew Research Center Publications, \\oiRedirect{textbook-civil_war_at_150}{Civil War at 150: Still Relevant, Still Divisive}, data collected between March 30 - April 3, 2011.}\n}\n\n@misc{data:mobileBrowse,\n  note={Pew Internet, \\oiRedirect{textbook-cell_internet_use_2012}{Cell Internet Use 2012}, data collected between March 15 - April 13, 2012.}\n}\n\n@article{news:mobileBrowseChinese,\n  author={Chang, S.}, \n  title = {The Chinese Love to Use Feature Phone to Access the Internet},\n  journal={M.I.C Gadget},\n   MONTH = {March 23},\n   YEAR    = {2012}\n}\n\n@misc{data:collegeWorthIt,\n  note={Pew Research Center Publications, \\oiRedirect{textbook-college_worth_it_2011}{Is College Worth It?}, data collected between March 15-29, 2011.}\n}\n\n@article{Ellis:2001,\n  title={{\\oiRedirect{textbook-color_pref_2001}{Color preferences according to gender and sexual orientation}}},\n  author={L Ellis and C Ficek},\n  journal={Personality and Individual Differences},\n  volume={31},\n  number={8},\n  pages={1375-1379},\n  year={2001},\n  publisher={Elsevier}\n}\n\n@misc{data:dailyShow,\n  note={The Pew Research Center, \\oiRedirect{textbook-americans_news_2010}{Americans Spending More Time Following the News}, data collected June 8-28, 2010.}\n}\n\n@misc{data:sleepCAandOR,\n  note={CDC, \\oiRedirect{textbook-Perceived_Insufficient_Rest_or_Sleep_Among_Adults}{Perceived Insufficient Rest or Sleep Among Adults --- United States, 2008}}\n}\n\n@misc{data:prop19_and_offshoreDrill,\n  note = {Survey USA, \\oiRedirect{textbook-SurveyUSA_16804}{Election Poll \\#16804}, data collected July 8-11, 2010.}\n}\n\n@article{news:fullBodyScan,\n  author={Condon, S.}, \n  title = {\\oiRedirect{textbook-airport_scanners_2010}{Poll: 4 in 5 Support Full-Body Airport Scanners}},\n  journal={CBS News},\n   MONTH = {November 15},\n   YEAR    = {2010}\n}\n\n@misc{data:sleepTransport,\n  note={National Sleep Foundation, \\oiRedirect{textbook-trans_workers_sleep_2012}{2012 Sleep in America Poll: Transportation Workers' Sleep}, 2012}\n}\n\n@article{Schmidt:2011,\n  title={\\oiRedirect{textbook-prenatal_vitamins_autism_2011}{Prenatal vitamins, one-carbon metabolism gene variants, and risk for autism}},\n  author={Schmidt, R.J. and Hansen, R.L. and Hartiala, J. and Allayee, H. and Schmidt, L.C. and Tancredi, D.J. and Tassone, F. and Hertz-Picciotto, I.},\n  journal={Epidemiology},\n  volume={22},\n  number={4},\n  pages={476},\n  year={2011}\n}\n\n@article{news:prenatalVitAutism,\n  author={Rabin, R.C.}, \n  title = {\\oiRedirect{textbook-nytimes_prenatal_vitamins_autism_2011}{Patterns: Prenatal Vitamins May Ward Off Autism}},\n  journal={New York Times},\n   MONTH = {June 13},\n   YEAR    = {2011}\n}\n\n@article{Lockman:2007,\n  title={\\oiRedirect{textbook-antiretroviral_therapy_2007}{Response to antiretroviral therapy after a single, peripartum dose of nevirapine}},\n  author={Lockman, S. and Shapiro, R.L. and Smeaton, L.M. and Wester, C. and Thior, I. and Stevens, L. and Chand, F. and Makhema, J. and Moffat, C. and Asmelash, A. and others},\n  journal={Obstetrical \\& gynecological survey},\n  volume={62},\n  number={6},\n  pages={361},\n  year={2007}\n}\n\n@misc{data:employmentDiabetes,\n  note={Gallup Wellbeing, \\oiRedirect{textbook-employed_americans_in_better_health_2012}{Employed Americans in Better Health Than the Unemployed}, data collected Jan. 2, 2011 - May 21, 2012.}\n}\n\n@misc{CreationismGallup,\n  note={Four in 10 Americans Believe in Strict Creationism, December 17, 2010, \\oiRedirect{textbook-strict_creationism_2010}{www.gallup.com/poll/145286/Four-Americans-Believe-Strict-Creationism.aspx}}\n}\n\n@article{Teng:2004,\n  title={Forage and bed sites characteristics of Indian muntjac (Muntiacus muntjak) in Hainan Island, China},\n  author={Teng, Liwei and Liu, Zhensheng and SONG, Yan-Ling and Zeng, Zhigao},\n  journal={Ecological Research},\n  volume={19},\n  number={6},\n  pages={675--681},\n  year={2004},\n  publisher={Wiley Online Library}\n}\n\n@article{Lucas:2011,\n  title={\\oiRedirect{textbook-coffee_caffeine_depression_2011}{Coffee, caffeine, and risk of depression among women}},\n  author={Lucas, M. and Mirzaei, F. and Pan, A. and Okereke, O.I. and Willett, W.C. and O'Reilly, E.J. and Koenen, K. and Ascherio, A.},\n  journal={Archives of internal medicine},\n  volume={171},\n  number={17},\n  pages={1571},\n  year={2011},\n  publisher={Am Med Assoc}\n}\n\n@article{news:coffeeDepression,\n  author={O'Connor, A.}, \n  title = {\\oiRedirect{textbook-coffee_depression_2011}{Coffee Drinking Linked to Less Depression in Women}},\n  journal={New York Times},\n   MONTH = {September 26},\n   YEAR    = {2011}\n}\n\n@misc{data:anes:2012,\n  note={The American National Election Studies ({\\oiRedirect{textbook-anes-2012}{ANES}}). The ANES 2012 Time Series Study [dataset]. Stanford University and the University of Michigan [producers].}\n}\n\n@misc{photo:barkingDeer,\n  note={Photo by Shrikant Rao from Flickr (\\oiRedirect{textbook-flickr_shrikant_rao_barking_deer}{http://flic.kr/p/4Xjdkk}), available under a \\oiRedirect{textbook-CC_BY_2}{CC BY 2.0 license}.}\n}\n\n% Chp 7 - Inference for means\n\n@misc{data:prius,\n  note = {Fuelecomy.gov, \\oiRedirect{textbook-toyota_prius_2012_mpg}{Shared MPG Estimates: Toyota Prius 2012}.}\n}\n\n@book{Graybill:1994,\n  title={Regression Analysis: Concepts and Applications},\n  author={Graybill, F.A. and Iyer, H.K.},\n  year={1994},\n  publisher={Duxbury Press},\n  pages={511--516}\n}\n\n@misc{data:oscars,\n  note = {Oscar winners from 1929 -- 2012, data up to 2009 from the \\oiRedirect{textbook-oscar_winners_up_to_2012}{Journal of Statistics Education data archive} and more current data from \\oiRedirect{textbook-wikipedia_org}{wikipedia.org}.}\n}\n\n@article{Scanlon:1993,\n  title={\\oiRedirect{textbook-Friday13_1993}{Is Friday the 13th Bad For Your Health?}},\n  author={Scanlon, T.J. and Luben, R.N. and Scanlon and F.L., Singleton, N.},\n  journal={BMJ},\n  volume={307},\n  pages={1584-1586},\n  year={1993}\n}\n\n@Book{ggplot2,\n    author = {Wickham, H.},\n    title = {\\oiRedirect{textbook-ggplot2_book}{ggplot2: elegant graphics for data analysis}},\n    publisher = {Springer New York},\n    year = {2009}\n}\n\n@misc{data:chickwts,\n  note = {\\oiRedirect{textbook-feed_and_chicken_weights}{Chicken Weights by Feed Type}, from the \\texttt{datasets} package in R.}\n}\n\n@misc{data:epaMPG,\n  note = {U.S. Department of Energy, \\oiRedirect{textbook-fuel_economy_data_2012}{Fuel Economy Data, 2012 Datafile}.}\n}\n\n@article{Oldham:2011,\n  title={\\oiRedirect{textbook-playing_computer_games_2011}{Playing a computer game during lunch affects fullness, memory for lunch, and later snack intake}},\n  author={Oldham-Cooper, R.E. and Hardman, C.A. and Nicoll, C.E. and Rogers, P.J. and Brunstrom, J.M.},\n  journal={The American Journal of Clinical Nutrition},\n  volume={93},\n  number={2},\n  pages={308},\n  year={2011},\n  publisher={Am Soc Nutrition}\n}\n\n@misc{data:prison,\n  note = {\\oiRedirect{textbook-prison_isolation_exp}{Prison isolation experiment, stat.duke.edu/resources/datasets/prison-isolation}.}\n}\n\n@misc{data:china,\n  note = {UNC Carolina Population Center, \\oiRedirect{textbook-china_health_nut_survey_2006}{China Health and Nutrition Survey, 2006}.}\n}\n\n@article{Mortada:2000,\n  title={Study of lead exposure from automobile exhaust as a risk for nephrotoxicity among traffic policemen.},\n  author={Mortada, WI and Sobh, MA and El-Defrawy, MM and Farahat, SE},\n  journal={American journal of nephrology},\n  volume={21},\n  number={4},\n  pages={274--279},\n  year={2000}\n}\n\n% Chp 8 - Simple linear regression\n\n@book{Hand:1994,\n  title={{A handbook of small data sets}},\n  author={Hand, D.J.},\n  year={1994},\n  publisher={Chapman \\& Hall/CRC}\n}\n\n@misc{data:trees,\n  note = {Source: R Dataset, \\oiRedirect{textbook-R_datasets_trees}{stat.ethz.ch/R-manual/R-patched/library/datasets/html/trees.html}}\n}\n\n@article{Benson:1993,\n  title={\\oiRedirect{textbook-birth_season_locomotion_1993}{Season of birth and onset of locomotion: Theoretical and methodological implications}},\n  author={Benson, J.B.},\n  journal={Infant behavior and development},\n  volume={16},\n  number={1},\n  pages={69-81},\n  issn={0163-6383},\n  year={1993},\n  publisher={Elsevier}\n}\n\n@misc{data:turkeyTourism,\n  note = {Association of Turkish Travel Agencies, \\oiRedirect{textbook-turkey_tourist_spending}{Foreign Visitors Figure \\& Tourist Spendings By Years}}\n}\n\n@misc{data:starbucksCals,\n  note={Source: Starbucks.com, collected on March 10, 2011, \\\\ \\oiRedirect{textbook-starbucks_com_menu_nutrition}{www.starbucks.com/menu/nutrition}}\n}\n\n@misc{data:urbanOwner,\n  note={United States Census Bureau, \\oiRedirect{textbook-census_urban_rural_2010}{2010 Census Urban and Rural Classification and Urban Area Criteria} and \\oiRedirect{textbook-housing_char_2010}{Housing Characteristics: 2010}.}\n}\n\n@book{Malkevitc+Lesser:2008,\n  title={{For All Practical Purposes: Mathematical Literacy in Today's World}},\n  author={Malkevitch, J. and Lesser, L.M.},\n  year={2008},\n  publisher={WH Freeman \\& Co}\n}\n\n@article{Hamermesh:2005,\n  title={Beauty in the classroom: Instructors’ pulchritude and putative pedagogical productivity},\n  author={Hamermesh, Daniel S and Parker, Amy},\n  journal={Economics of Education Review},\n  volume={24},\n  number={4},\n  pages={369--376},\n  year={2005},\n  publisher={Elsevier}\n}\n\n%%%%%%%%%%%%%\n% not sure which chapter, move later\n%%%%%%%%%%%%%\n\n@Book{data:quine,\n    title = {Modern Applied Statistics with S},\n    author = {W. N. Venables and B. D. Ripley},\n    publisher = {Springer},\n    edition = {Fourth Edition},\n    address = {New York},\n    year = {2002},\n    note = {\\href{http://www.stats.ox.ac.uk/pub/MASS4}{Data can also be found in the R MASS package}},\n}\n\n@misc{data:babies,\n  note = {Child Health and Development Studies, \\href{http://www.ma.hw.ac.uk/~stan/aod/library}{Baby weights data set}}\n}\n\n@article{King_Suamani_2018,\n  title={\\oiRedirect{textbook-King_Suamani_2018}{A Trial of a Triple-Drug Treatment for Lymphatic Filariasis}},\n  author={King, Christopher and Suamani, James and Sanuku, Nelly and Cheng, Yao-Chieh and Satofan, Samson and Mancuso, Brooke and Goss, Charles W and Robinson, Leanne J and Siba, Peter M and Weil, Gary J and Kazura, James W},\n  journal={New England Journal of Medicine},\n  volume={379},\n  pages={1801-1810},\n  year={2018}\n}\n\n@misc{bostonchildrenshospital:chickenpox,\n  note={Boston Children's Hospital, \\oiRedirect{textbook-bostonchildrenshospital_chickenpox_vaccine}{Chickenpox summary page}, referenced April 29, 2021.}\n}\n\n%%%%%%%%%%%%%\n% used in textbook (not in eoce)\n%%%%%%%%%%%%%\n\n@misc{data:facebookPrivacy,\n  note={Survey USA, \\oiRedirect{textbook-SurveyUSA_17960}{News Poll \\#17960}, data collected February 16-17, 2011.}\n}\n\n"
  },
  {
    "path": "extraTeX/data/data.tex",
    "content": "\\chapter{Data sets within the text}\n\\label{appendix_data}\n\\label{data_appendix}\n\n%A foundational principle that supports quality statistical\n%analysis is well-organized data.\n\n\\index{data|(}\n\nEach data set within the text is described in this appendix,\nand there is a corresponding page for each of these data sets at\n\\oiRedirect{data}\n    {\\color{black}\\textbf{openintro.org/data}}.\nThis page also includes additional data sets that can be\nused for honing your skills.\nEach data set has its own page with the following information:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item\n    List of the data set's variables.\n\\item\n    CSV download.\n\\item\n    R object file download.\n\\end{itemize}\n\n%\\vspace{10mm}\n\n\\newcommand{\\datawrap}[1]{#1 $\\to$}\n\\newcommand{\\seedataappendix}[1]{This data set\n    is described in Data Appendix~\\ref{#1}.}\n\\newcommand{\\seedataappendixplural}[1]{These data sets\n    are described in Data Appendix~\\ref{#1}.}\n\\newcommand{\\madeup}{This example was made up.}\n\n\n\n\\section{\\nameref{ch_intro_to_data}}\n\\label{ch_intro_to_data_data}\n\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[\\ref{basicExampleOfStentsAndStrokes}]\n    \\datawrap{\\datalink{stent30}, \\datalink{stent365}}\n    The stent data is split across two data sets,\n    one for days 0-30 results and one for days 0-365\n    results. \\\\\n    Chimowitz MI, Lynn MJ, Derdeyn CP, et al. 2011.\n    Stenting versus Aggressive Medical Therapy for\n    Intracranial Arterial Stenosis.\n    New England Journal of Medicine 365:993-1003.\n    \\oiRedirect{textbook-nejm_stent_study}\n        {www.nejm.org/doi/full/10.1056/NEJMoa1105335}. \\\\\n    NY Times article:\n    \\oiRedirect{textbook-nytimes_stent_study}\n        {www.nytimes.com/2011/09/08/health/research/08stent.html}.\n\n\\item[\\ref{dataBasics}]\n    \\datawrap{\\datalink{loan50},\n        \\datalink{loans\\_full\\_schema}}\n    This data comes from Lending Club\n    (\\oiRedirect{lendingclub-info-download-data}\n        {lendingclub.com}),\n    which provides a large set of data on the people who\n    received loans through their platform.\n    The data used in the textbook comes from a sample\n    of the loans made in Q1 (Jan, Feb, March) 2018.\n\\item[\\ref{dataBasics}]\n    \\datawrap{\\datalink{county}, \\datalink{county\\_complete}}\n    These data come from several government sources.\n    For those variables included in the\n    county data set, only the most recent data is reported,\n    as of what was available in late 2018.\n    Data prior to 2011 is all from\n    \\oiRedirect{census_gov}{census.gov},\n    where the specific Quick Facts page providing the data\n    is no longer available.\n    The more recent data comes from\n    \\oiRedirect\n        {ers_usda_gov-data_products-county_level_data_sets}\n        {USDA (ers.usda.gov)},\n    \\oiRedirect\n        {bls_gov-lau}\n        {Bureau of Labor Statistics (bls.gov/lau)},\n    \\oiRedirect\n        {census_gov-did-www-saipe}\n        {SAIPE (census.gov/did/www/saipe)},\n    and\n    \\oiRedirect\n        {census_gov-programs_surveys-acs}\n        {American Community Survey\n            (census.gov/programs-surveys/acs)}.\n\n\\item[\\ref{section_obs_data_sampling}]\n    \\datawrap{Nurses' Health Study}\n    For more information on this data set, see \\\\\n    \\oiRedirect{textbook-channing_nurse_study}\n        {www.channing.harvard.edu/nhs}\n\n\\item[\\ref{experimentsSection}]\n    The study we had in mind when discussing the\n    simple randomization (no blocking) study was \\\\\n    Anturane Reinfarction Trial Research Group. 1980.\n    \\emph{Sulfinpyrazone in the prevention of sudden\n    death after myocardial infarction.}\n    New England Journal of Medicine 302(5):250-256.\n\\end{itemize}\n\n\n\n\n\n\n\\section{\\nameref{ch_summarizing_data}}\n\\label{ch_summarizing_data_data}\n\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[\\ref{numericalData}]\n    \\datawrap{\\datalink{loan50}, \\datalink{county}}\n    \\seedataappendixplural{ch_intro_to_data_data}\n\n\\item[\\ref{categoricalData}]\n    \\datawrap{\\datalink{loan50}, \\datalink{county}}\n    \\seedataappendixplural{ch_intro_to_data_data}\n\n\\item[\\ref{caseStudyMalariaVaccine}]\n    \\datawrap{\\datalink{malaria}}\n    Lyke et al. 2017.\n    PfSPZ vaccine induces strain-transcending T cells\n    and durable protection against heterologous controlled\n    human malaria infection.\n    PNAS 114(10):2711-2716.\n    \\oiRedirect{lyke-ishizuka-2017}\n        {www.pnas.org/content/114/10/2711}\n\\end{itemize}\n\n\n\n\n\n\n\n\n\n\\section{\\nameref{ch_probability}}\n\\label{ch_probability_data}\n\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[\\ref{basicsOfProbability}]\n    \\datawrap{\\datalink{loan50}, \\datalink{county}}\n    \\seedataappendixplural{ch_intro_to_data_data}\n\\item[\\ref{basicsOfProbability}]\n    \\datawrap{\\datalink{playing\\_cards}}\n    Data set describing the 52 cards in a standard deck.\n\n\\item[\\ref{conditionalProbabilitySection}]\n    \\datawrap{\\datalink{family\\_college}}\n    Simulated data based on real population summaries at \\\\\n    \\oiRedirect{textbook-student_parent_college_2001}\n        {nces.ed.gov/pubs2001/2001126.pdf}.\n\\item[\\ref{conditionalProbabilitySection}]\n    \\datawrap{\\datalink{smallpox}}\n    Fenner F. 1988.\n    Smallpox and Its Eradication\n    (History of International Public Health, No. 6).\n    Geneva: World Health Organization. ISBN 92-4-156110-6.\n\n\\item[\\ref{conditionalProbabilitySection}]\n    \\datawrap{Mammogram screening, probabilities}\n    The probabilities reported were obtained using studies\n    reported at\n    \\oiRedirect{textbook-breastCancerDotOrg_20090831b}\n        {www.breastcancer.org}\n    and \\oiRedirect{textbook-ncbi_nih_breast_cancer}\n        {www.ncbi.nlm.nih.gov/pmc/articles/PMC1173421}. \n\n\\item[\\ref{conditionalProbabilitySection}]\n    \\datawrap{Jose campus visits, probabilities}\n    \\madeup{}\n\n\\item[\\ref{smallPop}]\n    No data sets were described in this section.\n\n\\item[\\ref{randomVariablesSection}]\n    \\datawrap{Course material purchases and probabilities}\n    \\madeup{}\n\n\\item[\\ref{randomVariablesSection}]\n    \\datawrap{Auctions for TV and toaster}\n    \\madeup{}\n\n\\item[\\ref{randomVariablesSection}]\n    \\datawrap{\\datalink{stocks\\_18}}\n    Monthly returns for Caterpillar, Exxon Mobil Corp,\n    and Google for November 2015 to October 2018.\n\n\\item[\\ref{contDist}]\n    \\datawrap{\\datalink{fcid}}\n    This sample can be considered a simple random sample\n    from the US population.\n    It relies on the USDA Food Commodity Intake Database.\n\n\\end{itemize}\n\n\n\n\n\\section{\\nameref{ch_distributions}}\n\\label{ch_distributions_data}\n\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[\\ref{normalDist}]\n    \\datawrap{SAT and ACT score distributions}\n    The SAT score data comes from the 2018 distribution,\n    which is provided at \\\\\n    {\\small\n    \\oiRedirect{textbook-collegeboard_sat_2018_score_distribution}\n        {reports.collegeboard.org/pdf/2018-total-group-sat-suite-assessments-annual-report.pdf}} \\\\\n    The ACT score data is available at \\\\\n    {\\footnotesize\n    \\oiRedirect{textbook-act_2018_score_distribution}\n        {act.org/content/dam/act/unsecured/documents/cccr2018/P\\_99\\_999999\\_N\\_S\\_N00\\_ACT-GCPR\\_National.pdf}} \\\\\n    We also acknowledge that the actual ACT score distribution\n    is \\emph{not} nearly normal.\n    However, since the topic is very accessible,\n    we decided to keep the context and examples.\n\\item[\\ref{normalDist}]\n    \\datawrap{Male heights}\n    The distribution is based on the\n    USDA Food Commodity Intake Database.\n\\item[\\ref{normalDist}]\n    \\datawrap{\\datalink{possum}}\n    The distribution parameters are based on a sample\n    of possums from Australia and New Guinea.\n    The original source of this data is as follows.\n    Lindenmayer DB, et al. 1995.\n    \\emph{Morphological variation among columns of the\n        mountain brushtail possum, Trichosurus caninus\n        Ogilby (Phalangeridae: Marsupiala)}.\n    Australian Journal of Zoology 43: 449-458.\n\n%\\item[\\ref{assessingNormal}]\n%    \\datawrap{\\datalink{male\\_heights\\_fcid}}\n%    This sample can be considered a simple random sample\n%    from the US population.\n%    It relies on the USDA Food Commodity Intake Database.\n%\\item[\\ref{assessingNormal}]\n%    \\datawrap{\\datalink{simulated\\_normal}}\n%    These data were simulated from a standard normal distribution.\n%    This data set includes three different data sets.\n%\\item[\\ref{assessingNormal}]\n%    \\datawrap{\\datalink{nba\\_players\\_19}}\n%    Summary information from the NBA players for the\n%    2018-2019 season.\n%    Data were retrieved from\n%    \\oiRedirect{data-nba_players_19}{www.nba.com/players}.\n%\\item[\\ref{assessingNormal}]\n%    \\datawrap{\\datalink{poker}}\n%    Poker winnings (and losses) for 50 days by a professional\n%    poker player, which represents their first 50 days trying\n%    to play for a living.\n%    Anonymity has been requested by the player.\n%\\item[\\ref{assessingNormal}]\n%    \\datawrap{\\datalink{simulated\\_dist}}\n%    Simulated data sets,\n%    not necessarily drawn from a normal distribution.\n%    This data set includes six different data sets.\n\n\\item[\\ref{geomDist}]\n    \\datawrap{Exceeding insurance deductible}\n    These statistics were made up but are possible\n    values one might observe for low-deductible plans.\n\n\\item[\\ref{binomialModel}]\n    \\datawrap{Exceeding insurance deductible}\n    These statistics were made up but are possible\n    values one might observe for low-deductible plans.\n\\item[\\ref{binomialModel}]\n    \\datawrap{Smoking friends}\n    Unfortunately, we don't currently have additional\n    information on the source for the 30\\% statistic,\n    so don't consider this one as fact since we cannot\n    verify it was from a reputable source.    \n\\item[\\ref{binomialModel}]\n    \\datawrap{US smoking rate}\n    The 15\\% smoking rate in the US figure is close to\n    the value from the Centers for Disease Control and\n    Prevention website, which reports a value of 14\\%\n    as of the 2017 estimate: \\\\\n    \\oiRedirect{cdc_gov-tobacco-data_statistics}\n        {cdc.gov/tobacco/data\\_statistics/fact\\_sheets/adult\\_data/cig\\_smoking/index.htm}\n\n\\item[\\ref{negativeBinomial}]\n    \\datawrap{Football kicker}\n    \\madeup{}\n\\item[\\ref{negativeBinomial}]\n    \\datawrap{Heart attack admissions}\n    This example was made up, though the heart attack\n    admissions are realistic for some hospitals.\n\n\\item[\\ref{poisson}]\n    \\datawrap{\\datalink{ami\\_occurrences}}\n    This is a simulated data set but resembles actual\n    AMI data for New York City based on typical AMI\n    incidence rates.\n\\end{itemize}\n\n\n\n\n\n\n\n\n\\section{\\nameref{ch_foundations_for_inf}}\n\\label{ch_foundations_for_inf_data}\n\n\\begin{itemize}\n\\item[\\ref{pointEstimates}]\n    \\datawrap{\\datalink{pew\\_energy\\_2018}}\n    The actual data has more observations than were referenced\n    in this chapter.\n    That is, we used a subsample since it helped smooth some\n    of the examples to have a bit more variability.\n    The \\data{pew\\us{}energy\\us{}2018} data set represents\n    the full data set for each of the different energy source\n    questions, which covers solar, wind, offshore drilling,\n    hydrolic fracturing, and nuclear energy.\n    The statistics used to construct the data are from\n    the following page:\n    \\begin{center}\n    \\oiRedirect{textbook-pew_2018_poll_on_solar_and_wind_expansion}\n        {{\\small{www.pewinternet.org/2018/05/14/majorities-see-government-efforts-to-protect-the-environment-as-insufficient/}}}\n    \\end{center}\n    \n\\item[\\ref{confidenceIntervals}]\n    \\datawrap{\\datalink{pew\\_energy\\_2018}}\n    See the details for this data set above\n    in the Section~\\ref{pointEstimates} data section.\n\\item[\\ref{confidenceIntervals}]\n    \\datawrap{\\datalink{ebola\\_survey}}\n    In New York City on October 23rd, 2014, a doctor who had\n    recently been treating Ebola patients in Guinea went to\n    the hospital with a slight fever and was subsequently\n    diagnosed with Ebola.\n    Soon thereafter, an NBC~4 New York/The Wall Street\n    Journal/Marist Poll found that\n    82\\% of New Yorkers favored a\n    ``mandatory 21-day quarantine for anyone who has come\n    in contact with an Ebola patient''.\n    This poll included responses of 1,042\n    New York adults between Oct 26th and~28th, 2014.\n    \\oiRedirect{textbook-maristpoll_ebola_201410}\n        {Poll ID NY141026 on maristpoll.marist.edu}.\n\n\\item[\\ref{hypothesisTesting}]\n    \\datawrap{\\datalink{pew\\_energy\\_2018}}\n    See the details for this data set above\n    in the Section~\\ref{pointEstimates} data section.\n\\item[\\ref{hypothesisTesting}]\n    \\datawrap{Rosling questions}\n    We noted much smaller samples than the Roslings'\n    describe in their book,\n    \\oiRedirect{amazon_factfulness}{Factfulness},\n    The samples we describe are similar but not\n    the same as the actual rates.\n    The approximate rates for the correct answers for the\n    two questions for (sometimes different) populations\n    discussed in the book, as reported in\n    \\oiRedirect{amazon_factfulness}{Factfulness},\n    are\n    \\begin{itemize}\n    \\item\n        80\\% of the world's 1 year olds have been vaccinated\n        against some disease:\n        13\\% get this correct (17\\% in the US).\n        \\oiRedirect{gapm-io-q9}{gapm.io/q9}\n    \\item\n        Number of children in the world in 2100:\n        9\\% correct.\n        \\oiRedirect{gapm-io-q5}{gapm.io/q5}\n    \\end{itemize}\n    Here are a few more questions and a rough percent\n    of people who get them correct:\n    \\begin{itemize}\n    \\item\n        In all low-income countries across the world today,\n        how many girls finish primary school: 20\\%, 40\\%, or 60\\%?\n        Answer: 60\\%.\n        About 7\\% of people get this question correct.\n        \\oiRedirect{gapm-io-q1}{gapm.io/q1}\n    \\item\n        What is the life expectancy of the world today:\n        50 years, 60 years, or 70 years?\n        Answer: 70 years.\n        In the US, about 43\\% of people get this question correct.\n        \\oiRedirect{gapm-io-q4}{gapm.io/q4}\n%    \\item\n%        How many of the world's 1 year old children today\n%        have been vaccinated against some disease:\n%        20\\%, 50\\%, or 80\\%?\n%        Answer: 80\\%.\n%        About 13\\% of people get this question correct.\n%        \\oiRedirect{gapm-io-q9}{gapm.io/q9}\n    \\item\n        In 1996, tigers, giant pandas, and black rhinos\n        were all listed as endangered.\n        How many of these three species are more\n        critically endangered today:\n        two of them,\n        one of them,\n        none of them?\n        Answer: none of them.\n        About 7\\% of people get this question correct.\n        \\oiRedirect{gapm-io-q11}{gapm.io/q11}\n    \\item\n        How many people in the world have some access\n        to electricity? 20\\%, 50\\%, 80\\%.\n        Answer: 80\\%.\n        About 22\\% of people get this correct.\n        \\oiRedirect{gapm-io-q12}{gapm.io/q12}\n    \\end{itemize}\n    For more information, check out the book,\n    \\oiRedirect{amazon_factfulness}{Factfulness}.\n\\item[\\ref{hypothesisTesting}]\n    \\datawrap{\\datalink{pew\\_energy\\_2018}}\n    See the details for this data set above\n    in the Section~\\ref{pointEstimates} data section.\n\\item[\\ref{hypothesisTesting}]\n    \\datawrap{\\datalink{nuclear\\_survey}}\n    A simple random sample of 1,028 US adults in March 2013\n    found that 56\\% of US adults support nuclear arms\n    reduction. \\\\\n    \\oiRedirect{textbook-nuclear_arms_reduction_201303}\n        {www.gallup.com/poll/161198/favor-russian-nuclear-arms-reductions.aspx}\n\\item[\\ref{hypothesisTesting}]\n    \\datawrap{Car manufacturing}\n    \\madeup{}\n\\item[\\ref{hypothesisTesting}]\n    \\datawrap{\\datalink{stent30}, \\datalink{stent365}}\n    \\seedataappendixplural{ch_intro_to_data_data}\n\n\\end{itemize}\n\n\n\n\n\n\\D{\\newpage}\n\n\\section{\\nameref{ch_inference_for_props}}\n\\label{ch_inference_for_props_data}\n\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[\\ref{singleProportion}]\n    \\datawrap{Payday loans}\n    The statistics come from the following source: \\\\\n    {\\footnotesize\\oiRedirect{pew-payday-loans-2017}\n        {pewtrusts.org/-/media/assets/2017/04/payday-loan-customers-want-more-protections-methodology.pdf}}\n\\item[\\ref{singleProportion}]\n    \\datawrap{Tire factory}\n    \\madeup{}\n\n\\item[\\ref{differenceOfTwoProportions}]\n    \\datawrap{\\datalink{cpr}}\n    B$\\ddot{\\text{o}}$ttiger et al.\n    \\emph{Efficacy and safety of thrombolytic therapy after\n        initially unsuccessful cardiopulmonary resuscitation:\n        a prospective clinical trial}.\n        The Lancet, 2001.\n\\item[\\ref{differenceOfTwoProportions}]\n    \\datawrap{\\datalink{fish\\_oil\\_18}}\n    Manson JE, et al. 2018.\n    \\emph{Marine n-3 Fatty Acids and Prevention of\n    Cardiovascular Disease and Cancer.}\n    NEJMoa1811403.\n\\item[\\ref{differenceOfTwoProportions}]\n    \\datawrap{\\datalink{mammogram}}\n    \\oiRedirect{textbook-90k_mammogram_study_2014}\n        {Miller AB. 2014.\n            \\emph{Twenty five year follow-up for breast cancer\n            incidence and mortality of the Canadian National\n            Breast Screening Study: randomised screening trial}.\n            BMJ 2014;348:g366.}\n\\item[\\ref{differenceOfTwoProportions}]\n    \\datawrap{\\datalink{drone\\_blades}}\n    The quality control data set for quadcopter drone blades\n    is a made-up data set for an example.\n    We provide the simulated data in the\n    \\data{drone\\us{}blades} data set.\n\n\\item[\\ref{oneWayChiSquare}]\n    \\datawrap{\\datalink{jury}}\n    The jury data set for examining discrimination\n    is a made-up data set an example.\n    We provide the simulated data in the \\data{jury} data set.\n\\item[\\ref{oneWayChiSquare}]\n    \\datawrap{\\datalink{sp500\\_1950\\_2018}}\n    Data is sourced from\n    \\oiRedirect{yahoo_finance}\n        {finance.yahoo.com}.\n\n\\item[\\ref{twoWayTablesAndChiSquare}]\n    \\datawrap{\\datalink{ask}}\n    Minson JA, Ruedy NE, Schweitzer ME.\n    \\emph{There is such a thing as a stupid question:\n    Question disclosure in strategic communication}. \\\\\n    {\\small\\oiRedirect{minson_ruedy_data_source}\n        {opim.wharton.upenn.edu/DPlab/papers/workingPapers/}}\\\\\n    {\\small\\oiRedirect{minson_ruedy_data_source}\n        {Minson\\_working\\_Ask\\%20(the\\%20Right\\%20Way)\\%20and\\%20You\\%20Shall\\%20Receive.pdf}}\n\n\\item[\\ref{twoWayTablesAndChiSquare}]\n    \\datawrap{\\datalink{diabetes2}}\n    Zeitler P, et al. 2012.\n    \\emph{A Clinical Trial to Maintain Glycemic Control\n    in Youth with Type~2 Diabetes}.\n    N Engl J Med.\n\n\\end{itemize}\n\n\n\n\n\n\n\\section{\\nameref{ch_inference_for_means}}\n\\label{ch_inference_for_means_data}\n\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[\\ref{oneSampleMeansWithTDistribution}]\n    \\datawrap{Risso's dolphins}\n    Endo T and Haraguchi K. 2009.\n    \\emph{High mercury levels in hair samples from\n    residents of Taiji, a Japanese whaling town}.\n    Marine Pollution Bulletin 60(5):743-747.\n\n    Taiji was featured in the movie\n    \\emph{The Cove}, and it is a significant source of dolphin\n    and whale meat in Japan.\n    Thousands of dolphins pass through the Taiji area annually,\n    and we assumes these 19 dolphins reasonably represent\n    a simple random sample from those dolphins.\n\\item[\\ref{oneSampleMeansWithTDistribution}]\n    \\datawrap{Croaker white fish}\n    \\oiRedirect{textbook-fda_mercury_in_fish_2010}\n        {fda.gov/food/foodborneillnesscontaminants/metals/ucm115644.htm}\n\\item[\\ref{oneSampleMeansWithTDistribution}]\n    \\datawrap{\\datalink{run17}}\n    \\oiRedirect{textbook-cherryblossom_org}{www.cherryblossom.org}\n\n\\item[\\ref{pairedData}]\n    \\datawrap{\\datalink{textbooks},\n        \\datalink{ucla\\_textbooks\\_f18}}\n    Data were collected by OpenIntro staff in 2010 and again\n    in 2018.\n    For the 2018 sample, we sampled 201 UCLA courses.\n    Of those, 68 required books that could be\n    found on Amazon.\n    The websites where information was retrieved: \\\\\n    \\oiRedirect{ucla_class_schedule}\n        {sa.ucla.edu/ro/public/soc},\n    \\oiRedirect{ucla_verbacompare}{ucla.verbacompare.com},\n    and \\oiRedirect{amazon}{amazon.com}.\n\n\\item[\\ref{differenceOfTwoMeans}]\n    \\datawrap{\\datalink{stem\\_cells}}\n    \\oiRedirect{textbook-menard_stem_cells_2005}\n        {Menard C, et al. 2005.\n            Transplantation of cardiac-committed mouse\n            embryonic\n            stem cells to infarcted sheep myocardium:\n            a preclinical study.\n            The Lancet: 366:9490, p1005-1012.}\n\\item[\\ref{differenceOfTwoMeans}]\n    \\datawrap{\\datalink{ncbirths}}\n    Birth records released by North Carolina in 2004.\n    Unfortunately, we don't currently have additional\n    information on the source for this data set.\n\\item[\\ref{differenceOfTwoMeans}]\n    \\datawrap{Exam versions}\n    \\madeup{}\n\n\\item[\\ref{PowerForDifferenceOfTwoMeans}]\n    \\datawrap{Blood pressure statistics}\n    The blood pressure standard deviation for patients\n    with blood pressure ranging from 140 to 180 mmHg\n    is guessed and may be a little\n    (but likely not dramatically)\n    imprecise from what we'd observe in actual data.\n\n\\item[\\ref{anovaAndRegrWithCategoricalVariables}]\n    \\datawrap{\\datalink{toy\\_anova}}\n    Data used for Figure~\\ref{toyANOVA},\n    where this data was made up.\n\\item[\\ref{anovaAndRegrWithCategoricalVariables}]\n    \\datawrap{\\datalink{mlb\\_players\\_18}}\n    Data were retrieved from\n    \\oiRedirect{mlb-stats}{mlb.mlb.com/stats}.\n    Only players with at least 100 at bats were considered\n    during the analysis.\n\\item[\\ref{anovaAndRegrWithCategoricalVariables}]\n    \\datawrap{\\datalink{classdata}}\n    \\madeup{}\n\n\\end{itemize}\n\n\n\n\n\n\n\\section{\\nameref{ch_regr_simple_linear}}\n\\label{ch_regr_simple_linear_data}\n\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[\\ref{fitting_line_to_data_section}]\n    \\datawrap{\\datalink{simulated\\_scatter}}\n    Fake data used for the first three plots.\n    The perfect linear plot uses group~4 data,\n    where \\var{group} variable in the data set\n    (Figure~\\ref{perfLinearModel}).\n    The group of 3 imperfect linear plots use groups~1-3\n    (Figure~\\ref{imperfLinearModel}).\n    The sinusoidal curve uses group~5 data\n    (Figure~\\ref{notGoodAtAllForALinearModel}).\n    The group of 3 scatterplots with residual plots use groups~6-8\n    (Figure~\\ref{sampleLinesAndResPlots}).\n    The correlation plots uses groups~9-19 data\n    (Figures~\\ref{posNegCorPlots} and~\\ref{corForNonLinearPlots}).\n\\item[\\ref{fitting_line_to_data_section}]\n    \\datawrap{\\datalink{possum}}\n    \\seedataappendix{ch_distributions_data}\n\n\\item[\\ref{fittingALineByLSR}]\n    \\datawrap{\\datalink{elmhurst}}\n    These data were sampled from a table of data for all\n    freshman from the 2011 class at Elmhurst College that\n    accompanied an article titled\n    \\emph{What Students Really Pay to Go to College}\n    published online by \\emph{The~Chronicle of Higher Education}:\n    \\oiRedirect{textbook-chronicle_elmhurst_article}\n        {chronicle.com/article/What-Students-Really-Pay-to-Go/131435}.\n\\item[\\ref{fittingALineByLSR}]\n    \\datawrap{\\datalink{simulated\\_scatter}}\n    The plots for things that can go wrong uses groups 20-23\n    (Figure~\\ref{whatCanGoWrongWithLinearModel}).\n\\item[\\ref{fittingALineByLSR}]\n    \\datawrap{\\datalink{mariokart}}\n    Auction data from Ebay (ebay.com) for the game Mario Kart\n    for the Nintendo Wii.\n    This data set was collected in early October, 2009.\n\n\\item[\\ref{typesOfOutliersInLinearRegression}]\n    \\datawrap{\\datalink{simulated\\_scatter}}\n    The plots for types of outliers uses groups 24-29\n    (Figure~\\ref{outlierPlots}).\n\n\\item[\\ref{inferenceForLinearRegression}]\n    \\datawrap{\\datalink{midterms\\_house}}\n    Data was retrieved from Wikipedia.\n\n\\end{itemize}\n\n\n\n\n\n\n\n\\section{\\nameref{ch_regr_mult_and_log}}\n\\label{ch_regr_mult_and_log_data}\n\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item[\\ref{introductionToMultipleRegression}]\n    \\datawrap{\\datalink{loans\\_full\\_schema}}\n    \\seedataappendix{ch_intro_to_data_data}\n\n\\item[\\ref{model_selection_section}]\n    \\datawrap{\\datalink{loans\\_full\\_schema}}\n    \\seedataappendix{ch_intro_to_data_data}\n\n\\item[\\ref{multipleRegressionModelAssumptions}]\n    \\datawrap{\\datalink{loans\\_full\\_schema}}\n    \\seedataappendix{ch_intro_to_data_data}\n\n\\item[\\ref{mario_kart_case_study}]\n    \\datawrap{\\datalink{mariokart}}\n    \\seedataappendix{ch_regr_simple_linear_data}\n\n\\item[\\ref{logisticRegression}]\n    \\datawrap{\\datalink{resume}}\n    Bertrand M, Mullainathan S. 2004.\n    \\emph{Are Emily and Greg More Employable than Lakisha and Jamal?\n    A Field Experiment on Labor Market Discrimination}.\n    The American Economic Review 94:4 (991-1013).\n    \\oiRedirect{resume-data-2004}\n        {www.nber.org/papers/w9873}\n\n    We did omit discussion of some structure in\n    the data for the analysis presented:\n    the experiment design included blocking,\n    where typically four resumes were sent to each job:\n    one for each inferred race/sex combination\n    (as inferred based on the first name).\n    We did not worry about this blocking aspect,\n    since accounting for the blocking would\n    \\emph{reduce} the standard error without notably\n    changing the point estimates for the\n    \\var{race} and \\var{sex} variables\n    versus the analysis performed in the section.\n    That is, the most interesting conclusions in the\n    study are unaffected even when completing a more\n    sophisticated analysis.\n\n%\\item[\\ref{logisticRegression}]\n%    \\datawrap{\\datalink{research\\_reply}}\n%    Milkman KL, Akinola M, Chugh D. 2015.\n%    What Happens Before?\n%    A Field Experiment Exploring How Pay and\n%    Representation Differentially Shape Bias\n%    on the Pathway Into Organizations.\n%    Journal of Applied Psychology, 100:6, p1678-1712.\n%\n%    This study highlights results where fictional students\n%    contacted faculty members.\n%    The outcome of interest was whether the faculty member\n%    would reply, and the variables of interest were the\n%    race and sex of the prospective student as well as\n%    demographics of the faculty member who received the message.\n%    The authors have made the data set publicly available,\n%    and we've put it into a CSV file that is friendly\n%    for downloading through the \\data{research\\_reply} data set.\n%    \\Comment{Either get this data set in a sharable form\n%      or remove this reference.}\n\n\\end{itemize}\n\n\\index{data|)}\n"
  },
  {
    "path": "extraTeX/eoceSolutions/eoceSolutions.tex",
    "content": "\\chapter{Exercise solutions}\n\\label{eoceSolutions}\n\n\n\n\n\n%_______________\n\\eocesolch{Introduction to data}\n\n\n\n%_______________\n\\begin{multicols}{2}\n\n% 1\n\n\\eocesol{(a)~Treatment: $10/43 = 0.23 \\rightarrow 23\\%$. \\\\\n(b)~Control: $2/46 = 0.04 \\rightarrow 4\\%$. \n(c)~A higher percentage of patients in the treatment group were pain \nfree 24 hours after receiving acupuncture. \n(d)~It is possible that the observed difference between the two group \npercentages is due to chance.}\n\n% 3\n\n\\eocesol{(a)~``Is there an association between air pollution exposure and preterm births?\"\n(b)~143,196 births in Southern California between 1989 and 1993.\n(c)~Measurements of carbon monoxide, nitrogen dioxide, ozone, and particulate \nmatter less than 10$\\mu g/m^3$ (PM$_{10}$) collected at air-quality-monitoring \nstations as well as length of gestation.\nContinuous numerical variables. }\n\n% 5\n\n\\eocesol{(a)~``Does explicitly telling children not to cheat affect their likelihood to \ncheat?\".\n(b)~160 children between the ages of 5 and 15.\n(c)~Four variables: (1) age (numerical, continuous), (2) sex (categorical), \n(3) whether they were an only child or not (categorical), (4) whether they \ncheated or not (categorical).}\n\n% 7\n\n\\eocesol{Explanatory: acupuncture or not.\nResponse: if the patient was pain free or not.}\n\n% 9\n\n\\eocesol{(a)~$50 \\times 3 = 150$. \n(b)~Four continuous numerical variables: sepal length, sepal width, petal length, and petal width. \n(c)~One categorical variable, species, with three levels: \\emph{setosa}, \\emph{versicolor}, and \\emph{virginica}.}\n\n% 11\n\n\\eocesol{(a)~Airport ownership status (public/private),\n    airport usage status (public/private),\n    latitude,\n    and longitude.\n(b)~Airport ownership status: categorical, not ordinal.\n    Airport usage status: categorical, not ordinal.\n    Latitude: numerical, continuous.\n    Longitude: numerical, continuous.}\n\n% 13\n\n\\eocesol{(a)~Population: all births, sample: 143,196 births between 1989 and 1993 in \nSouthern California. \n(b)~If births in this time span at the geography can be considered to be \nrepresentative of all births, then the results are generalizable to the \npopulation of Southern California. However, since the study is observational \nthe findings cannot be used to establish causal relationships.}\n\n% 15\n\n\\eocesol{(a)~Population: all asthma patients aged 18-69 who rely on medication for \nasthma treatment. Sample: 600 such patients.\n(b)~If the patients in this sample, who are likely not randomly sampled, can \nbe considered to be representative of all asthma patients aged 18-69 who rely \non medication for asthma treatment, then the results are generalizable to the \npopulation defined above. Additionally, since the study is experimental, the \nfindings can be used to establish causal relationships.}\n\n% 17\n\n\\eocesol{(a)~Observation.\n(b)~Variable.\n(c)~Sample statistic (mean).\n(d)~Population parameter (mean).}\n\n% 19\n\n\\eocesol{(a)~Observational.\n(b)~Use stratified sampling to randomly sample a fixed number of students, \nsay 10, from each section for a total sample size of 40 students.}\n\n% 21\n\n\\eocesol{(a)~Positive, non-linear, somewhat strong. Countries in which a higher \npercentage of the population have access to the internet also tend to have \nhigher average life expectancies, however rise in life expectancy trails \noff before around 80 years old.\n(b)~Observational.\n(c)~Wealth: countries with individuals who can widely afford the internet \ncan probably also afford basic medical care. (Note: Answers may vary.)}\n\n% 23\n\n\\eocesol{(a)~Simple random sampling is okay. In~fact, it's rare for simple random \nsampling to not be a reasonable sampling method! \n(b)~The student opinions may vary by field of study, so the stratifying \nby this variable makes sense and would be reasonable. \n(c)~Students of similar ages are probably going to have more similar \nopinions, and we want clusters to be diverse with respect to the outcome \nof interest, so this would \\textbf{not} be a good approach. (Additional \nthought: the clusters in this case may also have very different numbers \nof people, which can also create unexpected sample sizes.)}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 25\n\n\\eocesol{(a)~The cases are 200 randomly sampled men and women.\n(b)~The response variable is attitude towards a fictional microwave oven.\n(c)~The explanatory variable is dispositional attitude.\n(d)~Yes, the cases are sampled randomly.\n(e)~This is an observational study since there is no random assignment to \ntreatments.\n(f)~No, we cannot establish a causal link between the explanatory and response \nvariables since the study is observational.\n(g)~Yes, the results of the study can be generalized to the population at \nlarge since the sample is random.}\n\n% 27\n\n\\eocesol{(a)~Simple random sample. Non-response bias, if only those people who have \nstrong opinions about the survey responds his sample may not be representative \nof the population.\n(b)~Convenience sample. His sample may not be \nrepresentative of the population since it consists only of his friends. It is \nalso possible that the study will have non-response bias if some choose to not \nbring back the survey.\n(c)~Convenience sample. This will have a similar issues to handing out surveys \nto friends.\n(d)~Multi-stage sampling. If the classes are similar to each other with \nrespect to student composition this approach should not introduce bias, \nother than potential non-response bias.}\n\n% 29\n\n\\eocesol{(a)~Exam performance.\n(b)~Light level: fluorescent overhead lighting, yellow overhead lighting, no overhead \nlighting (only desk lamps).\n(c)~Sex: man, woman.}\n\n% 31\n\n\\eocesol{(a)~Experiment.\n(b)~Light level (overhead lighting, yellow overhead lighting, no overhead lighting) and \nnoise level (no noise, construction noise, and human chatter noise).\n(c)~Since the researchers want to ensure equal gender representation, sex will be a blocking variable.}\n\n% 33\n\n\\eocesol{Need randomization and blinding. One possible outline: \n(1)~Prepare two cups for each \nparticipant, one containing regular Coke and the other containing Diet Coke. Make sure \nthe cups are identical and contain equal amounts of soda. Label the cups A (regular) and \nB (diet). (Be sure to randomize A and B for each trial!)\n(2)~Give each participant the \ntwo cups, one cup at a time, in random order, and ask the participant to record a value \nthat indicates how much she liked the beverage.  Be sure that neither the participant nor \nthe person handing out the cups knows the identity of the beverage to make this a double-\nblind experiment. (Answers may vary.)}\n\n% 35\n\n\\eocesol{(a)~Observational study.\n(b)~Dog: Lucy. Cat: Luna.\n(c)~Oliver and Lily.\n(d)~Positive, as the popularity of a name for dogs increases, so does the \npopularity of that name for cats. }\n\n% 37\n\n\\eocesol{(a)~Experiment.\n(b)~Treatment: 25 grams of chia seeds twice a day, control: placebo. \n(c)~Yes, gender.\n(d)~Yes, single blind since the patients were blinded to the treatment \nthey received.\n(e)~Since this is an experiment, we can make a causal statement. However, since the \nsample is not random, the causal statement cannot be generalized to the population at \nlarge.}\n\n% 39\n\n\\eocesol{(a)~Non-responders may have a different response to this question, e.g. \nparents who returned the surveys likely don't have difficulty spending time \nwith their children.\n(b)~It is unlikely that the women who were reached at the same address 3 years \nlater are a random sample. These missing responders are probably renters \n(as opposed to homeowners) which means that they might be in a lower socio-\neconomic status than the respondents.\n(c)~There is no control group in this study, this is an observational study, \nand there may be confounding variables, e.g. these people may go running \nbecause they are generally healthier and/or do other exercises.}\n\n% 41\n\n\\eocesol{(a)~Randomized controlled experiment.\n(b)~Explanatory: treatment group (categorical, with 3 levels). Response variable: \nPsychological well-being.\n(c)~No, because the participants were volunteers.\n(d)~Yes, because it was an experiment.\n(e)~The statement should say ``evidence'' instead of ``proof''.}\n\n% 43\n\n\\eocesol{(a)~Categorical, non-ordinal: County, State, Driver's race. Numerical, discrete: No. of stops per year. Numerical, continuous: \\% searched, \\% drivers arrested.\n(b)~All categorical, non-ordinal.\n(c)~Response: whether the car was searched or not.\n    Explanatory: race of the driver.}\n\n\n\n%_______________\n\\end{multicols}\n\n\n\n%_______________\n\\eocesolch{Summarizing data}\n\n\n\n%_______________\n\\begin{multicols}{2}\n\n% 1\n\n\\eocesol{(a)~Positive association: mammals with longer gestation periods tend to live longer as \nwell.\n(b)~Association would still be positive.\n(c)~No, they are not independent. See part~(a).}\n\n% 3\n\n\\eocesol{The graph below shows a ramp up period.\nThere may also be a period of exponential growth at the start\nbefore the size of the petri dish becomes a factor in slowing growth. \\\\\n\\FigureFullPath[A graph is shown with a horizontal axis of \"time\" and a vertical axis labeled \"number of bacteria cells\". A curve is shown rising steeply on the left, and as it moves right, it rises more slow until it nearly stops rising as it reaches right side of the graph.]{0.25}{ch_summarizing_data/figures/eoce/reproducing_bacteria/reproducing_bacteria_sketch}}\n\n% 5\n\n\\eocesol{(a)~Population mean, $\\mu_{2007} = 52$; sample mean, $\\bar{x}_{2008} = 58$.\n(b)~Population mean, $\\mu_{2001} = 3.37$; sample mean, $\\bar{x}_{2012} = 3.59$.}\n\n% 7\n\n\\eocesol{Any 10 employees whose average number of days off is between the minimum and the mean \nnumber of days off for the entire workforce at this plant.}\n\n% 9\n\n\\eocesol{(a)~Dist~2 has a higher mean since $20 > 13$, and a higher standard deviation \nsince 20 is further from the rest of the data than 13.\n(b)~Dist~1 has a higher mean since $-20 > -40$, and Dist~2 has a \nhigher standard deviation since -40 is farther away from the rest of the data than -20.\n(c)~Dist~2 has a higher mean since all values in this distribution are higher \nthan those in Dist~1, but both distribution have the same standard deviation \nsince they are equally variable around their respective means.\n(d)~Both distributions have the same mean since they're both centered at 300, but \nDist~2 has a higher standard deviation since the observations are farther from \nthe mean than in Dist~1.}\n\n% 11\n\n\\eocesol{(a)~About 30.\n(b)~Since the distribution is right skewed the mean is higher than the median.\n(c)~Q1: between 15 and 20, Q3: between 35 and 40, IQR: about 20.\n(d)~Values that are considered to be unusually low or high lie more than 1.5$\\times$IQR \naway from the quartiles. Upper fence: Q3 + 1.5 $\\times$ IQR =  $37.5 + 1.5 \\times 20 = 67.5$; \nLower fence: Q1 - 1.5 $\\times$ IQR =  $17.5 - 1.5 \\times 20 =  -12.5$; The lowest AQI \nrecorded is not lower than 5 and the highest AQI recorded is not higher than 65, which \nare both within the fences. Therefore none of the days in this sample would be considered \nto have an unusually low or high AQI.}\n\n% 13\n\n\\eocesol{The histogram shows that the distribution is bimodal, which is not apparent in the box \nplot. The box plot makes it easy to identify more precise values of observations outside \nof the whiskers.}\n\n% 15\n\n\\eocesol{(a)~The distribution of number of pets per household is likely right skewed as there is a natural boundary at 0 and only a few people have many pets. Therefore the center would be best described by the median, and variability would be best described by the IQR.\n(b)~The distribution of number of distance to work is likely right skewed as there is a natural boundary at 0 and only a few people live a very long distance from work. Therefore the center would be best described by the median, and variability would be best described by the IQR.\n(c)~The distribution of heights of males is likely symmetric. Therefore the center would be best described by the mean, and variability would be best described by the standard deviation.}\n\n% 17\n\n\\eocesol{(a)~The median is a much better measure of the typical amount earned by these 42 \npeople. The mean is much higher than the income of 40 of the 42 people. This is \nbecause the mean is an arithmetic average and gets affected by the two extreme observations. The median does not get effected as much since it is robust to \noutliers.\n(b)~The IQR  is a much better measure of variability in the amounts earned by nearly \nall of the 42 people. The standard deviation gets affected greatly by the two high \nsalaries, but the IQR is robust to these extreme observations.}\n\n% 19\n\n\\eocesol{(a)~The distribution is unimodal and symmetric with a mean of about 25 minutes \nand a standard deviation of about 5 minutes. There does not appear to be any \ncounties with unusually high or low mean travel times. Since the distribution \nis already unimodal and symmetric, a log transformation is not necessary.\n(b)~Answers will vary. There are pockets of longer travel time around DC, \nSoutheastern NY, Chicago, Minneapolis, Los Angeles, and many other big cities. \nThere is also a large section of shorter average commute times that overlap \nwith farmland in the Midwest. Many farmers' homes are adjacent to their \nfarmland, so their commute would be brief, which may explain why the \naverage commute time for these counties is relatively low.}\n\n% 21\n\n\\eocesol{(a)~We see the order of the categories and the relative frequencies in the bar plot.\n(b)~There are no features that are apparent in the pie chart but not in the bar plot.\n(c)~We usually prefer to use a bar plot as we can also see the relative frequencies of the categories in this graph.}\n\n% 23\n\n\\eocesol{The vertical locations at which the ideological groups break into the Yes, No, \nand Not Sure categories differ, which indicates that likelihood of supporting \nthe DREAM act varies by political ideology. This suggests that the two variables \nmay be dependent.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 25\n\n\\eocesol{(a)~(i) False. Instead of comparing counts, we should compare percentages of people in each group who suffered cardiovascular problems.\n(ii)~True.\n(iii)~False. Association does not imply causation. We cannot infer a causal \nrelationship based on an observational study. The difference from part~(ii) \nis subtle.\n(iv)~True. \\\\\n(b)~Proportion of all patients who had cardiovascular problems: $\\frac{7,979}{227,571} \\approx 0.035$ \\\\\n(c)~The expected number of heart attacks in the rosiglitazone group, if having \ncardiovascular problems and treatment were independent, can be calculated as the \nnumber of patients in that group multiplied by the overall cardiovascular problem \nrate in the study: $67,593 * \\frac{7,979}{227,571} \\approx 2370$. \\\\\n(d)~(i)~$H_0$: The treatment and cardiovascular problems are independent. They have \nno relationship, and the difference in incidence rates between the rosiglitazone and \npioglitazone groups is due to chance.\n$H_A$: The treatment and cardiovascular problems are not independent. The difference \nin the incidence rates between the rosiglitazone and pioglitazone groups is not due \nto chance and rosiglitazone is associated with an increased risk of serious \ncardiovascular problems.\n(ii)~A higher number of patients with cardiovascular problems than expected under \nthe assumption of independence would provide support for the alternative hypothesis \nas this would suggest that rosiglitazone increases the risk of such problems.\n(iii)~In the actual study, we observed 2,593 cardiovascular events in the \nrosiglitazone group. In the 1,000 simulations under the independence model, we \nobserved somewhat less than 2,593 in every single simulation, which suggests that \nthe actual results did not come from the independence model. That is, the variables \ndo not appear to be independent, and we reject the independence model in favor of \nthe alternative. The study's results provide convincing evidence that rosiglitazone \nis associated with an increased risk of cardiovascular problems.}\n\n% 27\n\n\\eocesol{(a)~Decrease: the new score is smaller than the mean of the 24 previous scores.\n(b)~Calculate a weighted mean. Use a weight of 24 for the old mean and 1 for the new \nmean: $(24\\times 74 + 1\\times64)/(24+1) = 73.6$.\n%There are other ways to solve this \n%exercise that do not use a weighted mean.\n(c)~The new score is more than 1 standard deviation away from the previous mean, so \nincrease.}\n\n% 29\n\n\\eocesol{No, we would expect this distribution to be right skewed. There are two reasons \nfor this: (1)~there is a natural boundary at 0 (it is not possible to watch less \nthan 0 hours of TV), (2)~the standard deviation of the distribution is very large \ncompared to the mean.}\n\n% 31\n\n\\eocesol{The distribution of ages of best actress winners are right skewed with a \nmedian around 30 years. The distribution of ages of best actor winners \nis also right skewed, though less so, with a median around 40 years. The \ndifference between the peaks of these distributions suggest that best actress \nwinners are typically younger than best actor winners. The ages of best actress \nwinners are more variable than the ages of best actor winners. There are \npotential outliers on the higher end of both of the distributions. }\n\n% 33\n\n\\eocesol{\\FigureFullPath[A box plot is shown for \"Scores\" with the box spanning from about 72 to 82 and the median at about 78. The whiskers extend down to 66 and up to 94. A single point is shown below the lower whisker at about 57.]{0.25}{ch_summarizing_data/figures/eoce/stats_scores_box/stats_scores_boxplot}}\n\n\n\n%_______________\n\\end{multicols}\n\n\n\n%_______________\n\\eocesolch{Probability}\n\n\n\n%_______________\n\\begin{multicols}{2}\n\n% 1\n\n\\eocesol{(a)~False. These are independent trials.\n(b)~False. There are red face cards.\n(c)~True. A card cannot be both a face card and an ace.}\n\n% 3\n\n\\eocesol{(a)~10 tosses. Fewer tosses mean more variability in the sample fraction of heads, \nmeaning there's a better chance of getting at least 60\\% heads.\n(b)~100 tosses. More flips means the observed proportion of heads would often be \ncloser to the average, 0.50, and therefore also above 0.40.\n(c)~100 tosses. With more flips, the observed proportion of heads would often be \ncloser to the average, 0.50.\n(d)~10 tosses. Fewer flips would increase variability in the fraction of tosses \nthat are heads.}\n\n% 5\n\n\\eocesol{(a)~$0.5^{10}$ = 0.00098.\n(b)~$0.5^{10}$ = 0.00098.\n(c)~$P$(at least one tails) = $1 - P$(no tails) = $1 - (0.5^{10}) \\approx 1 - 0.001 = 0.999$.}\n\n% 7\n\n\\eocesol{(a)~No, there are voters who are both independent and swing voters. \\\\\n(b)\\\\\n\\FigureFullPath[A Venn diagram is shown for variables \"Independent\" and \"Swing\", where the two circles representing the variable are partially overlapping. The region of the \"Independent\" circle not overlapping the other circle is labeled with \"24\". The region of the \"Swing\" circle not overlapping the other circle is labeled with \"12\". The region where the two circles overlap is labeled with \"11\".]{0.25}{ch_probability/figures/eoce/swing_voters/swing_voters.pdf} \\\\\n(c)~Each Independent voter is either a swing voter or not. Since 35\\% of voters \nare Independents and 11\\% are both Independent and swing voters, the other 24\\% \nmust not be swing voters.\n(d)~0.47.\n(e)~0.53.\n(f)~P(Independent) $\\times$ P(swing) = $0.35\\times0.23 = 0.08$, which \ndoes not equal P(Independent and swing) = 0.11, so the events are dependent.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 9\n\n\\eocesol{(a)~If the class is not graded on a curve, they are independent. If graded on a \ncurve, then neither independent nor disjoint -- unless the instructor will only give \none A, which is a situation we will ignore in parts~(b) and~(c).\n(b)~They are probably not independent: if you study together, your study habits \nwould be related, which suggests your course performances are also related.\n(c)~No. See the answer to part~(a) when the course is not graded on a curve. More \ngenerally: if two things are unrelated (independent), then one occurring does not \npreclude the other from occurring.}\n\n% 11\n\n\\eocesol{(a)~$0.16 + 0.09 = 0.25$.\n(b)~$0.17 + 0.09 = 0.26$.\n(c)~Assuming that the education level of the husband and wife are independent: \n$0.25 \\times 0.26 = 0.065$. You might also notice we actually made a second \nassumption: that the decision to get married is unrelated to education level.\n(d)~The husband/wife independence assumption is probably not reasonable, because \npeople often marry another person with a comparable level of education. We will \nleave it to you to think about whether the second assumption noted in part~(c) is \nreasonable.}\n\n% 13\n\n\\eocesol{(a)~No, but we could if A and B are independent.\n(b-i)~0.21.\n(b-ii)~0.79.\n(b-iii)~0.3. \n(c)~No, because 0.1 $\\ne$ 0.21, where 0.21 was the value computed under \nindependence from part~(a).\n(d)~0.143.}\n\n% 15\n\n\\eocesol{(a)~No, 0.18 of respondents fall into this combination.\n(b)~$0.60 + 0.20 - 0.18 = 0.62$.\n(c)~$0.18 / 0.20 = 0.9$.\n(d)~$0.11 / 0.33 \\approx 0.33$.\n(e)~No, otherwise the answers to (c) and (d) would be the same.\n(f)~$0.06 / 0.34 \\approx 0.18$.}\n\n% 17\n\n\\eocesol{(a)~No. There are 6~females who like Five Guys Burgers.\n(b)~$162 / 248 = 0.65$.\n(c)~$181 / 252 = 0.72$.\n(d)~Under the assumption of a dating choices being independent of \nhamburger preference, which on the surface seems reasonable: \n$0.65 \\times 0.72 = 0.468$.\n(e)~$(252 + 6 - 1)/500 = 0.514$.}\n\n% 19\n\n\\eocesol{(a) \\\\\n\n\\FigureFullPath[A tree diagram with a primary branch \"Can construct box plots?\" and a secondary branch \"Passed?\". The primary \"Can construct box plots\" branching has two possibilities of \"Yes\" with probability 0.8 and \"No\" with probability 0.2. Each of these branches has two secondary branches. The \"Yes\" primary branch breaks into branches for \"Yes\" (for Passed) that has a conditional probability of 0.86 with a Yes-and-Yes final probability of 0.688, and a \"No\" secondary branch with a conditional probability of 0.14 with a Yes-and-No final probability of 0.112. The \"No\" primary branch from \"Can construct box plots\" has a branch of \"Yes\" that has a conditional probability of 0.65 with a No-and-Yes final probability of 0.13, and a \"No\" secondary branch with a conditional probability of 0.35 with a No-and-No final probability of 0.07.]{0.375}{ch_probability/figures/eoce/tree_drawing_box_plots/tree_drawing_box_plots}\n(b)~0.84}\n\n% 21\n\n\\eocesol{0.0714. Even when a patient tests positive for lupus, there is only a 7.14\\% \nchance that he actually has lupus. House may be right. \\\\\n\n\\FigureFullPath[A tree diagram with a primary branch \"Lupus\" and a secondary branch \"Result\" for the test of Lupus. The primary \"Lupus\" branching has two possibilities of \"Yes\" with probability 0.02 and \"No\" with probability 0.98. Each of these branches has two secondary branches. The \"Yes\" primary branch breaks into branches for \"Yes\" (for Result) that has a conditional probability of 0.98 with a Yes-and-Yes final probability of 0.0196, and a \"No\" secondary branch with a conditional probability of 0.02 with a Yes-and-No final probability of 0.0004. The \"No\" primary branch from \"Lupus\" has a secondary branch of \"Yes\" that has a conditional probability of 0.26 with a No-and-Yes final probability of 0.2548, and a \"No\" secondary branch with a conditional probability of 0.74 with a No-and-No final probability of 0.7252.]{0.375}{ch_probability/figures/eoce/tree_lupus/tree_lupus.pdf}}\n\n% 23\n\n\\eocesol{(a)~0.3.\n(b)~0.3.\n(c)~0.3.\n(d)~$0.3\\times0.3=0.09$.\n(e)~Yes, the population that is being sampled from is identical in each draw.}\n\n% 25\n\n\\eocesol{(a)~$2 / 9 \\approx 0.22$.\n(b)~$3 / 9 \\approx 0.33$.\n(c)~$\\frac{3}{10} \\times \\frac{2}{9} \\approx 0.067$.\n(d)~No, e.g. in this exercise, removing one chip meaningfully \nchanges the probability of what might be drawn next.}\n\n% 27\n\n\\eocesol{$P(^1$leggings, $^2$jeans, $^3$jeans$) = \\frac{5}{24} \\times \\frac{7}{23} \\times \\frac{6}{22} = 0.0173$. \nHowever, the person with leggings could have come 2nd or 3rd, and these each \nhave this same probability, so $3 \\times 0.0173 = 0.0519$.}\n\n% 29\n\n\\eocesol{(a)~13.\n(b)~No, these 27 students are not a random sample from the university's student \npopulation. For example, it might be argued that the proportion of smokers among \nstudents who go to the gym at 9 am on a Saturday morning would be lower than the \nproportion of smokers in the university as a whole.}\n\n% 31\n\n\\eocesol{(a)~E(X) = 3.59. SD(X) = 9.64.\n(b)~E(X) = -1.41. SD(X) = 9.64.\n(c)~No, the expected net profit is negative, so on average you expect to lose money.}\n\n% 33\n\n\\eocesol{5\\% increase in value.}\n\n% 35\n\n\\eocesol{E = -0.0526. SD = 0.9986.}\n\n% 37\n\n\\eocesol{Approximate answers are OK. \\\\\n(a)~$(29+32)/144 = 0.42$.\n(b)~$21/144 = 0.15$.\n(c)~$(26+12+15)/144 = 0.37$.}\n\n% 39\n\n\\eocesol{(a)~Invalid. Sum is greater than~1.\n(b)~Valid. Probabilities are between 0 and 1, and they sum to 1. In this class, \nevery student gets a~C.\n(c)~Invalid. Sum is less than~1.\n(d)~Invalid. There is a negative probability.\n(e)~Valid. Probabilities are between 0 and 1, and they sum to~1.\n(f)~Invalid. There is a negative probability.}\n\n% 41\n\n\\eocesol{0.8247. \\\\\n\\FigureFullPath[A tree diagram with a primary branch \"HIV\" and a secondary branch \"Result\" for the test of HIV. The primary \"HIV\" branching has two possibilities of \"Yes\" with probability 0.259 and \"No\" with probability 0.741. Each of these branches has two secondary branches. The \"Yes\" primary branch breaks into secondary branches for \"Yes\" (for Result) that has a conditional probability of 0.997 with a Yes-and-Yes final probability of 0.2582, and a \"No\" secondary branch with a conditional probability of 0.003 with a Yes-and-No final probability of 0.0008. The \"No\" primary branch from \"HIV\" has a secondary branch of \"Yes\" for \"Result\" that has a conditional probability of 0.074 with a No-and-Yes final probability of 0.0548, and a \"No\" secondary branch with a conditional probability of 0.926 with a No-and-No final probability of 0.6862.]{0.42}{ch_probability/figures/eoce/tree_hiv_swaziland/tree_hiv_swaziland.pdf}}\n\n% 43\n\n\\eocesol{(a)~E = \\$3.90. SD = \\$0.34. \\\\\n(b)~E = \\$27.30. SD = \\$0.89.}\n\n% 45\n\n\\eocesol{$Var\\left(\\frac{X_1 + X_2}{2}\\right)$ \\\\\n  $= Var\\left(\\frac{X_1}{2} + \\frac{X_2}{2}\\right)$ \\\\\n  $= \\frac{Var(X_1)}{2^2} + \\frac{Var(X_2)}{2^2}$ \\\\\n  $= \\frac{\\sigma^2}{4} + \\frac{\\sigma^2}{4}$ \\\\\n  $= \\sigma^2 / 2$ \\\\}\n\n% 47\n\n\\eocesol{$Var\\left(\\frac{X_1 + X_2 + \\dots + X_n}{n}\\right)$ \\\\\n  $= Var\\left(\\frac{X_1}{n} + \\frac{X_2}{n} + \\dots +\n      \\frac{X_n}{n}\\right)$ \\\\\n  $= \\frac{Var(X_1)}{n^2} + \\frac{Var(X_2)}{n^2} + \\dots +\n      \\frac{Var(X_n)}{n^2}$ \\\\\n  $= \\frac{\\sigma^2}{n^2} + \\frac{\\sigma^2}{n^2} + \\dots +\n      \\frac{\\sigma^2}{n^2}$ (there are $n$ of these terms) \\\\\n  $= n \\frac{\\sigma^2}{n^2}$ \\\\\n  $= \\sigma^2 / n$}\n\n\n\n%_______________\n\\end{multicols}\n\n\n\n%_______________\n\\eocesolch{Distributions of random variables}\n\n\n\n%_______________\n\\begin{multicols}{2}\n\n% 1\n\n\\eocesol{(a)~8.85\\%.\n(b)~6.94\\%.\n(c)~58.86\\%.\n(d)~4.56\\%. \\\\\n\\FigureFullPath[A normal distribution centered at 0 where a smaller left tail of the distribution has been shaded at and below a location labeled -1.35.]{0.23}{ch_distributions/figures/eoce/area_under_curve_1/zltNeg}\n\\FigureFullPath[A normal distribution centered at 0 where a smaller right tail of the distribution has been shaded at and above a location labeled 1.48.]{0.23}{ch_distributions/figures/eoce/area_under_curve_1/zgtPos}\n\\FigureFullPath[A normal distribution centered at 0 where a central region has been shaded. The region that remains unshaded is a large left tail up to just below the mean and a small right tail also remains unshaded.]{0.23}{ch_distributions/figures/eoce/area_under_curve_1/zBet}\n\\FigureFullPath[A normal distribution centered at zero where the two tails below a value of -2 and above a value of 2 have been shaded.]{0.23}{ch_distributions/figures/eoce/area_under_curve_1/zgtAbs}}\n\n% 3\n\n\\eocesol{(a)~Verbal: $N(\\mu = 151, \\sigma = 7)$, Quant: $N(\\mu = 153, \\sigma = 7.67)$.\n(b)~$Z_{VR} = 1.29$, $Z_{QR} = 0.52$. \\\\\n\\FigureFullPath[A normal distribution is shown along with 2 vertical lines specially marked. One is a little above the mean of the normal distribution at Z equals 0.52 and is labeled \"QR\". The second is a bit further above the mean at Z equals 1.29 and is labeled \"VR\"]{0.3}{ch_distributions/figures/eoce/GRE_intro/GRE_intro.pdf} \\\\\n(c)~She scored 1.29 standard deviations above the mean on the Verbal \nReasoning section and 0.52 standard deviations above the mean on the \nQuantitative Reasoning section.\n(d)~She did better on the Verbal Reasoning section since her Z-score on that \nsection was higher.\n(e)~$Perc_{VR} = 0.9007 \\approx 90\\%$, $Perc_{QR} = 0.6990 \\approx 70\\%$.\n(f)~$100\\% - 90\\% = 10\\%$ did better than her on VR, and $100\\% - 70\\% = 30\\%$\n did better than her on QR.\n(g)~We cannot compare the raw scores since they are on different scales. \nComparing her percentile scores is more appropriate when comparing her \nperformance to others.\n(h)~Answer to part (b) would not change as Z-scores can be calculated for \ndistributions that are not normal. However, we could not answer parts~(d)-(f) \nsince we cannot use the normal probability table to calculate probabilities \nand percentiles without a normal model.}\n\n% 5\n\n\\eocesol{(a)~$Z = 0.84$, which corresponds to approximately 159 on QR.\n(b)~$Z = -0.52$, which corresponds to approximately 147 on VR.}\n\n% 7\n\n\\eocesol{(a)~$Z = 1.2$, $P(Z > 1.2) = 0.1151$. \\\\\n(b)~$Z= -1.28 \\to 70.6\\degree$F or colder.}\n\n% 9\n\n\\eocesol{(a)~$N(25, 2.78)$.\n(b)~$Z = 1.08$, $P(Z > 1.08) = 0.1401$.\n(c)~The answers are very close because only the units were changed. (The only \nreason why they differ at all because 28\\degree C is \n82.4\\degree F, not precisely 83\\degree F.)\n(d)~Since $IQR = Q3 - Q1$, we first need to find $Q3$ and $Q1$ and take the \ndifference between the two. Remember that $Q3$ is the $75^{th}$ and $Q1$ is \nthe $25^{th}$ percentile of a distribution. Q1 = 23.13, Q3 = 26.86, IQR = 26.\n86 - 23.13 = 3.73.}\n\n% 11\n\n\\eocesol{(a)~No. The cards are not independent. For example, if the first card is an \nace of clubs, that implies the second card cannot be an ace of clubs. \nAdditionally, there are many possible categories, which would need to be \nsimplified.\n(b)~No. There are six events under consideration. The Bernoulli distribution \nallows for only two events or categories. Note that rolling a die could be a \nBernoulli trial if we simplify to two events, e.g. rolling a 6 and not rolling \na 6, though specifying such details would be necessary.}\n\n% 13\n\n\\eocesol{(a)~$0.875^2\\times 0.125 = 0.096$.\n(b)~$\\mu=8$, $\\sigma=7.48$.}\n\n% 15\n\n\\eocesol{If ${p}$ is the probability of a success, then the mean of a Bernoulli random variable $X$ is given by \\\\\n$\\mu = E[X] = P(X = 0) \\times 0 + P(X = 1) \\times 1$ \\\\\n$= (1 - p) \\times 0 + p\\times 1 = 0 + p = p$}\n\n% 17\n\n\\eocesol{(a)~Binomial conditions are met: \n(1)~Independent trials: In a random sample, whether or not one 18-20 year \nold has consumed alcohol does not depend on whether or not another one has.\n(2)~Fixed number of trials: $n = 10$.\n(3)~Only two outcomes at each trial: Consumed or did not consume alcohol.\n(4)~Probability of a success is the same for each trial: $p = 0.697$.\n(b)~0.203.\n(c)~0.203.\n(d)~0.167.\n(e)~0.997.}\n\n% 19\n\n\\eocesol{(a)~$\\mu = 35$, $\\sigma = 3.24$\n(b)~$Z = \\frac{45 - 35}{3.24} = 3.09$. 45 is more than 3 standard \ndeviations away from the mean, we can assume that it is an unusual \nobservation. Therefore yes, we would be surprised.\n(c)~Using the normal approximation, 0.0010. With 0.5 correction, 0.0017.}\n\n% 21\n\n\\eocesol{(a)~$1-0.75^3 = 0.5781$.\n(b)~0.1406.\n(c)~0.4219.\n(d)~$1-0.25^3=0.9844$.}\n\n% 23\n\n\\eocesol{(a)~Geometric distribution: 0.109.\n(b)~Binomial: 0.219.\n(c)~Binomial: 0.137.\n(d)~$1-0.875^6=0.551$.\n(e)~Geometric: 0.084.\n(f)~Using a binomial distribution with $n = 6$ and $p=0.75$, we see that $\\mu=4.5$, $\\sigma=1.06$, and $Z = 2.36$. Since this is not within 2 SD, it may be considered unusual.}\n\n% 25\n\n\\eocesol{(a)~$\\stackrel{Anna}{1/5}\\times\\stackrel{Ben}{1/4}\\times\\stackrel{Carl}{1/3}\\times\\stackrel{Damian}{1/2}\\times\\stackrel{Eddy}{1/1} = 1/5!=1/120$.\n(b)~Since the probabilities must add to 1, there must be $5!=120$ possible orderings.\n(c)~$8!=\\text{40,320}$.}\n\n% 27\n\n\\eocesol{(a)~Geometric, 0.0804.\n(b)~Binomial, 0.0322.\n(c)~Negative binomial, 0.0193.}\n\n% 29\n\n\\eocesol{(a)~Negative binomial with $n=4$ and $p=0.55$, where a success is defined here as a female student. The negative binomial setting is appropriate since the last trial is fixed but the order of the first 3 trials is unknown.\n(b)~0.1838.\n(c)~${3 \\choose 1} = 3$.\n(d)~In the binomial model there are no restrictions on the outcome of the last trial. In the negative binomial model the last trial is fixed. Therefore we are interested in the number of ways of orderings of the other $k - 1$ successes in the first $n - 1$ trials.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 31\n\n\\eocesol{(a)~Poisson with $\\lambda=75$.\n(b)~$\\mu=\\lambda=75$, $\\sigma=\\sqrt{\\lambda} = 8.66$.\n(c)~$Z=-1.73$. Since 60 is within 2 standard deviations of the mean, it would not generally be considered unusual. Note that we often use this rule of thumb even when the normal model does not apply.\n(d)~Using Poisson with $\\lambda = 75$: 0.0402.}\n\n% 33\n\n\\eocesol{(a)~$\\frac{\\lambda^k \\times e^{-\\lambda}}{k!}\n      = \\frac{6.5^5 \\times e^{-6.5}}{5!}\n      = 0.1454$ \\\\\n(b)~The probability will come to\n    $0.0015 + 0.0098 + 0.0318 = 0.0431$\n    (0.0430 if no rounding error). \\\\\n(c)~The number of people per car is $11.7 / 6.5 = 1.8$,\n    meaning people are coming in small clusters.\n    That is, if one person arrives, there's a chance\n    that they brought one or more other people in their\n    vehicle.\n    This means individuals (the people) are not independent,\n    even if the car arrivals are independent,\n    and this breaks a core assumption for the Poisson\n    distribution.\n    That is, the number of people visiting between\n    2pm and 3pm would not follow a Poisson distribution.}\n\n% 35\n\n\\eocesol{0 wins (-\\$3): 0.1458. 1 win (-\\$1): 0.3936. 2 wins (+\\$1): 0.3543. \n3 wins (+\\$3): 0.1063.}\n\n% 37\n\n\\eocesol{Want to find the probability that there will be 1,787 or more enrollees.\nUsing the normal approximation, with $\\mu = np = 2,500 \\times 0.7 = 1750$ and \n$\\sigma = \\sqrt{np(1-p)} = \\sqrt{2,500 \\times 0.7 \\times 0.3} \\approx 23$, \n$Z = 1.61$, and $P(Z > 1.61) = 0.0537$. With a 0.5 correction: 0.0559.}\n\n% 39\n\n\\eocesol{(a)~$Z=0.67$.\n(b)~$\\mu=\\$1650$, $x=\\$1800$.\n(c)~$0.67 = \\frac{1800-1650}{\\sigma} \\to \\sigma=\\$223.88$.}\n\n% 41\n\n\\eocesol{(a)~$(1-0.471)^2\\times0.471 = 0.1318$.\n(b)~$0.471^3 = 0.1045$.\n(c)~$\\mu = 1/0.471 = 2.12$, $\\sigma=\\sqrt{2.38} = 1.54$.\n(d)~$\\mu = 1/0.30 = 3.33$, $\\sigma=2.79$.\n(e)~When $p$ is smaller, the event is rarer, meaning the expected number of \ntrials before a success and the standard deviation of the waiting time are \nhigher.}\n\n% 43\n\n\\eocesol{$Z = 1.56$, $P(Z > 1.56) = 0.0594$, i.e. 6\\%.}\n\n% 45\n\n\\eocesol{(a)~$Z = 0.73$, $P(Z > 0.73) = 0.2327$.\n(b)~If you are bidding on only one auction and set a low maximum bid price, \nsomeone will probably outbid you. If you set a high maximum bid price, you \nmay win the auction but pay more than is necessary. If bidding on more than \none auction, and you set your maximum bid price very low, you probably won't \nwin any of the auctions. However, if the maximum bid price is even modestly \nhigh, you are likely to win multiple auctions.\n(c)~An answer roughly equal to the 10th percentile would be reasonable. \nRegrettably, no percentile cutoff point guarantees beyond any possible event \nthat you win at least one auction. However, you may pick a higher percentile \nif you want to be more sure of winning an auction.\n(d)~Answers will vary a little but should correspond to the answer in \npart~(c). We use the 10$^{th}$ percentile: $Z = -1.28 \\to \\$69.80$.}\n\n% 47\n\n\\eocesol{(a)~$Z = 3.5$, upper tail is 0.0002.\n    (More precise value: 0.000233, but we'll use\n    0.0002 for the calculations here.) \\\\\n(b)~$0.0002 \\times 2000 = 0.4$.\n    We would expect about 0.4 10 year olds\n    who are 76 inches or taller to show up. \\\\\n(c)~${{2000}\\choose{0}} (0.0002)^0 (1 - 0.0002)^{2000}\n    = 0.67029$. \\\\\n(d)~$\\frac{0.4^0 \\times e^{-0.4}}{0!}\n    = \\frac{1 \\times e^{-0.4}}{1}\n    = 0.67032$.}\n\n\n\n%_______________\n\\end{multicols}\n\n\n\n%_______________\n\\eocesolch{Foundations for inference}\n\n\n\n%_______________\n\\begin{multicols}{2}\n\n% 1\n\n\\eocesol{(a)~Mean. Each student reports a numerical value: a number of hours.\n(b)~Mean. Each student reports a number, which is a percentage, and we can \naverage over these percentages.\n(c)~Proportion. Each student reports Yes or No, so this is a categorical \nvariable and we use a proportion.\n(d)~Mean. Each student reports a number, which is a percentage like in part~(b).\n(e)~Proportion. Each student reports whether or not s/he expects to get a job, \nso this is a categorical variable and we use a proportion.}\n\n% 3\n\n\\eocesol{(a)~The sample is from all computer chips manufactured\n    at the factory during the week of production.\n    We might be tempted to generalize the population\n    to represent all weeks, but we should exercise\n    caution here since the rate of defects may change\n    over time.\n(b)~The fraction of computer chips manufactured\n    at the factory during the week of production\n    that had defects.\n(c)~Estimate the parameter using the data:\n    $\\hat{p} = \\frac{27}{212} = 0.127$.\n(d)~\\emph{Standard error} (or $SE$).\n(e)~Compute the $SE$ using\n    $\\hat{p} = 0.127$ in place of $p$:\n    $SE\n      \\approx \\sqrt{\\frac{\\hat{p}(1 - \\hat{p})}{n}}\n      = \\sqrt{\\frac{0.127(1 - 0.127)}{212}}\n      = 0.023$.\n(f)~The standard error is the\n    standard deviation of $\\hat{p}$.\n    A value of 0.10 would be about one standard\n    error away from the observed value, which would\n    not represent a very uncommon deviation.\n    (Usually beyond about 2 standard errors\n    is a good rule of thumb.)\n    The engineer should not be surprised.\n(g)~Recomputed standard error using $p = 0.1$:\n    $SE = \\sqrt{\\frac{0.1(1 - 0.1)}{212}}\n      = 0.021$.\n    This value isn't very different,\n    which is typical when the standard error\n    is computed using relatively similar\n    proportions (and even sometimes when\n    those proportions are quite different!).}\n\n% 5\n\n\\eocesol{(a)~Sampling distribution.\n(b)~If the population proportion is in the 5-30\\% range,\n    the success-failure condition would be satisfied and\n    the sampling distribution would be symmetric.\n(c)~We use the formula for the standard error:\n    $SE\n      = \\sqrt{\\frac{p (1 - p)}{n}}\n      = \\sqrt{\\frac{0.08 (1 - 0.08)}{800}}\n      = 0.0096$.\n(d)~Standard error.\n(e)~The distribution will tend to be more variable\n    when we have fewer observations per sample.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 7\n\n\\eocesol{Recall that the general formula is $point~estimate \\pm z^{\\star} \\times SE$.\nFirst, identify the three different values. The point estimate is 45\\%, \n$z^{\\star} = 1.96$ for a 95\\% confidence level, and $SE = 1.2\\%$. Then, plug the \nvalues into the formula:\n$ 45\\% \\pm 1.96 \\times 1.2\\% \\quad\\to\\quad (42.6\\%, 47.4\\%) $\nWe are 95\\% confident that the proportion of US adults who live with one or more \nchronic conditions is between 42.6\\% and 47.4\\%.}\n\n% 9\n\n\\eocesol{(a)~False. Confidence intervals provide a range of plausible values, and \nsometimes the truth is missed. A 95\\% confidence interval ``misses'' about 5\\% \nof the time.\n(b)~True. Notice that the description focuses on the true population value.\n(c)~True. If we examine the 95\\% confidence interval computed in\nExercise~\\ref{chronic_illness_intro}, we can see that 50\\% is not included in this interval.\nThis \nmeans that in a hypothesis test, we would reject the null hypothesis that the \nproportion is~0.5.\n(d)~False. The standard error describes the uncertainty in the overall estimate \nfrom natural fluctuations due to randomness, not the uncertainty corresponding \nto individuals' responses.}\n\n% 11\n\n\\eocesol{(a)~False. The point estimate is always in the confidence interval,\nand this is a non-sensical use of a confidence interval with a point estimate\n(because the point estimate is, by design, listed within the confidence interval).\n(b)~True.\n(c)~False. The confidence interval is not about a sample mean.\n(d)~False. To be more confident that we capture the parameter, we need a wider \ninterval. Think about needing a bigger net to be more sure of catching a fish in \na murky lake.\n(e)~True. Optional explanation: This is true since the normal model was used to \nmodel the sample mean. The margin of error is half the width of the interval, \nand the sample mean is the midpoint of the interval.\n(f)~False. In the calculation of the standard error, we divide the standard \ndeviation by the square root of the sample size. To cut the SE (or margin of \nerror) in half, we would need to sample $2^2 = 4$ times the number of people in \nthe initial sample.}\n\n% 13\n\n\\eocesol{(a)~The visitors are from a simple random sample,\n    so independence is satisfied.\n    The success-failure condition is also satisfied,\n    with both 64 and $752 - 64 = 688$ above 10.\n    Therefore, we can use a normal distribution to\n    model $\\hat{p}$ and construct a confidence interval.\n(b)~The sample proportion is $\\hat{p} = \\frac{64}{752} = 0.085$.\n    The standard error is\n    {\\footnotesize\\begin{align*}\n    SE\n      &= \\sqrt{\\frac{p (1 - p)}{n}}\n      \\approx \\sqrt{\\frac{\\hat{p} (1 - \\hat{p})}{n}} \\\\\n      &= \\sqrt{\\frac{0.085 (1 - 0.085)}{752}}\n      = 0.010\n    \\end{align*}}%\n(c)~For a 90\\% confidence interval,\n    use $z^{\\star} = 1.6449$.\n    The confidence interval is\n    $0.085 \\pm 1.6449 \\times 0.010 \\to\n    (0.0683, 0.1017)$.\n    We are 90\\% confident that 6.83\\% to 10.17\\%\n    of first-time site visitors will register using\n    the new design.}\n\n% 15\n\n\\eocesol{(a)~$H_0: p = 0.5$\n    (Neither a majority nor minority of students' grades improved)\n  $H_A: p \\neq 0.5$\n    (Either a majority or a minority of students' grades improved) \\\\\n(b)~$H_0: \\mu = 15$\n    (The average amount of company time each employee spends not \n    working is 15 minutes for March Madness.)\n  $H_A: \\mu \\neq 15$\n    (The average amount of company time each employee spends not \n    working is different than 15 minutes for March Madness.)}\n\n% 17\n\n\\eocesol{(1)~The hypotheses should be about the\n    population proportion ($p$), not the sample proportion.\n(2)~The null hypothesis should have an equal sign.\n(3)~The alternative hypothesis should have a not-equals\n    sign, and\n(4)~it should reference the null value, $p_0 = 0.6$,\n    not the observed sample proportion.\nThe correct way to set up these hypotheses is:\n$H_0: p = 0.6$ and\n$H_A: p \\neq 0.6$.}\n\n% 19\n\n\\eocesol{(a)~This claim is reasonable, since the entire interval\n    lies above 50\\%.\n(b)~The value of 70\\% lies outside of the interval,\n    so we have convincing evidence that the researcher's\n    conjecture is wrong.\n(c)~A~90\\% confidence interval will be narrower than a\n    95\\%~confidence interval.\n    Even without calculating the interval,\n    we can tell that 70\\% would not fall in the interval,\n    and we would reject the researcher's conjecture based\n    on a 90\\% confidence level as well.}\n\n% 21\n\n\\eocesol{(i)~Set up hypotheses. $H_0$: $p = 0.5$, $H_A$: $p \\neq 0.5$.\n  We will use a significance level of $\\alpha = 0.05$.\n(ii)~Check conditions: simple random sample gets us independence,\n  and the success-failure conditions is satisfied since\n  $0.5 \\times 1000 = 500$ for each group is at least~10.\n(iii)~Next, we calculate:\n  $SE = \\sqrt{0.5 (1 - 0.5) / 1000} = 0.016$.\n  $Z = \\frac{0.42 - 0.5}{0.016} = -5$,\n  which has a one-tail area of about 0.0000003,\n  so the p-value is twice this one-tail area at\n  0.0000006.\n(iv)~Make a conclusion:\n  Because the p-value is less than $\\alpha = 0.05$,\n  we reject the null hypothesis and conclude that the\n  fraction of US adults who believe raising the minimum\n  wage will help the economy is not 50\\%.\n  Because the observed value is less than 50\\% and we\n  have rejected the null hypothesis, we can conclude\n  that this belief is held by fewer than 50\\% of US adults.\n(For reference, the survey also explores support for\nchanging the minimum wage, which is a different\nquestion than if it will help the economy.)}\n\n% 23\n\n\\eocesol{If the p-value is 0.05, this means the test statistic would be\neither $Z = -1.96$ or $Z = 1.96$.\nWe'll show the calculations for $Z = 1.96$.\nStandard error:\n$SE = \\sqrt{0.3 (1 - 0.3) / 90} = 0.048$.\nFinally, set up the test statistic formula and solve\nfor $\\hat{p}$:\n$1.96 = \\frac{\\hat{p} - 0.3}{0.048}\n  \\to \\hat{p} = 0.394$\nAlternatively, if $Z = -1.96$ was used: $\\hat{p} = 0.206$.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 25\n\n\\eocesol{(a)~$H_0$: Anti-depressants do not affect the symptoms\n    of Fibromyalgia.\n    $H_A$: Anti-depressants do affect the symptoms of\n    Fibromyalgia (either helping or harming).\n(b)~Concluding that anti-depressants either help or worsen\n    Fibromyalgia symptoms when they actually do neither.\n(c)~Concluding that anti-depressants do not affect\n    Fibromyalgia symptoms when they actually do.}\n\n% 27\n\n\\eocesol{(a)~We are 95\\% confident that Americans spend an average\n    of 1.38 to 1.92 hours per day relaxing or pursuing\n    activities they enjoy.\n(b)~Their confidence level must be higher as the width\n    of the confidence interval increases as the confidence\n    level increases.\n(c)~The new margin of error will be smaller,\n    since as the sample size increases,\n    the standard error decreases,\n    which will decrease the margin of error.}\n\n% 29\n\n\\eocesol{(a)~$H_0$: The restaurant meets food safety and sanitation regulations.\n$H_A$: The restaurant does not meet food safety and sanitation regulations.\n(b)~The food safety inspector concludes that the restaurant does not meet food \nsafety and sanitation regulations and shuts down the restaurant when the \nrestaurant is actually safe.\n(c)~The food safety inspector concludes that the restaurant meets food safety \nand sanitation regulations and the restaurant stays open when the restaurant is \nactually not safe.\n(d)~A Type~1 Error may be more problematic for the restaurant owner since his \nrestaurant gets shut down even though it meets the food safety and sanitation \nregulations.\n(e)~A Type~2 Error may be more problematic for diners since the restaurant \ndeemed safe by the inspector is actually not.\n(f)~Strong evidence. Diners would rather a restaurant that meet the regulations get\nshut down than a restaurant that doesn't meet the regulations not get shut down.}\n\n% 31\n\n\\eocesol{(a)~$H_0: p_{unemp} = p_{underemp}$: The proportions of unemployed and \nunderemployed people who are having relationship problems are equal.\n$H_A: p_{unemp} \\ne p{underemp}$: The proportions of unemployed and \nunderemployed people who are having relationship problems are different.\n(b)~If in fact the two population proportions are equal, the probability of \nobserving at least a 2\\% difference between the sample proportions is \napproximately 0.35. Since this is a high probability we fail to reject the null \nhypothesis. The data do not provide convincing evidence that the proportion of \nof unemployed and underemployed people who are having relationship problems are \ndifferent.}\n\n% 33\n\n\\eocesol{Because 130 is inside the confidence interval,\nwe do not have convincing evidence that the true\naverage is any different than what the nutrition\nlabel suggests.}\n\n% 35\n\n\\eocesol{True. If the sample size gets ever larger, then the\nstandard error will become ever smaller.\nEventually, when the sample size is large enough and\nthe standard error is tiny, we can find statistically\nsignificant yet very small differences between the\nnull value and point estimate (assuming they are not\nexactly equal).}\n\n% 37\n\n\\eocesol{(a)~In effect, we're checking whether\n    men are paid more than women (or vice-versa),\n    and we'd expect these outcomes with either\n    chance under the null hypothesis:\n    \\begin{align*}\n    &H_0: p = 0.5\n    &&H_A: p \\neq 0.5\n    \\end{align*}\n    We'll use $p$ to represent the fraction of cases\n    where men are paid more than women. \\\\\n(b)~Below is the completion of the hypothesis test.\n  \\begin{itemize}\n  \\item\n    There isn't a good way to check independence here\n    since the jobs are not a simple random sample.\n    However, independence doesn't seem unreasonable,\n    since the individuals in each job are different from\n    each other.\n    The success-failure condition is met since we check\n    it using the null proportion:\n    $p_0 n = (1 - p_0) n = 10.5$ is greater than 10.\n  \\item\n    We can compute the sample proportion, $SE$, and\n    test statistic:\n    \\begin{align*}\n    \\hat{p} &= 19 / 21 = 0.905 \\\\\n    SE &= \\sqrt{\\frac{0.5 \\times (1 - 0.5)}{21}} = 0.109 \\\\\n    Z &= \\frac{0.905 - 0.5}{0.109} = 3.72\n    \\end{align*}\n    The test statistic $Z$ corresponds to an upper tail\n    area of about 0.0001, so the p-value is 2 times this\n    value: 0.0002.\n  \\item\n    Because the p-value is smaller than 0.05, we reject\n    the notion that all these gender pay disparities are\n    due to chance.\n    Because we observe that men are paid more in a higher proportion\n    of cases and we have rejected $H_0$, we can conclude that\n    men are being paid higher amounts in ways not explainable\n    by chance alone.\n  \\end{itemize}\n  If you're curious for more info around this topic,\n  including a discussion about adjusting for additional\n  factors that affect pay,\n  please see the following video by Healthcare Triage:\n  \\oiRedirect{textbook-yt_healthcare_triage_gender_pay_gap}\n      {youtu.be/aVhgKSULNQA}.}\n\n\n\n%_______________\n\\end{multicols}\n\n\n\\newpage\n\n\n%_______________\n\\eocesolch{Inference for categorical data}\n\n\n\n%_______________\n\\begin{multicols}{2}\n\n% 1\n\n\\eocesol{(a)~False. Doesn't satisfy success-failure condition.\n(b)~True. The success-failure condition is not satisfied. In most samples we \nwould expect $\\hat{p}$ to be close to 0.08, the true population proportion. \nWhile $\\hat{p}$ can be much above 0.08, it is bound below by 0, suggesting it \nwould take on a right skewed shape. Plotting the sampling distribution would \nconfirm this suspicion.\n(c)~False. $SE_{\\hat{p}} = 0.0243$, and $\\hat{p} = 0.12$ is only \n$\\frac{0.12 - 0.08}{0.0243} = 1.65$ SEs away from the mean, which would not \nbe considered unusual.\n(d)~True. $\\hat{p}=0.12$ is 2.32 standard errors away from the mean, which is \noften considered unusual.\n(e)~False. Decreases the SE by a factor of $1/\\sqrt{2}$.}\n\n% 3\n\n\\eocesol{(a)~True. See the reasoning of 6.1(b).\n(b)~True. We take the square root of the sample size in the SE formula.\n(c)~True. The independence and success-failure conditions are satisfied.\n(d)~True. The independence and success-failure conditions are satisfied.}\n\n% 5\n\n\\eocesol{(a)~False. A confidence interval is constructed to estimate the population \nproportion, not the sample proportion.\n(b)~True. 95\\% CI: $82\\%\\ \\pm\\ 2\\%$.\n(c)~True. By the definition of the confidence level.\n(d)~True. Quadrupling the sample size decreases the SE and ME by a factor \nof $1/\\sqrt{4}$.\n(e)~True. The 95\\% CI is entirely above 50\\%.}\n\n% 7\n\n\\eocesol{With a random sample, independence is \nsatisfied. The success-failure condition is also satisfied. \n$ME = z^{\\star} \\sqrt{ \\frac{\\hat{p} (1-\\hat{p})} {n} } \n= 1.96 \\sqrt{ \\frac{0.56 \\times  0.44}{600} }= 0.0397 \\approx 4\\%$}\n\n% 9\n\n\\eocesol{(a)~No. The sample only represents students who took the SAT, and this was \nalso an online survey.\n(b)~(0.5289, 0.5711). We are 90\\% confident that 53\\% to 57\\% of high school \nseniors who took the SAT are fairly certain that they will participate in a \nstudy abroad program in college.\n(c)~90\\% of such random samples would produce a 90\\% confidence interval \nthat includes the true proportion.\n(d)~Yes. The interval lies entirely above 50\\%.}\n\n% 11\n\n\\eocesol{(a)~We want to check for a majority (or minority),\n    so we use the following hypotheses:\n    \\begin{align*}\n    &H_0: p = 0.5\n    &&H_A: p \\neq 0.5\n    \\end{align*}\n    We have a sample proportion of $\\hat{p} = 0.55$\n    and a sample size of $n = 617$ independents. \\\\\n    Since this is a random sample, independence\n    is satisfied.\n    The success-failure condition is also satisfied:\n    $617 \\times 0.5$ and $617 \\times (1 - 0.5)$\n    are both at least 10 (we use the null proportion\n    $p_0 = 0.5$ for this check in a one-proportion\n    hypothesis test). \\\\\n    Therefore, we can model $\\hat{p}$ using a\n    normal distribution with a standard error of\n    \\begin{align*}\n    SE = \\sqrt{\\frac{p(1 - p)}{n}}\n      = 0.02\n    \\end{align*}\n    (We use the null proportion $p_0 = 0.5$\n    to compute the standard error for a\n    one-proportion hypothesis test.)\n    Next, we compute the test statistic:\n    \\begin{align*}\n    Z = \\frac{0.55 - 0.5}{0.02} = 2.5\n    \\end{align*}\n    This yields a one-tail area of 0.0062,\n    and a p-value of $2 \\times 0.0062 = 0.0124$. \\\\\n    Because the p-value is smaller than 0.05,\n    we reject the null hypothesis.\n    We have strong evidence that the support\n    is different from 0.5, and since the data\n    provide a point estimate above 0.5,\n    we have strong evidence to support this\n    claim by the TV pundit. \\\\\n(b)~No.\n    Generally we expect a hypothesis test\n    and a confidence interval to align,\n    so we would expect the confidence interval\n    to show a range of plausible values\n    entirely above 0.5.\n    However, if the confidence level is\n    misaligned (e.g. a 99\\% confidence level\n    and a $\\alpha = 0.05$ significance level),\n    then this is no longer generally true.}\n\n% 13\n\n\\eocesol{(a)~$H_0: p = 0.5$. $H_A: p \\neq 0.5$.\nIndependence (random sample) is satisfied,\nas is the success-failure conditions (using $p_0 = 0.5$,\nwe expect 40 successes and 40 failures).\n$Z = 2.91$ $\\to$ the one tail area is 0.0018,\nso the p-value is 0.0036.\nSince the p-value $< 0.05$, we reject the null hypothesis.\nSince we rejected $H_0$ and the point estimate suggests people are\nbetter than random guessing,\nwe can conclude the rate of correctly identifying a \nsoda for these people is significantly better than\njust by random guessing.\n(b)~If in fact people cannot tell the difference between diet and regular \nsoda and they were randomly guessing, the probability of getting\na random sample of \n80 people where 53 or more identify a soda correctly\n(or 53 or more identify a soda incorrectly)\nwould be 0.0036.}\n\n% 15\n\n\\eocesol{Because a sample proportion ($\\hat{p} = 0.55$) is available,\nwe use this for the sample size calculations.\nThe margin of error for a 90\\% confidence interval is\n$1.6449 \\times SE = 1.6449 \\times \\sqrt{\\frac{p(1 - p)}{n}}$.\nWe want this to be less than 0.01, where we use\n$\\hat{p}$ in place of $p$:\n\\begin{align*}\n1.6449 \\times \\sqrt{\\frac{0.55(1 - 0.55)}{n}} \\leq 0.01 \\\\\n1.6449^2 \\frac{0.55(1 - 0.55)}{0.01^2} \\leq n\n\\end{align*}\nFrom this, we get that $n$ must be at least 6697.}\n\n% 17\n\n\\eocesol{This is not a randomized experiment, and it is unclear whether people would \nbe affected by the behavior of their peers. That is, independence may not \nhold. Additionally, there are only 5 interventions under the provocative \nscenario, so the success-failure condition does not hold. Even if we consider \na hypothesis test where we pool the proportions, the success-failure \ncondition will not be satisfied. Since one condition is questionable and the \nother is not satisfied, the difference in sample proportions will not follow \na nearly normal distribution.}\n\n% 19\n\n\\eocesol{(a)~False. The entire confidence interval is above 0.\n(b)~True.\n(c)~True.\n(d)~True.\n(e)~False. It is simply the negated and reordered values: (-0.06,-0.02).}\n\n% 21\n\n\\eocesol{(a)~Standard error:\n    \\begin{align*}\n    SE\n      = \\sqrt{\\frac{0.79(1 - 0.79)}{347} +\n          \\frac{0.55(1 - 0.55)}{617}}\n      = 0.03\n    \\end{align*}\n    Using $z^{\\star} = 1.96$, we get:\n    \\begin{align*}\n    0.79 - 0.55 \\pm 1.96 \\times 0.03\n      \\to (0.181, 0.299)\n    \\end{align*}\n    We are 95\\% confident that the proportion\n    of Democrats who support the plan is 18.1\\%\n    to 29.9\\% higher than the proportion of\n    Independents who support the plan.\n(b)~True.}\n\n% 23\n\n\\eocesol{(a)~College grads: 23.7\\%. Non-college grads: 33.7\\%.\n(b)~Let $p_{CG}$ and $p_{NCG}$ represent the proportion of college graduates \nand non-college graduates who responded ``do not know\". \n$H_0: p_{CG} = p_{NCG}$. $H_A: p_{CG} \\ne p_{NCG}$. Independence is satisfied \n(random sample), and the success-failure \ncondition, which we would check using the pooled proportion \n($\\hat{p}_{\\textit{pool}} = 235/827 = 0.284$), is also satisfied. $Z = -3.18$ $\\to$ \np-value = 0.0014. Since the p-value is very small, we reject $H_0$. The data \nprovide strong evidence that the proportion of college graduates who do not \nhave an opinion on this issue is different than that of non-college \ngraduates. The data also indicate that fewer college grads say they ``do not \nknow'' than non-college grads (i.e. the data indicate the direction after we \nreject $H_0$).}\n\n% 25\n\n\\eocesol{(a)~College grads: 35.2\\%. Non-college grads: 33.9\\%.\n(b)~Let $p_{CG}$ and $p_{NCG}$ represent the proportion\nof college graduates \nand non-college grads who support offshore drilling.\n$H_0: p_{CG} = p_{NCG}$. \n$H_A: p_{CG} \\ne p_{NCG}$. Independence is satisfied (random sample),\nand the success-failure condition, which we would check \nusing the pooled proportion ($\\hat{p}_{\\textit{pool}} = 286/827 = 0.346$), is also \nsatisfied. $Z = 0.39$ $\\to$ p-value $=0.6966$. Since the p-value \n$> \\alpha$ (0.05), we fail to reject $H_0$. The data do not provide strong \nevidence of a difference between the proportions of college graduates\nand non-college graduates who support off-shore drilling in California.}\n\n% 27\n\n\\eocesol{Subscript $_C$ means control group. Subscript $_T$ means truck drivers. \n$H_0: p_C = p_T$. $H_A: p _C \\ne p_T$. Independence is satisfied (random \nsamples), as is the success-failure condition, which \nwe would check using the pooled proportion ($\\hat{p}_{\\textit{pool}} = 70/495 = 0.141$). \n$Z = -1.65$ $\\to$ p-value $ = 0.0989$. Since the p-value is high (default to alpha = 0.05), we fail to \nreject $H_0$. The data do not provide strong evidence that the rates of sleep \ndeprivation are different for non-transportation workers and truck drivers.}\n\n% 29\n\n\\eocesol{(a)~Summary of the study:\n\\begin{center}\\scriptsize\n\\begin{tabular}{l l c c c}\n        &           & \\multicolumn{2}{c}{\\textit{Virol. failure}}   &       \\\\\n\\cline{3-4}\n        &           & Yes       & No        & Total \\\\\n\\cline{2-5}\n\\multirow{2}{*}{\\textit{Treatment}}     & Nevaripine    & 26   & 94 & 120   \\\\\n        & Lopinavir & 10            & 110   & 120   \\\\\n\\cline{2-5}\n        & Total     & 36            & 204   & 240\n\\end{tabular}\n\\end{center}\n(b)~$H_0: p_N = p_L$. There is no difference in virologic failure rates between \nthe Nevaripine and Lopinavir groups. $H_A: p_N \\ne p_L$. There is some \ndifference in virologic failure rates between the Nevaripine and Lopinavir \ngroups.\n(c)~Random assignment was used, so the observations in each group are \nindependent. If the patients in the study are representative of those in the \ngeneral population (something impossible to check with the given information), \nthen we can also confidently generalize the findings to the population. The \nsuccess-failure condition, which we would check using the pooled proportion \n($\\hat{p}_{pool} = 36/240 = 0.15$), is satisfied. $Z = 2.89$ $\\to$ p-value \n$=0.0039$.\nSince the p-value is low, we reject $H_0$.\nThere is strong evidence of a difference in virologic failure rates between\nthe Nevaripine and Lopinavir groups.\nTreatment and virologic failure do not appear to be independent.}\n\n% 31\n\n\\eocesol{(a)~False. The chi-square distribution has one parameter called degrees of \nfreedom.\n(b)~True.\n(c)~True.\n(d)~False. As the degrees of freedom increases, the shape of the chi-square \ndistribution becomes more symmetric.}\n\n% 33\n\n\\eocesol{(a)~$H_0$: The distribution of the format of the book used by the students \nfollows the professor's predictions. $H_A$: The distribution of the format of \nthe book used by the students does not follow the professor's predictions.\n(b)~$E_{hard~copy} = 126 \\times  0.60 = 75.6$. \n$E_{print} = 126 \\times  0.25 = 31.5$.\n$E_{online} = 126 \\times  0.15 = 18.9$.\n(c)~Independence:  The sample is not random. However, if the professor has \nreason to believe that the proportions are stable from one term to the next \nand students are not affecting each other's study habits, independence is \nprobably reasonable. Sample size: All expected counts are at least 5.\n(d)~$\\chi^2 = 2.32$, $df=2$, p-value = 0.313.\n(e)~Since the p-value is large, we fail to reject $H_0$. The data do not \nprovide strong evidence indicating the professor's predictions were \nstatistically inaccurate.}\n\n% 35\n\n\\eocesol{(a)~Two-way table:\n\\begin{center}\\scriptsize\n\\begin{tabular}{l l c c c}\n& \\multicolumn{2}{c}{\\textit{Quit}} &       \\\\\n\\cline{2-3}\n\\textit{Treatment}      & Yes       & No        & Total \\\\\n\\hline\nPatch + support group   & 40            & 110   & 150   \\\\\nOnly patch          & 30            & 120   & 150   \\\\\n\\cline{1-4}\nTotal               & 70            & 230   & 300 \\\\\n\\cline{1-4}\n\\end{tabular}\n\\end{center}\n(b-i)~$E_{row_1, col_1} = \\frac{(row~1~total)\\times(col~1~total)}{table~total} = 35$. \nThis is lower than the observed value. \\\\\n(b-ii)~$E_{row_2, col_2} = \\frac{(row~2~total)\\times(col~2~total)}{table~total} = 115$. \nThis is lower than the observed value.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 37\n\n\\eocesol{$H_0$: The opinion of college grads and non-grads is not different on \nthe topic of drilling for oil and natural gas off the coast of \nCalifornia. $H_A$: Opinions regarding the drilling for oil and natural \ngas off the coast of California has an association with earning a \ncollege degree.\n\\begin{align*}\n&E_{row~1, col~1} = 151.5 && E_{row~1, col~2} = 134.5 \\\\\n&E_{row~2, col~1} = 162.1 && E_{row~2, col~2} = 143.9 \\\\\n&E_{row~3, col~1} = 124.5 && E_{row~3, col~2} = 110.5\n\\end{align*}\nIndependence: The samples are both random, unrelated, and from less \nthan 10\\% of the population, so independence between observations is \nreasonable. Sample size: All expected counts are at least 5.\n$\\chi^2 = 11.47$, $df = 2$ $\\to$ p-value = 0.003.\nSince the p-value $< \\alpha$, we reject $H_0$.  There is strong \nevidence that there is an association between support for off-shore \ndrilling and having a college degree.}\n\n% 39\n\n\\eocesol{No. The samples at the beginning and at the end of the semester are not \nindependent since the survey is conducted on the same students.}\n\n% 41\n\n\\eocesol{(a)~$H_0$:~The age of Los Angeles residents is independent of shipping carrier \npreference variable. $H_A$:~The age of Los Angeles residents is associated with \nthe shipping carrier preference variable. \n(b)~The conditions are not satisfied since some expected counts are below~5.}\n\n% 43\n\n\\eocesol{(a)~Independence is satisfied (random sample),\n    as is the success-failure condition (40 smokers, 160 \n    non-smokers).\n    The 95\\% CI: (0.145, 0.255).\n    We are 95\\% confident that 14.5\\% \n    to 25.5\\% of all students at this university smoke.\n(b)~We want $z^{\\star}SE$ to be no larger than 0.02 for\n    a 95\\% confidence level.\n    We use $z^{\\star}=1.96$ and plug in the point estimate\n    $\\hat{p}=0.2$ within the SE formula:\n    $1.96\\sqrt{0.2(1-0.2)/n} \\leq 0.02$.\n    The sample size $n$ should be at least 1,537.}\n\n% 45\n\n\\eocesol{(a)~Proportion of graduates from this university who found a job within one \nyear of graduating. $\\hat{p} = 348/400 = 0.87$.\n(b)~This is a random sample, so the \nobservations are independent.\nSuccess-failure condition is satisfied: 348 \nsuccesses, 52 failures, both well above~10.\n(c)~(0.8371, 0.9029). We are 95\\% confident that approximately 84\\% to 90\\% \nof graduates from this university found a job within one year of completing \ntheir undergraduate degree.\n(d)~95\\% of such random samples would produce a 95\\% confidence interval \nthat includes the true proportion of students at this university who found a \njob within one year of graduating from college.\n(e)~(0.8267, 0.9133). Similar interpretation as before.\n(f)~99\\% CI is wider, as we are more confident that the true proportion is \nwithin the interval and so need to cover a wider range.}\n\n% 47\n\n\\eocesol{Use a chi-squared goodness of fit test.\n$H_0$: Each option is equally likely.\n$H_A$: Some options are preferred over others.\nTotal sample size: 99.\nExpected counts: (1/3) * 99 = 33 for each option. These are all above 5, so \nconditions are satisfied.\n$df = 3 - 1 = 2$ and \n$\\chi^2 = \\frac{(43 - 33)^2}{33} + \\frac{(21 - 33)^2}{33} + \\frac{(35 - 33)^2}{33} = 7.52 \n\\rightarrow$ p-value $= 0.023$. Since \nthe p-value is less than 5\\%, we reject $H_0$. The data provide convincing \nevidence that some options are preferred over others.}\n\n% 49\n\n\\eocesol{(a)~$H_0: p = 0.38$. $H_A: p \\ne 0.38$. Independence (random sample)\nand the success-failure condition are satisfied. $Z=-20.5$ \n$\\to$ p-value $\\approx 0$. Since the p-value is very small, we reject $H_0$. \nThe data provide strong evidence that the proportion of Americans who only \nuse their cell phones to access the internet is different than the Chinese \nproportion of 38\\%, and the data indicate that the proportion is lower in \nthe US.\n(b)~If in fact 38\\% of Americans used their cell phones as a primary access \npoint to the internet, the probability of obtaining a random sample of 2,254 \nAmericans where 17\\% or less or 59\\% or more use their only their cell \nphones to access the internet would be approximately 0.\n(c)~(0.1545, 0.1855). We are 95\\% confident that approximately 15.5\\% to \n18.6\\% of all Americans primarily use their cell phones to browse the \ninternet.}\n\n\n\n%_______________\n\\end{multicols}\n\n\n\n%_______________\n\\eocesolch{Inference for numerical data}\n\n\n\n%_______________\n\\begin{multicols}{2}\n\n% 1\n\n\\eocesol{(a)~$df=6-1=5$, $t_{5}^{\\star} = 2.02$ (column with two tails of 0.10, \nrow with $df=5$).\n(b)~$df=21-1=20$, $t_{20}^{\\star} = 2.53$ (column with two tails of 0.02, \nrow with $df=20$).\n(c)~$df=28$, $t_{28}^{\\star} = 2.05$.\n(d)~$df=11$, $t_{11}^{\\star} = 3.11$.}\n\n% 3\n\n\\eocesol{(a)~0.085, do not reject $H_0$.\n(b)~0.003, reject $H_0$.\n(c)~0.438, do not reject $H_0$.\n(d)~0.042, reject $H_0$.}\n\n% 5\n\n\\eocesol{The mean is the midpoint: $\\bar{x} = 20$. Identify the margin of error: \n$ME = 1.015$, then use $t^{\\star}_{35} = 2.03$ and $SE=s/\\sqrt{n}$ in the \nformula for margin of error to identify $s = 3$.\\\\[6mm]}\n\n% 7\n\n\\eocesol{(a)~$H_0$: $\\mu = 8$ (New Yorkers sleep 8 hrs per night on average.) \n$H_A$: $\\mu \\neq 8$ (New Yorkers sleep less or more than 8 hrs per\nnight on average.)\n(b)~Independence: The sample is random.\nThe min/max suggest there are no concerning outliers.\n$T = -1.75$. $df=25-1=24$.\n(c)~ p-value $= 0.093$.\nIf in fact the true population mean of the \namount New Yorkers sleep per night was 8 hours,\nthe probability of getting a \nrandom sample of 25 New Yorkers where the average\namount of sleep is 7.73 hours\nper night or less (or 8.27 hours or more) is 0.093.\n(d)~Since p-value $>$ 0.05, do not reject $H_0$.\nThe data do not provide strong evidence that\nNew Yorkers sleep more or less than 8 hours per night\non average.\n(e)~No, since the p-value is smaller than $1 - 0.90 = 0.10$.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 9\n\n\\eocesol{$T$ is either -2.09 or 2.09.\nThen $\\bar{x}$ is one of the following:\n\\begin{align*}\n-2.09 &= \\frac{\\bar{x} - 60}{\\frac{8}{\\sqrt{20}}} \\ \\rightarrow \\ \\bar{x} = 56.26 \\\\\n2.09 &= \\frac{\\bar{x} - 60}{\\frac{8}{\\sqrt{20}}} \\ \\rightarrow \\ \\bar{x} = 63.74\n\\end{align*}}\n\n% 11\n\n\\eocesol{(a)~We will conduct a 1-sample $t$-test.\n    $H_0$: $\\mu = 5$. $H_A$: $\\mu \\neq 5$.\n    We'll use $\\alpha = 0.05$.\n    This is a random sample, so the observations are independent.\n    To proceed, we assume the distribution of years of piano\n    lessons is approximately normal.\n    $SE = 2.2 / \\sqrt{20} = 0.4919$.\n    The test statistic is $T = (4.6 - 5) / SE = -0.81$.\n    $df = 20 - 1 = 19$.\n    The one-tail area is about 0.21, so\n    the p-value is about 0.42, which is bigger than\n    $\\alpha = 0.05$ and we do not reject $H_0$.\n    That is, we do not have sufficiently strong evidence\n    to reject the notion that the average is 5 years. \\\\\n(b)~Using $SE = 0.4919$ and $t_{df = 19}^{\\star} = 2.093$,\n    the confidence interval is (3.57, 5.63).\n    We are 95\\% confident that the average number of\n    years a child takes piano lessons in this city is\n    3.57 to 5.63 years. \\\\\n(c)~They agree, since we did not reject the null hypothesis\n    and the null value of 5 was in the $t$-interval.}\n\n% 13\n\n\\eocesol{If the sample is large, then the margin of error will be about \n$1.96 \\times 100 / \\sqrt{n}$. We want this value to be less than 10, which \nleads to $n \\geq 384.16$, meaning we need a sample size of at least 385 (round \nup for sample size calculations!).}\n\n% 15\n\n\\eocesol{Paired, data are recorded in the same cities at two\ndifferent time points. \nThe air quality in a city at one point is not independent\nof the air quality in the same city at another time point.}\n\n% 17\n\n\\eocesol{(a)~Since it's the same students at the beginning and the end of the semester, \nthere is a pairing between the datasets, for a given student their beginning \nand end of semester grades are dependent.\n(b)~Since the subjects were sampled randomly, each observation in the men's \ngroup does not have a special correspondence with exactly one observation in \nthe other (women's) group.\n(c)~Since it's the same subjects at the beginning and the end of the study, \nthere is a pairing between the datasets, for a subject student their beginning \nand end of semester artery thickness are dependent.\n(d)~Since it's the same subjects at the beginning and the end of the study, \nthere is a pairing between the datasets, for a subject student their beginning \nand end of semester weights are dependent.}\n\n% 19\n\n\\eocesol{(a)~For each observation in one data set,\n    there is exactly one specially corresponding\n    observation in the other data set for the\n    same geographic location.\n    The data are paired.\n(b)~$H_0: \\mu_{\\text{diff}} = 0$\n    (There is no difference in average number\n    of days exceeding 90\\textdegree{}F in 1948\n    and 2018 for NOAA stations.)\n    $H_A: \\mu_{\\text{diff}} \\neq 0$\n    (There is a difference.)\n(c)~Locations were randomly sampled, so independence\n    is reasonable.\n    The sample size is at least 30, so we're just looking\n    for particularly extreme outliers:\n    none are present (the observation off left in the\n    histogram would be considered a clear outlier,\n    but not a particularly extreme one).\n    Therefore, the conditions are satisfied.\n(d)~$SE = 17.2 / \\sqrt{197} = 1.23$.\n    $T = \\frac{2.9 - 0}{1.23} = 2.36$\n    with degrees of freedom $df = 197 - 1 = 196$.\n    This leads to a one-tail area of 0.0096\n    and a p-value of about 0.019.\n(e)~Since the p-value is less than 0.05,\n    we reject $H_0$.\n    The data provide strong evidence that\n    NOAA stations observed more 90\\textdegree{}F\n    days in 2018 than in 1948.\n(f)~Type~1 Error, since we may have incorrectly\n    rejected $H_0$.\n    This error would mean that NOAA stations\n    did not actually observe a decrease, but the\n    sample we took just so happened to make it\n    appear that this was the case.\n(g)~No, since we rejected $H_0$,\n    which had a null value of 0.}\n\n% 21\n\n\\eocesol{(a)~$SE = 1.23$ and $t^{\\star} = 1.65$.\n    $2.9 \\pm 1.65 \\times 1.23 \\to (0.87, 4.93)$. \\\\\n(b)~We are 90\\% confident that there was an\n    increase of 0.87 to 4.93 in the average number\n    of days that hit 90\\textdegree{}F in 2018\n    relative to 1948 for NOAA stations. \\\\\n(c)~Yes, since the interval lies entirely above~0.}\n\n% 23\n\n\\eocesol{(a)~These data are paired. For example, the Friday the 13th in say, September \n1991, would probably be more similar to the Friday the 6th in September 1991 \nthan to Friday the 6th in another month or year. \\\\\n(b)~Let $\\mu_{\\textit{diff}} = \\mu_{sixth} - \\mu_{thirteenth}$. $H_0: \\mu_{\\textit{diff}} = 0$. \n$H_A: \\mu_{\\textit{diff}} \\ne 0$. \\\\\n(c)~Independence: The months selected are not random. However, if we think \nthese dates are roughly equivalent to a simple random sample of all such Friday \n6th/13th date pairs, then independence is reasonable.\nTo proceed, we must make this strong assumption,\nthough we should note this assumption in any reported results.\nNormality: With fewer than 10 observations,\nwe would need to see clear outliers to be concerned.\nThere is a borderline outlier on the right of the histogram of the differences,\nso we would want to report this in formal analysis results. \\\\\n(d)~$T = 4.93$ for $df = 10 - 1 = 9$ $\\to$ p-value = 0.001. \\\\\n(e)~Since p-value $<$ 0.05, reject $H_0$. The data provide strong evidence that \nthe average number of cars at the intersection is higher on Friday the \n6$^{\\text{th}}$ than on Friday the 13$^{\\text{th}}$. (We should exercise caution\nabout generalizing the interpretation to all intersections or roads.) \\\\\n(f)~If the average number of cars passing the intersection actually was the \nsame on Friday the 6$^{\\text{th}}$ and $13^{th}$, then the probability that we \nwould observe a test statistic so far from zero is less than 0.01. \\\\\n(g)~We might have made a Type~1 Error, i.e. incorrectly rejected the null \nhypothesis.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 25\n\n\\eocesol{(a)~$H_0: \\mu_{diff} = 0$. $H_A: \\mu_{diff} \\ne 0$.\n    $T=-2.71$. $df=5$.\n    p-value $= 0.042$.\n    Since p-value $<$ 0.05, reject $H_0$.\n    The data provide strong evidence that the average\n    number of traffic accident related emergency room\n    admissions are different between Friday the 6$^{\\text{th}}$\n    and Friday the 13$^{\\text{th}}$.\n    Furthermore, the data indicate that the direction of that\n    difference is that accidents are lower on Friday the\n    $6^{th}$ relative to Friday the 13$^{\\text{th}}$. \\\\\n(b)~(-6.49, -0.17). \\\\\n(c)~This is an observational study, not an experiment,\n    so we cannot so easily infer a causal intervention\n    implied by this statement.\n    It is true that there is a difference.\n    However, for example, this does not mean that\n    a responsible adult going out on Friday the $13^{th}$\n    has a higher chance of harm than on any other night.}\n\n% 27\n\n\\eocesol{(a)~Chicken fed linseed weighed an average of 218.75 grams\nwhile those fed horsebean weighed an average of 160.20 grams.\nBoth distributions are relatively symmetric with no apparent\noutliers.\nThere is more variability in the weights of chicken fed linseed. \\\\\n(b)~$H_0: \\mu_{ls} = \\mu_{hb}$. $H_A: \\mu_{ls} \\ne \\mu_{hb}$. \\\\\nWe leave the conditions to you to consider. \\\\\n$T=3.02$, $df = min(11, 9) = 9$ $\\to$ p-value $= 0.014$.\nSince p-value $<$ 0.05, reject $H_0$.\nThe data provide strong evidence that there is a\nsignificant difference between the average weights of \nchickens that were fed linseed and horsebean. \\\\\n(c)~Type~1 Error, since we rejected $H_0$. \\\\\n(d)~Yes, since p-value $>$ 0.01, we would not have rejected~$H_0$.}\n\n% 29\n\n\\eocesol{$H_0: \\mu_C = \\mu_S$. $H_A: \\mu_C \\ne \\mu_S$.\n$T = 3.27$, $df=11$ $\\to$ p-value $= 0.007$.\nSince p-value $< 0.05$, reject $H_0$.\nThe data provide strong evidence that the average weight\nof chickens that were fed casein is different than the\naverage weight of chickens that were fed soybean\n(with weights from casein being higher).\nSince this is a randomized experiment, the observed \ndifference can be attributed to the diet.}\n\n% 31\n\n\\eocesol{Let $\\mu_{diff} = \\mu_{pre} - \\mu_{post}$.\n$H_0: \\mu_{diff} = 0$:\nTreatment has no effect.\n$H_A: \\mu_{diff} \\neq 0$:\nTreatment has an effect on P.D.T. scores, either positive or negative.\nConditions:\nThe subjects are randomly assigned to treatments, so independence within\nand between groups is satisfied. \nAll three sample sizes are smaller than 30, so we look for clear outliers.\nThere is a borderline outlier in the first treatment group.\nSince it is borderline, we will proceed,\nbut we should report this caveat with any results.\nFor all three groups: $df=13$.\n$T_1 = 1.89 \\to$ p-value = 0.081,\n$T_2 = 1.35 \\to$ p-value = 0.200),\n$T_3 = -1.40 \\to$ (p-value = 0.185).\nWe do not reject the null hypothesis for any of these groups.\nAs earlier noted, there is some uncertainty about if\nthe method applied is reasonable for the first group.}\n\n% 33\n\n\\eocesol{Difference we care about: 40. Single tail of 90\\%: $1.28 \\times SE$. \nRejection region bounds: $\\pm 1.96 \\times SE$ (if 5\\% significance level). \nSetting $3.24 \\times SE = 40$, subbing in $SE = \\sqrt{\\frac{94^2}{n} + \n\\frac{94^2}{n}}$, and solving for the sample size $n$ gives 116 plots of \nland for each fertilizer.}\n\n% 35\n\n\\eocesol{Alternative.}\n\n% 37\n\n\\eocesol{$H_0$: $\\mu_1 = \\mu_2 = \\cdots = \\mu_6$. $H_A$: The average weight varies \nacross some (or all) groups. Independence: Chicks are randomly assigned to \nfeed types (presumably kept separate from one another), therefore \nindependence of observations is reasonable. Approx. normal: the distributions \nof weights within each feed type appear to be fairly symmetric. Constant \nvariance: Based on the side-by-side box plots, the constant variance \nassumption appears to be reasonable. There are differences in the actual \ncomputed standard deviations, but these might be due to chance as these are \nquite small samples. $F_{5,65} = 15.36$ and the p-value is approximately 0. \nWith such a small p-value, we reject $H_0$. The data provide convincing \nevidence that the average weight of chicks varies across some (or all) feed \nsupplement groups.}\n\n% 39\n\n\\eocesol{(a)~$H_0$: The population mean of MET for each group is equal to the others. \n$H_A$: At least one pair of means is different.\n(b)~Independence: We don't have any information on how the data were collected, \nso we cannot assess independence. To proceed, we must assume the subjects in each \ngroup are independent. In practice, we would inquire for more details. \nNormality: The data are bound below by zero and the standard deviations are larger \nthan the means, indicating very strong skew. However, since the sample sizes are \nextremely large, even extreme skew is acceptable. Constant variance: This condition \nis sufficiently met, as the standard deviations are reasonably consistent across groups.\n(c)~See below, with the last column omitted:\\\\[-2mm]\n\\begin{adjustwidth}{-4em}{-4em}\n{\\tiny\n\\begin{center}\n\\renewcommand{\\arraystretch}{1.25}\n\\begin{tabular}{lrrrr}\n  \\hline\n            & Df    & Sum Sq        & Mean Sq   & F value \\\\ \n  \\hline\ncoffee      & {\\textcolor{oiB}{{\\scriptsize 4}}}     & {\\textcolor{oiB}{{\\scriptsize 10508}}}       & {\\textcolor{oiB}{{\\scriptsize 2627}}}             & {\\textcolor{oiB}{{\\scriptsize 5.2}}} \\\\ \nResiduals       & {\\textcolor{oiB}{{\\scriptsize 50734}}} & 25564819     & {\\textcolor{oiB}{{\\scriptsize  504}}}         &  \\\\ \n   \\hline\nTotal           & {\\textcolor{oiB}{{\\scriptsize 50738}}} & 25575327 \\\\\n\\hline\n\\end{tabular}\n\\end{center}\n}\n\\end{adjustwidth} \\vspace{1mm}\n(d)~Since p-value is very small, reject $H_0$. The data provide convincing evidence \nthat the average MET differs between at least one pair of groups.}\n\n% 41\n\n\\eocesol{(a)~$H_0$: Average GPA is the same for all majors. $H_A$: At least one pair of means are different.\n(b)~Since p-value $>$ 0.05, fail to reject $H_0$. The data do not provide convincing evidence of a difference between the average GPAs across three groups of majors.\n(c)~The total degrees of freedom is $195 + 2 = 197$, so the sample size is $197+1=198$.}\n\n% 43\n\n\\eocesol{(a)~False. As the number of groups increases, so does the number of comparisons and hence the modified significance level decreases.\n(b)~True.\n(c)~True.\n(d)~False. We need observations to be independent regardless of sample size.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 45\n\n\\eocesol{(a)~$H_0$: Average score difference is the same for all treatments. $H_A$: At \nleast one pair of means are different.\n(b)~We should check conditions. If we look back to the earlier exercise, we \nwill see that the patients were randomized, so independence is satisfied. \nThere are some minor concerns about skew, especially with the third group, \nthough this may be acceptable. The standard deviations across the groups are \nreasonably similar. Since the p-value is less than 0.05, reject $H_0$. The \ndata provide convincing evidence of a difference between the average \nreduction in score among treatments.\n(c)~We determined that at least two means are different in part (b), so we \nnow conduct $K = 3\\times2/2 = 3$ pairwise $t$-tests that each use $\\alpha = 0.05/3 \n =  0.0167$ for a significance level. Use the following hypotheses for each \npairwise test. $H_0$: The two means are equal. $H_A$: The two means are \ndifferent. The sample sizes are equal and we use the pooled SD, so we can \ncompute $SE = 3.7$ with the pooled $df = 39$.\nLooking at the largest difference, Trmt 1 vs Trmt 3:\n$Z = \\frac{6.21 - (-3.21)}{3.7} = 2.52$ on $df = 39$ yields a p-value of 0.015.\nBecause this is smaller than $0.05 / 3 = 1.67$,\nwe have strong evidence to that this particular pair of groups are different.\nWhen doing similar calculations for Trmt 1 vs 2 or 2 vs 3, we do not\nfind any statistically significant difference.\n(Note that we get a different result if not using the pooled result.)}\n\n% 47\n\n\\eocesol{$H_0: \\mu_{T} = \\mu_{C}$. $H_A: \\mu_{T} \\ne \\mu_{C}$. $T=2.24$, $df=21$ $\\to$ \np-value $= 0.036$. Since p-value $<$ 0.05, reject $H_0$.\nThe data provide \nstrong evidence that the average food consumption by the patients in the \ntreatment and control groups are different. Furthermore, the data indicate \npatients in the distracted eating (treatment) group consume more food than \npatients in the control group.}\n\n% 49\n\n\\eocesol{False. While it is true that paired analysis requires equal sample sizes, \nonly having the equal sample sizes isn't, on its own, sufficient for doing \na paired test. Paired tests require that there be a special correspondence \nbetween each pair of observations in the two groups.}\n\n% 51\n\n\\eocesol{(a)~We are building a distribution of sample statistics, in this case the sample \nmean. Such a distribution is called a sampling distribution.\n(b)~Because we are dealing with the distribution of sample means, we need to \ncheck to see if the Central Limit Theorem applies. Our sample size is greater \nthan 30, and we are told that random sampling is employed. With these conditions \nmet, we expect that the distribution of the sample mean will be nearly normal \nand therefore symmetric.\n(c)~Because we are dealing with a sampling distribution, we measure its \nvariability with the standard error. $SE = 18.2 / \\sqrt{45} = 2.713$.\n(d)~The sample means will be more variable with the smaller sample size.}\n\n% 53\n\n\\eocesol{(a)~We should set 1.0\\% equal to 2.8 standard errors:\n    $2.8 \\times SE_{desired} = 1.0\\%$\n    (see Example~\\ref{sample_size_for_80_percent_power}\n    on page~\\pageref{sample_size_for_80_percent_power}\n    for details).\n    This means the standard error should be about $SE = 0.36\\%$\n    to achieve the desired statistical power. \\\\\n(b)~The margin of error was\n    $0.5 \\times (2.6\\% - (-0.2\\%)) = 1.4\\%$,\n    so the standard error in the experiment must have been\n    $1.96 \\times SE_{original} = 1.4\\%$\n    $\\to$\n    $SE_{original} = 0.71\\%$. \\\\\n(c)~The standard error decreases with the square root of the\n    sample size, so we should increase the sample size by\n    a factor of $1.97^2 = 3.88$. \\\\\n(d)~The team should run an experiment 3.88 times larger,\n    so they should have a random sample of 3.88\\% of their\n    users in each of the experiment arms in the new experiment.}\n\n% 55\n\n\\eocesol{Independence: it is a random sample,\nso we can assume that the students in this\nsample are independent of each other with \nrespect to number of exclusive relationships\nthey have been in.\nNotice that there are no students who have\nhad no exclusive relationships in the \nsample, which suggests some student responses\nare likely missing\n(perhaps only positive values were reported).\nThe sample size is at least 30, and there are\nno particularly extreme outliers, so the normality\ncondition is reasonable.\n90\\% CI: (2.97, 3.43).\nWe are 90\\% confident that undergraduate students\nhave been in 2.97 to 3.43 \nexclusive relationships, on average.}\n\n% 57\n\n\\eocesol{The hypotheses should be about the population mean ($\\mu$),\nnot the sample mean. \nThe null hypothesis should have an equal sign and the\nalternative hypothesis \nshould be about the null hypothesized value, not the observed\nsample mean. \nCorrection:\n\\begin{align*}\nH_0&: \\mu = 10~hours \\\\\nH_A&: \\mu \\neq 10~hours\n\\end{align*}\nA two-sided test allows us to consider the possibility\nthat the data show us something that we would find surprising.}\n\n\n\n%_______________\n\\end{multicols}\n\n\n\\newpage\n\n%_______________\n\\eocesolch{Introduction to linear regression}\n\n\n\n%_______________\n\\begin{multicols}{2}\n\n% 1\n\n\\eocesol{(a)~The residual plot will show randomly distributed residuals around 0. \nThe variance is also approximately constant.\n(b)~The residuals will show a fan shape, with higher variability for \nsmaller $x$. There will also be many points on the right above the line. \nThere is trouble with the model being fit here.}\n\n% 3\n\n\\eocesol{(a)~Strong relationship, but a straight line would not fit the data.\n(b)~Strong relationship, and a linear fit would be reasonable.\n(c)~Weak relationship, and trying a linear fit would be reasonable.\n(d)~Moderate relationship, but a straight line would not fit the data.\n(e)~Strong relationship, and a linear fit would be reasonable.\n(f)~Weak relationship, and trying a linear fit would be reasonable.}\n\n% 5\n\n\\eocesol{(a)~Exam 2 since there is less of a scatter in the plot of final \nexam grade versus exam 2. Notice that the relationship between \nExam 1 and the Final Exam appears to be slightly nonlinear.\n(b)~Exam 2 and the final are relatively close to each other \nchronologically, or Exam 2 may be cumulative so has greater \nsimilarities in material to the final exam. Answers may vary.}\n\n% 7\n\n\\eocesol{(a)~$r = -0.7$ $\\rightarrow$ (4).\n(b)~$r = 0.45$ $\\rightarrow$ (3).\n(c)~$r = 0.06$ $\\rightarrow$ (1).\n(d)~$r = 0.92$ $\\rightarrow$ (2).}\n\n% 9\n\n\\eocesol{(a)~The relationship is positive, weak, and possibly linear. However, \nthere do appear to be some anomalous observations along the left where \nseveral students have the same height that is notably far from the \ncloud of the other points. Additionally, there are many students who \nappear not to have driven a car, and they are represented by a set of \npoints along the bottom of the scatterplot.\n(b)~There is no obvious explanation why simply being tall should lead \na person to drive faster. However, one confounding factor is gender. \nMales tend to be taller than females on average, and personal \nexperiences (anecdotal) may suggest they drive faster. If we were to \nfollow-up on this suspicion, we would find that sociological studies \nconfirm this suspicion.\n(c)~Males are taller on average and they drive faster. The gender \nvariable is indeed an important confounding variable.}\n\n% 11\n\n\\eocesol{(a)~There is a somewhat weak, positive, possibly linear relationship \nbetween the distance traveled and travel time. There is clustering \nnear the lower left corner that we should take special note of.\n(b)~Changing the units will not change the form, direction or strength \nof the relationship between the two variables. If longer distances \nmeasured in miles are associated with longer travel time measured in \nminutes, longer distances measured in kilometers will be associated \nwith longer travel time measured in hours.\n(c)~Changing units doesn't affect correlation: $r = 0.636$.}\n\n% 13\n\n\\eocesol{(a)~There is a moderate, positive, and linear relationship between \nshoulder girth and height.\n(b)~Changing the units, even if just for one of the variables, will \nnot change the form, direction or strength of the relationship between \nthe two variables.}\n\n% 15\n\n\\eocesol{In each part, we can write the husband ages as a linear function of \nthe wife ages. \\\\\n(a)~$age_{H} = age_{W} + 3$. \\\\\n(b)~$age_{H} = age_{W} - 2$. \\\\\n(c)~$age_{H} = 2 \\times age_{W}$. \\\\\nSince the slopes are positive and these are perfect linear \nrelationships, the correlation will be exactly 1 in all three parts. \nAn alternative way to gain insight into this solution is to create a \nmock data set, e.g. 5 women aged 26, 27, 28, 29, and 30, then find the \nhusband ages for each wife in each part and create a scatterplot.}\n\n% 17\n\n\\eocesol{Correlation: no units. Intercept: kg. Slope: kg/cm.}\n\n% 19\n\n\\eocesol{Over-estimate. Since the residual is calculated as \n$observed\\ -\\ predicted$, a negative residual means that the \npredicted value is higher than the observed value.}\n\n% 21\n\n\\eocesol{(a)~There is a positive, very strong, linear association between the \nnumber of tourists and spending.\n(b)~Explanatory: number of tourists (in thousands). Response: \nspending (in millions of US dollars).\n(c)~We can predict spending for a given number of tourists using a \nregression line. This may be useful information for determining how \nmuch the country may want to spend in advertising abroad, or to \nforecast expected revenues from tourism.\n(d)~Even though the relationship appears linear in the scatterplot, \nthe residual plot actually shows a nonlinear relationship. This is \nnot a contradiction: residual plots can show divergences from \nlinearity that can be difficult to see in a scatterplot. A simple \nlinear model is inadequate for modeling these data. It is also \nimportant to consider that these data are observed sequentially, \nwhich means there may be a hidden structure not evident in the \ncurrent plots but that is important to consider.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 23\n\n\\eocesol{(a)~First calculate the slope: \n$b_1 = R\\times s_y/s_x = 0.636 \\times 113 / 99 = 0.726$. \nNext, make use of the fact that the regression line passes through \nthe point $(\\bar{x},\\bar{y})$: $\\bar{y} = b_0 + b_1 \\times \\bar{x}$. \nPlug in $\\bar{x}$, $\\bar{y}$, and $b_1$, and solve for $b_0$: 51. \nSolution: $\\widehat{travel~time} = 51 + 0.726 \\times distance$.\n(b)~$b_1$: For each additional mile in distance, the model predicts \nan additional 0.726 minutes in travel time. $b_0$: When the distance \ntraveled is 0 miles, the travel time is expected to be 51 minutes. It \ndoes not make sense to have a travel distance of 0 miles in this \ncontext. Here, the $y$-intercept serves only to adjust the height of \nthe line and is meaningless by itself.\n(c)~$R^2 = 0.636^2 = 0.40$. About 40\\% of the variability in travel \ntime is accounted for by the model, i.e. explained by the distance \ntraveled.\n(d)~$\\widehat{travel~time} = 51 + 0.726 \\times distance \n= 51 + 0.726 \\times 103 \\approx 126$ minutes. (Note: we should be \ncautious in our predictions with this model since we have not yet \nevaluated whether it is a well-fit model.)\n(e)~$e_i = y_i - \\hat{y}_i = 168 - 126 = 42$ minutes. A positive \nresidual means that the model underestimates the travel time.\n(f)~No, this calculation would require extrapolation.}\n\n% 25\n\n\\eocesol{(a)~$\\widehat{murder} = -29.901 + 2.559 \\times poverty\\%$.\n(b)~Expected murder rate in metropolitan areas with no poverty is -29.\n901 per million. This is obviously not a meaningful value, it just \nserves to adjust the height of the regression line.\n(c)~For each additional percentage increase in poverty, we expect \nmurders per million to be higher on average by 2.559.\n(d)~Poverty level explains 70.52\\% of the variability in murder rates \nin metropolitan areas.\n(e)~$\\sqrt{0.7052} = 0.8398$.}\n\n% 27\n\n\\eocesol{(a)~There is an outlier in the bottom right. Since it is far from the \ncenter of the data, it is a point with high leverage. It is also an \ninfluential point since, without that observation, the regression \nline would have a very different slope. \\\\\n(b)~There is an outlier in the bottom right. Since it is far from the \ncenter of the data, it is a point with high leverage. However, it \ndoes not appear to be affecting the line much, so it is not an \ninfluential point. \\\\\n(c)~The observation is in the center of the data (in the x-axis \ndirection), so this point does \\emph{not} have high leverage. This \nmeans the point won't have much effect on the slope of the line and \nso is not an influential point.}\n\n% 29\n\n\\eocesol{(a)~There is a negative, moderate-to-strong, somewhat linear \nrelationship between percent of families who own their home and the \npercent of the population living in urban areas in 2010. There is one \noutlier: a state where 100\\% of the population is urban. The \nvariability in the percent of homeownership also increases as we move \nfrom left to right in the plot.\n(b)~The outlier is located in the bottom right corner, horizontally \nfar from the center of the other points, so it is a point with high \nleverage. It is an influential point since excluding this point from \nthe analysis would greatly affect the slope of the regression line.}\n\n% 31\n\n\\eocesol{(a)~The relationship is positive, moderate-to-strong, and linear. \nThere are a few outliers but no points that appear to be influential. \\\\\n(b)~$\\widehat{weight} = -105.0113 + 1.0176 \\times height$. \\\\\nSlope: For each additional centimeter in height, the model\npredicts the average weight to be 1.0176 additional kilograms\n(about 2.2 pounds).   \\\\\nIntercept: People who are 0 centimeters tall are expected to weigh -\n105.0113 kilograms. This is obviously not possible. Here, the $y$-\nintercept serves only to adjust the height of the line and is \nmeaningless by itself. \\\\\n(c)~$H_0$: The true slope coefficient of height is zero \n($\\beta_1 = 0$). \\\\\n$H_A$: The true slope coefficient of height is \ndifferent than zero ($\\beta_1 \\neq 0$). \\\\\nThe p-value for the two-sided alternative hypothesis\n($\\beta_1 \\ne 0$) is incredibly small, so we reject $H_0$.\nThe data provide convincing evidence that height and \nweight are positively correlated.\nThe true slope parameter is indeed greater than~0. \\\\\n(d)~$R^2 = 0.72^2 = 0.52$. Approximately 52\\% of the variability in \nweight can be explained by the height of individuals.}\n\n% 33\n\n\\eocesol{(a)~$H_0$: $\\beta_1 = 0$. $H_A$: $\\beta_1 \\neq 0$.\nThe p-value, as reported in the table, is incredibly small\nand is smaller than 0.05, so we reject $H_0$.\nThe data provide convincing evidence that wives' and husbands' \nheights are positively correlated. \\\\\n(b)~$\\widehat{height}_{W} = 43.5755 + 0.2863 \\times height_{H}$. \\\\\n(c)~Slope: For each additional inch in husband's height, the average \nwife's height is expected to be an additional 0.2863 inches on \naverage. Intercept: Men who are 0 inches tall are expected to have \nwives who are, on average, 43.5755 inches tall. The intercept here is \nmeaningless, and it serves only to adjust the height of the line. \\\\\n(d)~The slope is positive, so $r$ must also be positive. \n$r = \\sqrt{0.09} = 0.30$. \\\\\n(e)~63.33. Since $R^2$ is low, the prediction based on this \nregression model is not very reliable. \\\\\n(f)~No, we should avoid extrapolating.}\n\n% 35\n\n\\eocesol{(a)~$H_0: \\beta_1 = 0; H_A: \\beta_1 \\ne 0$\n(b)~The p-value for this test is approximately 0, therefore we reject \n$H_0$. The data provide convincing evidence that poverty percentage \nis a significant predictor of murder rate.\n(c)~$n = 20, df = 18, T^*_{18} =  2.10$; $2.559 \\pm 2.10 \\times 0.390 \n= (1.74, 3.378)$; For each percentage point poverty is higher, murder \nrate is expected to be higher on average by 1.74 to 3.378 per million.\n(d)~Yes, we rejected $H_0$ and the confidence interval does not \ninclude 0.}\n\n% 37\n\n\\eocesol{(a)~True.\n(b)~False, correlation is a measure of the linear association \nbetween any two numerical variables.}\n\n% 39\n\n\\eocesol{(a)~The point estimate and standard error are $b_1 = 0.9112$ and \n$SE = 0.0259$. We can compute a T-score: $T = (0.9112 - 1)/0.0259 = -3.43$. \nUsing $df=168$, the p-value is about 0.001,\nwhich is less than $\\alpha = 0.05$.\nThat is, \nthe data provide strong evidence that the average difference between \nhusbands' and wives' ages has actually changed over time.\n(b)~$\\widehat{age}_W = 1.5740 + 0.9112 \\times age_{H}$.\n(c)~Slope: For each additional year in husband's age, the model predicts \nan additional 0.9112 years in wife's age. This means that wives' ages \ntend to be lower for later ages, suggesting the average gap of husband \nand wife age is larger for older people.\nIntercept: Men who are 0 years old are expected to have wives who are on \naverage 1.5740 years old. The intercept here is meaningless and serves only \nto adjust the height of the line.\n(d)~$R = \\sqrt{0.88} = 0.94$. The regression of wives' ages on husbands' \nages has a positive slope, so the correlation coefficient will be positive.\n(e)~$\\widehat{age}_W = 1.5740 + 0.9112 \\times 55 = 51.69$.\nSince $R^2$ is pretty high, the prediction based on this regression model \nis reliable.\n(f)~No, we shouldn't use the same model to predict an 85 year old man's \nwife's age. This would require extrapolation. The scatterplot from an \nearlier exercise shows that husbands in this data set are approximately \n20 to 65 years old. The regression model may not be reasonable outside \nof this range.}\n\n% 41\n\n\\eocesol{There is an upwards trend. However, the variability is higher for \nhigher calorie counts, and it looks like there might be two clusters \nof observations above and below the line on the right, so we should \nbe cautious about fitting a linear model to these data.}\n\n% 43\n\n\\eocesol{(a)~$r = -0.72 \\to (2)$\n(b)~$r = 0.07 \\to (4)$\n(c)~$r = 0.86 \\to (1)$\n(d)~$r = 0.99 \\to (3)$}\n\n\n\n%_______________\n\\end{multicols}\n\n\n\n%_______________\n\\eocesolch{Multiple and logistic regression}\n\n\n\n%_______________\n\\begin{multicols}{2}\n\n% 1\n\n\\eocesol{(a)~$\\widehat{baby\\_\\hspace{0.3mm}weight} = 123.05 - 8.94 \\times smoke$\n(b)~The estimated body weight of babies born to smoking mothers is 8.94 \nounces lower than babies born to non-smoking mothers.  \nSmoker: $123.05 - 8.94 \\times 1 = 114.11$ ounces. \nNon-smoker: $123.05 - 8.94 \\times 0 = 123.05$ ounces.\n(c)~$H_0$: $\\beta_1 = 0$. $H_A$: $\\beta_1 \\ne 0$. $T= -8.65$, and the p-value is \napproximately 0. Since the p-value is very small, we reject $H_0$. The data \nprovide strong evidence that the true slope parameter is different than 0 and \nthat there is an association between birth weight and smoking. Furthermore, \nhaving rejected $H_0$, we can conclude that smoking is associated with lower \nbirth weights.}\n\n% 3\n\n\\eocesol{(a)~$\\widehat{baby\\_weight} = -80.41 + 0.44 \\times gestation \n- 3.33 \\times parity - 0.01 \\times age + 1.15 \\times height \n+ 0.05 \\times weight - 8.40 \\times smoke$.\n(b)~$\\beta_{gestation}$: The model predicts a 0.44 ounce increase in the birth \nweight of the baby for each additional day of pregnancy, all else held constant.\n$\\beta_{age}$: The model predicts a 0.01 ounce decrease in the birth weight of \nthe baby for each additional year in mother's age, all else held constant. \n(c)~Parity might be correlated with one of the other variables in the model, \nwhich complicates model estimation.\n(d)~$\\widehat{baby\\_\\hspace{0.3mm}weight} = 120.58$.\n$e = 120 - 120.58 = -0.58$. The model over-predicts this baby's birth weight.\n(e)~$R^2 = 0.2504$. $R_{adj}^2 = 0.2468$.}\n\n% 5\n\n\\eocesol{(a)~(-0.32, 0.16). We are 95\\% confident that male students on average have GPAs \n0.32 points lower to 0.16 points higher than females when controlling for the \nother variables in the model.\n(b)~Yes, since the p-value is larger than 0.05 in all cases (not including the \nintercept).}\n\n% 7\n\n\\eocesol{Remove age.}\n\n% 9\n\n\\eocesol{Based on the p-value alone, either gestation or smoke should be added to the \nmodel first. However, since the adjusted $R^2$ for the model with gestation is \nhigher, it would be preferable to add gestation in the first step of the forward-\nselection algorithm. (Other explanations are possible. For instance, it would be \nreasonable to only use the adjusted $R^2$.)}\n\n% 11\n\n\\eocesol{She should use p-value selection since she is interested in finding out about \nsignificant predictors, not just optimizing predictions.}\n\n% 13\n\n\\eocesol{Nearly normal residuals:\nWith so many observations in the data set,\nwe look for particularly extreme outliers\nin the histogram and do not see any.\nvariability of residuals: The scatterplot of the residuals versus the fitted \nvalues does not show any overall structure. However, values that have very low \nor very high fitted values appear to also have somewhat larger outliers. In \naddition, the residuals do appear to have constant variability between the two \nparity and smoking status groups, though these items are relatively minor. \\\\ \nIndependent residuals: The scatterplot of residuals versus the order of data \ncollection shows a random scatter, suggesting that there is no apparent \nstructures related to the order the data were collected. \\\\ Linear relationships \nbetween the response variable and numerical explanatory variables: The residuals \nvs. height and weight of mother are randomly distributed around 0. The residuals \nvs. length of gestation plot also does not show any clear or strong remaining \nstructures, with the possible exception of very short or long gestations. The \nrest of the residuals do appear to be randomly distributed around 0. \\\\All \nconcerns raised here are relatively mild. There are some outliers, but there is \nso much data that the influence of such observations will be minor.}\n\n\\end{multicols}\n\\newpage\n\\begin{multicols}{2}\n\n% 15\n\n\\eocesol{(a)~There are a few potential outliers, e.g. on the left in the \n\\var{total\\_\\hspace{0.3mm}length} variable, but nothing that will be of serious \nconcern in a data set this large.\n(b)~When coefficient estimates are sensitive to which variables are included in \nthe model, this typically indicates that some variables are collinear. For \nexample, a possum's gender may be related to its head length, which would \nexplain why the coefficient (and p-value) for \\var{sex\\_\\hspace{0.3mm}male} \nchanged when we removed the \\var{head\\_\\hspace{0.3mm}length} variable. Likewise, \na possum's skull width is likely to be related to its head length, probably even \nmuch more closely related than the head length was to gender.}\n\n% 17\n\n\\eocesol{(a)~The logistic model relating $\\hat{p}_i$ to the predictors may be written as \n$\\log\\left( \\frac{\\hat{p}_i}{1 - \\hat{p}_i} \\right) \n= 33.5095 - 1.4207\\times sex\\_male_i - 0.2787 \\times skull\\_width_i \n+ 0.5687 \\times total\\_length_i - 1.8057 \\times tail\\_length_i$. \nOnly \\var{total\\_\\hspace{0.3mm}length} has a positive association with a possum \nbeing from Victoria.\n(b)~$\\hat{p} = 0.0062$. While the probability is very near zero, we have not run \ndiagnostics on the model. We might also be a little skeptical that the model \nwill remain accurate for a possum found in a US zoo. For example, perhaps the \nzoo selected a possum with specific characteristics but only looked in one \nregion. On the other hand, it is encouraging that the possum was caught in the \nwild. (Answers regarding the reliability of the model probability will vary.)}\n\n% 19\n\n\\eocesol{(a)~False.\n    When predictors are collinear, it means they are correlated,\n    and the inclusion of one variable can have a substantial\n    influence on the point estimate (and standard error) of\n    another.\n(b)~True.\n(c)~False.\n    This would only be the case if the data was from\n    an experiment and $x_1$ was one of the variables set by\n    the researchers.\n    (Multiple regression can be useful for forming hypotheses\n    about causal relationships, but it offers zero guarantees.)\n(d)~False.\n    We should check normality like we would for inference\n    for a single mean:\n    we look for particularly extreme outliers if $n \\geq 30$\n    or for clear outliers if $n < 30$.}\n\n% 21\n\n\\eocesol{(a)~\\resp{exclaim\\us{}subj} should be removed,\n    since it's removal reduces AIC the most\n    (and the resulting model has lower AIC\n    than the None Dropped model).\n(b)~Removing any variable will increase AIC,\n    so we should not remove any variables from\n    this set.}\n\n% 23\n\n\\eocesol{(a)~The equation is:\n    \\begin{align*}\n    \\log\\left(\\frac{p_i}{1 - p_i}\\right)\n      &= -0.8124 \\\\\n        &\\quad- 2.6351 \\times \\resp{to\\us{}multiple} \\\\\n        &\\quad + 1.6272 \\times \\resp{winner} \\\\\n        &\\quad- 1.5881 \\times \\resp{format} \\\\\n        &\\quad - 3.0467 \\times \\resp{re\\us{}subj}\n    \\end{align*}\n(b)~First find $\\log\\left(\\frac{p}{1 - p}\\right)$,\n    then solve for $p$:\n    \\begin{align*}\n    &\\log\\left(\\frac{p}{1 - p}\\right) \\\\\n      &\\quad= -0.8124\n          - 2.6351 \\times 0\n          + 1.6272 \\times 1 \\\\\n        &\\qquad- 1.5881 \\times 0\n          - 3.0467 \\times 0 \\\\\n      &\\quad= 0.8148 \\\\\n    &\\frac{p}{1 - p} = e^{0.8148}\n      \\quad\\to\\quad p = 0.693\n    \\end{align*}\n(c)~It should probably be pretty high, since it could\n    be very disruptive to the person using the email\n    service if they are missing emails that aren't spam.\n    Even only a 90\\% chance that a message is spam is\n    probably enough to warrant keeping it in the inbox.\n    Maybe a probability of 99\\% would be a reasonable\n    cutoff.\n    As for other ideas to make it even better,\n    it may be worth building a second model that tries\n    to classify the importance of an email message.\n    If we have both the spam model and the importance\n    model, we now have a better way to think about\n    cost-benefit tradeoffs.\n    For instance, perhaps we would be willing to\n    have a lower probability-of-spam threshold for\n    messages we were confident were not important,\n    and perhaps we want an even higher probability\n    threshold (e.g. 99.99\\%) for emails we are\n    pretty sure are important.}\n\n\n\n%_______________\n\\end{multicols}\n"
  },
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    "content": "\\index{probability sample|see{sample}}\n\\index{df|see{degrees of freedom (df)}}"
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    "path": "extraTeX/preamble/copyright.tex",
    "content": "\\chapter*{}\n\\vfill\n\n\\noindent%\nCopyright $\\copyright$ 2019. Fourth Edition. \\\\\nUpdated: \\versiondate. \\\\\n\n\\noindent%\nThis book may be downloaded as a free PDF at\n\\oiRedirect{os}\n    {\\color{black}\\textbf{openintro.org/book/os}}.\nThis textbook is also available under a\n\\oiRedirect{license}{Creative Commons license},\nwith the source files hosted on\n\\oiRedirect{os_source}{Github}. \\\\\n\n\\printlocation\n\n%\\noindent Modified versions of this textbook, including reformatted electronic versions, may not be redistributed under a title that suggests association with or endorsement by OpenIntro, e.g. it cannot be titled \\emph{OpenIntro Statistics}. %More information on branding restrictions for derivatives is available on the Rights page at~\\href{http://www.openintro.org/rights.php}{openintro.org}.\n\n"
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    "path": "extraTeX/preamble/copyright_derivative.tex",
    "content": "\\chapter*{}\n\\vfill\n\n% We encourage you to leave this page entirely intact.\n\n\\noindent $\\copyright$ 2015. This content is available under a Creative Commons Attribution-ShareAlike 3.0 Unported United States license. License details are available at the Creative Commons website: \\urlwofont{http://www.creativecommons.org} \\\\\n\n\\noindent For license and attribution guidance, see \\urlwofont{https://github.com/OpenIntroOrg/openintro-statistics/blob/master/LICENSE}\n"
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    "path": "extraTeX/preamble/preface.tex",
    "content": "\\chapter*{{\\color{oiB}Preface}}\n%\\chaptertext{}\n%\\sectiontext{}\n\n\\noindent%\nOpenIntro Statistics covers a first course in statistics,\nproviding a rigorous introduction to applied statistics\nthat is clear, concise, and accessible.\nThis book was written with the undergraduate level in mind,\nbut it's also popular in high schools and graduate courses.\n\\vspace{3mm}\n\nWe hope readers will take away three ideas from\nthis book in addition to forming a foundation of statistical\nthinking and methods.\\vspace{-1mm}\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item\n    Statistics is an applied field with a wide range\n    of practical applications.\n\\item\n    You don't have to be a math guru to learn\n    from real, interesting data.\n\\item\n    Data are messy, and statistical tools are imperfect.\n    But, when you understand the strengths and weaknesses of\n    these tools, you can use them to learn about the world.\n\\end{itemize}\n\n\n%\\subsection*{Is this a data science book?}\n%\n%\\noindent%\n%Short answer: yes.\n%Long answer: it depends what you mean by \\term{data science},\n%since two types of data scientists have emerged.\n%\\vspace{3mm}\n%\n%\\noindent%\n%Type~A data scientists focus on \\emph{analysis},\n%such as exploratory data analysis, inference,\n%model building, and other related topics.\n%Type~B data scientists focus on \\emph{building},\n%typically in the form of machine learning models\n%or other systems.\n%As you might expect, these two types share many skills,\n%though their main focuses differ.\n%This book focuses on skills most commonly used by\n%Type~A data scientists.\n%For more thoughts, please check out the following page:\n%\\begin{center}\n%\\oiRedirect{data_science_types}{{\\color{red}BROKEN}}\n%\\end{center}\n%\\vspace{3mm}\n%\n%\\noindent%\n\n\n\\subsection*{{\\color{oiB}Textbook overview}}\n\n\\noindent%\nThe chapters of this book are as follows:%\\vspace{2mm}\n\\begin{description}\n\\setlength{\\itemsep}{0mm}\n\\item[1. Introduction to data.]\n    Data structures, variables,\n    and basic data collection techniques.\n\\item[2. Summarizing data.]\n    Data summaries, graphics,\n    and a teaser of inference using randomization.\n\\item[3. Probability.]\n    Basic principles of probability.\n    %This chapter is not required for the later chapters.\n\\item[4. Distributions of random variables.]\n    The normal model and other key distributions.\n\\item[5. Foundations for inference.]\n    %Introduction to uncertainty in point estimates,\n    %confidence intervals, and hypothesis tests.\n    General ideas for statistical inference in the context\n    of estimating the population proportion.\n\\item[6. Inference for categorical data.]\n    Inference for proportions and tables using the normal\n    and chi-square distributions.\n\\item[7. Inference for numerical data.]\n    Inference for one or two sample means using the\n    \\mbox{$t$-distribution},\n    statistical power for comparing two groups,\n    and also comparisons of many\n    means using ANOVA.\n\\item[8. Introduction to linear regression.]\n    Regression for a numerical outcome with one predictor variable.\n    Most of this chapter could be covered after\n    Chapter~\\ref{introductionToData}.\n\\item[9. Multiple and logistic regression.]\n    Regression for numerical and categorical data\n    using many predictors. %for an accelerated course.\n\\end{description}\n\n\n%\\newpage\n\n\\noindent%\n\\emph{OpenIntro Statistics} supports flexibility\nin choosing and ordering topics.\nIf the main goal is to reach multiple regression\n(Chapter~\\ref{ch_regr_mult_and_log})\nas quickly as possible, then the following are the\nideal prerequisites:\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n\\item Chapter~\\ref{ch_intro_to_data},\n    Sections~\\ref{numericalData},\n    and Section~\\ref{categoricalData} for a solid\n    introduction to data structures and statistical\n    summaries that are used throughout the book.\n\\item Section~\\ref{normalDist}\n    for a solid understanding of the normal distribution.\n\\item Chapter~\\ref{ch_foundations_for_inf}\n    to establish the core set of inference tools.\n%\\item Section~\\ref{oneSampleMeansWithTDistribution}\n%    and Chapter~\\ref{ch_regr_simple_linear}\n%    provide required for multiple regression with a numerical\n%    outcome.\n%    For the remaining chapters, they could be tackled in\n%    almost any order, with the exception that\n%    \n%    \n%    come before Chapter~\\ref{ch_regr_mult_and_log}.\n\\item Section~\\ref{oneSampleMeansWithTDistribution}\n    to give a foundation for the $t$-distribution\n\\item Chapter~\\ref{ch_regr_simple_linear}\n    for establishing ideas and principles for single\n    predictor regression.\n%    introduce the \n%    which introduces the $t$-distribution, should come before\n%    Section~\\ref{oneSampleMeansWithTDistribution}\n%Chapters~\\ref{ch_inference_for_props}-\\ref{ch_regr_mult_and_log},\n%    could be tackled in\n%    almost any order, with the exception that\n%    Section~\\ref{oneSampleMeansWithTDistribution}\n%    and Chapter~\\ref{ch_regr_simple_linear}\n%    come before Chapter~\\ref{ch_regr_mult_and_log}.\n%\\item Sections~\\ref{ch_inference_for_props}\n%    and~\\ref{} are recommended before logistic regression.\n\\end{itemize}\n%One conspicuously missing topic from the list above is the\n%chapter on Probability.\n%While useful for a deeper understanding of the calculations,\n%especially for anyone looking to take a second course in\n%statistics, it is not required reading when the focus is on\n%applied data analysis.\n\n\n\\subsection*{{\\color{oiB}Examples and exercises}}\n%, and appendices}\n\n\\noindent%\nExamples are provided to establish an understanding of how\nto apply methods\n\n\\begin{examplewrap}\n\\begin{nexample}{This is an example.\n    When a question is asked here, where can the answer be found?}\n  The answer can be found here, in the solution section\n  of the example!\n\\end{nexample}\n\\end{examplewrap}\n\n\\noindent%\nWhen we think the reader should be ready to try determining\nthe solution to an example, we frame it as Guided Practice.\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nThe reader may check or learn the answer to any Guided Practice\nproblem by reviewing the full solution in a footnote.\\footnotemark{}\n%Readers are strongly encouraged to attempt these practice problems.\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{Guided Practice problems are intended to stretch\n  your thinking, and you can check yourself by reviewing the\n  footnote solution for any Guided Practice.}\n\n\\noindent%\nExercises are also provided at the end of each section\nas well as review exercises at the end of each chapter.\nSolutions are given for odd-numbered exercises in\nAppendix~\\ref{eoceSolutions}.\n%Probability tables for the normal, $t$,\n%and chi-square distributions are in\n%Appendix~\\ref{distributionTables}.\n\n\n\\subsection*{{\\color{oiB}Additional resources}}\n\nVideo overviews, slides, statistical software labs,\ndata sets used in the textbook,\nand much more are readily available at\\\\[-5mm]\n\\begin{center}\n\\oiRedirect{os}\n    {\\color{black}\\textbf{openintro.org/os}}\n\\end{center}\n%Data sets for this textbook are available on the website\n%and in a companion R package.\\footnote{Diez DM,\n%    Barr CD, \\c{C}etinkaya-Rundel M. 2015.\n%    \\texttt{openintro}: OpenIntro data sets and supplement\n%    functions.\n%    \\oiRedirect{textbook-github_openintro}\n%        {github.com/OpenIntroOrg/openintro-r-package}.}\n%All of these resources are free and may be used with\n%or without this textbook as a companion.\nWe also have improved the ability to access data in this book\nthrough the addition of Appendix~\\ref{appendix_data},\nwhich provides additional information for each of the data sets\nused in the main text and is new in the Fourth Edition.\nOnline guides to each of these data sets are also provided at\n\\oiRedirect{data}\n    {\\color{black}\\textbf{openintro.org/data}}\nand through a\n\\oiRedirect{textbook-github_openintro}\n    {companion R~package}.\n% Official:\n% http://www.openintro.org/package/openintro\n% Currently redirect it to:\n% http://openintrostat.github.io/openintro-r-package/\n\\vspace{3mm}\n\n\\noindent%\nWe appreciate all feedback as well as reports of any\ntypos through the website.\nA short-link to report a new typo or review known typos is\n\\oiRedirect{os_typos}\n    {\\color{black}\\textbf{openintro.org/os/typos}}. \\vspace{3mm}\n\n\\noindent%\nFor those focused on statistics at the high school level,\nconsider\n\\oiRedirect{textbook-books}\n    {\\emph{Advanced High School Statistics}},\nwhich is a version of \\emph{OpenIntro Statistics} that has\nbeen heavily customized by \\oiRedirect{people}{Leah Dorazio}\nfor high school courses and\nAP\\textsuperscript{\\textregistered} Statistics.\n\n\n\\subsection*{{\\color{oiB}Acknowledgements}}\nThis project would not be possible without the passion and\ndedication of many more people beyond those on the author list.\nThe authors would like to thank the\n\\oiRedirect{textbook-openintro_about}{OpenIntro Staff}\nfor their involvement and ongoing contributions.\nWe~are also very grateful to the hundreds of students\nand instructors who have provided us with valuable feedback\nsince we first started posting book content in~2009. \\vspace{3mm}\n\n\\noindent%\nWe also want to thank the many teachers who helped review\nthis edition, including\nLaura Acion,\n\\oiRedirect{matthew_e_aiello-lammens}\n    {Matthew E. Aiello-Lammens},\n\\oiRedirect{jonathan_akin}{Jonathan Akin},\nStacey C. Behrensmeyer,\nJuan Gomez,\nJo Hardin,\n\\oiRedirect{nicholas_horton}{Nicholas Horton},\n\\oiRedirect{danish_khan}{Danish Khan},\n\\oiRedirect{peter_hm_klaren}{Peter H.M. Klaren},\nJesse Mostipak,\nJon C. New,\nMario Orsi,\nSteve Phelps,\nand David Rockoff.\nWe appreciate all of their feedback, which helped\nus tune the text in significant ways and greatly\nimproved this book.\n"
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    "content": "\\chapter*{Feedback Instructions}\n%\\chaptertext{}\n%\\sectiontext{}\n\nThis is a review copy of an unfinished version of the\nFourth Edition of OpenIntro Statistics.\nPlease read these next few pages before reviewing this book.\n\n\n\\subsection*{What *not* to watch for}\n\n\\noindent%\nThere are several components that you should ignore.\n\\begin{enumerate}\n\\setlength{\\itemsep}{0mm}\n\\item\n    \\textbf{End-of-section/chapter exercises\n    and odd-numbered solutions will be included\n    in the final version.}\n    The newer exercises are not yet ready for sharing,\n    so we've omitted exercises from this review copy\n    to avoid any confusion.\n\\item\n    \\Comment{This is comment text that we are using to\n    call out items and that you can consider as FYIs.}\n    There's a big dot in the margin that makes it easy\n    to spot these notes.\n\\item\n    There are plenty of formatting issues,\n    e.g. awkward page breaks or footnotes on the wrong page.\n    These issues will be fixed during final textbook formatting.\n\\item\n    There are some broken references such as\n    ``Figure~\\ref{}'' or ``Section~\\ref{}''.\n    Any such references will be fixed before\n    the Fourth Edition is released.\n\\end{enumerate}\n\n\n\\subsection*{We will send a survey for you to complete}\n\n\\noindent%\nWe will send you a survey by December 31st.\nResponding to this survey by January 7th will be most\nhelpful to us, which is when we will be starting to\nincorporate significant amounts of feedback.\n\n\n\\subsection*{Sending feedback as you read}\n\n\\noindent%\nIf you are browsing through the book and think,\n``Hey, they should add / do / change / etc [thing]'',\nsend a note to \\url{os4@openintro.org}\nor via\n\\href{http://www.openintro.org/os4}{\\texttt{openintro.org/os4}}\n\\vspace{3mm}\n\n\\noindent%\nBelow are specific topics where you may\nwant to voice your thoughts:\n\\begin{enumerate}\n\\item\n    If you are reading an example\n    or case study and think that there's an\n    interesting comment that might be made on\n    confounding variables or on what a multivariate\n    analysis would be like, please let us know.\n    We'll be adding such comments and discussion\n    during January and February.\n\\item\n    If you read the new\n    \\emph{Foundations for Inference} chapter,\n    what do you think about it?\n    Do you like, dislike, or not care that we\n    now introduce inference using proportions\n    before means?\n\\item\n    We have also reversed the ordering of the two chapters\n    covering inference for proportions / means.\n    Do we move too quickly or too slowly in spots\n    for either section?\n    Which spots require more explanation or examples?\n\\item\n    The new case study for logistic regression\n    covers a sensitive yet important topic:\n    racial discrimination.\n    If you read this section, do you think the\n    topic was presented and discussed in an\n    appropriately respectful and responsible way?\n\n    We will also be getting a thorough\n    review by subject-matter experts for this section.\n%\\item\n%    In newer examples, we more strongly suggest software\n%    over using tables for finding tail areas.\n%    We are planning to do further changes around wording\n%    in existing examples and would like feedback on this\n%    direction.\n%\\item\n%    The 3rd Edition launched with only black-and-white\n%    paperbacks, and a year after launch we made\n%    full color hardcovers available.\n%    How important is it to you that we offer\n%    (1) full-color books available and/or\n%    (2) hardcover textbooks available?\n%    (Our tentative plan is to launch with\n%    a black-and-white paperback and also\n%    a full-color paperback, where the expected\n%    prices are \\$20 and \\$35, respectively.)\n%\\item\n    \n\\end{enumerate}\n\n\n\\subsection*{Some of the changes already implemented}\n\n\\noindent%\nThe following sections contained notable updates\nin content or examples:\n\\begin{itemize}\n%\\setlength{\\itemsep}{0mm}\n\\item 1.2,\n\\item 1.3.4,\n\\item all of Chapter~\\ref{ch_summarizing_data},\n\\item some loan data examples in 3.1,\n\\item stock return examples in 3.4,\n\\item (nothing notable in Chapter~\\ref{ch_distributions}]),\n\\item all of Chapter~\\ref{ch_foundations_for_inf},\n\\item 6.1.2,\n\\item 6.1.3,\n\\item 6.3.5,\n\\item 6.4,\n\\item 7.1.5,\n\\item 7.2,\n\\item 7.5 (updated MLB data),\n\\item 8.4 (updated election data),\n\\item 9.4\n\\end{itemize}\n\n\n\\noindent%\nHere are some special callouts for changes made:\n\\begin{description}\n\\item[Stylistic.]\n    Each section now starts at the top of a page.\n    Section, subsection, term boxes, tip boxes,\n    examples, and guided practice\n    all have updated appearances.\n    \n    There are some bugs with spacing here and there,\n    e.g. with sections and the horizontal lines,\n    that we are still working out.\n    \n    Video and slide icons / links have also been removed,\n    since these will be presented in a different way\n    in the Fourth Edition.\n\\item[Graphics and statistical summaries get their own chapter.]\n    The first chapter of the Third Edition has been\n    broken into two chapters in the Fourth Edition.\n\\item[Inference: proportions before means.]\n    We introduce inference using proportion before means\n    in the Fourth Edition.\n    The inference of proportions chapter also now\n    precedes the inference for means chapter.\n\\item[Simulation and randomization.]\n    Two sections in the inference for proportions\n    in small sample situations have been removed\n    and will become online extras in about April.\n    The randomization case study section near the start of the\n    textbook was retained with a new case study.\n\\item[Lots of new examples.]\n    We have replaced or updated many older or less interesting\n    data sets with new case studies to make the book more\n    engaging for both students and teachers.\n    (A few lingering instances remain that will be resolved\n    before the Fourth Edition is complete.)\n    If any data sets strike you as outdated or uninteresting,\n    please send a note.\n\\item\n    \n\\end{description}\n\n\n\\subsection*{Changes in progress or that will be completed}\n\n\\noindent%\nFor reference, we will go to print in April.\n\n\\noindent%\nBelow are tentative changes, and we welcome\nfeedback and suggestions on these plans.\n\\begin{enumerate}\n%\\item\n%    As earlier mentioned, exercises will be moved to the\n%    end of sections, and there will be some new exercises\n%    in the new edition.\n\\item\n    \\textbf{We are moving all data references into an\n    appendix and out of footnotes in the text}\n    (you can observe in the book that many footnotes\n    for references have disappeared).\n    Our goals with this change are to\n    \\begin{enumerate}\n    \\item\n        simplify reading for the large majority of readers, and\n    \\item\n        provide a place where we can provide a complete\n        list of all data sets in the text.\n    \\end{enumerate}\n    The appendix will also include links (in the PDF)\n    to pages dedicated to each data set\n    and a CSV download link.\n\\item\n    \\textbf{We are tentatively planning to place exercises\n    at the end of each section.}\n    We would also include a handful of exercises at the\n    end of each chapter that would be more comprehensive.\n\\item\n    Create a couple lead-in pages for each chapter that\n    stand out more strongly.\n    Designs have been drawn up but are not yet implemented\n    in the \\LaTeX{} source files.\n\\item\n    Replace the Mario Kart auction data in\n    Chapter~9 with a new data set that is to-be-determined.\n\\item\n    We are cutting out the condition that the\n    \\emph{sample size needs to be $\\leq 10\\%$ of the\n    population size}.\n    It will be mentioned briefly as a consideration\n    but no longer included as a condition.\n%    We've received several cases of feedback that this\n%    is confusing (often asked: why is collecting more data bad?),\n%    or that it is not practically relevant except\n%    in very rare cases.\n%    If you are concerned about this change,\n%    please let us know.\n\\item\n    The discussion of statistical vs practical significance\n    is not in the new \\emph{Foundations for Inference} chapter.\n    However, it will be added back into the book before the\n    Fourth Edition is released in a location to-be-determined.\n\\item\n    We will be completing a thorough review of the inference\n    chapters to ensure they read well in their new order.\n    Most especially, we want to be confident the 2-prop\n    description is reasonable since it is no longer preceded\n    by the 2-mean scenario.\n\\item\n    We may add a new section on graphics\n    that would follow the sections on summarizing numerical\n    and categorical data.\n\\item\n    We may include some basics on R code at the end\n    of some sections.\n    If this is of particular interest to you,\n    please let us know.\n\\item\n    We may include some blank pages in the Fourth Edition\n    launch if we plan to add specific types of new content.\n    This strategy would allow us to add extra (non-critical)\n    content later without affecting page numbering of\n    textbooks already purchased or downloaded.\n\\item\n    You'll also find several comments throughout the book\n    that callout additional items.\n\\end{enumerate}\n\n\n\n\n\n\n\n"
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    "content": "%-------------------------------------------------------------\n% 5 Section Headers\n\n% 5.0 Spacings\n\\newcommand{\\sectionheaderspaceA}{1mm}\n\\newcommand{\\sectionheaderspaceB}{4mm}\n\\newcommand{\\sectionheaderspaceC}{2mm}\n\\newcommand{\\sectionheaderspaceD}{6mm}\n\\newcommand{\\setupfont}\n    {\\normalfont\\LARGE\\bfseries\\fontfamily{phv}\\selectfont}\n\\newcommand{\\setupfontsectionexercises}\n    {\\normalfont\\bfseries\\fontfamily{phv}\\selectfont}\n\\newcommand{\\setfontsize}[1]{\\fontsize{#1}{#1}\\selectfont}\n\n% 5.1 Chapter\n\\newcommand{\\titlebreak}[1][5mm]{\\\\[#1]}\n\\newcommand{\\chaptertitle}[2][\\chaptertitlefontsize]{{\\color{white}\\titlerule[1.5mm]}\\addvspace{\\sectionheaderspaceB}\n    {\\setupfont{}\\color{chaptertitlegray}\\setfontsize{#1}#2} \\\\[2mm]\n        {\\color{white}\\titlerule[1.5mm]}\n\n~\\vspace{15mm}\n\n}\n\\titleformat{\\chapter}[display]\n{\\color{oiB}\\normalfont\\Huge\\bfseries\\raggedright}{\\chaptertitlename\\\n\\thechapter}{20pt}{{\\setupfont{}\\Huge #1}}\n\\newenvironment{chapterpage}[1]{\n%\\noindent\\begin{fullminipage}[left=\\chapterpagepaddingleftright{},right=\\chapterpagepaddingleftright{},top=\\chapterpagepadding{},bottom=\\chapterpagepadding{},bgcolor=seaBackground]\n%\\begin{mdframed}[%\n%    topline=false,\n%    rightline=false,\n%    leftline=false,\n%    bottomline=false,\n%    innerleftmargin=\\chapterpagepaddingleftinner{},\n%    innerrightmargin=\\chapterpagepaddingrightinner{},\n%    innertopmargin=\\chapterpagepaddingtopinner{},\n%    innerbottommargin=\\chapterpagepaddingbottominner{},\n%    backgroundcolor=seaBackground]\n\\chapter{#1}\n}{\n%\\end{mdframed}\n%\\end{fullminipage}\n\\newpage\n}\n\\titleformat{\\chapter}\n    {}\n    {\\setupfont{}\\setfontsize{\\chapterXfontsize}\\color{oiB}Chapter~\\thechapter}% \\quad #1}\n    {1em}\n    {}\n    []\n\\titlespacing{\\chapter}\n   {0pt}% left\n   {0pt}% before sep\n   {\\baselineskip}% after sep% 5.3 Subsection\n\\newcommand{\\chaptersection}[1]{\\noindent\\Large\\fontfamily{phv}%\n\\selectfont\\textbf{\\ref{#1}~\\nameref{#1}} \\\\[4mm]}\n\n% 5.1.1 Chapter introduction\n\\newcommand{\\chapterintro}[1]{\n%  \\fancyhead[RE]{}\n%  \\fancyhead[LO]{}\n\n%  ~\\vspace{25mm}\n\n%  {\\color{oiB}\\titlerule[1.5mm]}\\addvspace{7mm}\n\n%  {\\Large\n%  \\begingroup\\onehalfspacing\n%  \\noindent%\n  #1%\\vspace{7mm}\\par\n%  \\endgroup}\n\n%  {\\color{oiB}\\titlerule[0.5mm]}\n\n  \\vfill\n\n%  {\\color{oiB}\\titlerule[0.5mm]}\n  \n%  ~\\vspace{5mm}\n  \n  {\\Large\\noindent%\n  For videos, slides, and other resources, please visit \\\\[2mm]\n  \\oiRedirect{os}{\\textbf{\\color{oiB}www.openintro.org/os}}}%\n      \\vspace{5mm}\n\n  ~\\newpage\n  %\\fancyhead[RO,LE]{\\thepage}\n%  \\fancyhead[RE]{\\leftmark}\n%  \\fancyhead[LO]{\\rightmark}\n}\n\n% 5.2 Section\n\\newcommand{\\clearpageforsection}{\\clearpage}\n\\let\\oldsection\\section\n\\renewcommand\\section{\\clearpageforsection\\oldsection}\n%\\titleformat{\\section}\n%    {{\\color{oiR}\\titlerule[1mm]}\\addvspace{\\sectionheaderspaceA}\\color{oiB}\\setupfont{}}\n%    {\\color{oiB}\\thesection \\quad #1}\n%    {1em}\n%    {}\n%    [{\\color{oiR}\\titlerule[1mm]}\\addvspace{4mm}]\n\\titlespacing{\\section}\n   {0pt}% left\n   {0pt}% before sep\n   {\\baselineskip}% after sep% 5.3 Subsection\n%\\titleformat{\\subsection}\n%    {{\\color{grayDark}\\titlerule[0.1mm]}\\vspace{2mm}\\color{oiB}\\normalfont\\large%\n%        \\bfseries\\fontfamily{phv}%\n%        \\selectfont}\n%    {\\color{oiB}\\thesubsection}\n%    {1em}\n%    {#1}\n\\titlespacing{\\subsection}\n   {0pt}% left\n   {8mm}% before sep\n   {\\baselineskip}% after sep% 5.3 Subsection\n\n\n% 5.4 Section Introduction\n\\newcommand{\\sectionintro}[1]{\\begin{spacing}{1.1}\\large\n#1\n\\end{spacing}}\n\\newcommand{\\nsubsection}[1]{\\subsection{\\MakeUppercase{#1}}}\n\n\n% 5.5 Review Exercises\n\\newcommand{\\reviewexercisesheader}[1][Chapter exercises]\n  {\n    \\clearpageforsection\n    {{\\color{oiR}\\titlerule[1mm]}%\n        \\vspace*{\\sectionheaderspaceC}\n    \\noindent\\setupfont{}\\color{oiB}#1 \\\\[-1mm]\n    {\\color{oiR}\\titlerule[1mm]} \\\\[2mm]\n    }\n}\n\\newcommand{\\exercisesheader}[1][Exercises]\n  {\n    \\clearpageforsection\n    {{\\color{oiR}\\titlerule[1.0mm]}%\n        \\vspace*{\\sectionheaderspaceC}\n    \\noindent\\setupfont{}\\large\\color{oiB}\\textbf{#1}\n    } \\vspace*{\\sectionheaderspaceD{}}\n  }\n"
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    "content": "% 1 Page Parameters\n% 2 Special Commands for Editions\n% 3 Content Modifications\n% 4 Counters and Parameters\n% 5 Section Coloring\n% 6 Utilities\n% 7 \n% 8 Figures and Captions\n% 9 Examples and Exercises\n% 10 Special Boxes\n\n%\\renewcommand\\chapter{\\if@openright\\cleardoublepage\\else\\clearpage\\fi\n%                    \\thispagestyle{fancy}%\n%                    \\global\\@topnum\\z@\n%                    \\@afterindentfalse\n%                    \\secdef\\@chapter\\@schapter}\n\\fancypagestyle{plain}{%\n\\fancyhf{} % clear all header and footer fields\n\\fancyhead[RO,RE]{\\thepage} %RO=right odd, RE=right even\n\\renewcommand{\\headrulewidth}{0pt}\n\\renewcommand{\\footrulewidth}{0pt}}\n\\raggedbottom\n\n\n\\newcommand{\\stdspace}[0]{3mm}\n\\newcommand{\\stdvspace}[0]{\\vspace{\\stdspace{}}}\n\\newcommand{\\stdaddvspace}[0]{\\addvspace{\\stdspace{}}}\n\n\n\n\n%-------------------------------------------------------------\n% 1 Page Parameters\n% 1.1\n\\setlength\\paperheight{11in}\n\\setlength\\paperwidth{8.5in}\n\\newcommand{\\officialtextheight}{9.7in}\n\\newcommand{\\officialtextwidth}{6in}\n%\\setlength\\paperheight{10in}\n%\\setlength\\paperwidth{8in}\n%\\newcommand{\\officialtextheight}{8.7in}\n%\\newcommand{\\officialtextwidth}{6in}\n\\newcommand{\\officialvoffset}{-0.6in}\n\\setlength\\textheight{\\officialtextheight}\n\\setlength\\textwidth{\\officialtextwidth}\n\\setlength\\voffset{\\officialvoffset}\n\\renewcommand{\\baselinestretch}{1.0}\n% 1.2 Margin Size\n\\setlength\\hoffset{0.25in}\n% 1.2.1 Even\n\\setlength\\oddsidemargin{0in}\n\\setlength\\evensidemargin{0in}\n% 1.2.2 Slightly offset\n%\\setlength\\oddsidemargin{0.08in}\n%\\setlength\\evensidemargin{-0.08in}\n% 1.2.3 Significant offset\n% WARNING: The chapter pages will show partially hidden page numbers.\n%\\setlength\\oddsidemargin{0.2in}\n%\\setlength\\evensidemargin{-0.2in}\n% 1.3 PDF Parameters\n%\\setlength\\paperheight{11in}\n%\\setlength\\textheight{8.25in}\n%\\setlength\\paperwidth{8.5in}\n%\\setlength\\textwidth{5.45in}\n%\\setlength\\voffset{-10mm}\n%\\setlength\\oddsidemargin{0.75in}\n%\\setlength\\evensidemargin{0.75in}\n% 1.4 Margin Spacing\n\\setlength{\\marginparsep}{5mm}\n\\setlength{\\marginparwidth}{20mm}\n% 1.5 Page Header\n\\pagestyle{fancy}\n\\renewcommand{\\headrulewidth}{0pt}\n\\fancyhead[RO,LE]{\\thepage}\n\\fancyhead[RE]{\\leftmark}\n\\fancyhead[LO]{\\rightmark}\n\\fancyfoot[c]{}\n\\fancyheadoffset[RO,LE]{0.9in}\n\n\n\n% Tablet Version\n%\\setlength\\paperheight{8.82in}\\setlength\\textheight{8.25in}\\setlength\\paperwidth{5.7in}\\setlength\\textwidth{5.45in}\\setlength\\voffset{-23.5mm}\\setlength\\hoffset{-27mm}\\setlength\\oddsidemargin{5mm}\\setlength\\evensidemargin{5mm}\\setlength{\\marginparsep}{5mm}\\setlength{\\marginparwidth}{35mm}\\fancyheadoffset[RO,LE]{0.2in}\n\n\n\n\n%-------------------------------------------------------------\n% 2 Special Commands for Editions\n\\newcommand{\\referrer}{os4_pdf}\n\\newcommand{\\vspaceB}[1]{}\n\\newcommand{\\hspaceB}[1]{}\n\\newcommand{\\textB}[1]{}\n\\newcommand{\\textC}[1]{}\n\\newcommand{\\D}[1]{#1}\n\n\n\n%-------------------------------------------------------------\n% 3 Content Modifications\n\\newcommand{\\APVersion}[2]{#2}\n\\newcommand{\\MultipleRegression}[2]{#1}\n\\newcommand{\\MultipleRegressionChapter}[2]{#1}\n\\newcommand{\\SimulationAndRandomization}[1]{#1}\n\\newcommand{\\ANOVASection}[2]{#1}\n\\newcommand{\\GLMSection}[2]{#1}\n\n\n\n\n%-------------------------------------------------------------\n% 4 Counters and Parameters\n% 4.1 Counters\n\\newcounter{alwaysOne}\n\\setcounter{alwaysOne}{1}\n\\newcounter{alwaysTwo}\n\\setcounter{alwaysTwo}{2}\n\\newcounter{alwaysThree}\n\\setcounter{alwaysThree}{3}\n\\newcounter{alwaysFour}\n\\setcounter{alwaysFour}{4}\n\\newcounter{withinChNum}[chapter]\n\\setcounter{withinChNum}{0}\n\\newcounter{eoce}[chapter]\n\\renewcommand{\\theeoce}\n    {\\arabic{chapter}.\\arabic{eoce}}\n\\newcounter{eocesolch}\n\\setcounter{eocesolch}{0}\n\\newcounter{eocesol}[eocesolch]\n\\renewcommand{\\theeocesol}\n     {\\arabic{eocesolch}.\\arabic{eocesol}}\n\\newcounter{eoceNeedSolution}[chapter]\n\\renewcommand{\\theeoceNeedSolution}\n    {\\arabic{chapter}.\\arabic{eoceNeedSolution}}\n\\newcounter{eoceReplace}[chapter]\n\\renewcommand{\\theeoceReplace}\n    {\\arabic{chapter}.\\arabic{eoceReplace}}\n\\newcounter{eoceFF}[chapter]\n\\renewcommand{\\theeoceFF}\n    {\\arabic{chapter}.\\arabic{eoceFF}}\n% 4.2 Parameters\n\\newlength{\\exampleAboveBar}\n\\newlength{\\exampleBelowBar}\n\\setlength{\\exampleAboveBar}{-3.15mm}\n\\setlength{\\exampleBelowBar}{-1.15mm}\n\\newlength{\\nexampleAboveBar}\n\\newlength{\\nexampleBelowBar}\n\\setlength{\\nexampleAboveBar}{-1mm}\n\\setlength{\\nexampleBelowBar}{-1mm}\n% 4.3 Chapter Declarations\n\\newcommand\\includechapter[2]{\n  \\setcounter{chapter}{#1}\n  \\addtocounter{chapter}{-1}\n  \\normalsize\n  \\include{#2/TeX/#2}\n  \\newpage\\input{#2/TeX/review_exercises}\n  }\n\n\n\n%-------------------------------------------------------------\n% 5 Section Headers\n%\n% See headers.tex file for main chapters.\n\\newcommand{\\chapterpagepaddingtopinner}[0]{35mm} % 45mm\n\\newcommand{\\chapterpagepaddingbottominner}[0]{25mm}\n\\newcommand{\\chapterXfontsize}[0]{92}\n\\newcommand{\\chaptertitlefontsize}[0]{30}\n\n\n%-------------------------------------------------------------\n% 6 Utilities\n% 6.1 Helpful Editing Commands\n\\newcommand\\Add[1]{\\marginpar[{\\Huge\\color{oiR}$\\bullet$}]{\\Huge\\color{oiR}$\\bullet$}{\\color{oiB}#1}}\n\\newcommand\\Cut[1]{\\marginpar[{\\Huge\\color{oiR}$\\bullet$}]{\\Huge\\color{oiR}$\\bullet$}{\\color{oiGC}#1}}\n%\\newcommand\\Comment[1]{\\marginpar[{\\Huge\\color{oiR}$\\bullet$}]{\\Huge\\color{oiR}$\\bullet$} {\\color{oiG}{[#1]}}}\n\\newcommand{\\note}[1]{\\Comment{#1}}\n% 6.2 Special Symbols\n\\newcommand{\\degree}{\\ensuremath{^\\circ}}\n\\newcommand{\\R}{\\textbf{\\textsf{R}}}\n% 6.3 Text Commands (Terms, Data, Variable, Response)\n\\newcommand{\\term}[1]{\\textbf{#1}\\index{#1|textbf}}\n\\newcommand{\\termsub}[2]{\\textbf{#1}\\index{#2|textbf}}\n\\newcommand{\\termni}[1]{\\textbf{#1}}\n\\newcommand{\\hiddenterm}[1]{#1\\index{#1|textbf}}\n\\newcommand{\\indexthis}[2]{#1\\index{#2}}\n\\newcommand{\\termO}[1]{\\textbf{\\color{termOColor}#1}}\n\\newenvironment{data}[1]{\\texttt{#1}}{}\n\\newcommand{\\datalink}[1]{\\index{#1|textbf}\\texttt{\\oiRedirect{data_#1}{#1}}}\n\\newenvironment{var}[1]{\\texttt{#1}}{}\n\\newenvironment{resp}[1]{\\texttt{#1}}{}\n\\newcommand{\\lmlevel}[1]{:~\\emph{#1}}{}\n\\newenvironment{calctext}[1]{{\\color{oiB}\\texttt{#1}}}{}\n\\newenvironment{calctextmath}[1]{{\\color{oiB}\\mathtt{#1}}}{}\n\\newenvironment{calcbutton}[1]{{\\color{oiB}\\texttt{#1}}}{}\n\\newcommand{\\codeindent}{\\hspace{5mm}}\n% 6.4 Highlighting\n\\newenvironment{highlight}{\\textbf}{}\n\\newcommand{\\highlightO}[1]{\\textbf{\\color{oiB}#1}}\n\\newcommand{\\highlightT}[1]{\\emph{\\color{oiR}#1}}\n% 6.5 Lengths\n\\setlength{\\parindent}{0.3in}\n% 6.6 Hyperreferences\n\\newcommand{\\urlwofont}[1]{\\urlstyle{same}\\url{#1}}\n\\newcommand{\\oiRedirect}[2]{\\href{http://www.openintro.org/redirect.php?go=#1&referrer=\\referrer}{#2}}\n\\newcommand{\\videoicon}[1][4.5mm]{\\includegraphics[height=#1]{extraTeX/icons/video_camera.png}~}\n\\newcommand{\\CalculatorVideos}[1]{}%{\\begin{tipBox}{\\tipBoxTitle[\\videoicon]{Calculator videos}\n%Videos covering #1 using TI and Casio graphing calculators are available at \\mbox{\\oiRedirect{textbook-openintro_videos}{openintro.org/videos}}.}\n%\\end{tipBox}}\n\\newcommand{\\videohref}[2][4.5mm]{\\oiRedirect{#2}{\\raisebox{-0.3mm}[0pt]{\\includegraphics[height=#1]{extraTeX/icons/video_camera.png}}}}\n\\newcommand{\\slideshref}[2][4.5mm]{\\oiRedirect{#2}{\\raisebox{-0.3mm}[0pt]{\\includegraphics[height=#1]{extraTeX/icons/slides.png}}}}\n\\newcommand{\\videomarginhref}[2][4mm]{\\oiRedirect{#2}{\\raisebox{-3mm}[0pt]{\\includegraphics[height=#1]{extraTeX/icons/video_camera.png}}}}\n\\newcommand{\\sectionvideohref}[2][6mm]{\\oiRedirect{#2}{\\raisebox{-0.5mm}[0pt]{\\includegraphics[height=#1]{extraTeX/icons/video_camera.png}}}}\n\\newcommand{\\sectionslideshref}[2][6mm]{\\oiRedirect{#2}{\\raisebox{-0.5mm}[0pt]{\\includegraphics[height=#1]{extraTeX/icons/slides.png}}}}\n\\newcommand{\\MarginVideo}[1]{\\marginpar[{\\videomarginhref{#1}}]{{\\videomarginhref{#1}}}}\n% 6.7 Helper commands\n\\newcommand{\\us}[0]{\\_\\hspace{0.3mm}}\n%\\newcommand{\\quadplus}[0]{\\quad + \\quad}\n\\newcommand{\\indfunc}[2]{\\var{#1}_{\\resp{#2}}}\n\n\n%-------------------------------------------------------------\n% 7 \n\n\n\n%-------------------------------------------------------------\n% 8 Figures and Captions\n% 8.1 & 8.2 Table & Figure Numbering\n% Thanks @Herbert on StackExchange for helping clean up this style code!\n% http://tex.stackexchange.com/questions/176978/latex-numbering-in-counters-appears-to-have-changed/177045?noredirect=1#comment409945_177045\n\\makeatletter\n\\let\\c@table\\c@figure\n\\makeatother\n% 8.3 Caption Width\n\\newlength{\\mycaptionwidth}\n\\setlength{\\mycaptionwidth}{0.825\\textwidth}\n\\captionsetup{width=\\mycaptionwidth}\n\\newcommand{\\Figure}[3][]{\\includegraphics[width=#2\\textwidth]{\\chapterfolder/figures/#3/#3}}\n\\newcommand{\\Figures}[4][]{\\includegraphics[width=#2\\textwidth]{\\chapterfolder/figures/#3/#4}}\n\\newcommand{\\Figuress}[4][]{\\includegraphics[width=#2]{\\chapterfolder/figures/#3/#4}}\n\\newcommand{\\FigureFullPath}[3][]{\\includegraphics[width=#2\\textwidth]{#3}}\n\\newcommand{\\chapterfolder}{}\n\n\n\n\n%-------------------------------------------------------------\n% 9 Examples and Exercises\n% 9.1 Exercises, within the text\n% 9.1.1 Exercise Environment\n\\newcommand{\\excolor}[1]{{\\color{excolor}#1}}\n\\newenvironment{exercise}\n{\n\\begin{itemize}\\item[\\color{oiB}$\\bigodot$]\\refstepcounter{equation}\\noindent\\normalsize\\textbf{\\color{oiB}Guided Practice \\theexercise}%\\hspace{3mm}\n\n}\n{\\normalsize\n\n\\stdaddvspace{}\n\\end{itemize}}\n% 9.1.2 Exercise Fine Tuning\n\\newcommand\\theexercise{\\thechapter.\\arabic{equation}}\n% 9.2 Examples\n% 9.2.1 Example Environment\n\\newcommand\\theexample{\\thechapter.\\arabic{equation}}\n\\newenvironment{example}[1]\n{\\begin{itemize}\n\\item[\\color{oiB}\\Large$\\CIRCLE$]\\refstepcounter{equation}\\noindent\\textbf{\\color{oiB}Example \\theexample}\n\n#1\\vspace{\\exampleAboveBar}\n\n{\\color{examplegray}\\rule{20mm}{0.1mm}}\n\n\\vspace{\\exampleBelowBar}\n\n\\normalsize}{\n\n\\end{itemize}\n\\stdaddvspace{}\n}\n% 9.2.2 Wrappers\n%\\reversemarginpar\n\\def\\warningsymbol{\\protect\\marginsymbolhelper}\n\\def\\marginsymbolhelper{\\tabto*{0mm} {\\dbend} \\tabto*{0mm}}\n\\newcommand{\\exampleicon}[1]{\\vspace{#1} \\raggedleft\\includegraphics[width=5mm]{extraTeX/icons/example.png}\\hspace{2mm}\\ }\n\\newenvironment{gpewrapper}[1]{\\addvspace{4mm}\n\n\\noindent\\hspace{-12.45mm}\\begin{minipage}[c]{\\textwidth+8mm}\n\\begin{minipage}[c]{8.4mm}\n\\hspace{0.5mm}\\includegraphics[width=5mm]{extraTeX/icons/#1.png}\n\\end{minipage}\\begin{minipage}[c]{\\textwidth-0.45mm}\\begin{mdframed}[%\n    topline=false,\n    rightline=false,\n    bottomline=false,\n    linewidth=0.5mm,\n    linecolor=oiB]}{\\end{mdframed}\\end{minipage}\\end{minipage}\n\n    \\addvspace{4mm}}\n\\newenvironment{examplewrap}\n    {\\begin{gpewrapper}{example}}\n    {\\end{gpewrapper}}\n\\newenvironment{exercisewrap}\n    {\\begin{gpewrapper}{guided_practice}}\n    {\\end{gpewrapper}}\n% 9.2.3 Example Title\n\\newcommand{\\exampletitle}[1]{\\textbf{\\color{oiB}\\small\\fontfamily{phv}%\n\\selectfont{\\MakeUppercase{Example~#1}}} \\\\[1mm]}\n\\newcommand{\\exercisetitle}[1]{\\textbf{\\color{oiB}\\small\\fontfamily{phv}%\n\\selectfont{\\MakeUppercase{Guided Practice~#1}}} \\\\[1mm]}\n% 9.2.4 NEW Example and Guided Practice Environment\n\\newcommand{\\exspace}{\\stdvspace{}}\n\\newenvironment{nexample}[1]{\\addvspace{6mm}\n\n\\refstepcounter{equation}\\exampletitle{\\theexample}\n#1\n\n\\addvspace{\\nexampleAboveBar}\n\n{\\color{examplegray}\\rule{20mm}{0.1mm}}\n\n\\addvspace{\\nexampleBelowBar}\n\n\\setlength{\\parskip}{2mm}}{}\n\\newenvironment{nexercise}{\\addvspace{6mm}\n\n\\refstepcounter{equation}\\exercisetitle{\\theexample}}{}\n\n% 9.3 EOCEs: End of Chapter Exercises\n% 9.3.1 Environment\n\\newenvironment{eoce}[2][]\n{\\refstepcounter{eoce}\\noindent\\small\\textbf{\\textcolor{oiB}{{\\hypersetup{linkcolor=oiB}{\\fontfamily{phv}\\selectfont\\ref{eoce_sol_\\arabic{chapter}_\\arabic{eoce}}}}\\label{eoce_\\arabic{chapter}_\\arabic{eoce}}}}\\hspace{2mm} #1#2\n\n\\addvspace{4mm}\n\n}\n%{\\em #2 } $\\:$ \\\\ }\n{}\n% 9.3.2 EOCE Solutions\n\\newcommand{\\eocesolch}[1]{\n\\refstepcounter{eocesolch}\\noindent\\textbf{\\color{oiB}\\arabic{eocesolch}\\hspace{2mm}#1}\n\n\\addvspace{2mm}\n\n}\n{\n\\newcommand{\\eocesol}[1]{\\refstepcounter{eocesol}\\noindent\\textbf{\\color{oiB}{\\hypersetup{linkcolor=oiB}{\\fontfamily{phv}\\selectfont\\ref{eoce_\\arabic{eocesolch}_\\arabic{eocesol}}}}\\label{eoce_sol_\\arabic{eocesolch}_\\arabic{eocesol}}}\\hspace{2mm}{\\small#1}\\makebox[0pt]{\\color{white}\\tiny \\refstepcounter{eocesol}\\label{eoce_sol_\\arabic{eocesolch}_\\arabic{eocesol}}}\n\n\\addvspace{1mm}\n\n}\n% 9.3.3 EOCE Utilities\n\\newcommand{\\qt}[2][.]{{\\fontfamily{phv}\\selectfont\\textcolor{oiB}{\\textbf{#2#1}}}}\n\\newcommand{\\qtq}[1]{{\\fontfamily{phv}\\selectfont\\textcolor{oiB}{\\textbf{#1?}}}}\n\\newcommand{\\ec}[1]{\\textcolor{oiB}{\\footnotesize{~(#1)}}}% 9.3.4 EOCE Roman Parts\n\\newenvironment{romanparts}{\n\\begin{enumerate}[I.]\n\\setlength{\\itemsep}{0mm}\n}{\\end{enumerate}}\n% 9.3.5 EOCE Parts\n\\newenvironment{parts}{\n%\\vspace{-0.25cm}\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}}\n{\\end{enumerate}}\n% 9.3.6 EOCE Subparts\n\\newenvironment{subparts}{\n\\begin{enumerate}[i.]\n\\setlength{\\itemsep}{0mm}}\n{\\end{enumerate}}\n% 9.3.7 EOCE hyp environment\n\\newenvironment{hyp}{\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n}\n{\\end{itemize}\n}\n% 9.3.8 cond environment\n\\newenvironment{cond}{\n\\begin{enumerate}[1.]\n\\setlength{\\itemsep}{0mm}\n}\n{\\end{enumerate}\n}\n% 9.3.9 Exercise fixes required.\n\\newcommand{\\eoceNeedSolution}[1][]\n{\\textbf{\\refstepcounter{eoceNeedSolution} \\color{red}ADD SOLUTION. #1}}\n\\newcommand{\\eoceReplace}[1][]\n{\\textbf{\\refstepcounter{eoceReplace} \\color{red}REPLACE THIS EXERCISE. #1}}\n\\newcommand{\\eoceFF}[1][]\n{\\textbf{\\refstepcounter{eoceFF} \\color{red}FINAL FORMATTING.}}\n\n\n\n\n\n%-------------------------------------------------------------\n% 10 Special Boxes\n% 10.1.1 Term Box\n\\newcommand\\tBoxTitleBuffer{\\\\[1.5mm]}\n\\newenvironment{tBoxTitle}[2][\\tBoxTitleBuffer]{\\textbf{\\color{oiB}#2} #1\n}{}\n\\newenvironment{termBox}[1]{\n\\addvspace{4mm}\n\\noindent{\\color{oiB}\\framebox[\\textwidth][c]{\\framebox[\\textwidth-3mm][l]{ \\\\\n\t\\vspace{0.5cm} \\\\\n\t\\begin{minipage}[b]{\\textwidth-3mm}\n\t\t\\begin{minipage}[t]{2mm}\n\t\t\t\\hspace{2mm}\n\t\t\\end{minipage}\n\t\t\\begin{minipage}[b]{\\textwidth-10mm}\n\t\t\t\\color{black}\\ \\\\[-0.7mm]\n\t\t\t#1\n\t\t\t\n\t\t\t\\vspace{1mm}\n\t\t\\end{minipage}\n\t\\end{minipage}}}}\n}{\n\n\\addvspace{1mm}}\n% 10.2 Tip Box\n\\newenvironment{tipBoxTitle}[2][TIP:\\ ]{\\textbf{\\color{oiB}#1#2}\\\\[0.3mm]}{}\n\\newenvironment{tipBox}[1]{\n\\addvspace{4mm}\n\\noindent{\\color{oiB}\\framebox[\\textwidth][l]{ \\\\\n\t\\vspace{5mm} \\\\\n\t\\begin{minipage}[b]{\\textwidth-4mm}\n\t\t\\begin{minipage}[t]{2mm}\n\t\t\t\\hspace{2mm}\n\t\t\\end{minipage}\n\t\t\\begin{minipage}[b]{\\textwidth-8mm}\n\t\t\t\\color{black}\\ \\\\[-0.7mm]\n\t\t\t#1\n\t\t\t\n\t\t\t\\vspace{1mm}\n\t\t\\end{minipage}\n\t\\end{minipage}}}\n}{\n\n\\addvspace{1mm}}\n% 10.3 Caution Box\n\\newenvironment{caution}[2]{\n\\addvspace{4mm}\n\\noindent{\\color{oiB}\\framebox[\\textwidth][l]{ \\\\\n\t\\vspace{5mm} \\\\\n\t\\begin{minipage}[b]{\\textwidth-4mm}\n\t\t\\begin{minipage}[t]{2mm}\n\t\t\t\\hspace{2mm}\n\t\t\\end{minipage}\n\t\t\\begin{minipage}[b]{\\textwidth-8mm}\n\t\t\t\\textbf{\\color{oiB}Caution: #1} \\\\[1mm]\n\t\t\t\\color{black}#2\n\t\t\\end{minipage}\n\t\\end{minipage}}}\n}{\n\n\\addvspace{1mm}}\n% 10.4 One Box\n\\newenvironment{onebox}[1]{\n\\addvspace{4mm}\n\\noindent\\begin{minipage}{\\textwidth}\n\\noindent\\rule{\\textwidth}{0.3pt}\\vspace{-6mm}\n\\begin{mdframed}[%\n    topline=false,\n    rightline=false,\n    leftline=false,\n    bottomline=false,\n    backgroundcolor=grayBackground]\n\\textbf{\\color{oiB}\\small\\fontfamily{phv}%\n\\selectfont{\\MakeUppercase{#1}}} \\\\[1mm]}{\n\\end{mdframed}\\vspace{-4.2mm}\n\\rule{\\textwidth}{0.3pt}\n\\end{minipage}\n\n\\addvspace{4mm}}\n\n\n\n"
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    "content": "% 1 Page Parameters\n% 2 Special Commands for Editions\n% 3 Content Modifications\n% 4 Counters and Parameters\n% 5 Section Coloring\n% 6 Utilities\n% 7 \n% 8 Figures and Captions\n% 9 Examples and Exercises\n% 10 Special Boxes\n\n%\\renewcommand\\chapter{\\if@openright\\cleardoublepage\\else\\clearpage\\fi\n%                    \\thispagestyle{fancy}%\n%                    \\global\\@topnum\\z@\n%                    \\@afterindentfalse\n%                    \\secdef\\@chapter\\@schapter}\n\\fancypagestyle{plain}{%\n\\fancyhf{} % clear all header and footer fields\n\\fancyhead[RO,RE]{\\thepage} %RO=right odd, RE=right even\n\\renewcommand{\\headrulewidth}{0pt}\n\\renewcommand{\\footrulewidth}{0pt}}\n\\raggedbottom\n\n\n\\newcommand{\\stdspace}[0]{3mm}\n\\newcommand{\\stdvspace}[0]{\\vspace{\\stdspace{}}}\n\\newcommand{\\stdaddvspace}[0]{\\addvspace{\\stdspace{}}}\n\n\n\n\n%-------------------------------------------------------------\n% 1 Page Parameters\n% 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Parameters\n%\\setlength\\paperheight{11in}\n%\\setlength\\textheight{8.25in}\n%\\setlength\\paperwidth{8.5in}\n%\\setlength\\textwidth{5.45in}\n%\\setlength\\voffset{-10mm}\n%\\setlength\\oddsidemargin{0.75in}\n%\\setlength\\evensidemargin{0.75in}\n% 1.4 Margin Spacing\n\\setlength{\\marginparsep}{5mm}\n\\setlength{\\marginparwidth}{20mm}\n% 1.5 Page Header\n\\pagestyle{fancy}\n\\renewcommand{\\headrulewidth}{0pt}\n\\fancyhead[RO,LE]{\\thepage}\n\\fancyhead[RE]{\\leftmark}\n\\fancyhead[LO]{\\rightmark}\n\\fancyfoot[c]{}\n\\fancyheadoffset[RO,LE]{0.9in}\n\n\n\n% Tablet Version\n%\\setlength\\paperheight{8.82in}\\setlength\\textheight{8.25in}\\setlength\\paperwidth{5.7in}\\setlength\\textwidth{5.45in}\\setlength\\voffset{-23.5mm}\\setlength\\hoffset{-27mm}\\setlength\\oddsidemargin{5mm}\\setlength\\evensidemargin{5mm}\\setlength{\\marginparsep}{5mm}\\setlength{\\marginparwidth}{35mm}\\fancyheadoffset[RO,LE]{0.2in}\n\n\n\n\n%-------------------------------------------------------------\n% 2 Special Commands for Editions\n\\newcommand{\\referrer}{os4_pdf}\n\\newcommand{\\vspaceB}[1]{}\n\\newcommand{\\hspaceB}[1]{}\n\\newcommand{\\textB}[1]{}\n\\newcommand{\\textC}[1]{}\n\\newcommand{\\D}[1]{#1}\n\n\n\n%-------------------------------------------------------------\n% 3 Content Modifications\n\\newcommand{\\APVersion}[2]{#2}\n\\newcommand{\\MultipleRegression}[2]{#1}\n\\newcommand{\\MultipleRegressionChapter}[2]{#1}\n\\newcommand{\\SimulationAndRandomization}[1]{#1}\n\\newcommand{\\ANOVASection}[2]{#1}\n\\newcommand{\\GLMSection}[2]{#1}\n\n\n\n\n%-------------------------------------------------------------\n% 4 Counters and Parameters\n% 4.1 Counters\n\\newcounter{alwaysOne}\n\\setcounter{alwaysOne}{1}\n\\newcounter{alwaysTwo}\n\\setcounter{alwaysTwo}{2}\n\\newcounter{alwaysThree}\n\\setcounter{alwaysThree}{3}\n\\newcounter{alwaysFour}\n\\setcounter{alwaysFour}{4}\n\\newcounter{withinChNum}[chapter]\n\\setcounter{withinChNum}{0}\n\\newcounter{eoce}[chapter]\n\\renewcommand{\\theeoce}\n    {\\arabic{chapter}.\\arabic{eoce}}\n\\newcounter{eocesolch}\n\\setcounter{eocesolch}{0}\n\\newcounter{eocesol}[eocesolch]\n\\renewcommand{\\theeocesol}\n     {\\arabic{eocesolch}.\\arabic{eocesol}}\n\\newcounter{eoceNeedSolution}[chapter]\n\\renewcommand{\\theeoceNeedSolution}\n    {\\arabic{chapter}.\\arabic{eoceNeedSolution}}\n\\newcounter{eoceReplace}[chapter]\n\\renewcommand{\\theeoceReplace}\n    {\\arabic{chapter}.\\arabic{eoceReplace}}\n\\newcounter{eoceFF}[chapter]\n\\renewcommand{\\theeoceFF}\n    {\\arabic{chapter}.\\arabic{eoceFF}}\n% 4.2 Parameters\n\\newlength{\\exampleAboveBar}\n\\newlength{\\exampleBelowBar}\n\\setlength{\\exampleAboveBar}{-3.15mm}\n\\setlength{\\exampleBelowBar}{-1.15mm}\n\\newlength{\\nexampleAboveBar}\n\\newlength{\\nexampleBelowBar}\n\\setlength{\\nexampleAboveBar}{-1mm}\n\\setlength{\\nexampleBelowBar}{-1mm}\n% 4.3 Chapter Declarations\n\\newcommand\\includechapter[2]{\n  \\setcounter{chapter}{#1}\n  \\addtocounter{chapter}{-1}\n  \\normalsize\n  \\include{#2/TeX/#2}\n  \\newpage\\input{#2/TeX/review_exercises}\n  }\n\n\n\n%-------------------------------------------------------------\n% 5 Section Headers\n%\n% See headers.tex file for main chapters.\n\\newcommand{\\chapterpagepaddingtopinner}[0]{35mm} % 45mm\n\\newcommand{\\chapterpagepaddingbottominner}[0]{25mm}\n\\newcommand{\\chapterXfontsize}[0]{92}\n\\newcommand{\\chaptertitlefontsize}[0]{30}\n\n\n%-------------------------------------------------------------\n% 6 Utilities\n% 6.1 Helpful Editing Commands\n\\newcommand\\Add[1]{\\marginpar[{\\Huge\\color{oiR}$\\bullet$}]{\\Huge\\color{oiR}$\\bullet$}{\\color{oiB}#1}}\n\\newcommand\\Cut[1]{\\marginpar[{\\Huge\\color{oiR}$\\bullet$}]{\\Huge\\color{oiR}$\\bullet$}{\\color{oiGC}#1}}\n%\\newcommand\\Comment[1]{\\marginpar[{\\Huge\\color{oiR}$\\bullet$}]{\\Huge\\color{oiR}$\\bullet$} {\\color{oiG}{[#1]}}}\n\\newcommand{\\note}[1]{\\Comment{#1}}\n% 6.2 Special Symbols\n\\newcommand{\\degree}{\\ensuremath{^\\circ}}\n\\newcommand{\\R}{\\textbf{\\textsf{R}}}\n% 6.3 Text Commands (Terms, Data, Variable, Response)\n\\newcommand{\\term}[1]{\\textbf{#1}\\index{#1|textbf}}\n\\newcommand{\\termsub}[2]{\\textbf{#1}\\index{#2|textbf}}\n\\newcommand{\\termni}[1]{\\textbf{#1}}\n\\newcommand{\\hiddenterm}[1]{#1\\index{#1|textbf}}\n\\newcommand{\\indexthis}[2]{#1\\index{#2}}\n\\newcommand{\\termO}[1]{\\textbf{\\color{termOColor}#1}}\n\\newenvironment{data}[1]{\\texttt{#1}}{}\n\\newcommand{\\datalink}[1]{\\index{#1|textbf}\\texttt{\\oiRedirect{data_#1}{#1}}}\n\\newenvironment{var}[1]{\\texttt{#1}}{}\n\\newenvironment{resp}[1]{\\texttt{#1}}{}\n\\newcommand{\\lmlevel}[1]{:~\\emph{#1}}{}\n\\newenvironment{calctext}[1]{{\\color{oiB}\\texttt{#1}}}{}\n\\newenvironment{calctextmath}[1]{{\\color{oiB}\\mathtt{#1}}}{}\n\\newenvironment{calcbutton}[1]{{\\color{oiB}\\texttt{#1}}}{}\n\\newcommand{\\codeindent}{\\hspace{5mm}}\n% 6.4 Highlighting\n\\newenvironment{highlight}{\\textbf}{}\n\\newcommand{\\highlightO}[1]{\\textbf{\\color{oiB}#1}}\n\\newcommand{\\highlightT}[1]{\\emph{\\color{oiR}#1}}\n% 6.5 Lengths\n\\setlength{\\parindent}{0.3in}\n% 6.6 Hyperreferences\n\\newcommand{\\urlwofont}[1]{\\urlstyle{same}\\url{#1}}\n\\newcommand{\\oiRedirect}[2]{\\href{http://www.openintro.org/redirect.php?go=#1&referrer=\\referrer}{#2}}\n\\newcommand{\\videoicon}[1][4.5mm]{\\includegraphics[height=#1]{extraTeX/icons/video_camera.png}~}\n\\newcommand{\\CalculatorVideos}[1]{}%{\\begin{tipBox}{\\tipBoxTitle[\\videoicon]{Calculator videos}\n%Videos covering #1 using TI and Casio graphing calculators are available at 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Example and Guided Practice Environment\n\\newcommand{\\exspace}{\\stdvspace{}}\n\\newenvironment{nexample}[1]{\\addvspace{6mm}\n\n\\refstepcounter{equation}\\exampletitle{\\theexample}\nExample problem: #1\n\n\\addvspace{\\nexampleAboveBar}\n\n{\\color{examplegray}\\rule{20mm}{0.1mm}}\n\n\\addvspace{\\nexampleBelowBar}\n\n\\setlength{\\parskip}{2mm}Solution to the example:}{\n\n\\MakeUppercase{Example~\\theexample{} Has Ended.}}\n\\newenvironment{nexercise}{\\addvspace{6mm}\n\n\\refstepcounter{equation}\\exercisetitle{\\theexample}}{ Go to the preceding footnote link for the Guided Practice solution.\n\n\\MakeUppercase{Guided Practice~\\theexample{} Has Ended.}}\n\n% 9.3 EOCEs: End of Chapter Exercises\n% 9.3.1 Environment\n\\newenvironment{eoce}[2][]\n{\\refstepcounter{eoce}\\noindent\\small\\textbf{\\textcolor{oiB}{{\\hypersetup{linkcolor=oiB}{\\fontfamily{phv}\\selectfont\\ref{eoce_sol_\\arabic{chapter}_\\arabic{eoce}}}}\\label{eoce_\\arabic{chapter}_\\arabic{eoce}}}}\\hspace{2mm} #1#2\n\n\\addvspace{4mm}\n\n}\n%{\\em #2 } $\\:$ \\\\ }\n{}\n% 9.3.2 EOCE Solutions\n\\newcommand{\\eocesolch}[1]{\n\\refstepcounter{eocesolch}\\noindent\\textbf{\\color{oiB}\\arabic{eocesolch}\\hspace{2mm}#1}\n\n\\addvspace{2mm}\n\n}\n{\n\\newcommand{\\eocesol}[1]{\\refstepcounter{eocesol}\\noindent\\textbf{\\color{oiB}{\\hypersetup{linkcolor=oiB}{\\fontfamily{phv}\\selectfont\\ref{eoce_\\arabic{eocesolch}_\\arabic{eocesol}}}}\\label{eoce_sol_\\arabic{eocesolch}_\\arabic{eocesol}}}\\hspace{2mm}{\\small#1}\\makebox[0pt]{\\color{white}\\tiny \\refstepcounter{eocesol}\\label{eoce_sol_\\arabic{eocesolch}_\\arabic{eocesol}}}\n\n\\addvspace{1mm}\n\n}\n% 9.3.3 EOCE Utilities\n\\newcommand{\\qt}[2][.]{{\\fontfamily{phv}\\selectfont\\textcolor{oiB}{\\textbf{#2#1}}}}\n\\newcommand{\\qtq}[1]{{\\fontfamily{phv}\\selectfont\\textcolor{oiB}{\\textbf{#1?}}}}\n\\newcommand{\\ec}[1]{\\textcolor{oiB}{\\footnotesize{~(#1)}}}% 9.3.4 EOCE Roman Parts\n\\newenvironment{romanparts}{\n\\begin{enumerate}[I.]\n\\setlength{\\itemsep}{0mm}\n}{\\end{enumerate}}\n% 9.3.5 EOCE Parts\n\\newenvironment{parts}{\n%\\vspace{-0.25cm}\n\\begin{enumerate}[(a)]\n\\setlength{\\itemsep}{0mm}}\n{\\end{enumerate}}\n% 9.3.6 EOCE Subparts\n\\newenvironment{subparts}{\n\\begin{enumerate}[i.]\n\\setlength{\\itemsep}{0mm}}\n{\\end{enumerate}}\n% 9.3.7 EOCE hyp environment\n\\newenvironment{hyp}{\n\\begin{itemize}\n\\setlength{\\itemsep}{0mm}\n}\n{\\end{itemize}\n}\n% 9.3.8 cond environment\n\\newenvironment{cond}{\n\\begin{enumerate}[1.]\n\\setlength{\\itemsep}{0mm}\n}\n{\\end{enumerate}\n}\n% 9.3.9 Exercise fixes required.\n\\newcommand{\\eoceNeedSolution}[1][]\n{\\textbf{\\refstepcounter{eoceNeedSolution} \\color{red}ADD SOLUTION. #1}}\n\\newcommand{\\eoceReplace}[1][]\n{\\textbf{\\refstepcounter{eoceReplace} \\color{red}REPLACE THIS EXERCISE. #1}}\n\\newcommand{\\eoceFF}[1][]\n{\\textbf{\\refstepcounter{eoceFF} \\color{red}FINAL 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    "path": "extraTeX/tables/TeX/chiSquareTable.tex",
    "content": "\\section{Chi-Square Probability Table}\n\\label{chiSquareProbabilityTable}\n\nA \\term{chi-square probability table} may be used\nto find tail areas of a chi-square distribution.\nThe \\term{chi-square table} is partially shown in\nFigure~\\ref{chiSquareProbabilityTableShort},\nand the complete table may be found on\npage~\\pageref{fullChiSqTable}.\nWhen using a chi-square table, we examine a particular\nrow for distributions\nwith different degrees of freedom, and we identify a range for\nthe area (e.g. 0.025 to 0.05).\nNote that the chi-square table provides upper tail values,\nwhich is different than the normal and $t$-distribution tables.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{r | rrrr | rrrr |}\n  \\hline\nUpper tail & 0.3 & 0.2 & 0.1 & 0.05 & 0.02 & 0.01 & 0.005 & 0.001 \\\\ \n  \\hline\n%df \\hfill 1 & \\footnotesize 1.07 & \\footnotesize 1.64 & \\footnotesize 2.71 & \\footnotesize 3.84 & \\footnotesize 5.41 & \\footnotesize 6.63 & \\footnotesize 7.88 & \\footnotesize 10.83 \\\\ \ndf \\hfill 2 & \\footnotesize 2.41 & \\footnotesize \\highlightO{3.22} & \\footnotesize \\highlightO{4.61} & \\footnotesize 5.99 & \\footnotesize 7.82 & \\footnotesize 9.21 & \\footnotesize 10.60 & \\footnotesize 13.82 \\\\ \n  \\em3 & \\em\\footnotesize 3.66 & \\em\\footnotesize 4.64 & \\em\\footnotesize \\highlightT{6.25} & \\em\\footnotesize 7.81 & \\em\\footnotesize 9.84 & \\em\\footnotesize 11.34 & \\em\\footnotesize 12.84 & \\em\\footnotesize 16.27 \\\\ \n  4 & \\footnotesize 4.88 & \\footnotesize 5.99 & \\footnotesize 7.78 & \\footnotesize 9.49 & \\footnotesize 11.67 & \\footnotesize 13.28 & \\footnotesize 14.86 & \\footnotesize 18.47 \\\\ \n  5 & \\footnotesize 6.06 & \\footnotesize 7.29 & \\footnotesize 9.24 & \\footnotesize 11.07 & \\footnotesize 13.39 & \\footnotesize 15.09 & \\footnotesize 16.75 & \\footnotesize 20.52 \\\\ \n  \\hline\n  6 & \\footnotesize 7.23 & \\footnotesize 8.56 & \\footnotesize 10.64 & \\footnotesize 12.59 & \\footnotesize 15.03 & \\footnotesize 16.81 & \\footnotesize 18.55 & \\footnotesize 22.46 \\\\ \n  7 & \\footnotesize 8.38 & \\footnotesize 9.80 & \\footnotesize 12.02 & \\footnotesize 14.07 & \\footnotesize 16.62 & \\footnotesize 18.48 & \\footnotesize 20.28 & \\footnotesize 24.32 \\\\ \n  \\hline\n\\end{tabular}\n\\caption{A section of the chi-square table. A complete table is in Appendix~\\ref{chiSquareProbabilityTable}.}\n\\label{chiSquareProbabilityTableShort}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{app_chiSquareAreaAbove6Point25WithDF3}\n    shows a chi-square distribution with 3 degrees of freedom\n    and an upper shaded tail starting at 6.25.\n    Use Figure~\\ref{chiSquareProbabilityTableShort}\n    to estimate the shaded area.}\n  This distribution has three degrees of freedom,\n  so only the row with 3 degrees of freedom (df) is relevant.\n  This row has been italicized in the table.\n  Next, we see that the value -- 6.25 -- falls in the column\n  with upper tail area 0.1.\n  That is, the shaded upper tail of\n  Figure~\\ref{app_chiSquareAreaAbove6Point25WithDF3}\n  has area 0.1.\n\n  This example was unusual, in that we observed the\n  \\emph{exact} value in the table.\n  In the next examples, we encounter situations where\n  we cannot precisely estimate the tail area and must\n  instead provide a range of values.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}\n\\centering\n\\subfigure[]{\n\\FigureFullPath[A chi-square distribution with 3 degrees of freedom is shown, where the area above 6.25 is shaded and appears to represent roughly 10\\% of the area under the distribution.]{0.475}{ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove6Point25WithDF3/chiSquareAreaAbove6Point25WithDF3}\n\\label{app_chiSquareAreaAbove6Point25WithDF3}\n}\n\\subfigure[]{\n\\FigureFullPath[A chi-square distribution with 2 degrees of freedom is shown, where the area above 4.3 is shaded and appears to represent roughly 10\\% of the area under the distribution.]{0.475}{ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove4Point3WithDF2/chiSquareAreaAbove4Point3WithDF2}\n\\label{app_chiSquareAreaAbove4Point3WithDF2}\n}\n\\subfigure[]{\n\\FigureFullPath[A chi-square distribution with 5 degrees of freedom is shown, where the area above 5.1 is shaded and appears to represent roughly 40\\% of the area under the distribution.]{0.475}{ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove5Point1WithDF5/chiSquareAreaAbove5Point1WithDF5}\n\\label{app_chiSquareAreaAbove5Point1WithDF5}\n}\n\\subfigure[]{\n\\FigureFullPath[A chi-square distribution with 7 degrees of freedom is shown, where the area above 11.7 is shaded and appears to represent roughly 10\\% of the area under the distribution.]{0.475}{ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove11Point7WithDF7/chiSquareAreaAbove11Point7WithDF7}\n\\label{app_chiSquareAreaAbove11Point7WithDF7}\n}\n%\\subfigure[]{\n%\\includegraphics[width=0.475\\textwidth]{ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove10WithDF4/chiSquareAreaAbove10WithDF4}\n%\\label{app_chiSquareAreaAbove10WithDF4}\n%}\n%\\subfigure[]{\n%\\includegraphics[width=0.475\\textwidth]{ch_inference_for_props/figures/arrayOfFigureAreasForChiSquareDistribution/chiSquareAreaAbove9Point21WithDF3/chiSquareAreaAbove9Point21WithDF3}\n%\\label{app_chiSquareAreaAbove9Point21WithDF3}\n%}\n\\caption{\n\\textbf{\\subref{app_chiSquareAreaAbove6Point25WithDF3}}~Chi-square distribution with 3~degrees of freedom, area above 6.25 shaded.\n\\textbf{\\subref{app_chiSquareAreaAbove4Point3WithDF2}}~2~degrees of freedom, area above 4.3 shaded.\n\\textbf{\\subref{app_chiSquareAreaAbove5Point1WithDF5}}~5~degrees of freedom, area above 5.1 shaded.\n\\textbf{\\subref{app_chiSquareAreaAbove11Point7WithDF7}}~7~degrees of freedom, area above 11.7 shaded.\n%\\textbf{\\subref{app_chiSquareAreaAbove10WithDF4}}~4~degrees of freedom, area above 10 shaded.\n%\\textbf{\\subref{app_chiSquareAreaAbove9Point21WithDF3}}~3~degrees of freedom, area above 9.21 shaded.\n}\n\\label{arrayOfFigureAreasForChiSquareDistributionChiSqAppendix}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{\n    Figure~\\ref{app_chiSquareAreaAbove4Point3WithDF2}\n    shows the upper tail of a chi-square distribution\n    with 2~degrees of freedom.\n    The area above value 4.3 has been shaded;\n    find this tail area.}\n  The cutoff 4.3 falls between the second and third columns\n  in the 2~degrees of freedom row.\n  Because these columns correspond to tail areas of 0.2 and 0.1,\n  we can be certain that the area shaded in\n  Figure~\\ref{app_chiSquareAreaAbove4Point3WithDF2}\n  is between 0.1 and 0.2.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{app_chiSquareAreaAbove5Point1WithDF5} shows an upper tail for a chi-square distribution with 5 degrees of freedom and a cutoff of 5.1. Find the tail area.}\nLooking in the row with 5 df, 5.1 falls below the smallest cutoff for this row (6.06). That means we can only say that the area is \\emph{greater than} 0.3.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{Figure~\\ref{app_chiSquareAreaAbove11Point7WithDF7}\n    shows a cutoff of 11.7 on a chi-square distribution with\n    7~degrees of freedom.\n    Find the area of the upper tail.}\n  The value 11.7 falls between 9.80 and 12.02 in the 7 df row.\n  Thus, the area is between 0.1 and 0.2.\n\\end{nexample}\n\\end{examplewrap}\n\n%\\begin{exercisewrap}\n%\\begin{nexercise}\n%Figure~\\ref{app_chiSquareAreaAbove10WithDF4} shows a cutoff of 10 on a chi-square distribution with 4 degrees of freedom. Find the area of the upper tail.\\footnotemark\n%\\end{nexercise}\n%\\end{exercisewrap}\n%\\footnotetext{The area is between 0.02 and 0.05.}\n%\n%\\begin{exercisewrap}\n%\\begin{nexercise}\n%Figure~\\ref{app_chiSquareAreaAbove9Point21WithDF3} shows a cutoff of 9.21 with a chi-square distribution with 3 df. Find the area of the upper tail.\\footnotemark\n%\\end{nexercise}\n%\\end{exercisewrap}\n%\\footnotetext{Between 0.02 and 0.05.}\n\n%\\begin{figure}[hhh]\n%\\centering\n%\\includegraphics[height=1.5in]{extraTeX/tables/figures/chiSquareTail/chiSquareTail}\n%\\caption{Areas in the chi-square table always refer to the right tail.}\n%\\end{figure}\n\n\\begin{center}\n\\begin{tabular}{r | rrrr | rrrr |}\n  \\hline\nUpper tail & 0.3 & 0.2 & 0.1 & 0.05 & 0.02 & 0.01 & 0.005 & 0.001 \\\\ \n  \\hline\ndf \\hfill 1 & \\footnotesize 1.07 & \\footnotesize 1.64 & \\footnotesize 2.71 & \\footnotesize 3.84 & \\footnotesize 5.41 & \\footnotesize 6.63 & \\footnotesize 7.88 & \\footnotesize 10.83 \\\\ \n  2 & \\footnotesize 2.41 & \\footnotesize 3.22 & \\footnotesize 4.61 & \\footnotesize 5.99 & \\footnotesize 7.82 & \\footnotesize 9.21 & \\footnotesize 10.60 & \\footnotesize 13.82 \\\\ \n  3 & \\footnotesize 3.66 & \\footnotesize 4.64 & \\footnotesize 6.25 & \\footnotesize 7.81 & \\footnotesize 9.84 & \\footnotesize 11.34 & \\footnotesize 12.84 & \\footnotesize 16.27 \\\\ \n  4 & \\footnotesize 4.88 & \\footnotesize 5.99 & \\footnotesize 7.78 & \\footnotesize 9.49 & \\footnotesize 11.67 & \\footnotesize 13.28 & \\footnotesize 14.86 & \\footnotesize 18.47 \\\\ \n  5 & \\footnotesize 6.06 & \\footnotesize 7.29 & \\footnotesize 9.24 & \\footnotesize 11.07 & \\footnotesize 13.39 & \\footnotesize 15.09 & \\footnotesize 16.75 & \\footnotesize 20.52 \\\\ \n  \\hline\n  6 & \\footnotesize 7.23 & \\footnotesize 8.56 & \\footnotesize 10.64 & \\footnotesize 12.59 & \\footnotesize 15.03 & \\footnotesize 16.81 & \\footnotesize 18.55 & \\footnotesize 22.46 \\\\ \n  7 & \\footnotesize 8.38 & \\footnotesize 9.80 & \\footnotesize 12.02 & \\footnotesize 14.07 & \\footnotesize 16.62 & \\footnotesize 18.48 & \\footnotesize 20.28 & \\footnotesize 24.32 \\\\ \n  8 & \\footnotesize 9.52 & \\footnotesize 11.03 & \\footnotesize 13.36 & \\footnotesize 15.51 & \\footnotesize 18.17 & \\footnotesize 20.09 & \\footnotesize 21.95 & \\footnotesize 26.12 \\\\ \n  9 & \\footnotesize 10.66 & \\footnotesize 12.24 & \\footnotesize 14.68 & \\footnotesize 16.92 & \\footnotesize 19.68 & \\footnotesize 21.67 & \\footnotesize 23.59 & \\footnotesize 27.88 \\\\ \n  10 & \\footnotesize 11.78 & \\footnotesize 13.44 & \\footnotesize 15.99 & \\footnotesize 18.31 & \\footnotesize 21.16 & \\footnotesize 23.21 & \\footnotesize 25.19 & \\footnotesize 29.59 \\\\ \n  \\hline\n  11 & \\footnotesize \\footnotesize 12.90 & \\footnotesize 14.63 & \\footnotesize 17.28 & \\footnotesize 19.68 & \\footnotesize 22.62 & \\footnotesize 24.72 & \\footnotesize 26.76 & \\footnotesize 31.26 \\\\ \n  12 & \\footnotesize 14.01 & \\footnotesize 15.81 & \\footnotesize 18.55 & \\footnotesize 21.03 & \\footnotesize 24.05 & \\footnotesize 26.22 & \\footnotesize 28.30 & \\footnotesize 32.91 \\\\ \n  13 & \\footnotesize 15.12 & \\footnotesize 16.98 & \\footnotesize 19.81 & \\footnotesize 22.36 & \\footnotesize 25.47 & \\footnotesize 27.69 & \\footnotesize 29.82 & \\footnotesize 34.53 \\\\ \n  14 & \\footnotesize 16.22 & \\footnotesize 18.15 & \\footnotesize 21.06 & \\footnotesize 23.68 & \\footnotesize 26.87 & \\footnotesize 29.14 & \\footnotesize 31.32 & \\footnotesize 36.12 \\\\ \n  15 & \\footnotesize 17.32 & \\footnotesize 19.31 & \\footnotesize 22.31 & \\footnotesize 25.00 & \\footnotesize 28.26 & \\footnotesize 30.58 & \\footnotesize 32.80 & \\footnotesize 37.70 \\\\ \n  \\hline\n  16 & \\footnotesize 18.42 & \\footnotesize 20.47 & \\footnotesize 23.54 & \\footnotesize 26.30 & \\footnotesize 29.63 & \\footnotesize 32.00 & \\footnotesize 34.27 & \\footnotesize 39.25 \\\\ \n  17 & \\footnotesize 19.51 & \\footnotesize 21.61 & \\footnotesize 24.77 & \\footnotesize 27.59 & \\footnotesize 31.00 & \\footnotesize 33.41 & \\footnotesize 35.72 & \\footnotesize 40.79 \\\\ \n  18 & \\footnotesize 20.60 & \\footnotesize 22.76 & \\footnotesize 25.99 & \\footnotesize 28.87 & \\footnotesize 32.35 & \\footnotesize 34.81 & \\footnotesize 37.16 & \\footnotesize 42.31 \\\\ \n  19 & \\footnotesize 21.69 & \\footnotesize 23.90 & \\footnotesize 27.20 & \\footnotesize 30.14 & \\footnotesize 33.69 & \\footnotesize 36.19 & \\footnotesize 38.58 & \\footnotesize 43.82 \\\\ \n  20 & \\footnotesize 22.77 & \\footnotesize 25.04 & \\footnotesize 28.41 & \\footnotesize 31.41 & \\footnotesize 35.02 & \\footnotesize 37.57 & \\footnotesize 40.00 & \\footnotesize 45.31 \\\\ \n  \\hline\n  25 & \\footnotesize 28.17 & \\footnotesize 30.68 & \\footnotesize 34.38 & \\footnotesize 37.65 & \\footnotesize 41.57 & \\footnotesize 44.31 & \\footnotesize 46.93 & \\footnotesize 52.62 \\\\ \n  30 & \\footnotesize 33.53 & \\footnotesize 36.25 & \\footnotesize 40.26 & \\footnotesize 43.77 & \\footnotesize 47.96 & \\footnotesize 50.89 & \\footnotesize 53.67 & \\footnotesize 59.70 \\\\ \n  40 & \\footnotesize 44.16 & \\footnotesize 47.27 & \\footnotesize 51.81 & \\footnotesize 55.76 & \\footnotesize 60.44 & \\footnotesize 63.69 & \\footnotesize 66.77 & \\footnotesize 73.40 \\\\ \n  50 & \\footnotesize 54.72 & \\footnotesize 58.16 & \\footnotesize 63.17 & \\footnotesize 67.50 & \\footnotesize 72.61 & \\footnotesize 76.15 & \\footnotesize 79.49 & \\footnotesize 86.66 \\\\ \n  \\hline\n\\end{tabular}\n\\label{fullChiSqTable}\n\\end{center}\n"
  },
  {
    "path": "extraTeX/tables/TeX/tTable.tex",
    "content": "\\section{$\\pmb{t}$-Probability Table}\n\\label{tDistributionTable}\n\nA \\termsub{$\\pmb{t}$-probability table}\n    {t-probability table@$t$-probability table}\nmay be used to\nfind tail areas of a $t$-distribution using a T-score,\nor vice-versa.\nSuch a table lists T-scores and the corresponding percentiles.\nA partial\n\\termsub{$\\pmb{t}$-table}{t-table@$t$-table}\nis shown in Figure~\\ref{tTableSample},\nand the complete table starts on page~\\pageref{tTableFirstPage}.\nEach row in the $t$-table represents a $t$-distribution with\ndifferent degrees of freedom.\nThe columns correspond to tail probabilities.\nFor instance, if we know we are working with the\n$t$-distribution with $df=18$, we can examine row 18,\nwhich is highlighted in Figure~\\ref{tTableSample}.\nIf we want the value in this row that identifies the T-score\n(cutoff) for an upper tail of 10\\%, we can look in the column\nwhere \\emph{one tail} is 0.100.\nThis cutoff is 1.33.\nIf we had wanted the cutoff for the lower 10\\%, we would\nuse -1.33.\nJust like the normal distribution,\nall $t$-distributions are symmetric.\n\n\\begin{figure}[hht]\n\\centering\n\\begin{tabular}{r | rrr rr}\none tail & \\hspace{1.5mm}  0.100 & \\hspace{1.5mm} 0.050 & \\hspace{1.5mm} 0.025 & \\hspace{1.5mm} 0.010 & \\hspace{1.5mm} 0.005  \\\\\ntwo tails & 0.200 & 0.100 & 0.050 & 0.020 & 0.010 \\\\\n\\hline\n{$df$} \\hfill 1  &  {\\normalsize  3.08} & {\\normalsize  6.31} & {\\normalsize 12.71} & {\\normalsize 31.82} & {\\normalsize 63.66}  \\\\ \n2  &  {\\normalsize  1.89} & {\\normalsize  2.92} & {\\normalsize  4.30} & {\\normalsize  6.96} & {\\normalsize  9.92}  \\\\ \n3  &  {\\normalsize  1.64} & {\\normalsize  2.35} & {\\normalsize  3.18} & {\\normalsize  4.54} & {\\normalsize  5.84}  \\\\ \n$\\vdots$ & $\\vdots$ &$\\vdots$ &$\\vdots$ &$\\vdots$ & \\\\\n17  &  {\\normalsize  1.33} & {\\normalsize  1.74} & {\\normalsize  2.11} & {\\normalsize  2.57} & {\\normalsize  2.90}  \\\\ \n\\highlightO{18}  &  \\highlightO{\\normalsize  1.33} & \\highlightO{\\normalsize  1.73} & \\highlightO{\\normalsize  2.10} & \\highlightO{\\normalsize  2.55} & \\highlightO{\\normalsize  2.88}  \\\\ \n19  &  {\\normalsize  1.33} & {\\normalsize  1.73} & {\\normalsize  2.09} & {\\normalsize  2.54} & {\\normalsize  2.86}  \\\\ \n20  &  {\\normalsize  1.33} & {\\normalsize  1.72} & {\\normalsize  2.09} & {\\normalsize  2.53} & {\\normalsize  2.85}  \\\\ \n$\\vdots$ & $\\vdots$ &$\\vdots$ &$\\vdots$ &$\\vdots$ & \\\\\n400  &  {\\normalsize  1.28} & {\\normalsize  1.65} & {\\normalsize  1.97} & {\\normalsize  2.34} & {\\normalsize  2.59}  \\\\ \n500  &  {\\normalsize  1.28} & {\\normalsize  1.65} & {\\normalsize  1.96} & {\\normalsize  2.33} & {\\normalsize  2.59}  \\\\ \n$\\infty$  &  {\\normalsize  1.28} & {\\normalsize  1.64} & {\\normalsize  1.96} & {\\normalsize  2.33} & {\\normalsize  2.58}  \\\\ \n\\end{tabular}\n\\caption{An abbreviated look at the $t$-table.\n    Each row represents a different $t$-distribution.\n    The columns describe the cutoffs for specific tail areas.\n    The row with $df=18$ has been \\highlightO{highlighted}.}\n\\label{tTableSample}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{What proportion of the $t$-distribution with\n    18 degrees of freedom falls below -2.10?}\n  Just like a normal probability problem, we first draw the\n  picture and shade the area below -2.10:\n  \\begin{center}\n  \\FigureFullPath[A t-distribution is shown, which is centered at zero. The left tail below -2.1 is shaded, which appears to represent about 2\\% to 5\\% of the area under the distribution.]{0.5}{ch_inference_for_means/figures/tDistDF18LeftTail2Point10/tDistDF18LeftTail2Point10}\n  \\end{center}\n  To find this area, we first identify the appropriate row:\n  $df = 18$.\n  Then we identify the column containing the absolute value\n  of -2.10;\n  it~is the third column.\n  Because we are looking for just one tail, we examine the\n  top line of the table, which shows that a one tail area\n  for a value in the third row corresponds to 0.025.\n  That is, 2.5\\% of the distribution falls below -2.10.\n\n  In the next example we encounter a case where the exact\n  T-score is not listed in the table.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{A $t$-distribution with 20 degrees of freedom\n    is shown in the left panel of\n    Figure~\\ref{tDistAppendixTwoEx}.\n    Estimate the proportion of the distribution falling\n    above~1.65.}\n  We identify the row in the $t$-table using the degrees\n  of freedom: $df=20$.\n  Then we look for 1.65; it is not listed.\n  It falls between the first and second columns.\n  Since these values bound 1.65, their tail areas will\n  bound the tail area corresponding to 1.65.\n  We identify the one tail area of the first and\n  second columns, 0.050 and 0.10, and we conclude that\n  between 5\\% and 10\\% of the distribution is more than\n  1.65 standard deviations above the mean.\n  If we like, we can identify the precise area using\n  statistical software: 0.0573.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{figure}[h]\n\\centering\n\\FigureFullPath[Two figures are shown. First, the t-distribution with 20 degrees of freedom is shown, with the area above 1.65 shaded, which appears to represent 5\\% to 10\\% of the area under the distribution. The second distribution is a t-distribution with 475 degrees of freedom, with the area further than 2 units from 0 shaded, which corresponds to the area to the left of -2 and the area to the right of positive 2 and appears to represent about 5\\% of the area under the curve.]{0.85}{ch_inference_for_means/figures/tDistAppendixTwoEx/tDistAppendixTwoEx}\n\\caption{Left: The $t$-distribution with 20 degrees of freedom,\n    with the area above 1.65 shaded.\n    Right: The $t$-distribution with 475 degrees of freedom,\n    with the area further than 2 units from 0 shaded.}\n\\label{tDistAppendixTwoEx}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{A $t$-distribution with 475 degrees of freedom\n    is shown in the right panel of\n    Figure~\\ref{tDistAppendixTwoEx}.\n    Estimate the proportion of the distribution falling more\n    than 2 units from the mean (above or below).}\n  As before, first identify the appropriate row: $df=475$.\n  This row does not exist!\n  When this happens, we use the next smaller row, which in\n  this case is $df = 400$.\n  Next, find the columns that capture 2.00;\n  because $1.97 < 3 < 2.34$, we use the third and fourth columns.\n  Finally, we find bounds for the tail areas by looking at\n  the two tail values: 0.02 and 0.05.\n  We use the two tail values because we are looking for two\n  symmetric tails in the $t$-distribution.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{exercisewrap}\n\\begin{nexercise}\nWhat proportion of the $t$-distribution with 19 degrees of freedom falls above -1.79 units?\\footnotemark{}\n\\end{nexercise}\n\\end{exercisewrap}\n\\footnotetext{We find the shaded area \\emph{above} -1.79 (we leave the picture to you). The small left tail is between 0.025 and 0.05, so the larger upper region must have an area between 0.95 and 0.975.}\n\n\\begin{examplewrap}\n\\begin{nexample}{Find the value of $t_{18}^{\\star}$\n    using the $t$-table, where $t_{18}^{\\star}$\n    is the cutoff for the $t$-distribution with\n    18 degrees of freedom where 95\\% of the distribution\n    lies between -$t_{18}^{\\star}$ and +$t_{18}^{\\star}$.}\n  For a 95\\% confidence interval, we want to find\n  the cutoff $t^{\\star}_{18}$ such that 95\\% of the\n  $t$-distribution is between -$t^{\\star}_{18}$\n  and $t^{\\star}_{18}$;\n  this is the same as where the two tails have a total\n  area of 0.05.\n  We look in the $t$-table on page~\\pageref{tTableSample},\n  find the column with area totaling 0.05 in the two tails\n  (third column), and then the row with 18 degrees of\n  freedom: $t^{\\star}_{18} = 2.10$.\n\\end{nexample}\n\\end{examplewrap}\n\n\n\\newpage\n\n\\begin{center}\n\\FigureFullPath[Three bell-shaped distributions are shown. The first two are labeled \"One Tail\" and again \"One Tail\", where the first shows a small left tail in the distribution and the second shows a small right-tail in the distribution. The third distribution shown is labeled \"Two Tails\", and it shows both the left and right tail shaded (where those tail areas are the same).]{}{extraTeX/tables/figures/tTails/tTails}\n\\end{center}\n\n\\begin{center}\n\\begin{tabular}{r | rrr rr}\n\\hline\none tail & \\hspace{1.5mm}  0.100 & \\hspace{1.5mm} 0.050 & \\hspace{1.5mm} 0.025 & \\hspace{1.5mm} 0.010 & \\hspace{1.5mm} 0.005  \\\\\ntwo tails & 0.200 & 0.100 & 0.050 & 0.020 & 0.010 \\\\\n\\hline\n{df} \\hfill 1  &  {\\normalsize  3.08} & {\\normalsize  6.31} & {\\normalsize 12.71} & {\\normalsize 31.82} & {\\normalsize 63.66}  \\\\ \n2  &  {\\normalsize  1.89} & {\\normalsize  2.92} & {\\normalsize  4.30} & {\\normalsize  6.96} & {\\normalsize  9.92}  \\\\ \n3  &  {\\normalsize  1.64} & {\\normalsize  2.35} & {\\normalsize  3.18} & {\\normalsize  4.54} & {\\normalsize  5.84}  \\\\ \n4  &  {\\normalsize  1.53} & {\\normalsize  2.13} & {\\normalsize  2.78} & {\\normalsize  3.75} & {\\normalsize  4.60}  \\\\ \n5  &  {\\normalsize  1.48} & {\\normalsize  2.02} & {\\normalsize  2.57} & {\\normalsize  3.36} & {\\normalsize  4.03}  \\\\ \n\\hline\n6  &  {\\normalsize  1.44} & {\\normalsize  1.94} & {\\normalsize  2.45} & {\\normalsize  3.14} & {\\normalsize  3.71}  \\\\ \n7  &  {\\normalsize  1.41} & {\\normalsize  1.89} & {\\normalsize  2.36} & {\\normalsize  3.00} & {\\normalsize  3.50}  \\\\ \n8  &  {\\normalsize  1.40} & {\\normalsize  1.86} & {\\normalsize  2.31} & {\\normalsize  2.90} & {\\normalsize  3.36}  \\\\ \n9  &  {\\normalsize  1.38} & {\\normalsize  1.83} & {\\normalsize  2.26} & {\\normalsize  2.82} & {\\normalsize  3.25}  \\\\ \n10  &  {\\normalsize  1.37} & {\\normalsize  1.81} & {\\normalsize  2.23} & {\\normalsize  2.76} & {\\normalsize  3.17}  \\\\ \n\\hline\n\\hline\n11  &  {\\normalsize  1.36} & {\\normalsize  1.80} & {\\normalsize  2.20} & {\\normalsize  2.72} & {\\normalsize  3.11}  \\\\ \n12  &  {\\normalsize  1.36} & {\\normalsize  1.78} & {\\normalsize  2.18} & {\\normalsize  2.68} & {\\normalsize  3.05}  \\\\ \n13  &  {\\normalsize  1.35} & {\\normalsize  1.77} & {\\normalsize  2.16} & {\\normalsize  2.65} & {\\normalsize  3.01}  \\\\ \n14  &  {\\normalsize  1.35} & {\\normalsize  1.76} & {\\normalsize  2.14} & {\\normalsize  2.62} & {\\normalsize  2.98}  \\\\ \n15  &  {\\normalsize  1.34} & {\\normalsize  1.75} & {\\normalsize  2.13} & {\\normalsize  2.60} & {\\normalsize  2.95}  \\\\ \n\\hline\n16  &  {\\normalsize  1.34} & {\\normalsize  1.75} & {\\normalsize  2.12} & {\\normalsize  2.58} & {\\normalsize  2.92}  \\\\ \n17  &  {\\normalsize  1.33} & {\\normalsize  1.74} & {\\normalsize  2.11} & {\\normalsize  2.57} & {\\normalsize  2.90}  \\\\ \n18  &  {\\normalsize  1.33} & {\\normalsize  1.73} & {\\normalsize  2.10} & {\\normalsize  2.55} & {\\normalsize  2.88}  \\\\ \n19  &  {\\normalsize  1.33} & {\\normalsize  1.73} & {\\normalsize  2.09} & {\\normalsize  2.54} & {\\normalsize  2.86}  \\\\ \n20  &  {\\normalsize  1.33} & {\\normalsize  1.72} & {\\normalsize  2.09} & {\\normalsize  2.53} & {\\normalsize  2.85}  \\\\ \n\\hline\n\\hline\n21  &  {\\normalsize  1.32} & {\\normalsize  1.72} & {\\normalsize  2.08} & {\\normalsize  2.52} & {\\normalsize  2.83}  \\\\ \n22  &  {\\normalsize  1.32} & {\\normalsize  1.72} & {\\normalsize  2.07} & {\\normalsize  2.51} & {\\normalsize  2.82}  \\\\ \n23  &  {\\normalsize  1.32} & {\\normalsize  1.71} & {\\normalsize  2.07} & {\\normalsize  2.50} & {\\normalsize  2.81}  \\\\ \n24  &  {\\normalsize  1.32} & {\\normalsize  1.71} & {\\normalsize  2.06} & {\\normalsize  2.49} & {\\normalsize  2.80}  \\\\ \n25  &  {\\normalsize  1.32} & {\\normalsize  1.71} & {\\normalsize  2.06} & {\\normalsize  2.49} & {\\normalsize  2.79}  \\\\ \n\\hline\n26  &  {\\normalsize  1.31} & {\\normalsize  1.71} & {\\normalsize  2.06} & {\\normalsize  2.48} & {\\normalsize  2.78}  \\\\ \n27  &  {\\normalsize  1.31} & {\\normalsize  1.70} & {\\normalsize  2.05} & {\\normalsize  2.47} & {\\normalsize  2.77}  \\\\ \n28  &  {\\normalsize  1.31} & {\\normalsize  1.70} & {\\normalsize  2.05} & {\\normalsize  2.47} & {\\normalsize  2.76}  \\\\ \n29  &  {\\normalsize  1.31} & {\\normalsize  1.70} & {\\normalsize  2.05} & {\\normalsize  2.46} & {\\normalsize  2.76}  \\\\ \n30  &  {\\normalsize  1.31} & {\\normalsize  1.70} & {\\normalsize  2.04} & {\\normalsize  2.46} & {\\normalsize  2.75}  \\\\ \n\\hline\n\\end{tabular}\n\\label{tTableFirstPage}\n\\end{center}\n\n\n\\newpage\n\n\\begin{center}\n\\FigureFullPath[Three bell-shaped distributions are shown. The first two are labeled \"One Tail\" and again \"One Tail\", where the first shows a small left tail in the distribution and the second shows a small right-tail in the distribution. The third distribution shown is labeled \"Two Tails\", and it shows both the left and right tail shaded (where those tail areas are the same).]{}{extraTeX/tables/figures/tTails/tTails}\n\\end{center}\n\n\\begin{center}\n\\begin{tabular}{r | rrr rr}\n\\hline\none tail & \\hspace{1.5mm}  0.100 & \\hspace{1.5mm} 0.050 & \\hspace{1.5mm} 0.025 & \\hspace{1.5mm} 0.010 & \\hspace{1.5mm} 0.005  \\\\\ntwo tails & 0.200 & 0.100 & 0.050 & 0.020 & 0.010 \\\\\n\\hline\n{df} \\hfill 31  &  {\\normalsize  1.31} & {\\normalsize  1.70} & {\\normalsize  2.04} & {\\normalsize  2.45} & {\\normalsize  2.74}  \\\\ \n32  &  {\\normalsize  1.31} & {\\normalsize  1.69} & {\\normalsize  2.04} & {\\normalsize  2.45} & {\\normalsize  2.74}  \\\\ \n33  &  {\\normalsize  1.31} & {\\normalsize  1.69} & {\\normalsize  2.03} & {\\normalsize  2.44} & {\\normalsize  2.73}  \\\\ \n34  &  {\\normalsize  1.31} & {\\normalsize  1.69} & {\\normalsize  2.03} & {\\normalsize  2.44} & {\\normalsize  2.73}  \\\\ \n35  &  {\\normalsize  1.31} & {\\normalsize  1.69} & {\\normalsize  2.03} & {\\normalsize  2.44} & {\\normalsize  2.72}  \\\\ \n\\hline\n36  &  {\\normalsize  1.31} & {\\normalsize  1.69} & {\\normalsize  2.03} & {\\normalsize  2.43} & {\\normalsize  2.72}  \\\\ \n37  &  {\\normalsize  1.30} & {\\normalsize  1.69} & {\\normalsize  2.03} & {\\normalsize  2.43} & {\\normalsize  2.72}  \\\\ \n38  &  {\\normalsize  1.30} & {\\normalsize  1.69} & {\\normalsize  2.02} & {\\normalsize  2.43} & {\\normalsize  2.71}  \\\\ \n39  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.02} & {\\normalsize  2.43} & {\\normalsize  2.71}  \\\\ \n40  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.02} & {\\normalsize  2.42} & {\\normalsize  2.70}  \\\\ \n\\hline\n\\hline\n41  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.02} & {\\normalsize  2.42} & {\\normalsize  2.70}  \\\\ \n42  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.02} & {\\normalsize  2.42} & {\\normalsize  2.70}  \\\\ \n43  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.02} & {\\normalsize  2.42} & {\\normalsize  2.70}  \\\\ \n44  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.02} & {\\normalsize  2.41} & {\\normalsize  2.69}  \\\\ \n45  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.01} & {\\normalsize  2.41} & {\\normalsize  2.69}  \\\\ \n\\hline\n46  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.01} & {\\normalsize  2.41} & {\\normalsize  2.69}  \\\\ \n47  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.01} & {\\normalsize  2.41} & {\\normalsize  2.68}  \\\\ \n48  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.01} & {\\normalsize  2.41} & {\\normalsize  2.68}  \\\\ \n49  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.01} & {\\normalsize  2.40} & {\\normalsize  2.68}  \\\\ \n50  &  {\\normalsize  1.30} & {\\normalsize  1.68} & {\\normalsize  2.01} & {\\normalsize  2.40} & {\\normalsize  2.68}  \\\\ \n\\hline\n\\hline\n%55  &  {\\normalsize  1.30} & {\\normalsize  1.67} & {\\normalsize  2.00} & {\\normalsize  2.40} & {\\normalsize  2.67}  \\\\ \n60  &  {\\normalsize  1.30} & {\\normalsize  1.67} & {\\normalsize  2.00} & {\\normalsize  2.39} & {\\normalsize  2.66}  \\\\ \n%65  &  {\\normalsize  1.29} & {\\normalsize  1.67} & {\\normalsize  2.00} & {\\normalsize  2.39} & {\\normalsize  2.65}  \\\\ \n70  &  {\\normalsize  1.29} & {\\normalsize  1.67} & {\\normalsize  1.99} & {\\normalsize  2.38} & {\\normalsize  2.65}  \\\\ \n%75  &  {\\normalsize  1.29} & {\\normalsize  1.67} & {\\normalsize  1.99} & {\\normalsize  2.38} & {\\normalsize  2.64}  \\\\ \n%\\hline\n80  &  {\\normalsize  1.29} & {\\normalsize  1.66} & {\\normalsize  1.99} & {\\normalsize  2.37} & {\\normalsize  2.64}  \\\\ \n%85  &  {\\normalsize  1.29} & {\\normalsize  1.66} & {\\normalsize  1.99} & {\\normalsize  2.37} & {\\normalsize  2.63}  \\\\ \n90  &  {\\normalsize  1.29} & {\\normalsize  1.66} & {\\normalsize  1.99} & {\\normalsize  2.37} & {\\normalsize  2.63}  \\\\ \n%95  &  {\\normalsize  1.29} & {\\normalsize  1.66} & {\\normalsize  1.99} & {\\normalsize  2.37} & {\\normalsize  2.63}  \\\\ \n100  &  {\\normalsize  1.29} & {\\normalsize  1.66} & {\\normalsize  1.98} & {\\normalsize  2.36} & {\\normalsize  2.63}  \\\\ \n\\hline\n%\\hline\n%120  &  {\\normalsize  1.29} & {\\normalsize  1.66} & {\\normalsize  1.98} & {\\normalsize  2.36} & {\\normalsize  2.62}  \\\\ \n%140  &  {\\normalsize  1.29} & {\\normalsize  1.66} & {\\normalsize  1.98} & {\\normalsize  2.35} & {\\normalsize  2.61}  \\\\ \n150  &  {\\normalsize  1.29} & {\\normalsize  1.66} & {\\normalsize  1.98} & {\\normalsize  2.35} & {\\normalsize  2.61}  \\\\ \n%160  &  {\\normalsize  1.29} & {\\normalsize  1.65} & {\\normalsize  1.97} & {\\normalsize  2.35} & {\\normalsize  2.61}  \\\\ \n%180  &  {\\normalsize  1.29} & {\\normalsize  1.65} & {\\normalsize  1.97} & {\\normalsize  2.35} & {\\normalsize  2.60}  \\\\ \n200  &  {\\normalsize  1.29} & {\\normalsize  1.65} & {\\normalsize  1.97} & {\\normalsize  2.35} & {\\normalsize  2.60}  \\\\ \n%\\hline\n300  &  {\\normalsize  1.28} & {\\normalsize  1.65} & {\\normalsize  1.97} & {\\normalsize  2.34} & {\\normalsize  2.59}  \\\\ \n400  &  {\\normalsize  1.28} & {\\normalsize  1.65} & {\\normalsize  1.97} & {\\normalsize  2.34} & {\\normalsize  2.59}  \\\\ \n500  &  {\\normalsize  1.28} & {\\normalsize  1.65} & {\\normalsize  1.96} & {\\normalsize  2.33} & {\\normalsize  2.59}  \\\\ \n\\hline\n\\hline\n$\\infty$  &  {\\normalsize  1.28} & {\\normalsize  1.645} & {\\normalsize  1.96} & {\\normalsize  2.33} & {\\normalsize  2.58}  \\\\ \n\\hline\n\\end{tabular}\n\\end{center}\n\n"
  },
  {
    "path": "extraTeX/tables/TeX/zTable.tex",
    "content": "\\chapter{Distribution tables}\n\\label{distributionTables}\n\n\\section{Normal Probability Table}\n\\label{normalProbabilityTable}\n\nA \\term{normal probability table} may be used to\nfind percentiles of a normal distribution using a Z-score,\nor vice-versa.\nSuch a table lists Z-scores and the corresponding percentiles.\nAn abbreviated probability table is provided in\nFigure~\\ref{zTableShort} that we'll use for the examples\nin this appendix.\nA~full table may be found on page~\\pageref{normTableSide1}.\n\n\\begin{figure}[h]\n\\centering\n\\begin{tabular}{c | rrrrr | rrrrr |}\n  \\cline{2-11}\n&&&& \\multicolumn{4}{c}{Second decimal place of $Z$} &&& \\\\\n  \\cline{2-11}\n$Z$ & \\highlightT{0.00} & 0.01 & 0.02 & 0.03 &\n    \\highlightO{0.04} & 0.05 & 0.06 & 0.07 & 0.08 & 0.09 \\\\\n  \\hline\n  \\hline\n0.0 & \\footnotesize{0.5000} & \\footnotesize{0.5040} & \\footnotesize{0.5080} & \\footnotesize{0.5120} & \\footnotesize{0.5160} & \\footnotesize{0.5199} & \\footnotesize{0.5239} & \\footnotesize{0.5279} & \\footnotesize{0.5319} & \\footnotesize{0.5359} \\\\\n  0.1 & \\footnotesize{0.5398} & \\footnotesize{0.5438} & \\footnotesize{0.5478} & \\footnotesize{0.5517} & \\footnotesize{0.5557} & \\footnotesize{0.5596} & \\footnotesize{0.5636} & \\footnotesize{0.5675} & \\footnotesize{0.5714} & \\footnotesize{0.5753} \\\\\n  0.2 & \\footnotesize{0.5793} & \\footnotesize{0.5832} & \\footnotesize{0.5871} & \\footnotesize{0.5910} & \\footnotesize{0.5948} & \\footnotesize{0.5987} & \\footnotesize{0.6026} & \\footnotesize{0.6064} & \\footnotesize{0.6103} & \\footnotesize{0.6141} \\\\\n%  May comment out 0.0-0.2 to make extra space. Then insert the following line:\n%  $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ \\\\\n  0.3 & \\footnotesize{0.6179} & \\footnotesize{0.6217} & \\footnotesize{0.6255} & \\footnotesize{0.6293} & \\footnotesize{0.6331} & \\footnotesize{0.6368} & \\footnotesize{0.6406} & \\footnotesize{0.6443} & \\footnotesize{0.6480} & \\footnotesize{0.6517} \\\\\n  0.4 & \\footnotesize{0.6554} & \\footnotesize{0.6591} & \\footnotesize{0.6628} & \\footnotesize{0.6664} & \\footnotesize{0.6700} & \\footnotesize{0.6736} & \\footnotesize{0.6772} & \\footnotesize{0.6808} & \\footnotesize{0.6844} & \\footnotesize{0.6879} \\\\\n  \\hline\n  0.5 & \\footnotesize{0.6915} & \\footnotesize{0.6950} & \\footnotesize{0.6985} & \\footnotesize{0.7019} & \\footnotesize{0.7054} & \\footnotesize{0.7088} & \\footnotesize{0.7123} & \\footnotesize{0.7157} & \\footnotesize{0.7190} & \\footnotesize{0.7224} \\\\\n  0.6 & \\footnotesize{0.7257} & \\footnotesize{0.7291} & \\footnotesize{0.7324} & \\footnotesize{0.7357} & \\footnotesize{0.7389} & \\footnotesize{0.7422} & \\footnotesize{0.7454} & \\footnotesize{0.7486} & \\footnotesize{0.7517} & \\footnotesize{0.7549} \\\\\n  0.7 & \\footnotesize{0.7580} & \\footnotesize{0.7611} & \\footnotesize{0.7642} & \\footnotesize{0.7673} & \\footnotesize{0.7704} & \\footnotesize{0.7734} & \\footnotesize{0.7764} & \\footnotesize{0.7794} & \\footnotesize{0.7823} & \\footnotesize{0.7852} \\\\\n\\highlightO{0.8} & \\footnotesize{0.7881} & \\footnotesize{0.7910} & \\footnotesize{0.7939} & \\footnotesize{0.7967} & \\highlightO{\\footnotesize{0.7995}} & \\footnotesize{0.8023} & \\footnotesize{0.8051} & \\footnotesize{0.8078} & \\footnotesize{0.8106} & \\footnotesize{0.8133} \\\\\n  0.9 & \\footnotesize{0.8159} & \\footnotesize{0.8186} & \\footnotesize{0.8212} & \\footnotesize{0.8238} & \\footnotesize{0.8264} & \\footnotesize{0.8289} & \\footnotesize{0.8315} & \\footnotesize{0.8340} & \\footnotesize{0.8365} & \\footnotesize{0.8389} \\\\\n  \\hline\n  \\hline\n  \\highlightT{1.0} & \\highlightT{\\footnotesize{0.8413}}\n    & \\footnotesize{0.8438} & \\footnotesize{0.8461} & \\footnotesize{0.8485} & \\footnotesize{0.8508} & \\footnotesize{0.8531} & \\footnotesize{0.8554} & \\footnotesize{0.8577} & \\footnotesize{0.8599} & \\footnotesize{0.8621} \\\\\n  1.1 & \\footnotesize{0.8643} & \\footnotesize{0.8665} & \\footnotesize{0.8686} & \\footnotesize{0.8708} & \\footnotesize{0.8729} & \\footnotesize{0.8749} & \\footnotesize{0.8770} & \\footnotesize{0.8790} & \\footnotesize{0.8810} & \\footnotesize{0.8830} \\\\\n  $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &\n      $\\vdots$ &   $\\vdots$ &   $\\vdots$ &   $\\vdots$ &\n      $\\vdots$ &   $\\vdots$ &   $\\vdots$ \\\\\n   \\hline\n\\end{tabular}\n\\caption{A section of the normal probability table.\n    The percentile for a normal random variable with $Z=1.00$\n    has been \\highlightT{highlighted}, and the percentile\n    closest to 0.8000 has also been \\highlightO{highlighted}.}\n\\label{zTableShort}\n\\end{figure}\n\nWhen using a normal probability table to find a percentile\nfor $Z$ (rounded to two decimals),\nidentify the proper row in the normal probability\ntable up through the first decimal, and then determine the\ncolumn representing the second decimal value.\nThe intersection of this row and column is the percentile\nof the observation.\nFor instance, the percentile of $Z = 0.45$ is shown in row\n$0.4$ and column $0.05$ in Figure~\\ref{zTableShort}:\n0.6736, or the $67.36^{th}$ percentile.\n\n\\begin{figure}[h]\n  \\centering\n  \\FigureFullPath[Two normal distributions are shown. The first is labeled \"Negative Z\", where the left tail of the distribution is shaded up to a location that is to the left of the center of the distribution (where Z would be about -1). The second normal distribution is labeled \"Positive Z\", where the left tail of the distribution is shaded up to a location that is to the right of the center of the distribution (where Z would be about positive 1). The area in the first plot is smaller, less than 50\\% of the area under the distribution, while the area in the second plot is larger and represents well over 50\\% of the area under the distribution.]{0.8}{ch_distributions/figures/normalTails/normalTails}\n  \\caption{The area to the left of $Z$ represents the\n      percentile of the observation.}\n\\end{figure}\n\n\\begin{examplewrap}\n\\begin{nexample}{\n    SAT scores follow a normal distribution, $N(1100, 200)$.\n    Ann earned a score of 1300 on her SAT with\n    a corresponding Z-score of $Z = 1$.\n    She would like to know what percentile she falls in among\n    all SAT test-takers.}\n  Ann's \\term{percentile} is the percentage of people who\n  earned a lower SAT score than her.\n  We shade the area representing those individuals in the\n  following graph:\n  \\begin{center}\n  \\FigureFullPath[A normal distribution is shown that is centered at 1100 with a standard deviation of 200. The region to the left of 1300 (where Z equals 1) is shaded, which appears to represent about 80\\% of the area under the distribution.]{0.45}{ch_distributions/figures/satBelow1300/satBelow1300}\n  \\end{center}\n  The total area under the normal curve is always equal to~1,\n  and the proportion of people who scored below Ann on the SAT\n  is equal to the \\emph{area} shaded in the graph.\n  We find this area by looking in row $1.0$ and column $0.00$\n  in the normal probability table:~0.8413.\n  In other words, Ann is in the $84^{th}$ percentile of\n  SAT takers.\n\\end{nexample}\n\\end{examplewrap}\n\n\\begin{examplewrap}\n\\begin{nexample}{How do we find an upper tail area?}\n  The normal probability table \\emph{always} gives the area\n  to the left.\n  This means that if we want the area to the right,\n  we first find the lower tail and then subtract it from~1.\n  For instance, 84.13\\% of SAT takers scored below Ann,\n  which means 15.87\\% of test takers scored higher than Ann.\n\\end{nexample}\n\\end{examplewrap}\n\nWe can also find the Z-score associated with a percentile.\nFor example, to identify $Z$ for the $80^{th}$ percentile,\nwe look for the value closest to 0.8000 in the middle portion\nof the table: 0.7995.\nWe determine the Z-score for the $80^{th}$ percentile by\ncombining the row and column Z values: 0.84.\n\n\\begin{examplewrap}\n\\begin{nexample}{Find the SAT score for the $80^{th}$ percentile.}\n  We look for the are to the value in the table closest to 0.8000.\n  The closest value is 0.7995, which corresponds to $Z = 0.84$,\n  where 0.8 comes from the row value and 0.04 comes from the\n  column value.\n  Next, we set up the equation for the Z-score and the unknown\n  value $x$ as follows, and then we solve for $x$:\n  \\begin{align*}\n  Z = 0.84 = \\frac{x - 1100}{200}\n  \\quad\\to\\quad x = 1268\n  \\end{align*}\n  The College Board scales scores to increments of 10,\n  so the $80^{th}$ percentile is 1270.\n  (Reporting 1268 would have been perfectly okay for our purposes.)\n\\end{nexample}\n\\end{examplewrap}\n\n%\\noindent%\n%Remember: to find the area to the right, calculate 1 minus the area to the left.\\vspace{1mm}\n%\\begin{center}\n%\\includegraphics[width=0.55\\textwidth]{extraTeX/tables/figures/normalTails/subtractingArea/subtractingArea}\\vspace{3mm}\n%\\end{center}\nFor additional details about working with the normal distribution and the normal probability table, see Section~\\ref{normalDist}, which starts on page~\\pageref{normalDist}.\n\n\\begin{table}[p]\n\\begin{center}{\\small\n\\FigureFullPath[A normal distribution is shown and labeled \"Negative Z\", where the left tail of the distribution is shaded up to a location that is to the left of the center of the distribution (where Z would be about -1). This shaded area represents less than 50\\% of the area under the normal distribution.]{0.5}{extraTeX/tables/figures/normalTails/normalTailLeft} \\vspace{2mm} \\\\\n\\begin{tabular}{| rrrrr | rrrrr | c}\n  \\cline{1-10}\n&&& \\multicolumn{4}{c}{Second decimal place of $Z$} &&& \\\\\n  \\cline{1-10}\n0.09 &  0.08 &  0.07 &  0.06 &  0.05 &  0.04 &  0.03 &  0.02 &  0.01 &  0.00 & $Z$  \\\\\n  \\hline\n  \\hline\n\\footnotesize{0.0002} & \\footnotesize{0.0003} & \\footnotesize{0.0003} & \\footnotesize{0.0003} & \\footnotesize{0.0003} & \\footnotesize{0.0003} & \\footnotesize{0.0003} & \\footnotesize{0.0003} & \\footnotesize{0.0003} & \\footnotesize{0.0003} & $-3.4$ \\\\\n  \\footnotesize{0.0003} & \\footnotesize{0.0004} & \\footnotesize{0.0004} & \\footnotesize{0.0004} & \\footnotesize{0.0004} & \\footnotesize{0.0004} & \\footnotesize{0.0004} & \\footnotesize{0.0005} & \\footnotesize{0.0005} & \\footnotesize{0.0005} & $-3.3$ \\\\\n  \\footnotesize{0.0005} & \\footnotesize{0.0005} & \\footnotesize{0.0005} & \\footnotesize{0.0006} & \\footnotesize{0.0006} & \\footnotesize{0.0006} & \\footnotesize{0.0006} & \\footnotesize{0.0006} & \\footnotesize{0.0007} & \\footnotesize{0.0007} & $-3.2$ \\\\\n  \\footnotesize{0.0007} & \\footnotesize{0.0007} & \\footnotesize{0.0008} & \\footnotesize{0.0008} & \\footnotesize{0.0008} & \\footnotesize{0.0008} & \\footnotesize{0.0009} & \\footnotesize{0.0009} & \\footnotesize{0.0009} & \\footnotesize{0.0010} & $-3.1$ \\\\\n  \\footnotesize{0.0010} & \\footnotesize{0.0010} & \\footnotesize{0.0011} & \\footnotesize{0.0011} & \\footnotesize{0.0011} & \\footnotesize{0.0012} & \\footnotesize{0.0012} & \\footnotesize{0.0013} & \\footnotesize{0.0013} & \\footnotesize{0.0013} & $-3.0$ \\\\\n    \\hline\n    \\hline\n  \\footnotesize{0.0014} & \\footnotesize{0.0014} & \\footnotesize{0.0015} & \\footnotesize{0.0015} & \\footnotesize{0.0016} & \\footnotesize{0.0016} & \\footnotesize{0.0017} & \\footnotesize{0.0018} & \\footnotesize{0.0018} & \\footnotesize{0.0019} & $-2.9$ \\\\\n  \\footnotesize{0.0019} & \\footnotesize{0.0020} & \\footnotesize{0.0021} & \\footnotesize{0.0021} & \\footnotesize{0.0022} & \\footnotesize{0.0023} & \\footnotesize{0.0023} & \\footnotesize{0.0024} & \\footnotesize{0.0025} & \\footnotesize{0.0026} & $-2.8$ \\\\\n  \\footnotesize{0.0026} & \\footnotesize{0.0027} & \\footnotesize{0.0028} & \\footnotesize{0.0029} & \\footnotesize{0.0030} & \\footnotesize{0.0031} & \\footnotesize{0.0032} & \\footnotesize{0.0033} & \\footnotesize{0.0034} & \\footnotesize{0.0035} & $-2.7$ \\\\\n  \\footnotesize{0.0036} & \\footnotesize{0.0037} & \\footnotesize{0.0038} & \\footnotesize{0.0039} & \\footnotesize{0.0040} & \\footnotesize{0.0041} & \\footnotesize{0.0043} & \\footnotesize{0.0044} & \\footnotesize{0.0045} & \\footnotesize{0.0047} & $-2.6$ \\\\\n  \\footnotesize{0.0048} & \\footnotesize{0.0049} & \\footnotesize{0.0051} & \\footnotesize{0.0052} & \\footnotesize{0.0054} & \\footnotesize{0.0055} & \\footnotesize{0.0057} & \\footnotesize{0.0059} & \\footnotesize{0.0060} & \\footnotesize{0.0062} & $-2.5$ \\\\\n    \\hline\n  \\footnotesize{0.0064} & \\footnotesize{0.0066} & \\footnotesize{0.0068} & \\footnotesize{0.0069} & \\footnotesize{0.0071} & \\footnotesize{0.0073} & \\footnotesize{0.0075} & \\footnotesize{0.0078} & \\footnotesize{0.0080} & \\footnotesize{0.0082} & $-2.4$ \\\\\n  \\footnotesize{0.0084} & \\footnotesize{0.0087} & \\footnotesize{0.0089} & \\footnotesize{0.0091} & \\footnotesize{0.0094} & \\footnotesize{0.0096} & \\footnotesize{0.0099} & \\footnotesize{0.0102} & \\footnotesize{0.0104} & \\footnotesize{0.0107} & $-2.3$ \\\\\n  \\footnotesize{0.0110} & \\footnotesize{0.0113} & \\footnotesize{0.0116} & \\footnotesize{0.0119} & \\footnotesize{0.0122} & \\footnotesize{0.0125} & \\footnotesize{0.0129} & \\footnotesize{0.0132} & \\footnotesize{0.0136} & \\footnotesize{0.0139} & $-2.2$ \\\\\n  \\footnotesize{0.0143} & \\footnotesize{0.0146} & \\footnotesize{0.0150} & \\footnotesize{0.0154} & \\footnotesize{0.0158} & \\footnotesize{0.0162} & \\footnotesize{0.0166} & \\footnotesize{0.0170} & \\footnotesize{0.0174} & \\footnotesize{0.0179} & $-2.1$ \\\\\n  \\footnotesize{0.0183} & \\footnotesize{0.0188} & \\footnotesize{0.0192} & \\footnotesize{0.0197} & \\footnotesize{0.0202} & \\footnotesize{0.0207} & \\footnotesize{0.0212} & \\footnotesize{0.0217} & \\footnotesize{0.0222} & \\footnotesize{0.0228} & $-2.0$ \\\\\n    \\hline\n    \\hline\n  \\footnotesize{0.0233} & \\footnotesize{0.0239} & \\footnotesize{0.0244} & \\footnotesize{0.0250} & \\footnotesize{0.0256} & \\footnotesize{0.0262} & \\footnotesize{0.0268} & \\footnotesize{0.0274} & \\footnotesize{0.0281} & \\footnotesize{0.0287} & $-1.9$ \\\\\n  \\footnotesize{0.0294} & \\footnotesize{0.0301} & \\footnotesize{0.0307} & \\footnotesize{0.0314} & \\footnotesize{0.0322} & \\footnotesize{0.0329} & \\footnotesize{0.0336} & \\footnotesize{0.0344} & \\footnotesize{0.0351} & \\footnotesize{0.0359} & $-1.8$ \\\\\n  \\footnotesize{0.0367} & \\footnotesize{0.0375} & \\footnotesize{0.0384} & \\footnotesize{0.0392} & \\footnotesize{0.0401} & \\footnotesize{0.0409} & \\footnotesize{0.0418} & \\footnotesize{0.0427} & \\footnotesize{0.0436} & \\footnotesize{0.0446} & $-1.7$ \\\\\n  \\footnotesize{0.0455} & \\footnotesize{0.0465} & \\footnotesize{0.0475} & \\footnotesize{0.0485} & \\footnotesize{0.0495} & \\footnotesize{0.0505} & \\footnotesize{0.0516} & \\footnotesize{0.0526} & \\footnotesize{0.0537} & \\footnotesize{0.0548} & $-1.6$ \\\\\n  \\footnotesize{0.0559} & \\footnotesize{0.0571} & \\footnotesize{0.0582} & \\footnotesize{0.0594} & \\footnotesize{0.0606} & \\footnotesize{0.0618} & \\footnotesize{0.0630} & \\footnotesize{0.0643} & \\footnotesize{0.0655} & \\footnotesize{0.0668} & $-1.5$ \\\\\n    \\hline\n  \\footnotesize{0.0681} & \\footnotesize{0.0694} & \\footnotesize{0.0708} & \\footnotesize{0.0721} & \\footnotesize{0.0735} & \\footnotesize{0.0749} & \\footnotesize{0.0764} & \\footnotesize{0.0778} & \\footnotesize{0.0793} & \\footnotesize{0.0808} & $-1.4$ \\\\\n  \\footnotesize{0.0823} & \\footnotesize{0.0838} & \\footnotesize{0.0853} & \\footnotesize{0.0869} & \\footnotesize{0.0885} & \\footnotesize{0.0901} & \\footnotesize{0.0918} & \\footnotesize{0.0934} & \\footnotesize{0.0951} & \\footnotesize{0.0968} & $-1.3$ \\\\\n  \\footnotesize{0.0985} & \\footnotesize{0.1003} & \\footnotesize{0.1020} & \\footnotesize{0.1038} & \\footnotesize{0.1056} & \\footnotesize{0.1075} & \\footnotesize{0.1093} & \\footnotesize{0.1112} & \\footnotesize{0.1131} & \\footnotesize{0.1151} & $-1.2$ \\\\\n  \\footnotesize{0.1170} & \\footnotesize{0.1190} & \\footnotesize{0.1210} & \\footnotesize{0.1230} & \\footnotesize{0.1251} & \\footnotesize{0.1271} & \\footnotesize{0.1292} & \\footnotesize{0.1314} & \\footnotesize{0.1335} & \\footnotesize{0.1357} & $-1.1$ \\\\\n  \\footnotesize{0.1379} & \\footnotesize{0.1401} & \\footnotesize{0.1423} & \\footnotesize{0.1446} & \\footnotesize{0.1469} & \\footnotesize{0.1492} & \\footnotesize{0.1515} & \\footnotesize{0.1539} & \\footnotesize{0.1562} & \\footnotesize{0.1587} & $-1.0$ \\\\\n    \\hline\n    \\hline\n  \\footnotesize{0.1611} & \\footnotesize{0.1635} & \\footnotesize{0.1660} & \\footnotesize{0.1685} & \\footnotesize{0.1711} & \\footnotesize{0.1736} & \\footnotesize{0.1762} & \\footnotesize{0.1788} & \\footnotesize{0.1814} & \\footnotesize{0.1841} & $-0.9$ \\\\\n  \\footnotesize{0.1867} & \\footnotesize{0.1894} & \\footnotesize{0.1922} & \\footnotesize{0.1949} & \\footnotesize{0.1977} & \\footnotesize{0.2005} & \\footnotesize{0.2033} & \\footnotesize{0.2061} & \\footnotesize{0.2090} & \\footnotesize{0.2119} & $-0.8$ \\\\\n  \\footnotesize{0.2148} & \\footnotesize{0.2177} & \\footnotesize{0.2206} & \\footnotesize{0.2236} & \\footnotesize{0.2266} & \\footnotesize{0.2296} & \\footnotesize{0.2327} & \\footnotesize{0.2358} & \\footnotesize{0.2389} & \\footnotesize{0.2420} & $-0.7$ \\\\\n  \\footnotesize{0.2451} & \\footnotesize{0.2483} & \\footnotesize{0.2514} & \\footnotesize{0.2546} & \\footnotesize{0.2578} & \\footnotesize{0.2611} & \\footnotesize{0.2643} & \\footnotesize{0.2676} & \\footnotesize{0.2709} & \\footnotesize{0.2743} & $-0.6$ \\\\\n  \\footnotesize{0.2776} & \\footnotesize{0.2810} & \\footnotesize{0.2843} & \\footnotesize{0.2877} & \\footnotesize{0.2912} & \\footnotesize{0.2946} & \\footnotesize{0.2981} & \\footnotesize{0.3015} & \\footnotesize{0.3050} & \\footnotesize{0.3085} & $-0.5$ \\\\\n    \\hline\n  \\footnotesize{0.3121} & \\footnotesize{0.3156} & \\footnotesize{0.3192} & \\footnotesize{0.3228} & \\footnotesize{0.3264} & \\footnotesize{0.3300} & \\footnotesize{0.3336} & \\footnotesize{0.3372} & \\footnotesize{0.3409} & \\footnotesize{0.3446} & $-0.4$ \\\\\n  \\footnotesize{0.3483} & \\footnotesize{0.3520} & \\footnotesize{0.3557} & \\footnotesize{0.3594} & \\footnotesize{0.3632} & \\footnotesize{0.3669} & \\footnotesize{0.3707} & \\footnotesize{0.3745} & \\footnotesize{0.3783} & \\footnotesize{0.3821} & $-0.3$ \\\\\n  \\footnotesize{0.3859} & \\footnotesize{0.3897} & \\footnotesize{0.3936} & \\footnotesize{0.3974} & \\footnotesize{0.4013} & \\footnotesize{0.4052} & \\footnotesize{0.4090} & \\footnotesize{0.4129} & \\footnotesize{0.4168} & \\footnotesize{0.4207} & $-0.2$ \\\\\n  \\footnotesize{0.4247} & \\footnotesize{0.4286} & \\footnotesize{0.4325} & \\footnotesize{0.4364} & \\footnotesize{0.4404} & \\footnotesize{0.4443} & \\footnotesize{0.4483} & \\footnotesize{0.4522} & \\footnotesize{0.4562} & \\footnotesize{0.4602} & $-0.1$ \\\\\n  \\footnotesize{0.4641} & \\footnotesize{0.4681} & \\footnotesize{0.4721} & \\footnotesize{0.4761} & \\footnotesize{0.4801} & \\footnotesize{0.4840} & \\footnotesize{0.4880} & \\footnotesize{0.4920} & \\footnotesize{0.4960} & \\footnotesize{0.5000} & $-0.0$ \\\\\n    \\hline\n\\multicolumn{11}{l}{{\\normalsize$^*$For $Z \\leq -3.50$, the probability is less than or equal to $0.0002$.}}\n\\end{tabular}}\n\\label{normTableSide1}\n\\end{center}\n\\end{table}\n\n\\begin{table}[p]\n\\begin{center}{\\small\n\\FigureFullPath[A normal distribution is shown with a label \"Positive Z\", where the left tail of the distribution is shaded up to a location that is to the right of the center of the distribution (where Z would be about positive 1). The region that is shaded is more than 50\\% of the area under the distribution.]{0.5}{extraTeX/tables/figures/normalTails/normalTailRight} \\vspace{2mm} \\\\\n\\begin{tabular}{c | rrrrr | rrrrr |}\n  \\cline{2-11}\n&&&& \\multicolumn{4}{c}{Second decimal place of $Z$} &&& \\\\\n  \\cline{2-11}\n$Z$ & 0.00 & 0.01 & 0.02 & 0.03 & 0.04 & 0.05 & 0.06 & 0.07 & 0.08 & 0.09 \\\\\n  \\hline\n  \\hline\n0.0 & \\footnotesize{0.5000} & \\footnotesize{0.5040} & \\footnotesize{0.5080} & \\footnotesize{0.5120} & \\footnotesize{0.5160} & \\footnotesize{0.5199} & \\footnotesize{0.5239} & \\footnotesize{0.5279} & \\footnotesize{0.5319} & \\footnotesize{0.5359} \\\\\n  0.1 & \\footnotesize{0.5398} & \\footnotesize{0.5438} & \\footnotesize{0.5478} & \\footnotesize{0.5517} & \\footnotesize{0.5557} & \\footnotesize{0.5596} & \\footnotesize{0.5636} & \\footnotesize{0.5675} & \\footnotesize{0.5714} & \\footnotesize{0.5753} \\\\\n  0.2 & \\footnotesize{0.5793} & \\footnotesize{0.5832} & \\footnotesize{0.5871} & \\footnotesize{0.5910} & \\footnotesize{0.5948} & \\footnotesize{0.5987} & \\footnotesize{0.6026} & \\footnotesize{0.6064} & \\footnotesize{0.6103} & \\footnotesize{0.6141} \\\\\n  0.3 & \\footnotesize{0.6179} & \\footnotesize{0.6217} & \\footnotesize{0.6255} & \\footnotesize{0.6293} & \\footnotesize{0.6331} & \\footnotesize{0.6368} & \\footnotesize{0.6406} & \\footnotesize{0.6443} & \\footnotesize{0.6480} & \\footnotesize{0.6517} \\\\\n  0.4 & \\footnotesize{0.6554} & \\footnotesize{0.6591} & \\footnotesize{0.6628} & \\footnotesize{0.6664} & \\footnotesize{0.6700} & \\footnotesize{0.6736} & \\footnotesize{0.6772} & \\footnotesize{0.6808} & \\footnotesize{0.6844} & \\footnotesize{0.6879} \\\\\n  \\hline\n  0.5 & \\footnotesize{0.6915} & \\footnotesize{0.6950} & \\footnotesize{0.6985} & \\footnotesize{0.7019} & \\footnotesize{0.7054} & \\footnotesize{0.7088} & \\footnotesize{0.7123} & \\footnotesize{0.7157} & \\footnotesize{0.7190} & \\footnotesize{0.7224} \\\\\n  0.6 & \\footnotesize{0.7257} & \\footnotesize{0.7291} & \\footnotesize{0.7324} & \\footnotesize{0.7357} & \\footnotesize{0.7389} & \\footnotesize{0.7422} & \\footnotesize{0.7454} & \\footnotesize{0.7486} & \\footnotesize{0.7517} & \\footnotesize{0.7549} \\\\\n  0.7 & \\footnotesize{0.7580} & \\footnotesize{0.7611} & \\footnotesize{0.7642} & \\footnotesize{0.7673} & \\footnotesize{0.7704} & \\footnotesize{0.7734} & \\footnotesize{0.7764} & \\footnotesize{0.7794} & \\footnotesize{0.7823} & \\footnotesize{0.7852} \\\\\n  0.8 & \\footnotesize{0.7881} & \\footnotesize{0.7910} & \\footnotesize{0.7939} & \\footnotesize{0.7967} & \\footnotesize{0.7995} & \\footnotesize{0.8023} & \\footnotesize{0.8051} & \\footnotesize{0.8078} & \\footnotesize{0.8106} & \\footnotesize{0.8133} \\\\\n  0.9 & \\footnotesize{0.8159} & \\footnotesize{0.8186} & \\footnotesize{0.8212} & \\footnotesize{0.8238} & \\footnotesize{0.8264} & \\footnotesize{0.8289} & \\footnotesize{0.8315} & \\footnotesize{0.8340} & \\footnotesize{0.8365} & \\footnotesize{0.8389} \\\\\n  \\hline\n  \\hline\n  1.0 & \\footnotesize{0.8413} & \\footnotesize{0.8438} & \\footnotesize{0.8461} & \\footnotesize{0.8485} & \\footnotesize{0.8508} & \\footnotesize{0.8531} & \\footnotesize{0.8554} & \\footnotesize{0.8577} & \\footnotesize{0.8599} & \\footnotesize{0.8621} \\\\\n  1.1 & \\footnotesize{0.8643} & \\footnotesize{0.8665} & \\footnotesize{0.8686} & \\footnotesize{0.8708} & \\footnotesize{0.8729} & \\footnotesize{0.8749} & \\footnotesize{0.8770} & \\footnotesize{0.8790} & \\footnotesize{0.8810} & \\footnotesize{0.8830} \\\\\n  1.2 & \\footnotesize{0.8849} & \\footnotesize{0.8869} & \\footnotesize{0.8888} & \\footnotesize{0.8907} & \\footnotesize{0.8925} & \\footnotesize{0.8944} & \\footnotesize{0.8962} & \\footnotesize{0.8980} & \\footnotesize{0.8997} & \\footnotesize{0.9015} \\\\\n  1.3 & \\footnotesize{0.9032} & \\footnotesize{0.9049} & \\footnotesize{0.9066} & \\footnotesize{0.9082} & \\footnotesize{0.9099} & \\footnotesize{0.9115} & \\footnotesize{0.9131} & \\footnotesize{0.9147} & \\footnotesize{0.9162} & \\footnotesize{0.9177} \\\\\n  1.4 & \\footnotesize{0.9192} & \\footnotesize{0.9207} & \\footnotesize{0.9222} & \\footnotesize{0.9236} & \\footnotesize{0.9251} & \\footnotesize{0.9265} & \\footnotesize{0.9279} & \\footnotesize{0.9292} & \\footnotesize{0.9306} & \\footnotesize{0.9319} \\\\\n  \\hline\n  1.5 & \\footnotesize{0.9332} & \\footnotesize{0.9345} & \\footnotesize{0.9357} & \\footnotesize{0.9370} & \\footnotesize{0.9382} & \\footnotesize{0.9394} & \\footnotesize{0.9406} & \\footnotesize{0.9418} & \\footnotesize{0.9429} & \\footnotesize{0.9441} \\\\\n  1.6 & \\footnotesize{0.9452} & \\footnotesize{0.9463} & \\footnotesize{0.9474} & \\footnotesize{0.9484} & \\footnotesize{0.9495} & \\footnotesize{0.9505} & \\footnotesize{0.9515} & \\footnotesize{0.9525} & \\footnotesize{0.9535} & \\footnotesize{0.9545} \\\\\n  1.7 & \\footnotesize{0.9554} & \\footnotesize{0.9564} & \\footnotesize{0.9573} & \\footnotesize{0.9582} & \\footnotesize{0.9591} & \\footnotesize{0.9599} & \\footnotesize{0.9608} & \\footnotesize{0.9616} & \\footnotesize{0.9625} & \\footnotesize{0.9633} \\\\\n  1.8 & \\footnotesize{0.9641} & \\footnotesize{0.9649} & \\footnotesize{0.9656} & \\footnotesize{0.9664} & \\footnotesize{0.9671} & \\footnotesize{0.9678} & \\footnotesize{0.9686} & \\footnotesize{0.9693} & \\footnotesize{0.9699} & \\footnotesize{0.9706} \\\\\n  1.9 & \\footnotesize{0.9713} & \\footnotesize{0.9719} & \\footnotesize{0.9726} & \\footnotesize{0.9732} & \\footnotesize{0.9738} & \\footnotesize{0.9744} & \\footnotesize{0.9750} & \\footnotesize{0.9756} & \\footnotesize{0.9761} & \\footnotesize{0.9767} \\\\\n  \\hline\n  \\hline\n  2.0 & \\footnotesize{0.9772} & \\footnotesize{0.9778} & \\footnotesize{0.9783} & \\footnotesize{0.9788} & \\footnotesize{0.9793} & \\footnotesize{0.9798} & \\footnotesize{0.9803} & \\footnotesize{0.9808} & \\footnotesize{0.9812} & \\footnotesize{0.9817} \\\\\n  2.1 & \\footnotesize{0.9821} & \\footnotesize{0.9826} & \\footnotesize{0.9830} & \\footnotesize{0.9834} & \\footnotesize{0.9838} & \\footnotesize{0.9842} & \\footnotesize{0.9846} & \\footnotesize{0.9850} & \\footnotesize{0.9854} & \\footnotesize{0.9857} \\\\\n  2.2 & \\footnotesize{0.9861} & \\footnotesize{0.9864} & \\footnotesize{0.9868} & \\footnotesize{0.9871} & \\footnotesize{0.9875} & \\footnotesize{0.9878} & \\footnotesize{0.9881} & \\footnotesize{0.9884} & \\footnotesize{0.9887} & \\footnotesize{0.9890} \\\\\n  2.3 & \\footnotesize{0.9893} & \\footnotesize{0.9896} & \\footnotesize{0.9898} & \\footnotesize{0.9901} & \\footnotesize{0.9904} & \\footnotesize{0.9906} & \\footnotesize{0.9909} & \\footnotesize{0.9911} & \\footnotesize{0.9913} & \\footnotesize{0.9916} \\\\\n  2.4 & \\footnotesize{0.9918} & \\footnotesize{0.9920} & \\footnotesize{0.9922} & \\footnotesize{0.9925} & \\footnotesize{0.9927} & \\footnotesize{0.9929} & \\footnotesize{0.9931} & \\footnotesize{0.9932} & \\footnotesize{0.9934} & \\footnotesize{0.9936} \\\\\n  \\hline\n  2.5 & \\footnotesize{0.9938} & \\footnotesize{0.9940} & \\footnotesize{0.9941} & \\footnotesize{0.9943} & \\footnotesize{0.9945} & \\footnotesize{0.9946} & \\footnotesize{0.9948} & \\footnotesize{0.9949} & \\footnotesize{0.9951} & \\footnotesize{0.9952} \\\\\n  2.6 & \\footnotesize{0.9953} & \\footnotesize{0.9955} & \\footnotesize{0.9956} & \\footnotesize{0.9957} & \\footnotesize{0.9959} & \\footnotesize{0.9960} & \\footnotesize{0.9961} & \\footnotesize{0.9962} & \\footnotesize{0.9963} & \\footnotesize{0.9964} \\\\\n  2.7 & \\footnotesize{0.9965} & \\footnotesize{0.9966} & \\footnotesize{0.9967} & \\footnotesize{0.9968} & \\footnotesize{0.9969} & \\footnotesize{0.9970} & \\footnotesize{0.9971} & \\footnotesize{0.9972} & \\footnotesize{0.9973} & \\footnotesize{0.9974} \\\\\n  2.8 & \\footnotesize{0.9974} & \\footnotesize{0.9975} & \\footnotesize{0.9976} & \\footnotesize{0.9977} & \\footnotesize{0.9977} & \\footnotesize{0.9978} & \\footnotesize{0.9979} & \\footnotesize{0.9979} & \\footnotesize{0.9980} & \\footnotesize{0.9981} \\\\\n  2.9 & \\footnotesize{0.9981} & \\footnotesize{0.9982} & \\footnotesize{0.9982} & \\footnotesize{0.9983} & \\footnotesize{0.9984} & \\footnotesize{0.9984} & \\footnotesize{0.9985} & \\footnotesize{0.9985} & \\footnotesize{0.9986} & \\footnotesize{0.9986} \\\\\n  \\hline\n  \\hline\n  3.0 & \\footnotesize{0.9987} & \\footnotesize{0.9987} & \\footnotesize{0.9987} & \\footnotesize{0.9988} & \\footnotesize{0.9988} & \\footnotesize{0.9989} & \\footnotesize{0.9989} & \\footnotesize{0.9989} & \\footnotesize{0.9990} & \\footnotesize{0.9990} \\\\\n  3.1 & \\footnotesize{0.9990} & \\footnotesize{0.9991} & \\footnotesize{0.9991} & \\footnotesize{0.9991} & \\footnotesize{0.9992} & \\footnotesize{0.9992} & \\footnotesize{0.9992} & \\footnotesize{0.9992} & \\footnotesize{0.9993} & \\footnotesize{0.9993} \\\\\n  3.2 & \\footnotesize{0.9993} & \\footnotesize{0.9993} & \\footnotesize{0.9994} & \\footnotesize{0.9994} & \\footnotesize{0.9994} & \\footnotesize{0.9994} & \\footnotesize{0.9994} & \\footnotesize{0.9995} & \\footnotesize{0.9995} & \\footnotesize{0.9995} \\\\\n  3.3 & \\footnotesize{0.9995} & \\footnotesize{0.9995} & \\footnotesize{0.9995} & \\footnotesize{0.9996} & \\footnotesize{0.9996} & \\footnotesize{0.9996} & \\footnotesize{0.9996} & \\footnotesize{0.9996} & \\footnotesize{0.9996} & \\footnotesize{0.9997} \\\\\n  3.4 & \\footnotesize{0.9997} & \\footnotesize{0.9997} & \\footnotesize{0.9997} & \\footnotesize{0.9997} & \\footnotesize{0.9997} & \\footnotesize{0.9997} & \\footnotesize{0.9997} & \\footnotesize{0.9997} & \\footnotesize{0.9997} & \\footnotesize{0.9998} \\\\\n   \\hline\n\\multicolumn{11}{l}{{\\normalsize$^*$For $Z \\geq 3.50$, the probability is greater than or equal to $0.9998$.}}\n\\end{tabular}}\n\\end{center}\n\\end{table}\n"
  },
  {
    "path": "extraTeX/tables/code/chiSquareProbTable.R",
    "content": "library(xtable)\n\nDF    <- c(seq(0.5, 3, 0.5), 4:20, 25, 30, 40, 50)\ntails <- c(0.3, 0.2, 0.1, 0.05, 0.02, 0.01, 0.005, 0.001)\n\ncst <- matrix(NA, length(DF), length(tails))\nfor (i in 1:nrow(cst)) {\n  for (j in 1:ncol(cst)) {\n    cst[i,j] <- round(qchisq(1-tails[j], DF[i]), 2)\n  }\n}\ncolnames(cst) <- tails\nrow.names(cst) <- DF\n\nxtable(cst)\n"
  },
  {
    "path": "extraTeX/tables/code/normalProbTable.R",
    "content": "library(xtable)\n\n\n# _____ Negative Z Table _____ #\nz <- matrix(NA, 39, 10)\nfor (i in 1:39) {\n  for (j in 1:9) {\n    z[i,j] <- -((39 - i) / 10 + (10 - j) / 100) + 0.01\n  }\n}\nZ <- matrix(NA, 39, 10)\nfor(i in 1:39){\n  for(j in 1:9){\n    hold <- format(c(round(pnorm(z[i, j]), 4), 0.1234))[1]\n    Z[i,j] <- paste('scriptsize{', hold, '}', sep='')\n  }\n  hold <- format(c(z[i, 9], 0.1))[1]\n  Z[i,10] <- paste('$', hold, '$', sep='')\n}\ntmp  <- c(round(pnorm(seq(-3.89, -0.09, 0.1)), 4), 0.0001)\nhold <- as.character(format(tmp)[1:39])\nrownames(Z) <- paste('scriptsize{', hold, '}', sep='')\ncolnames(Z) <- format(seq(0.08, -0.01, -0.01))\nxtable(Z[5:39, ])\n\n\n# _____ Positive Z Table _____ #\nz <- matrix(NA, 39, 10)\nfor (i in 1:39) {\n  for (j in 1:10) {\n    z[i,j] <- (i - 1) / 10 + (j - 1) / 100\n  }\n}\nZ <- matrix(NA, 39, 10)\nfor (i in 1:39) {\n  for (j in 1:10) {\n    hold <- format(c(round(pnorm(z[i,j]), 4), 0.1234))[1]\n    Z[i,j] <- paste('scriptsize{', hold, '}', sep='')\n  }\n}\nhold <- as.character(format(seq(0, 3.8, 0.1)))\nrownames(Z) <- hold\ncolnames(Z) <- format(seq(0, 0.09, 0.01))\nxtable(Z[1:35, ])\n"
  },
  {
    "path": "extraTeX/tables/figures/chiSquareTail/chiSquareTail.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF('chiSquareTail.pdf', 3.5, 2.1,\n      mar = c(2, 1, 0.5, 1),\n      mgp = c(3, 0.8, 0))\nX <- seq(0, 25, 0.05)\nY <- dchisq(X, 3.5)\n\nplot(X, Y, type = 'l', axes = FALSE, xlim = c(0, 15))\naxis(1)\nthese <- which(X > 5.79)\npolygon(c(X[these[1]], X[these], X[rev(these)[1]]),\n        c(0, Y[these], 0), col = COL[1])\nlines(X, Y)\nabline(h = 0)\ndev.off()\n"
  },
  {
    "path": "extraTeX/tables/figures/normalTails/normalTails.R",
    "content": "library(openintro)\ndata(COL)\n\nGeneratePlot <- function(X, Y, label, start = -10, end = 10) {\n  plot(X, Y,\n       type = 'l',\n       axes = FALSE,\n       xlim = c(-3.4, 3.4))\n  axis(1,\n       at = c(-5, 0, 5),\n       label = c(-5, label, 5),\n       cex.axis = 0.7,\n       tick = FALSE)\n  these <- which(start < X & X < end)\n  polygon(c(X[these[1]], X[these],X[rev(these)[1]]),\n          c(0, Y[these], 0),\n          col = COL[1])\n  lines(X, Y)\n  abline(h = 0)\n  lines(c(0, 0),\n        dnorm(0) * c(0.01, 0.99),\n        col = COL[1],\n        lty = 3)\n}\n\nX <- seq(-4, 4, 0.01)\nY <- dnorm(X)\n\nmyPDF('normalTails.pdf', 4.5, 1.3,\n      mar = c(1.3, 1, 0.5, 1),\n      mgp = c(3, -0.2, 0),\n      mfrow = 1:2)\nGeneratePlot(X, Y, \"Negative Z\", -10, -0.801)\nGeneratePlot(X, Y, \"Positive Z\", -10, 0.801)\ndev.off()\n\n\n\nmyPDF('normalTailLeft.pdf', 2.75, 1.05,\n      mar = c(0.9, 1, 0.1, 3.05),\n      mgp = c(3, -0.2, 0))\nGeneratePlot(X, Y, \"Negative Z\", -10, -0.801)\ndev.off()\n\n\nmyPDF('normalTailRight.pdf', 2.75, 1.05,\n      mar = c(0.9, 2.9, 0.1, 1),\n      mgp = c(3, -0.2, 0))\nGeneratePlot(X, Y, \"Positive Z\", -10, 0.801)\ndev.off()\n"
  },
  {
    "path": "extraTeX/tables/figures/normalTails/subtractingArea/subtractingArea.R",
    "content": "library(openintro)\ndata(COL)\n\nAddShadedPlot <- function(x, y, offset,\n                          shade.start = -8,\n                          shade.until = 8) {\n  lines(x + offset, y)\n  lines(x + offset, rep(0, length(x)))\n  these <- which(shade.start <= x & x <= shade.until)\n  polygon(c(x[these[1]], x[these], x[rev(these)[1]]) + offset,\n          c(0, y[these], 0),\n          col = COL[1])\n  lines(x + offset, y)\n}\nAddText <- function(x, text) {\n  text(x, 0.549283, text, cex = 1.69238)\n}\n\npdf('subtractingArea.pdf', 8, 1.67)\npar(las = 1,\n    mar = rep(0, 4),\n    mgp = c(3, 0, 0))\nX <- seq(-3.2, 3.2, 0.01)\nY <- dnorm(X)\n\nplot(X, Y,\n     type = 'l',\n     axes = FALSE,\n     xlim = c(-3.4, 16 + 3.4),\n     ylim = c(0, 0.622))\n\nAddShadedPlot(X, Y, 0)\nAddText(0, format(c(1, 0.0001), scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 8, -8, 0.43)\nAddText(8, format(0.6664, scientific = FALSE)[1])\n\nAddShadedPlot(X, Y, 16, 0.43, 8)\nAddText(16, format(0.3336, scientific = FALSE)[1])\n\nlines(c(3.72, 4.28), rep(0.549283, 2), lwd = 2)\nlines(c(3, 8 - 3), c(0.2, 0.2), lwd = 3)\n\ntext(12, 0.549283,\n     ' = ',\n     cex = 1.69238)\nsegments(c(11, 11), c(0.17, 0.23), c(13, 13), lwd = 3)\ndev.off()\n"
  },
  {
    "path": "extraTeX/tables/figures/tTails/tTails.R",
    "content": "library(openintro)\ndata(COL)\n\nmyPDF(\"tTails.pdf\", 6, 1.6,\n      mfrow = c(1, 3),\n      mar = c(3.3, 0.5, 0.5, 0.5))\nnormTail(L = -1.2, df = 8, col = COL[1])\nmtext(\"One Tail\", 1, line = 2.1, cex = 0.75)\nnormTail(U = 1.2, df = 8, col = COL[1])\nmtext(\"One Tail\", 1, line = 2.1, cex = 0.75)\nnormTail(L = -1.2, U = 1.2, df = 8, col = COL[1])\nmtext(\"Two Tails\", 1, line = 2.1, cex = 0.75)\ndev.off()\n"
  },
  {
    "path": "fullminipage.sty",
    "content": "%%\n%% This is file `fullminipage.sty',\n%% generated with the docstrip utility.\n%%\n%% The original source files were:\n%%\n%% fullminipage.dtx  (with options: `package')\n%% \n%% This is a generated file.\n%% \n%% Copyright 2012 Christian Schneider <software(at)chschneider(dot)eu>\n%% \n%% This file is part of fullminipage.\n%% \n%% fullminipage is free software: you can redistribute it and/or modify\n%% it under the terms of the GNU General Public License version 3 as\n%% published by the Free Software Foundation, not any later version.\n%% \n%% fullminipage is distributed in the hope that it will be useful,\n%% but WITHOUT ANY WARRANTY; without even the implied warranty of\n%% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n%% GNU General Public License for more details.\n%% \n%% You should have received a copy of the GNU General Public License\n%% along with fullminipage.  If not, see <http://www.gnu.org/licenses/>.\n%% \n%% WARNING: THIS IS ALPHA SOFTWARE AND MAY CONTAIN SERIOUS BUGS!\n%% \n\\NeedsTeXFormat{LaTeX2e}[1999/12/01]\n\\ProvidesPackage{fullminipage}\n  [2014/07/06 v0.1.1 fullpage minipage environment]\n\\RequirePackage{keyval}\n\\RequirePackage{color}\n\\define@key{fullminipage}{left}{\\def\\fullminipage@left{#1}}\n\\define@key{fullminipage}{right}{\\def\\fullminipage@right{#1}}\n\\define@key{fullminipage}{top}{\\def\\fullminipage@top{#1}}\n\\define@key{fullminipage}{bottom}{\\def\\fullminipage@bottom{#1}}\n\\define@key{fullminipage}{alignment}{\\def\\fullminipage@alignment{#1}}\n\\define@key{fullminipage}{bgcolor}[black]{\\def\\fullminipage@bgcolor{#1}}\n\\define@key{fullminipage}{background}{\\def\\fullminipage@background{#1}}\n\\define@key{fullminipage}{pagebreak}{\\def\\fullminipage@pagebreak{#1}}\n\\newenvironment{fullminipage}[1][]{%\n  \\begingroup\n    \\setkeys{fullminipage}{left=\\z@,right=\\z@,top=\\z@,bottom=\\z@,%\n                           alignment=t,background={},pagebreak=\\newpage}%\n    \\@ifundefined{twocolumn@sw}{}%\n      {\\twocolumn@sw{\\setkeys{fullminipage}{pagebreak=\\clearpage}}{}}%\n    \\if@twocolumn\\setkeys{fullminipage}{pagebreak=\\clearpage}\\fi%\n    \\setkeys{fullminipage}{#1}%\n    \\fullminipage@pagebreak\n    \\thispagestyle{empty}%\n    \\@tempdima=-1in\n    \\advance\\@tempdima by-\\voffset\n    \\advance\\@tempdima by-\\topmargin\n    \\advance\\@tempdima by-\\headheight\n    \\advance\\@tempdima by-\\headsep\n    \\@tempdimb=\\@tempdima\n    \\advance\\@tempdima by-\\parskip\n    \\advance\\@tempdima by-\\topskip\n    \\advance\\@tempdima by\\fullminipage@top\n    \\vspace*{\\@tempdima}%\n    \\@tempdima=\\paperheight\n    \\advance\\@tempdima by\\@tempdimb\n    \\advance\\@tempdima by-\\textheight\n    \\advance\\@tempdima by-\\fullminipage@bottom\n    \\enlargethispage{\\@tempdima}%\n    \\leftmargin=-1in\n    \\advance\\leftmargin by-\\hoffset\n    \\if@twoside\n      \\ifodd\\value{page}%\n        \\advance\\leftmargin by-\\oddsidemargin\n      \\else\n        \\advance\\leftmargin by-\\evensidemargin\n      \\fi\n    \\else\n      \\advance\\leftmargin by-\\oddsidemargin\n    \\fi\n    \\advance\\leftmargin by\\fullminipage@left\n    \\linewidth=\\paperwidth\n    \\advance\\linewidth by-\\fullminipage@left\n    \\advance\\linewidth by-\\fullminipage@right\n    \\parshape \\@ne \\leftmargin \\linewidth\n    \\nointerlineskip\n    \\noindent\n    \\vsize=\\paperheight\n    \\advance\\vsize by-\\fullminipage@top\n    \\advance\\vsize by-\\fullminipage@bottom\n    \\begin{picture}(0,0)\n      \\@ifundefined{fullminipage@bgcolor}{}{%\n        \\put(0,0){\\makebox(0,0)[bl]%\n          {\\color{\\fullminipage@bgcolor}{\\rule{\\linewidth}{\\vsize}}}%\n        }%\n      }%\n      \\put(0,0){\\makebox(0,0)[bl]%\n        {\\fullminipage@background}%\n      }%\n    \\end{picture}%\n    \\begin{minipage}[b][\\vsize][\\fullminipage@alignment]{\\linewidth}\n}%\n{%\n    \\end{minipage}%\n    \\parfillskip=\\z@\n    \\fullminipage@pagebreak\n  \\endgroup\n}\n\\endinput\n%%\n%% End of file `fullminipage.sty'.\n"
  },
  {
    "path": "main.tex",
    "content": "\\documentclass[10pt,openany]%,oneside]\n{book}\n\\newcommand{\\versiondate}[0]{Dec 30th, 2024}\n\n\\usepackage{\n  amsmath, calc,\n  caption, changepage,\n  endnotes, enumerate,\n  epstopdf,\n  fancyhdr,\n  fix-cm,\n  fncychap,\n  footmisc,\n  fullminipage,\n  geometry, graphicx,\n  ifthen, lscape,\n  makeidx, manfnt,\n  marginnote,\n  mdframed,\n  multicol, multirow,\n  setspace, soul,\n  tabto,\n  textcomp,\n  %tocloft,\n  varioref, verbatim,\n  wasysym, wrapfig\n}\n\\usepackage{subfigure}\n\\usepackage[explicit]{titlesec}\n%\\usepackage[usenames,dvipsnames]{color}  \n%\\newcommand{\\href}[2]{#2} \\newcommand{\\url}[1]{#1} \\newcommand{\\urlstyle}[1]{}\n\\include{extraTeX/style/colorsV1}\n\\newcommand{\\printlocation}[0]{}\n\n\\newcommand{\\chapterpagepadding}[0]{7mm}\n\\newcommand{\\chapterpagepaddingleftright}[0]{\\chapterpagepadding{}}\n\\newcommand{\\chapterpagepaddingleftinner}[0]{25mm}\n\\newcommand{\\chapterpagepaddingrightinner}[0]{30mm}\n\n% _____ (1) PDF _____ %\n\\usepackage[bookmarksnumbered, colorlinks = false, pdfborder = {0 0 0}, urlcolor = oiGB, colorlinks=true, linkcolor = oiGB, citecolor = oiGB, backref = true]{hyperref}\n\n% _____ (2) PDF -- screenreader _____ %\n% !!!!!\n% 0. Uncomment out the following package:\n%      \\usepackage{pdfcomment}\n% 1. Use the `style_simple` instead of `style`.\n% 2. Use the `headers_simple` instead of `headers`.\n% 3. Adjust the TOC depth to 3.\n% !!!!!\n\n% _____ (3) B&W Paperback _____ %\n%\\definecolor{oiB}{rgb}{0,0,0}\\definecolor{chaptertitlegray}{rgb}{0,0,0}\\usepackage[bookmarksnumbered, colorlinks = false, pdfborder = {0 0 0}, urlcolor = oiB, colorlinks=true, linkcolor = oiB, citecolor = oiB, backref = false]{hyperref}\n\n% _____ (4) Hardcover _____ %\n%\\definecolor{oiB}{rgb}{0,0,0}\\definecolor{chaptertitlegray}{rgb}{0,0,0}\\PassOptionsToPackage{hyperref}{colorlinks=false,pdfborder={0 0 0},urlcolor= black,colorlinks=black,linkcolor=black, citecolor=black,backref=true}\n% !!!!!\n% Also must \\include{extraTeX/style/hardcover} below.\n% !!!!!\n% \\renewcommand{\\printlocation}[0]{\\noindent Printed in China. \\\\}\n\n% _____ (5) Color Paperback _____ %\n%\\definecolor{chaptertitlegray}{rgb}{0,0,0}\\usepackage[bookmarksnumbered, pdfborder = {0 0 0}, urlcolor = black, colorlinks=true, linkcolor = black, citecolor = black, backref = true]{hyperref}\\renewcommand{\\chapterpagepaddingleftright}[0]{15mm} \\renewcommand{\\chapterpagepaddingleftinner}[0]{17mm}\\renewcommand{\\chapterpagepaddingrightinner}[0]{22mm}\n\n\n\n\n\\usepackage[style=authortitle-ibid, maxnames=2,natbib=true,sortcites=true,block=space,backend=bibtex]{biblatex}\n\\bibliography{eoce.bib}\n\n\\makeindex\n\\include{extraTeX/style/style}\n%\\include{extraTeX/style/style_simple}\n%\\include{extraTeX/style/tablet}\n%\\include{extraTeX/style/video}\n% The following style file supports an 8.25 x 11 paper size.\n%\\include{extraTeX/style/hardcover}\n\\include{extraTeX/preamble/title}%_derivative}\n\\date{}\n\\renewcommand\\contentsname{Table of Contents}\n\\setcounter{tocdepth}{1} % standard version\n%\\setcounter{tocdepth}{3} % screen reader version\n%\\renewcommand{\\cftchapfont}{\\scshape}\n%\\renewcommand{\\cftsecfont}{\\bfseries}\n\n\\begin{document}\n%\\include{extraTeX/preamble/review_copy}\n\\renewcommand{\\thepage}{}\n\\maketitle\n\\include{extraTeX/preamble/copyright}%_derivative}\n\\renewcommand{\\thepage}{\\arabic{page}}\n\\tableofcontents\n\\include{extraTeX/preamble/preface}\n\\normalsize\n\n\\begingroup\n\\include{extraTeX/style/headers}\n%\\include{extraTeX/style/headers_simple}\n\\includechapter{1}{ch_intro_to_data}\n\\includechapter{2}{ch_summarizing_data}\n\\includechapter{3}{ch_probability}\n\\includechapter{4}{ch_distributions}\n\\includechapter{5}{ch_foundations_for_inf}\n\\includechapter{6}{ch_inference_for_props}\n\\includechapter{7}{ch_inference_for_means}\n\\includechapter{8}{ch_regr_simple_linear}\n\\includechapter{9}{ch_regr_mult_and_log}\n\\endgroup\n\n\\begingroup\n\\include{extraTeX/style/style_appendices}\n\\appendix{}\n\\addtocontents{toc}{\\protect\\setcounter{tocdepth}{0}}\\include{extraTeX/eoceSolutions/eoceSolutions}\n\\include{extraTeX/data/data}\n\\include{extraTeX/tables/TeX/zTable}\n\\include{extraTeX/tables/TeX/tTable}\n\\include{extraTeX/tables/TeX/chiSquareTable}\n\\endgroup\n\n\\include{extraTeX/index/index}\n\\printindex\n\n\\end{document}"
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